Pub Date : 2022-11-26DOI: 10.1142/s0219843622500190
Dong Huang, Jong Hoon-Yang
Today, with the rapid development of science and technology, the value of education is more and more valued by people, but the research and development of quality education in film and television are still relatively traditional. The objective is to explore the application and effect of the combination of artificial intelligence and deep learning algorithm in the quality education of youth film and television. In view of the current problems in the quality education of film and television, this work innovatively introduces the augmented reality (AR) technology, and applies artificial intelligence and AR technology to the quality education of film and television for young people. At the same time, a deep learning algorithm is introduced to build a youth film and television quality education system based on artificial intelligence combined with deep learning, and further empirical analysis of the system is carried out in the form of a questionnaire survey. The questionnaire survey shows that, from the perspective of various dimensions of learning attitude, the two groups of learners had significant differences in emotional experience and self-cognition ([Formula: see text]), and more than 90% of the teenagers thought the system resource interface was beautiful; from the perspective of the perceived usefulness of the system, 68.24% of the teenagers believe that the system is easy to operate and useful, and as many as 82.82% of the teenagers believe that the system improves their interest in learning about quality education courses in film and television. Therefore, it is found that the constructed system can improve the learning interest of young people in the quality education course of film and television and make their learning attitude more positive, providing experimental reference for the latter research in the field of youth education.
{"title":"Artificial Intelligence Combined with Deep Learning in Film and Television Quality Education for the Youth","authors":"Dong Huang, Jong Hoon-Yang","doi":"10.1142/s0219843622500190","DOIUrl":"https://doi.org/10.1142/s0219843622500190","url":null,"abstract":"Today, with the rapid development of science and technology, the value of education is more and more valued by people, but the research and development of quality education in film and television are still relatively traditional. The objective is to explore the application and effect of the combination of artificial intelligence and deep learning algorithm in the quality education of youth film and television. In view of the current problems in the quality education of film and television, this work innovatively introduces the augmented reality (AR) technology, and applies artificial intelligence and AR technology to the quality education of film and television for young people. At the same time, a deep learning algorithm is introduced to build a youth film and television quality education system based on artificial intelligence combined with deep learning, and further empirical analysis of the system is carried out in the form of a questionnaire survey. The questionnaire survey shows that, from the perspective of various dimensions of learning attitude, the two groups of learners had significant differences in emotional experience and self-cognition ([Formula: see text]), and more than 90% of the teenagers thought the system resource interface was beautiful; from the perspective of the perceived usefulness of the system, 68.24% of the teenagers believe that the system is easy to operate and useful, and as many as 82.82% of the teenagers believe that the system improves their interest in learning about quality education courses in film and television. Therefore, it is found that the constructed system can improve the learning interest of young people in the quality education course of film and television and make their learning attitude more positive, providing experimental reference for the latter research in the field of youth education.","PeriodicalId":50319,"journal":{"name":"International Journal of Humanoid Robotics","volume":null,"pages":null},"PeriodicalIF":1.5,"publicationDate":"2022-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45299571","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-11-23DOI: 10.1142/s0219843622500177
Li Tan, Ningpei Ran
With the rapid development of artificial intelligence, the related technologies and applications that accompany it emerge as the times require. The industry based on artificial intelligence is booming. Image recognition and target tracking technology are widely used in various fields, especially in the fields of security monitoring and augmented reality. Combined with the characteristics of athletes’ sports, an auxiliary information system is developed to supervise and guide the training in real time. It can track and analyze the characteristics of individual athletes’ sports function, the arrangement of coaches’ training plan, the state of brain function, the index of routine physiology and biochemistry, nutrition regulation, and the condition of injuries and injuries in the middle of the day, so as to reveal the athletes’ training in the middle of the day the changing rule of various indexes in the training state. Based on the mobile artificial intelligence terminal technology, this paper develops and designs a monitoring system for athletes’ training process in C/S mode. GPS is used to obtain athletes’ position information in real time and provide real-time guidance for athletes.
