Pub Date : 2022-12-05DOI: 10.37394/23205.2022.21.38
Ghouraf Djamel Eddine, Naceri Abdellatif
This paper proposes the Meta-heuristics approaches using genetic algorithms (GA) and particle swarm optimization (PSO) for tuning power system stabilizer PSS parameters. In this work we have proposed a multi-objective function based on two objectives: first maximize the stability margin by increasing the damping factors and second minimize the eigenvalues real parts. For the effectiveness function proposed check, we compared it with mono-objective function. The simulation results, by comparative study between genetic algorithms and particle swarm optimizations techniques via multi objective and mono objective functions proved the efficiency of the PSS adapted by multi-objective function based genetic algorithms in comparison with particle swarm optimization, it’s enhanced stability of power system works under different operating modes and different network configurations. The simulation results obtained under developed graphical user interface (GUI).
{"title":"Efficient Multi-objective Optimizers by Meta-heuristics for Power System Control","authors":"Ghouraf Djamel Eddine, Naceri Abdellatif","doi":"10.37394/23205.2022.21.38","DOIUrl":"https://doi.org/10.37394/23205.2022.21.38","url":null,"abstract":"This paper proposes the Meta-heuristics approaches using genetic algorithms (GA) and particle swarm optimization (PSO) for tuning power system stabilizer PSS parameters. In this work we have proposed a multi-objective function based on two objectives: first maximize the stability margin by increasing the damping factors and second minimize the eigenvalues real parts. For the effectiveness function proposed check, we compared it with mono-objective function. The simulation results, by comparative study between genetic algorithms and particle swarm optimizations techniques via multi objective and mono objective functions proved the efficiency of the PSS adapted by multi-objective function based genetic algorithms in comparison with particle swarm optimization, it’s enhanced stability of power system works under different operating modes and different network configurations. The simulation results obtained under developed graphical user interface (GUI).","PeriodicalId":332148,"journal":{"name":"WSEAS TRANSACTIONS ON COMPUTERS","volume":"192 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116521967","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-11-10DOI: 10.37394/23205.2022.21.37
Z. Zubi, Eman Jibril Idris
The Arabic language has many different dialects and it must be recognized before using the automatic speech recognition (ASR). On the other hand, it is observed in all Arab countries that the standard Arabic language is widely written and used in an official speech, newspapers, public administration, and schools but it is not used in daily conversations instead the dialect is widely spoken in daily life and rarely written. In this paper, we examine the difficult task of properly identifying various Arabic dialects and propose a system developed to identify a set of four regional and modern standard Arabic speeches, based on speech recognition using Hidden Markov Models (HMMs) algorithms. HMMs have become a very popular way to build a speech recognition system. It is set as hidden states and possibilities of transition from one state to another. Due to the similarities and differences between the Arabic dialects, speeches collected from the ADI5 datasets were retrieved from the MGB-3 challenge source. We proposed an Arabic Dialect Identification System called "Building a System for Arabic Dialects Identification based on Speech Recognition using Hidden Markov Models (HMMs)" that takes Input as speech utterances and produces output as dialect being spoken. During the training phase, speech utterances from one or more dialects were analyzed to capture the important properties of audio signals in terms of time and frequency. During the testing phase, previously unseen test utterances were utilized to the system, and the system outputs the dialect associated with the model of dialect that most closely matches the test utterance. The proposed model of the system shows promising results of the model for each dialect match.
