With the deepening reform of the power market and carbon market, great progress has been made in informatization. Power information may be stored in many scattered places, and it is difficult to share data between different departments or systems. This leads to fragmentation and redundancy of information and makes information exchange difficult. Blockchain can improve the reliability of Power-Carbon Management System (briefly described as PCMS for convenience) data processing. PCMS informatization has become the basis for improving the quality and efficiency of project management and maximizing the environmental and economic benefits of the project. Because the power information management system can effectively control the flow of information and resource allocation. Due to the requirement of low-carbon and stable power production, PCMS attaches great importance to the application and implementation of information in power management, but does not attach enough importance to the informatization of power production management. Therefore, this paper analyzed the current situation, characteristics and existing problems of PCMS through machine learning algorithm, then constructed the design principles, and finally proposed the optimization path of PCMS according to the principles. The information collection ability and system control ability of the optimized PCMS were better than the original PCMS. The information collection ability was 14.2% higher than the original, and the system control ability was 9.8% higher than the original. In general, both blockchain and machine learning can improve the data reliability of PCMS.
{"title":"Power-Carbon Information Management System Based on Machine Learning","authors":"Ruohan Wang, Yunlong Chen, Entang Li, Hongwei Xing, Jianhui Zhang, Jing Li","doi":"10.1142/s0129156424400214","DOIUrl":"https://doi.org/10.1142/s0129156424400214","url":null,"abstract":"With the deepening reform of the power market and carbon market, great progress has been made in informatization. Power information may be stored in many scattered places, and it is difficult to share data between different departments or systems. This leads to fragmentation and redundancy of information and makes information exchange difficult. Blockchain can improve the reliability of Power-Carbon Management System (briefly described as PCMS for convenience) data processing. PCMS informatization has become the basis for improving the quality and efficiency of project management and maximizing the environmental and economic benefits of the project. Because the power information management system can effectively control the flow of information and resource allocation. Due to the requirement of low-carbon and stable power production, PCMS attaches great importance to the application and implementation of information in power management, but does not attach enough importance to the informatization of power production management. Therefore, this paper analyzed the current situation, characteristics and existing problems of PCMS through machine learning algorithm, then constructed the design principles, and finally proposed the optimization path of PCMS according to the principles. The information collection ability and system control ability of the optimized PCMS were better than the original PCMS. The information collection ability was 14.2% higher than the original, and the system control ability was 9.8% higher than the original. In general, both blockchain and machine learning can improve the data reliability of PCMS.","PeriodicalId":35778,"journal":{"name":"International Journal of High Speed Electronics and Systems","volume":"9 12","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140976586","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 : 2024-04-22DOI: 10.1142/s0129156424400226
M. Shur
Terahertz (THz) sensing technology enables 6G communication, detection of biological and chemical hazardous agents, cancer detection, monitoring of industrial processes and products, and detection of mines and explosives. THz sensors support security in buildings, airports, and other public spaces. They found important applications in radioastronomy and space research and, more recently, in Artificial Intelligence-driven THz sensing of MMICs and VLSI. Exploding demand for data transfers will require using the 300 GHz band after 2028 or even before and will make the deployment of THz sensing electronics inevitable. This paper discusses the new physics of THz sensing and THz sensing devices. It also reviews the THz sensing market, and key THz sensor companies.
