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IEEE/SICE International Symposium on System Integration

作者： Linghao Ye Daisuke Chugo Satoshi Muramatsu Sho Yokota Hiroshi Hashimoto Graduate School of Science and Technology Kwansei Gakuin University Sanda Japan School of Information Science and Technology Tokai University Hiratsuka Japan Faculty of Science and Engineering Toyo University Kawagoe Saitama Japan Master Program of Innovation for Design and Engineering Advanced Institute of Industrial Technology Shinagawa Tokyo Japan

ISBN: (数字)9798331531614

ISBN: (纸本)9798331531621

The objective of this paper is to develop a robot system that can transmit the video taken by the drone to the computer in real time, use YOLO to recognize the image of pedestrians, and enable the ground robot to plan the path to avoid pedestrians. External sensors, such as laser range finders (LRF) and CCD cameras, are often used on mobile robots. As we all know, these sensors are susceptible to occlusion. Therefore, even if the mobile robot avoids the pedestrian in front of it, it is likely to collide with other pedestrians that cannot be detected due to occlusion. In this study, we used a drone to shoot videos at high altitudes, transferred the videos to a computer, used YOLO on the computer to perform image recognition of pedestrians around the robot, and passed the recognition results to ROS, which enabled it to plan a path to avoid pedestrians around it. As a result, showed that the videos shot at high altitudes could effectively identify pedestrians around the robot after being recognized by YOLO.

关键词： YOLO Pedestrians Image recognition Robot kinematics System integration Real-time systems Sensors Mobile robots Collision avoidance Drones

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Analyzing the Imitation Behaviors of Gorilla Chest-Beating Based on Human Pose Estimation

Analyzing the Imitation Behaviors of Gorilla Chest-Beating B...

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Cyber science and Technology Congress (CyberSciTech), IEEE

作者： Yu Qi Siyu Xiong Chongyang Zhang Bo Wu Graduate School of Bionics Computer and Media Sciences Tokyo University of Technology Tokyo Japan Department of Information Engineering Hebei Institute of Mechanical and Electrical Technology Xingtai China School of Computer Science Tokyo University of Technology Tokyo Japan

ISBN: (数字)9798331532093

ISBN: (纸本)9798331532109

In animal exercise classes course for acting majors, students enhance their physical flexibility and expressiveness by imitating the behaviors and actions of various animals. The gorilla's chest-beating action is a common exercise in this course. The accuracy of students' body postures when imitating animals often depends on the instructor's experience and personal preferences, leading to students with different levels and physical characteristics not receiving targeted guidance and practice. Human pose estimation technology is already quite mature, and we hope to apply it to animal exercise courses to analyze students' body movements when imitating animals, such as chest-beating. Therefore, this paper utilizes Mediapipe technology to detect the elbow joint angles of students imitating the gorilla's chest-beating action, a two-way analysis of variance (ANOVA) was employed to examine the effects of experience and gender on joint angle variations. Additionally, the results of the U-test analyzing the differences between the gorilla and the participants confirmed that male students' elbow joint angles are closer to those of the gorilla than those of female students when mimicking the gorilla's chest-beating action. This has significant implications for improving the precision and personalization of performance training, and it also provides valuable insights for further research and practice in related fields.

关键词： Training Accuracy Animals Pose estimation Standardization Behavioral sciences Elbow Analysis of variance

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A Design and Implementation of Water Heater Model Plant with Temperature Estimation Model for IOT Studies 7

A Design and Implementation of Water Heater Model Plant with...

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7th International Conference on Vocational Education and Electrical engineering, ICVEE 2024

作者： Pradhana, Anak Agung Surya Funabiki, Nobuo Kotama, I Nyoman Darma Desnanjaya, I Gusti Made Ngurah Radhitya, Made Leo Mahajaya, Nyoman Sarasuartha Okayama University Graduate School of Environmental Life Natural Science and Technology Okayama Japan Okayama University Department of Electrical and Communication Engineering Okayama Japan Institute of Business and Technology Indonesia Department of Computer System Engineering Denpasar Indonesia Institute of Business and Technology Indonesia Department of Informatic Technology Denpasar Indonesia Institut Teknologi Dan Bisnis Stikom Departement of Magister Information Systems Denpasar Indonesia

ISBN: (纸本)9798331505103

This study presents the development of an Internet of Things (IoT) system for a water heater model, focusing on enhancing reliability during sensor malfunctions that could disrupt operations. Using the SEMAR IoT platform, the system efficiently collects and analyzes data, while the integration of the Holt-Winter algorithm ensures accurate temperature estimates, even with unreliable or missing sensor data. The algorithm successfully auto-calibrates sensors, predicts temperature at specific time intervals based on real sensor behavior, and fills in missing data during sensor failures. Tests show strong correlations between estimated and actual temperatures, allowing uninterrupted operation. This solution offers a flexible, cost-effective alternative to traditional redundancy, ensuring system reliability during sensor failures. Future research will explore alternative predictive models and conduct real-world testing to validate the system's resilience under complex conditions. © 2024 IEEE.