{"title":"Applying Artificial Intelligence Technology to Analyze the Athletes’ Training Under Sports Training Monitoring System","authors":"Li Tan, Ningpei Ran","doi":"10.1142/s0219843622500177","DOIUrl":"https://doi.org/10.1142/s0219843622500177","url":null,"abstract":"With the rapid development of artificial intelligence, the related technologies and applications that accompany it emerge as the times require. The industry based on artificial intelligence is booming. Image recognition and target tracking technology are widely used in various fields, especially in the fields of security monitoring and augmented reality. Combined with the characteristics of athletes’ sports, an auxiliary information system is developed to supervise and guide the training in real time. It can track and analyze the characteristics of individual athletes’ sports function, the arrangement of coaches’ training plan, the state of brain function, the index of routine physiology and biochemistry, nutrition regulation, and the condition of injuries and injuries in the middle of the day, so as to reveal the athletes’ training in the middle of the day the changing rule of various indexes in the training state. Based on the mobile artificial intelligence terminal technology, this paper develops and designs a monitoring system for athletes’ training process in C/S mode. GPS is used to obtain athletes’ position information in real time and provide real-time guidance for athletes.","PeriodicalId":50319,"journal":{"name":"International Journal of Humanoid Robotics","volume":null,"pages":null},"PeriodicalIF":1.5,"publicationDate":"2022-11-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48215396","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-11-23DOI: 10.1142/s0219843622500207
Xiaolan Han, Dong Huang, Sang Eun-Lee, Jong Hoon-Yang
This work is to explore the application of intelligent algorithms based on deep learning in human–computer interaction systems, hoping to promote the development of human–computer interaction systems in the field of behavior recognition. Firstly, the design scheme of the human–computer interaction system is presented, and the establishment of the robot visual positioning system is emphasized. Then, the fast-region convolutional neural networks (fast-RCNN) algorithm is introduced, and it is combined with deep convolutional residual network (ResNet101). A candidate region extraction algorithm based on ResNet and long short-term memory network is proposed, and a residual network (ResNet) for spatial context memory is proposed. Both algorithms are employed in human–computer interaction systems. Finally, the performance of the algorithm and the human–computer interaction system are analyzed and characterized. The results show that the proposed candidate region extraction algorithm can significantly reduce the loss value of training set and test set after training. In addition, the corresponding accuracy, recall, and [Formula: see text]-value of the model are all above 0.98, which proves that the model has a good detection accuracy. Spatial context memory ResNet shows good accuracy in speech expression detection. The detection accuracy of single attribute, double attribute, and multi-attribute speech expression is above 89%, and the detection accuracy is good. In summary, the human–computer interaction system shows good performance in capturing target objects, even for unlabeled objects, the corresponding grasping success rate is 95%. Therefore, this work provides a theoretical basis and reference for the application of intelligent optimization algorithm in human–computer interaction system.