{"title":"Arabic Dialects System using Hidden Markov Models (HMMs)","authors":"Z. Zubi, Eman Jibril Idris","doi":"10.37394/23205.2022.21.37","DOIUrl":"https://doi.org/10.37394/23205.2022.21.37","url":null,"abstract":"The Arabic language has many different dialects and it must be recognized before using the automatic speech recognition (ASR). On the other hand, it is observed in all Arab countries that the standard Arabic language is widely written and used in an official speech, newspapers, public administration, and schools but it is not used in daily conversations instead the dialect is widely spoken in daily life and rarely written. In this paper, we examine the difficult task of properly identifying various Arabic dialects and propose a system developed to identify a set of four regional and modern standard Arabic speeches, based on speech recognition using Hidden Markov Models (HMMs) algorithms. HMMs have become a very popular way to build a speech recognition system. It is set as hidden states and possibilities of transition from one state to another. Due to the similarities and differences between the Arabic dialects, speeches collected from the ADI5 datasets were retrieved from the MGB-3 challenge source. We proposed an Arabic Dialect Identification System called \"Building a System for Arabic Dialects Identification based on Speech Recognition using Hidden Markov Models (HMMs)\" that takes Input as speech utterances and produces output as dialect being spoken. During the training phase, speech utterances from one or more dialects were analyzed to capture the important properties of audio signals in terms of time and frequency. During the testing phase, previously unseen test utterances were utilized to the system, and the system outputs the dialect associated with the model of dialect that most closely matches the test utterance. The proposed model of the system shows promising results of the model for each dialect match.","PeriodicalId":332148,"journal":{"name":"WSEAS TRANSACTIONS ON COMPUTERS","volume":"159 2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128915846","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-11-07DOI: 10.37394/23205.2022.21.36
Mohsen A. Hassan, Aliaa Youssif, Osama Imam, A. Ghoneim
Stock market (SM) prediction methods can be divided into two categories based on the number of information sources used: single-source methods and dual-source approaches. To estimate the price of a stock, single-source approaches rely solely on numerical data. The Efficient Market Hypothesis (EMH), [1]. States that the stock price will represent all important information. Different sources of information might complement one another and influence the stock price. Machine learning and deep learning techniques have long been used to anticipate stock market movements, [2], [3]. The researcher gathered the dataset, [4], [5], [6], [7]. The dataset contains the date of the reading, the opening price, the high and low value of the stock, news about the stock, and the volume. The researcher uses a variety of machine Learning and deep learning approaches to compare performance and prediction error rates, in addition, the researcher also compared the effect of adding the news text as a feature and as a label model. and using a dedicated model for news sentiment analysis by applying the FinBERT word embedding and using them to construct a Long Short-Term Memory (LSTM). From our observation, it is evident that Deep learning-based models performed better than their Machine learning counterparts. The author shows that information extracted from news sources is better at predicting rather than its direction of price movement. And the best-performing model without news is the LSTM with an RMSE of 0.0259 while the best-performing model with news is the LSTM with a stand-alone and LSTM model for news yields RMSE of 0.0220.
{"title":"On the Impact of News for Reliable Stock Market Predictions: An LSTM-based Ensemble using FinBERT Word-Embeddings","authors":"Mohsen A. Hassan, Aliaa Youssif, Osama Imam, A. Ghoneim","doi":"10.37394/23205.2022.21.36","DOIUrl":"https://doi.org/10.37394/23205.2022.21.36","url":null,"abstract":"Stock market (SM) prediction methods can be divided into two categories based on the number of information sources used: single-source methods and dual-source approaches. To estimate the price of a stock, single-source approaches rely solely on numerical data. The Efficient Market Hypothesis (EMH), [1]. States that the stock price will represent all important information. Different sources of information might complement one another and influence the stock price. Machine learning and deep learning techniques have long been used to anticipate stock market movements, [2], [3]. The researcher gathered the dataset, [4], [5], [6], [7]. The dataset contains the date of the reading, the opening price, the high and low value of the stock, news about the stock, and the volume. The researcher uses a variety of machine Learning and deep learning approaches to compare performance and prediction error rates, in addition, the researcher also compared the effect of adding the news text as a feature and as a label model. and using a dedicated model for news sentiment analysis by applying the FinBERT word embedding and using them to construct a Long Short-Term Memory (LSTM). From our observation, it is evident that Deep learning-based models performed better than their Machine learning counterparts. The author shows that information extracted from news sources is better at predicting rather than its direction of price movement. And the best-performing model without news is the LSTM with an RMSE of 0.0259 while the best-performing model with news is the LSTM with a stand-alone and LSTM model for news yields RMSE of 0.0220.","PeriodicalId":332148,"journal":{"name":"WSEAS TRANSACTIONS ON COMPUTERS","volume":"100 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127108185","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-09-01DOI: 10.37394/23205.2022.21.35
Liukui Chen, Weiye Sun, Li Tang, H. Jiang, Zuojin Li
This paper proposes a deep learning scheme to automatically carry out reading recognition in wheel mechanical water meter images. Aiming at these early water meters deployed in old residential compounds, this method based on deep neural networks employs a coarse-to-fine reading recognition strategy, firstly, by means of an improved U-Net to locate the reading area of the dial on a large scale, and then the single character segmentation is performed according to the structural features of the dial, and finally carry out reading recognition through the improved VGG16. Experimental result shows that the proposed scheme can reduce the information interference of non-interested regions, effectively extract and identify reading results, and the recognition accuracy of 95.6% is achieved on the dataset in this paper. This paper proposes a new solution for the current situation of manual meter reading, which is time-consuming and labor-intensive, errors occur frequently; and the transformation cost is high and difficult to implement. It provides technical support for automatic reading recognition of wheel mechanical water meters.
