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IEEE Annual International Symposium Virtual Reality

作者： Yiwei Bao Jiaxi Wang Zhimin Wang Feng Lu State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang University Beijing China

Recent research based on hand-eye coordination has shown that gaze could improve object selection and translation experience under certain scenarios in AR. However, several limitations still exist. Specifically, we investigate whether gaze could help object selection with heavy 3D occlusions and help 3D object translation in the depth dimension. In addition, we also investigate the possibility of reducing the gaze calibration burden before use. Therefore, we develop new methods with proper gaze guidance for 3D interaction in AR, and also an implicit online calibration method. We conduct two user studies to evaluate different interaction methods and the results show that our methods not only improve the effectiveness of occluded objects selection but also alleviate the arm fatigue problem significantly in the depth translation task. We also evaluate the proposed implicit online calibration method and find its accuracy comparable to standard 9 points explicit calibration, which makes a step towards practical use in the real world.

关键词： Three-dimensional displays Design methodology Estimation User interfaces Fatigue Calibration Task analysis

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Improving Motor Imagery Classification in BCI systems Using EMD and Multi-Layer CNNs

Improving Motor Imagery Classification in BCI Systems Using ...

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International eConference on computer and Knowledge engineering (ICCKE)

作者： Reza Arghand Ali Chaibakhsh Moein Radman Intelligent Systems and Advanced Control Lab Faculty of Mechanical Engineering University of Guilan Rasht Guilan Iran Brain-Computer Interfacing and Neural Engineering Laboratory University of Essex School of Computer Science and Electronic Engineering Colchester United Kingdom

ISBN: (数字)9798331511272

ISBN: (纸本)9798331511289

Brain-computer Interface (BCI) systems are relatively new technologies that could play a significant role in aiding the recovery of impaired activities resulting from neuromuscular disabilities in affected individuals. Accurate recognition and classification of motor imagery in BCI systems present a challenge, leading to extensive research in recent years aimed at improving the accuracy of these systems. In this study, a combination of the Empirical Mode Decomposition (EMD) method and a multi-layer Convolutional Neural Network (CNN) is employed. Initially, the signal is decomposed into Intrinsic Mode Functions (IMFs) using EMD, and all IMFs across all trials are analyzed to select the best ones, which are then fed into the CNN as inputs. Additionally, the study incorporates the fusion of three CNN networks, each corresponding to a different IMF. The features extracted from these networks are combined and used to train an SVM classifier. The proposed method achieved an accuracy of 86.1% on the BCI-2a 2008 dataset, outperforming other state-of-the-art approaches.

关键词： Support vector machines Knowledge engineering Accuracy Image recognition Empirical mode decomposition Neuromuscular Motors Feature extraction Brain-computer interfaces Convolutional neural networks

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Adaptive Stiffness Control of Series Elastic Actuator Manipulators Based on Dynamic System

Adaptive Stiffness Control of Series Elastic Actuator Manipu...

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IEEE International Conference on Automation science and engineering (CASE)

作者： Kunlin Guo Chao Zeng Weiyong Si Ning Wang Chenguang Yang Key Laboratory of Autonomous Systems and Networked Control School of Automation Science and Engineering South China University of Technology Guangzhou China Department of Computer Science University of Liverpool Liverpool UK School of Computer Science and Electronic Engineering University of Essex Colchester Bristol Robotics Laboratory University of the West of England Bristol UK

ISBN: (数字)9798350358513

ISBN: (纸本)9798350358520

In traditional control methods, Series Elastic Actuator (SEA) joint manipulators are limited by their hardware and can only perform simple tasks with low stiffness. To address this issue, we propose a stiffness adjustment control strategy for SEA manipulators based on Dynamic systems (DS) with good generalization performance. By enhancing the generalization capability of DS in complex tasks and combining posture control, the SEA manipulators can autonomously adjust control gains and postures according to task requirements to achieve higher stiffness at the end-effector. Experimental results involving the activation of air switches with different stiffness levels and installation angles have demonstrated the effectiveness of our proposed method. The results indicate that compared to traditional methods of adjusting the control gain and retraining DS for similar tasks, our approach exhibits superior generalization performance while maintaining end-effector stiffness during interactions.

