Pub Date : 2024-08-09DOI: 10.1142/s0129156424400925
Yuan Zhang
With the rapid development of computer and Internet technology, using computer vision technology to evaluate the dynamic and static balance ability of athletes has become a research hotspot. This study combines the theory of functional training of human muscle groups and delves into the relationship between body function training and dynamic and static balance ability. By developing a computer vision-based algorithm, we successfully evaluated the dynamic and static balance abilities of athletes. To verify the effectiveness and reliability of the algorithm, we used simulation training experiments and comparative analysis methods. The simulation training experiment simulated the dynamic and static balance training of athletes in a real environment, and quantitatively evaluated their balance ability through this algorithm. In addition, we also compared the changes in balance ability of athletes before and after receiving functional training to verify the effectiveness of functional training in improving balance ability. The results of these validation methods all demonstrate good consistency and accuracy, further confirming the correctness of the research results on the dynamic and static balance ability of athletes based on functional training. This study not only provides new tools and methods for athlete balance training, but also a beneficial attempt for the application of artificial intelligence in the field of sports.
{"title":"Evaluation of Dynamic and Static Balance Ability of Athletes Based on Computer Vision Technology","authors":"Yuan Zhang","doi":"10.1142/s0129156424400925","DOIUrl":"https://doi.org/10.1142/s0129156424400925","url":null,"abstract":"With the rapid development of computer and Internet technology, using computer vision technology to evaluate the dynamic and static balance ability of athletes has become a research hotspot. This study combines the theory of functional training of human muscle groups and delves into the relationship between body function training and dynamic and static balance ability. By developing a computer vision-based algorithm, we successfully evaluated the dynamic and static balance abilities of athletes. To verify the effectiveness and reliability of the algorithm, we used simulation training experiments and comparative analysis methods. The simulation training experiment simulated the dynamic and static balance training of athletes in a real environment, and quantitatively evaluated their balance ability through this algorithm. In addition, we also compared the changes in balance ability of athletes before and after receiving functional training to verify the effectiveness of functional training in improving balance ability. The results of these validation methods all demonstrate good consistency and accuracy, further confirming the correctness of the research results on the dynamic and static balance ability of athletes based on functional training. This study not only provides new tools and methods for athlete balance training, but also a beneficial attempt for the application of artificial intelligence in the field of sports.","PeriodicalId":35778,"journal":{"name":"International Journal of High Speed Electronics and Systems","volume":"27 20","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141924654","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 : 2024-08-09DOI: 10.1142/s0129156424400500
Hong Yang, Xiaokai Meng, Hua Yu, Yang Bai, Yu Han, Yongxin Liu
In recent years, knowledge graphs have had a significant impact across diverse domains. Notably, the power knowledge graph has garnered considerable attention as high-performance database. However, its untapped reasoning capabilities offer an enticing avenue for exploration. One of the main reasons is the sparsity of power grid datasets, especially electrical equipment knowledge graph. Because of the scarcity of high-risk records, there exists a large number of long tail entities and long tail relations. To address this challenge, we introduce a novel text-based model called GELMSP (Graph Embedding using Language Model with Self-learned Prompts). We employ a bi-encoder structure along with a contrastive learning strategy to expedite the training process. Additionally, our approach incorporates a self-learned prompt mechanism that generates prompts for specific situations without the need for any additional information, known as self-learning. This harnesses the power of pre-trained language models to comprehend the semantic nuances within the entities and relationships of the knowledge graph. Adopting this innovative method enables our model to effectively handle sparse datasets, leading to a comprehensive understanding of interconnectedness within the knowledge graph. Additionally, we demonstrate the efficacy of our model through extensive experiments and comparisons against baseline methods, reaffirming its potential in advancing the state-of-the-art in electrical equipment defect diagnosis.
