Journal of Computational Methods in Sciences and Engineering - Volume 24, issue 3

Authors: Wang, Fei | Fan, Zikai | Miao, Yun | Ren, Jiayi | Luo, Yuchao

Article Type: Research Article

Abstract: Generating as-built Building information models (BIMs) is promising in power substation construction projects because they can reflect the actual conditions of facilities. However, traditional manual-designed BIMs are different from real-world scenarios due to reality gaps. In this paper, we present a new method of reconstructing the layout of power equipment and facilities in substations using LIDAR point clouds. The proposed method extracts electric equipment and facilities via object segmentation and model retrieval. In particular, we investigate PFH, FPFH and SHOT descriptors for the 3D-SIFT keypoints in the 3D shape retrieval of complex electric equipment and facilities. After the best-match model …is retrieved from a model library, the layout of typical electric equipment and facilities is reconstructed by aligning the model to the scene point cloud via point cloud registration. Experimental results validate the effectiveness of the proposed method. The proposed method enhances the efficiency of generating 3D models of power substations. Show more

Keywords: Reconstruction, substation, point cloud registration, point cloud segmentation

DOI: 10.3233/JCM-247162

Citation: Journal of Computational Methods in Sciences and Engineering, vol. 24, no. 3, pp. 1269-1281, 2024

Authors: Sun, Yong | Yang, Yiwei | Chen, Jing | Xue, Keli | Li, Min

Article Type: Research Article

Abstract: This paper addresses prevalent issues of suboptimal compatibility between the heating exchanger and the thermal storage unit, poor safety performance, and overall insufficient heat exchange efficiency within the application of heating exchangers in electric heating solid energy storage heating systems. Experimental testing and numerical simulation studies were conducted. The research investigates the effect of the temperature of inlet air as well as velocity on the heat exchange performance of the heating exchanger as well as temperature variations of single-row heat pipes. Drawing upon these change patterns, an optimized heating exchanger structure is proposed and subsequently investigated through optimization simulation studies. …The study results indicate that the best overall optimization effect is achieved with a heating exchanger arranged with finned tube combinations of 4 mm in two rows, 6 mm in two rows, 8 mm in two rows, 10 mm in two rows, 12 mm in two rows, and 14 mm in ten rows arranged successively from front to back. When the heating exchanger’s inlet air speed is relatively high, this combination’s heat exchange capacity surpasses the original structure. Additionally, the uniformity of air-side temperature drop improved by 44.89%, while the finned area was reduced by 25.62%. Show more

Keywords: Electric heating solid energy storage heating system, finned tube heating exchanger, heat exchange performance, temperature non-uniformity coefficient

DOI: 10.3233/JCM-247137

Citation: Journal of Computational Methods in Sciences and Engineering, vol. 24, no. 3, pp. 1283-1302, 2024

Authors: Zhao, Wei | Qi, Jian

Article Type: Research Article

Abstract: With the rapid development of information technology, the field of education is undergoing a profound change, in which intelligent hybrid learning and virtual reality technology are increasingly valued. This study proposes an intelligent hybrid learning method based on virtual reality for student performance improvement. This paper reveals the limitations of traditional learning methods in meeting the needs of modern education, and expounds the theoretical basis of intelligent hybrid learning and virtual reality technology. This paper collects and processes a large amount of learning data, based on which a new model of student learning performance prediction is established. The verification results …of the model show that the model in this study has excellent performance in predicting students’ learning performance. This paper gives some suggestions for future educational practice and research. In general, this study provides a new learning method for the field of education and has important reference value for education reform and teaching practice. Show more

Keywords: Intelligent blended learning, virtual reality technology, student learning performance

DOI: 10.3233/JCM-247198

Citation: Journal of Computational Methods in Sciences and Engineering, vol. 24, no. 3, pp. 1303-1316, 2024

Authors: Cen, Haifeng | Xu, Yuan | Sun, Kaiyuan | Tian, Hao | Chen, Kun | Lin, Lin

Article Type: Research Article

Abstract: This paper examines the use of an Uninterruptible Power Supply (UPS) to enhance the operational efficiency of data centers. It focuses on developing an optimal energy scheduling strategy for a data center equipped with UPS, using the Markov decision process (MDP) framework. The MDP framework simulates the decision-making process involved in minimizing energy costs. Each unit’s available power output in the data center is treated as a Markov state, taking into account the uncertainty associated with renewable distributed generation. This uncertainty drives the system to transition to other Markov states in subsequent decision times. A recursive optimization model is established …for each Markov state at each decision time to guide state-based operations, which includes determining the unit output while considering both current and future costs. The challenge of dealing with high dimensionality, arising from a substantial number of states and actions in the model, is effectively addressed by adopting an approximate dynamic programming (ADP) method. This approach incorporates decision-state and forwards dynamic algorithms to tackle the complexity of the MDP-based model. By employing ADP, the computational burden is reduced, enabling efficient and practical solutions to be obtained. Show more

Keywords: Data center, Markov decision process, reinforcement learning, UPS

DOI: 10.3233/JCM-247149

Citation: Journal of Computational Methods in Sciences and Engineering, vol. 24, no. 3, pp. 1317-1329, 2024

Authors: Yao, Zhifeng | Xu, Ye

Article Type: Research Article

Abstract: The conventional genetic algorithm (GA) for path planning exists several drawbacks, such as uncertainty in the direction of robot movement, circuitous routes, low convergence rates, and prolonged search time. To solve these problems, this study introduces an improved GA-based path-planning algorithm that adopts adaptive regulation of crossover and mutation probabilities. This algorithm uses a hybrid selection strategy that merges elite, tournament, and roulette wheel selection methods. An adaptive approach is implemented to control the speed of population evolution through crossover and mutation. Combining with a local search operation enhances the optimization capability of the algorithm. The proposed algorithm was compared …with the traditional GA through simulations, demonstrating shorter path lengths and reduced search times. Show more

Keywords: Genetic algorithm, hybrid selection strategy, adaptive strategy, local search

DOI: 10.3233/JCM-247133

Citation: Journal of Computational Methods in Sciences and Engineering, vol. 24, no. 3, pp. 1331-1340, 2024

Authors: Liang, Junhua | Zhao, Zhisheng | Ma, Sujing | Wang, Jiaju

Article Type: Research Article

Abstract: Blended learning is the latest and inevitable trend in the development of education. Although blended learning research is on the rise, fewer studies examine the learning behaviour of college students in blended learning environments. This study aimed to investigate the learning behaviours of students in the field of computer science and examine these behaviours using data mining algorithms, taking the teaching practice of the Digital Signal Processing course as a case study. A total of 18 behavioural indicators were extracted and divided into three categories: basic learning behaviours, self-regulated learning behaviours, and extended learning behaviours. Data analysis of the …behavioural indicators yielded the following conclusions: (1) Students did not have the habit of watching course playback and were less receptive to multiple online learning platforms; (2) Students’ midterm performance and duration of livestream watching directly affected their basic learning behaviours, with all indicators of self-regulated and extended learning behaviours showing significant correlations; (3) The clustering of learning behaviours yielded four different learner patterns, which calls for personalised teaching strategies; (4) The random forest algorithm had an accuracy of 95.4% in predicting performance of the four types of learners. Show more

Keywords: Behavioural analysis, blended teaching, self-regulated learning, learning behaviour

DOI: 10.3233/JCM-247160

Citation: Journal of Computational Methods in Sciences and Engineering, vol. 24, no. 3, pp. 1341-1353, 2024

Authors: Wang, Xiaomei | Jing, Huifeng | Guo, Yuyue

Article Type: Research Article

Abstract: To examine the impact and mechanisms of the aerobics exercise on gut flora and gastrointestinal hormones in type 2 diabetes rats. Methods Adult male SD rats aged 8 weeks were divided into 5 groups at random (n = 10): a peaceful comparison group (N), a diabetic comparison group (D), a diabetic low-intensity exercise group (LD), a diabetic middle-intensity exercise group (MD), and a diabetes highintensity exercise group. (HD). The rat groups LD, MD, and HD performed aerobic exercise five times per week for a total of 6 weeks and compared general condition, blood glucose, blood lipids, …insulin, leptin, gastrin (GAS) and motilin ( MOT), gastrointestinal motility, and intestinal flora. Results The levels of insulin, leptin, total cholesterol, triglycerides, E. coli, LDL-cholesterol, LDL-cholesterol, insulin, and gastric residual rate were significantly different from group N. Conclusion: Type 2 diabetic rats have gut flora imbalance and gastrointestinal hormone changes. Aerobic exercise with different intensity can alleviate gastrointestinal hormone imbalance and intestinal flora imbalance in type 2 diabetes rats, and the impact of moderate intensity exercise is the strongest. Show more

Keywords: Aerobic exercise, type 2 diabetes;intestinal flora, gastrointestinal hormones

DOI: 10.3233/JCM-247158

Citation: Journal of Computational Methods in Sciences and Engineering, vol. 24, no. 3, pp. 1355-1362, 2024