关键词： Temperature measurement

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MERNet: Multimodal Emotion Recognition in Conversation Network via Cross-Modal Knowledge Distillation and Contrastive Learning

MERNet: Multimodal Emotion Recognition in Conversation Netwo...

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2025 IEEE International Conference on Big Data and Smart Computing, BigComp 2025

作者： Hwang, Sooyeon Gweon, Gahgene Kwon, Eunseong Seoul National University Interdisciplinary Program in Artificial Intelligence Korea Republic of Seoul National University Graduate School of Convergence Science and Technology Korea Republic of POSTECH Dept. of Computer Science and Engineering Korea Republic of

Multimodal Emotion Recognition in Conversation (ERC) is a task of predicting the emotion of each utterance in a conversation by utilizing both verbal and non-verbal modalities. However, existing approaches often struggle to bridge cross-modal gaps, resulting in misaligned features and frequent misclassification of minority emotions into semantically similar majority emotions. To address these challenges, we propose MERNet, a framework that employs cross-modal knowledge distillation and contrastive learning to align multimodal features and effectively distinguish subtle emotions in conversations. Our framework consists of two stages: 1) guiding non-verbal modalities with the text modality to transfer knowledge and align their features, and 2) applying contrastive learning with emotion labels as anchors to distinguish subtle differences between similar emotions and address the class imbalance problem. Experiments conducted on two benchmark datasets, IEMOCAP and MELD, demonstrate that our MERNet outperforms existing state-of-the-art models. © 2025 IEEE.

关键词： Contrastive Learning

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MERNet: Multimodal Emotion Recognition in Conversation Network via Cross-Modal Knowledge Distillation and Contrastive Learning

MERNet: Multimodal Emotion Recognition in Conversation Netwo...

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International Conference on Big Data and Smart Computing (BIGCOMP)

作者： Sooyeon Hwang Gahgene Gweon Eunseong Kwon Interdisciplinary Program in Artificial Intelligence Seoul National University South Korea Graduate School of Convergence Science and Technology Seoul National University South Korea Dept. of Computer Science and Engineering POSTECH South Korea

ISBN: (数字)9798331529024

ISBN: (纸本)9798331529031

关键词： Knowledge engineering Bridges Emotion recognition Visualization Computational modeling Contrastive learning Benchmark testing Linguistics Big Data

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Harmonious Music-driven Group Choreography with Trajectory-Controllable Diffusion 39

Harmonious Music-driven Group Choreography with Trajectory-C...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Dai, Yuqin Zhu, Wanlu Li, Ronghui Ren, Zeping Zhou, Xiangzheng Ying, Jixuan Li, Jun Yang, Jian PCA Lab Key Lab of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China Shenzhen International Graduate School Tsinghua University Shenzhen China

ISBN: (纸本)157735897X

Creating group choreography from music is crucial in cultural entertainment and virtual reality, with a focus on generating harmonious movements. Despite growing interest, recent approaches often struggle with two major challenges: multi-dancer collisions and single-dancer foot sliding. To address these challenges, we propose a Trajectory-Controllable Diffusion (TCDiff) framework, which leverages non-overlapping trajectories to ensure coherent and aesthetically pleasing dance movements. To mitigate collisions, we introduce a Dance-Trajectory Navigator that generates collision-free trajectories for multiple dancers, utilizing a distance-consistency loss to maintain optimal spacing. Furthermore, to reduce foot sliding, we present a footwork adaptor that adjusts trajectory displacement between frames, supported by a relative forward-kinematic loss to further reinforce the correlation between movements and trajectories. Experiments demonstrate our method’s superiority. © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

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Development of a digital twin environment for smart collision avoidance algorithms for mobile robots using reinforcement learning

Development of a digital twin environment for smart collisio...

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SICE Annual Conference

作者： Natsumi Matsumoto Kazuyuki Kobayashi Tomoyuki Ohkubo Kaiqiao Tian Nashwan J Sebi Ka C Cheok Cai Changqing Graduate School of Science and Engineering Hosei University Tokyo Japan Faculty of Advanced Engineering Nippon Institute of Technology Saitama Japan Electrical & Computer Engineering Department School of Engineering & Computer Science Oakland University Rochester USA School of Electrical Engineering and Information Technology Changchun Institute of Technology China

This paper describes a study on building a digital twin environment using point cloud data for mobile robots. A digital twin environment for mobile robots is a method to reproduce almost the same environment in a simulation space as if it were a twin, using data from an actual natural environment. In fact, in the development of mobile robots, experiments on public urban area are conducted, however, they are incredibly time-consuming and labor-intensive, so it would be helpful if a realistic simulation environment could be created. This study focuses on building a digital twin environment using point-cloud-data for mobile robots. In order to verify the effectiveness of this approach, we constructed the environment using data from a real mobile robot that has been driven. The navigation of the mobile robot was performed in the constructed simulation environment, and it was possible to realize a navigation run in the driving environment that was almost the same as the run of the real mobile robot.

