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IEEE International Conference on Industrial technology (ICIT)

作者： Sichao Zhang Wei Liang Yinlong Zhang Zhibo Pang State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang China Chinese Academy of Sciences Institutes for Robotics and Intelligent Manufacturing Shenyang China University of Chinese Academy of Sciences Beijing China Department of Automation Technology ABB Corporate Research Sweden Vasteras Sweden

ISBN: (纸本)9781728119496

Automatic Guided Vehicle (AGV) semantic attack is now becoming an important research topic in digital workshops. Unfortunately, the conventional attack detections could trigger the wrong attack detection alarms, because of the unexpected wireless radio frequency interference. This paper investigates a new method for AGV semantic attack detection based on Hidden Markov Model (HMM). Our method is able to discriminate the typical wireless retransmission in presence of environment interference or in the context of AGV semantic attack by comparing the hidden state transition probability with the predefined threshold. Our model has been evaluated on our developed platform. The extensive experimental results and analysis prove the effectiveness of our method.

关键词： Wireless communication Conferences Semantics Hidden Markov models Interference Markov processes Radiofrequency interference

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Tackling Instance-Dependent Label Noise via a Universal Probabilistic Model

arXiv

引用

arXiv 2021年

作者： Wang, Qizhou Han, Bo Liu, Tongliang Niu, Gang Yang, Jian Gong, Chen Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information of MoE School of Computer Science and Engineering Nanjing University of Science and Technology China Department of Computer Science Hong Kong Baptist University Hong Kong Trustworthy Machine Learning Lab School of Computer Science Faculty of Engineering The University of Sydney Australia Japan Department of Computing Hong Kong Polytechnic University Hong Kong

The drastic increase of data quantity often brings the severe decrease of data quality, such as incorrect label annotations, which poses a great challenge for robustly training Deep Neural Networks (DNNs). Existing learning methods with label noise either employ ad-hoc heuristics or restrict to specific noise assumptions. However, more general situations, such as instance-dependent label noise, have not been fully explored, as scarce studies focus on their label corruption process. By categorizing instances into confusing and unconfusing instances, this paper proposes a simple yet universal probabilistic model, which explicitly relates noisy labels to their instances. The resultant model can be realized by DNNs, where the training procedure is accomplished by employing an alternating optimization algorithm. Experiments on datasets with both synthetic and real-world label noise verify that the proposed method yields significant improvements on robustness over state-of-the-art counterparts. Copyright © 2021, The Authors. All rights reserved.

关键词： Deep neural networks

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Myoelectric Prosthetic Hand with Adjustable Tightening Socket for Varied Residual Limb Shapes*

Myoelectric Prosthetic Hand with Adjustable Tightening Socke...

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IEEE International Conference on Cyborg and Bionic systems (CBS)

作者： Yuki Inoue Yuki Kuroda Yoshiko Yabuki Yusuke Yamanoi Xu Yong Xiaobei Jing Hiroshi Yokoi Department of Mechanical and Intelligent Engineering The University of Electro-Communications Chofu Tokyo JAPAN OMRON SINIC X Corporation Bunkyo-ku Tokyo JAPAN department of Electrical Engineering Faculty of Engineering Tokyo University of Science Katushika-ku Tokyo JAPAN CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institute of Advanced Technology (SIAT) Chinese Academy of Sciences (CAS) and the SIAT Branch Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen CHINA Department of Mechanical and Intelligent Engineering Center for Neuroscience and Biomedical Engineering The University of Electro-Communications Chofu Tokyo JAPAN Mu-BORG K.K. Chofu JAPAN

ISBN: (数字)9798350388039

ISBN: (纸本)9798350388046

The prosthetic socket serves as an interface between the body and the machine. If the socket is not well-fitted to the residual limb, pain can occur. The shape of the residual limb changes over time as surgical scars stabilize or the patient’s body weight fluctuates. A socket with a cutout on the side can be tightened from the outside to adjust its circumference and accommodate these changes. However, when the circumference of the rigid socket is adjusted to fit, soft skin can become trapped due to friction between the skin and the socket. This phenomenon causes pain due to pinching. Consequently, this mechanism cannot be used for myoelectric prosthetic hands, where direct contact between the socket and the skin is necessary. In this study, we developed a socket with a variable circumference that suppresses skin pinching. We applied it to participants with different residual limb shapes. A Box and Block Test (BBT) was performed using two types of sockets. The first used a conventional socket designed to fit their residual limb shape. The second used the proposed socket, which was designed to fit the residual limb’s shape of one out of two participants and adapted to the residual limb’s changing shape. The results of the BBT showed not much difference in the number of blocks moved between the conventional socket and the proposed socket. Therefore, the performance of the proposed socket was comparable to that of conventional sockets and could be applied to different residual limbs. In other words, the proposed socket was shown to adapt to changes in the residual limb without causing pain. Sockets with the proposed functionality could be used in a variety of bio-interfaces, allowing for flexible connections between the body and machines.

