Journal of Intelligent & Fuzzy Systems - Volume 40, issue 4

Authors: Yang, Yongfen

Article Type: Research Article

Abstract: Intelligent video analysis has broad application prospects. How to automatically analyze and identify human behavior in video has attracted extensive attention from researchers at home and abroad. Moreover, researching effective video behavior recognition algorithms and designing efficient behavior recognition systems has important theoretical and practical value. This paper studies the nonlinear classification technique and applies the video behavior recognition algorithm to basketball recognition. Moreover, this paper studies the classical convolutional neural network model and several improvements. In addition, this paper explains the advantages of convolutional neural networks in feature extraction compared with traditional neural networks and analyzes the performance of …the algorithm by designing actual experiments. The research results show that the algorithm can quickly identify multiple players on the field, and the method can effectively deal with occlusion and other issues with high accuracy and real-time. Show more

Keywords: Nonlinear classification, basketball, sports, neural network, recognition model, feature recognition

DOI: 10.3233/JIFS-189577

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 4, pp. 7567-7576, 2021

Authors: Chen, Weiran | Li, Xiuhong | Chen, Xiaoran | Xiong, Yan

Article Type: Research Article

Abstract: Dress fatigue can affect the efficiency of sports, especially for running, the dress fatigue has a greater impact on it. Moreover, at present, there are few studies on dress fatigue. Based on this, this study is based on BP neural network, and uses surface electromyography theory and muscle fatigue measurement method to perform fatigue measurement. The fatigue threshold analysis is mainly carried out by the experimental method, and the prediction model of the wearing fatigue threshold based on BP neural network is constructed based on the actual demand. Moreover, this paper verifies the reliability of threshold distribution by experimental analysis …combined with model analysis. In addition, the study sets the organizational structure and clothing pressure as verification indicators to analyze the performance of the model. The research results show that the model constructed in this study can effectively analyze the mechanism of fatigue impact of running dress, and this paper can provide reference for the study of dress fatigue. Show more

Keywords: BP neural network, running movement, dress fatigue, impact mechanism

DOI: 10.3233/JIFS-189578

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 4, pp. 7577-7587, 2021

Authors: Hao, Zongshuai | Wang, Xin | Zheng, Shoucun

Article Type: Research Article

Abstract: At present, there are efficiency problems in related algorithms for athlete detection and recognition. Based on this, this study analyzes the characteristics of athletes’ sports process. In this study, the Otsu method was used to perform grayscale feature processing. At the same time, based on the Harris corner extraction algorithm, this study proposes that the multi-target tracking combined with the corner feature of the target can be used to track different parts of the athlete as different target areas. In addition, this study uses a sequential algorithm to perform connected component labeling. Finally, in order to test the performance and …recognition efficiency of the proposed algorithm, the performance of the algorithm is explored through experimental analysis. The research shows that the algorithm has good performance and has certain practical effects, and it has certain reference significance for subsequent related research. Show more

Keywords: Visual image, sports, athlete, motion detection, skeletal model

DOI: 10.3233/JIFS-189579

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 4, pp. 7589-7599, 2021

Authors: Liu, Xiujuan | Zhao, Huifeng

Article Type: Research Article

Abstract: At present, the dairy brand loyalty evaluation model is not perfect, and the dairy brand loyalty measurement model for the consumer-oriented industry needs to be further studied. Through machine learning methods, online consumer brand product purchase behaviors are clustered to achieve clustering of users with similar loyalty and to measure online dairy brand loyalty. This study has the advantages of applying machine learning to processing online consumer big data, that is, it has advantages when processing high-dimensional data, when processing data in multiple ways, and when analyzing data with high complexity algorithms. The independent variables, dependent variables, and adjusted variables …in the model are measured in the form of a Likert five-level scale. Moreover, this study combines with actual cases to make adjustments to the measurement of dairy brand loyalty and verifies the model performance through simulation experiments. The research results show that the validity of the scale structure is good, and the research model has certain practical effects. Show more

Keywords: Machine learning, clustering algorithm, dairy brand loyalty, simulation model

DOI: 10.3233/JIFS-189580

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 4, pp. 7601-7612, 2021

Authors: Preetha, P. | Mallika, R.

Article Type: Research Article

Abstract: Attention Deficit Hyperactivity Disorder (ADHD) is one of the major mental-health disorders worldwide. ADHD is typically characterized by impaired executive function, impulsivity, hyperactivity and with respect to these behavioral symptoms, diagnosis of ADHD is performed. These symptoms are obviously seen at in early stage. Serious impairments and substantial burdens are induced for society as well as to families. However, for ADHD, there is no diagnostic laboratory in current scenario. Psychological tests like Brown Attention Deficit Disorder Scale (BADDS), Conners Parent Rating Scale and ADHD Rating Scale (ADHD-RS) are carried out for ADHD diagnosis. Tedious and complex clinical analysis are needed …in this testing and this makes low efficiency of the diagnostic process. A traditional diagnosis technique of ADHD produces degraded results. So, enhanced extreme learning machine is incorporated with existing techniques for avoiding the issues of performance degradation. There is a need to enhance the classifier performance further and there is a chance for unwanted noise in input samples, which may degrade the performance of classifier. For avoiding these issues, an enhanced and automated ADHS diagnosis technique is proposed. First stage is pre-processing, and it is carried out based on min max normalization and feature extraction is a next stage, which is carried out through Fast Independent Component Analysis and third stage is a Deep Extreme Learning Machine (DELM) based ADHD identification and classification. Extreme Learning Machine with Kernel (KELM) and Multilayer Extreme Learning Machine (MLELM) algorithm are combined in this method and it is termed as deep extreme learning machine (DELM). Collection of neuro images are used for quantitative and qualitative analysis and with respect to f-measure, recall, precision and accuracy, robustness of proposed technique is demonstrated. Show more

Keywords: Deep extreme learning machine, min max normalization, psychological tests, attention Deficit hyperactivity disorder impaired executive function.

DOI: 10.3233/JIFS-189581

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 4, pp. 7613-7621, 2021

Authors: Sadhana, S. | Mallika, R.

Article Type: Research Article

Abstract: Blindness is one of the serious issues in the present medical world scenario mainly caused by Diabetic Retinopathy (DR). It is a diabetes complication, that is produced due to the problems in retina blood vessel. For clinical treatment, it will be extremely helpful, if diabetic retinopathy is detected in early stages. In recent years, the manual detection of DR consumes more time and moreover, the detection of DR in early stages is still a challenging task. In order to avoid these issues, this research work focus on an automated as well as effective solution for detecting DR symptoms from retinal …images and requires less time for accurate detection. A Novel histogram equalization technique is used for performing contrast enhancement and equalization in initial pre-processing stage. Then, from these pre-processed images, image patches are extracted regularly. Improved Discrete Curvelet Transform based Grey Level Co-occurrence Matrix (IDCT-GLCM) is used in second stage for extracting features. Then, extracted features are given to Classifier. At last, an Improved Alexnet model-based CNN (IAM-CNN) classification approach is used for diagnosing DR from digital fundus images. In terms of accuracy, specificity and sensitivity, effectiveness and efficiency of proposed method is shown by extensive simulation results. Show more

Keywords: Improved alexnet model based CNN (IAM-CNN), improved discrete curvelet transform based grey level co-occurrence matrix (IDCT-GLCM), novel histogram equalization, diabetic retinopathy

DOI: 10.3233/JIFS-189582

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 4, pp. 7623-7634, 2021

Authors: Alphonse, P.J.A. | Sriharsha, K.V.