{"title":"Artificial Intelligence-Oriented User Interface Design and Human Behavior Recognition based on Human–Computer Nature Interaction","authors":"Xiaolan Han, Dong Huang, Sang Eun-Lee, Jong Hoon-Yang","doi":"10.1142/s0219843622500207","DOIUrl":"https://doi.org/10.1142/s0219843622500207","url":null,"abstract":"This work is to explore the application of intelligent algorithms based on deep learning in human–computer interaction systems, hoping to promote the development of human–computer interaction systems in the field of behavior recognition. Firstly, the design scheme of the human–computer interaction system is presented, and the establishment of the robot visual positioning system is emphasized. Then, the fast-region convolutional neural networks (fast-RCNN) algorithm is introduced, and it is combined with deep convolutional residual network (ResNet101). A candidate region extraction algorithm based on ResNet and long short-term memory network is proposed, and a residual network (ResNet) for spatial context memory is proposed. Both algorithms are employed in human–computer interaction systems. Finally, the performance of the algorithm and the human–computer interaction system are analyzed and characterized. The results show that the proposed candidate region extraction algorithm can significantly reduce the loss value of training set and test set after training. In addition, the corresponding accuracy, recall, and [Formula: see text]-value of the model are all above 0.98, which proves that the model has a good detection accuracy. Spatial context memory ResNet shows good accuracy in speech expression detection. The detection accuracy of single attribute, double attribute, and multi-attribute speech expression is above 89%, and the detection accuracy is good. In summary, the human–computer interaction system shows good performance in capturing target objects, even for unlabeled objects, the corresponding grasping success rate is 95%. Therefore, this work provides a theoretical basis and reference for the application of intelligent optimization algorithm in human–computer interaction system.","PeriodicalId":50319,"journal":{"name":"International Journal of Humanoid Robotics","volume":null,"pages":null},"PeriodicalIF":1.5,"publicationDate":"2022-11-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43104190","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-11-21DOI: 10.1142/s0219843622500189
Haishan Ye
The present work aims to promote the development of intelligent image processing technology for badminton robots and optimize the application effect of badminton robots in national fitness. Firstly, the problems and common needs of the badminton robot currently in use are investigated. Secondly, a shuttlecock aerodynamic model is established to simulate the effects of air resistance and gravity on the aerial flight of shuttlecock. Besides, the convolution neural network (CNN) is combined with traditional image processing technology to denoise and recognize the collected shuttlecock images. Finally, the badminton robot vision system is constructed and its performance is tested. The results demonstrate that the image denoising method based on CNN and the traditional image processing method can effectively process and denoise the captured moving image. Under the noise level of [Formula: see text], the peak signal-to-noise ratio index of this method is better than the Gaussian Scale Model, k-Singular Value Decomposition, and Color Names methods, slightly better than that of the Multilayer Perceptron (MLP) network. In terms of the time consumed in processing the same number of pictures, the method reported here takes the least time. But when [Formula: see text], the MLP method has a better denoising effect because the noise is overlarge and the features are not easy to learn. Moreover, the detection accuracy of the optimized Single Shot MultiBox Detector (SSD) method adopted here is 79.0%. This accuracy is 1.7% higher than that of the traditional SSD method, and 2.3% higher than that of Faster Region-Convolutional Neural Network based on Region Proposal Network. The optimized network structure reported here provides a certain idea for the software design of the badminton robot.
{"title":"Intelligent Image Processing Technology for Badminton Robot under Machine Vision of Internet of Things","authors":"Haishan Ye","doi":"10.1142/s0219843622500189","DOIUrl":"https://doi.org/10.1142/s0219843622500189","url":null,"abstract":"The present work aims to promote the development of intelligent image processing technology for badminton robots and optimize the application effect of badminton robots in national fitness. Firstly, the problems and common needs of the badminton robot currently in use are investigated. Secondly, a shuttlecock aerodynamic model is established to simulate the effects of air resistance and gravity on the aerial flight of shuttlecock. Besides, the convolution neural network (CNN) is combined with traditional image processing technology to denoise and recognize the collected shuttlecock images. Finally, the badminton robot vision system is constructed and its performance is tested. The results demonstrate that the image denoising method based on CNN