Please help us test our new pre-print finding feature by giving the pre-print link a rating. A 5 star rating indicates the linked pre-print has the exact same content as the published article.
Authors:Sixian Chan, Yating Liu, Xiaotian Pan, Yanjing Lei Abstract: International Journal of Humanoid Robotics, Ahead of Print. In recent years, jointly utilizing local and global features to improve model performance is becoming an important approach for person re-identification. If the relationship between body parts is not considered, it is easy to confuse the identity differentiation of different persons with similar attributes in the corresponding parts. To solve this problem, we propose a feature fusion-based method for person re-identification, which contains three core parts: an adjacency module, a counterfactual attention module and a global difference pooling module. First, an adjacency module is designed to consider the relationship between adjacent body parts and make the features more discriminative. Next, a counterfactual attention module is proposed to conduct counterfactual intervention analysis and encourage the network to learn more useful attention to obtain more fine-grained features. Then, a global difference pooling module is used to learn the global features of a person’s image itself and pay more attention to the important features of the human body. Through the fusion of local features and global features, our model can effectively distinguish the identities of different people with similar attributes in the corresponding parts. Finally, we conduct a large number of experiments and achieve outstanding results on Market-1501, CUHK03 and Msmt17. Citation: International Journal of Humanoid Robotics PubDate: 2023-05-11T07:00:00Z DOI: 10.1142/S0219843623500044
Please help us test our new pre-print finding feature by giving the pre-print link a rating. A 5 star rating indicates the linked pre-print has the exact same content as the published article.
Authors:Ratan Das, Ahmed Chemori, Neelesh Kumar Abstract: International Journal of Humanoid Robotics, Ahead of Print. Estimation and control of zero-moment point (ZMP) is a widely used concept for planning the locomotion of bipedal robots and is commonly measured using integrated joint angle encoders and foot force sensors. Contemporary methods for ZMP measurement involve built-in contact sensors such as joint encoders or instrumented foot force sensors. This paper presents a novel approach for computing ZMP for a humanoid robot using inertial sensor-based wireless foot sensor modules (WFSMs). The developed WFSMs, strapped at different limb segments of a bipedal robot, measure lower limb joint angles in real time. The joint angle trajectories, further transformed into Cartesian position coordinates, are used for estimating the ZMP positions of humanoid robots using the planar biped model. The whole framework is presented through experimental studies for different real-life walking scenarios. Since the modules work based on the limb motion and inclination, any ground unevenness would be automatically reflected in the module output. Hence, this measurement process can be a convenient method for applications requiring humanoid control on uneven surfaces/outdoor terrains. To compare the performance of the proposed model, ZMP is simultaneously measured from inbuilt foot force sensors and joint encoders of the robot. Statistical tests exhibit a high linear correlation between the proposed method with integrated encoders and foot force sensors (Pearson’s coefficient, [math]). Results indicate that ZMP estimated by WFSM is a viable method to monitor the dynamic gait balance of a humanoid robot and has potential application in outdoor and uneven terrains. Citation: International Journal of Humanoid Robotics PubDate: 2023-03-13T07:00:00Z DOI: 10.1142/S0219843623500032
Please help us test our new pre-print finding feature by giving the pre-print link a rating. A 5 star rating indicates the linked pre-print has the exact same content as the published article.
Authors:Laura Aymerich-Franch, Iliana Ferrer Abstract: International Journal of Humanoid Robotics, Ahead of Print. One of the major areas where social robots are finding their place in society is for healthcare-related applications. Yet, very little research has mapped the deployment of socially assistive robots (SARs) in real settings. By using a documentary research method, we traced back 279 experiences of SARs deployments in hospitals, elderly care centers, occupational health centers, private homes, and educational institutions worldwide that involved 52 different robot models. We retrieved, analyzed, and classified the functions that SARs develop in these experiences, the areas in which they are deployed, the principal manufacturers, and the robot models that are being adopted. The functions we identified for SARs are entertainment, companionship, telepresence, edutainment, providing general and personalized information or advice, monitoring, promotion of physical exercise and rehabilitation, testing and pre-diagnosis, delivering supplies, patient registration, giving location indications, patient simulator, protective measure enforcement, medication and well-being adherence, translating and having conversations in multiple languages, psychological therapy, patrolling, interacting with digital devices, and disinfection. Our work provides an in-depth picture of the current state of the art of SARs’ deployment in real scenarios for healthcare-related applications and contributes to understanding better the role of these machines in the healthcare sector. Citation: International Journal of Humanoid Robotics PubDate: 2023-03-10T08:00:00Z DOI: 10.1142/S0219843623500020
Please help us test our new pre-print finding feature by giving the pre-print link a rating. A 5 star rating indicates the linked pre-print has the exact same content as the published article.