Article Type: Research Article

Abstract: Depth data from conventional cameras in monitoring fields provides a thorough assessment of human behavior. In this context, the depth of each viewpoint must be calculated using binocular stereo, which requires two cameras to retrieve 3D data. In networked surveillance environments, this drives excess energy and also provides extra infrastructure. We launched a new computational photographic technique for depth estimation using a single camera based on the ideas of perspective projection and lens magnification property. The person to camera distance (or depth) is obtained from understanding the focal length, field of view and magnification characteristics. Prior to finding distance, initially …real height is estimated using Human body anthropometrics. These metrics are given as inputs to the Gradient-Boosting machine learning algorithm for estimating Real Height. And then magnification and Field of View measurements are extracted for each sample. The depth (or distance) is predicted on the basis of the geometrical relationship between field of view, magnification and camera at object distance. Using physical distance and height measurements taken in real time as ground truth, experimental validation is performed and it is inferred that with in 3m–7 m range, both in indoor and outdoor environments, the camera to person distance (Preddist ) anticipated from field of view and magnification is 91% correlated with actual depth at a confidence point of 95% with RMSE of 0.579. Show more

Keywords: Focal length, magnification, field of view, sensor size, perspective projection, anthropometrics

DOI: 10.3233/JIFS-189583

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 4, pp. 7635-7651, 2021

Authors: Thamizh Thentral, T.M. | Jegatheesan, R. | Subramani, C.

Article Type: Research Article

Abstract: The source current harmonics reduction techniques were found to be unpredictable and disparity under different loading conditions. The presence of uncertainity issue in harmonics elimination is due to nonlinear loads. Filters can be used to eliminate the harmonics and power quality issues. But these filters are not cost effective to provide dynamic performance under various loading conditions. The target of this paper is to minimize source current harmonics with optimum voltage stability under different loading conditions. A new Unified Power Flow controller is developed whose series compensator is replaced by modular multilevel converter to achieve high modular level with reduced …harmonics and fast current limiting during the fault short circuit and shunt compensator is replaced with four switches and one capacitor combination to achieve the twin benefit of more reliable power system and good voltage stability for different loadings. DDSRF (Decoupled Double Synchronous Reference Frame) theory is utilized in the proposed converter for generating the reference current from the AC supply. DDSRF theory generates sinusoidal harmonics with the opposite phase to the load current. The UPFC can suck or injects the responsive power in the PCC. After DDSRF theory, hysteresis controller is used to produce PWM pulse for the shunt and series compensator. The proposed DDSRF theory is compared with existing dq theory to show its effectiveness in terms of THD analysis. The PI and fuzzy logic methodology is utilized to control the capacitor DC rail voltage. The proposed approach is simulated using Matlab under various loading condition and hardware is developed using Spartan 6E FPGA Controller. Show more

Keywords: UPFC, PWM, DDSRF theory, series compensator, shunt compensator, MMC, four switch inverter

DOI: 10.3233/JIFS-189584

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 4, pp. 7653-7665, 2021

Article Type: Other

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 4, pp. 7667-7667, 2021

Authors: Hsieh, Wen-Hsiang

Article Type: Editorial

DOI: 10.3233/JIFS-189586

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 4, pp. 7669-7669, 2021

Authors: Wen, Bor-Jiunn | Lin, Yung-Sheng | Tu, Hsing-Min | Hsieh, Cheng-Chang

Article Type: Research Article

Abstract: This study proposes a cloud tele-measurement technique on an electromechanical system, and uses a neural network algorithm based on principal-component analysis (PCA) to quickly diagnose its performance. Three vibration, three temperature, electrical voltage, and current sensors were mounted on the electromechanical system, and the external braking device was used to provide different load-states to simulate the operating states of the motor under different conditions. Moreover, a single-chip multiprocessor was used through the sensor to instantly measure the various load-state simulations of the motor. The operating states of the electromechanical system were classified as normal, abnormal, and required-to-be-turned-off states using a …principal-component Bayesian neural network algorithm (PBNNA), to enable their quick diagnosis. Furthermore, PBNNA successfully reduces the dimensionality of the multivariate dataset for rapid analysis of the electromechanical system’s performance. The accuracy rates of health-diagnosis based on the Bayesian neural network algorithm and PBNNA models were obtained as 97.7% and 98%, respectively. Finally, the single-chip multiprocessor based on PBNNA is used to automatically upload the measurement and analysis results of the electromechanical system to the cloud website server. The establishment of this model system can optimize prediction judgment and decision-making based on the damage situation to achieve the goals of intelligence and optimization of factory reconstruction. Show more

Keywords: Tele-measurement, electromechanical system, principal-component bayesian neural network algorithm, health-diagnosis, cloud website server

DOI: 10.3233/JIFS-189587

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 4, pp. 7671-7680, 2021

Authors: Cheng, Cheng-Feng | Chen, Ta-Cheng

Article Type: Research Article

Abstract: This study aims to explore the configurations of potential relevant antecedents in 3D printing medical Market for achieving high user satisfaction from both the suppliers’ and users’ perspectives. The important antecedents in this study include relationship marketing, innovation, 3D printing perceived values, and 3D printing perceived risk. Firstly, this study investigates the relationships among potential relevant antecedents and user satisfaction. Furthermore, to explore the gap between users’ evaluation and innovation suppliers’ perception, this study addresses this issue based on both perspectives of suppliers and buyers. To assess the applicability of the proposed model, we employed questionnaires survey and collected primary …data from 3D printing suppliers and their customers. Moreover, the fuzzy set qualitative comparative analysis (fsQCA) approach has been applied for evaluating the effectiveness of relationship marketing and innovation in 3D printing medical market. Finally, the numerical results indicate that there is one causal configuration (i.e. , 1A) found to be sufficient for high user satisfaction for the perspectives of 3D printing suppliers and three configurations for the perspectives of 3D printing customers. In the perspectives of 3D printing suppliers, the combination of relationship marketing, innovation, and 3D printing perceived value is sufficient conditions causing high user satisfaction. However, there are three causal configurations (i.e. , 1B, 2B, and 3B) found to be sufficient for high user satisfaction for the perspectives of 3D printing customers. Show more

Keywords: Relationship marketing, innovation, perceived value, perceived risk, user satisfaction, fuzzy set qualitative comparative analysis

DOI: 10.3233/JIFS-189588

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 4, pp. 7681-7690, 2021

Authors: Chen, Yen-Hung | Chang, Arthur | Huang, ChunWei

Article Type: Research Article

Abstract: The cloud computing and Internet of Things (IoT) have become two key technologies to meet future business requirements. However, a massive scale of Distributed Denial-of-Service (DDoS) has been widely applied to congest network critical links and to paralyze the cloud and IoT service. This is mainly due to DDoS is easily implemented, obfuscated, and occulted by launching large-scale legitimate low-speed flows and rolling target links to paralyze target network areas. Many metrics and risk access management frameworks to evaluate the impact of DDoS are proposed. However, they all lack time granularity to evaluate the cost of different scales of attacks …in IoT or large-scale network structure. This study proposes an AI Driven Evaluation framework, called ADE, that applies Convolution Neural Networks to statistically evaluate the network status through end-to-end functionality (Input: network status; Output: DDoS detected or not) without any manual intervention. ADE provides quantitative security risk analysis by using learning time as the control variable, network structure as the independent variable, and time to identify DDoS as the dependent variable. The learning time to detect DDoS event and recover the system is then applied to evaluate the scale of this DDoS, the reasonability of the regulated RTO, and the vulnerability of the current net-work topology and the improvement due to the new security solution. The experiment results demonstrate the contributions of ADE are (1) providing objective and quantitative analytical security risk assessment indicator, (2) providing an autonomic DDoS defense framework without any manual intervention which allows cloud computing and Internet of Things company focuses on their service and leaves security defending to ADE, and (3) demonstrating the possibility of AI assisted risk assessment which enables security defense solution buyer with less security domain experts to evaluate suitable network defense strategy. Show more