and the traditional image processing method can effectively process and denoise the captured moving image. Under the noise level of [Formula: see text], the peak signal-to-noise ratio index of this method is better than the Gaussian Scale Model, k-Singular Value Decomposition, and Color Names methods, slightly better than that of the Multilayer Perceptron (MLP) network. In terms of the time consumed in processing the same number of pictures, the method reported here takes the least time. But when [Formula: see text], the MLP method has a better denoising effect because the noise is overlarge and the features are not easy to learn. Moreover, the detection accuracy of the optimized Single Shot MultiBox Detector (SSD) method adopted here is 79.0%. This accuracy is 1.7% higher than that of the traditional SSD method, and 2.3% higher than that of Faster Region-Convolutional Neural Network based on Region Proposal Network. The optimized network structure reported here provides a certain idea for the software design of the badminton robot.","PeriodicalId":50319,"journal":{"name":"International Journal of Humanoid Robotics","volume":null,"pages":null},"PeriodicalIF":1.5,"publicationDate":"2022-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45922473","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-11-21DOI: 10.1142/s0219843622500165
Kuan Yang, Hongkai Wang
The purpose is to improve the application of museum robots in museum scenes, enhance the service capabilities of robots in museums, break tourists’ boring concepts of museum environment, manual explanation, services, etc., and promote tourists’ exhibition experience. A method for sentiment analysis of humanoid robots in museums is proposed by studying the transformation of museums with the help of artificial intelligence (AI) technology, as well as the function and significance of museums in history education. First, the function of museums in history education and the role of AI in constructing intelligent museums are described. Second, on account of the multimodal sentiment analysis method of speech and emotion, a scenario model of the visitor museum is established. An uncertain reasoning method for robot service tasks based on Multi-entity Bayesian network (MEBN) is also proposed. Finally, the proposed model is validated by experiments. The results show that compared with the recognition rates of Arousal and Valence dimensions, the consistency correlation coefficient value of the Kalman filter is higher. The Consistency Correlation Coefficient (CCC) value of the Arousal dimension is 0.703, and the CCC value of the Valence dimension is 0.766. Besides, in different tour times, the proportion of services that tourists want to be provided with varies in different emotional states. From time [Formula: see text]1 to time [Formula: see text]2, the proportion of tourists who want to hear explanations of cultural relics dropped by 11.5%, while the proportion of tourists who want to be provided with tea service increased by 24%. This indicates that when the Kalman filter algorithm performs continuous emotion recognition of a multimodal fusion, the final emotion recognition accuracy is higher, and emotion analysis can help humanoid robots to be more intelligent and humanized. The proposed sentiment analysis based on the multimodal analysis and MEBN’s uncertainty reasoning method for robot service tasks not only broadens the practical application field of intelligent robots under human–computer interaction technology but also has important research significance for the innovative education development of museum history education.
{"title":"The Application of Interactive Humanoid Robots in the History Education of Museums Under Artificial Intelligence","authors":"Kuan Yang, Hongkai Wang","doi":"10.1142/s0219843622500165","DOIUrl":"https://doi.org/10.1142/s0219843622500165","url":null,"abstract":"The purpose is to improve the application of museum robots in museum scenes, enhance the service capabilities of robots in museums, break tourists’ boring concepts of museum environment, manual explanation, services, etc., and promote tourists’ exhibition experience. A method for sentiment analysis of humanoid robots in museums is proposed by studying the transformation of museums with the help of artificial intelligence (AI) technology, as well as the function and significance of museums in history education. First, the function of museums in history education and the role of AI in constructing intelligent museums are described. Second, on account of the multimodal sentiment analysis method of speech and emotion, a scenario model of the visitor museum is established. An uncertain reasoning method for robot service tasks based on Multi-entity Bayesian network (MEBN) is also proposed. Finally, the proposed model is validated by experiments. The results show that compared with the recognition rates of Arousal and Valence dimensions, the consistency correlation coefficient value of the Kalman filter is higher. The Consistency Correlation Coefficient (CCC) value of the Arousal dimension is 0.703, and the CCC value of the Valence dimension is 0.766. Besides, in different tour times, the proportion of services that tourists want to be