{"title":"Research on Automatic Reading Recognition of Wheel Mechanical Water Meter Based on Improved U-Net and VGG16","authors":"Liukui Chen, Weiye Sun, Li Tang, H. Jiang, Zuojin Li","doi":"10.37394/23205.2022.21.35","DOIUrl":"https://doi.org/10.37394/23205.2022.21.35","url":null,"abstract":"This paper proposes a deep learning scheme to automatically carry out reading recognition in wheel mechanical water meter images. Aiming at these early water meters deployed in old residential compounds, this method based on deep neural networks employs a coarse-to-fine reading recognition strategy, firstly, by means of an improved U-Net to locate the reading area of the dial on a large scale, and then the single character segmentation is performed according to the structural features of the dial, and finally carry out reading recognition through the improved VGG16. Experimental result shows that the proposed scheme can reduce the information interference of non-interested regions, effectively extract and identify reading results, and the recognition accuracy of 95.6% is achieved on the dataset in this paper. This paper proposes a new solution for the current situation of manual meter reading, which is time-consuming and labor-intensive, errors occur frequently; and the transformation cost is high and difficult to implement. It provides technical support for automatic reading recognition of wheel mechanical water meters.","PeriodicalId":332148,"journal":{"name":"WSEAS TRANSACTIONS ON COMPUTERS","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124434555","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-07-15DOI: 10.37394/23205.2022.21.34
Feng Li, Lingling Wang
In recent years, with the rapid development of economy, the country's various construction is thriving, and remarkable achievements. At the same time, resources and the environment have been seriously damaged. This phenomenon is directly related to the irrationality of garbage classification and delivery, and the contradiction between the two is becoming increasingly acute as people strongly reflect the problem of environmental pollution but do nothing about it. This paper designs a garbage image classification system based on deep learning, the main research content is to compare multiple deep learning neural network models, find the optimal classifier, develop web applications and deploy neural networks, which includes image data acquisition, image pre-processing, and comparison of VGG16, Inception, and Resnet neural network model accuracy.
{"title":"Application of Deep Learning Based on Garbage Image Classification","authors":"Feng Li, Lingling Wang","doi":"10.37394/23205.2022.21.34","DOIUrl":"https://doi.org/10.37394/23205.2022.21.34","url":null,"abstract":"In recent years, with the rapid development of economy, the country's various construction is thriving, and remarkable achievements. At the same time, resources and the environment have been seriously damaged. This phenomenon is directly related to the irrationality of garbage classification and delivery, and the contradiction between the two is becoming increasingly acute as people strongly reflect the problem of environmental pollution but do nothing about it. This paper designs a garbage image classification system based on deep learning, the main research content is to compare multiple deep learning neural network models, find the optimal classifier, develop web applications and deploy neural networks, which includes image data acquisition, image pre-processing, and comparison of VGG16, Inception, and Resnet neural network model accuracy.","PeriodicalId":332148,"journal":{"name":"WSEAS TRANSACTIONS ON COMPUTERS","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122365029","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-07-05DOI: 10.37394/23205.2022.21.32
M. Sabrigiriraj, K. Manoharan, R. Karthik
The modern scientific growths in optical networks based on wavelength division multiplexing (WDM) are more attractive to satisfy the high bandwidth requirements of the modern internet infrastructure. Moreover, they potential to satisfy the future bandwidth requirements.WDM optical networks act as the backbone for telecommunication and high-performance communication networks. Multicast communication is the simultaneous transmission of data from one source node to many destination nodes available in the network over wavelength division multiplexing. It is extensively deployed in high performance computing and communication networks. In this article, a linear array and ring networks are extended by directly linking all nodes which are separated by two intermediate nodes with additional fibers which is referred as linear array and ring network with 3-length extension. The wavelength allotment methods are proposed to realize one-to-many communication over wavelength division multiplexing optical linear array and ring with 2-length extension under longest link first routing and the minimum wavelength number needed is determined. The minimum wavelength number needed to support for the extended linear array is reduced approximately by 20% when compared with that of a linear array network and for the extended unidirectional ring is reduced approximately by 25% when compared with that of a unidirectional ring network. And also, for the extended bidirectional ring the minimum wavelength number is reduced approximately by 33% when compared with that of bidirectional ring network.
