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New Family of Voltage-Source Converters Derived Using New Basic Cell for Microgrid Applications 3

New Family of Voltage-Source Converters Derived Using New Ba...

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3rd international conference on High Voltage Engineering and Power Systems, ICHVEPS 2021

作者： Dahono, Pekik Argo Dahono, Andriazis Institut Teknology Bandung School of Electrical Engineering and Informatics Bandung Indonesia

ISBN: (纸本)9781665443548

A new family of voltage-source converters for microgrid applications is proposed in this paper. The proposed voltage-source converters are constructed by using a new basic cell that is called modified Cuk dc-dc power converter. By using the proposed basic cell, the obtained voltage-source converters have very small input and output ripples with efficiency higher than the conventional ones. The concept is then extended to modular multilevel voltage-source converters. The obtained multilevel converters have very small input and output ripples with self-voltage balancing capability. Conventional control methods that have been developed for conventional voltage-source converters can be applied directly to the proposed voltage-source converters. Several simulated and experimental results are included here to show the basic performance of the proposed converters. © 2021 IEEE.

关键词： Cells

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A depthwise separable convolution based 6D pose estimation network by efficient 2D-3D feature fusion

A depthwise separable convolution based 6D pose estimation n...

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2021 IEEE international conference on Real-Time Computing and robotics, RCAR 2021

作者： Feng, Qi Gu, Chaochen Qin, Jiani Xu, Rui Shanghai Jiao Tong University Department of Automation Shanghai200240 China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai200240 China

ISBN: (纸本)9781665436786

Precise 6D pose estimation of the target object is an essential prerequisite for robots to understand the real world. Previous 6D pose estimation methods based on 3D data usually have problems such as long model training time, imperfect feature extraction, redundant network model parameters, and complicated follow-up processing steps. This paper proposes a 2D-3D feature fusion module that could enhance feature extraction for the 6D pose estimation network. Furthermore, we compress the size of model parameters by adopting depthwise separable convolution to accelerate training speed and to reduce memory consumption. The experiment results on LineMOD dataset show the effectiveness of our method. Our method achieves on par or better performance than the state-of-art methods for 6D pose estimation and reduces model training time and the number of model parameters simultaneously. © 2021 IEEE.

关键词： Convolution

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Gait Phase Detection and Prediction with Machine Learning Models Based on sEMG

Gait Phase Detection and Prediction with Machine Learning Mo...

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Artificial Intelligence, Human-Computer Interaction and robotics (AIHCIR), international conference on

作者： Youping Sun Jiachen Wang Zixian Wang Jiasen Li Yibin Li Rui Song Huanghe Zhang The Center for Robotics School of Control Science and Engineering Shandong University Jinan Shandong China

ISBN: (数字)9798331534035

ISBN: (纸本)9798331534042

In this study, we utilized surface electromyog-raphy (sEMG) to detect and predict five key gait phases: heel strike(HS), maximum stance flexion(MSF), maximum stance extension(MSE), toe-off(TO), and maximum swing flex-ion(MSF). These gait phases provide an in-depth understanding of gait dynamics. Data preprocessing employed a sliding window technique to process the sEMG signals. We demonstrate that artificial neural networks (ANN), support vector machines (SVM), and k-nearest neighbors (KNN) can effectively detect and predict gait phases from sEMG signals. The performance of these three fundamental machine learning models was evaluated using hold-out validation, 10-fold cross-validation(10-Fold), and leave-one-subject-out cross-validation(LOOCV). The 2-phase detection achieved an accu-racy of 0.9879 ± 0.015, while the 5-phase detection yielded 0.9681 ± 0.0040. In prediction, the 2-phase achieved 0.974 ± 0.013, and the 5-phase reached 0.978 ± 0.016. Our results confirm the effectiveness of machine learning models in de-tecting and predicting gait phases using sEMG. The findings suggest that the proposed method has potential for personalized gait rehabilitation and fall prevention, offering a promising direction for clinical applications.