{"title":"Sensing Using Terahertz Radiation","authors":"M. Shur","doi":"10.1142/s0129156424400226","DOIUrl":"https://doi.org/10.1142/s0129156424400226","url":null,"abstract":"Terahertz (THz) sensing technology enables 6G communication, detection of biological and chemical hazardous agents, cancer detection, monitoring of industrial processes and products, and detection of mines and explosives. THz sensors support security in buildings, airports, and other public spaces. They found important applications in radioastronomy and space research and, more recently, in Artificial Intelligence-driven THz sensing of MMICs and VLSI. Exploding demand for data transfers will require using the 300 GHz band after 2028 or even before and will make the deployment of THz sensing electronics inevitable. This paper discusses the new physics of THz sensing and THz sensing devices. It also reviews the THz sensing market, and key THz sensor companies.","PeriodicalId":35778,"journal":{"name":"International Journal of High Speed Electronics and Systems","volume":"3 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140675262","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 : 2024-04-22DOI: 10.1142/s0129156424400196
Jingang Fan, Yan Yang, Jiabao Liu
With the advent of the big data era, data-driven decision-making and analysis are increasingly valued in various fields. Especially in the field of education, how to use big data technology to better understand student needs, optimize the education process, and improve education quality has become an important research topic. This paper will explore the application of decision trees and related analysis algorithms in the analysis of college students’ physical fitness, in order to provide scientific basis for improving the physical health level of college students. This paper studies the application of DT (decision tree) and correlation analysis algorithm in the analysis of college students’ physical fitness. In this paper, the method of big data and DM (data mining) is proposed to extract the rules contained in the data information, so as to directly provide auxiliary decision-making for physical fitness test and analysis. The research results show that through training the training set, a good classification accuracy rate is achieved, and through optimizing the depth, the accuracy rate can reach more than 85.033%. Using DM technology as a carrier, this paper digs into the rules behind the new knowledge of college students’ physical fitness, and digs out the previously unknown, implied and potentially useful information and knowledge.
{"title":"Research on the Application of Decision Tree and Correlation Analysis Algorithm in College Students’ Physical Fitness Analysis","authors":"Jingang Fan, Yan Yang, Jiabao Liu","doi":"10.1142/s0129156424400196","DOIUrl":"https://doi.org/10.1142/s0129156424400196","url":null,"abstract":"With the advent of the big data era, data-driven decision-making and analysis are increasingly valued in various fields. Especially in the field of education, how to use big data technology to better understand student needs, optimize the education process, and improve education quality has become an important research topic. This paper will explore the application of decision trees and related analysis algorithms in the analysis of college students’ physical fitness, in order to provide scientific basis for improving the physical health level of college students. This paper studies the application of DT (decision tree) and correlation analysis algorithm in the analysis of college students’ physical fitness. In this paper, the method of big data and DM (data mining) is proposed to extract the rules contained in the data information, so as to directly provide auxiliary decision-making for physical fitness test and analysis. The research results show that through training the training set, a good classification accuracy rate is achieved, and through optimizing the depth, the accuracy rate can reach more than 85.033%. Using DM technology as a carrier, this paper digs into the rules behind the new knowledge of college students’ physical fitness, and digs out the previously unknown, implied and potentially useful information and knowledge.","PeriodicalId":35778,"journal":{"name":"International Journal of High Speed Electronics and Systems","volume":"30 21","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140674984","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 : 2024-04-18DOI: 10.1142/s0129156424400202
M. Dub, P. Sai, A. Krajewska, D. B. But, Yu. Ivonyak, P. Prystawko, J. Kacperski, G. Cywiński, S. Rumyantsev, W. Knap, M. Słowikowski, M. Filipiak
The grating-gate plasmonic crystal system represents a compelling arena for investigating strong light-matter interactions and diverse plasmon resonances. This study reviews the recent discovery of two distinctive terahertz phases of AlGaN/GaN plasmonic crystals that arise from varying the modulation of a two-dimensional electron density beneath the metallic gratings: the delocalized phase at weak modulation and the localized phase at strong modulation. Notably, we delve into an impact of the grating filling factor on the electrically driven transition between these phases. Our findings underscore the critical role of specific metal grating geometry parameters in facilitating this transition. Moreover, we explore the potential of utilizing graphene-based gratings as alternatives to metallic gratings. Through the integration of graphene, grown by Chemical Vapor Deposition method on copper foil and then transferred to the high electron mobility AlGaN/GaN heterostructures, we achieve an effective modulation of broadband absorption by free charge carriers within the 0.5–6 THz range via electrical biasing of the graphene electrode. However, while this approach successfully modulates absorption in a wide THz range, it does not elicit plasmon resonances within the graphene-based grating-gate plasmonic crystals. This intriguing observation poses a significant unresolved question warranting further theoretical and experimental exploration in subsequent studies.