Keywords: SDN, machine learning, IoT, mobile broadband, convolutional neural networks, distributed denial-of-service

DOI: 10.3233/JIFS-189589

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 4, pp. 7691-7699, 2021

Authors: Hsieh, Yi-Chih | You, Peng-Sheng

Article Type: Research Article

Abstract: In this study, we investigate a new multi-maintenance with sequential operation (MMSO) problem, in which a variety of tasks must be processed on multiple machines. In the MMSO problem, each task has multiple sequential operations that must be processed for each machine. In addition to maintenance, the MMSO problem has many other practical applications, such as physical examination scheduling. The proposed MMSO, which is an NP-hard problem, generalizes typical job shop scheduling problems. Thus, a novel encoding scheme, which is embedded into an immune-based algorithm (IBA), is proposed in this study to convert any sequence of random numbers into a …feasible solution of the MMSO problem to solve the MMSO problem. Numerical results of applications in aircraft maintenance and physical examination scheduling are reported and compared with those of particle swarm optimization and genetic algorithm. Experimental results show that IBA outperforms the two other algorithms. Show more

Keywords: Scheduling, maintenance, sequential operations, immune-based algorithm, optimization

DOI: 10.3233/JIFS-189590

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 4, pp. 7701-7710, 2021

Authors: You, Peng-Sheng | Hsieh, Yi-Chih

Article Type: Research Article

Abstract: Leveraging their networks, bike rental companies usually provide customers with services for renting and returning bikes at different bike stations. Over time, however, rental networks may encounter problems with unbalanced bike stocks. The potential imbalance between supply and demand at bike stations may result in lost sales for stations with relatively high demand and underutilization for stations with relatively low demand. This paper proposed a constrained mixed-integer programming model that uses operator-based redistribution and user-based price approach to rebalance bikes across bike stations. This paper aims to maximize total profit over a planning horizon by determining operator-based bike transfers and …dynamic pricing. The proposed model is a non-deterministic polynomial-time problem, and thus, a heuristic was developed based on linear programming and evolutionary computation to perform model solving. Numerical experiments reveal that the proposed method performed better than Lingo, a well-known commercial software. Sensitivity analyses were also performed to investigate the impact of changes in system parameters on computational results. Show more

Keywords: Bike-rental, rebalancing, operator-based redistribution, user-based price, mixed integer programming, heuristic

DOI: 10.3233/JIFS-189591

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 4, pp. 7711-7722, 2021

Authors: Wang, Sheng-Chuan | Chen, Ta-Cheng

Article Type: Research Article

Abstract: Multi-objective competitive location problem with cooperative coverage for distance-based attractiveness is introduced in this paper. The potential facilities compete to be selected to serve all demand points which are determined by maximizing total collective attractiveness of all demand points from assigned facilities and minimizing the fixed and distance costs between all demand points and selected facilities. Facility attractiveness is represented as a coverage of the facility with full, partial and none coverage corresponding to maximum full and partial coverage radii. Cooperative coverage, which the demand point is covered by at least one facility, is also considered. The problem is formulated …as a multi-objective optimization model and solution procedure based on elitist non-dominated sorting genetic algorithms (NSGA-II) is developed. Experimental example demonstrates the best non-dominated solution sets obtained by developed solution procedure. Contributions of this paper include introducing competitive location problem with facility attractiveness as a distance-based coverage of the facility, re-categorizing facility coverage classification and developing solution procedure base upon NSGA-II. Show more

Keywords: Competitive location problem, facility attractiveness, distance-based coverage, cooperative coverage, elitist non-dominated sorting genetic algorithms (NSGA-II)

DOI: 10.3233/JIFS-189592

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 4, pp. 7723-7734, 2021

Authors: Heo, Tak-Sung | Kim, Jong-Dae | Park, Chan-Young | Kim, Yu-Seop

Article Type: Research Article

Abstract: Sentence similarity evaluation is a significant task used in machine translation, classification, and information extraction in the field of natural language processing. When two sentences are given, an accurate judgment should be made whether the meaning of the sentences is equivalent even if the words and contexts of the sentences are different. To this end, existing studies have measured the similarity of sentences by focusing on the analysis of words, morphemes, and letters. To measure sentence similarity, this study uses Sent2Vec, a sentence embedding, as well as morpheme word embedding. Vectors representing words are input to the 1-dimension convolutional neural …network (1D-CNN) with various sizes of kernels and bidirectional long short-term memory (Bi-LSTM). Self-attention is applied to the features transformed through Bi-LSTM. Subsequently, vectors undergoing 1D-CNN and self-attention are converted through global max pooling and global average pooling to extract specific values, respectively. The vectors generated through the above process are concatenated to the vector generated through Sent2Vec and are represented as a single vector. The vector is input to softmax layer, and finally, the similarity between the two sentences is determined. The proposed model can improve the accuracy by up to 5.42% point compared with the conventional sentence similarity estimation models. Show more

Keywords: Sentence similarity, siamese network, Sent2vec, convolutional neural network, self-attention

DOI: 10.3233/JIFS-189593

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 4, pp. 7735-7744, 2021

Authors: Oh, Byoung-Doo | Lee, Yoon-Kyoung | Song, Hye-Jeong | Kim, Jong-Dae | Park, Chan-Young | Kim, Yu-Seop

Article Type: Research Article

Abstract: Speech pathology is a scientific study of speech disorders. In this field, the study also analyzes and evaluates language abilities for the purpose of improving speech and hearing. Speech therapy first performs evaluation of speech ability, which is expensive. In order to solve this problem, software methodologies have been applied to language analysis, but most of them have been applied to only part of the whole process. In this study, the degree of language development is judged by determining the age group of the speaker (Pre-school children, Elementary school, Middle and high school, Adults, and Senior citizen) using deep learning …and simple statistics. We use transcription data from the counseling contents and multi-kernel CNN model. At this time, in order to understand the characteristics of Korean language belonging agglutinative languages, experiments are carried out in words, morphemes, characters, Jamo, and Jamo with POS tag-level. And we analyze the distribution of the results for each sentence of the speakers to predict their age groups and to check the degree of language development. The proposed model shows an average accuracy of about 74.6 %. Show more

Keywords: Language analysis, age group analysis, convolutional neural networks, deep learning, statistical analysis

DOI: 10.3233/JIFS-189594

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 4, pp. 7745-7754, 2021

Authors: Chen, Lili

Article Type: Research Article

Abstract: These years, the laboratory safety situation in Colleges and universities has improved, but the safety problem still needs to be improved. Based on the research basis of previous literature, this paper extracts the causes of laboratory accidents in Colleges and universities in the past six years, uses the grey system theory to carry out correlation analysis, sorts the correlation degree of influencing factors of laboratory safety behavior, and through empirical analysis, the organization system, safety education, laboratory environment management, foundation safety ,professional safety show a decreasing relationship in the influencing factors of laboratory safety. On the basis of this conclusion, …the safety management path of related laboratories is proposed: Improve the laboratory organization and management system; carry out safety education regularly; strict professional operation process; orderly laboratory environment; effective basic safety management. These path studies have positive practical significance for laboratory safety management. Show more

Keywords: University Laboratory Safety, analysis of grey correlation degree, Security management path

DOI: 10.3233/JIFS-189596

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 4, pp. 7755-7762, 2021

Authors: Hsieh, Yi-Chih | You, Peng-Sheng | Chuang, Hao-Chun

Article Type: Research Article

Abstract: In this paper, we study the forest harvesting problem (FHP). A forest is assumed to be divided into several identical square units, and each unit has its harvesting value based on its type. Harvesting a unit will affect the growth and values of its neighboring units. In this FHP, the best harvesting plan of a unit must be identified to maximize three various objectives simultaneously. The FHP is a multiobjective mathematical and an NP-hard problem. We apply three artificial intelligence algorithms, namely, immune algorithm, genetic algorithm, and particle swarm optimization, for maximizing the weighted objective to solve the FHP. We …also solve the following two sets of test problems: (i) a set of randomly generated FHP problems and (ii) a practical problem in Taiwan. Numerical results show the performance of the three algorithms for the test problems. Finally, we compare and discuss the effects of various weights for the three objectives. Show more