{"title":"Electrical Equipment Knowledge Graph Embedding Using Language Model with Self-learned Prompts","authors":"Hong Yang, Xiaokai Meng, Hua Yu, Yang Bai, Yu Han, Yongxin Liu","doi":"10.1142/s0129156424400500","DOIUrl":"https://doi.org/10.1142/s0129156424400500","url":null,"abstract":"In recent years, knowledge graphs have had a significant impact across diverse domains. Notably, the power knowledge graph has garnered considerable attention as high-performance database. However, its untapped reasoning capabilities offer an enticing avenue for exploration. One of the main reasons is the sparsity of power grid datasets, especially electrical equipment knowledge graph. Because of the scarcity of high-risk records, there exists a large number of long tail entities and long tail relations. To address this challenge, we introduce a novel text-based model called GELMSP (Graph Embedding using Language Model with Self-learned Prompts). We employ a bi-encoder structure along with a contrastive learning strategy to expedite the training process. Additionally, our approach incorporates a self-learned prompt mechanism that generates prompts for specific situations without the need for any additional information, known as self-learning. This harnesses the power of pre-trained language models to comprehend the semantic nuances within the entities and relationships of the knowledge graph. Adopting this innovative method enables our model to effectively handle sparse datasets, leading to a comprehensive understanding of interconnectedness within the knowledge graph. Additionally, we demonstrate the efficacy of our model through extensive experiments and comparisons against baseline methods, reaffirming its potential in advancing the state-of-the-art in electrical equipment defect diagnosis.","PeriodicalId":35778,"journal":{"name":"International Journal of High Speed Electronics and Systems","volume":"80 19","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141922437","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 : 2024-08-09DOI: 10.1142/s0129156424401013
Rong Diao
Basketball is a competitive sport, and athletes often choose to increase training intensity and difficulty in order to achieve better results. This inevitably leads to joint damage. Therefore, this study aims to explore prevention strategies for joint injuries during basketball overload training based on adjustable embedded systems. This study proposes a prevention strategy based on an adjustable embedded system to address the issue of athlete joint injuries during basketball overload training. This system integrates advanced sensor technology and real-time data analysis algorithms, aiming to monitor and analyze key parameters of basketball players during overload training in real time. The system consists of adjustable hardware devices and intelligent software platforms, which can accurately capture key information such as athlete’s motion data, strength output, and joint angles. Based on the results of real-time data analysis, the system can intelligently adjust training intensity, frequency, and methods. In addition, the system can provide personalized training suggestions based on the individual characteristics and training goals of athletes, helping them improve their technical movements and optimize training effectiveness. Through extensive experimental verification and practical application, this study demonstrates the effectiveness of joint injury prevention strategies for basketball overload training based on adjustable embedded systems. This system can not only effectively prevent joint injuries among athletes, but also improve the scientific and targeted nature of training, providing strong support for the training of basketball players.
{"title":"Analysis of Joint Injury Prevention in Basketball Overload Training Based on Adjustable Embedded Systems","authors":"Rong Diao","doi":"10.1142/s0129156424401013","DOIUrl":"https://doi.org/10.1142/s0129156424401013","url":null,"abstract":"Basketball is a competitive sport, and athletes often choose to increase training intensity and difficulty in order to achieve better results. This inevitably leads to joint damage. Therefore, this study aims to explore prevention strategies for joint injuries during basketball overload training based on adjustable embedded systems. This study proposes a prevention strategy based on an adjustable embedded system to address the issue of athlete joint injuries during basketball overload training. This system integrates advanced sensor technology and real-time data analysis algorithms, aiming to monitor and analyze key parameters of basketball players during overload training in real time. The system consists of adjustable hardware devices and intelligent software platforms, which can accurately capture key information such as athlete’s motion data, strength output, and joint angles. Based on the results of real-time data analysis, the system can intelligently adjust training intensity, frequency, and methods. In addition, the system can provide personalized training suggestions based on the individual characteristics and training goals of athletes, helping them improve their technical movements and optimize training effectiveness. Through extensive experimental verification and practical application, this study demonstrates the effectiveness of joint injury prevention strategies for basketball overload training based on adjustable embedded systems. This system can not only effectively prevent joint injuries among athletes, but also improve the scientific and targeted nature of training, providing strong support for the training of basketball players.","PeriodicalId":35778,"journal":{"name":"International Journal of High Speed Electronics and Systems","volume":"4 11","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141925398","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 : 2024-07-25DOI: 10.1142/s0129156424400792
Yashun Wang, Wei Xu
Extracting to enhance the accuracy of diagnosing bearing faults in steam turbines, a novel approach focused on extracting key fault features from vibration signals is introduced. Recognizing the complex, non-linear, and non-stationary nature of bearing vibration signals, our strategy involves a sensitivity analysis utilizing a multivariate diagnostic algorithm. The process begins with collecting vibration data from defective bearings via the TMI system. This data is then subjected to Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN), enabling the integration of adaptive noise for the extraction of in-depth information. Following this, an analysis in both time and frequency domains — post Fast Fourier Transform (FFT) — is conducted on the decomposed signals, forming the basis of a diagnostic features database. To streamline data analysis and boost the model’s computational efficiency, a combination of eXtreme Gradient Boosting (XGBoost) and Mutual Information Criterion (MIC) is applied for dimensionality reduction. Furthermore, a deep belief network (DBN) is implemented to develop a precise fault diagnosis model for the bearings in rotating machinery. By incorporating sensitivity analysis, a diagnostic matrix is crafted, facilitating highly accurate fault identification. The superiority of this diagnostic algorithm is corroborated by testing with real on-site data and a benchmark database, demonstrating its enhanced diagnostic capabilities relative to other feature selection techniques.