关键词： Actuators computer aided software engineering Automation Adaptive systems Control systems End effectors Hardware Dynamical systems Manipulator dynamics

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Sensitivity Decouple Learning for Image Compression Artifacts Reduction

arXiv

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arXiv 2024年

作者： Ma, Li Zhao, Yifan Peng, Peixi Tian, Yonghong Huawei Technologies Company Ltd Shenzhen518129 China State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang University Beijing100191 China School of Electronics and Computer Engineering School of Computer Science Peking University Beijing100871 China PengCheng Laboratory Shenzhen518055 China

With the benefit of deep learning techniques, recent researches have made significant progress in image compression artifacts reduction. Despite their improved performances, prevailing methods only focus on learning a mapping from the compressed image to the original one but ignore the intrinsic attributes of the given compressed images, which greatly harms the performance of downstream parsing tasks. Different from these methods, we propose to decouple the intrinsic attributes into two complementary features for artifacts reduction, i.e., the compression-insensitive features to regularize the high-level semantic representations during training and the compressionsensitive features to be aware of the compression degree. To achieve this, we first employ adversarial training to regularize the compressed and original encoded features for retaining high-level semantics, and we then develop the compression quality-aware feature encoder for compression-sensitive features. Based on these dual complementary features, we propose a Dual Awareness Guidance Network (DAGN) to utilize these awareness features as transformation guidance during the decoding phase. In our proposed DAGN, we develop a cross-feature fusion module to maintain the consistency of compression-insensitive features by fusing compression-insensitive features into the artifacts reduction baseline. Our method achieves an average 2.06 dB PSNR gains on BSD500, outperforming state-of-the-art methods, and only requires 29.7 ms to process one image on BSD500. Besides, the experimental results on LIVE1 and LIU4K also demonstrate the efficiency, effectiveness, and superiority of the proposed method in terms of quantitative metrics, visual quality, and downstream machine vision tasks. Copyright © 2024, The Authors. All rights reserved.

关键词： Deep learning

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Lag Consensus for Second-Order Nonlinear Multi-Agent systems on the Basis of Passivity 13

Lag Consensus for Second-Order Nonlinear Multi-Agent Systems...

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13th IEEE International Conference on CYBER Technology in Automation, Control, and Intelligent systems, CYBER 2023

作者： Liu, Ru-Yue Han, Xiao Wang, Jin-Liang School of Mathematical Sciences Tiangong University Tianjin300387 China School of Mechanical Engineering Tiangong University Tianjin300387 China School of Computer Science and Technology Tianjin Key Laboratory of Autonomous Intelligence Technology and Systems Tiangong University Tianjin300387 China

ISBN: (纸本)9798350315196

This paper investigates lag consensus of second-order nonlinear multi-agent systems (MASs) on the basis of passivity. on one hand, the passivity of single agent is addressed by utilizing the appropriate controller. On the other hand, a criterion is derived for assuring lag consensus of MASs with the help of the passivity of single agent. Finally, the designed strategies are verified through a numerical example. © 2023 IEEE.

关键词： Multi agent systems

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Design of Distributed Nonlinear Observers Without Using Global Inputs

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IEEE Transactions on Automatic Control 2025年

作者： Cao, Ganghui Wang, Jinzhi Polycarpou, Marios M. Peking University State Key Laboratory for Turbulence and Complex Systems Department of Mechanics and Engineering Science College of Engineering Beijing100871 China University of Cyprus KIOS Research and Innovation Center of Excellence and the Department of Electrical and Computer Engineering Nicosia1678 Cyprus

This paper investigates the design of distributed observers for a class of nonlinear systems. The designed distributed observers reside in a network of sensor nodes. The communication links in the network enable each sensor node to estimate all states of the nonlinear system, even though each one can only collect a small portion of the system's outputs. However, all inputs of the system are still needed at each node to carry out a correct state estimation. For this problem, the paper further proposes a distributed nonlinear observer without using global inputs, in which each sensor node only needs partial input information of the nonlinear system. © 1963-2012 IEEE.

关键词： Nonlinear simulations

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Semantic Heads Segmentation and Counting in Crowded Retail Environment with Convolutional Neural Networks Using Top View Depth Images

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SN computer science 2023年第1期4卷 61页

作者： Abed, Almustafa Akrout, Belhassen Amous, Ikram Department of Computer Science College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz University Alkharj 11942 Saudi Arabia Multimedia Information Systems and Advanced Computing Laboratory (MIRACL) Sfax University Sfax 3021 Tunisia ENET’COM University of Sfax National School of Electronics and Telecommunications of Sfax Road Tunis City El Ons Sfax 3018 Tunisia

In recent years, researchers have developed several techniques to accurately count the number of people in a crowded retail environment for human behavior analysis, ranging from vision and feature based approaches to machine-learning and deep learning approaches. Due to the availability and affordability of recent advanced technologies such as depth sensors and high computing powers as well as the availability of big data, the need for building accurate models to detect and count people in crowded environments arises. Deep learning approaches has proven to be highly accurate especially when there is enough data to train the model and high computation powers. People detection and counting is a challenging problem. due to several issues such as occlusion, light variations, complex backgrounds to name a few. To cope with these issues, we utilize a top-view configuration depth data to train our model. We propose a convolutional encoder decoder architecture consisting of a resnet50 encoder trained on the ImageNet dataset as a transfer learning technique and we built the decoder part of the model as a novel contribution, for segmenting and counting customers heads in a crowded retail stores where there are more than 6 individuals per square meter without compromising people privacy because the camera does not record people’s faces. The objective of our method is to segment and count people using the publicly available TV-Head Dataset and People Counting DataSet (PCDS). The results demonstrate that our model is robust and can be used for real time people counting with accurate results. © 2022, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： computer vision Convolutional neural networks Intelligent retail environment People counting Top-view configuration