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Joint Frame Structure and Beamforming Design for Integrated Localization and Communication Network

Joint Frame Structure and Beamforming Design for Integrated ...

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IEEE International Conference on Communications in China (ICCC)

作者： Yexin Shi Wei Qi Rui Yin Wenjin Yu Jie Qian Zhihui Ye Celimuge Wu College of Information Science & Electronic Engineering Zhejiang University China School of Information & Electronic Engineering Hangzhou City University China College of Computer Science and Technology Zhejiang University Hangzhou China Graduate School of Informatics and Engineering The University of Electro-Communications Japan

This paper investigates a joint localization and communication system. We integrate the dual function in the same devices to reduce cost, and jointly optimize the frame structure and beamforming schemes for uplink and downlink to improve localization accuracy and transmission rate. First, the impact of number of pilots and beamforming on localization accuracy and transmission rates is analyzed. Accordingly, we formulate an optimization problem with the goal of minimizing the Cramér-Rao lower bound (CRLB) while satisfying the transmission requirements. To solve this problem, a joint optimization scheme for frame structure and beamformer is designed based on the alternating optimization (AO) algorithm. Finally, numerical simulation results show that compared to traditional methods, the proposed schemes improve the localization accuracy and ensures the transmission rate for users.

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Density Based Spatial Clustering of Applications with Noise and Sentence Bert Embedding for Indonesian Utterance Clustering

Density Based Spatial Clustering of Applications with Noise ...

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computer science, information Technology and engineering (ICCoSITE), International Conference on

作者： Muhammad Fikri Hasani Yaya Heryadi Yulyani Arifin Lukas Wayan Suparta Computer Science Department BINUS Graduate Program Computer Science School of Computer Science Bina Nusantara University Jakarta Indonesia Computer Science Department BINUS Graduate Program Computer Science Bina Nusantara University Jakarta Indonesia Cognitive Engineering Research Group (CERG) Universitas Katolik Indonesia Atma Jaya Jakarta Indonesia Department of Electrical Engineering Faculty of Industrial Technology Institut Teknologi Nasional Yogyakarta Yogyakarta Indonesia

Task oriented chatbots are a sub-topic related to chatbots, where chatbots will perform certain tasks with specific goals. One part of creating a task-oriented chatbot is doing intent classification. Intent classification is a task of text classification. As in general text classification, the required dataset requires a label to carry out the classification process. To speed up and help the utterance analysis process, there is already a method, namely clustering, and Density-based clustering is a part of clustering that can determine cluster patterns based on arbitrary data, with DBScan as one of its algorithms. This research used 10000 client utterance data of awhatsapp based e-commerce conversation. SentenceBert also used as a state of art sentence embedding. This research yield silhouette score of 0.327 as the best result from eps of 0.1 and MinPts of 95. However, based on the cluster result, sentences labelled as noise can be further clustered. Text Preprocessing, text augmentation and sentence embedding techniques can be explored to increase the cluster performance.

关键词： computer science Art Text categorization Clustering algorithms Oral communication Chatbots Classification algorithms

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Optimal adaptive neuro-fuzzy inference system with biogeography-based optimization for numbers of COVID-19 vaccination prediction

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Journal of Intelligent and Fuzzy Systems 2023年第3期45卷 4639-4650页

作者： Hsu, Pi-Shan Huang, Chien-Chung Sung, Wei-Ying Tsai, Han-Ying Wu, Zih-Xin Lin, Ting-Yu Lin, Kuo-Ping Liu, Gia-Shie Department of Family Medicine Taichung Veterans General Hospital Taichung City Taiwan Graduate Institute of Microbiology and Public Health College of Veterinary Medicine National Chung-Hsing University Taichung Taiwan Computer Communication Center Taichung Veterans General Hospital Taiwan Department of Industrial Engineering and Enterprise Information Tunghai University Taichung City Taiwan School of Accounting College of Business University of Economics Ho Chi Minh City Viet Nam Department of Information Management Lunghwa University of Science and Technology Taoyuan Taiwan

This study attempts to develop the adaptive neuro-fuzzy inference system (ANFIS) with biogeography-based optimization (BBO) (ANFIS-BBO) for a case study of the actual number of COVID-19 vaccinations in a medical center, considering the variables of the date and time of vaccination, the brand of vaccine, and the number of open appointments on the government network platform in Taiwan. The COVID-19 has brought about a great burden on the health and economy of the world since the end of 2019. Many scholars have proposed a prediction model for the number of confirmed cases and deaths. However, there is still a lack of research in the prediction model for mass vaccination. In this study, ANFIS-BBO is developed to predict the number of COVID-19 vaccination, and three other forecasting models, support vector machines (SVM), least-square support vector machines (LSSVM) and general regression neural network (GRNN) are employed for forecasting the same data sets. Empirical results show that the ANFIS-BBO with trapezoidal membership function model can achieve better performance than other methods and provide robust predictions for the actual number of COVID-19 mass vaccination. © 2023 - IOS Press. All rights reserved.

关键词： COVID-19

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