关键词： Limbs Shape Pain Sockets Human-machine systems Friction Surgery Skin Interviews Prosthetic hand

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$run$runData: Re-Distributing Data via Piggybacking for Geo-Distributed Data Analytics Over Edges

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IEEE Transactions on Parallel and Distributed systems 2021年第1期33卷 40-55页

作者： Yibo Jin Zhuzhong Qian Song Guo Sheng Zhang Lei Jiao Sanglu Lu Department of Computer Science and Technology State Key Laboratory for Novel Software Technology Nanjing University Nanjing Jiangsu China Department of Computing The Hong Kong Polytechnic University Hung Hom Hong Kong Hong Kong Polytechnic University Shenzhen Research Institute Shenzhen Guangdong China Department of Computer and Information Science University of Oregon Eugene OR USA

Efficiently analyzing geo-distributed datasets is emerging as a major demand in a cloud-edge system. Since the datasets are often generated in closer proximity to end users, traditional works mainly focus on offloading proper tasks from those hotspot edges to the datacenter to decrease the overall completion time of submitted jobs in a one-shot manner. However, optimizing the completion time of current job alone is insufficient in a long-term scope since some datasets would be used multiple times. Instead, optimizing the data distribution is much more efficient and could directly benefit forthcoming jobs, although it may postpone the execution of current one. Unfortunately, due to the throwaway feature of data fetcher, existing data analytics systems fail to re-distribute corresponding data out of hotspot edges after the execution of data analytics. In order to minimize the overall completion time for a sequence of jobs as well as to guarantee the performance of current one, we propose to re-distribute the data along with task offloading, and formulate corresponding ε-bounded data-driven task scheduling problem over wide area network under the consideration of edge heterogeneity. We design an online schema run Data, which offloads proper tasks and related data via piggybacking to the datacenter based on delicately calculated probabilities. Through rigorous theoretical analysis, run Data is proved concentrated on its optimum with high probability. We implement run Data based on Spark and HDFS. Both testbed results and trace-driven simulations show that run Data re-distributes proper data via piggybacking and achieves up to 37 percent reduction on average response time compared with state-of-the-art schemas.

关键词： Task analysis Data analysis Wide area networks Data models Servers Optimization Videos

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Overview of the Tenth Dialog System technology Challenge: DSTC10

引用

IEEE/ACM Transactions on Audio Speech and Language Processing 2024年 32卷 765-778页

作者： Yoshino, Koichiro Chen, Yun-Nung Crook, Paul Kottur, Satwik Li, Jinchao Hedayatnia, Behnam Moon, Seungwhan Fei, Zhengcong Li, Zekang Zhang, Jinchao Feng, Yang Zhou, Jie Kim, Seokhwan Liu, Yang Jin, Di Papangelis, Alexandros Gopalakrishnan, Karthik Hakkani-Tur, Dilek Damavandi, Babak Geramifard, Alborz Hori, Chiori Shah, Ankit Zhang, Chen Li, Haizhou Sedoc, Joao D'haro, Luis F. Banchs, Rafael Rudnicky, Alexander Guardian Robot Project R-IH RIKEN 2-2-2 Hikaridai Seika Shoraku619-0288 Japan Information Science Nara Institute of Science and Technology Ikoma630-0101 Japan Computer Science and Information Engineering National Taiwan University Taipei10617 Taiwan Inc. Palo AltoCA95054 United States Alexa AI *** Inc. SunnyvaleCA94089 United States Meta Seattle RedmondWA98052 United States Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China Tencent AI Lab Beijing Beijing China Kexueyuan South Road Zhongguancun Beijing100190 China Beijing 100190 China Alexa AI *** Inc. SunnyvaleCA United States 1120 Enterprise way Sunnyvale94089 United States *** Inc. SeattleWA United States Menlo Park CA United States Audio and Speech Group Mitsubishi Electric Research Laboratories CambridgeMA02139-1955 United States Carnegie Mellon University Department of Language and Information Technologies or just Carnegie Mellon University Pittsburgh United States National University of Singapore Singapore Singapore Department of Electrical and Computer Engineering National University of Singapore Singapore Singapore Shenzhen Research Institute of Big Data School of Data Science Chinese University of Hong Kong Shenzhen518172 China New York University New YorkNY United States ETSI de Telecomunicacion - Speech Technology and Machine Learning Group Universidad Politecnica de Madrid Ciudad Universitaria Madrid28040 Spain Nanyang Technological University Singapore Singapore Carnegie Mellon University PittsburghPA United States

