Journal of Computational Methods in Sciences and Engineering - Volume 24, issue 2

Authors: Yang, Hanqi | Wang, Xiaoyu

Article Type: Research Article

Abstract: The global market competition is becoming increasingly fierce, and manufacturing enterprises need to invest and expand. However, the traditional financial optimization of manufacturing enterprises has faced problems such as low efficiency and inaccurate search for the optimal solution, which has made manufacturing enterprises likely to face financial risks. The investment portfolio can enable enterprises to obtain the maximum profit on a certain risk level, or reduce their investment risk on a certain return level as far as possible. If the combinatorial optimization is realized, it can be applied to the optimal selection of manufacturing enterprises’ financialization. This article analyzed the …respective characteristics of Genetic Algorithm (GA) and Simulated Annealing (SA) algorithms, and analyzed the combination of GA and SA algorithms to solve the optimal investment portfolio through GA-SA algorithm, thereby helping manufacturing enterprises to make the optimal choice for financialization. The experimental results of this article indicated that the GA-SA algorithm solved the problem of GA algorithm easily falling into local optima, SA algorithm’s initial temperature and generation mechanism, and improved the efficiency of finding the optimal solution. Meanwhile, the experimental results showed that the average optimal solutions of Genetic Algorithm, Simulated Annealing algorithm, and GA-SA algorithms for 36 stock portfolios in Enterprise 1 were 69, 69, and 107, respectively. The average optimal solutions of the three algorithms for 36 stock portfolios in Enterprise 2 were 73, 90, and 112, respectively. This proves that the number of optimal solutions searched by GA-SA algorithm is higher than that of GA and SA algorithm, and also proves that it is effective to use GA-SA algorithm to optimize investment portfolio and help manufacturing enterprises to make optimal financial choices. Show more

Keywords: Simulated annealing, genetic algorithm, portfolio theory, manufacturing enterprise

DOI: 10.3233/JCM-247179

Citation: Journal of Computational Methods in Sciences and Engineering, vol. 24, no. 2, pp. 623-638, 2024

Authors: Ma, Xiaowen

Article Type: Research Article

Abstract: Aiming to address the timely dissemination of news information, this work explores the clever utilization of data mining (DM) technology and deep learning (DL) algorithms to construct an intelligent real-time news image acquisition system to meet the urgency of news dissemination needs. First, this work introduces an intelligent real-time news image acquisition system and provides a detailed analysis of its principles and advantages. Throughout this process, the crucial role of DM technology in news image classification and automation is emphasized, especially in dealing with rapidly evolving news events. Next, the work establishes an intelligent real-time news image acquisition model based …on DL algorithms, which integrates the essence of DM technology. Through this fusion, the research objective is to enhance the performance of the news image acquisition system to achieve higher real-time and accuracy, which is vital for the swift delivery of news information. Finally, this work investigates the application of the intelligent news image acquisition system in network communication to ensure its adaptability to various network communication scenarios while maintaining accuracy and real-time capabilities. The research results demonstrate that the application of DM technology in combination with DL algorithms can effectively meet the practical needs of the news industry, enhancing the automation of news image processing and enabling faster information delivery to the audience. Notably, the AlexNet model employed performs exceptionally well, achieving recognition rates of up to 99.6% after data augmentation or equalization processing, with an accuracy of 90.9% and a high specificity of 93.38%. This indicates outstanding overall classification accuracy and negative class accuracy, even when distinguishing between news and non-news scenarios. These results clearly underscore the connection between DM technology and news acquisition and editing practices, and emphasize its potential to improve the efficiency and accuracy of real-time information dissemination. The research’s contribution and innovation lie in the fusion of DM technology with DL algorithms to build an intelligent real-time news image acquisition system. This fusion enhances the automation and classification performance of news images, thereby improving the real-time and accuracy of news information. Furthermore, the work strongly emphasizes improving the real-time and accuracy of the news image acquisition system to ensure the swift delivery of information, which is of utmost importance in rapidly changing news events. Show more

Keywords: Deep learning, data mining, real-time image acquisition, network security, AlexNet model

DOI: 10.3233/JCM-237131

Citation: Journal of Computational Methods in Sciences and Engineering, vol. 24, no. 2, pp. 639-656, 2024

Authors: Huang, Furong

Article Type: Research Article

Abstract: As global economic integration continues to advance, international trade has become increasingly vital for the economic development and growth of nations. This research aims to assess the trends in industrial technology security within China’s international trade and provide practical guidance for policy-making, corporate strategies, and international cooperation. The significance of the rising trend in security within China’s international trade industry lies in its establishment of a robust foundation for the long-term development of China’s international trade, contributing to its cooperation and competitiveness with other countries. In addressing the limitations of traditional measurement methods and providing a more comprehensive and accurate …assessment of industrial technology security, this research presents an approach based on a discrete Hopfield Neural Network (HNN) for evaluating industrial technology security in international trade. This method integrates multiple indicators, including technology gap rates, to construct the Superior Quality Engineering (SQE) comprehensive evaluation model. The research employs a combination model of “entropy-grey relational-Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS)-discrete HNN” to assess industrial technology security. This research evaluates international trade industry technology security using patent data from 2015 to 2022 as samples. The results indicate an overall upward trend in security in China’s international trade industry. Within this trend, the research observes a stepwise increase in scale components, leading to continuous improvement in security. In terms of quality components, although security develops relatively slowly overall, it exhibits a trend of initial gradual decline followed by rapid growth. Regarding efficiency components, there is overall slow growth with periodic fluctuations. This research outcome provides substantial support for the research of industrial technology in international trade. The proposed method can assist businesses in evaluating their technological security in international trade and offer robust support for international trade decision-making. Show more

Keywords: Measurement, international trade, technological innovation, high-tech industries, discrete hopfield neural network, gray relation

DOI: 10.3233/JCM-237128

Citation: Journal of Computational Methods in Sciences and Engineering, vol. 24, no. 2, pp. 657-674, 2024

Authors: Meng, Yan | Jungjin, Kim | Hak-Chun, Lee | Je, Cho Dong | Cheng, Peiyun

Article Type: Research Article

Abstract: With the rapid development of Artificial Intelligence (AI) and Big Data (BD), they provide people with new ways and tools in information acquisition, processing and dissemination. The purpose of this study is to deeply discuss the influence of AI and BD technologies on the development of Information Security (InfoSec) in media economy, and analyze the opportunities and challenges it brings. Firstly, the evaluation system of the influence of BD technology on the development of InfoSec, a media enterprise, is constructed. Then, this study discusses the development mode of the multilateral platform of media economy supported by BD technology. Finally, taking …S company and X platform as examples, this study analyses the influence of BD technology on the development of InfoSec and the development mode of media economy supported by BD technology. The research results show that with the support of BD technology, the proportion of InfoSec products in enterprises has reached 0.2426, and the InfoSec score of S company has reached 77.74, reaching a high level. The profit margin of digital network media is three times that of traditional media. The number of users of multilateral digital media economic platform X has increased by 21%, and its turnover has increased by 54%. AI and BD technologies have brought opportunities and challenges to the InfoSec development of media economy. While applying these technologies, it is necessary to take effective measures to protect users’ privacy and strengthen the information review and verification mechanism to ensure the sustainable development of media economy. The uniqueness of this study lies in the construction of a comprehensive BD technology impact assessment system and the discussion of the development model of multilateral media economic platform, which provides a powerful reference and guidance for media enterprises. In addition, this study also reveals the positive influence of BD technology on InfoSec and the development of media economy, which provides beneficial enlightenment for the future development of media industry. In response to the challenges of privacy and InfoSec, this study puts forward important policy and practical suggestions to ensure the sustainable growth of media economy and the protection of users’ rights and interests. Therefore, this study has important contribution and innovative value in both theory and practice. Show more