Keywords: Forest harvesting problem, optimization, immune algorithm, genetic algorithm, particle swarm optimization

DOI: 10.3233/JIFS-189597

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 4, pp. 7763-7774, 2021

Authors: Ding, Ing-Jr | Zheng, Nai-Wei | Hsieh, Meng-Chuan

Article Type: Research Article

Abstract: With fast developments of artificial intelligence, human behaviors can be further acknowledged by means of the biometric information of hand gesture actions made by the person. Such hand gesture information revealing the specific intention of the person will be undoubtedly a critical clue to cognize human behaviors. Furthermore, identity recognition of the hand gesture-making person is one of the most important technique issues in hand gesture recognition applications. This work explores hand gesture intention-based identity recognition where various deep learning recognition strategies are presented. The well-know image sensor of Leap Motion Controller (LMC) is employed in this work for acquisitions …of active hand gesture data. This paper presents four different deep learning strategies for hand gesture intention-based identity recognition, all of which are based on the deep learning model of the visual geometry group (VGG)-type convolution neural network (CNN). The presented deep learning strategies to perform hand gesture intention-based identity recognition are typical VGG-16 CNN deep learning, dynamic time warping (DTW) classifications with VGG-16 CNN extracted deep learning features, DTW classifications by VGG-16 CNN extracted deep learning features with principal component analysis (PCA) data reduction, and PCA centroid classifications using VGG-16 CNN extracted deep learning features with PCA. Compared with traditional hand gesture recognition by classifications of only the geometrical space feature of LMC 3D-(x, y, z) data without any deep learning, most of presented VGG-CNN based deep learning approaches have more outstanding performances on recognition accuracy. In the situation of real-time recognition that considers both of recognition accuracy and computation time, PCA centroid classifications by VGG-16 CNN extracted deep learning features with PCA reduction, FC1-PCA and FC2-PCA features that are estimated from the first and the second fully connected (FC) layer of VGG-CNN respectively (i.e. FC1 and FC2 layers) and then significantly reduced the data dimension by PCA, apparently performs best among all presented deep learning strategies. Show more

Keywords: Human behavior cognition, hand gesture action, hand gesture intention-based identity recognition, LMC sensor, VGG-CNN deep learning feature

DOI: 10.3233/JIFS-189598

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 4, pp. 7775-7788, 2021

Authors: Pan, Nan | Pan, Dilin | Hou, Zhanwei | Jiang, Xuemei | Liu, Yi

Article Type: Research Article

Abstract: Conventional methods can scarcely achieve the rapid comparison of line traces. In this study, trace detection signals based on a single-point laser were collected and smoothed. The multi-scale wavelet coefficient of the trace detection signals was obtained after noise reduction by the dual-tree complex wavelet algorithm to minimize the adverse effects of data size on successive comparison calculations. Batch similarity comparison of trace features was achieved using multiple comparison strategies, including an optimized dynamic time regulation algorithm and the recognition of the changing rate gradient. Based on the results of boosting fusion multi-strategy comparison, optimized comparisons were achieved by machine …learning, and a model for the rapid comparison of trace features was established. Finally, the viability of the proposed algorithm was verified by experiments. Show more

Keywords: Multi-strategy mode, line traces, dual tree complex wavelet, batch comparison

DOI: 10.3233/JIFS-189599

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 4, pp. 7789-7794, 2021

Authors: Shen, Xin | Shu, Hongchun | Cao, Min | Qian, Junbing | Pan, Nan

Article Type: Research Article

Abstract: Power quality of distribution network is an emerging issue due to rapid increase in usage of non-linear loads on the one hand and utilization of sensitive devices on the other hand. Especially, harmonic emission is an important concern in both electric utilities and end users of electric power. Therefore, an accurate and rapid harmonic analysis method is of interest. New technologies have enabled the investigation of electricity consumption mode at an unprecedented scale and in multiple dimensions. However, an effective method that can capture the complexity of all the factors relevant to understanding a phenomenon such as ultrahigh harmonics (2–15 kHz). …How to detect the super high order harmonic accurately has become the premise and foundation of the study of super high order harmonic. The key challenge in developing such approaches is the identification of effective models to provide a comprehensive and relevant systems view. An ideal method can identify super high harmonics and predict outcomes, by measured data across several dimensions variation. In this paper, the data integration, current methods and available implementation is discussed. Finally, the current challenges in integrative methods is discussed. Show more

Keywords: Power quality, harmonic emission, unprecedented scale, multiple dimensions

DOI: 10.3233/JIFS-189600

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 4, pp. 7795-7802, 2021

Authors: Shen, Xin | Shu, Hongchun | Cao, Min | Pan, Nan | Qian, Junbing

Article Type: Research Article

Abstract: In distribution networks with distributed power supplies, distributed power supplies can also be used as backup power sources to support the grid. If a distribution network contains multiple distributed power sources, the distribution network becomes a complex power grid with multiple power supplies. When a short-circuit fault occurs at a certain point on the power distribution network, the size, direction and duration of the short-circuit current are no longer single due to the existence of distributed power, and will vary with the location and capacity of the distributed power supply system. The change, in turn, affects the current in the …grid, resulting in the generation and propagation of additional current. This power grid of power electronics will cause problems such as excessive standard mis-operation, abnormal heating of the converter and component burnout, and communication system failure. It is of great and practical significance to study the influence of distributed power in distributed power distribution networks. Show more

Keywords: Distributed power, distribution network, malfunction, harmonic, power electronics

DOI: 10.3233/JIFS-189601

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 4, pp. 7803-7810, 2021

Authors: Qian, Junbing | Zhao, Chengjun | Pan, Nan | Xie, Tao

Article Type: Research Article

Abstract: In order to effectively control the vibration transmitted from the ground to the precision equipment, permanent magnet linear synchronous motor (PMLSM) can be effectively applied in the active vibration absorber systems due to its good characteristics. The design of a PMLSM is very important because of the special requirements of the damping system on the response speed, working temperature, working bandwidth and installation size of the PMLSM. In this paper, the multi-objective design problem of the PMLSM in vibration damping system is proposed. Based on the equivalent magnetic circuit analysis of the PML SM’s air gap magnetic field, the performance …and design of the PMLSM are effectively analyzed in combination with the design objectives. The design and performance of the prototype meet the design requirements. Show more

Keywords: Damping, amplitude-frequency characteristics, Laplace transform, linear synchronous motor

DOI: 10.3233/JIFS-189602

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 4, pp. 7811-7818, 2021

Authors: Qian, Junbing | Xu, Zhongru | Luo, Yongyou | Pan, Nan | Liu, Yi

Article Type: Research Article

Abstract: Most of the underwater salvage operations work in shallow waters. The underwater environment is complex and varied. There are many risks and unpredictable conditions such as turbulence, eddies, wind, waves and deep water pressure. The motion and control cause serious interference, and the flexibility of automatic stabilization and multi-dimensional motion under external disturbances is increasingly becoming a key element in the design process of underwater robots. In this paper, the structure, driving and control design of an underwater dynamic search and underwater robot based on 6-DOF driving is proposed, and its dynamics and control system are analyzed. Different from the …traditional underwater robot technology, the method proposed in this paper is more suitable for shallow water area and multi DOF driving control technology. The driving structure and electronic device of the robot are introduced. Several experiments were carried out in the controlled environment. The experimental results demonstrate the correctness and effectiveness of the design and analysis. Show more

