Pub Date : 2024-07-12DOI: 10.1142/s0129156424400421
Shijie Shi, Ruifen Zhang, Rong Zhang, Chaoying Fu, Ziji Wang
In recent years, a large number of renewable energy power plants have been built all over the country, which has led to a sharp increase in the pressure on the current centralized electricity trading platform. There are data storage bottlenecks and data privacy problems in the current blockchain-based power transaction and access control system. In this paper, the interstellar file system is proposed to store the data of the new distributed power trading platform in a distributed way, so as to improve the storage capacity of the physical nodes; An improved ciphertext strategy attribute encryption scheme, which is lightweight, traceable and supports outsourced decryption combined with state secret algorithm, is adopted to integrate the user’s unique identity into the user’s private key and part of the expensive decryption calculation is outsourced to the cloud server to realize fine-grained data access control in the electricity market. The experimental results show that compared to the basic CP-ABE scheme, when the encryption attribute is 100, the decryption time of the proposed scheme is reduced from 556[Formula: see text]ms to 1.1[Formula: see text]ms; Compared to blockchain-based solutions alone, when storing data of 15[Formula: see text]MB, GAS consumption decreases from 2968738133 to 89360. The scheme can greatly improve the storage capacity of the system, improve the operational efficiency and meet the actual performance requirements.
{"title":"Power Trading and Access Control Scheme Based on IPFS and Blockchain","authors":"Shijie Shi, Ruifen Zhang, Rong Zhang, Chaoying Fu, Ziji Wang","doi":"10.1142/s0129156424400421","DOIUrl":"https://doi.org/10.1142/s0129156424400421","url":null,"abstract":"In recent years, a large number of renewable energy power plants have been built all over the country, which has led to a sharp increase in the pressure on the current centralized electricity trading platform. There are data storage bottlenecks and data privacy problems in the current blockchain-based power transaction and access control system. In this paper, the interstellar file system is proposed to store the data of the new distributed power trading platform in a distributed way, so as to improve the storage capacity of the physical nodes; An improved ciphertext strategy attribute encryption scheme, which is lightweight, traceable and supports outsourced decryption combined with state secret algorithm, is adopted to integrate the user’s unique identity into the user’s private key and part of the expensive decryption calculation is outsourced to the cloud server to realize fine-grained data access control in the electricity market. The experimental results show that compared to the basic CP-ABE scheme, when the encryption attribute is 100, the decryption time of the proposed scheme is reduced from 556[Formula: see text]ms to 1.1[Formula: see text]ms; Compared to blockchain-based solutions alone, when storing data of 15[Formula: see text]MB, GAS consumption decreases from 2968738133 to 89360. The scheme can greatly improve the storage capacity of the system, improve the operational efficiency and meet the actual performance requirements.","PeriodicalId":35778,"journal":{"name":"International Journal of High Speed Electronics and Systems","volume":"14 12","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141654129","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 : 2024-07-12DOI: 10.1142/s0129156424400615
Billel Smaani, Samir Labiod, M. S. Benlatreche, Boudjemaa Mehimmedetsi, Ramakant Yadav, Husien Salama
The gate-all-around junctionless field-effect transistor (GAA JL FET)-based biosensor has recently attracted worldwide attention due to its good sensitivity to gate-all-around architecture and overall conduction mechanism. The effect of temperature usually affects the performance of transistors and sensors. Therefore, the impact of temperature on the 3D GAA JL FET-based biosensor has been investigated in this work. The dielectric modulation (DM) approach has been considered for including biomolecules. Consequently, the main proprieties of this biosensor have been investigated by ranging the temperature from 77[Formula: see text]K to 400[Formula: see text]K. The simulated results showed that the on-state current lowers as the temperature rises, but the off-state current increases. The off-current variation concerning the temperature is higher than the on-current change. Also, this type of biosensor appears to have a finer threshold voltage. Furthermore, the obtained results reveal that the current sensitivity is increased when ranging from temperature from 200[Formula: see text]K to 400[Formula: see text]K, and deteriorates for lower temperature values, like 100[Formula: see text]K and 77[Formula: see text]K. In addition, the GAA JL FET-based biosensor is more reliable for the detection of neutral biomolecules at high temperatures.