Keywords: Artificial intelligence, big data technology, media economy, information security, multilateral platforms

DOI: 10.3233/JCM-237126

Citation: Journal of Computational Methods in Sciences and Engineering, vol. 24, no. 2, pp. 675-695, 2024

Authors: Zhang, Zhongpeng | Wang, Guibao

Article Type: Research Article

Abstract: This work aims to advance the security management of complex networks to better align with evolving societal needs. The work employs the Ant Colony Optimization algorithm in conjunction with Long Short-Term Memory neural networks to reconstruct and optimize task networks derived from time series data. Additionally, a trend-based noise smoothing scheme is introduced to mitigate data noise effectively. The approach entails a thorough analysis of historical data, followed by applying trend-based noise smoothing, rendering the processed data more scientifically robust. Subsequently, the network reconstruction problem for time series data originating from one-dimensional dynamic equations is addressed using an algorithm based …on the principles of Stochastic Gradient Descent (SGD). This algorithm decomposes time series data into smaller samples and yields optimal learning outcomes in conjunction with an adaptive learning rate SGD approach. Experimental results corroborate the remarkable fidelity of the weight matrix reconstructed by this algorithm to the true weight matrix. Moreover, the algorithm exhibits efficient convergence with increasing data volume, manifesting shorter time requirements per iteration while ensuring the attainment of optimal solutions. When the sample size remains constant, the algorithm’s execution time is directly proportional to the square of the number of nodes. Conversely, as the sample size scales, the SGD algorithm capitalizes on the availability of more information, resulting in improved learning outcomes. Notably, when the noise standard deviation is 0.01, models predicated on SGD and the Least-Squares Method (LSM) demonstrate reduced errors compared to instances with a noise standard deviation of 0.1, highlighting the sensitivity of LSM to noise. The proposed methodology offers valuable insights for advancing research in complex network studies. Show more

Keywords: Heuristic algorithm, long short-term memory neural network, optimal task network security, topology time series, stochastic gradient descent

DOI: 10.3233/JCM-237124

Citation: Journal of Computational Methods in Sciences and Engineering, vol. 24, no. 2, pp. 697-714, 2024

Authors: Liu, Peijun

Article Type: Research Article

Abstract: English reading and writing are important parts of language teaching. In order to improve the English reading and writing ability of college students, the TLBO (teaching learning-based optimization) algorithm is used in this research to improve the way that English reading and writing are taught in colleges and universities. It is chosen as the primary model for this study. The TLBO algorithm is further optimized in this paper, and a convergence analysis is performed between the optimized model M-TLBO (multi-learning teaching learning-based optimization) algorithm and other TLBO algorithms in order to address the issues that the TLBO algorithm has an …excessively single teaching ability and readily settles into local optimal solutions for some large-scale complex problems. In terms of stability and convergence accuracy, M-TLBO outperforms other algorithms. In order to investigate the impact of the M-TLBO algorithm on students’ writing performance, this paper uses the teaching-learning optimization algorithm to conduct a pre-and post-test on students’ English reading and writing performance in five dimensions. The study’s findings revealed that students’ pre-test writing scores had a mean value of 8.4770 and a standard deviation of 1.72449, and that their post-study writing scores had increased by 5.05 points. The English reading and writing information-based teaching model can improve students’ English writing performance. It is hoped to promote the development of English teaching and improve the efficiency of students’ English learning. Show more

Keywords: Intelligent optimization algorithm, TLBO algorithm, M-TLBO model, information-based teaching, English reading and writing, teaching model

DOI: 10.3233/JCM-237101

Citation: Journal of Computational Methods in Sciences and Engineering, vol. 24, no. 2, pp. 715-730, 2024

Authors: Xia, Yunqing

Article Type: Research Article

Abstract: With the development of technology and the widespread collection of data, high-dimensional data analysis has become a research hotspot in many fields. Traditional parameter methods often face problems such as dimensional disasters in high-dimensional data analysis. Non parametric methods have broad application prospects in high-dimensional data because they do not rely on specific parameter distribution assumptions. The Bayesian rule is more suitable for dealing with noise and outliers in high-dimensional data because it takes uncertainty into account. Therefore, it is of great significance to combine non parametric methods with Bayesian methods for application research in high-dimensional data analysis. In this …paper, the nonparametric Bayesian method was applied to the analysis of high-dimensional data, and the Dirichlet process Mixture model was used to cluster high-dimensional data. The regression analysis of high-dimensional data was carried out through the prediction model of nonparametric Bayesian regression. In this paper, the nonparametric Bayesian method based on Bayesian sparse linear model was used for feature selection of high-dimensional data. In order to determine the superiority of nonparametric Bayesian methods in high-dimensional data analysis, this paper conducted experiments on nonparametric Bayesian methods and traditional parametric methods in high-dimensional data analysis from five aspects of cluster analysis, classification analysis, regression analysis, feature selection and anomaly detection, and evaluated them through multiple indicators. This article explored the application of non parametric Bayesian methods in high-dimensional data analysis from these aspects through simulation experiments. The experimental results show that the clustering accuracy of the non parametric Bayesian clustering algorithm was 0.93, and the accuracy of the non parametric Bayesian classification algorithm was between 0.93 and 0.99; the coefficient of determination of nonparametric Bayesian regression algorithm was 0.98; the F1 values of non parametric Bayesian methods in anomaly detection ranged from 0.86 to 0.91, which was superior to traditional methods. Non parametric Bayesian methods have broad application prospects in high-dimensional data analysis, and can be applied in multiple fields such as clustering, classification, regression, etc. Show more

Keywords: High dimensional data, nonparametric bayesian method, cluster analysis, classification analysis, regression analysis, feature selection

DOI: 10.3233/JCM-237104

Citation: Journal of Computational Methods in Sciences and Engineering, vol. 24, no. 2, pp. 731-743, 2024

Authors: Dong, Peilin | Wang, Xiaoyu | Shi, Zhouhao

Article Type: Research Article

Abstract: The financial market has randomness, and the prediction of the financial market is an important task in the financial market. In traditional financial market prediction models, the prediction results are often unsatisfactory. So it needs to introduce new models for financial analysis. To solve this problem, this paper analyzed a financial market trend prediction model based on LSTM (Long Short-Term Memory) NN (Neural Network) algorithm, and conducted an empirical analysis on the Shanghai stock index dataset. This paper first introduced the LSTM NN algorithm, and then divided it into training set, test set and comparison set according to the data …characteristics. At last, this paper used the data preprocessing method to verify the LSTM NN algorithm. The experimental results showed that the LSTM NN algorithm analyzed in this paper can effectively improve the generalization ability of financial market trend prediction models while ensuring the prediction accuracy. Through experimental analysis, this paper found that the average accuracy rate of using LSTM NN algorithm was 2.25% higher than that of using traditional NN algorithm. This research is primarily aimed at developing effective methods for predicting stock market trends in the continuously evolving Chinese securities market. The core objective is to empower investors with precise guidance by enabling them to make well-informed investment decisions. Achieving accurate predictions holds the potential to significantly impact economic operations in a positive way. Therefore, this research direction is of paramount importance, offering substantial value both in academic exploration and practical application. Show more

Keywords: Long short-term memory, neural network algorithm, financial market, prediction model

DOI: 10.3233/JCM-237097

Citation: Journal of Computational Methods in Sciences and Engineering, vol. 24, no. 2, pp. 745-755, 2024