This article introduces the Tenth Dialog System technology Challenge (DSTC-10). This edition of the DSTC focuses on applying end-to-end dialog technologies for five distinct tasks in dialog systems, namely 1. Incorporation of Meme images into open domain dialogs, 2. Knowledge-grounded Task-oriented Dialogue Modeling on Spoken Conversations, 3. Situated Interactive Multimodal dialogs, 4. Reasoning for Audio Visual Scene-Aware Dialog, and 5. Automatic Evaluation and Moderation of Open-domainDialogue systems. This article describes the task definition, provided datasets, baselines, and evaluation setup for each track. We also summarize the results of the submitted systems to highlight the general trends of the state-of-the-art technologies for the tasks. © 2023 The Authors.

关键词： Job analysis

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Improving diversity of neural text generation via inverse probability weighting

arXiv

引用

arXiv 2021年

作者： Zhang, Xinran Sun, Maosong Liu, Jiafeng Li, Xiaobing Department of Music Artificial Intelligence Music Information Technology Central Conservatory of Music Beijing China Department of Computer Science and Technology Tsinghua University Beijing China Institute for Artificial Intelligence Tsinghua University Beijing China State Key Lab on Intelligent Technology and Systems Tsinghua University Beijing China

The neural text generation suffers from the text degeneration issue such as repetition. Traditional stochastic sampling methods only focus on truncating the unreliable "tail" of the distribution, and do not address the "head" part, which we show might contain tedious or even repetitive candidates with high probability that lead to repetition loops. They also do not consider the issue that human text does not always favor high-probability words. Inspired by these, in this work we propose a heuristic sampling method. We propose to use interquartile range of the predicted distribution to determine the "head" part, then permutate and rescale the "head" with inverse probability. This aims at decreasing the probability for the tedious and possibly repetitive candidates with higher probability, and increasing the probability for the rational but more surprising candidates with lower probability. The proposed algorithm provides a reasonable permutation on the predicted distribution which enhances diversity without compromising rationality of the distribution. We use pre-trained language model to compare our algorithm with traditional methods. Results show that our algorithm can effectively increase the diversity of generated samples while achieving close resemblance to human text. Copyright © 2021, The Authors. All rights reserved.

关键词： Heuristic methods

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Optimizing the fluidity in thin-section investment casting for vacuum induction molten AZ91D-1 %wt. CaO

引用

Journal of Magnesium and Alloys 2025年

作者： Carneiro, V.H. Lopes, V. Duarte, I. Puga, H. INESC TEC - Institute for Systems and Computer Engineering Technology and Science R. Dr. Roberto Frias Porto4200-465 Portugal Department of Engineering School of Science and Technology - Universidade de Trás-os-Montes e Alto Douro Quinta de Prados Vila Real5001-801 Portugal INL – International Iberian Nanotechnology Laboratory Avenida Mestre José Veiga Braga4715-330 Portugal CMEMS-UMinho Department of Mechanical Engineering University of Minho Campus de Azurém Guimarães4800-058 Portugal TEMA - Centre for Mechanical Technology and Automation Department of Mechanical Engineering University of Aveiro Campus Universitário de Santiago Aveiro3810-193 Portugal LASI - Intelligent Systems Associate Laboratory Campus Azurém Guimarães4800-058 Portugal

This paper discusses an experimental investigation into the fluidity of AZ91D-1 %wt. CaO magnesium melt via induction for thin-section investment casting. Plaster molds with thin spiral cavities (0.5 to 1.5 mm square sections) were designed and manufactured to assess the impact of casting conditions on filling length, as magnesium alloys cause severe melting and melt-mold exothermic reactions, making investment casting challenging. Combinations of traditional Mg-mold reaction mitigation techniques, such as applying a protective mold coating (Yttria) and vacuum, were examined to determine their role in the filling process. The results suggest that when induction is employed to melt reactive alloys, these methods are not always beneficial, as initially thought. Particularly at higher melt temperatures, the combination of Yttria-coated molds with low-pressure vacuum induction significantly reduce fluidity: vacuum induced melt levitation which promotes oxidation with the residual atmosphere;and Yttria-coating cracking due to thermal stress during the mold fabrication slows filling and promotes significant melt-mold reaction. This study shows that best results to investment cast thin-sections are obtained by avoiding both vacuum and protective coatings, providing a viable route for the precision manufacturing of stent biomedical devices. © 2025 Chongqing University