基于全栅极无结场效应晶体管（GAA JL FET）的生物传感器因其对全栅极结构和整体传导机制的良好灵敏度，最近引起了全世界的关注。温度效应通常会影响晶体管和传感器的性能。因此，本文研究了温度对基于 3D GAA JL FET 的生物传感器的影响。本研究考虑采用介电调制（DM）方法来纳入生物分子。因此，在 77[式：见正文]K至 400[式：见正文]K的温度范围内研究了这种生物传感器的主要特性。模拟结果表明，随着温度的升高，通态电流降低，但失态电流增加。关态电流随温度的变化高于导通电流的变化。此外，这种生物传感器似乎具有更精细的阈值电压。此外，获得的结果表明，当温度从 200[式：见正文]K到 400[式：见正文]K时，电流灵敏度会增加，而当温度值较低时，如 100[式：见正文]K和 77[式：见正文]K，电流灵敏度会降低。此外，基于 GAA JL FET 的生物传感器在高温下检测中性生物分子时更为可靠。
{"title":"Temperature Effect Assessment on the Gate-All-Around Junctionless FET for Bio-Sensing Applications","authors":"Billel Smaani, Samir Labiod, M. S. Benlatreche, Boudjemaa Mehimmedetsi, Ramakant Yadav, Husien Salama","doi":"10.1142/s0129156424400615","DOIUrl":"https://doi.org/10.1142/s0129156424400615","url":null,"abstract":"The gate-all-around junctionless field-effect transistor (GAA JL FET)-based biosensor has recently attracted worldwide attention due to its good sensitivity to gate-all-around architecture and overall conduction mechanism. The effect of temperature usually affects the performance of transistors and sensors. Therefore, the impact of temperature on the 3D GAA JL FET-based biosensor has been investigated in this work. The dielectric modulation (DM) approach has been considered for including biomolecules. Consequently, the main proprieties of this biosensor have been investigated by ranging the temperature from 77[Formula: see text]K to 400[Formula: see text]K. The simulated results showed that the on-state current lowers as the temperature rises, but the off-state current increases. The off-current variation concerning the temperature is higher than the on-current change. Also, this type of biosensor appears to have a finer threshold voltage. Furthermore, the obtained results reveal that the current sensitivity is increased when ranging from temperature from 200[Formula: see text]K to 400[Formula: see text]K, and deteriorates for lower temperature values, like 100[Formula: see text]K and 77[Formula: see text]K. In addition, the GAA JL FET-based biosensor is more reliable for the detection of neutral biomolecules at high temperatures.","PeriodicalId":35778,"journal":{"name":"International Journal of High Speed Electronics and Systems","volume":"64 8","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141654514","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 : 2024-07-12DOI: 10.1142/s0129156424400561
Aalvee Asad Kausani, Caiwen Ding, Mehdi Anwar
Memristors as non-volatile memory devices have been recognized for executing in-memory computation in neuromorphic hardware. In this paper, a multilayer neural network has been developed with memristor-bridges as electrical synapses and trained with modified-chip-in-the-loop technique for an image classification task. Modeling the ideal conduction behavior of memristors by their device-physics inspired analytical model has yielded satisfactory performance. However, repeated voltage cycling degrades the resistance window of memristors by aggregating conductive residuals in filamentary memristors. Therefore, emulation of such nonideality has demonstrated compromised results. To improve the performance, refresh pulses have been introduced to the devices in between write pulses to eradicate the fundamental reason of the degradation — i.e., the residuals. It has been observed that improvement of performance is contingent upon the refreshment frequency, and frequent refreshment has the ability to restore performance to a level closely approaching its ideal emulation.