Authors: Weng, Minming

Article Type: Research Article

Abstract: In the background of the Internet, traditional media companies are facing very serious challenges. This paper firstly gave a brief introduction to the development of traditional media, pointed out the necessity of digital transformation of traditional media companies, and analyzed Phoenix Media using political, economic, social and technological (PEST) analysis and strength, weaknesses, opportunity, and threat (SWOT) analysis. Moreover, the current development of the company was analyzed, and its digital transformation path was elaborated. Finally, the existing problems of the company were pointed out, and some suggestions were given. This study provides a reference for traditional media companies to carry …out digital transformation. Show more

Keywords: Internet, traditional media, digitalization, transformation path

DOI: 10.3233/JCM-237092

Citation: Journal of Computational Methods in Sciences and Engineering, vol. 24, no. 2, pp. 757-768, 2024

Authors: Sun, Ji

Article Type: Research Article

Abstract: OBJECTIVES: This paper aims to analyze factors affecting the employment quality of contemporary college students to improve it. METHODS: Data related to the influencing factors and employment quality were collected by means of a questionnaire. After verifying the effectiveness of the data, a structural equation model of employment quality was constructed. FINDINGS: The data collected by the questionnaire were effective. The structural equation model analysis results showed that individual factors, educational factors, and environmental factors were all significantly related to employment quality. NOVELTY: The novelty of this article lies in …the use of a structural equation model to quantitatively analyze the relationship between influencing factors and employment quality, providing an effective reference for improving the employment quality of college students. Show more

Keywords: Employment quality, college students, structural equation model, influencing factors

DOI: 10.3233/JCM-237094

Citation: Journal of Computational Methods in Sciences and Engineering, vol. 24, no. 2, pp. 769-777, 2024

Authors: Ma, Yujiang

Article Type: Research Article

Abstract: Dance performance is an art form, which needs to cultivate students’ dance skills, artistic accomplishment and stage performance ability. Sequential quadratic programming algorithm is an optimization algorithm that can be used to solve complex optimization problems. In this paper, Sequential Quadratic Programming (SQP) is applied to explore the training mode of dance performers in colleges to help dance performers develop the optimal training plan and program. Aiming at the problems existing in the training mode of dance performance talents in colleges, this paper put forward an optimization method based on SQP algorithm, and implemented its optimization scheme in actual colleges. …In the planning of dance performance talent training mode, particle swarm optimization (PSO) is used to optimize SQP algorithm, so that it can have higher planning efficiency. This paper studied the goal and index system of the training of dance performance talents in colleges, generated a personalized training program, and further improved the scientific and practical effectiveness of the training mode. This paper investigated the current situation of dance performance talent training in several dance schools in a certain province of China. The survey data include practical curriculum planning, teachers’ teaching philosophy and teaching content. Combined with SQP algorithm, the teaching and training program is optimized. After evaluation, it can be concluded that the SQP algorithm optimized by PSO shows good stability and accuracy. It can calculate the optimal solution of the cultivation scheme, and when calculating the optimal solution, the running time of the Central Processing Unit (CPU) was only 5.6 s, which can further improve the efficiency of the planning. Finally, through the satisfaction and resource utilization test, it can be found that the number of people who are very satisfied with the teaching content of the dance performance talent training program optimized by SQP increased from 38.4% to 52.4%. After optimization, the average utilization rate of teaching resources reached 88.1%. It can be concluded that SQP algorithm can provide scientific basis for dance education institutions to improve the training mode of dance talents. This can help dance education institutions better improve the training mode, and improve the overall quality and dance skills of dance students. Show more

Keywords: Talent training model, dance performance in colleges, sequential quadratic programming, resource allocation, effect evaluation, particle swarm optimization

DOI: 10.3233/JCM-237106

Citation: Journal of Computational Methods in Sciences and Engineering, vol. 24, no. 2, pp. 779-795, 2024

Authors: Chen, Zhiliang | Wang, Juan | Wei, Miao

Article Type: Research Article

Abstract: For the power generation prediction of traditional hydropower stations, most of them only use time series prediction and neglect to study the spatial topological relationship of hydropower stations in the river basin, so that it is difficult to fully explore the characteristic relationship of space power stations. In this paper, a research method for power generation prediction of hydropower in river basin hydropower stations based on multi-head attention map convolutional neural network is proposed. This method establishes a first-level node neighborhood feature map based on the spatial geographic distribution information of hydropower stations in the basin, and uses the method …of graph convolution to carry out node feature mining and feature learning, so as to transform the power generation capacity evaluation problem of the hydropower station in the basin into the node prediction problem in the graph, which is different from the global normalization rule. the multi-head attention mechanism introduced further improves the information aggregation quality of the graph node, and uses the historical temperature, power generation, electricity price, unit status and other data of each hydropower station in the basin for training. The reasoning results show that the proposed method achieves higher accuracy than other schemes, and the power prediction method is conducive to the formulation of power plans of hydropower stations in the basin, and can also play a positive role in guiding the site selection of hydropower plants. Show more

Keywords: Spatial topology, multi-head attention, graph convolutional neural networks, hydroelectric power stations, power prediction

DOI: 10.3233/JCM237076

Citation: Journal of Computational Methods in Sciences and Engineering, vol. 24, no. 2, pp. 797-811, 2024

Authors: Ran, Jingfei | Ma, Hui | Ran, Runyang

Article Type: Research Article

Abstract: With the rapid development of social mode virtualization and electronic component technology, the application of data science and Internet of Things (IoT) technology in the field of e-commerce is gradually increasing. This study aims to explore how these emerging technologies can enhance the advantage of Chinese e-commerce companies in international competition. By comprehensively analyzing the massive data generated by online social networking and the application of IoT sensor technology in logistics and enterprise management, this paper proposes a decision support model based on data analysis. Research methods include data collection, data analysis and case studies. The results of the study …show that data analytics and IoT technologies can effectively improve the efficiency of e-commerce operations and customer experience. The conclusion is that these technologies not only contribute to the domestic development of e-commerce enterprises, but also play a non-negligible role in international competition. This research has important implications for understanding the practical applications and potential of new technologies in the field of e-commerce. Show more

Keywords: Electronic commerce, Internet of Things, big data, supply chain management, decision support model

DOI: 10.3233/JCM-237067

Citation: Journal of Computational Methods in Sciences and Engineering, vol. 24, no. 2, pp. 813-822, 2024

Authors: Zhang, Kefeng | He, Ming | Guo, Junxin

Article Type: Research Article

Abstract: Water Turbine Unit is the core equipment of water power generation. It is the most important research object in hydroelectric power. The analysis of daily monitoring data of Water Turbines can evaluate its running state and avoid the loss caused by failure. In this paper, A hybrid neural network architecture which CNN combines Transformer is proposed to evaluate the health state of water turbines based on multivariate long time series data. It has two core components: (i) dilated convolution applies to capture low-level and local semantic information, then the output of convolutional layers is divided into subseries-level patches by time, …these patches are regarded as input tokens of Transformer layers; (ii) utilizing self-attention of Transformer to extract high-level and global semantic information. Patching design naturally has benefit as follow: (i) local semantic information is retained in Transformer input tokens and high-level semantic confirm to common sense of human understanding through low-level construction; (ii) the length of Transformer input sequence is greatly shorted and attention can be more concentrated compared with point-wise form. Meanwhile, the computation and memory usage reduces at the same time. The experimental result indicates that the hybrid architecture can achieves excellent performance in time series understanding. Show more

Keywords: CNN and Transformer hybrid, health condition, water turbine, multivariate time series, time series analysis

DOI: 10.3233/JCM-237074

Citation: Journal of Computational Methods in Sciences and Engineering, vol. 24, no. 2, pp. 823-834, 2024

Authors: Jiang, Xiaobo | Jiang, Yunchuan | Liu, Leping | Xia, Meng | Jiang, Yunlu