{"title":"Wide-Sense Nonblocking Multicast in WDM Optical Linear Array and Ring Networks with 3-Length Extension Under Longest Link First Routing","authors":"M. Sabrigiriraj, K. Manoharan, R. Karthik","doi":"10.37394/23205.2022.21.32","DOIUrl":"https://doi.org/10.37394/23205.2022.21.32","url":null,"abstract":"The modern scientific growths in optical networks based on wavelength division multiplexing (WDM) are more attractive to satisfy the high bandwidth requirements of the modern internet infrastructure. Moreover, they potential to satisfy the future bandwidth requirements.WDM optical networks act as the backbone for telecommunication and high-performance communication networks. Multicast communication is the simultaneous transmission of data from one source node to many destination nodes available in the network over wavelength division multiplexing. It is extensively deployed in high performance computing and communication networks. In this article, a linear array and ring networks are extended by directly linking all nodes which are separated by two intermediate nodes with additional fibers which is referred as linear array and ring network with 3-length extension. The wavelength allotment methods are proposed to realize one-to-many communication over wavelength division multiplexing optical linear array and ring with 2-length extension under longest link first routing and the minimum wavelength number needed is determined. The minimum wavelength number needed to support for the extended linear array is reduced approximately by 20% when compared with that of a linear array network and for the extended unidirectional ring is reduced approximately by 25% when compared with that of a unidirectional ring network. And also, for the extended bidirectional ring the minimum wavelength number is reduced approximately by 33% when compared with that of bidirectional ring network.","PeriodicalId":332148,"journal":{"name":"WSEAS TRANSACTIONS ON COMPUTERS","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128436253","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-07-05DOI: 10.37394/23205.2022.21.33
V. Riznyk
This paper involves techniques for optimization of encoding design based on the remarkable geometric property of ring symmetry which contains two complementary asymmetries as the World harmony law for improving the quality indices of one- and multidimensional cyclic codes with respect to performance reliability, transmission speed, and transmission content, using vector data coding. These design techniques make it possible to configure encoding system with minimized number of digit weights, while maintaining or improving on error protection, security, and function of autocorrelation. Such sets are t-dimensional vectors, each of them together with all their modular sums enumerate the set node points grid of the coordinate system with the corresponding sizes and dimensionality. Systemic researches based on remarkable geometric properties of multi-modular mathematical structures such as “Glory to Ukraine Star” (GUS) combinatorial configurations demonstrated.
{"title":"Optimization of Encoding Design Based on the Spatial Geometry Remarkable Properties","authors":"V. Riznyk","doi":"10.37394/23205.2022.21.33","DOIUrl":"https://doi.org/10.37394/23205.2022.21.33","url":null,"abstract":"This paper involves techniques for optimization of encoding design based on the remarkable geometric property of ring symmetry which contains two complementary asymmetries as the World harmony law for improving the quality indices of one- and multidimensional cyclic codes with respect to performance reliability, transmission speed, and transmission content, using vector data coding. These design techniques make it possible to configure encoding system with minimized number of digit weights, while maintaining or improving on error protection, security, and function of autocorrelation. Such sets are t-dimensional vectors, each of them together with all their modular sums enumerate the set node points grid of the coordinate system with the corresponding sizes and dimensionality. Systemic researches based on remarkable geometric properties of multi-modular mathematical structures such as “Glory to Ukraine Star” (GUS) combinatorial configurations demonstrated.","PeriodicalId":332148,"journal":{"name":"WSEAS TRANSACTIONS ON COMPUTERS","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128789305","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-07-02DOI: 10.37394/23205.2022.21.31
M. Seda
The Steiner tree problem in graphs involves finding a minimum cost tree which connects a defined subset of the vertices. This problem generalises the minimum spanning tree problem, in contrast, it is NP-complete and is usually solved for large instances by deterministic or stochastic heuristic methods and approximate algorithms. In this paper, however, we focus on a different approach, based on the formulation of a mixed integer programming model and its modification for solving in the professional optimization tool GAMS, which is now capable of solving even large instances of problems of exponential complexity.