Authors: Li, Qiang | Pan, Xiaoli | Fan, Wenyan | Zhou, Ying | Liu, Yongfu | Yu, Wangxin | Li, Dongyu | Li, Wenyue | Li, Weibin

Article Type: Research Article

Abstract: The study investigates the impact of different drying methods on the selection of thin-layer drying kinetic models, parameters, and quality for Exocarpium Citreic Grandis. This study investigates the drying characteristics, index constituents content, and microscopic structures of Exocarpium Citreic Grandis slices, subjected to three drying techniques: HAD (at 50, 60, 70 ∘ C), VD (at 50, 60, 70 ∘ C), and MVD (at 1000, 1500, 2000W). A thin-layer drying kinetic model was established. The findings demonstrated that the drying process was primarily dominated by the falling rate phase. When the drying temperature was 70 …∘ C and the microwave power was 2000W, the HAD VD, and MVD took 120, 360, and 20 minutes respectively. By fitting six commonly used thin-layer drying models, it was discovered that the optimal mathematical models for HAD, VD, and MVD were the Page model, the Logarithmic model, and the Page model, respectively. The highest average R 2 values were 0.9963, 0.9965, and 0.9964, and the lowest average RMSE values were 0.01782, 0.01704, and 0.0174 respectively. The effective diffusion coefficient increased with the drying temperature and microwave power, with MVD having the maximum coefficient. As the temperature and microwave power increased, the contents of naringin and rhoifolin decreased. However, the naringin content in MVD was 23.05% and 45.71% higher compared to hot air and VD respectively. The cross-section of Exocarpium Citreic Grandis dried via microwave vacuum exhibited a porous honeycomb structure with uniformly distributed spaces and larger pores, reflecting an expansion effect. The HAD process also resulted in a honeycomb-like structure, but with smaller pores. The VD process resulted in a layered structure with significant cell collapse. Microwave vacuum drying demonstrates superior energy efficiency and product quality compared to hot air and vacuum drying methods. The study finds that microwave drying does not have a destructive impact on the active compounds of Exocarpium Citreic Grandis. To facilitate large-scale, continuous production, microwave drying can be practically applied in industrial processing. Show more

Keywords: Exocarpium Citreic Grandis, microwave vacuum, hot air drying, vacuum drying, thin-layer drying model

DOI: 10.3233/JCM-247143

Citation: Journal of Computational Methods in Sciences and Engineering, vol. 24, no. 3, pp. 1363-1377, 2024

Authors: Liu, Xiaomei | Hu, Xuewei

Article Type: Research Article

Abstract: Conventional brute-force attacks can now be detected and identified based on statistical analysis of logs and traffic data. However, they fail to detect low-frequency and distributed brute-force attack behaviors. To address different attack methods, new detection techniques have emerged. This study compares various machine learning algorithms and selects two methods, namely the clustering algorithm k-means and bdscan, as well as the decision tree algorithm for data learning. In one approach, normal user login data is integrated with enterprise email log data. The data is first statistically analyzed and filtered, followed by quantifying data characteristics using information entropy. Subsequently, machine learning …algorithms are employed for classification, and the results are visualized for display. In another approach, labeled raw data is used to train a model using the decision tree algorithm. By comparing the two analysis results, a more accurate model can be obtained. These analytical methods can help enterprises strengthen email security and defend against low-frequency and distributed brute-force attacks. Show more

Keywords: Brute-force attack, low-frequency, distributed, machine learning algorithms

DOI: 10.3233/JCM-247147

Citation: Journal of Computational Methods in Sciences and Engineering, vol. 24, no. 3, pp. 1379-1393, 2024

Authors: Sun, Hua | Su, Kaifeng | Yang, Yifan

Article Type: Research Article

Abstract: As an important part of automotive shock absorber, the columnar parts in automotive shock absorber will inevitably have machining defects during the process, which will not only degrade the performance of the parts, but also degrade or even fail the performance of the final shock absorber after assembly. Yolov5, as a target detection algorithm, has received much attention due to its high accuracy and fast operation speed. However, the algorithm faces some challenges when applied in a practical industrial environment. In this paper, improvement measures are proposed to address the limitations of sample collection and the high speed of pipeline …recognition in industrial environments. The network model is optimized and designed. Firstly, the ASPP module is replaced by the SPP module thus improving the viewability throughout the process providing recognition accuracy. Secondly, the Conv and C3 layers of Yolov5s are replaced by Transformer to obtain higher recognition accuracy. By improving and optimizing the above methods, we can better cope with the improvement of detection accuracy under small sample conditions. Experiments show that the method can significantly improve the detection accuracy and operation speed of Yolov5s under the hardware condition of lower computing power, which is more suitable for industrial scenario application scenarios. Show more

Keywords: Target detection, YOLOv5, SPP module, APPF module, Transformer

DOI: 10.3233/JCM-247145

Citation: Journal of Computational Methods in Sciences and Engineering, vol. 24, no. 3, pp. 1395-1404, 2024

Authors: Wu, Xingping | Yuan, Qiheng | Zhou, Chunlei | Chen, Xiang | Xuan, Donghai | Song, Jinwei

Article Type: Research Article

Abstract: In the field of electric carbon, the mapping relationship between carbon emission flow calculation and power flow calculation was studied by combining techniques such as current trajectory tracking, carbon flow trajectory analysis, power system flow calculation methods, and electric network analysis theory. By delving into the mechanism between these two factors, a better understanding of the correlation between them can be achieved. In addition, by using time series data, graph attention neural networks (GNN), distributed computing technology, and spatiotemporal computing engines, carbon emission fluctuations can be decomposed and a high-frequency “energy-electricity-carbon” integrated dynamic emission factor can be achieved. Through the …spatiotemporal distribution patterns of this dynamic factor in multiple dimensions, the carbon emissions from key industries in cities can be accurately calculated. In this paper, the LSTM-GAT model is used as the core to construct a key carbon emission prediction model for cities. The study focuses on the power plant, chemical industry, steel, transportation industry, and construction industry, which are high energy-consuming industries with an annual electricity consumption of more than 100 million kWh in a major city of China. By analyzing the entire life cycle from power generation to electricity consumption and conducting current flow analysis, monthly, weekly, and daily carbon emission calculations were performed. Additionally, other factors such as the industrial development index, GDP, coverage area of power generation enterprises, regional population, size, and type of power-consuming units were included in the comprehensive calculation to build a measurement system. By conducting experiments and analyzing historical data, we have found that the LSTM-GAT model outperforms the single models of GCN, GAT, LSTM, GRU, and RNN in terms of lower error values and higher accuracy. The LSTM-GAT model is better suited for predicting carbon emissions and related indicators with an accuracy rate of 89.5%. Our predictions show that the carbon emissions will exhibit a slow growth trend in the future, while the carbon emission intensity will decrease. This information can provide a scientific basis for government decision-making. Show more

Keywords: Electric carbon, dynamic emission factor, LSTM-GAT, predict carbon emissions

DOI: 10.3233/JCM-247139

Citation: Journal of Computational Methods in Sciences and Engineering, vol. 24, no. 3, pp. 1405-1421, 2024

Authors: Wang, Chong

Article Type: Research Article

Abstract: The TFAHP-FAHP algorithm model constructed in this article can preferably avoid subjective assumptions caused by human factor. The new energy vehicle power battery is a core component for improving the performance of electric vehicles, and constitutes one of the most valuable parts of the invention patent for new energy vehicles. Patent pledge financing is a brand new form with great potential. However, as a result of the imperfect system of the patent pledge financing, banks and other financial institutions are exposed to an increased risk in the process of the patent pledge financing, which seriously limits the development of the …patent pledge financing for power battery. In view of the above issues, this paper uses the SPSS reliability and validity tests in order to develop a relatively complete and accurate index system. TFAHP algorithm model and fuzzy comprehensive are combined to determine the final correction coefficient. In this study, the income method is used to determine the pledge valuation value of a new energy vehicle power battery patent portfolio, and the AHP algorithm method is used to determine the weight of the value of the invention patent portfolio within the patent portfolio. As a result, in this paper, the pledge value of the patent portfolio is multiplied by a proportion of the invention patent portfolio’s value, and then multiplied by a correction coefficient to obtain the final new energy vehicle power battery invention patent portfolio pledge value. Using this reference value, technology-based energy companies can pledge financing for new energy vehicle power battery invention patent portfolios. Show more

Keywords: TFAHP algorithm, FAHP algorithm, invention patent portfolio for new energy power battery, SPSS reliability and validity test algorithm

DOI: 10.3233/JCM-247154

Citation: Journal of Computational Methods in Sciences and Engineering, vol. 24, no. 3, pp. 1423-1439, 2024