{"title":"Performance Improvement of Degrading Memristor-Bridge-Based Multilayer Neural Network with Refresh Pulses","authors":"Aalvee Asad Kausani, Caiwen Ding, Mehdi Anwar","doi":"10.1142/s0129156424400561","DOIUrl":"https://doi.org/10.1142/s0129156424400561","url":null,"abstract":"Memristors as non-volatile memory devices have been recognized for executing in-memory computation in neuromorphic hardware. In this paper, a multilayer neural network has been developed with memristor-bridges as electrical synapses and trained with modified-chip-in-the-loop technique for an image classification task. Modeling the ideal conduction behavior of memristors by their device-physics inspired analytical model has yielded satisfactory performance. However, repeated voltage cycling degrades the resistance window of memristors by aggregating conductive residuals in filamentary memristors. Therefore, emulation of such nonideality has demonstrated compromised results. To improve the performance, refresh pulses have been introduced to the devices in between write pulses to eradicate the fundamental reason of the degradation — i.e., the residuals. It has been observed that improvement of performance is contingent upon the refreshment frequency, and frequent refreshment has the ability to restore performance to a level closely approaching its ideal emulation.","PeriodicalId":35778,"journal":{"name":"International Journal of High Speed Electronics and Systems","volume":"29 7","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141655066","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 : 2024-07-02DOI: 10.1142/s012915642440038x
Lili Cai
Wake-up receivers (WuRxs) allow wireless sensor nodes to run on battery power while maintaining asynchronous, low-latency communication. This paper focuses on WuRxs based on low-frequency pattern matchers (LFPMs). Many recent studies either investigate physical WuRx implementations or simulate WuRx-based protocols. Our goal is to address the challenges that arise when realizing WuRx-based protocols in hardware. These challenges are, that a packet activates unwanted WuRxs, an unreliable address space, and missing cluster broadcast capabilities. The proposed separation sequences and run-length limited patterns ensure a reliable address space. WuRxs based on LFPMs use a fixed pattern matching. Cluster broadcasts are enabled by the proposed variable Manchester coding. Typically, LFPMs use Manchester coding with an efficiency of only 0.5 bit/symbol. We introduce two non-Manchester coding techniques with higher efficiency: lookup table-based coding with an efficiency of 0.71 and 3S2B coding with an efficiency of 0.67.
{"title":"A Design Pattern for a Single Reliable Addressing Wake-up Receiver Based on Low-Frequency Pattern Matcher","authors":"Lili Cai","doi":"10.1142/s012915642440038x","DOIUrl":"https://doi.org/10.1142/s012915642440038x","url":null,"abstract":"Wake-up receivers (WuRxs) allow wireless sensor nodes to run on battery power while maintaining asynchronous, low-latency communication. This paper focuses on WuRxs based on low-frequency pattern matchers (LFPMs). Many recent studies either investigate physical WuRx implementations or simulate WuRx-based protocols. Our goal is to address the challenges that arise when realizing WuRx-based protocols in hardware. These challenges are, that a packet activates unwanted WuRxs, an unreliable address space, and missing cluster broadcast capabilities. The proposed separation sequences and run-length limited patterns ensure a reliable address space. WuRxs based on LFPMs use a fixed pattern matching. Cluster broadcasts are enabled by the proposed variable Manchester coding. Typically, LFPMs use Manchester coding with an efficiency of only 0.5 bit/symbol. We introduce two non-Manchester coding techniques with higher efficiency: lookup table-based coding with an efficiency of 0.71 and 3S2B coding with an efficiency of 0.67.","PeriodicalId":35778,"journal":{"name":"International Journal of High Speed Electronics and Systems","volume":"9 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141684580","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 : 2024-06-10DOI: 10.1142/s0129156424400317
Bingwu Pang
As a traditional Chinese martial art, Taijiquan has a remarkable effect on the rehabilitation of joint injury with its unique movement. In this paper, the influence of Taijiquan on joint injury is analyzed by using the local depth feature representation method of image sampling. Then, the local feature coding algorithm is introduced, and the problems existing in the rehabilitation of joint injury are analyzed. An analysis algorithm of the influence of Taijiquan on joint injury based on CV model was proposed, and the effectiveness of the algorithm was verified. The results show that the proposed algorithm improves the MS-COCO dataset by 0.2%, 0.88%, 1.86% and 3.18%, respectively, compared with Hash Net. On the 15Scene dataset, CNN-VLAD’s classification results were 4.1% higher than those of the TNNCV model. On the Caltech 256 data set, the classification accuracy of SMVLADC algorithm is 7.7% higher than CNN-VLAD algorithm. This shows that the proposed algorithm is effective, and the local depth features extracted by CNN are more effective than the traditional artificial features. At the same time, the superiority of CV model based on improved significant regional features is further verified. This study provides a new theoretical basis and practical method for the rehabilitation treatment of joint injury by Taijiquan.