{"title":"Steiner Tree Problem in Graphs and Mixed Integer Linear Programming-Based Approach in GAMS","authors":"M. Seda","doi":"10.37394/23205.2022.21.31","DOIUrl":"https://doi.org/10.37394/23205.2022.21.31","url":null,"abstract":"The Steiner tree problem in graphs involves finding a minimum cost tree which connects a defined subset of the vertices. This problem generalises the minimum spanning tree problem, in contrast, it is NP-complete and is usually solved for large instances by deterministic or stochastic heuristic methods and approximate algorithms. In this paper, however, we focus on a different approach, based on the formulation of a mixed integer programming model and its modification for solving in the professional optimization tool GAMS, which is now capable of solving even large instances of problems of exponential complexity.","PeriodicalId":332148,"journal":{"name":"WSEAS TRANSACTIONS ON COMPUTERS","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126352658","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-07-02DOI: 10.37394/23205.2022.21.30
Kamel Maaloul, Lejdel Brahim
Ambient air pollution is the most harmful environmental risk to health. As urban air quality improves, health costs from air pollution-related diseases diminish. This is why air pollution is a major challenge for the public and government around the world. Deployment of the Internet of Things-based sensors has considerably changed the dynamics of predicting air quality. Air pollution can be predicted using machine learning algorithms Data-based sensors in the context of smart cities. In this paper, we performed pollution forecasting using machine learning techniques while presenting a comparative study to determine the best model to accurately predict air quality. Random Forest is an efficient algorithm capable of detecting air quality.
{"title":"Comparative Analysis of Machine Learning for Predicting Air Quality in Smart Cities","authors":"Kamel Maaloul, Lejdel Brahim","doi":"10.37394/23205.2022.21.30","DOIUrl":"https://doi.org/10.37394/23205.2022.21.30","url":null,"abstract":"Ambient air pollution is the most harmful environmental risk to health. As urban air quality improves, health costs from air pollution-related diseases diminish. This is why air pollution is a major challenge for the public and government around the world. Deployment of the Internet of Things-based sensors has considerably changed the dynamics of predicting air quality. Air pollution can be predicted using machine learning algorithms Data-based sensors in the context of smart cities. In this paper, we performed pollution forecasting using machine learning techniques while presenting a comparative study to determine the best model to accurately predict air quality. Random Forest is an efficient algorithm capable of detecting air quality.","PeriodicalId":332148,"journal":{"name":"WSEAS TRANSACTIONS ON COMPUTERS","volume":"140 12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129016074","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-06-30DOI: 10.37394/23205.2022.21.29
Jin Xian, Samba Aimé Hervé
At present, the motion control algorithms of lower limb exoskeleton robots have errors in tracking the desired trajectory of human hip and knee joints, which leads to poor follow-up performance of the human-machine system. Therefore, an iterative learning control algorithm is proposed to track the desired trajectory of human hip and knee joints. In this paper, the experimental platform of lower limb exoskeleton rehabilitation robot is built, and the control system software and hardware design and robot prototype function test are carried out. On this basis, a series of experiments are carried out to verify the rationality of the robot structure and the feasibility of the control method. Firstly, the dynamic model of the lower limb exoskeleton robot is established based on the structure analysis of the human lower limb; secondly, the servo control model of the lower limb exoskeleton robot is established based on the iterative learning control algorithm; finally, the exponential gain closed-loop system is designed by using MATLAB software. The relationship between convergence speed and spectral radius is analyzed, and the expected trajectory of hip joint and knee joint is obtained. The simulation results show that the algorithm can effectively improve the gait tracking accuracy of the lower limb exoskeleton robot and improve the follow-up performance of the human-machine system.
{"title":"Semiconductor Devices, Microwave, Numrobust Iterative Learning Control Algorithm for Lower Limb Rehabilitation Proactive Human-robot Collaborationerical Methods, Thermal Model, Analysis, Optimization","authors":"Jin Xian, Samba Aimé Hervé","doi":"10.37394/23205.2022.21.29","DOIUrl":"https://doi.org/10.37394/23205.2022.21.29","url":null,"abstract":"At present, the motion control algorithms of lower limb exoskeleton robots have errors in tracking the desired trajectory of human hip and knee joints, which leads to poor follow-up performance of the human-machine system. Therefore, an iterative learning control algorithm is proposed to track the desired trajectory of human hip and knee joints. In this paper, the experimental platform of lower limb exoskeleton rehabilitation robot is built, and the control system software and hardware design and robot prototype function test are carried out. On this basis, a series of experiments are carried out to verify the rationality of the robot structure and the feasibility of the control method. Firstly, the dynamic model of the lower limb exoskeleton robot is established based on the structure analysis of the human lower limb; secondly, the servo control model of the lower limb exoskeleton robot is established based on the iterative learning control algorithm; finally, the exponential gain closed-loop system is designed by using MATLAB software. The relationship between convergence speed and spectral radius is analyzed, and the expected trajectory of hip joint and knee joint is obtained. The simulation results show that the algorithm can effectively improve the gait tracking accuracy of the lower limb exoskeleton robot and improve the follow-up performance of the human-machine system.","PeriodicalId":332148,"journal":{"name":"WSEAS TRANSACTIONS ON COMPUTERS","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130776651","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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