Authors: Chen, Mingming | Bao, Jin | Li, Zhixin | Li, Jun

Article Type: Research Article

Abstract: Wireless charging technology provides a convenient and safe method for electric vehicles. To meet the needs of commercial operations, it is essential to accurately measure the active power at the transmitter end of the wireless charging system. However, due to the high-frequency and high-voltage, measuring the voltage at the transmitting coil is challenging. In response to this issue, this paper focuses on the bilateral LCC compensation network and analyzes the harmonic components of the current and voltage at transmitting coil. It derives the voltage relationship between compensation capacitor and the transmitting coil and indirectly measure voltage at transmitting coil, finally …proposing an active power calculation method for the transmitting coil. An experimental prototype of a wireless charging system utilizing bilateral LCC compensation is built. Simulations and experiments verify that the active power consumed by the transmitting coil is mainly the fundamental component, and the impact of the sampling frequencies and power levels on the measurement accuracy is discussed. Show more

Keywords: Wireless charging system, bilateral LCC compensation network, current characteristics, active power measurement

DOI: 10.3233/JCM-247164

Citation: Journal of Computational Methods in Sciences and Engineering, vol. 24, no. 3, pp. 1441-1456, 2024

Authors: Qiu, Lingling

Article Type: Research Article

Abstract: In order to enhance the performance of intelligent image recognition, this study optimizes the image recognition model through lightweight convolutional neural networks (CNNs) and cloud computing technology. The study begins by introducing the relevant theories and models of edge computing (EC) and lightweight CNNs models. Next, this study focuses on optimizing traditional image recognition models. Finally, the effectiveness and reliability of the proposed model are experimentally validated. The experimental results indicate that, when recognizing 1000 images, the average recognition times per image on cloud servers and edge servers are 13.33 ms and 50.11 ms, respectively. Despite the faster speed of …cloud servers, the performance of edge servers can be improved by stacking servers. When the number of edge servers reaches 4, their recognition speed surpasses that of the cloud server model. Additionally, comparing the latency and processing time between EC and cloud computing architectures, it is observed that, with an increase in the number of processed images, the average processing time per image in the EC architecture remains relatively stable and consistent. In contrast, the average processing time gradually increases in the cloud computing architecture. This indicates a significant impact of the number of images on the processing rate of the cloud computing architecture. Therefore, as the time gap in processing between cloud computing and EC increases, the advantages of the EC architecture become more apparent. This study’s significance lies in advancing the development of deep learning technology and providing possibilities for its widespread practical application. The contribution of this study lies in promoting the development of EC and lightweight neural network models, offering valuable references and guidance for practical applications in related fields. Show more

Keywords: Edge computing, lightweight CNNs, image recognition, cloud computing technology, edge server, Elastic Compute Service

DOI: 10.3233/JCM-247187

Citation: Journal of Computational Methods in Sciences and Engineering, vol. 24, no. 3, pp. 1457-1471, 2024

Authors: Wu, Yongyi | Jiang, Jingfeng | Wen, Zhendan

Article Type: Research Article

Abstract: This work addresses issues such as inadequate teaching methods, a lack of teaching resources, and low proactiveness in current accounting education. It introduces a novel teaching approach termed “Just-in-Time Teaching (JITT) cloud teaching,” which integrates “real-time teaching” with the Internet of Things-based “cloud teaching model” specifically for accounting education. First, the current status of accounting education in secondary vocational schools is investigated through a questionnaire survey. Subsequently, adjustments are made to the traditional teaching model, considering the limitations in teaching media creation channels and challenges in teaching activities. The teaching content of accounting education is designed in terms of mind …maps, curriculum type, and problem design. The findings indicate: (1) Almost half of the surveyed teachers have heard of but never used the JITT cloud teaching, and the proportion is the largest. Some teachers have used but disapprove of JITT cloud teaching. (2) The proportion of students using website learning resources is 43.81%, while the proportion using mobile learning applications is 38.34%. (3) There is a significant difference between the traditional teaching mode and the JITT teaching mode in terms of “classroom teaching” and “sense of responsibility”. The average values under the JITT teaching mode have significantly improved compared to the traditional one. (4) The experimental group has a higher proportion of students scoring 90–100, which is 58%, significantly higher than the control group. The above research results indicate that there are still many possibilities for the practical application of the JITT teaching method in the future Moreover, applying the JITT cloud teaching model contributes to enhancing teaching quality and supports students’ learning. Show more

Keywords: JITT cloud teaching, accounting teaching, artificial intelligence, implementation and evaluation, Internet of Things

DOI: 10.3233/JCM-247189

Citation: Journal of Computational Methods in Sciences and Engineering, vol. 24, no. 3, pp. 1473-1493, 2024

Authors: Hu, Guo | Chen, Hao | Li, Xuan | Chelli, Karim | Alzyoud, Adel Ali Yassin

Article Type: Research Article

Abstract: AIMS: In order to implement the relevant laws, regulations and technical policies of national urban planning and environmental protection, and meet people’s living and human health, since the 21st century, various countries have adopted various methods to improve the urban ecological environment in varying degrees. The influence of the environment on people is first through the human visual response to the brain, so that the human brain can respond quickly and affect their human health. However, human modernization has brought many serious adverse factors to mankind. We must attach great importance to it. METHODS: Using the …methods of field investigation, mathematical analysis and observation, and using artistic mathematical methods from the perspective of visual communication, the effects of these factors were analyzed. RESULTS: The analysis results show that more than 80% of people believe that the stronger the artistry of urban environment, the better human health and the longer life span. Only 10% of people are indifferent to the artistry of urban environment. 10% thought it was OK to have a place to live. CONCLUSION: 85% of people require that the urban environment should first have artistic beauty. Human beings must consider the artistry of the urban environment as much as possible in the process of urban modernization. The stronger the artistry of the environment, the more beautiful the modern environment will be. With a beautiful environment, human beings will live longer and longer. Effective measures must be taken to avoid excessive confusion in the construction and layout of cities. Show more

Keywords: Mathematical analysis, urban lighting, architecture, artistry, visual communication

DOI: 10.3233/JCM-247156

Citation: Journal of Computational Methods in Sciences and Engineering, vol. 24, no. 3, pp. 1495-1505, 2024

Authors: Cang, He | Feng, Dan

Article Type: Research Article

Abstract: In order to improve the security and performance of the oral English instant translation model, this paper optimizes the instant translation model through the Internet of Things (IoT) security technology and deep learning technology. In this paper, the real-time translation model based on deep learning and IoT technology is analyzed in detail to show the application of these two technologies in the real-time translation model, and the related information security issues are discussed. Meanwhile, this paper proposes a method combining deep learning network and IoT technology to further improve the security of instant translation model. The experimental results show that …under the optimized model, the parameter upload time is 60 seconds, the aggregation calculation time is 6.5 seconds, and the authentication time is 7.5 seconds. Moreover, the average recognition accuracy of the optimized model reaches 93.1%, and it is superior to the traditional machine translation method in accuracy and real-time, which has wide practical value and application prospects. Therefore, the research has certain reference significance for improving the security of the English corpus oral instant translation model. Show more

Keywords: Information security, Internet of Things technology, deep learning, oral instant translation model, English corpus

DOI: 10.3233/JCM-247183

Citation: Journal of Computational Methods in Sciences and Engineering, vol. 24, no. 3, pp. 1507-1522, 2024

Authors: Liang, Xiaolong | Pan, Derun | Yu, Jiayi

Article Type: Research Article

Abstract: This study aims to overcome the impact of complex environmental backgrounds on the recognition of wildlife in monitoring images, thereby exploring the role of a deep learning-based intelligent wildlife recognition system in biodiversity conservation. The automatic identification of wildlife images is conducted based on convolutional neural networks (CNNs). Target detection technology, based on regression algorithms, is initially employed to extract Regions of Interest (ROI) containing wildlife from images. The wildlife regions in monitoring images are detected, segmented, and converted into ROI images. A dual-channel network model based on Visual Geometry Group 16 (VGG16) is implemented to extract features from sample …images. Finally, these features are input into a classifier to achieve wildlife recognition. The proposed optimized model demonstrates superior recognition performance for five wildlife species, caribou, lynx, mule deer, badger, and antelope, compared to the dual-channel network model based on VGG16. The optimized model achieves a Mean Average Precision (MAP) of 0.714, with a maximum difference of 0.145 compared to the other three network structures, affirming its effectiveness in enhancing the accuracy of automatic wildlife recognition. The model effectively addresses the issue of low recognition accuracy caused by the complexity of background information in monitoring images, achieving high-precision recognition and holding significant implications for the implementation of biodiversity conservation laws. Show more

Keywords: Deep learning, convolutional neural networks, intelligent identification of images, wild animals, dual-channel network, biodiversity conservation law

DOI: 10.3233/JCM-247185

Citation: Journal of Computational Methods in Sciences and Engineering, vol. 24, no. 3, pp. 1523-1538, 2024