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Multi-scale discriminative regions attention network for fine-grained vehicle classification

Journal of Network Intelligence

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Journal of Network Intelligence 2021年第2期6卷 164-177页

作者： Rong, Wen-Zhong Han, Jin Cai, Ying-Hao Liu, Gen College of Computer Science and Engineering Shandong University of Science and Technology Qingdao266590 China The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China

Fine-grained vehicle classification is a challenging task in computer vision due to the low intra-class variance. Some methods have been developed to improve the accuracy of fine-grained vehicle classification by improving the ability of discriminative features extraction, but there is still room for further improvement in the localization accuracy of vehicle discriminative regions. Based on deep convolutional neural networks, we focus on finding a more efficient structure that pays more attention to the discriminative image regions to enhance the ability of fine-grained vehicle classification. We propose a novel Multi-Scale Discriminative Regions Attention Network (MS-DRAN), which extracts feature maps through ResNet-50 backbone network and generates multi-scale feature maps by a Feature Pyramid Network (FPN). Then, MS-DRAN generates discriminative regions attention maps on the multi-scale feature maps. The attention maps from shallow layer perform pixel-level multiplication with the feature maps from deeper layer. By this way, the network gradually extracts a more discriminating feature maps for classification. We also design a multi-task loss function to associate the classification results for each scale and optimize the network parameter in training. Lastly, we validate the MS-DRAN on Stanford Cars-196 dataset and CompCars dataset and achieves 94.3% and 98.1% in accuracy, respectively. ©2021 ISSN 2414-8105 (Online).

关键词： Classification (of information)

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Reducing idleness in financial cloud services via multi-objective evolutionary reinforcement learning based load balancer

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science China(Information sciences) 2024年第2期67卷 20-40页

作者： Peng YANG Laoming ZHANG Haifeng LIU Guiying LI Department of Statistics and Data Science Southern University of Science and Technology Guangdong Provincial Key Laboratory of Brain-Inspired Intelligent Computation Department of Computer Science and EngineeringSouthern University of Science and Technology Academy for Advanced Interdisciplinary Studies Southern University of Science and Technology Guangdong OPPO Mobile Telecommunications Corp. Ltd Research Institute of Trustworthy Autonomous Systems Southern University of Science and Technology

In recent years, various companies have started to shift their data services from traditional data centers to the cloud. One of the major motivations is to save on operational costs with the aid of cloud elasticity. This paper discusses an emerging need from financial services to reduce the incidence of idle servers retaining very few user connections, without disconnecting them from the server side. This paper considers this need as a bi-objective online load balancing problem. A neural network based scalable policy is designed to route user requests to varied numbers of servers for the required elasticity. An evolutionary multi-objective training framework is proposed to optimize the weights of the policy. Not only is the new objective of idleness reduced by over 130% more than traditional industrial solutions, but the original load balancing objective itself is also slightly improved. Extensive simulations with both synthetic and real-world data help reveal the detailed applicability of the proposed method to the emergent problem of reducing idleness in financial services.

关键词： evolutionary reinforcement learning evolutionary multi-objective optimization load balance cloud computing

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MEAT: Median-Ensemble Adversarial Training for Improving Robustness and Generalization

MEAT: Median-Ensemble Adversarial Training for Improving Rob...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Zhaozhe Hu Jia-Li Yin Bin Chen Luojun Lin Bo-Hao Chen Ximeng Liu Fujian Province Key Laboratory of Information Security and Network Systems Fuzhou China College of Computer Science and Big Data Fuzhou University Fuzhou China Department of Computer Science and Engineering Yuan Ze University Taiwan

Self-ensemble adversarial training methods improve model robustness by ensembling models at different training epochs, such as model weight averaging (WA). However, previous research has shown that self-ensemble defense methods in adversarial training (AT) still suffer from robust overfitting, which severely affects the generalization performance. Empirically, in the late phases of training, the AT becomes more overfitting to the extent that the individuals for weight averaging also suffer from overfitting and produce anomalous weight values, which causes the self-ensemble model to continue to undergo robust overfitting due to the failure in removing the weight anomalies. To solve this problem, we aim to tackle the influence of outliers in the weight space in this work and propose an easy-to-operate and effective Median-Ensemble Adversarial Training (MEAT) method to solve the robust overfitting phenomenon existing in self-ensemble defense from the source by searching for the median of the historical model weights. Experimental results show that MEAT achieves the best robustness against the powerful AutoAttack and can effectively allievate the robust overfitting. We further demonstrate that most defense methods can improve robust generalization and robustness by combining with MEAT.

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