Authors:Sangjin Ko, Jaclyn Barnes, Jiayuan Dong, Chung Hyuk Park, Ayanna Howard, Myounghoon Jeon Abstract: International Journal of Humanoid Robotics, Ahead of Print. As the influence of social robots in people’s daily lives grows, research on understanding people’s perception of robots including sociability, trust, acceptance, and preference becomes more pervasive. Research has considered visual, vocal, or tactile cues to express robots’ emotions, whereas little research has provided a holistic view in examining the interactions among different factors influencing emotion perception. We investigated multiple facets of user perception on robots during a conversational task by varying the robots’ voice types, appearances, and emotions. In our experiment, 20 participants interacted with two robots having four different voice types. While participants were reading fairy tales to the robot, the robot gave vocal feedback with seven emotions and the participants evaluated the robot’s profiles through post surveys. The results indicate that (1) the accuracy of emotion perception differed depending on presented emotions, (2) a regular human voice showed higher user preferences and naturalness, (3) but a characterized voice was more appropriate for expressing emotions with significantly higher accuracy in emotion perception, and (4) participants showed significantly higher emotion recognition accuracy with the animal robot than the humanoid robot. A follow-up study ([math]) with voice-only conditions confirmed that the importance of embodiment. The results from this study could provide the guidelines needed to design social robots that consider emotional aspects in conversations between robots and users. Citation: International Journal of Humanoid Robotics PubDate: 2023-02-22T08:00:00Z DOI: 10.1142/S0219843623500019
Please help us test our new pre-print finding feature by giving the pre-print link a rating. A 5 star rating indicates the linked pre-print has the exact same content as the published article.
Authors:Dong Huang, Jong Hoon-Yang Abstract: International Journal of Humanoid Robotics, Ahead of Print. 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 ([math]), 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. Citation: International Journal of Humanoid Robotics PubDate: 2022-11-26T08:00:00Z DOI: 10.1142/S0219843622500190
Please help us test our new pre-print finding feature by giving the pre-print link a rating. A 5 star rating indicates the linked pre-print has the exact same content as the published article.
Authors:Li Tan, Ningpei Ran Abstract: International Journal of Humanoid Robotics, Ahead of Print. 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. Citation: International Journal of Humanoid Robotics PubDate: 2022-11-23T08:00:00Z DOI: 10.1142/S0219843622500177
Please help us test our new pre-print finding feature by giving the pre-print link a rating. A 5 star rating indicates the linked pre-print has the exact same content as the published article.
Authors:Xiao Han, Dong Huang, Sang Eun-Lee, Jong Hoon-Yang Abstract: International Journal of Humanoid Robotics, Ahead of Print. 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 [math]-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. Citation: International Journal of Humanoid Robotics PubDate: 2022-11-23T08:00:00Z DOI: 10.1142/S0219843622500207
Please help us test our new pre-print finding feature by giving the pre-print link a rating. A 5 star rating indicates the linked pre-print has the exact same content as the published article.
Authors:Kuan Yang, Hongkai Wang Abstract: International Journal of Humanoid Robotics, Ahead of Print. 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 [math]1 to time [math]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. Citation: International Journal of Humanoid Robotics PubDate: 2022-11-21T08:00:00Z DOI: 10.1142/S0219843622500165
Please help us test our new pre-print finding feature by giving the pre-print link a rating. A 5 star rating indicates the linked pre-print has the exact same content as the published article.
Authors:Haishan Ye Abstract: International Journal of Humanoid Robotics, Ahead of Print. 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 [math], 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 [math], 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. Citation: International Journal of Humanoid Robotics PubDate: 2022-11-21T08:00:00Z DOI: 10.1142/S0219843622500189
Please help us test our new pre-print finding feature by giving the pre-print link a rating. A 5 star rating indicates the linked pre-print has the exact same content as the published article.