Authors: Ni, Junhong | Hu, Xiaorui

Article Type: Research Article

Abstract: In order to solve the problems of monthly electricity generation forecasting being limited by the lack of actual data source, and the large errors caused by the influence of various factors such as weather and holidays, and the limitations of the applicable scenarios of the existing research results, a monthly electricity generation forecasting model based on similar month screening and Seasonal and Trend decomposition using Loess (STL) was proposed in this paper. The complementary advantages of Multiple Linear Regression (MLR) and Improved Random Forest Regression (RFR) are utilized to achieve the monthly electricity generation prediction in the province. This prediction …model does not require a large number of data to obtain a better prediction accuracy, and breaks through the limitations of the existing monthly electricity prediction model that are only suitable for a certain industry or a certain region. Experiments performed on an actual electric power generation series validate the efficiency of the proposed model. Show more

Keywords: Monthly electricity generation forecast, STL decomposition, similar month screening, multiple linear regression, random forest

DOI: 10.3233/JCM-247141

Citation: Journal of Computational Methods in Sciences and Engineering, vol. 24, no. 3, pp. 1539-1556, 2024

Authors: Yin, Li | Chen, Yijun

Article Type: Research Article

Abstract: The application of computer information management system (IMS for short here) in university management faces problems such as incomplete system software and complex system design. Applying clustering algorithms (CA for short here) to computer student IMS can help optimize the system’s overall effectiveness. This article constructed a computer student IMS based on computer technology and applied it to the management of college students. This article also combined CA to conduct relevant effectiveness tests on the system, in order to optimize the overall effectiveness of the system. Under the algorithm in this article, the average connection speed for each user accessing …the system was 9.17 Mbps. The average reaction time was 0.34 seconds, the average security level was 92.47%, and the highest memory usage rate of the system was 34.27%; Under the decision tree algorithm, the average connection speed of each user accessing the system was 8.82 Mbps, and the average reaction time reached 0.64 s. The average security level was 88.41%, and the highest memory usage rate was 42.58%. Under the artificial neural network algorithm, the average connection speed of the system was 8.47 Mbps, the average response time was 0.86 s, and the highest memory usage rate was 45.97%. Analyzing the data reveals that the algorithm introduced in this paper significantly enhances system connection speed and reduces reaction time. This improvement not only enhances security measures but also minimizes memory usage, effectively optimizing the overall efficiency of the system. Show more

Keywords: Computer information management system, university management work, student information management, system safety, clustering algorithm

DOI: 10.3233/JCM-247177

Citation: Journal of Computational Methods in Sciences and Engineering, vol. 24, no. 3, pp. 1557-1569, 2024

Authors: Qi, Ma

Article Type: Research Article

Abstract: An improved genetic algorithm is proposed to optimize the deep neural network algorithm for visual style conversion in visual media. It consists of two parts: optimizing the deep neural network algorithm design and designing a video style conversion model. The genetic algorithm selection strategy is enhanced to optimize the neural network structure. A non-recursive neural network is used to handle temporal inconsistency in a single frame. Experimental results on the Heart dataset show that the accuracy of the optimized deep neural network algorithm is 0.8913, outperforming other algorithms like the generative adversarial dual neural network (0.8696), ant colony optimization (0.8651), …active network (0.8536), genetic algorithm (0.8566), and particle swarm algorithm (0.8558). Moreover, the optimized algorithm achieves high temporal stability and running speed in single and multi-style conversion networks. In conclusion, the proposed strategy using improved genetic algorithms to optimize deep neural network algorithms for visual style conversion offers effective solutions with high application value in terms of accuracy, temporal stability, and running speed. Show more

Keywords: Genetic algorithm; deep learning; visual media; style conversion

DOI: 10.3233/JCM-247194

Citation: Journal of Computational Methods in Sciences and Engineering, vol. 24, no. 3, pp. 1571-1584, 2024

Authors: Chen, Xiaoxin | Wu, Meng | Wang, Mangning

Article Type: Research Article

Abstract: This paper aims to improve the level of social credit system and the accuracy and efficiency of bank users’ credit scoring by using business intelligence technology based on deep neural network (DNN). Firstly, based on the theory of personal credit evaluation factors, a comprehensive credit evaluation factor system is constructed, taking into account social and economic background, consumption habits, behavior patterns and other factors. Meanwhile, back propagation neural network (BPNN) theory is introduced as the core method of modeling to cope with the nonlinear relationship in the credit scoring task and the demand of large-scale data processing. Secondly, by analyzing …the operation process of BPNN in detail, the specific application in credit scoring model is emphasized. Finally, on the basis of theory and operation, this paper implements a credit scoring model for bank users based on BPNN theory. The experimental results show that the model realized in this paper can automatically discover the key attributes and internal rules in the sampled data, and adjust the weight and threshold of the network by modifying the parameters and network structure to meet the expected requirements. The accuracy of the credit score of the predicted sample data reaches 99.5%, and the prediction error is very small, which has a good prediction effect. This paper provides a feasible solution for business intelligence and DNN in the field of credit scoring, and also provides strong empirical support for improving the level of social credit system. Show more

Keywords: Bank users, back propagation neural network, credit risk, credit score, index system

DOI: 10.3233/JCM-247181

Citation: Journal of Computational Methods in Sciences and Engineering, vol. 24, no. 3, pp. 1585-1604, 2024

Authors: Zhu, Dengyun | Jing, Rong | Guo, Qi | Zhang, Dongjiao | Wan, Fucheng

Article Type: Research Article

Abstract: Word2vec is often used in text sentiment analysis to generate word vector, which maps the same word into the same vector. Although Word2vec plays a very good effect in the initial model training task, it still cannot solve the problems of polysemy and new use of old words, which leads to inaccurate extracted features and affects the final classification results. In this paper, BERT model was used to vectorize the review text of tourist attractions, and fusion attention mechanism and long and short-term memory model were used to extract the emotional features of the text for classification at the feature …extraction layer. The emotional accuracy of the model proposed in this paper reached 95.79% in the review text of tourist attractions. Show more

Keywords: Sentiment analysis, deep learning, BERT model, attention mechanism

DOI: 10.3233/JCM-247135

Citation: Journal of Computational Methods in Sciences and Engineering, vol. 24, no. 3, pp. 1605-1615, 2024

Authors: Yue, Yinglong

Article Type: Research Article

Abstract: The study designed a risk assessment scheme to reduce the risk of highway bridge construction in highland mountainous areas, and optimised the existing hierarchical analysis method used for risk weight calculation, using entropy weight and fuzzy numbers for improvement, and designed an optimised fuzzy hierarchical entropy weight comprehensive risk assessment model. The results found that the maximum affiliation degree of site safety management risk is 0.39, which is a low-level risk; the maximum affiliation degree of personnel safety and operation quality category is 0.42, which is an intermediate risk; the maximum affiliation degree of machinery and equipment is 0.40, which …is a high-level risk; the maximum affiliation degree of construction materials is 0.69, which is a low-level risk; and the maximum affiliation degree of environment category is 0.51, which is an intermediate risk. The maximum affiliation of the overall construction risk is 0.369, which indicates that the fuzzy comprehensive evaluation of the project is an intermediate risk. The results of the study show that the proposed construction risk assessment scheme for highway bridges in highland mountainous areas can provide certain reference for the construction of highland mountainous areas and avoid the corresponding safety risks. Show more

Keywords: Plateau, bridge construction, risk assessment, triangular fuzzy numbers, AHP

DOI: 10.3233/JCM-247192

Citation: Journal of Computational Methods in Sciences and Engineering, vol. 24, no. 3, pp. 1617-1630, 2024

Authors: Yue, Junping

Article Type: Research Article

Abstract: In today’s information age, network public opinion has an increasing impact on the educational environment of colleges and universities, and has a profound impact on students’ career planning, initiative and employment perception. In view of this situation, this study discusses the evaluation and guidance of university network public opinion environment based on fuzzy evaluation method. Firstly, the theory of fuzzy evaluation method is elaborated in detail, and its advantages and challenges in decision making are discussed. Then, the fuzzy evaluation method is applied to the evaluation of the network public opinion environment in colleges and universities, and the relationship between …students’ entrepreneurial education, entrepreneurial intention, entrepreneurial intention, entrepreneurial behavior and the establishment of new enterprises is deeply studied. Finally, by optimizing the application of fuzzy evaluation method, the accuracy and efficiency of evaluating the network public opinion environment in colleges and universities are improved. This study provides a scientific and systematic evaluation tool and guidance strategy for the network public opinion environment for researchers and practitioners in related fields, so as to promote the improvement of the educational environment and the development of students. Show more

Keywords: Fuzzy evaluation method, network public opinion, university education environment, entrepreneurship education, entrepreneurial intention

DOI: 10.3233/JCM-247196

Citation: Journal of Computational Methods in Sciences and Engineering, vol. 24, no. 3, pp. 1631-1647, 2024