Article Type: Research Article

Abstract: In order to solve the problem of low accuracy of time dimension feature extraction and classification of high-dimensional large data streams, this paper proposes a time dimension feature extraction and classification algorithm of high-dimensional large data streams based on unsupervised learning. Analyze the trend of high-dimensional data flow changes under machine learning, and achieve dimensionality reduction of high-dimensional large traffic time dimensional data through local save projection. Analyze the spatial relationship between feature attributes and feature space, segment and fit high-dimensional big data streams and time dimensional feature data streams, further segment time dimensional sequences using sliding windows, and complete …feature extraction through discrete dyadic wavelet transform. According to the clustering algorithm, cluster the time dimension feature data stream, calculate the cosine similarity of the feature data, model the time dimension feature stream of training samples, use the feature classification function to minimize the classification loss, and use unsupervised learning to achieve the final classification task. The test results show that this method can improve the temporal feature extraction and classification accuracy streams. Show more

Keywords: Unsupervised learning, high dimensional big data flow, time dimension characteristics, low dimensional space, sliding window, discrete dyadic wavelet transform

DOI: 10.3233/JCM-237085

Citation: Journal of Computational Methods in Sciences and Engineering, vol. 24, no. 2, pp. 835-848, 2024

Authors: Zhang, Jun | Chen, Zongren | Wu, Weimei | Shao, Liuyang | Deng, Kaihuan | Gao, Shixiong

Article Type: Research Article

Abstract: Fault detection for photovoltaic power generation system is a challenging problem in condition monitoring and troubleshooting, which aims to maintain the safe operation of equipment and improve the benefit of photovoltaic industry. Aiming at the problems of frequent failures of photovoltaic power generation system, large amount of operating data and difficult to obtain fault samples, we propose an unsupervised fault detection approach for photovoltaic power generation system via bidirectional long/short memory deep auto-encoder which combines the auto-encoder in deep learning with the Bi-directional Long Short-Term Memory (BiLSTM). Specifically, We first take the statistical feature enhanced as the input of an …auto-encoder based on BiLSTM. Then, we build a simulation model of Grid-connected PV system. Finally, we use the operation results under normal conditions to train the fault detection model to obtain the reconstruction error and determine the fault detection threshold, so as to judge the anomalies of the photovoltaic power generation system. We simulate the shadow occlusion fault and verify the effectiveness of the proposed method, and the fault detection accuracy of 0.95 is achieved. Compare with other models, the results show that it could set up better dependence on multi-dimensional data in time sequences, effectively testing solar panel failures and solving insufficient data labels problems. Show more

Keywords: Fault detection, deep learning, photovoltaic power generation system, deep auto-encoder, bidirectional long/short memory

DOI: 10.3233/JCM-237070

Citation: Journal of Computational Methods in Sciences and Engineering, vol. 24, no. 2, pp. 849-861, 2024

Authors: Chen, Yafang

Article Type: Research Article

Abstract: With the rapid development of deep learning technology, its application in various fields is increasingly extensive. Especially in the field of education, the application of deep learning technology has brought great challenges and changes to the traditional teaching mode. This research is aimed at the application of deep learning in intelligent English teaching mode. Firstly, the theory of deep learning is studied in depth, and the application cases of deep learning in other fields are discussed. Secondly, the research designs and implements an intelligent English teaching model based on deep learning, and carries out a lot of experiments and tests. …The experimental results show that this new teaching mode can effectively improve the efficiency and effect of students’ English learning. However, it is also found that the model has some problems, such as model training needs a lot of computing resources, has certain requirements for hardware equipment, and some students have poor adaptability to the new learning mode. To solve these problems, a series of solutions are proposed. In general, although there are still some challenges in the application of deep learning in intelligent English teaching mode, its potential is huge and it has a profound impact on improving the quality of teaching. Show more

Keywords: Deep learning, intelligent English teaching, teaching efficiency, learning effect, solution strategy

DOI: 10.3233/JCM-237054

Citation: Journal of Computational Methods in Sciences and Engineering, vol. 24, no. 2, pp. 863-877, 2024

Authors: Zhou, Ya | Fu, Shaopeng

Article Type: Research Article

Abstract: Low-carbon building is an unavoidable development trend in the construction industry, especially in the critical moment of global warming, it is necessary to make a comprehensive evaluation of low-carbon buildings. At this stage, low carbon building has become an important direction in the construction field, and whether the low carbon building reaches the corresponding standards and the advantages played by the low carbon field need to be assessed with perfect evaluation indexes. Based on this, this paper constructs a low-carbon building evaluation system from the whole life cycle of the building using the hierarchical analysis method (AHP) and BP neural …network method. Firstly, the definition and influencing factors of low-carbon buildings are analyzed, secondly, the evaluation index system of low-carbon buildings is constructed, and then the evaluation index system of low-carbon buildings is verified by using the hierarchical analysis method, and the results show that the evaluation results based on the hierarchical analysis and the BP neural network method are more accurate than those of the traditional hierarchical analysis method. The results show that the evaluation results based on hierarchical analysis and BP neural network are more accurate than the traditional hierarchical analysis method. It shows that the BP neural network method can effectively reduce the influence of subjective factors in the hierarchical analysis method and improve the objectivity of the evaluation results. On this basis, this paper proposes countermeasures to promote the development of low-carbon buildings, in order to provide a certain reference for the long-term development of low-carbon buildings. Show more

Keywords: Low carbon building, evaluation method, hierarchical analysis method, BP neural network

DOI: 10.3233/JCM-237018

Citation: Journal of Computational Methods in Sciences and Engineering, vol. 24, no. 2, pp. 879-890, 2024

Authors: Su, Xuan | Gao, Han

Article Type: Research Article

Abstract: In the past few decades, China’s power demand has been increasing, and the power fiber plays a key role in ensuring the orderly dispatching of all links of the power system. The study used a wavelet decomposition and reconstruction method, which is a signal processing technique used to decompose complex optical power data into low-frequency and high-frequency signals with different frequency components. Through this decomposition, we can more clearly observe periodic fluctuations, trend changes, and noise components in optical power data. The study also examined different prediction models, including GRU, LSTM, ARMA), etc. The performance of these models in predicting …optical power trends is then analyzed, taking into account their accuracy, stability, and computational efficiency. Finally, we carefully evaluated the GRU-ARMA combined prediction model and determined its superior performance in predicting optical power trends. The outcomes show that after adjusting the input data length of the gating cycle cell model and the relevant parameters of the autoregressive sliding mean model, the residual mean value was 0.0141. At the same time, the root mean square error calculated by the combined prediction model of the gating cycle unit-autoregressive moving mean model was 0.000618, which successfully improved the accuracy of predicting the optical power trend of power fiber. This research result provides an important reference for the aging state assessment of power fiber lines, and has an important practical application value for the maintenance of power fiber lines. Show more

Keywords: Feature extraction, network situation, fault diagnosis, security early warning, power system

DOI: 10.3233/JCM-247293

Citation: Journal of Computational Methods in Sciences and Engineering, vol. 24, no. 2, pp. 891-905, 2024

Authors: Wang, Yu | Tang, Bihong

Article Type: Research Article

Abstract: With the increasing level of grid intelligence and the related demand response database expanding, it is important to study a compound problem data governance method for demand response, while the traditional data governance methods have problems such as not considering data temporality and ignoring the impact of noise and duplicate data on data repair. As a result, this project will develop an anomaly data extraction and repair model based on two-way long and short memory networks, and repair the anomaly data by respective noise smoothing, missing data filling, and duplicate data cleaning. The paper also provides an adaptive moment estimation …approach for optimisation to raise the model’s accuracy. The outcomes demonstrated that the study model’s precision for anomalous data extraction was 100% and its recall rate was 80%, which was a significant improvement over the previous state. In terms of anomalous data repair, the research model had the root mean square error value and lowest mean absolute percentage error value when compared with related models, at 0.0049 MPa and 1.375% respectively. Both the abnormal data extraction and repair performance of the research model are greatly improved over the related models, and have important value in the abnormal data governance of demand response databases. Show more