{"title":"Study on Joint Injury of Taijiquan Movement Based on Computer Image Analysis","authors":"Bingwu Pang","doi":"10.1142/s0129156424400317","DOIUrl":"https://doi.org/10.1142/s0129156424400317","url":null,"abstract":"As a traditional Chinese martial art, Taijiquan has a remarkable effect on the rehabilitation of joint injury with its unique movement. In this paper, the influence of Taijiquan on joint injury is analyzed by using the local depth feature representation method of image sampling. Then, the local feature coding algorithm is introduced, and the problems existing in the rehabilitation of joint injury are analyzed. An analysis algorithm of the influence of Taijiquan on joint injury based on CV model was proposed, and the effectiveness of the algorithm was verified. The results show that the proposed algorithm improves the MS-COCO dataset by 0.2%, 0.88%, 1.86% and 3.18%, respectively, compared with Hash Net. On the 15Scene dataset, CNN-VLAD’s classification results were 4.1% higher than those of the TNNCV model. On the Caltech 256 data set, the classification accuracy of SMVLADC algorithm is 7.7% higher than CNN-VLAD algorithm. This shows that the proposed algorithm is effective, and the local depth features extracted by CNN are more effective than the traditional artificial features. At the same time, the superiority of CV model based on improved significant regional features is further verified. This study provides a new theoretical basis and practical method for the rehabilitation treatment of joint injury by Taijiquan.","PeriodicalId":35778,"journal":{"name":"International Journal of High Speed Electronics and Systems","volume":"118 28","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141362917","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 : 2024-06-04DOI: 10.1142/s0129156424400342
Xiaoru Gou
Based on the Light GCN-CSCM model, this study for recommending online English. With the popularity of the Internet, online English teaching platforms are booming, but learners still face challenges in choosing the right content from numerous resources. This study aims using social network information, combined with the Light GCN-CSCM model, to achieve accurate and personalized English teaching resource recommendations. This paper introduces the principle of the Light GCN-CSCM model and applies it to online English teaching resource recommendations. Methods such as data preprocessing, model realization, integration and optimization of the recommendation system are designed, and appropriate evaluation indexes are selected for evaluation. The effectiveness and performance advantages of the proposed method are verified by experiments on real data sets. The Light GCN-CSCM model-based online English teaching resource recommendation method has achieved significant improvement in the accuracy of personalized recommendations and user satisfaction. This study constructed an efficient recommendation system by in-depth analyzing the characteristics of online English teaching resources and the needs of users. This system can provide customized teaching resources for users based on their learning habits, levels, and interests, greatly improving the pertinence and efficiency of learning.