Authors: Hou, Qingmin | Xiao, Guanghua | Xu, Fangmin | Eddine, Hassan Nasser

Article Type: Research Article

Abstract: Pressure and flow rate are the most important hydraulic parameters in natural gas pipeline flow, and leak rate is the most crucial parameter after a leak accident occurs. The study of these parameters is vital to the safey of natural gas pipelines and risk assessment after accidents. In this research, based on the conservation laws universally applicable to the motion of objects, we establish the fundamental control equation group for natural gas flow. Then, for both normal and leak conditions of pipelines, we use the characteristic line method to derive the corresponding difference equations for the fundamental control equations, thereby …providing calculation methods for pressure and flow rate. Finally, we investigate the calculation method of natural gas pipeline leak rate under different leak aperture sizes, and validate the accuracy of this method through simulation examples. Show more

Keywords: Hydraulic parameters, method of characteristic line, normal and leak conditions, leak rate, natural gas pipelines

DOI: 10.3233/JCM-247151

Citation: Journal of Computational Methods in Sciences and Engineering, vol. 24, no. 3, pp. 1649-1664, 2024

Authors: Chen, Jing | Wang, Xiaoxuan | Wu, Yujing

Article Type: Research Article

Abstract: The use of image fusion technology in the area of information processing is continuing to advance in depth thanks to ongoing hardware advancements and related research. An enhanced convolutional neural network approach is developed to fuse visible and infrared images, and image pre-processing is carried out utilising an image alignment method with edge detection in order to gain more accurate and trustworthy image information. The performance of the fast wavelet decomposition, convolutional neural network, and modified convolutional neural network techniques is compared and examined using four objective assessment criteria. The experimental findings demonstrated that the picture alignment was successful with …an offset error of fewer than 3 pixels in the horizontal direction and an angle error of less than 0.3∘ in both directions. The revised convolutional neural network method increased the information entropy, mean gradient, standard deviation, and edge detection information by an average of 46.13%, 39.40%, 19.91%, and 3.72%. The runtime of the modified approach was lowered by 19.42% when compared to the convolutional neural network method, which enhanced the algorithm’s performance and boosted the effectiveness of picture fusion. The image fusion accuracy reached 98.61%, indicating that the method has better fusion performance and is of practical value for improving image fusion quality. Show more

Keywords: Image fusion, convolutional neural networks, residual networks, visible and infrared images, information entropy

DOI: 10.3233/JCM-247272

Citation: Journal of Computational Methods in Sciences and Engineering, vol. 24, no. 3, pp. 1665-1678, 2024

Authors: Hu, Fengyu

Article Type: Research Article

Abstract: Sustainable and environmentally friendly construction is the way of the future for building projects, and it is a practical illustration of how sustainable development is being implemented in the construction sector, where the control objectives are interdependent and constricting. However, when green construction is carried out, a conundrum arises since important factors like cost, environmental preservation, and safety cannot be addressed simultaneously. This issue limits the promotion of green construction. In order to solve the obstacles in the green construction process, the study chose to introduce three objectives to establish a multi-objective optimization model for project optimization. The local search …idea in the mountain climbing algorithm was introduced into the non controlled sorting genetic algorithm to improve it, and a green construction multi-objective optimization model was established. Experimental verification of the feasibility and efficiency of improving non controlled sorting genetic algorithms; And evaluate and solve the established sustainable construction project. The results represent the maximum optimization values for each objective. The construction period is mainly distributed between 183–245 days, the cost distribution is between 16.6855 million yuan and 200861 million yuan, the quality distribution is between 0.864 and 0.878, the safety distribution is between 0.874 and 0.999, the environmental distribution is between 133.76 and 190.72, and the resource distribution is between 0.834 and 0.999, all of which meet the standards. Provide theoretical solutions for managers to manage green construction projects. Show more

Keywords: NSGA-II algorithm, hill climbing searching, environmental construction, multi-objective model, project management

DOI: 10.3233/JCM-247275

Citation: Journal of Computational Methods in Sciences and Engineering, vol. 24, no. 3, pp. 1679-1694, 2024

Authors: Peng, Yunxiang | Tian, Guixian

Article Type: Research Article

Abstract: With the deepening of enterprise financialization, the trend of “moving away from reality to emptiness” has increased the difficulty of financial management in the manufacturing industry. This paper selects the data of A-share main board listed companies from 2012 to 2021 to study the motivation of financial investment in the manufacturing industry and its impact on financial risk. The research results show that the main motivation of listed companies’ financial investment in the manufacturing industry is “substitution” motivation. With the purpose of maximizing profits, the excessive allocation of monetary assets, especially long-term financial assets, increases financial risks of enterprises. Furthermore, …the financial risk caused by the financial investment of state-owned enterprises is greater. Show more

Keywords: Manufacturing, financialization investment, “substitution” motivation, “reservoir” motivation

DOI: 10.3233/JCM-247270

Citation: Journal of Computational Methods in Sciences and Engineering, vol. 24, no. 3, pp. 1695-1708, 2024

Authors: Huang, Yang

Article Type: Research Article

Abstract: College students are learning a foreign language must know how to translate the spoken or written content from the respective language into English. These approaches do not help the college students to develop the capacity for rational thinking and adequate the motivation for the English translation. The educational principles are not in line with the qualities of the students in the typical English translation classroom teaching, and the teaching methods are out-dated. In the older process of the teaching English translation, many unreliable, vague aspects need to be considered, such as recognizing students’ fundamental English knowledge, unique circumstances, language proficiency, …cultural differences, and the ambiguity of the source language. The main issue with the current English translation evaluation methodology is that it cannot be easily to deal with thecomplex fuzzy indices when judging the accuracy of the student translations. An algorithm named FCAM-AHP-ANFIS is proposed to provide an effective and accurate method for evaluating and predicting students’ English translation outcomes to overcome the traditional shortcomings. According to the proposed approach, students can learn about passive translation, but they may struggle to actively improve their translation skills. College students can benefit from the decision-making aid provided by the extensive evaluation technique due to its high availability and precision. The fundamental benefit of the fuzzy technique over more traditional forms of the assessment is that it accounts for the ambiguity and uncertainty of the making judgments at the human level and provides a coherent framework that includes the indistinct findings of the several steps in evaluating an English translation. The Fuzzy Comprehensive Assessment Model (FCAM) is a decision-making method that uses the fuzzy logic to assess the quality of English translations among the college students. The Analytic Hierarchy Process (AHP) is employed to calculate each criterion’s relative importance and determine the optimal weighting for each criterion utilized in the assessment model. The Adaptive Neuro-Fuzzy Inference System (ANFIS) is used to analyze the translated data and generate predictions for the students’ translation outcomes. The experimental outcomes show the accuracy of the English translation assessment scores are 95.6% with 97% precision, 96% recall, and 96.5% of f1-score metric in addition to Root Mean Square (RMSE) and Mean Absolute Error (MAE). Show more

Keywords: Fuzzy comprehensive assessment model, analytic hierarchy process, adaptive neuro-fuzzy inference system, English translation and ranking scores

DOI: 10.3233/JCM-247281

Citation: Journal of Computational Methods in Sciences and Engineering, vol. 24, no. 3, pp. 1709-1725, 2024

Authors: Yang, Yi

Article Type: Research Article

Abstract: With the rapid development of Internet technology, foreign trade has been integrated with it, resulting in the rapid development of cross-border e-commerce, and for all kinds of enterprises to bring rich profits. However, in the fierce market competition, many enterprises ignore the importance of supply chain in the process of operation, which leads to the frequent bankruptcy of enterprises. To solve this problem, the research focuses on the supply chain performance evaluation of cross-border e-commerce enterprises, and proposes an improved error inverse propagation algorithm supply chain performance evaluation model. The results show that the model has improved the service capability …of cross-border e-commerce, the performance of suppliers and the supply chain. The average relative error of the artificial neural network algorithm and the error reverse propagation algorithm is 3.26% and 10.23% respectively, while the average relative error of the expected output and actual output of the artificial neural network algorithm is 2.11%, and the average relative error of the expected output value and actual output of the error reverse propagation algorithm is 6.78%. It can be seen that the artificial neural network algorithm can effectively improve the performance level of the supply chain, and under this algorithm, the objectivity of the weights and the accuracy and efficiency of the prediction results are guaranteed. Therefore, this study has important scientific value and practical significance for understanding and improving the supply chain management of cross-border e-commerce enterprises. Show more

Keywords: Cross-border e-commerce, BP neural network, supply chain performance, LMBP algorithm, supply chain management, performance evaluation

DOI: 10.3233/JCM-247290

Citation: Journal of Computational Methods in Sciences and Engineering, vol. 24, no. 3, pp. 1727-1740, 2024

Authors: Tan, Jie

Article Type: Research Article

Abstract: With the widespread application of digital images, image processing technology plays an important role in fields such as computer vision and image analysis. Based on the orthogonal matching pursuit algorithm, an image processing method is proposed. In the process, sparse representation and reconstruction algorithm are used for image compressed sensing to complete image sampling operation. Afterwards, the theory of overcomplete sparse representation is introduced to optimize sparse representation, and an overcomplete dictionary is used to remove Gaussian noise, achieving the goal of image processing. The experimental results indicate that the research method do not show significant deficiencies in signal reconstruction …when testing reconstructed signals under sparsity of 8; When testing the calculation time, the calculation time of the research method is about 0.212 s when the sparsity is 5 in the Lenna; In the error test, the mean square difference of the research method in the Lenna is stable at about 14.6; When conducting application analysis, the variance eigenvalues of the research method remained below 9.4. This indicates that the research method has good performance and can effectively process images, providing new technical support for image processing. Show more