Keywords: Complex underwater environment, underwater salvage operation, dynamics, driving system, multiple degrees of freedom

DOI: 10.3233/JIFS-189603

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 4, pp. 7819-7828, 2021

Authors: Pan, Nan | Qian, Junbing | Zhao, Chengjun

Article Type: Research Article

Abstract: It can divide the atomization effect in the direction of the nozzle axial injection into the jet area and the non-jet area by using the second crushing theory. On this basis, according to the feed liquid atomization particles discrete degree index of characteristics particle size of feed liquid atomization, it divides the injection zone into the atomization area and the diffusion area, so as to realize the axial direction of jet nozzle injection zone, atomization zone and the diffusion zone accurately. Simulation and experiment are used to verify the three zones of atomization nozzle. The division of three zones drives …the study from the whole space of liquid distribution in the roller to atomization zone, clears the key zone of the roller in tobacco primary processing, and provides a basis for further work. Show more

Keywords: Roller, nozzle, injection zone, atomization zone, diffusion zone, atomized particle size

DOI: 10.3233/JIFS-189604

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 4, pp. 7829-7836, 2021

Authors: Lilan, Haoqi | Qian, Junbing | Pan, Nan

Article Type: Research Article

Abstract: Nozzle spray atomization is widely used in industrial and agricultural production processes and is a very complicated physical change. The spray atomization of the nozzle is a process in which the droplets are continuously broken into finer particles under the action of force, in order to study the effect of nozzle atomization, that is, droplet size distribution characteristics. The experimental average mathematical model of droplet size distribution was established by introducing Sauter Mean Diameter (SMD). The droplet size distribution in the atomization field of the nozzle is studied by simulation. In the experimental study, the high-speed camera, external mixing air …atomizing nozzle platform experimental device and image processing were used, and the atomization field was divided into multiple observation areas. Through the measurement of several local observation areas, the droplet size distribution of the whole atomization field is constructed. It provides a reference for the study of the atomization field of the nozzle and a basis for the intuitive understanding of the droplet size distribution in the atomization field of the nozzle. The effective atomization area of the nozzle atomization was selected to study the influence of the liquid flow rate, the liquid temperature and the nozzle pressure on the atomized particle size distribution of the externally mixed atomizing nozzle. The internal law is obtained, which provides a basis and reference for effectively controlling the atomization effect in the atomization field. Show more

Keywords: Nozzle atomization, particle size, liquid, atomization field, image processing, Sauter average diameter

DOI: 10.3233/JIFS-189605

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 4, pp. 7837-7847, 2021

Authors: Zhao, Chengjun | Pan, Nan | Jiang, Xuemei | Pan, Dilin | Liu, Yi

Article Type: Research Article

Abstract: The linear trace indicates the external morphological structure of the contact portion of clamping and cutting tools, which is not easy to be destroyed, has a high occurrence rate and high significant on identification. It is of great significance for prosecutor to determine the nature of the case and determine the tools used in the crime so as to find the criminals. The traditional linear trace analyzing methods include microscopy, manual comparison of characteristics, image recognition and three-dimensional scanning methods. The single-point laser picks up the toolmark detection signal, and the longest common substring is obtained after noise reduction. In …addition, the improved dynamic programming algorithm calculates and generates matching results. Finally, the effectiveness of the algorithm is verified by the actual detection data. Show more

Keywords: Linear trace, single-point laser, longest common substring, dynamic programming, matching calculation

DOI: 10.3233/JIFS-189606

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 4, pp. 7849-7855, 2021

Authors: Pan, Nan | Jiang, Xuemei | Pan, Dilin | Liu, Yi

Article Type: Research Article

Abstract: Bullet trace detection’s main task is determining the type of bullet and the identity of the gun that fired it. In order to solve conventional bullet trace matching method problems, a bullet fast matching method based on single point laser detection is proposed. First, adaptive control of the bullet center position and cylinder axis were performed. Then, a laser displacement sensor was implemented to perform a 360° detection in order to trace rifling on the bullet surface in the circumferential direction; grayscale morphological filtering was implemented in order to de-noise detection data, and the Pearson correlation coefficient was implemented in …order to perform the trace similarity matching calculation, thereby achieving a fast bullet trace matching. Moreover, the algorithm’s effectiveness was verified via practical testing data. Show more

Keywords: Single point laser, bullet, adaptive control, grayscale morphological filtering, Pearson correlation coefficient

DOI: 10.3233/JIFS-189607

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 4, pp. 7857-7862, 2021

Authors: Chen, Yenming J. | Tsai, Jinn-Tsong | Huang, Wei-Tai | Ho, Wen-Hsien

Article Type: Research Article

Abstract: The uncertainty issue in real-work optimization affects the level of optimization significantly. Because most future uncertainties cannot be foreseen in advance, the optimization must take the uncertainties as a risk in an intelligent way in the process of computation algorithm. Based on our risk-sensitive filtering algorithm, this study adopts a model-predictive control to construct a risk-averse, predictable model that can be used to regulate the level of a real-world system. Our model is intelligent in that the predictive model needs not to identify the system parameters in advance, and our algorithm will learn the parameters through data. When the real-world …system is under the disturbance of unexpected events, our model can still maintain suitable performance. Our results show that the intelligent model designed in this study can learn the system parameters in a real-world system and minimize unexpected real-world disturbances. Through the learning process, our model is robust, and the optimal performance can still be retained even the system parameters deviate from expected, e.g., material shortage in a supply chain due to earthquake. When parameter error risks occur, the control rules can still drive the overall system with a minimal performance drop. Show more

Keywords: Intelligent optimization, model-predictive control, risk-sensitive filtering, robust algorithm

DOI: 10.3233/JIFS-189608

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 4, pp. 7863-7873, 2021

Authors: Lee, Bor-Hon | Yang, Albert Jing-Fuh | Chen, Yenming J.

Article Type: Research Article

Abstract: A large categories of time series fluctuate dramatically, for example, prices of agriculture produce. Traditional methods in time series and stochastic prediction may not capture such dynamics. This paper tries to use machine learning to tune the model for a real situation by establishing a price determination mechanism on the model of stochastic automata (SA) and evolutionary game (EG). Time series volatility attributed to the chaotic process can be obtained through the learning algorithm of Markov Chain Monte Carlo (MCMC). Using machine learning through the chaotic analysis of stochastic automata and evolutionary games, we find that a more spatially aggregated …distribution (smaller entropy) leads to larger time series fluctuations, regardless of the initial distribution of crops. By integrating the factors discovered in this study, we can develop a better learning algorithm in a highly volatile time series in agriculture prices. Show more

Keywords: Distribution entropy, spatial diffusion, stochastic automata (SA), evolutionary game (EG), machine learning

DOI: 10.3233/JIFS-189609

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 4, pp. 7875-7881, 2021

Authors: Chen, Yao-Mei | Chen, Yenming J. | Tsai, Yun-Kai | Ho, Wen-Hsien | Tsai, Jinn-Tsong