Keywords: Machine learning, data extraction, data repair, Bi-LSTM, Adam’s algorithm

DOI: 10.3233/JCM-247295

Citation: Journal of Computational Methods in Sciences and Engineering, vol. 24, no. 2, pp. 907-920, 2024

Authors: Ke, Qingpai | Dong, Di | Liu, Tong | Xiong, Shibin | Jiang, Wei | Shi, Xuntao | Lou, Rongbo

Article Type: Research Article

Abstract: As the renewable energy integrates in the power system, the interactions between source, load, and storage equipment in the distribution network become frequent. The original distribution network could not meet the growing demand of the power grid. The use of controlled power electronic devices for flexible interconnection transformation of the distribution network could help improve the controllability, reliability, and safety of the power system and promote the consumption of distributed power sources. It is an important means of evolving towards future intelligent distribution networks. Typical devices for flexible interconnection are discussed of the operating characteristics. In addition, the core technologies …of flexible interconnected distribution network system are introduced from the view of point of dispatching. Finally, to achieve a wider application of flexible interconnected distribution networks, the development trend is prospected. Show more

Keywords: Energy router, flexible interconnected distribution network system, operation scheduling, soft open point, power electronic transformer

DOI: 10.3233/JCM-247297

Citation: Journal of Computational Methods in Sciences and Engineering, vol. 24, no. 2, pp. 921-933, 2024

Authors: Dou, Zhenlan | Zhang, Chunyan | Duan, Chuanxu | Wen, Xuan | Sun, Chen

Article Type: Research Article

Abstract: A novel multi-stage time scale economic dispatch scheme is proposed for virtual power plants, taking into account the uncertainties arising from the connection of distribution network sources. This research introduces specific scheduling schemes tailored to various time scales within distribution networks, including a fuzzy optimized day ahead scheduling scheme, an intra-day scheduling scheme combined with Deep Q Network, and an adaptive optimized real-time scheduling scheme. This plan mainly considers the impact of photovoltaic output and conducts scheduling one day in advance through fuzzy optimization. In the intraday scheduling, different strategies were adopted in the study. By combining with Deep Q …Network, research on scheduling for intraday demand within the power system. The analysis is conducted through rigorous modeling. Experimental tests were conducted to evaluate the performance of the proposed schemes. The day ahead dispatching primarily considers the impact of photovoltaic output and calculates the cost associated with each link in the grid under three different meteorological conditions. In the intra-day scheduling, the total costs for Scenario 1, Scenario 2, and Scenario 3 are found to be 34,724.5 yuan, 36,296.5 yuan, and 33,275.8 yuan, respectively. Notably, strategies 1 and 2 demonstrate lower costs compared to the pre-day scheduling, with the exception of Scenario 3. In real-time scheduling, considering the matching between sources and sources, the matching rate between sources and sources can be maintained at over 95%, and the stability and cost of the power grid have significantly decreased. In summary, by proposing a multi-stage time scale economic scheduling scheme, this study fully considers the uncertainty of the power supply of the distribution network access, as well as the different needs of day, day and real-time scheduling, providing an effective solution for the power dispatching of virtual power plants and providing important technical support for the reliability and economy of the power system. Show more

Keywords: Multi-time scale, grid dispatching, virtual power plant, DQN, fuzzy optimization, adaptive optimization

DOI: 10.3233/JCM-247299

Citation: Journal of Computational Methods in Sciences and Engineering, vol. 24, no. 2, pp. 935-953, 2024

Authors: Che, Kai | Yang, Peng | Gong, Yunqian | Chen, Chuanmin | Liu, Songtao | Li, Ni | Lin, Shanshan

Article Type: Research Article

Abstract: In recent years, wireless charging technology for electric vehicles has gained significant attention. To accurately analyze the distribution characteristics of the electromagnetic field during the wireless charging process of electric vehicles, a finite element-based electromagnetic analysis method was employed. Applied in the commercial simulation software, the electromagnetic environment of the resonant coil and electric vehicle model was simulated under high-power charging conditions, resulting in an overall electromagnetic field distribution for the electric vehicle. The results indicated that within the coil region, the magnetic induction intensity in the central area of the coil was zero, and it increased as the distance …from the center of the coil grew. Outside the coil region, the magnetic induction intensity gradually decreased. The electric field intensity of the resonant coil was maximum in the central area of the coil, and it weakened as the distance from the center of the coil increased. When a magnetic shielding resonant coil was used, the electromagnetic field was confined between the shielding materials, and the magnetic field rapidly attenuated on both sides of the magnetic shield. The electromagnetic field energy of the electric vehicle body was mainly concentrated at the bottom of the vehicle near the coil. When the coil was located in the front of the car body, the maximum electric field intensity distribution in the car body was 8.50 V/m, and the maximum magnetic induction intensity was 0.024 μ T. When the coil was located in the middle of the car body, the maximum electric field intensity was 2.31 V/m, the maximum magnetic induction intensity was 0.019 μ T. As the distance from the coil position increased, the energy weakened. Show more

Keywords: Wireless charging system, resonant coil, finite element simulation, electromagnetic field distribution

DOI: 10.3233/JCM-247302

Citation: Journal of Computational Methods in Sciences and Engineering, vol. 24, no. 2, pp. 955-973, 2024

Authors: Zhu, Yansha | Qin, Jiansen

Article Type: Research Article

Abstract: With the rapid development of information technology, the Internet of Things (IoT) and big data have gradually become the core tools of modern enterprise management and decision-making. This study explores the application and potential value of IoT and big data in the field of enterprise assets and accounting. Through strategic questionnaire design and multi-channel data collection, a corresponding data model is constructed, and the effectiveness of the model is further verified. The results show that these technologies can not only improve the management efficiency of enterprises, but also provide more accurate and timely data support for corporate decision-making. However, in …practical applications, enterprises also need to face data integrity, technology updates, and a host of other challenges. This study not only provides enterprises with an opportunity to gain an in-depth understanding of these technologies, but also provides valuable implications for future research and applications. Show more

Keywords: Internet of Things, big data, enterprise asset management, accounting

DOI: 10.3233/JCM-247304

Citation: Journal of Computational Methods in Sciences and Engineering, vol. 24, no. 2, pp. 975-989, 2024

Authors: Qin, Jiansen | Zhu, Yansha

Article Type: Research Article

Abstract: With the rapid development of information technology, Internet of Things technology has become a hot topic in today’s society. This study aims to explore how IoT technology impacts various aspects of financial management, in particular how it changes the collection, processing, and parsing of financial data. Through in-depth literature review, questionnaire survey and experimental design, the research found that IoT technology has a significant positive effect on sales, transaction times and inventory control. But at the same time, data security, privacy concerns and initial technology investment are also challenges that enterprises need to consider when adopting these technologies. Overall, IoT …technology offers tremendous opportunities for financial management, but businesses must fully assess the potential risks and benefits when implementing it. Show more

Keywords: Internet of Things technology, financial management, data analysis, technology input, data security

DOI: 10.3233/JCM-247306

Citation: Journal of Computational Methods in Sciences and Engineering, vol. 24, no. 2, pp. 991-1008, 2024