Keywords: Image processing, orthogonal matching pursuit, sparse representation, compressed sensing, gaussian noise

DOI: 10.3233/JCM-247284

Citation: Journal of Computational Methods in Sciences and Engineering, vol. 24, no. 3, pp. 1741-1753, 2024

Article Type: Editorial

DOI: 10.3233/JCM-230000

Citation: Journal of Computational Methods in Sciences and Engineering, vol. 24, no. 3, pp. 1757-1757, 2024

Authors: Yan, Chen

Article Type: Research Article

Abstract: The ultimate bearing capacity test effect directly influences the safety performance of the design of building structures. To enhance the safety of building structures, applying the BP neural network algorithm to their ultimate bearing capacity test is studied to improve the test effect. The shear wave velocity of the building structure during stress is collected using the static probing technique. The input samples of the BP neural network are the building structure’s shear wave velocity and construction parameters. They are processed by dimensionality reduction through principal component analysis. The firework algorithm is used to optimize the weight of the BP …neural network. An early termination training method is designed, and the optimal weight is combined to train the BP neural network. After training, the samples are input after dimensionality reduction, and the building structure’s ultimate bearing capacity test results are output. Experimental results show that this method can effectively collect the shear wave velocity of building structures and complete the dimensionality reduction of samples. Under different coaxial stresses, this method can effectively measure the ultimate bearing capacity, about 3800 kN. After parameter optimization, the test value of this method is very close to the target value; that is, the ultimate bearing capacity test precision is high. Show more

Keywords: BP neural network, architectural results, ultimate bearing capacity, test application, static probing technology, firework algorithm

DOI: 10.3233/JCM-230001

Citation: Journal of Computational Methods in Sciences and Engineering, vol. 24, no. 3, pp. 1759-1770, 2024

Authors: Min, Quanzhang

Article Type: Research Article

Abstract: This study designs an automatic monitoring and alarm system for integrated meteorological observation based on the cloud platform to improve the automatic monitoring ability for integrated meteorological observation and the operation and maintenance management level of meteorological departments. A meteorological data collector was used to collect comprehensive meteorological observation data, transmit the collected comprehensive meteorological observation data to the main control processing module, preprocess the comprehensive meteorological observation data, and finally transmit the pre-processed comprehensive meteorological observation data to the cloud service module through the data transmission module. In the cloud service module, cloud computing and an improved K-means algorithm …were used to analyze the comprehensive meteorological observation data, mine abnormal comprehensive meteorological observation data, and send alarm information to the application module. Users can view the comprehensive meteorological observation data in the cloud server on the client in real-time, thus realizing automatic monitoring and alarm for comprehensive meteorological observation. Results show that the average transmission rate of various meteorological comprehensive observation data transmitted by the system is as high as 99.48%, thereby achieving clustering of abnormal meteorological comprehensive observation data. Upon detecting abnormal meteorological data, the system sends alarm information to the mobile phone client to complete the monitoring of abnormal meteorological information. Show more

Keywords: Cloud platform, comprehensive meteorological observation, automation, monitoring alarm system, sensor, data acquisition

DOI: 10.3233/JCM-230002

Citation: Journal of Computational Methods in Sciences and Engineering, vol. 24, no. 3, pp. 1771-1784, 2024

Authors: Yuan, Yaodong | Xu, Hongyan | Krishnamurthy, M. | Vijayakumar, P.

Article Type: Research Article

Abstract: The visual analysis method of educational data statistics based on big data mining is studied to improve students’ academic performance. Introducing the Mahalanobis distance and covariance matrix into the Fuzzy C-Means (FCM) clustering algorithm improves the FCM clustering algorithm. Through the improvement of the FCM clustering algorithm, the education data is mined from the massive original education data. The mining results are analyzed statistically, and the statistical analysis chart of education data is drawn. By improving the force-guided layout algorithm, the mined educational data points are written into the elastic graph layout to realize the visual layout. The ECharts data …visualization analysis component presents the visual layout results of education data points and the statistical analysis charts of education data. Experiments show that this method can effectively mine educational data and draw statistical analysis charts of educational data. Among them, learning analysis data occupy the highest proportion (15%), and privacy protection data occupy the lowest proportion (only 1%). The method can effectively lay out the educational data points and has a better visual effect. This method can effectively present the results of statistical analysis of educational data in visual form, in which learning analysis data is the most important. Show more

Keywords: Big data mining, educational data, statistical analysis, visualization, Mahalanobis distance, force-guided layout

DOI: 10.3233/JCM-230003

Citation: Journal of Computational Methods in Sciences and Engineering, vol. 24, no. 3, pp. 1785-1793, 2024

Authors: Qiao, Fengfeng | Chen, Xinchen | Ding, Zenghui

Article Type: Research Article

Abstract: With the constant goal of improving the quality of higher education, quality evaluation is a widely concerned problem, prompting this study to construct a higher education quality evaluation method based on a neural network model. First, an Attention Relevance Confidence Satisfaction (ARCS) model was constructed herein. This was done through two rounds of screening; the evaluation indexes of higher education quality were selected, and an evaluation index system was finally constructed. Then, the weight of each evaluation index was calculated using the constructed ARCS model. According to the 1–9 grading scale, an index scoring matrix of industry-education integration was established. …Afterwards, the higher education quality evaluation score was obtained based on the neural network model, and the evaluation effect level was determined. The experimental results showed that the quality evaluation effect of the proposed method in the past five years showed an overall rising trend, even already reaching the top level. Moreover, the denoised experimental dataset was finally divided into a test dataset (28%) and an experimental dataset (72%), with the proposed method exhibiting a favorable effect. Show more

Keywords: Neural network model, higher education, quality evaluation, evaluation index system, ARCS model

DOI: 10.3233/JCM-230004

Citation: Journal of Computational Methods in Sciences and Engineering, vol. 24, no. 3, pp. 1795-1805, 2024

Authors: Ma, Qingxiang

Article Type: Research Article

Abstract: A prevention and control tracking system based on three-dimensional (3D) face recognition was designed to improve the target tracking accuracy of the prevention and control tracking system. The ARM control chip of TMS320DM6446 was selected as the control chip of the ARM control module. The CMOS image acquisition sensor of the image acquisition module collected face images. The collected images were transmitted to the 3D face recognition module. The 3D face recognition module used the Gabor wavelet algorithm to extract the 3D face contour features of the face image. Moreover, the LDA algorithm was used to recognize faces based on …3D face contour features. The 3D face recognition results were compared with the faces in the face library to determine whether prevention and control tracking were necessary. When prevention and control tracking was needed, the GPS tracking and positioning module embedded in the mobile device terminal of the target object was started. The GPS tracking and positioning module was used to prevent and control the tracking of the target. The results of prevention and control tracking were displayed to the system users using a VGA display. The experimental results indicated that the designed system could accurately recognize faces and achieve prevention and control tracking of the target based on the face recognition results. Show more

Keywords: 3D face recognition, prevention and control tracking system, control chip, Gabor wavelet, contour features, LDA algorithm

DOI: 10.3233/JCM-230005

Citation: Journal of Computational Methods in Sciences and Engineering, vol. 24, no. 3, pp. 1807-1823, 2024

Authors: Li, Zhen | Guo, Xinyao | Si, Qingmin | Fu, Shuai | Lin, Chen

Article Type: Research Article

Abstract: The 10,000-hour rate of civil aviation incidents is an important index parameter to measure flight safety. Predicting the development trend of the 10,000-hour rate of civil aviation incidents plays an important role in aviation accident prevention and safety decision-making. Many complex factors influence the occurrence of civil aviation incidents, so the 10,000-hour rate of civil aviation incidents changes randomly and volatilely. This study proposed the idea of prediction by combining the grey GM (1, 1) model and the Markov model. Specifically, the grey GM (1, 1) prediction model was constructed using the statistical data on the 10,000-hour rate of civil …aviation incidents in China during 2005–2020. On this basis, a grey Markov prediction model was established. The prediction of the 10,000-hour rate of incidents in 2021 based on the two models showed that the grey Markov model displayed higher prediction accuracy than the grey GM (1, 1) model and conformed to the change laws of the 10,000-hour rate data of civil aviation incidents better. Moreover, the grey Markov model could effectively improve the accuracy of the grey prediction model, compensate for its deficiencies, and facilitate the mastery of the change laws of civil aviation incidents, providing a reliable basis for aviation safety management and incident prevention. Show more

Keywords: Civil aviation safety, 10, 000-hour rate of incidents, grey prediction, Markov prediction

DOI: 10.3233/JCM-230006

Citation: Journal of Computational Methods in Sciences and Engineering, vol. 24, no. 3, pp. 1825-1837, 2024