Article Type: Research Article

Abstract: A multi-layer convolutional neural network (MCNN) with hyperparameter optimization (HyperMCNN) is proposed for classifying human electrocardiograms (ECGs). For performance tests of the HyperMCNN, ECG recordings for patients with cardiac arrhythmia (ARR), congestive heart failure (CHF), and normal sinus rhythm (NSR) were obtained from three PhysioNet databases: MIT-BIH Arrhythmia Database, BIDMC Congestive Heart Failure Database, and MIT-BIH Normal Sinus Rhythm Database, respectively. The MCNN hyperparameters in convolutional layers included number of filters, filter size, padding, and filter stride. The hyperparameters in max-pooling layers were pooling size and pooling stride. Gradient method was also a hyperparameter used to train the MCNN model. …Uniform experimental design approach was used to optimize the hyperparameter combination for the MCNN. In performance tests, the resulting 16-layer CNN with an appropriate hyperparameter combination (16-layer HyperMCNN) was used to distinguish among ARR, CHF, and NSR. The experimental results showed that the average correct rate and standard deviation obtained by the 16-layer HyperMCNN were superior to those obtained by a 16-layer CNN with a hyperparameter combination given by Matlab examples. Furthermore, in terms of performance in distinguishing among ARR, CHF, and NSR, the 16-layer HyperMCNN was superior to the 25-layer AlexNet, which was the neural network that had the best image identification performance in the ImageNet Large Scale Visual Recognition Challenge in 2012. Show more

Keywords: Convolutional neural network, hyperparameter, human electrocardiogram, PhysioNet, uniform experimental design approach

DOI: 10.3233/JIFS-189610

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 4, pp. 7883-7891, 2021

Authors: Chou, Fu-I | Ho, Wen-Hsien | Chen, Yenming J. | Tsai, Jinn-Tsong

Article Type: Research Article

Abstract: This study proposes a framework implementing triangular estimation for better modeling and forecasting time series. In order to improve the performance of estimation, we employ two sources of triangulation to generate a time series, which is statistically indistinguishable with the latent time series hidden in a system. Thanks to Bayesian hierarchical estimation, which is akin to deep learning but more sophisticate and longer history, the framework has been validated by a large amount of records in vegetable auctions. The hierarchical Bayesian estimation and Monte Carlo Markov Chain particle filters used in hidden Markov model are appreciated during the massive bootstrapping …of data. Our results demonstrate excellent estimation performance in discovering hidden states. Show more

Keywords: Generative estimation, time series forecasting, triangulation data assimilation

DOI: 10.3233/JIFS-189611

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 4, pp. 7893-7899, 2021

Authors: Ouyang, Chen-Sen | Chen, Yenming J. | Tsai, Jinn-Tsong | Chang, Yiu-Jen | Huang, Tian-Hsiang | Hwang, Kao-Shing | Ho, Yuan-Chih | Ho, Wen-Hsien

Article Type: Research Article

Abstract: Atrial fibrillation (AF) is a type of paroxysmal cardiac disease that presents no obvious symptoms during onset, and even the electrocardiograms (ECG) results of patients with AF appear normal under a premorbid status, rendering AF difficult to detect and diagnose. However, it can result in deterioration and increased risk of stroke if not detected and treated early. This study used the ECG database provided by the Physionet website (https://physionet.org ), filtered data, and employed parameter-extraction methods to identify parameters that signify ECG features. A total of 31 parameters were obtained, consisting of P-wave morphology parameters and heart rate variability parameters, …and the data were further examined by implementing a decision tree, of which the topmost node indicated a significant causal relationship. The experiment results verified that the P-wave morphology parameters significantly affected the ECG results of patients with AF. Show more

Keywords: Atrial fibrillation, electrocardiogram (ECG), data mining, decision tree

DOI: 10.3233/JIFS-189612

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 4, pp. 7901-7908, 2021

Authors: Ko, Joonho | Cho, Hyun Woong | Kim, Jung In | Kim, Hyunmyung | Lee, Young-Joo | Suh, Wonho

Article Type: Research Article

Abstract: Transportation system management and traveler information systems evolve with the development of data communications and intelligence of traffic simulations. Variety of roadside and mobile sensing platforms will be deployed to allow communication between vehicles with Dedicated Short Range Communications (DSRC). Traffic data received from moving vehicles will be transmitted to each individual vehicle and traffic management center to provide real time traffic information. Microscopic traffic simulation models will be used for generating intelligence from real time data in the form of traffic analysis and prediction, since they have the highest detailed level of prediction such as vehicle / driver characteristics …and have the capability to capture dynamically changing traffic conditions through the simulation. In this study, three communication methods for data communication and intelligence in traffic simulation environments are used including Ethernet, off-the-shelf wireless network, and one commercial network provider for data communication. Simulation time is measured and statistically analyzed using three different communication methods and one non-communication case. Also, traffic simulation performance is investigated to demonstrate the intelligence of traffic simulation tools in modeling traffic congestion. Show more

Keywords: Traffic simulation environments, data communication, intelligence of traffic simulation, simulation analysis, network simulation

DOI: 10.3233/JIFS-189613

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 4, pp. 7909-7916, 2021

Authors: Ko, Joonho | Cho, Hyun Woong | Kim, Jung In | Kim, Hyunmyung | Lee, Young-Joo | Suh, Wonho

Article Type: Research Article

Abstract: Traffic simulation tools are becoming more popular as complexity and intelligence are growing in transportation systems. The need for more accurate and intelligent traffic modeling is increasing rapidly as transportation systems are having more congestion problems. Although traffic simulation models have been continuously updated to represent various traffic conditions more realistically, most simulation models still have limitations in overcapacity congested traffic conditions. In traditional traffic simulation models, when there is no available space due to traffic congestion, additional traffic demand may never be allowed to enter the network. The objective of this paper is to investigate one possible method to …address the issue of unserved vehicles in overcapacity congested traffic conditions using the VISSIM trip chain. The VISSIM trip chain is used for this analysis as it has the advantage of holding a vehicle without eliminating it when traffic congestion prevents its entrance onto a network. This will allow the vehicle to enter when an acceptable gap becomes available on the entry link. To demonstrate the difference between the simulation using standard traffic input and the trip chain method, a sample congested traffic network is built and congested traffic scenarios are created. Also, simulations with different minimum space headway parameters in the priority rules are analyzed to model congested traffic conditions more realistically. This will provide the insight about the sensitivity of the model to this parameter. Based on the analysis conducted it is concluded that, with appropriate calibrations, the trip chain feature in VISSIM has the potentials to be useful in modeling overcapacity congested traffic conditions more realistically. Show more

Keywords: Traffic simulation environments, traffic congestion modeling, intelligence of traffic simulation, simulation analysis, network simulation

DOI: 10.3233/JIFS-189614

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 4, pp. 7917-7923, 2021

Authors: Chen, Joy Iong-Zong | Hengjinda, P.

Article Type: Research Article

Abstract: Smart Robot embedded with GMM-UBM (Gaussian mixture model- universal background model) based on the machine learning scheme is presented in the article. Authors have designed a smart robot for the farmer and which is designed controlled by the concept of machine learning. On the other hand, the techniques of machine learning are applied to develop a smart robot for helping farmers recognize the environment conditions, e.g . weather, and disease protection in rice or plant. The smart robot is implemented to detect and to recognize the environment conditions around a fixed area. The sensing way through vision devices, such as …camera, look like a human’s eye to distinguish various types of target. The QR code is deployed to simulate working conditions allows the robot to separate conditions and act according to conditions precisely. Besides, the smart robot is embedded with GMM-UBM algorithm for promoting the accuracy of recognition from the deployment of machine learning. The smart robot, mainly combines with AI (Artificial intelligence) techniques, consists of the following equipments: 1) a control movement subsystem, 2) a sensor control subsystem, and 3) an analysis subsystem. The researcher has determined the condition of the message options via QR code. In addition, the contents of the QR code tag will be processed a text message and saved to a memory device, once the reading is finished. The data analysis subsystem then reads the text and recommends the robot to move according to the specified conditions. The results from QR code data allow the smart robot to accurately collect many kinds of prefer data (e.g ., climate data) in the farm at the specified location. Show more

Keywords: Artificial intelligence, GMM-UBM, machine learning, smart robot, vision devices