{"title":"Design of Online English Teaching Resource Recommendation Method Based on Light GCN-CSCM Model","authors":"Xiaoru Gou","doi":"10.1142/s0129156424400342","DOIUrl":"https://doi.org/10.1142/s0129156424400342","url":null,"abstract":"Based on the Light GCN-CSCM model, this study for recommending online English. With the popularity of the Internet, online English teaching platforms are booming, but learners still face challenges in choosing the right content from numerous resources. This study aims using social network information, combined with the Light GCN-CSCM model, to achieve accurate and personalized English teaching resource recommendations. This paper introduces the principle of the Light GCN-CSCM model and applies it to online English teaching resource recommendations. Methods such as data preprocessing, model realization, integration and optimization of the recommendation system are designed, and appropriate evaluation indexes are selected for evaluation. The effectiveness and performance advantages of the proposed method are verified by experiments on real data sets. The Light GCN-CSCM model-based online English teaching resource recommendation method has achieved significant improvement in the accuracy of personalized recommendations and user satisfaction. This study constructed an efficient recommendation system by in-depth analyzing the characteristics of online English teaching resources and the needs of users. This system can provide customized teaching resources for users based on their learning habits, levels, and interests, greatly improving the pertinence and efficiency of learning.","PeriodicalId":35778,"journal":{"name":"International Journal of High Speed Electronics and Systems","volume":"4 12","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141387847","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 : 2024-05-21DOI: 10.1142/s0129156424400287
Jing Lin, Dan Li
In this age of big data, education researchers are reconceptualizing and re-evaluating the value of education data. Therefore, we need to use educational data mining methods for data analysis to better guide teaching. The informatization level of colleges and universities is improving year by year, and the entire training data of students from enrollment to graduation is stored. These datasets are collected, stored, and kept by different departments, contain a large amount of regular and relevant information, and truly record the growth footprint of students. Traditional educational decision-making has not yet fully explored and used the valuable information hidden in data resources. Although some scholars have carried out research related to campus data mining at this stage, there are still many problems that have not yet been solved in the application of decision-making in colleges and universities. This paper is based on the idea of data-driven decision-making, combined with the data characteristics of campus big data, and establishes a model solution for student behavior analysis and behavior prediction by applying multiple machine learning algorithms. On the basis of the analysis of students’ academic behavior performance in the context of multi-category educational data, we proposed a cluster analysis framework for processing multi-type campus big data, and described the group characteristics of the clustering results. By introducing the K-prototype algorithm, we effectively solved the multi-category problem where traditional clustering algorithms (such as K-Means, etc.) cannot adapt to the attributes of educational data. The research results show that innovative educational decision-making models and methods are based on the idea of “data-prediction-decision”, which promotes the application research of big data science in the area of education.
{"title":"Design of Fast Mining Algorithm for Educational Sport Course Data Based on Cluster Analysis","authors":"Jing Lin, Dan Li","doi":"10.1142/s0129156424400287","DOIUrl":"https://doi.org/10.1142/s0129156424400287","url":null,"abstract":"In this age of big data, education researchers are reconceptualizing and re-evaluating the value of education data. Therefore, we need to use educational data mining methods for data analysis to better guide teaching. The informatization level of colleges and universities is improving year by year, and the entire training data of students from enrollment to graduation is stored. These datasets are collected, stored, and kept by different departments, contain a large amount of regular and relevant information, and truly record the growth footprint of students. Traditional educational decision-making has not yet fully explored and used the valuable information hidden in data resources. Although some scholars have carried out research related to campus data mining at this stage, there are still many problems that have not yet been solved in the application of decision-making in colleges and universities. This paper is based on the idea of data-driven decision-making, combined with the data characteristics of campus big data, and establishes a model solution for student behavior analysis and behavior prediction by applying multiple machine learning algorithms. On the basis of the analysis of students’ academic behavior performance in the context of multi-category educational data, we proposed a cluster analysis framework for processing multi-type campus big data, and described the group characteristics of the clustering results. By introducing the K-prototype algorithm, we effectively solved the multi-category problem where traditional clustering algorithms (such as K-Means, etc.) cannot adapt to the attributes of educational data. The research results show that innovative educational decision-making models and methods are based on the idea of “data-prediction-decision”, which promotes the application research of big data science in the area of education.","PeriodicalId":35778,"journal":{"name":"International Journal of High Speed Electronics and Systems","volume":"102 11","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141115852","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 : 2024-05-21DOI: 10.1142/s0129156424400263
Hua Chen, Shaohua Liu
The theme of today’s world is peace and development. A stable external environment has made people’s average life expectancy gradually increased, and the world is rapidly aging. Aging has brought many problems, such as the increase in the number of patients with limb dysfunction due to various diseases, which has gradually increased the demand for rehabilitation training. With traditional rehabilitation training methods, the training scenes are single and boring, and patients are prone to resisting. This paper designs and implements a real-time rehabilitation training guidance system based on self-powered sensors for the rehabilitation training needs of stroke patients. The system uses self-powered sensors to collect human motion information in real time, and compares it with the key posture sequences in the standard motion library to obtain corresponding matching results and guide patients to perform correct rehabilitation training. Using the rotation quaternion of 25 bone points in the patient’s rehabilitation exercise to calculate and update the rotation quaternion of the corresponding bone point of the character model, the function of the character model to follow the patient’s mirror motion is realized. This allows patients to control the completion of their rehabilitation movements without the need for medical staff to accompany them. And the stability of the system is optimized based on the particle swarm optimization algorithm. After traversal optimization, the current sensitivity coefficient of the model is reduced by about 75% compared with that before the correction, indicating that the current stability of the model obtained at this time has been improved to a certain extent. However, in the regression model of the self-powered sensor established by the particle swarm optimization algorithm, its parameters are reduced by about 82% compared with those before the correction, which shows that the current stability of the model has been greatly improved at this time, and the operating current of the receiving loop has been greatly improved.