Authors:Bin He, Hao Liu, Caiyue Xu, Yafei Wang, Ping Lu, Yanmin Zhou Abstract: International Journal of Humanoid Robotics, Ahead of Print. Robots are now working outside of industrial fences more and more closely with humans. Safety is the primary requirement for intimate human–robot interactions. Contacts could happen at any and multipoints of robot. Tactile sensors have great potentials for contact sensing. However, their implementation for the whole-body compliance of robots upon unknown contacts is still challenging. In this work, a systematic solution is proposed. A dual-arm humanoid platform is constructed with distributed tactile sensors on its arms and body. Cheap and easy accessed resistive flexible tactile sensors are used. A data collection and signal processing system is developed for the sensing system with scalable capabilities. External contacts on the robot can be monitored by a visualized system. The multipoint contact force is calculated with the sensor positions taken into consideration. A PD controller-based compliant force control algorithm is proposed in the joint space of the robot. Particularly, a geometry-based force propagation method is introduced in order to achieve overall whole-body compliance of the robot. Experiments verified the whole-body compliance of the robot arms. Safety could then be maintained for frequent human–robot interactions. Citation: International Journal of Humanoid Robotics PubDate: 2022-09-12T07:00:00Z DOI: 10.1142/S0219843622500141
Please help us test our new pre-print finding feature by giving the pre-print link a rating. A 5 star rating indicates the linked pre-print has the exact same content as the published article.
Authors:Ling Zhao Abstract: International Journal of Humanoid Robotics, Ahead of Print. The research expects to promote “artificial intelligence (AI) [math] Education” and cultivate high-quality art design talents with international perspectives. First, random sampling recruits 100 Art Design sophomores in a university and divides them into a test group and a control group. The Questionnaire Survey (QS) is used to obtain research data, including students’ satisfaction with the curriculum, training mode, and innovation environment. Meanwhile, some deficiencies in the traditional educational model are revealed alongside targeted suggestions. Then, a comparative analysis is performed on the design works of the two groups and the student’s evaluation of the existing and proposed models. The result proves the effectiveness of the proposed international art design talents-oriented Human–Computer Interaction (HCI)-based training model. The research results imply that students are less satisfied with the existing curriculum, training mode, and innovative environment. About 40% of students hold a neutral attitude towards the current curriculum. They hope that the school provides a more comprehensive and flexible curriculum, personalized training methods, and a relaxed learning environment conducive to creativity. Students in the test and the control groups have gained significant differences in the scores of design works. The test group has scored relatively high, and their works contain more international elements than the control group. Meanwhile, the test group’s works reflect a deeper understanding of theoretical knowledge. They give a high evaluation of the proposed talent training model. Thus, the proposed HCI-based new talent training model is effective. Therefore, the proposal is of great significance for promoting the “AI [math] Education” and talent training models. Citation: International Journal of Humanoid Robotics PubDate: 2022-07-27T07:00:00Z DOI: 10.1142/S0219843622500128
Please help us test our new pre-print finding feature by giving the pre-print link a rating. A 5 star rating indicates the linked pre-print has the exact same content as the published article.
Authors:Xue Zheng Abstract: International Journal of Humanoid Robotics, Ahead of Print. Individuals use language in a wide range of contexts. It is a major part of the culture. Teaching students how to speak English in a different manner requires adopting cultural attitudes and behaviors. This learning style has a tremendous sense of belonging, community, and intent. In addition, it motivates learners to create a difference in their neighborhoods and communities around the world. A simple way to incorporate culture into the curriculum is to use the abilities and narratives of the wider community. Multilingual classrooms present an incredible task for English teachers because of the students’ wide range of linguistic backgrounds. Because they are afraid of committing mistakes, the students in multilingual classrooms lack self-confidence to communicate in English. Therefore, in this paper, Robot Interaction for Social Cultural Education (RI-SCE) method is proposed to overcome the challenges mentioned above. It uses Deep Machine language and Artificial Intelligence to interact with robots-based computer vision for cultural psychology of English cultural education. As a result, the simulation shows the importance of robot translation in performance, accuracy, efficiency, security, and flexibility compared to the other available models. The model proposed here achieves standard accuracy of 95.2%. Citation: International Journal of Humanoid Robotics PubDate: 2022-06-29T07:00:00Z DOI: 10.1142/S0219843622500062
Please help us test our new pre-print finding feature by giving the pre-print link a rating. A 5 star rating indicates the linked pre-print has the exact same content as the published article.