{"title":"Complete Ensemble Empirical Mode Decomposition with Adaptive Noise to Extract Deep Information of Bearing Fault in Steam Turbines via Deep Belief Network","authors":"Yashun Wang, Wei Xu","doi":"10.1142/s0129156424400792","DOIUrl":"https://doi.org/10.1142/s0129156424400792","url":null,"abstract":"Extracting to enhance the accuracy of diagnosing bearing faults in steam turbines, a novel approach focused on extracting key fault features from vibration signals is introduced. Recognizing the complex, non-linear, and non-stationary nature of bearing vibration signals, our strategy involves a sensitivity analysis utilizing a multivariate diagnostic algorithm. The process begins with collecting vibration data from defective bearings via the TMI system. This data is then subjected to Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN), enabling the integration of adaptive noise for the extraction of in-depth information. Following this, an analysis in both time and frequency domains — post Fast Fourier Transform (FFT) — is conducted on the decomposed signals, forming the basis of a diagnostic features database. To streamline data analysis and boost the model’s computational efficiency, a combination of eXtreme Gradient Boosting (XGBoost) and Mutual Information Criterion (MIC) is applied for dimensionality reduction. Furthermore, a deep belief network (DBN) is implemented to develop a precise fault diagnosis model for the bearings in rotating machinery. By incorporating sensitivity analysis, a diagnostic matrix is crafted, facilitating highly accurate fault identification. The superiority of this diagnostic algorithm is corroborated by testing with real on-site data and a benchmark database, demonstrating its enhanced diagnostic capabilities relative to other feature selection techniques.","PeriodicalId":35778,"journal":{"name":"International Journal of High Speed Electronics and Systems","volume":"12 9","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141803342","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 : 2024-07-25DOI: 10.1142/s012915642440072x
Gayanath W. Fernando, Donal Sheets, Jason Hancock, Arthur Ernst, R. Matthias Geilhufe
Materials with perovskite structure are known to exhibit fascinating physical properties such as high-temperature superconductivity, negative thermal expansion (NTE) and colossal magnetoresistance. However, transition metal trifluoride perovskites are less well studied compared to their oxide counterparts though they display marked differences such as NTE behavior in ScF3. Doping of such MF3 perovskites has been the focus of the experimental work of Morelock et al. [1] which provides a comprehensive structural study of the material class Sc1[Formula: see text]Ti[Formula: see text]F3. As shown in Fig. 1, there is a structural phase transition assumed to be tied to tilting of corner sharing octahedrons in this class of crystal structures which is believed to have an electrostatic dipolar origin, seen for example even in AlF3 [2]. However, the insulating and magnetic properties of TiF3 are closely related to the transition metal 3d-electrons. The extra valence [Formula: see text]-electron that Ti carries compared to Sc [1, 3] gives rise to unusual electronic and magnetic properties.