{"title":"Grating-Gate AlGaN/GaN Plasmonic Crystals for Terahertz Waves Manipulation","authors":"M. Dub, P. Sai, A. Krajewska, D. B. But, Yu. Ivonyak, P. Prystawko, J. Kacperski, G. Cywiński, S. Rumyantsev, W. Knap, M. Słowikowski, M. Filipiak","doi":"10.1142/s0129156424400202","DOIUrl":"https://doi.org/10.1142/s0129156424400202","url":null,"abstract":"The grating-gate plasmonic crystal system represents a compelling arena for investigating strong light-matter interactions and diverse plasmon resonances. This study reviews the recent discovery of two distinctive terahertz phases of AlGaN/GaN plasmonic crystals that arise from varying the modulation of a two-dimensional electron density beneath the metallic gratings: the delocalized phase at weak modulation and the localized phase at strong modulation. Notably, we delve into an impact of the grating filling factor on the electrically driven transition between these phases. Our findings underscore the critical role of specific metal grating geometry parameters in facilitating this transition. Moreover, we explore the potential of utilizing graphene-based gratings as alternatives to metallic gratings. Through the integration of graphene, grown by Chemical Vapor Deposition method on copper foil and then transferred to the high electron mobility AlGaN/GaN heterostructures, we achieve an effective modulation of broadband absorption by free charge carriers within the 0.5–6 THz range via electrical biasing of the graphene electrode. However, while this approach successfully modulates absorption in a wide THz range, it does not elicit plasmon resonances within the graphene-based grating-gate plasmonic crystals. This intriguing observation poses a significant unresolved question warranting further theoretical and experimental exploration in subsequent studies.","PeriodicalId":35778,"journal":{"name":"International Journal of High Speed Electronics and Systems","volume":" 13","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140687612","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 : 2024-04-18DOI: 10.1142/s0129156424400184
Dongxun Yang, M. Tonouchi
Laser-induced Terahertz (THz) Emission Spectroscopy (TES) has demonstrated its potential utility in the realm of Metal-Oxide-Semiconductor (MOS) devices as an expedient and noncontact estimation methodology. Owing to its discerning response to the interface electric field, the amplitude of the THz emission peak in time-domain spectroscopy encapsulates rich information regarding MOS properties, notably the flat-band voltage. This paper concentrates on the precise quantitative estimation of the flat-band voltage within the Si MOS structure, elucidating the intricacies of the estimation process through the THz emission model.
激光诱导太赫兹（THz）发射光谱（TES）作为一种便捷的非接触式估算方法，已在金属氧化物半导体（MOS）器件领域展现出其潜在的实用性。由于太赫兹发射峰的振幅对界面电场有明显的响应，时域光谱中的太赫兹发射峰包含了有关 MOS 特性的丰富信息，尤其是平带电压。本文主要研究如何精确定量估计硅 MOS 结构中的平带电压，通过太赫兹发射模型阐明估计过程的复杂性。
{"title":"Quantifying Flat-Band Voltage in Si Metal-Oxide-Semiconductor Structures: An Evaluation via Terahertz Emission Spectroscopy (TES)","authors":"Dongxun Yang, M. Tonouchi","doi":"10.1142/s0129156424400184","DOIUrl":"https://doi.org/10.1142/s0129156424400184","url":null,"abstract":"Laser-induced Terahertz (THz) Emission Spectroscopy (TES) has demonstrated its potential utility in the realm of Metal-Oxide-Semiconductor (MOS) devices as an expedient and noncontact estimation methodology. Owing to its discerning response to the interface electric field, the amplitude of the THz emission peak in time-domain spectroscopy encapsulates rich information regarding MOS properties, notably the flat-band voltage. This paper concentrates on the precise quantitative estimation of the flat-band voltage within the Si MOS structure, elucidating the intricacies of the estimation process through the THz emission model.","PeriodicalId":35778,"journal":{"name":"International Journal of High Speed Electronics and Systems","volume":" 13","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140687318","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 : 2024-04-13DOI: 10.1142/s0129156424400172
Yan Yang, Jingang Fan, Jiabao Liu