Authors: Mou, Dan

Article Type: Research Article

Abstract: Tourism has had some negative effects while generating positive results. The carbon emissions produced by tourism, which is not a “smokeless industry” in traditional cognition, account for a certain proportion of the global greenhouse gas emissions. Tourism transportation, tourist accommodation, and other tourism activities all contribute to the carbon emission of tourism, and various tourism activities not only stimulate the economy but also increase air pollution. As a big industry, tourism’s growth and development have continuously increased energy consumption, and the pressure on energy conservation and emission reduction has also been greatly aggravated. In this study, the tourism carbon emissions …in each province of China were estimated using a “top-down” calculation model, the tourism energy consumption factors were decomposed using a logarithmic mean Divisia index model, and the driving factors of tourism carbon emissions were analyzed through a panel data model. Results show that the tourism carbon emissions in China rapidly increased from 360.74 million tons in 2006 to 853.28 million tons in 2021. The driving factors of tourism energy consumption in China are economic development, energy efficiency, and population, while the inhibiting factors are tourism intensity and energy structure. The per capita GDP, the proportion of the tertiary industry, the turnover of tourists, and the level of urbanization all significantly promote the growth of tourism carbon emissions in China at 1%. The research results are of great significance to the proposal of measures for tourism carbon emission reduction in combination with the situation of various provinces and cities, promoting regional economic development and boosting the development of tourism in China under the background of a low-carbon economy. Show more

Keywords: LMDI, panel data model, tourism, energy consumption, carbon emissions, driving factors

DOI: 10.3233/JCM-230007

Citation: Journal of Computational Methods in Sciences and Engineering, vol. 24, no. 3, pp. 1839-1849, 2024

Authors: Sundaresan, Yuvaraj Gandhi | Thiyagarajan, Revathi

Article Type: Research Article

Abstract: The difficulty of scheduling jobs or workloads increases due to the stochastic and transient characteristics of the cloud network. As a key prerequisite for establishing QoS, it asserts that effective work scheduling must be developed and executed. Maximum profit is made possible for cloud service providers by proper resource management. The most effective scheduling algorithm considers resources given by providers rather than the task set that users have accumulated. This paper developed a model that works in a two-level hierarchical model comprising global scheduling and local schedules to handle the heterogeneous type of request in real-time. These two levels of …scheduling communicate with each other to produce an optimal scheduling scheme. Initially, all the requests are passed to the global scheduler, whose task is to categorize the type of request and pass it to the corresponding queue for assigning it to the related local scheduler using a parabolic intuitionistic fuzzy scheduler. In this work, the heterogeneous types of files are handled by maintaining different queues, in which each queue handles only a specific type of file like text doc, audio, image and video. Once the type of req is initiated by the clients, the global scheduler identifies the type of request and passes it to their relevant queue. In the next level, the local scheduler is assigned to each type of web server cluster. Once the work request is dispatched from the global workload scheduler, it is allocated to the local queue of the local scheduler, which allocates the resources of web servers by adapting the Quantum Honey Badger Algorithm, which searches the best-suited server for completing the assigned work based on the available resource parameters. Show more

Keywords: Work load scheduling, Intuitionistic fuzzy, quantum theory, honey badger algorithm, resource allocation, heterogenous work, cloud network

DOI: 10.3233/JCM-230008

Citation: Journal of Computational Methods in Sciences and Engineering, vol. 24, no. 3, pp. 1851-1862, 2024

Authors: Shan, Zhengyi | Zhu, Shihong

Article Type: Research Article

Abstract: By constructing an evaluation system for the high-quality development of innovation and entrepreneurship, an evaluation index system was established in this study from five aspects: the background, process, input, output, and transformation of innovation and entrepreneurship, and analytic hierarchy process (AHP) and entropy method (EM) were adopted to perform combination weighting. Then, the core of each subsystem and the comprehensive score were calculated based on the TOPSIS method, the high-quality development level of urban innovation and entrepreneurship in 19 vice-ministerial cities like Beijing, Shanghai, Tianjin, Chongqing, Guangzhou, Shenzhen, and Chengdu in China was measured, and the innovation and entrepreneurship development …level and structural characteristics were analyzed from five aspects. The results show that Shenzhen, Shanghai, Beijing, Nanjing, and Guangzhou take the lead in the high-quality development of innovation and entrepreneurship, while Xi’an, Chengdu, Hangzhou, Wuhan, Qingdao, Jinan, and Ningbo are in the medium level. Chongqing, Shenyang, Dalian, Harbin, Xiamen, and Changchun perform poorly in the development of innovation and entrepreneurship with problems of interregional large gradient difference in capacity and unbalanced development, which provides an important reference for understanding the current situation, advantages, and disadvantages of innovation and entrepreneurship education development in various economic zones. Show more

Keywords: Innovation and entrepreneurship, entropy method, analytic hierarchy process, TOPSIS method, level measurement

DOI: 10.3233/JCM-230009

Citation: Journal of Computational Methods in Sciences and Engineering, vol. 24, no. 3, pp. 1863-1876, 2024

Authors: Xu, Yebiao

Article Type: Research Article

Abstract: Global warming is one of the key issues attracting international concern. The carbon dioxide emission produced by energy combustion is the main cause of the greenhouse effect, and reducing carbon emissions is considered the most effective way to deal with the greenhouse effect. The extensive production mode characterized by high energy consumption, high emission, and low efficiency in China’s construction industry intensifies the contradiction between economic development and resources and the environment, and the growth under this mode is at the expense of consuming a lot of resources and energy. The improvement of carbon emission efficiency is an effective means …of achieving the goal of economic growth and carbon emission reduction simultaneously, making it necessary to accurately measure the carbon emission efficiency of the construction industry in each province, determine the influencing factors, and formulate reasonable emission reduction policies for this industry. In this study, an input-output index system of carbon emission efficiency of China’s construction industry was constructed, the carbon emission efficiency of the construction industry in each province was evaluated using the super-efficiency SBM model, and the factors affecting the carbon emission efficiency of this industry were analyzed via the Tobit model. The results showed that the average value of carbon emission efficiency of the construction industry generally showed a rising trend in a fluctuating way during the study period. From 2014 to 2022, the average carbon emission efficiency of the national construction industry presented an upward trend, from 1.122 in 2014 to 1.148 in 2022; the regional economic level (p = 0.020 < 0.05) and human capital level (p = 0.000 < 0.01) exerted obvious promoting effects on the carbon emission efficiency of China’s construction industry, while the urbanization development (p = 0.049 < 0.05) generated evident negative effects on carbon emission efficiency of this industry. The research results have important reference values for making cross-provincial emission reduction plans for the construction industry, promoting its carbon emission efficiency, and driving the research and development of green building materials and clean energy. Show more

Keywords: Super-efficiency SBM, Tobit model, China’s construction industry, carbon emission efficiency, energy consumption

DOI: 10.3233/JCM-230010

Citation: Journal of Computational Methods in Sciences and Engineering, vol. 24, no. 3, pp. 1877-1887, 2024

Authors: Yin, Zhixiang | Yin, Zongyi | Ye, Jiamei | Liu, Runchang

Article Type: Research Article

Abstract: Nowadays, the demand for risk response is increasing in countries worldwide, leading to the development of emergency-related industries as strategic emerging sectors. However, the emergency logistics industry is facing increasingly critical distribution issues. This study applies K-means clustering analysis to convert multiple distribution centers into multiple single distribution center problems. It then compares and analyzes the vehicle routing model with time windows for emergency logistics delivery in multiple distribution centers using guided local search (GLS), taboo search (TS), and simulated annealing (SA) algorithm. The results demonstrate that the GLS algorithm outperformed both the SA and TS algorithm in optimizing emergency …logistics delivery paths for multiple distribution centers. The GLS algorithm proved to be more effective in solving this problem. This study confirms the contemporary value of emergency logistics distribution problems and offers practical insights into optimizing emergency logistics distribution paths in multiple distribution centers. Show more

Keywords: Emergency logistics, distribution path, K-means, guided local search algorithm, multiple distribution centers

DOI: 10.3233/JCM-230011

Citation: Journal of Computational Methods in Sciences and Engineering, vol. 24, no. 3, pp. 1889-1902, 2024

Authors: Chung, Yao-Liang | Chung, Hung-Yuan | Yang, Zheng-Hua | Pichappan, Pit

Article Type: Research Article

Abstract: This study aimed to apply a microprocessor on a wireless automated vehicle to achieve real-time tracking of moving objects. The targets captured by the camera on the vehicle were first separated from their background through background subtraction. Next, morphological processing was performed to remove unnecessary information. An enhanced seeded region growing method was used to achieve image segmentation by labeling and segmenting the targets effectively, thus enhancing the accuracy and resolving the problem of object concealment. The corresponding red, green, and blue colors of each target were calculated through a color space, which was then converted into an enhanced luminance/chroma …blue/chroma red (YUV) color space for color histogram modeling and storage, so as to increase the system’s tracking speed. The enhanced YUV colors also achieved accurate tracking in dark places. After inputting the next image, an enhanced agglomerative hierarchical clustering method was used to agglomerate and connect pixels with the same YUV for tracking. A proportional-integral-derivative controller controlled the motors on the camera lens and the vehicle so that the target could be tracked properly in real-time. The experimental results revealed that our proposed tracking method performed better than conventional tracking methods. Show more