Authors: Huang, Xiumei | Jiang, Dewei | Zhu, Kexin

Article Type: Research Article

Abstract: The analysis of the multi-scale evaluation of port city’s international trade goal is conducive to the sustainable development of port city’s international trade. In order to make a more in-depth study on the realization ability of port city’s international trade sustainable development goal, this paper proposes a new multi-scale evaluation method of port city’s international trade goal. This method selects the evaluation indexes, uses the improved normalization method to process the indexes, uses the combination of AHP and factor analysis method to form the subjective and objective combination weighting method, brings the processed indexes into the least square optimization combination …evaluation model, calculates the index weight, and uses the fuzzy evaluation method to carry out multi-scale index evaluation on the international trade of port cities to realize its multi-scale evaluation and analysis. The results show that the standardized index of Shanghai’s foreign trade dependence is 0.0056, indicating its independence in international trade. In the comprehensive evaluation, the evaluation values of Shanghai, Tianjin, and Guangzhou are 92.56, 87.89, and 88.45, respectively, which are very close to the actual results, which shows that the accuracy of the evaluation method is high, and provides a theoretical basis for the sustainable development of international trade in port cities. Show more

Keywords: Port city, international trade target, AHP, factor analysis method, fuzzy evaluation method and multi-scale indicator evaluation

DOI: 10.3233/JCM-247288

Citation: Journal of Computational Methods in Sciences and Engineering, vol. 24, no. 2, pp. 1009-1023, 2024

Authors: Zhao, Xiaojuan

Article Type: Research Article

Abstract: In the context of the wide application of big data technology, it is particularly important to optimize the allocation of teaching methods and learning resources. This study first expounds the key role of big data in the optimization of teaching methods and the allocation of learning resources, and emphasizes how big data technology promotes the transformation and development of education and teaching models. Based on the analysis of traditional models of teaching method optimization and learning resource allocation, this study proposes a new model driven by big data. By accurately identifying students’ learning needs and behavior patterns, the model optimizes …teaching methods and allocation of learning resources. This study introduces the whole process of data collection, cleaning, analysis and modeling. In the process, it shows how big data can be integrated, analyzed, and applied to further support the construction and validation of models. Through empirical research and effect evaluation, this study proves the validity of the model of teaching method optimization and learning resource allocation driven by big data, and demonstrates how big data can promote educational equity and improve educational quality. Show more

Keywords: Big data, teaching method optimization, learning resource allocation, data-driven model

DOI: 10.3233/JCM-247277

Citation: Journal of Computational Methods in Sciences and Engineering, vol. 24, no. 2, pp. 1025-1040, 2024

Authors: He, Panpan | Wang, Jingjing

Article Type: Research Article

Abstract: With the rapid development of big data and cloud computing, the field of physical education has begun to actively explore the application of these new technologies. Big data can collect and analyze a large amount of teaching information, help understand students’ learning needs and preferences, optimize resource allocation, and improve teaching efficiency. Cloud computing can realize the online and personalized teaching resources and services, providing convenient and rich learning experience. This study first analyzes the role and influence of big data and cloud computing in the optimization of physical education teaching resources and service mode, and then verifies the actual …effects of these technologies through empirical research, analyzes the existing problems and potential challenges, and puts forward corresponding solutions and suggestions. The results show that big data and cloud computing help to improve the efficiency and user satisfaction of physical education, and have important value in promoting the modernization of physical education. Show more

Keywords: Big data, cloud computing, physical education teaching, resource optimization, service model

DOI: 10.3233/JCM-247279

Citation: Journal of Computational Methods in Sciences and Engineering, vol. 24, no. 2, pp. 1041-1056, 2024

Authors: Zhang, Ke

Article Type: Research Article

Abstract: To improve the application of behavior detection technology in college education, the study proposes a new model built on deep CNN, which is used for student behavior detection and analysis in college labor education courses. The study first analyzed the target detection algorithm, and optimized the selected You Only Look Once version 5 (YOLOv5) algorithm and its network structure with a series of improvements, and based on this, embedded the attention module into the algorithm structure to finally obtain a new model, namely YOLOv5-O. After a series of experiments, YOLOv5-O reached an average accuracy of 90.1% on the test set, …while the application test in the actual teaching environment showed that its average accuracy was 86.7%. This result is obviously superior to the existing technology, which proves the validity of the study and provides strong data support for the automatic detection of student behavior. In addition, in the teaching experiment, YOLOv5-O assisted teaching achieved the most significant teaching effect, and students’ achievement improved the most. The feasibility of this method is verified. Show more

Keywords: Behavior detection, college education, deep convolutional neural network, target detection algorithm, YOLOv5, attention module

DOI: 10.3233/JCM-247308

Citation: Journal of Computational Methods in Sciences and Engineering, vol. 24, no. 2, pp. 1057-1069, 2024

Authors: Wang, Qin | Zhang, Feng

Article Type: Research Article

Abstract: From four dimensions of resource, environment, ecology (green ecology) and social economy, correlation analysis method and principal component analysis method of SPSS are used to screen evaluation indexes to design a scientific and reasonable evaluation indexes system of resource and environmental carrying capacity in Xiong’an New Area in line with regional characteristics. Based on fuzzy comprehensive evaluation method, resource and environmental carrying capacity of the New Area are evaluated. The results show that: the resource and environmental carrying capacity of the New Area from 2014 to 2018 are 0.3229, 0.3556, 0.3521, 0.3859 and 0.4445 respectively, with an average annual growth …rate of 8.32% and a steady growth trend. Then, some countermeasures to improve resource and environmental carrying capacity of the New Area are presented from three aspects of development and utilization of land resource, development and utilization of water resource and protection and utilization of water environment. Show more

Keywords: Xiong’an New Area, resource and environmental carrying capacity, correlation analysis, principal component analysis, fuzzy comprehensive evaluation method, entropy method

DOI: 10.3233/JCM-247311

Citation: Journal of Computational Methods in Sciences and Engineering, vol. 24, no. 2, pp. 1071-1084, 2024

Authors: Chen, Dongya

Article Type: Research Article

Abstract: n smart cities, sanitation workers are the key to urban construction. Accurate targeting of sanitation workers can help managers better monitor and manage them. Pedestrian detection is the core and key component of object detection technology. The difficulty of pedestrian detection in the actual feature recognition is still how to quickly and accurately identify the identity in the complex video scene. To realize the effective detection of sanitation workers, the study designs an identity identification scheme conducive to the friendly management of smart urban management. Since the optical flow feature extraction method and HSV color space extraction method can not …meet the actual detection efficiency, this study innovatively integrates the two methods to improve the detection accuracy of the mode. Meanwhile, the study also introduces PCA algorithm to identify the specific identity of sanitation workers. In the actual detection of sanitation workers, the identification rate of two sanitation workers is high, and the similarity is 98.61%. This technology greatly reduces the false detection rate of actual detection and improves the detection accuracy. Show more

Keywords: Smart urban management, identification, optical flow characteristics, HSV space, technology integration, PCA

DOI: 10.3233/JCM-247313

Citation: Journal of Computational Methods in Sciences and Engineering, vol. 24, no. 2, pp. 1085-1099, 2024

Authors: Qi, Shaobo

Article Type: Research Article

Abstract: As China’s economy progresses towards high-quality development, the construction of science and technology think tanks, with talent construction as the key, has become an important supporting force and occupies a unique position in the national innovation system. In recent years, with the rapid expansion of the number of think tank talents, how to evaluate and manage think tank talents has become a key factor in the development of think tank influence and competitiveness. Based on the experience of talent team construction at home and abroad, Taking the typical science and technology innovation think tanks of Henan Province as an example, …this paper analyzes the current situation and status quo of science and technology innovation think tank talents, and finds problems in the talents, Summarize the factors that affect the talent construction and development of science and technology innovation think tanks, From the perspective of six dimensions of competency model, four first-level indicators of talent quantity, talent quality, talent ability and talent contribution of science and technology innovation think tank and 16 subordinate second-level indicators are constructed for talent evaluation of think tank. Ahp is adopted to set questionnaires and collect data with Likert5-level scale, and yaahp software is used to verify the weight verification evaluation effect. Invite experts in the field to score the evaluation indicators. The research results show that the talent evaluation index of science and technology innovation think tank based on competency model pays more attention to the standardization, integration and systematization of the talent evaluation of the think tank, and the system is scientific and reasonable. It can provide reference and reference for promoting the construction, evaluation and development management of scientific and technological innovation think tanks. Show more