Each student in an adaptive education system has significant differences in knowledge background, ability level and cognitive style. Therefore, to build an adaptive teaching system, it is necessary to establish an operable, reasonable and individualized student model by clarifying students’ abilities and differences. The improved Apriori algorithm under big data is the most classic association rule algorithm, which is generated by a set of candidates, and it uses the iterative method of hierarchical search to traverse a set of frequency items in the transaction database. After finding the set of frequency items, select the association according to the trust rules. This paper studies how to apply the improved Apriori algorithm to an adaptive online education system in a big data environment. An evolutionary algebra is taken with mean fit of 80, population size of 20, mean fit of 0.28, population size of 60, mean fit of 0.26, population size of 80, mean fit of 0.25, mean population size of 0.25. The population size is 200, and the average fitting is 0.24. The larger the error, the smaller the error between each indicator of the test paper and the corresponding value specified by the user. The improved Apriori algorithm in the big data environment has designed five themes of rule mining, which are mainly used for class management: class linkage, class category linkage, student basic information linkage, lecture and basic information linkage, and lecture mode linkage. They play the role of teaching assistants with a specific role.
{"title":"Research on the Application of Improved Apriori Algorithm in Sport-Adaptive Online Teaching System Under Big Data Environment","authors":"Yan Yang, Jingang Fan, Jiabao Liu","doi":"10.1142/s0129156424400172","DOIUrl":"https://doi.org/10.1142/s0129156424400172","url":null,"abstract":"Each student in an adaptive education system has significant differences in knowledge background, ability level and cognitive style. Therefore, to build an adaptive teaching system, it is necessary to establish an operable, reasonable and individualized student model by clarifying students’ abilities and differences. The improved Apriori algorithm under big data is the most classic association rule algorithm, which is generated by a set of candidates, and it uses the iterative method of hierarchical search to traverse a set of frequency items in the transaction database. After finding the set of frequency items, select the association according to the trust rules. This paper studies how to apply the improved Apriori algorithm to an adaptive online education system in a big data environment. An evolutionary algebra is taken with mean fit of 80, population size of 20, mean fit of 0.28, population size of 60, mean fit of 0.26, population size of 80, mean fit of 0.25, mean population size of 0.25. The population size is 200, and the average fitting is 0.24. The larger the error, the smaller the error between each indicator of the test paper and the corresponding value specified by the user. The improved Apriori algorithm in the big data environment has designed five themes of rule mining, which are mainly used for class management: class linkage, class category linkage, student basic information linkage, lecture and basic information linkage, and lecture mode linkage. They play the role of teaching assistants with a specific role.","PeriodicalId":35778,"journal":{"name":"International Journal of High Speed Electronics and Systems","volume":"51 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140707305","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 : 2024-04-12DOI: 10.1142/s0129156424400081
Yundong Hu, Jintao Chen
Electricity is an indispensable resource in daily life. While it brings great convenience to people, it also brings some safety hazards, especially in the event of power failure, which may cause significant harm. Therefore, monitoring and warning of power faults are very important. Traditional power grounding fault monitoring and warning systems have problems such as untimely monitoring, inaccurate warning, and high error rates in fault location. In order to solve the above problems, this paper uses wireless networks to construct a power grounding fault monitoring and early warning system. The wireless network collected fault data based on the complexity of power grounding fault data, and used threshold monitoring method to analyze the collected data. The wireless network was used to construct a prediction model to monitor grounding faults. Through experiments, it can be found that the accuracy of the wireless network-based power fault monitoring system for predicting grounding faults was over 92.57%, and the average warning accuracy of 20 experiments was 93.856%. This paper studied a wireless network-based algorithm for monitoring and warning of power grounding faults, which can effectively improve the monitoring and warning capabilities of power systems, and reduce the risk of power equipment faults and the probability of power accidents.