Keywords: Object tracking, seeded region growing method, YUV color space, agglomerative hierarchical clustering method, PID control

DOI: 10.3233/JCM-230012

Citation: Journal of Computational Methods in Sciences and Engineering, vol. 24, no. 3, pp. 1903-1919, 2024

Authors: Wang, Baohua | Du, Yunchao

Article Type: Research Article

Abstract: Intelligent interconnection and big data will be the core content of the future competition, and a unified digital platform construction of the automobile manufacturing industry will inevitably become an important support for the future development from large to strong. Using literature research and expert consultation, 14 influencing factors of digital platform construction in the automobile manufacturing industry were sorted out. this study uses ISM (Interpretative structural modelling) model to stratify the influencing factors of digital platform construction of the automobile manufacturing industry, draw a multi-layer hierarchical structure diagram of influencing factors, and uses the MICMAC (Matrix impacts cross-reference multiplication applied …to a classification) method to analyze the dependence and driving force of the main influencing factors. The results show that 14 factors are more scientific and reasonable as influencing factors of digital platform construction in the automobile manufacturing industry. A1, A3, B1, B3, C3, D2, D3 are the top-level influencing factors. C1 and C2 are the bottom influencing factors, highlighting that technical factors are still the fundamental factors affecting the digital platform of the automobile manufacturing industry. C1, D1 and C2 are autonomous factors with a high driving force and play an important role in promoting digital platform construction in the automobile manufacturing industry. The research results have important reference value for accelerating the digital transformation of the automobile manufacturing industry, enhancing the core competitiveness of automobile industry enterprises, and improving the monitoring degree of operation status of the automobile industry market. Show more

Keywords: ISM-MICMAC, automobile manufacturing, digital platform, dependence, driving force, influencing factor

DOI: 10.3233/JCM-230013

Citation: Journal of Computational Methods in Sciences and Engineering, vol. 24, no. 3, pp. 1921-1930, 2024

Authors: Wang, Pei

Article Type: Research Article

Abstract: The high-quality development of the logistics industry, which is an essential and strategic industry supporting the national economic operation and a fundamental component of modern industrial system construction, is not only a key component of the high-quality development of the national economy but also the main driving force for the high-quality growth of the national economy. As the supporting industry of the national economy, the logistics industry will also face spatial disequilibrium during development. Therefore, to achieve the coordinated development of the logistics industry, the high-quality development and the spatial-temporal unbalanced development status of the logistics industry in each province …must be figured out first. This research established a comprehensive evaluation system for the logistics industry development, which included 14 basic indexes based on the provincial-level panel data of 30 provinces in China during 2009–2020. Then, the regional logistics development level score in China was measured using the entropy weight TOPSIS method, and the differences in the regional logistics development level in China and the dynamic evolution law of their distribution were deeply explored through the Dagum Gini coefficient model. The research results revealed that the evaluation index system (14 basic indexes) for the regional logistics industry development level in China was relatively scientific and reasonable; the regional logistics industry development level in China was increasing year by year, showing a steady upward trend, and the imbalance in the eastern, central, and western regions regarding the regional logistics development was shrinking year by year; the average intergroup contribution rate was 36.33%, the intragroup contribution rate was 31.49%, and the contribution rate of intensity of trans variation was 32.19%, proving that the regional differences exerted a most extraordinary influence on the spatial differences in the regional logistics industry development level in China. The research results have important reference value for summarizing the meaning of high-quality logistics industry development, constructing the evaluation index system for logistics industry development, and exploring the reasons for the temporal and spatial differences in logistics industry development in China. Show more

Keywords: Entropy weight TOPSIS, Dagum model, regional logistics, development level, difference

DOI: 10.3233/JCM-230014

Citation: Journal of Computational Methods in Sciences and Engineering, vol. 24, no. 3, pp. 1931-1942, 2024

Authors: Diao, Xueying

Article Type: Research Article

Abstract: Excessive emission of greenhouse gases leads to the increasing greenhouse effect, adversely affecting the global climate. Carbon dioxide is the dominant part of greenhouse gases; reducing its emission is the most important way to solve the climate problem. Aiming at the characteristics of the dangerous goods transportation market and the development of the carbon tax policy, the carbon tax cost and the cost of dangerous goods transportation are introduced, and the characteristics of the dangerous goods transportation and the road traffic network and the road traffic in each period are fully analyzed. The optimization model of the path of the …hazardous goods vehicles is established with the optimization objective of the lowest total cost. Then, by analyzing the advantages and disadvantages of bacterial foraging algorithm (BFA) and ant colony algorithm (ACO), the hybrid BFA-ACO algorithm is established by combining the two, and the replication and convergence operations of bacterial foraging algorithm are introduced into the ACO algorithm to improve the convergence speed and global convergence ability of the algorithm. The hybrid algorithm is then used to optimize and solve the path optimization model of hazardous materials vehicles and compared with the classical algorithms Genetic Algorithm (GA) and ACO for solving the path of dangerous materials vehicles. A comparison of the optimization results reveals that optimizing the model by bacterial foraging-ACO algorithm is better than optimizing the model by a single algorithm. Show more

Keywords: Hazardous materials transportation, path optimization, carbon tax, disinfection costs, bacterial foraging-ant colony algorithm

DOI: 10.3233/JCM-230015

Citation: Journal of Computational Methods in Sciences and Engineering, vol. 24, no. 3, pp. 1943-1954, 2024

Authors: Vijayachandran, Vipin | R, Suchithra

Article Type: Research Article

Abstract: Data collection using local differential privacy (LDP) has mainly been studied for homogeneous data. Several data categories, including key-value pairs, must be estimated simultaneously in real-world applications, including the frequency of keys and the mean values within each key. It is challenging to achieve an acceptable utility-privacy tradeoff using LDP for key-value data collection since the data has two aspects, and a client could have multiple key-value pairs. Current LDP approaches are not scalable enough to handle large and small datasets. When the dataset is small, there is insufficient data to calculate statistical parameters; when the dataset is enormous, such …as in streaming data, there is a risk of data leakage due to the high availability of too much information. The result is unsuitable for examination due to the substantial amount of randomization used in some methods. Existing LDP approaches are mostly restricted to basic data categories like category and numerical values. To address these difficulties, this research developed the DKVALP (Differentially private key-value pairs) algorithm, which ensures differential privacy in key-value pair data. This DKVALP is a lightweight, differentially private data algorithm that generates random noise using an updated Laplace algorithm to ensure differential privacy for the data. According to execution outputs on synthetic and real-world datasets, the proposed DKVALP framework offers improved usefulness for both frequency and mean predictions over the similar LDP security as conventional approaches. Show more

Keywords: Differential privacy, local differential privacy, Laplace algorithm, back key-value pairs, improved Laplace algorithm and DKVALP

DOI: 10.3233/JCM-230016

Citation: Journal of Computational Methods in Sciences and Engineering, vol. 24, no. 3, pp. 1955-1970, 2024

Authors: Zhang, Ranran

Article Type: Research Article

Abstract: As a booster of the national economy and a catalyst of industrial development, the logistics industry uniquely maintains stable economic operations and promotes industrial structural adjustment. The development of China’s logistics industry has broadened the international market and accelerated the exchange and cooperation of logistics industries between different countries. The “Belt & Road Initiative” will continue to provide impetus for the development of China’s logistics industry, which can guarantee the infrastructure interconnection of the logistics industry and ensure the fundamental implementation of the initiative. Based on theoretical analysis and the panel data of 30 provinces in China during 2005–2020, whether …the “Belt & Road Initiative” had obvious policy effects on regional logistics development in China was analyzed. The empirical research results showed that the “Belt & Road Initiative” could accelerate the high-quality development of the logistics industry in the provinces along the route during the research period. Industrial proportion, per capita GDP, import and export amount of goods, investment in fixed assets of the whole society, and science and technology input positively affected the development of regional logistics industries. The regression coefficient of the energy structure in the logistics industry was negative but not significant. The research results have important decision-making reference values in promoting the regional advantages of modern logistics industries, promoting the convenience of logistics trade, improving the scientific and technological level of the logistics industry, and using other exogenous policy variables to boost the high-quality development of modern logistics industries under the background of the “Belt & Road Initiative”. Show more

Keywords: Difference-in-difference model, “Belt & Road Initiative”, regional logistics, regional economy, policy effect, effect evaluation

DOI: 10.3233/JCM-230017

Citation: Journal of Computational Methods in Sciences and Engineering, vol. 24, no. 3, pp. 1971-1980, 2024