{"title":"Research on Positioning Tracking Mode of Sports Rehabilitation Training Based on Self-Powered Sensor Based on Particle Swarm Optimization Algorithm","authors":"Hua Chen, Shaohua Liu","doi":"10.1142/s0129156424400263","DOIUrl":"https://doi.org/10.1142/s0129156424400263","url":null,"abstract":"The theme of today’s world is peace and development. A stable external environment has made people’s average life expectancy gradually increased, and the world is rapidly aging. Aging has brought many problems, such as the increase in the number of patients with limb dysfunction due to various diseases, which has gradually increased the demand for rehabilitation training. With traditional rehabilitation training methods, the training scenes are single and boring, and patients are prone to resisting. This paper designs and implements a real-time rehabilitation training guidance system based on self-powered sensors for the rehabilitation training needs of stroke patients. The system uses self-powered sensors to collect human motion information in real time, and compares it with the key posture sequences in the standard motion library to obtain corresponding matching results and guide patients to perform correct rehabilitation training. Using the rotation quaternion of 25 bone points in the patient’s rehabilitation exercise to calculate and update the rotation quaternion of the corresponding bone point of the character model, the function of the character model to follow the patient’s mirror motion is realized. This allows patients to control the completion of their rehabilitation movements without the need for medical staff to accompany them. And the stability of the system is optimized based on the particle swarm optimization algorithm. After traversal optimization, the current sensitivity coefficient of the model is reduced by about 75% compared with that before the correction, indicating that the current stability of the model obtained at this time has been improved to a certain extent. However, in the regression model of the self-powered sensor established by the particle swarm optimization algorithm, its parameters are reduced by about 82% compared with those before the correction, which shows that the current stability of the model has been greatly improved at this time, and the operating current of the receiving loop has been greatly improved.","PeriodicalId":35778,"journal":{"name":"International Journal of High Speed Electronics and Systems","volume":"125 23","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141115341","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 : 2024-05-21DOI: 10.1142/s0129156424400305
Fuxing Ma
Track and field teaching has always been an important part in school physical education (PE). With the deepening of curriculum reform and the continuous growth of IT, universities have gradually broken the old instructional mode, and have set up online teaching platforms and developed new instructional modes. How to integrate modern teaching and learning theory into the new teaching technology platform is the requirement of the times and the inevitable theme of the current PE reform. In this article, the track and field instructional resources under the platform of instructional resources management are studied, and the classification mining algorithm is used to mine and analyze the students’ interest data, so as to find out the rules and patterns of users’ access to instructional resources, thus further optimizing the allocation of users’ access to instructional resources, improving the efficiency of users’ access to instructional resources and the utilization rate of instructional resources. Experiments show that the improved collaborative filtering (CF) algorithm based on deep learning is superior to the other two algorithms in recommendation error, and the error is reduced by 10.69% compared with the traditional CF algorithm.