DOI: 10.3233/JIFS-189615

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 4, pp. 7925-7937, 2021

Authors: Yu, Meng | Lu, Bao | Li, Xiong | Li, Wenfeng

Article Type: Research Article

Abstract: Online Distance teaching for multiple smart classrooms by famous teachers, as an effective solver for the problem of lack of excellent teachers, has become a new popular teaching mode. However, one of the key problems to be solved urgently for this teaching mode is how to monitor children’s class status and effectively feedback their listening standing to teachers. Installation of intelligent pressure cushion on the chair of smart classroom to monitor children’s classroom state can be a powerful way to improve teaching effectiveness for the online distance teaching mode. This paper presents a new method for monitoring children’s classroom behavior …based on intelligent cushion, which can identify basic children’s classroom behavior by classifying the original intelligent cushion pressure signal and evaluating the effectiveness of the classifier. To be concrete, the present method uses intelligent pressure cushion to collect data and denoises the original data by digital filter, and then extracts the time-domain and frequency-domain features of time-series pressure signals based on sliding time window. Finally, it uses machine learning to identify children’s status. In addition, by feature selection to reduce the data dimension, integrating different classifier to classify the extracted features, the efficiency of the present method is greatly improved. Show more

Keywords: Smart cushion, child behavior recognition, pressure sensor

DOI: 10.3233/JIFS-189616

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 4, pp. 7939-7949, 2021

Authors: Pan, Nan | Jiang, Xuemei | Pan, Dilin | Liu, Yi

Article Type: Research Article

Abstract: This article has been retracted, and the online PDF has been watermarked “RETRACTED”. The retraction notice is available at https://doi.org/10.3233/JIFS-219327 .

DOI: 10.3233/JIFS-189617

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 4, pp. 7951-7956, 2021

Authors: Liu, Hsiao-Man | Huang, Chung-Chi | Huang, Chung-Lin | Ke, Yen-Ting

Article Type: Research Article

Abstract: This study proposes a health assessment and predictive assistance system for intelligent health monitoring. Through machine learning, the tool features a customized set of quantitative measurements and web analysis systems for physical and mental fitness. The system replaces the manpower and time requirements of the past necessary to conduct interviews and keep paper records, allowing users to observe and analyze physical and mental fitness status through the webpage. To achieve this, ECG, EEG, and EMAS are used to follow physiological, psychological, and meridian energy states. ASP.NET software is used as a development tool for the system cloud page, which constructs, …documents, evaluates, and predicts functions for the smart health assistance system. The measurement data is entered and recorded in the cloud database. The data is used to construct an assessment and prediction of the user’s state of mind and body through machine learning methods, as well as the individual’s physical and mental fitness. Show more

Keywords: Intelligent assessment, intelligent prediction, somatic fitness, healthcare, machine learning

DOI: 10.3233/JIFS-189618

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 4, pp. 7957-7967, 2021

Authors: Huang, Chung-Lin | Huang, Chung-Chi

Article Type: Research Article

Abstract: Knowledge graphs are useful sources for various AI applications, however the basic paradigm to support pilot training is still unclear. In the paper, It is proposed to generate the customized knowledge graph of flight trainings using machine learning method for the flight training program. In order to provide the successful key to the further understanding of the learning problems between the students and the instructors. In this research, we collected data from an aeronautical academic in Taiwan that students were trained for Recreation Pilot License Program. We performed a test on 24 students at the first of each training course, …16 data of collected been used on building the module, 8 of them used to exam the module. There are 12 courses in the training program, and 30 hours total time were suggested by academic. The score which we applied on test were based on LCG method which is the sum of Maneuver and SRM Grades. For the indicators of course component in Learner Centered Grading, namely (a) CCS1: Operation & Effect of Controls; (b) CCS2: Straight & Level; (c) CCS3: Climbing & Descending; (d) CCS4: Turning; (e) CCS5: Stalling; (f) CCS6: Revision; (g) CCS7: Circuits; (h) CCS8: Cross-Wind Training; (i) CCS9: Circuit Emergency; (j) CCS10: Solo Circuit; (k) CCS11: Forced Landing; and (l) CCS12: Precautionary & Searching Landing. Through the method of Knowledge Graph, we deduct and predict the number of hours that need to be added for each student’s learning. Using the dynamic knowledge graph to display the key issues of the course learning continuously, and make follow-up decisions for the students, instructors and airliners. Show more

Keywords: Customized knowledge graph, FAA-industry training standards, machine learning

DOI: 10.3233/JIFS-189619

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 4, pp. 7969-7979, 2021

Authors: Wen, Bor-Jiunn | Kao, Chia-Hung | Yeh, Che-Chih

Article Type: Research Article

Abstract: Labor force is gradually becoming insufficient owing to the aging population. The quality and safety of workforces are increasingly important, and thus, a set of intelligent wearable devices that assist the transport of loads by laborers, provide auxiliary standing support, and prevent falls were designed in this study. By applying an auxiliary force to the knee joint externally, an intelligent wearable device saves labor and reduces the burden on this joint, thereby protecting it. This study utilizes a Bayesian backpropagation algorithm for intelligent control. The intelligent wearable device provides the most suitable velocity and torsion depending on the initial driving …torsion of the user by a Bayesian backpropagation algorithm based on the current angle position, velocity, and torsion load of the device motor, thereby achieving an intelligent control effect of auxiliary standing support. A triaxial accelerometer is utilized to sense a fall and prevent it by a so-called fuzzy-Bayesian backpropagation control (FBC). Eventually, this study successfully designed and manufactured an intelligent wearable device by the FBC method. For a single motor control, two knee auxiliary devices can generate a torsion of 18.6 Nm. For dual motor control, two knee auxiliary devices can generate a torsion of 43.2 Nm. Thus, the laborers can not only perform their work efficiently and safely but also reduce costs and raise the working market competitiveness. Show more

Keywords: Intelligent wearable device, auxiliary stand, falling prevention, fuzzy-bayesian backpropagation control

DOI: 10.3233/JIFS-189620

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 4, pp. 7981-7991, 2021

Authors: Pan, Nan | Shen, Xin | Guo, Xiaojue | Cao, Min | Pan, Dilin

Article Type: Research Article

Abstract: In recent years, electricity stealing has been repeatedly prohibited, and as the methods of stealing electricity have become more intelligent and concealed, it is growing increasingly difficult to extract high-dimensional data features of power consumption. In order to solve this problem, a correlation model of power-consumption data based on convolutional neural networks (CNN) is established. First, the original user signal is preprocessed to remove the noise. The user signal with a fixed signal length is then intercepted and the parallel class labelled. The segmented user signals and corresponding labels are input into the convolutional neural network for training, and the …trained convolutional neural network is then used to detect and classify the test user signals. Finally, the actual steal leak dataset is used to verify the effectiveness of this algorithm, which proves that the algorithm can effectively carry out anti–-electricity stealing by warning of abnormal power consumption behavior. There are lots of line traces on the surface of the broken ends which left in the cable cutting case crime scene along the high-speed railway in China. The line traces usually present nonlinear morphological features and has strong randomness. It is not very effective when using existing image-processing and three-dimensional scanning methods to do the trace comparison, therefore, a fast algorithm based on wavelet domain feature aiming at the nonlinear line traces is put forward to make fast trace analysis and infer the criminal tools. The proposed algorithm first applies wavelet decomposition to the 1-D signals which picked up by single point laser displacement sensor to partially reduce noises. After that, the dynamic time warping is employed to do trace feature similarity matching. Finally, using linear regression machine learning algorithm based on gradient descent method to do constant iteration. The experiment results of cutting line traces sample data comparison demonstrate the accuracy and reliability of the proposed algorithm. Show more

Keywords: Anti–electricity stealing, high-dimensional data features, convolutional neural network, early warning