{"title":"Exploration on Wireless Network-Based Monitoring and Early Warning Algorithms for Power Grounding Faults","authors":"Yundong Hu, Jintao Chen","doi":"10.1142/s0129156424400081","DOIUrl":"https://doi.org/10.1142/s0129156424400081","url":null,"abstract":"Electricity is an indispensable resource in daily life. While it brings great convenience to people, it also brings some safety hazards, especially in the event of power failure, which may cause significant harm. Therefore, monitoring and warning of power faults are very important. Traditional power grounding fault monitoring and warning systems have problems such as untimely monitoring, inaccurate warning, and high error rates in fault location. In order to solve the above problems, this paper uses wireless networks to construct a power grounding fault monitoring and early warning system. The wireless network collected fault data based on the complexity of power grounding fault data, and used threshold monitoring method to analyze the collected data. The wireless network was used to construct a prediction model to monitor grounding faults. Through experiments, it can be found that the accuracy of the wireless network-based power fault monitoring system for predicting grounding faults was over 92.57%, and the average warning accuracy of 20 experiments was 93.856%. This paper studied a wireless network-based algorithm for monitoring and warning of power grounding faults, which can effectively improve the monitoring and warning capabilities of power systems, and reduce the risk of power equipment faults and the probability of power accidents.","PeriodicalId":35778,"journal":{"name":"International Journal of High Speed Electronics and Systems","volume":"5 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140710529","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 : 2024-03-28DOI: 10.1142/s0129156424400135
Yun Liu, Yizhe Wang, Wujian Deng
The characteristics of hyperspectral remote sensing images such as inconspicuous feature representativeness, single feature level, and complex information content, can lead to unstable classification results. We propose a lightweight dense network model that injects channel attention in the form of dense connections between network layers (DSE-DN) for the classification of hyperspectral images. In the DSE-DN network, principal component analysis (PCA) is applied to reduce redundancy in the hyperspectral images. Subsequently, a densely connected network is constructed, incorporating channel attention mechanisms through dense connections to enhance the analysis of spectral image features. Finally, the processed hyperspectral images are classified using a fully interconnected layer. We assess two classical hyperspectral datasets and construct 2DCNN, 3DCNN, ResNet, and the network that injects channel attention layer by layer to compare with DSE-DN. The experimental results indicate the utility of the DSE-DN network in hyperspectral image classification and its superiority over other networks.
{"title":"A Study for Design of Lightweight Dense Connection Network on Hyperspectral Image Classification","authors":"Yun Liu, Yizhe Wang, Wujian Deng","doi":"10.1142/s0129156424400135","DOIUrl":"https://doi.org/10.1142/s0129156424400135","url":null,"abstract":"The characteristics of hyperspectral remote sensing images such as inconspicuous feature representativeness, single feature level, and complex information content, can lead to unstable classification results. We propose a lightweight dense network model that injects channel attention in the form of dense connections between network layers (DSE-DN) for the classification of hyperspectral images. In the DSE-DN network, principal component analysis (PCA) is applied to reduce redundancy in the hyperspectral images. Subsequently, a densely connected network is constructed, incorporating channel attention mechanisms through dense connections to enhance the analysis of spectral image features. Finally, the processed hyperspectral images are classified using a fully interconnected layer. We assess two classical hyperspectral datasets and construct 2DCNN, 3DCNN, ResNet, and the network that injects channel attention layer by layer to compare with DSE-DN. The experimental results indicate the utility of the DSE-DN network in hyperspectral image classification and its superiority over other networks.","PeriodicalId":35778,"journal":{"name":"International Journal of High Speed Electronics and Systems","volume":"25 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140372191","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 : 2024-03-25DOI: 10.1142/s0129156424400111
Xue Jiang
In the long history of human beings, with the continuous exploration and research of natural phenomena and social life, many scientific fields have emerged, and robots are the product of this technological development to a certain stage. At present, there are hundreds of different types of robots applied in production and daily life in the world, which have achieved significant economic benefits. However, its technical issues have gradually emerged. For example, the shortcomings in visual perception and other aspects cannot be effectively addressed. Object recognition is not precise enough, and information resources cannot be effectively utilized to achieve control functions. These are the main factors that constrain the further progress and improvement of robots. The emergence of big data and Artificial Intelligence (AI) has brought unprecedented opportunities to robots. Especially, the application of big data analysis in intelligent manufacturing and smart city construction is becoming increasingly widespread, thus providing new solutions for robot services. They not only enable people to quickly and accurately grasp a large amount of valuable knowledge, but also better tap into the enormous potential contained in human intelligence, which largely drives the robot industry towards intelligence. By summarizing the existing research results, this paper explored the development trend of robot object recognition systems, and focused on its key technologies, the feature matching-based pattern recognition and acceleration strategy-based detection efficiency improvement. In response to the current problems, corresponding solutions were proposed and comparative experiments were designed. This proved that the anti-interference detection accuracy of the robot object recognition system based on big data and AI algorithm improved by about 12.48%, thus hoping to provide reference for future robot system development.