Authors:Lei Liu Abstract: International Journal of Humanoid Robotics, Ahead of Print. According to a study of health centers across the country, the physical health state is evaluated through a log-based, multi-access physical monitoring program and the accompanying challenges they face in their lifestyle. The deficiency of important nutrients is causing organ degradation, which in turn causes a wide range of health issues, especially for newborns, children, and adults. The physical activities of children and teenagers must be constantly monitored to eliminate issues in their lives through a smart environment. Physical monitoring systems with many access points, information needs, and accurate health-status diagnoses are becoming increasingly important in today’s fast-paced world. In eliminating problems from their lives, a smart environment must constantly monitor the physical activities of children and teenagers. There is a growing need for physical monitoring systems with multiple access points, information needs, and accurate health-status diagnoses in today’s human–robot interactive communication process rapidly changing world. Smart-log patches incorporating researchers have developed and tested sensors for the Internet of Things (IoT) in this study. The smart-log patch is a Bayesian deep learning network system that is based on edge computing (BDLN-EC) to infer and recognize various physical data gathered from people. Deep learning-driven wireless communication is described in signal analysis, encoding and decoding, security and privacy, channel estimation, and compression sensing. Deep learning-driven wireless connectivity intuitions and methodologies are the focus of our work. Wearable IoT systems with multimedia capabilities have been tested and evaluated for accuracy, efficacy, error, and energy usage. Citation: International Journal of Humanoid Robotics PubDate: 2022-06-04T07:00:00Z DOI: 10.1142/S0219843622500086
Please help us test our new pre-print finding feature by giving the pre-print link a rating. A 5 star rating indicates the linked pre-print has the exact same content as the published article.
Authors:Ahmed Altameem, M. Shaheer Akhtar, Torki Altameem, H. Fouad, Sukumaran Anil, Young Soon Kim, Ahmed E. Youssef Abstract: International Journal of Humanoid Robotics, Ahead of Print. Human–computer interaction (HCI) is deployed in various real-time applications, including healthcare, for automated patient response. In such applications, robot-assisted interactive scenarios are modeled to handle patient queries and provide precise information. Timely query sensing and accurate data analysis are required to achieve accurate patient responses. In this study, responsive policy decision (RPD) using manifold mediator learning (MML) is introduced to improve data detection accuracy and accuracy in robot-assisted HCI applications. The initial decision-making process in data analytics is based on interaction stages and medical data detection. After identifying the most appropriate policy, respondents are provided with time-based responses based on the patient’s queries. When it comes to improving the accuracy of data analysis decisions, machine learning uses policies based on interaction stages and previous state efficiency of HCI responses. The experimental analysis proves the reliability of the proposed method by improving the accuracy of data analysis and reducing its complexity and response time for the varying queries and time intervals. Citation: International Journal of Humanoid Robotics PubDate: 2022-05-18T07:00:00Z DOI: 10.1142/S0219843622400072
Please help us test our new pre-print finding feature by giving the pre-print link a rating. A 5 star rating indicates the linked pre-print has the exact same content as the published article.
Authors:Zhixian Qin, Zhao Dan Xu, Quan Cai Sun, Parthasarathy Poovendran, P. Balamurugan Abstract: International Journal of Humanoid Robotics, Ahead of Print. Substation equipment inspection is essential for the power industry. The expansion of the smart grid scale improves the transmission capacity and enhances the likelihood of power plant facilities failure. To ensure the safety of the electric power supply, it is essential to inspect substation equipment. Metal commercial equipment can be traversed by remote inspection robots equipped with magnetic wheels. It is possible to use robots like this to examine equipment and pipelines remotely. In many cases, these gadgets are able to scale vertical surfaces and even traverse obstacles with a variety of shapes. Finally, researchers in the field of robotics have indicated that challenges such as restricted onboard battery capacity, undependable line fault detection, electrical insulation, power mechanism, and advanced control techniques for outer wind disruption are highly promising research areas. To build an unmanned, intelligent, and succeeded substation, the substation progressively implements inspection robots instead of physical exertion. Hence, in this paper, the mobile-based Intelligent Tracking Framework (MITF) has been proposed using inspection robots. This inspection robot is autonomous and can be used for various tracking tools: visual, infrared, and partial charge–discharge camera. The robot is integrated with a camera and thermal infrared imager sensors that have been collectively designated as workload. These inspection sensors are used to detect environmental parameters such as reading meters, evaluation thermoelectric temperature. The accurate localization of working loads and the inspection robot electromagnetic interference within substations have been resolved. This mobile robot delivers innovative monitoring and precise detection for the unmanned substation and smart substation. The suggested approach’s effectiveness is verified through experiment results based on the electrical equipment of the substation. The experimental outcome of the proposed method boosts the Meter Reading Analysis (94.19%), Transmission Capacity Analysis (98.5%), Workload Analysis (98.9%), Temperature Analysis (97.6%), and Safety Analysis (95.41%). Citation: International Journal of Humanoid Robotics PubDate: 2022-05-13T07:00:00Z DOI: 10.1142/S0219843622400035
Please help us test our new pre-print finding feature by giving the pre-print link a rating. A 5 star rating indicates the linked pre-print has the exact same content as the published article.