DOI: 10.3233/JIFS-189621

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 4, pp. 7993-7999, 2021

Authors: Jeong, Sang-Ki | Ji, Dea-Hyeong | Oh, Ji-Youn | Seo, Jung-Min | Choi, Hyeung-Sik

Article Type: Research Article

Abstract: In this study, to effectively control small unmanned surface vehicles (USVs) for marine research, characteristics of ocean current were learned using the long short-term memory (LSTM) model algorithm of a recurrent neural network (RNN), and ocean currents were predicted. Using the results, a study on the control of USVs was conducted. A control system model of a small USV equipped with two rear thrusters and a front thruster arranged horizontally was designed. The system was also designed to determine the output of the controller by predicting the speed of the following currents and utilizing this data as a system disturbance …by learning data from ocean currents using the LSTM algorithm of a RNN. To measure ocean currents on the sea when a small USV moves, the speed and direction of the ship’s movement were measured using speed, azimuth, and location (latitude and longitude) data from GPS. In addition, the movement speed of the fluid with flow velocity is measured using the installed flow velocity measurement sensor. Additionally, a control system was designed to control the movement of the USV using an artificial neural network-PID (ANN-PID) controller [12 ]. The ANN-PID controller can manage disturbances by adjusting the control gain. Based on these studies, the control results were analyzed, and the control algorithm was verified through a simulation of the applied control system [8, 9 ]. Show more

Keywords: USV (Unmanned surface vehicles), RNN (Recurrent neural network), LSTM (Long short-term memory models), ANN-PID (Artificial neural networks-PID)

DOI: 10.3233/JIFS-189622

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 4, pp. 8001-8011, 2021

Authors: Choi, Hey-Min | Kim, Min-Kyu | Yang, Hyun

Article Type: Research Article

Abstract: Recently, abnormally high water temperature (AHWT) phenomena are occurring more often due to the global warming and its impact. These phenomena have damaged extensively to the maritime economy around the southern coast of Korea and caused an illness by exacerbating the propagation of Vibrio pathogens. To mitigate damages by AHWT phenomena, it is necessary to respond as quickly as possible or predict them in advance. In this study, therefore, we proposed a deep learning-based methodology to predict the occurrences of AHWT phenomena using the long short-term memory (LSTM) model. First, a LSTM model was trained using the satellite-derived water temperature …data over the past ten years. Then, the water temperatures after a few days were estimated using the trained LSTM model. In a performance evaluation, when estimating water temperatures after one-day, the model achieved results of 1.865 and 0.412 in terms of mean absolute percentage error (MAPE) and root mean square error (RMSE), respectively. Second, we developed a decision algorithm based on the Markov state transition in order to predict the AHWT occurrence probability. As a result, we obtained 0.88 of F1 score for predicting AHWT phenomena after 1 day in case of the southern coast of Korea. Show more

Keywords: Long short-term memory, deep learning, satellite data, abnormally high water temperature

DOI: 10.3233/JIFS-189623

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 4, pp. 8013-8020, 2021

Authors: Phawinee, Suphawimon | Cai, Jing-Fang | Guo, Zhe-Yu | Zheng, Hao-Ze | Chen, Guan-Chen

Article Type: Research Article

Abstract: Internet of Things is considerably increasing the levels of convenience at homes. The smart door lock is an entry product for smart homes. This work used Raspberry Pi, because of its low cost, as the main control board to apply face recognition technology to a door lock. The installation of the control sensing module with the GPIO expansion function of Raspberry Pi also improved the antitheft mechanism of the door lock. For ease of use, a mobile application (hereafter, app) was developed for users to upload their face images for processing. The app sends the images to Firebase and then …the program downloads the images and captures the face as a training set. The face detection system was designed on the basis of machine learning and equipped with a Haar built-in OpenCV graphics recognition program. The system used four training methods: convolutional neural network, VGG-16, VGG-19, and ResNet50. After the training process, the program could recognize the user’s face to open the door lock. A prototype was constructed that could control the door lock and the antitheft system and stream real-time images from the camera to the app. Show more

Keywords: Face recognition, intelligent lock, ResNet, deep learning

DOI: 10.3233/JIFS-189624

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 4, pp. 8021-8031, 2021

Authors: Wen, Yafeng

Article Type: Research Article

Abstract: With the promotion of BIM Technology, prefabricated building is developed rapidly in China. However, BIM technology has been only partially applied to prefabricated building, and there is still a gap between prefabricated building and intelligent construction. This paper focus on BIM 5D, together with relevant information technologies, all of which will be highly integrated and applied to prefabricated building, with the mission to get related information and enable the rapid flow of information, as well as bringing human perception, memory, knowledge and wisdom into prefabricated building, driving the development of prefabricated buildings to intelligence and leanness. Intelligent construction is …an innovated construction model based on the combination of latest information technology and engineering construction. Thus, it is particularly important to train personnel with corresponding knowledge structure, knowledge system and professional ability for intelligent construction. This paper also discusses about how to train personnel on prefabricated building and intelligent construction. Show more

Keywords: BIM5D, prefabricated building, intelligent construction, personnel training

DOI: 10.3233/JIFS-189625

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 4, pp. 8033-8041, 2021

Authors: Hsieh, Wen-Hsiang | Chen, Yi-Syun | Wu, Shang-Teh

Article Type: Research Article

Abstract: Iterative Learning Control is a branch of intelligent control which combines artificial intelligence and control theory. This objective of this study aims at reducing the cyclic error of an inverse ball screw transmission system by using iterative learning control approach. Firstly, kinematic and dynamic analyses are conducted by using the vectorial loop closure and Lagrange equations, respectively. Then, system identification is performed followed by controller design. Moreover, controller parameters are optimized to minimize the error. Finally, the feasibility and the effectiveness of the proposed approach are verified by computer simulation and prototype experiment. The experimental results showed that the reducing …percentage of the square error sum of the output speed is 90.64% by using PID control only. If ILC is applied additionally, the error is further reduced to 94.21%. Therefore, the proposed approach is not only feasible and but also effective. Show more

Keywords: Ball screw, ILC controller, PID controller, Oldham coupling

DOI: 10.3233/JIFS-189627

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 4, pp. 8043-8052, 2021

Authors: Lai, Yi-Horng

Article Type: Research Article

Abstract: OBJECTIVES: Efavirenz therapy plays an important role in controlling the progression of HIV/AIDS. However, efavirenz often causes short-term side effects for the central nervous system, and it remained controversial as to whether efavirenz leads to depression or even suicidal attempt when applied for a longer period of time. The purpose of this study is to determine the association between the use of efavirenz and depressive disorders. METHODS: This study explored the use of efavirenz on HIV-infected patients using National Health Insurance Research Database (NHIRD) in Taiwan by Bayesian survival analysis and investigated whether the use of efavirenz has …the risk of depressive disorders. To reduce the dependence of statistical modeling assumptions, this study applied propensity score matching to research data. RESULTS: Based on the result of this study, it can be found that the use of efavirenz (HR = 1.009, 95% CI=–0.505 0.554), gender (HR = 0.324, 95% CI = –2.544 0.381) were not significantly associated with the occurrence of depressive disorders, whereas age of HIV diagnosis (HR = 1.021, 95% CI = 0.011 0.055) was significantly associated with the occurrence of depressive disorders. This study concludes that the use of efavirenz does not in-crease the risk of depressive disorders among HIV-treated patients. CONCLUSIONS: For the care of HIV-infected patients (especially the older ones), the psychological harm from society, such as lack of social support, social stigma or unemployment is higher than the harm of medicine. Show more

Keywords: Human immunodeficiency virus (HIV), active antiretroviral therapy, depressive disorder, propensity score matching, Bayesian cox regression

DOI: 10.3233/JIFS-189628

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 4, pp. 8053-8062, 2021