{"title":"Investigation of Artificial Intelligence Algorithms in Robot Object Recognition Systems Under the Background of Big Data","authors":"Xue Jiang","doi":"10.1142/s0129156424400111","DOIUrl":"https://doi.org/10.1142/s0129156424400111","url":null,"abstract":"In the long history of human beings, with the continuous exploration and research of natural phenomena and social life, many scientific fields have emerged, and robots are the product of this technological development to a certain stage. At present, there are hundreds of different types of robots applied in production and daily life in the world, which have achieved significant economic benefits. However, its technical issues have gradually emerged. For example, the shortcomings in visual perception and other aspects cannot be effectively addressed. Object recognition is not precise enough, and information resources cannot be effectively utilized to achieve control functions. These are the main factors that constrain the further progress and improvement of robots. The emergence of big data and Artificial Intelligence (AI) has brought unprecedented opportunities to robots. Especially, the application of big data analysis in intelligent manufacturing and smart city construction is becoming increasingly widespread, thus providing new solutions for robot services. They not only enable people to quickly and accurately grasp a large amount of valuable knowledge, but also better tap into the enormous potential contained in human intelligence, which largely drives the robot industry towards intelligence. By summarizing the existing research results, this paper explored the development trend of robot object recognition systems, and focused on its key technologies, the feature matching-based pattern recognition and acceleration strategy-based detection efficiency improvement. In response to the current problems, corresponding solutions were proposed and comparative experiments were designed. This proved that the anti-interference detection accuracy of the robot object recognition system based on big data and AI algorithm improved by about 12.48%, thus hoping to provide reference for future robot system development.","PeriodicalId":35778,"journal":{"name":"International Journal of High Speed Electronics and Systems","volume":"3 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140382146","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 : 2024-03-22DOI: 10.1142/s0129156424400159
M. Hasan, Nezih Pala, Michael Shur
We report on sub-terahertz plasmonic wave generation in the 2DEG channel of diamond TeraFET when biased by a DC current at the drain. Our numerical results demonstrated that p-diamond can support resonant oscillation of 300 GHz at room temperature, allowing it to function as a sub-THz emitter. We investigated the impact of different channel lengths, gate biases, drift velocities, and temperatures on the fundamental mode oscillation. The model incorporated the decay factors owing to electron scattering and electron fluid viscosity.
{"title":"Current-Driven Terahertz Oscillations in Diamond TeraFET","authors":"M. Hasan, Nezih Pala, Michael Shur","doi":"10.1142/s0129156424400159","DOIUrl":"https://doi.org/10.1142/s0129156424400159","url":null,"abstract":"We report on sub-terahertz plasmonic wave generation in the 2DEG channel of diamond TeraFET when biased by a DC current at the drain. Our numerical results demonstrated that p-diamond can support resonant oscillation of 300 GHz at room temperature, allowing it to function as a sub-THz emitter. We investigated the impact of different channel lengths, gate biases, drift velocities, and temperatures on the fundamental mode oscillation. The model incorporated the decay factors owing to electron scattering and electron fluid viscosity.","PeriodicalId":35778,"journal":{"name":"International Journal of High Speed Electronics and Systems","volume":" 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140387398","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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