{"title":"A Brief Review of Electronic and Magnetic Structure of TIF3","authors":"Gayanath W. Fernando, Donal Sheets, Jason Hancock, Arthur Ernst, R. Matthias Geilhufe","doi":"10.1142/s012915642440072x","DOIUrl":"https://doi.org/10.1142/s012915642440072x","url":null,"abstract":"Materials with perovskite structure are known to exhibit fascinating physical properties such as high-temperature superconductivity, negative thermal expansion (NTE) and colossal magnetoresistance. However, transition metal trifluoride perovskites are less well studied compared to their oxide counterparts though they display marked differences such as NTE behavior in ScF3. Doping of such MF3 perovskites has been the focus of the experimental work of Morelock et al. [1] which provides a comprehensive structural study of the material class Sc1[Formula: see text]Ti[Formula: see text]F3. As shown in Fig. 1, there is a structural phase transition assumed to be tied to tilting of corner sharing octahedrons in this class of crystal structures which is believed to have an electrostatic dipolar origin, seen for example even in AlF3 [2]. However, the insulating and magnetic properties of TiF3 are closely related to the transition metal 3d-electrons. The extra valence [Formula: see text]-electron that Ti carries compared to Sc [1, 3] gives rise to unusual electronic and magnetic properties.","PeriodicalId":35778,"journal":{"name":"International Journal of High Speed Electronics and Systems","volume":"48 9","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141805015","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 : 2024-07-25DOI: 10.1142/s012915642440055x
R. Gudlavalleti, J. Chandy, E. Heller, F. Jain
This paper presents in-memory computing using fast write/erase quantum dot (QD) nonvolatile random access memory (NVRAM). In comparison to NVMs, multi-state NVRAMs offer enhanced Compute-In-Memory capability for applications in deep neural network architecture. Dot product is the methodology that enables an array structure for multiply and accumulate (MAC) operation. We show an approach to dot product computation using multi-state quantum dot channel (QDC) FETs and QD-NVRAM.
{"title":"In-Memory Computing Using Dot-Product via Multi-Bit QD-NVRAMs","authors":"R. Gudlavalleti, J. Chandy, E. Heller, F. Jain","doi":"10.1142/s012915642440055x","DOIUrl":"https://doi.org/10.1142/s012915642440055x","url":null,"abstract":"This paper presents in-memory computing using fast write/erase quantum dot (QD) nonvolatile random access memory (NVRAM). In comparison to NVMs, multi-state NVRAMs offer enhanced Compute-In-Memory capability for applications in deep neural network architecture. Dot product is the methodology that enables an array structure for multiply and accumulate (MAC) operation. We show an approach to dot product computation using multi-state quantum dot channel (QDC) FETs and QD-NVRAM.","PeriodicalId":35778,"journal":{"name":"International Journal of High Speed Electronics and Systems","volume":"44 13","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141805492","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 : 2024-07-25DOI: 10.1142/s0129156424400524
J. Grasso, Rahul Raman, Brian G. Willis
Modern integrated circuits have active components on the order of nanometers. However, optical devices are often limited by diffraction effects with dimensions measured in wavelengths. Nanoscale photodetectors capable of converting light into electrical signals are necessary for the miniaturization of optoelectronic applications. Strong coupling of light and free electrons in plasmonic nanostructures overcomes these limitations by confining light into sub-wavelength volumes with intense local electric fields. Localized electric fields are intensified at nanorod ends and in nanogap regions between nanostructures. Hot carriers generated within these high-field regions from nonradiative decay of surface plasmons can be injected into the conduction band of adjacent semiconductors, enabling sub-bandgap photodetection. The optical properties of these plasmonic photodetectors can be tuned by modifying antenna materials and geometric parameters like size, thickness, and shape. Electrical interconnects provide connectivity to convert light into electrical signals. In this work, interconnected nanogap antennas fabricated with 35 nm gaps are encapsulated with ALD-deposited TiO2, enabling photodetection via Schottky barrier junctions. Photodetectors with high responsivity (12[Formula: see text][Formula: see text]A/mW) are presented for wavelengths below the bandgap of TiO2 (3.2[Formula: see text]eV). These plasmonic nanogap antennas are sub-wavelength, tunable photodetectors with sub-bandgap responsivity for a broad spectral range.