provided with varies in different emotional states. From time [Formula: see text]1 to time [Formula: see text]2, the proportion of tourists who want to hear explanations of cultural relics dropped by 11.5%, while the proportion of tourists who want to be provided with tea service increased by 24%. This indicates that when the Kalman filter algorithm performs continuous emotion recognition of a multimodal fusion, the final emotion recognition accuracy is higher, and emotion analysis can help humanoid robots to be more intelligent and humanized. The proposed sentiment analysis based on the multimodal analysis and MEBN’s uncertainty reasoning method for robot service tasks not only broadens the practical application field of intelligent robots under human–computer interaction technology but also has important research significance for the innovative education development of museum history education.","PeriodicalId":50319,"journal":{"name":"International Journal of Humanoid Robotics","volume":null,"pages":null},"PeriodicalIF":1.5,"publicationDate":"2022-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44818652","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-05-18DOI: 10.1142/s0219843622500074
Guoyu Zuo, Zichen Xu, Gao Huang
Efficient and collision-free coordination of two robot arms is increasingly needed in various service-oriented robotic applications. This paper proposes a dual arm coordination algorithm to improve the efficiency of coordination by considering both robot’s actions and operating sequences of the tasks that need to use two arms to complete complex operations. Teleoperation demonstration is first performed to obtain the robot’s human-like motion trajectories, so as to reduce the probability of the collisions between the two arms. The coordination diagram in time domain is then designed to more clearly represent the situations of trajectory collisions and find the collision-free coordination action law. A Coordination Pair Generator (CPG) is designed to reorganize the operating sequences according to the characteristics of input trajectories and the action coordination. The effectiveness and efficiency of our method are verified on the simulation and physical experiments which execute the drug sorting task in nursing homes, respectively, on the ABB YuMi robot model and self-developed robot system. According to the experiment results, the operation time has been reduced by 9% and the collision area has been reduced by 7.5%.
{"title":"Dual Arm Coordination with Coordination Diagram Based on Teleoperation Demonstration","authors":"Guoyu Zuo, Zichen Xu, Gao Huang","doi":"10.1142/s0219843622500074","DOIUrl":"https://doi.org/10.1142/s0219843622500074","url":null,"abstract":"<p>Efficient and collision-free coordination of two robot arms is increasingly needed in various service-oriented robotic applications. This paper proposes a dual arm coordination algorithm to improve the efficiency of coordination by considering both robot’s actions and operating sequences of the tasks that need to use two arms to complete complex operations. Teleoperation demonstration is first performed to obtain the robot’s human-like motion trajectories, so as to reduce the probability of the collisions between the two arms. The coordination diagram in time domain is then designed to more clearly represent the situations of trajectory collisions and find the collision-free coordination action law. A Coordination Pair Generator (CPG) is designed to reorganize the operating sequences according to the characteristics of input trajectories and the action coordination. The effectiveness and efficiency of our method are verified on the simulation and physical experiments which execute the drug sorting task in nursing homes, respectively, on the ABB YuMi robot model and self-developed robot system. According to the experiment results, the operation time has been reduced by 9% and the collision area has been reduced by 7.5%.</p>","PeriodicalId":50319,"journal":{"name":"International Journal of Humanoid Robotics","volume":null,"pages":null},"PeriodicalIF":1.5,"publicationDate":"2022-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138531137","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-08-20DOI: 10.1142/s0219876220990017
{"title":"Author Index Volume 17 (2020)","authors":"","doi":"10.1142/s0219876220990017","DOIUrl":"https://doi.org/10.1142/s0219876220990017","url":null,"abstract":"","PeriodicalId":50319,"journal":{"name":"International Journal of Humanoid Robotics","volume":null,"pages":null},"PeriodicalIF":1.5,"publicationDate":"2020-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43325643","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-08-29DOI: 10.1142/s0219876219990019
{"title":"Author Index Volume 16 (2019)","authors":"","doi":"10.1142/s0219876219990019","DOIUrl":"https://doi.org/10.1142/s0219876219990019","url":null,"abstract":"","PeriodicalId":50319,"journal":{"name":"International Journal of Humanoid Robotics","volume":null,"pages":null},"PeriodicalIF":1.5,"publicationDate":"2019-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48025078","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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