{"title":"Track and Field Teaching Based on Computer Network Resources","authors":"Fuxing Ma","doi":"10.1142/s0129156424400305","DOIUrl":"https://doi.org/10.1142/s0129156424400305","url":null,"abstract":"Track and field teaching has always been an important part in school physical education (PE). With the deepening of curriculum reform and the continuous growth of IT, universities have gradually broken the old instructional mode, and have set up online teaching platforms and developed new instructional modes. How to integrate modern teaching and learning theory into the new teaching technology platform is the requirement of the times and the inevitable theme of the current PE reform. In this article, the track and field instructional resources under the platform of instructional resources management are studied, and the classification mining algorithm is used to mine and analyze the students’ interest data, so as to find out the rules and patterns of users’ access to instructional resources, thus further optimizing the allocation of users’ access to instructional resources, improving the efficiency of users’ access to instructional resources and the utilization rate of instructional resources. Experiments show that the improved collaborative filtering (CF) algorithm based on deep learning is superior to the other two algorithms in recommendation error, and the error is reduced by 10.69% compared with the traditional CF algorithm.","PeriodicalId":35778,"journal":{"name":"International Journal of High Speed Electronics and Systems","volume":"39 9","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141113696","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 : 2024-05-15DOI: 10.1142/s0129156424400299
Keming Wang, Chengli Wang, Wenbing Jin
The embedded system of intelligent reservoir dam achieves the integration and efficient utilization of water conservancy dam system data through multi-channel data collection and analysis calculated by computer technology, CNC system, and neural network. Compared with traditional data collection and processing methods, both timeliness and accuracy have been greatly improved. This study aims to develop a multi-channel sensor data acquisition device for reservoir dams based on embedded system technology. This device can collect real-time and efficient data from sensors in various parts of the dam, ensuring the safe operation of the reservoir dam. By using advanced embedded system technology, this device has advantages such as low power consumption, high stability, and real-time data transmission. The Analytic Hierarchy Process (AHP) was used to study the embedded multi-channel sensor data acquisition device for reservoir dams in multiple directions and factors. The AHP method provides an effective means for problem decision-making in complex situations. Referring to the AHP method, the factors that affect reservoir dams can be divided into different levels. Compare the importance of two random factors in each level to obtain a specific quantitative expression of the relative important factors on a scale. Then repeat this step to obtain the weight ranking for different levels. At the same time, the device monitors key parameters such as temperature, humidity, displacement, and pressure in various parts of the dam through multiple sensors, providing strong support for early warning and decision-making of reservoir dams. The results of this study have important practical significance and application value for improving the safety and stability of reservoir dams.
{"title":"Research and Development of an Embedded Multi-Channel Sensor Data Acquisition Device for Reservoir Dams","authors":"Keming Wang, Chengli Wang, Wenbing Jin","doi":"10.1142/s0129156424400299","DOIUrl":"https://doi.org/10.1142/s0129156424400299","url":null,"abstract":"The embedded system of intelligent reservoir dam achieves the integration and efficient utilization of water conservancy dam system data through multi-channel data collection and analysis calculated by computer technology, CNC system, and neural network. Compared with traditional data collection and processing methods, both timeliness and accuracy have been greatly improved. This study aims to develop a multi-channel sensor data acquisition device for reservoir dams based on embedded system technology. This device can collect real-time and efficient data from sensors in various parts of the dam, ensuring the safe operation of the reservoir dam. By using advanced embedded system technology, this device has advantages such as low power consumption, high stability, and real-time data transmission. The Analytic Hierarchy Process (AHP) was used to study the embedded multi-channel sensor data acquisition device for reservoir dams in multiple directions and factors. The AHP method provides an effective means for problem decision-making in complex situations. Referring to the AHP method, the factors that affect reservoir dams can be divided into different levels. Compare the importance of two random factors in each level to obtain a specific quantitative expression of the relative important factors on a scale. Then repeat this step to obtain the weight ranking for different levels. At the same time, the device monitors key parameters such as temperature, humidity, displacement, and pressure in various parts of the dam through multiple sensors, providing strong support for early warning and decision-making of reservoir dams. The results of this study have important practical significance and application value for improving the safety and stability of reservoir dams.","PeriodicalId":35778,"journal":{"name":"International Journal of High Speed Electronics and Systems","volume":"63 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140973623","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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