Authors:Xingyun Gong, Xiaojun Li Abstract: International Journal of Humanoid Robotics, Ahead of Print. Cognitive psychology is a science of human knowledge, which means that people perceive, acquire, memorize, think, and comprehend intellectual capabilities. The psychological strategy involves controlling every action and status of the human body. The problematic states of psychological facts include mental disorders like depression, stress, anxiety, and inferiority complex, leading to memory loss. The emerged technique of cognitive psychological managing framework using artificial intelligence (CPMF-AI) is introduced. The proposed framework is extended to forecast the psychological standards of the human brain for practical well-being. There are four methods to monitor memory power, stress, and other human mental disorders. They are distant neural systems (DNS), convolutional psychology tracking systems (CPTS), intelligent neural systems (INS), and memory-building strategies (MBS). Besides language aspects, physical aspects play a vital part in human–robot interaction (HRI) and make the difference compared to the more limited HRI communication. These methodologies are integrated into four case studies to detect neural passage systems for monitoring mental issues. The simulation analysis helps enhance the framework’s accuracy and minimize the error rate. Thus, the proposed system of cognitive technology is comparatively better than the existing methods. Citation: International Journal of Humanoid Robotics PubDate: 2022-04-28T07:00:00Z DOI: 10.1142/S0219843622400059
Please help us test our new pre-print finding feature by giving the pre-print link a rating. A 5 star rating indicates the linked pre-print has the exact same content as the published article.
Authors:Minghong She, Min Xiao, Yongchi Zhao Abstract: International Journal of Humanoid Robotics, Ahead of Print. The new area of education has progressively become where virtual reality interacts and integrates. The influence of credit management in the learning process is improved by digital twin technology. The next study paradigms of learning space will be the reconstruction of smart learning spaces based on digital twins. Hence, this paper, Digital Twin Approach (DTA), has been proposed to reconstruct the learning area and encourage the education revolution and a new learning community. In real-time manual interaction, the simulated twin will imitate diverse scenarios and create situations through AI decision-making and automatic twin implementation. The use of digital twin technology offers a new technology integration and development path to smart-university management in the investigation of the construction of smart university facilities. Thus, the experimental results show that the DTA technology can motivate students to study and boost learning when effectively utilized. Citation: International Journal of Humanoid Robotics PubDate: 2022-04-21T07:00:00Z DOI: 10.1142/S0219843622500050
Please help us test our new pre-print finding feature by giving the pre-print link a rating. A 5 star rating indicates the linked pre-print has the exact same content as the published article.
Authors:Bing Li Abstract: International Journal of Humanoid Robotics, Ahead of Print. Design of intelligent research systems is considered as one of the most prominent developments in multi-modal information domains in our day-to-day life. While significant growth in computer-aided English teaching methods (CAETMs) has made a progression over the past few years using techniques such as computational intelligence, biological computing aspects within the artificial intelligence domain. All the research in English teaching structures has been automated through online cloud-based applications and progressing at a rapid rate. But there are still a number of subjects that need to be explored in terms of its design, implementation, deployment of intelligent methods, and multi-agent systems in a real-world environment. However establishing teaching research subjects with novel techniques and methodologies utilized in computer vision in support of deep learning, semantic models in healthcare, organizations, and education sector are in need of further research with innovative and creative ideas. This paper will provide the emerging CAETM that solved the global needs of people in the educational domain. We will also discuss the improvements that need to be done in English teaching methods with digital computing solutions. Citation: International Journal of Humanoid Robotics PubDate: 2022-04-13T07:00:00Z DOI: 10.1142/S0219843622400047
Hybrid journal (It can contain Open Access articles)
[121 journals]