关键词： Support vector machines Phase measurement Prevention and mitigation Machine learning Artificial neural networks Predictive models Nearest neighbor methods Phase detection Surface treatment Robots

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ToF 3D vision detection and localization of soft packaging bags based on deep learning

ToF 3D vision detection and localization of soft packaging b...

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2021 IEEE international conference on Real-Time Computing and robotics, RCAR 2021

作者： Shen, Chengyang Chen, Weidong Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Department of Automation

ISBN: (纸本)9781665436786

The detection of soft packaging bags is a key step in the process of soft packaging bags unpacking. Time-of-flight(ToF) camera is used as vision sensor and a multi-scale detection method based on deep learning is proposed to solve the deformation and shielding problems of soft packaging bags. This method is improved on the basis of YOLOv3. Aiming at the texture disorder and shape change caused by the deformation of the soft packaging bags, the deformation convolution is used to replace the standard convolution for feature extraction. In view of the slow detection speed of YOLOv3, the inverted residual module based on depth separable convolution is used to replace the residual module. Aiming at the problem of non-detection caused by the occlusion between soft packaging bags, the loss function in YOLOv3 is improved and the size of anchor box is adjusted. The test is carried out when there were different degrees of shielding between the soft packaging bags and different degrees of deformation of the soft packaging bags. The experimental results show that the detection speed of this method reaches 0.8s/frame, the recall rate reaches 99%, and the relative positioning accuracy reaches 2 cm. © 2021 IEEE.

关键词： Convolution

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Optimized HMI Strategies for Collaborative Driving Interfaces in L3+ Autonomous Vehicles

Optimized HMI Strategies for Collaborative Driving Interface...

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robotics, Artificial Intelligence and Intelligent control (RAIIC), international conference on

作者： Banben He Yongliang Zhang Lin Li Xiaocui Wang Jingqiang Jin Tian Lan Research& Development Institute Dongfeng Motor Group Wuhan China

ISBN: (数字)9798350352627

ISBN: (纸本)9798350352634

Amidst the pivotal transition of autonomous vehicles from Level 2 to Level 3, a fundamental paradigm shift occurs, moving from a shared human-machine control framework to a complete transfer of driving responsibilities. In this evolving context, the automated system assumes a primary role in executing driving tasks, while the driver transitions to a supervisory role, overseeing system behavior and intervening only when deemed necessary. This transition necessitates addressing complex challenges encompassing safety, liability, workload distribution, alongside fostering human-machine trust and enhancing system interpretability. To address these multifaceted challenges, we introduce a comprehensive human-machine shared driving interaction design strategy, referred to as the TSE design method. This strategy encapsulates the entire driving journey, commencing with pre-journey training and preparation, encompassing safe driving practices during the journey, and culminating in post-journey performance evaluation. Our primary emphasis lies in the intricate analysis of human-machine safety interaction strategies during the actual driving phase. Based on the current system status and road conditions, the automated system dynamically apportions driving tasks to the driver, considering factors such as the driver’s workload, engagement level, and the complexity of take-over tasks. Furthermore, we delve into the intersectional impact of various factors, including the driver’s mental model, attention allocation, situational awareness, human-machine trust, system transparency, and interpretive interfaces, on the overall performance of the human-machine shared driving system. To demonstrate the practical application of our design principles, we present concrete design exemplars derived from driver training sessions and take-over interaction design. These instances illustrate the effectiveness of our TSE design method in enhancing the safety, trust, and interpretability of the human-

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Optimizing Logistics in E-Commerce Using Deep Random Forest for Enhanced User Satisfaction 3

Optimizing Logistics in E-Commerce Using Deep Random Forest ...