Keywords: Competency model, science and technology innovation think tanks, talent evaluation

DOI: 10.3233/JCM-247315

Citation: Journal of Computational Methods in Sciences and Engineering, vol. 24, no. 2, pp. 1101-1117, 2024

Authors: Xu, Meng | Chen, Ming | Pan, Fangyuan

Article Type: Research Article

Abstract: The energy factor is the foundation of economic and social development, and it is also an important prerequisite for the effective operation of the elderly care industry. Based on the perspective of energy factor input, this paper constructs the DPSR mechanism model of the energy-elderly care industry system. Taking Shanghai as an example, through its 2010–2019 15 index data such as energy factors and industrial economy, the Entropy-TOPSIS method is comprehensively used to evaluate the energy-elderly care industry system. Finally, the following conclusions are drawn: (1) The energy factor has an important impact on the operation of the elderly care …industry through its effect on the driving force module, the pressure module and the state module; (2) The elderly care industry has an important impact on the energy factor through the conduction of the response module; (3) The evaluation results of the Shanghai energy-elderly care industry system showed a continuous improvement trend from 2010 to 2019. Show more

Keywords: Energy elements, DPSR model, TOPSIS method, systematic evaluation

DOI: 10.3233/JCM-247317

Citation: Journal of Computational Methods in Sciences and Engineering, vol. 24, no. 2, pp. 1119-1130, 2024

Authors: Wang, Juan | Ming, Qingzhong | Qin, Wen

Article Type: Research Article

Abstract: Mountain is the most traditional tourism destination in China. On the basis of analyzing the particularity and its spatial differentiation of human-environment relationship in mountain tourism destination, this paper conducts empirical analysis in Yulong Snow Mountain by land use type classification. Firstly, ENVI is used to preprocess the Landsat-8 images of the research area. Then the visual interpretation is performed by ArcGIS to classify land use types. Based on the above, the differentiation status of human-environment relationship is analyzed. It concluded that: (1) Under the influence of natural factors such as piedmont, climate, altitude, gradient, slope, geological environment, and geomorphic …characteristics, the uneven spatial distribution of human-environment relationship in mountain tourism destination is greatly affected by vegetation, rainfall, heat distribution, wind direction, suitability for human activities, and especially the topography; (2) The Yulong Snow Mountain forms three distinct levels of spatial differentiation in human-environment relationships between the east and west slopes, between high, medium and low elevations, and between different gradients. Show more

Keywords: Human-environment relationship, mountain tourism, spatial differentiation, mountain destination, Yulong Snow Mountain

DOI: 10.3233/JCM-247320

Citation: Journal of Computational Methods in Sciences and Engineering, vol. 24, no. 2, pp. 1131-1139, 2024

Authors: Chen, Hongyin | Dou, Zhenlan | Li, Jianfeng | Wang, Songcen | Zhang, Chunyan | Li, Dezhi | Liu, Yang | Pang, Jingshuai | Zhang, Baihan

Article Type: Research Article

Abstract: Because the global climate change intensifies as well as the natural disasters frequently occur, extreme events have caused serious impacts on the energy system in urban areas, and at the same time, they have brought great challenges to the supply and scheduling of urban energy systems. Therefore, in order to better integrate and manage various energy resources in urban areas, a Deep Q-Leaning Network-Quasi Upper Confidence Bound model is innovatively constructed using deep reinforcement learning technology to learn the state and behavior mapping relationship of energy system. Use deep learning to fit complex nonlinear models to optimize the entire energy …system. Compare and verify the experiment with the real energy system. The improved Deep reinforcement learning algorithm is compared with Q-learning model, PDWoLF PHC algorithm model, Quasi Upper Confidence Bound algorithm model and deep Q-Leaning Network algorithm model. The results show that the research algorithm has the smallest instantaneous error value and absolute value of frequency deviation for area control, and the average value of the research algorithm in the absolute value of the frequency deviation is reduced by 45%–73% compared to other algorithms; over time, the unit output power of the research algorithm is able to flexibly track the stochastic square wave loads. Therefore, the proposed system strategies can provide feasible solutions to meet the challenges of extreme events and promote the sustainable development and safe operation of urban energy systems. Show more

Keywords: Integrated energy systems, multi-intelligence, deep reinforcement learning, sampling mechanisms, extreme events

DOI: 10.3233/JCM-247322

Citation: Journal of Computational Methods in Sciences and Engineering, vol. 24, no. 2, pp. 1141-1156, 2024

Authors: Wang, Hengjiang | Cui, Fang | Ni, Mao | Zhou, Ting

Article Type: Research Article

Abstract: With the development of modern society, business organizations have higher and higher requirements for the efficiency of cloud computing services. In order to improve the comprehensive computing capability of cloud computing network, it is very important to optimize its end-side computing power. This research takes the Hadoop platform as the computing end-side cloud computing network structure as the research object, and designs a Hadoop end-side multi-granularity and multi-level multi-level network that integrates the Graphics processing unit (GPU) and the information transfer interface (Multi Point Interface, MPI). Hierarchical computing power optimization scheduling model and improved microservice deployment s11trategy that integrates multi-level …resources. The performance verification experiment results show that the mean value of all node balance ratios of the original strategy and the improved strategy on computing resource-oriented, memory resource-oriented, and disk resource-oriented microservices are 0.13 and 0.12, 0.21 and 0.17, and 0.22 and 0.19, respectively. The value of the service instance cost in the scheme using the critical path optimization scheduling strategy is always at a low level, while the instance cost value of the native strategy is significantly higher than the former. It can be seen that the end-side computing power optimization scheduling model designed in this study can indeed play a role in improving the computing performance of the end-side computing power network. Show more

Keywords: Multi granularity, multi level, computational power network, scheduling strategy, micro service deployment

DOI: 10.3233/JCM-247324

Citation: Journal of Computational Methods in Sciences and Engineering, vol. 24, no. 2, pp. 1157-1171, 2024

Authors: Li, Ningchuan

Article Type: Research Article

Abstract: In order to achieve the goal of dynamically adjusting daily passenger flow to effectively control the overall efficiency of the transportation system, this study constructs a real-time monitoring and prediction system for subway passenger flow based on front-end voice processing technology and support vector machine models. The study first conducted a railway passenger flow analysis, and then used a support vector machine model to construct a preliminary prediction system. In order to achieve global optimization, the study also introduced particle swarm optimization algorithm to construct an optimization prediction model based on PSO-SVM. The results show that the proposed PSO-SVM method …has undergone 48 iterations of training, and the predicted values closely match the actual passenger flow curve. The maximum RE error is 2%, and the overall prediction error is 98%. The decision coefficient of PSO-SVM is 0.998932. Therefore, this indicates that it has high performance and feasibility in predicting and controlling passenger flow during peak hours of urban rail transit. Show more

Keywords: Urban rail transit, passenger flow, support vector machine, genetic algorithm, particle swarm

DOI: 10.3233/JCM-247338

Citation: Journal of Computational Methods in Sciences and Engineering, vol. 24, no. 2, pp. 1173-1187, 2024