{"title":"Interconnected Plasmonic Nanogap Antennas for Sub-Bandgap Photodetection via Hot Carrier Injection","authors":"J. Grasso, Rahul Raman, Brian G. Willis","doi":"10.1142/s0129156424400524","DOIUrl":"https://doi.org/10.1142/s0129156424400524","url":null,"abstract":"Modern integrated circuits have active components on the order of nanometers. However, optical devices are often limited by diffraction effects with dimensions measured in wavelengths. Nanoscale photodetectors capable of converting light into electrical signals are necessary for the miniaturization of optoelectronic applications. Strong coupling of light and free electrons in plasmonic nanostructures overcomes these limitations by confining light into sub-wavelength volumes with intense local electric fields. Localized electric fields are intensified at nanorod ends and in nanogap regions between nanostructures. Hot carriers generated within these high-field regions from nonradiative decay of surface plasmons can be injected into the conduction band of adjacent semiconductors, enabling sub-bandgap photodetection. The optical properties of these plasmonic photodetectors can be tuned by modifying antenna materials and geometric parameters like size, thickness, and shape. Electrical interconnects provide connectivity to convert light into electrical signals. In this work, interconnected nanogap antennas fabricated with 35 nm gaps are encapsulated with ALD-deposited TiO2, enabling photodetection via Schottky barrier junctions. Photodetectors with high responsivity (12[Formula: see text][Formula: see text]A/mW) are presented for wavelengths below the bandgap of TiO2 (3.2[Formula: see text]eV). These plasmonic nanogap antennas are sub-wavelength, tunable photodetectors with sub-bandgap responsivity for a broad spectral range.","PeriodicalId":35778,"journal":{"name":"International Journal of High Speed Electronics and Systems","volume":"41 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141805122","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 : 2024-07-25DOI: 10.1142/s0129156424400810
Lin Jian
The application of multimedia technology in physical education classroom teaching has become increasingly widespread. It not only enriches teaching methods and makes the classroom more lively and interesting, but also enhances students’ learning interest and participation. Through multimedia technology, teachers can more intuitively display the essentials of actions, helping students better understand and learn. In addition, multimedia technology can also provide rich information resources to help students understand more sports knowledge and improve their overall quality. This paper introduces the basic concepts and characteristics of multimedia technology, including its interactivity, integration and innovation. Then, the application status of multimedia technology in physical education is deeply analyzed, and the main problems in teaching practice are revealed, such as the lack of technical equipment, the lack of teachers’ technical ability and the lack of teaching resources. Then, from three aspects of teaching content, teaching method and teaching effect, the influence of multimedia technology on PE classroom teaching quality is analyzed in detail. Physical education teachers should actively guide students to participate in multimedia learning, such as through interactive games, online discussions, etc., to stimulate students’ interest and initiative in learning, and improve learning effectiveness. Physical education teachers need to continuously learn and improve their multimedia technology application abilities, and master the latest teaching techniques and tools, in order to better integrate multimedia technology into classroom teaching. Schools can establish a multimedia teaching resource library to centrally manage excellent multimedia teaching resources, facilitate teachers to search and use, and improve teaching efficiency. Including strengthening equipment investment, improving teachers’ technical ability and developing high quality teaching resources.
{"title":"Analysis of the Quality of PE Classroom Teaching with Multimedia Technology","authors":"Lin Jian","doi":"10.1142/s0129156424400810","DOIUrl":"https://doi.org/10.1142/s0129156424400810","url":null,"abstract":"The application of multimedia technology in physical education classroom teaching has become increasingly widespread. It not only enriches teaching methods and makes the classroom more lively and interesting, but also enhances students’ learning interest and participation. Through multimedia technology, teachers can more intuitively display the essentials of actions, helping students better understand and learn. In addition, multimedia technology can also provide rich information resources to help students understand more sports knowledge and improve their overall quality. This paper introduces the basic concepts and characteristics of multimedia technology, including its interactivity, integration and innovation. Then, the application status of multimedia technology in physical education is deeply analyzed, and the main problems in teaching practice are revealed, such as the lack of technical equipment, the lack of teachers’ technical ability and the lack of teaching resources. Then, from three aspects of teaching content, teaching method and teaching effect, the influence of multimedia technology on PE classroom teaching quality is analyzed in detail. Physical education teachers should actively guide students to participate in multimedia learning, such as through interactive games, online discussions, etc., to stimulate students’ interest and initiative in learning, and improve learning effectiveness. Physical education teachers need to continuously learn and improve their multimedia technology application abilities, and master the latest teaching techniques and tools, in order to better integrate multimedia technology into classroom teaching. Schools can establish a multimedia teaching resource library to centrally manage excellent multimedia teaching resources, facilitate teachers to search and use, and improve teaching efficiency. Including strengthening equipment investment, improving teachers’ technical ability and developing high quality teaching resources.","PeriodicalId":35778,"journal":{"name":"International Journal of High Speed Electronics and Systems","volume":"52 31","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141804912","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 : 2024-07-25DOI: 10.1142/s0129156424400457
Yuanyuan Yang, Liang Chen
In order to deeply analyze the causes of English learners’ procrastination in e-learning and its influence on learning effect, an artificial intelligence (AI)-based method is designed to analyze the influencing factors of procrastination. By using K-means algorithm, this method divides learners’ online learning procrastination into two categories: active procrastination and passive procrastination, and collects corresponding learning state data samples. Then, taking into account various factors, including students, teachers, and the environment, we identified 11 key factors that may contribute to learning procrastination. Then, using the artificial intelligence-based procrastination factor ranking analysis model and the cuckoo search algorithm-trained XGBoost model, we trained multiple decision tree models to learn and predict the association between these influencing factors and different procrastination types of learning states. The experimental results show that after the application of this method, through in-depth analysis of the phenomenon of procrastination in students’ online English learning, different types of procrastination and their influencing factors are successfully identified, and an effective intervention model is designed based on the analysis results, which significantly improves students’ learning efficiency and provides strong support for the intervention of procrastination. It is proved that this method has certain significance for the accurate analysis of learning delay factors and effective intervention of procrastination in English e-learning.