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3rd IEEE international conference on automation and Computation, AUTOCOM 2025

作者： Nisha, G.Anne Sreetha, P. Seetha, A. Manickam, K. Jagannathan, Sharath Kumar Sekar, S.D. Kyoto Computer Gakuin Department of Informatics Kyoto Japan Kpr Institute of Engineering and Technology Department of Biomedical Engineering Tamil Nadu India S. A. Engineering College Department of Information Technology Thiruverkadu Chennai India Sona College of Technology Department of Mathematics Tamil Nadu India Data Science Institute Saint Peter's University NJ United States R.M.K.Engineering College Department of Mechanical Engineering TamilNadu Chennai India

ISBN: (纸本)9798331542375

Operational efficiency is one of the most important factors affecting customer satisfaction directly in e-commerce. Still, constant addressing of timely delivery, path simplification, and reduction of delays remains challenging problems. Deep Random Forest (DRF) is a new approach presented in this work to examine and forecast significant logistics metrics including delivery times and satisfaction scores. Development of this approach targeted to solve these issues. The model makes use of e-commerce past performance including order volumes, customer comments, and delivery times. Findings: The suggested approach achieves 10% increase in user satisfaction, 12% cost reduction, and 15% faster delivery times than baseline methods. This is accomplished by means of DRF, applied to examine trends and carry out predictive modifications. Results and Methodology: These results outperform those of the benchmarks established by Gradient Boosting and XGBoost, which displayed respective rises in satisfaction of 9% and 8%. Apart from improving the quality of service, the use of DRF not only strengthened user loyalty but also contributed to improve the transportation efficiency. This approach exposes the transforming opportunities of artificial intelligence in logistics management for e-commerce systems. This approach guarantees operations with a customer-centric concentration benefiting from measurable advantages. © 2025 IEEE.

关键词： Marketplaces

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Handling of Computationally Demanding Digital controllers in Power System Dynamic Simulations

Handling of Computationally Demanding Digital Controllers in...

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international conference on Energy Transition in the Mediterranean Area (SyNERGY MED)

作者： Mehran Jafari Gautier Bureau Marco Chiaramello Adrien Guironnet Patrick Panciatici Petros Aristidou Dept. of Electrical Eng. Computer Eng. & Informatics Cyprus University of Technology Limassol Cyprus Réseau de Transport d’ Électricité (RTE) France

ISBN: (数字)9798350375923

ISBN: (纸本)9798350375930

Nowadays Smart grids utilize digital controllers to guarantee the reliable and efficient operation of their components. However, the numerical simulation of these types of controllers is challenging due to the numerous discrete time events introduced. The interpolation-based method (IBM) can effectively simulate smart digital controllers alongside the differential-algebraic equations of the system under control. Compared to the traditional step-reduction method (SRM) that reduces the time step for each time event during the simulation, IBM allows the use of variable time steps and solves the system while ensuring simulation accuracy. However, in the case of computationally demanding controllers, the computational performance of IBM suffers significantly. In this paper, a modified version of IBM is introduced, which can handle computationally demanding controllers in dynamic simulation of power systems. The performance of the proposed method is assessed and compared to SRM and IBM by simulating a test system with different controllers.

关键词： Accuracy Computational modeling Power system dynamics control systems Numerical simulation Mathematical models Smart grids Power system reliability Reliability

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AI Micro Motion Sensors for Screening Sarcopenia-prone Elderly Subjects

AI Micro Motion Sensors for Screening Sarcopenia-prone Elder...

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international conference on Micro/Nano Sensors for AI, Healthcare, and robotics (NSENS)

作者： Keer Wang Hongyu Zhang Clio Yuen Man Cheng Meng Chen King Wai Chiu Lai Calvin Kalun Or Yong Hu Arul Lenus Roy Vellaisamy Cindy Lo Kuen Lam Ning Xi Vivian Weiqun Lou Wen Jung Li Department of Mechanical Engineering City University of Hong Kong China Department of Social Work and Social Administration Sau Po Centre on Ageing The University of Hong Kong China Department of Biomedical Engineering Centre for Robotics and Automation City University of Hong Kong China Department of Industrial and Manufacturing System Engineering The University of Hong Kong China Department of Orthpedics & Traumatology Li Ka-Shing Faculty of Medicine The University of Hong Kong China Department of Science and Engineering City University of Hong Kong China Department of Family Medicine and Primary Care Li Ka-Shing Faculty of Medicine The University of Hong Kong China