Authors: Zheng, Xiaodong | Zhang, Rui

Article Type: Research Article

Abstract: In order to improve the communication quality and transmission rate of WLAN, the key technologies of smart antenna in WLAN based on adaptive array are proposed. The adaptive array is used to suppress the WLAN interference signal, and the feedback from the output is used to adjust the weight loaded on the signal, and the optimal weight corresponding to the linear constraint minimum variance is obtained to achieve beamforming optimization. On this basis, the optimal antenna selection algorithm and the sub-optimal antenna selection algorithm are designed, and the sub-channel matrix with the maximum capacity can be obtained by repeated iterative …calculation, so as to realize the smart antenna selection and further optimize the performance of WLAN. Experimental results show that the smart antenna signal has 100% coverage in WLAN compared with conventional methods. When the transmission rate is greater than 35 mb/s, the delay of the study method is not jitter compared to the contrast method. When the transmission rate is greater than 35 mb/s, the packet loss rate of the proposed method remains stable, while the packet loss rate of the two comparison methods begins to increase. When the transmission rate is greater than 12 mb/s, the throughput of the proposed method is more stable than the other methods. In conclusion, this method has the characteristics of large signal coverage, low delay, low packet loss rate and high throughput, and can be widely used in practice. The contribution of this study is that the weight of the antenna array can be dynamically adjusted through the adaptive algorithm, thus optimizing the quality of the received signal and improving the receiving sensitivity. Show more

Keywords: Adaptive array, WLAN, smart antenna, beamforming optimization, subchannel matrix

DOI: 10.3233/JCM-247326

Citation: Journal of Computational Methods in Sciences and Engineering, vol. 24, no. 2, pp. 1189-1205, 2024

Authors: Guan, Zhanrong

Article Type: Research Article

Abstract: With the rapid development of computer network technology, it is often necessary to collect weak signals to collect favorable information. The development of signal detection technology is ongoing; however, various issues arise during the detection process. These issues include low efficiency and a high signal noise threshold. However, many problems will be encountered in the process of detection. In order to solve these problems, the nonlinear chaos theory is introduced to detect signals, and the simulation experiments of weak pulse signals and weak partial discharge signals are carried out respectively. The experimental results showed that the detection effect was remarkable …in the quasi periodic state, and it had a good detection effect for weak pulse signals. At a signal-to-noise ratio of - 25 dB, double coupling system, two-way ring coupling system, and single ring coupling system displayed detection success rates exceeding 98%. Meanwhile, the detection success rate of the strong coupling system was only 12%. Even at a noise signal ratio as low as - 40 dB, the dual coupling system still maintained a detection success rate above 80%. The simulation results of partial discharge signal detection showed that there was a high fluctuation only at 2 ms, and the rest was basically stable at about 0 V, indicating that the system had a strong suppression effect on Gaussian white noise. When comparing the simulation results of the detection of the new chaotic system and the double coupling system, it was found that the new chaotic system has a superior impact in detecting weakly attenuated partial discharge signals. Through analysis of the system’s dynamic behavior, the research confirms its rich dynamic characteristics and sheds light on the reasons for phase state mutation and missed detection. The noise system is utilized for comparing the performance of various systems, with the goal of enhancing the system’s detection capability. Show more

Keywords: Nonlinear system, differential equation, chaos, electrical signal, pulse

DOI: 10.3233/JCM-247329

Citation: Journal of Computational Methods in Sciences and Engineering, vol. 24, no. 2, pp. 1207-1221, 2024

Authors: Yan, Xiangqin | Zhai, Lei | Feng, Zhe

Article Type: Research Article

Abstract: Safety monitoring is an important part of bridge engineering construction and operation. At present, there is room for promoting the health monitoring and evaluation of small and medium-sized concrete bridges. In view of this, the study first models the spatial model and physical parameters of the bridge, and then builds the data of vehicle load and vehicle type. To reduce the complexity of data mapping, wavelet packet decomposition is used to analyze the data structure. And the physical field effect analysis is abandoned to directly mine the data relationship at both ends by using deep neural network. The data decomposition …results show that the method can discard the temperature-induced effect. And the local decomposition results of the data meet the input of the neural network. The data measured by the sensor is added to the depth learning model for fitting. The overall and local fitting rates are more than 92%. The loss function converges quickly, and there is no gradient explosion. The model predicts the bridge structural damage caused by vehicle stress of four load categories, and the results show that the average fitting rate is 89.72%. Therefore, the identification path of the proposed deep learning model has positive significance for the evaluation of bridge structural damage. The main contribution of the study is to propose a deep learning-based method for bridge structural damage assessment. By modeling the spatial model and physical parameters of the bridge and combining data from vehicle load and vehicle type, the data structure was analyzed using the wavelet packet decomposition method to eliminate temperature-induced effects and data from sensor measurements were added to the deep learning model for fitting. This finding has positive implications for bridge structural damage assessment and can provide effective pathways and methods to monitor and evaluate the health status of small and medium-sized concrete bridges. This has important practical application value for the construction and operation of bridge engineering. Show more

Keywords: Deep learning, bridge, structural damage, assessment role, wavelet packet decomposition

DOI: 10.3233/JCM-247331

Citation: Journal of Computational Methods in Sciences and Engineering, vol. 24, no. 2, pp. 1223-1236, 2024

Authors: Li, Li | Du, Lei | Hu, Yixuan

Article Type: Research Article

Abstract: Spacecraft cluster is a new way of multi-vehicle cooperation and a major problem in the current space distribution system. A large number of core technologies of spacecraft cluster technology need to be simulated, analyzed and verified on the ground. On this basis, this article proposes the construction of a spacecraft system model and a dynamic model, and designs a path planning method that combines the spacecraft system model with the a* algorithm. And use the Morphin search tree algorithm to improve the A-star algorithm, and then simulate and analyze the spacecraft formation control scheme. The simulation results of trajectory planning …show that after running the model for 21 ms, the improved A* algorithm achieved the expected error. Its MAE value is 10 - 4 , achieving good path planning effect. The research plan can effectively achieve control of spacecraft formation and meet the needs of collaborative tasks and navigation route planning. Show more

Keywords: Improved A* algorithm, path planning, spacecraft formation flight, attitude control, morphin search tree algorithm

DOI: 10.3233/JCM-247333

Citation: Journal of Computational Methods in Sciences and Engineering, vol. 24, no. 2, pp. 1237-1251, 2024

Authors: Xue, Rui | Cai, Yan

Article Type: Research Article

Abstract: Parallel Support Vector Machine (SVM) based on big data has achieved some results in data mining, but due to the complexity of the data itself and a large amount of noisy data, its execution efficiency and classification accuracy in the big data environment are very low. In order to eliminate noise, a noise reduction method based on Noise Cleaning (NC) strategy was proposed, and redundant training samples in big data environments were deleted; Introduce an improved Artificial Fish Swarm Algorithm (IAFSA) to obtain the final Parallel SVM algorithm using mutual information and artificial fish swarm algorithm based on MapReduce (MIAFSA-PSVM) …classification model. The results indicate that when compared to CMI-PSVM, the execution time of MIAFSA-PSVM algorithm is higher on the NDC dataset with the largest data size, The SVM parameter optimization algorithm based on MapReduce and cuckoo search (CSSVM-MR) and the particle swarm optimization based parallel support vector machine ensemble algorithm (PSO-PSVM) decreased by 40.1%, 79.3%, and 51.7%, respectively. This indicates that GIESVM-MR and MIAFSA-PSVM have strong adaptability to big data environments and high classification accuracy. Show more

Keywords: Big data, parallel SVM, GIESVM-MR, MIAFSA-PSVM, NC, GDC, IAFSA

DOI: 10.3233/JCM-247335

Citation: Journal of Computational Methods in Sciences and Engineering, vol. 24, no. 2, pp. 1253-1266, 2024