{"title":"A Study on the Influencing Factors of Online Learning Procrastination of English Learners Based on Artificial Intelligence","authors":"Yuanyuan Yang, Liang Chen","doi":"10.1142/s0129156424400457","DOIUrl":"https://doi.org/10.1142/s0129156424400457","url":null,"abstract":"In order to deeply analyze the causes of English learners’ procrastination in e-learning and its influence on learning effect, an artificial intelligence (AI)-based method is designed to analyze the influencing factors of procrastination. By using K-means algorithm, this method divides learners’ online learning procrastination into two categories: active procrastination and passive procrastination, and collects corresponding learning state data samples. Then, taking into account various factors, including students, teachers, and the environment, we identified 11 key factors that may contribute to learning procrastination. Then, using the artificial intelligence-based procrastination factor ranking analysis model and the cuckoo search algorithm-trained XGBoost model, we trained multiple decision tree models to learn and predict the association between these influencing factors and different procrastination types of learning states. The experimental results show that after the application of this method, through in-depth analysis of the phenomenon of procrastination in students’ online English learning, different types of procrastination and their influencing factors are successfully identified, and an effective intervention model is designed based on the analysis results, which significantly improves students’ learning efficiency and provides strong support for the intervention of procrastination. It is proved that this method has certain significance for the accurate analysis of learning delay factors and effective intervention of procrastination in English e-learning.","PeriodicalId":35778,"journal":{"name":"International Journal of High Speed Electronics and Systems","volume":"59 10","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141805512","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 : 2024-07-25DOI: 10.1142/s012915642440069x
B. Saman, E. Heller, F. Jain
Static random-access memory (SRAM) is an essential component in the architecture of modern microprocessors and VLSI circuits. The problems of high power consumption, large area, circuit complexity, and data stability against noise are among the most important indicators of performance and obstacles to the current use of SRAM. Ternary, quaternary, and higher-order logic (MLV) systems have shown the potential in overcoming these limitations in increasing the information density compared to the traditional binary system. The quantum dot channel field-effect transistor (QDC-FET) and quantum well Spatial Wavefunction Switched field-effect transistor (SWS-FET) are a new alternative with multiple operating states, low power consumption, and smaller footprints. This work presents a new four-state SRAM design that uses SWS-FET and compares it with Voltage-Mode CMOS Quaternary logic design. In addition, this work studies the noise margin in the memory circuit of the quadrilateral logic system and its effect on data stability. Furthermore, this study shows the reliability of quaternary SRAM design by evaluating the impact of errors.
{"title":"The Static Noise Margin (SNM) of Quaternary SRAM using Quantum SWS-FET","authors":"B. Saman, E. Heller, F. Jain","doi":"10.1142/s012915642440069x","DOIUrl":"https://doi.org/10.1142/s012915642440069x","url":null,"abstract":"Static random-access memory (SRAM) is an essential component in the architecture of modern microprocessors and VLSI circuits. The problems of high power consumption, large area, circuit complexity, and data stability against noise are among the most important indicators of performance and obstacles to the current use of SRAM. Ternary, quaternary, and higher-order logic (MLV) systems have shown the potential in overcoming these limitations in increasing the information density compared to the traditional binary system. The quantum dot channel field-effect transistor (QDC-FET) and quantum well Spatial Wavefunction Switched field-effect transistor (SWS-FET) are a new alternative with multiple operating states, low power consumption, and smaller footprints. This work presents a new four-state SRAM design that uses SWS-FET and compares it with Voltage-Mode CMOS Quaternary logic design. In addition, this work studies the noise margin in the memory circuit of the quadrilateral logic system and its effect on data stability. Furthermore, this study shows the reliability of quaternary SRAM design by evaluating the impact of errors.","PeriodicalId":35778,"journal":{"name":"International Journal of High Speed Electronics and Systems","volume":"35 25","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141806114","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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