ISBN: (数字)9798350375213

ISBN: (纸本)9798350375220

Sarcopenia, a condition characterized by age-related muscle mass and function decline, poses significant risks including falls, fractures, gait disorders, and even mortality. This study aimed to develop an AI motion sensor system utilizing micro inertial measurement units (μIMUs) to screen sarcopeniaprone elderly subjects. Subjects within the age range of 65–84 years performed single sit-to-stand and 5-time chair stand while wearing two μIMUs. K-Nearest-Neighbours (KNN) algorithms were employed to collect and analyze motion data from the tests. The 53 subjects were categorized as either healthy or sarcopeniaprone, with the sarcopenia-prone group further classified into three levels based on their condition severity. The highest classification accuracy achieved was 94.64% for distinguishing between healthy and sarcopenia-prone subjects, and 90.44% for differentiating various sarcopenia-prone risk levels. This AI motion sensor system demonstrates potential as a cost-effective and accessible approach for large-scale sarcopenia screening. Further refinement of this method could enable remote health monitoring and telerehabilitation programs catering to older adults.

关键词： Legged locomotion Measurement units Medical services Muscles Robot sensing systems Motion detection Sensors

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Tooth segmentation of dental mesh based on improved region growing

Tooth segmentation of dental mesh based on improved region g...

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2021 IEEE international conference on Real-Time Computing and robotics, RCAR 2021

作者： Gu, Chenlan Zhao, Ning Wang, Hesheng Shanghai Jiao Tong University Department of Automation Shanghai200240 China Shanghai Ninth People's Hospital Shanghai Jiao Tong University School of Medicine Department of Orthodontics Shanghai200240 China Shanghai Jiao Tong University Institute of Medical Robotics Key Laboratory of System Control and Information Processing of Ministry of Education Department of Automation Shanghai200240 China

ISBN: (纸本)9781665436786

Computer-aided design (CAD) systems have gained more and more concern in orthodontics over the last decades. Aimed at tooth segmentation task of three dimension (3D) dental models in orthodontic CAD systems, we propose an efficient and robust framework based on geometric features. To make region growing algorithm flexible and apply it to 3D tooth segmentation, we extend the region similarity description from single-parameter to multi-parameter. A parameter adaptive method is designed to get rid of manual parameter adjustment and raise efficiency. Moreover, we provide a multi-level label optimization algorithm for segmentation refinement. Some experiments are conducted with various dental models from real cases to validate various algorithmic components. The results demonstrate that our pipeline is superior on efficiency and robustness. © 2021 IEEE.

关键词： Efficiency

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Mitigating the Impact of Packet Corruption Loss in rdMA Networks with Switch Assistance

Mitigating the Impact of Packet Corruption Loss in RDMA Netw...

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Cloud Computing, Big Data Applications and Software Engineering (CBASE), international conference on

作者： Anbang Wan School of Informatics Xiamen University Xiamen China

ISBN: (数字)9798331506582

ISBN: (纸本)9798331506599

As cloud computing increases its demand for high-performance networks, rdMA networks have become a mainstream technology to support high-speed data transmission. Traditionally, rdMA networks are assumed lossless by enabling Priority Flow control (PFC). However, packet loss due to packet corruption is still inevitable and common, which has a serious impact on the performance of rdMA networks. Therefore, this paper proposes Protego, a solution to mitigate the impact of packet corruption loss in rdMA networks. Protego leverages the switch's in-network vision and packet processing capabilities to detect packet corruption loss, and helps recovery to alleviate performance degradation. Protego considers different timeout situations caused by packet loss and provides fine-grained processing, and intercepts redundant packets in advance to save bandwidth. Protego is well compatible with the rdMA protocol, and our experiments show the effectiveness of Protego.

关键词： Degradation Cloud computing Protocols Flow production systems Packet loss Switches Bandwidth Big Data applications Data communication Software engineering

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