关键词： Investment casting

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A Powered Prosthetic Hand with Vision System for Enhancing the Anthropopathic Grasp

arXiv

引用

arXiv 2024年

作者： Xu, Yansong Wang, Xiaohui Li, Junlin Zhang, Xiaoqian Li, Feng Gao, Qing Fu, Chenglong Leng, Yuquan The State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang110169 China University of Chinese Academy of Sciences Beijing100049 China School of Electronics and Communication Engineering Sun Yat-sen University Shenzhen518107 China Department of Mechanical and Energy Engineering Southern University of Science and Technology Shenzhen518055 China

The anthropomorphism of grasping process significantly benefits the experience and grasping efficiency of prosthetic hand wearers. Currently, prosthetic hands controlled by signals such as brain-computer interfaces (BCI) and electromyography (EMG) face difficulties in precisely recognizing the amputees’ grasping gestures and executing anthropomorphic grasp processes. Although prosthetic hands equipped with vision systems enables the objects’ feature recognition, they lack perception of human grasping intention. Therefore, this paper explores the estimation of grasping gestures solely through visual data to accomplish anthropopathic grasping control and the determination of grasping intention within a multi-object environment. To address this, we propose the Spatial Geometry-based Gesture Mapping (SG-GM) method, which constructs gesture functions based on the geometric features of the human hand grasping processes. It’s subsequently implemented on the prosthetic hand. Furthermore, we propose the Motion Trajectory Regression-based Grasping Intent Estimation (MTR-GIE) algorithm. This algorithm predicts pre-grasping object utilizing regression prediction and prior spatial segmentation estimation derived from the prosthetic hand’s position and trajectory. The experiments were conducted to grasp 8 common daily objects including cup, fork, etc. The experimental results presented a similarity coefficient R2 of grasping process of 0.911, a Root Mean Squared Error (RMSE) of 2.47°, a success rate of grasping of 95.43%, and an average duration of grasping process of 3.07±0.41 s. Furthermore, grasping experiments in a multi-object environment were conducted. The average accuracy of intent estimation reached 94.35%. Our methodologies offer a groundbreaking approach to enhance the prosthetic hand’s functionality and provides valuable insights for future research. Copyright © 2024, The Authors. All rights reserved.

关键词： Electromyography

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Learning-based Event-triggered Adaptive Optimal Output Regulation of Linear Discrete-time systems 10

Learning-based Event-triggered Adaptive Optimal Output Regul...

引用

10th IEEE Data Driven Control and Learning systems Conference, DDCLS 2021

作者： Zhao, Fuyu Gao, Weinan Liu, Tengfei Jiang, Zhong-Ping State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang110819 China College of Engineering and Science Florida Institute of Technology Department of Mechanical and Civil Engineering MelbourneFL32901 United States New York University Six MetroTech Center Department of Electrical and Computer Engineering BrooklynNY11201 United States

ISBN: (纸本)9781665424233

In this paper, a data-driven event-triggered output-feedback control approach is proposed to solve the problem of adaptive optimal output regulation for uncertain discrete-time linear systems when only the output information is available. A crucial strategy is to develop a novel co-design scheme for the event-triggering mechanism and the data-driven optimal controller. Theoretical analysis and an application to a LCL coupled inverter-based distributed generation system demonstrate the effectiveness of the proposed learning-based, event-triggered, adaptive optimal controller design with output-feedback. © 2021 IEEE.

关键词： Digital control systems

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Multi-level feature fusion model-based real-time person re-identification for forensics

Multi-level feature fusion model-based real-time person re-i...

引用

作者： Wang, Shiqin Xu, Xin Liu, Lei Tian, Jing School of Computer Science and Technology Wuhan University of Science and Technology Wuhan430065 China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan University of Science and Technology Wuhan430065 China Artificial Intelligence Institute Wuhan University of Science and Technology Wuhan430065 China Institute of Systems Science National University of Singapore Singapore119615 Singapore

Person forensics aims to retrieve the specified person across non-overlapping cameras. It is difficult owing to the appearance variations caused by occlusion, human pose change, background clutter, illumination variation, etc. In this scenario, current models face great challenges in extracting effective features. Recent deep learning models mainly focus on extracting representative deep features to cope with appearance variations, while handcrafted features are not fully explored. In this paper, a multi-level feature fusion model (MFFM) is designed to combine both deep features and handcrafted features in real time. MFFM is first utilized to describe person appearance. Then, local binary pattern (LBP) and histogram of oriented gradient (HOG) are extracted to cope with geometric change and illumination variance. Using LBP and HOG, 11.89% on the CUHK03, 15.30% on the Market-1501 and 8.25% on the VIPeR top-1 recognition accuracy improvement for the proposed method are achieved with only 9.66%, 4.90%, and 7.59% extra processing time. Experimental results indicate MFFM can achieve the best performance compared to the state-of-the-art models on the Market1501, CUHK03, and VIPeR datasets. © 2019, Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Local binary pattern

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