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2023 International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2023

作者： Xie, Mangang An, Baozhen Song, Changhao Wang, Qianqian Zhou, Meng Jia, Xiangdong Northwest Normal University College of Computer Science and Engineering Lanzhou730070 China Guangdong Provincial Key Laboratory of Short-Range Wireless Detection and Communication Guangzhou510640 China Zhejiang University School of Information and Electronic Engineering Hangzhou310027 China

ISBN: (纸本)9798350324174

A real-time Internet of Things (IoT) status update system is considered, where the IoT device needs to send not only the interested sensing information (payload) but also the controlling information (extra) to the destination for improving the quality of service. To simultaneously transmit these two types of information, a promising coding scheme, i.e., free-ride code, is utilized, in which the 5G low-density parity-check coded sensing sequence is superposed on randomly coded controlling sequence and is delivered to the destination without consuming additional bandwidth and power resources. The average age of information (AAoI) is evaluated for both two types of information. Numerical simulations indicate that the AAoI of extra can be reduced by free-ride codes without impairing the one of payload. © 2023 IEEE.

关键词： Internet of things

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VSLink: A Fast and Pervasive Approach to Physical Cyber Space Interaction via Visual SLAM

VSLink: A Fast and Pervasive Approach to Physical Cyber Spac...

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International Conference on Mobile Ad-hoc and Sensor Networks, MSN

作者： Han Zhou Jiaming Huang Hongchang Fan Geng Ren Yi Gao Wei Dong Zhejiang Key Lab of Service Robot College of Computer Science Zhejiang University Alibaba Group

With the fast growth of the Internet of Things, people now are surrounded by plenty of devices. To achieve efficient interaction with these devices, human-device interaction technologies are evolving. Because existing methods (mobile App) require users to remember the mapping between the real-world device and the digital one, an important point is to break such a gap. In this paper, we propose VSLink, which offers human-device interaction in an Augmented-Reality-like manner. VSLink achieves fast object identification and pervasive interaction for fusing the physical and cyberspace. To improve processing speed and accuracy, VSLink adopts a two-step object identification method to locate the interaction targets. In VSLink, visual SLAM and object detection neural networks detect stable/-movable objects separately, and detection prior from SLAM is sent to neural networks which enables sparse-convolution-based inference acceleration. VSLink offers a platform where the user could customize the interaction target, function, and interface. We evaluated VSLink in an environment containing multiple objects to interact with. The results showed that it achieves a 33% network inference acceleration on state-of-the-art networks, and enables object identification with 30FPS video input.

关键词： Visualization Simultaneous localization and mapping Neural networks Cyberspace Object detection Sensors Mobile applications

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Unsupervised feature selection via joint local learning and group sparse regression

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Frontiers of Information Technology & Electronic Engineering 2019年第4期20卷 538-553页

作者： Yue WU Can WANG Yue-qing ZHANG Jia-jun BU Zhejiang Provincial Key Laboratory of Service Robot College of Computer Science and Technology Zhejiang University Alibaba-Zhejiang University Joint Institute of Frontier Technologies

Feature selection has attracted a great deal of interest over the past decades. By selecting meaningful feature subsets, the performance of learning algorithms can be effectively improved. Because label information is expensive to obtain, unsupervised feature selection methods are more widely used than the supervised ones. The key to unsupervised feature selection is to find features that effectively reflect the underlying data distribution. However,due to the inevitable redundancies and noise in a dataset, the intrinsic data distribution is not best revealed when using all features. To address this issue, we propose a novel unsupervised feature selection algorithm via joint local learning and group sparse regression(JLLGSR). JLLGSR incorporates local learning based clustering with group sparsity regularized regression in a single formulation, and seeks features that respect both the manifold structure and group sparse structure in the data space. An iterative optimization method is developed in which the weights finally converge on the important features and the selected features are able to improve the clustering results. Experiments on multiple real-world datasets(images, voices, and web pages) demonstrate the effectiveness of JLLGSR.

关键词： Unsupervised Local learning Group sparse regression Feature selection

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Short-Term Load Forecasting with TDCI: Temporal Decomposition and Channel Independence 7

Short-Term Load Forecasting with TDCI: Temporal Decompositio...

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7th International Conference on Electrical, Mechanical and computer Engineering, ICEMCE 2023

作者： Li, Wenxue Cheng, Chi Xu, Peihua Liu, Renfeng Zhang, Yun Zhou, Huabing College of Computer Science and Engineering Wuhan Institute of Technology Hubei Prov. Wuhan430205 China Wuhan Institute of Technology Hubei Provincial Key Laboratory of Intelligent Robot Hubei Prov. Wuhan430205 China Hubei Meteorological Service Center Wuhan430079 China School of Educational Information Technology Central China Normal University Faculty of Artificial Intelligence Education Wuhan430079 China School of Mathematics and Computer Science Wuhan Polytechnic University Wuhan430023 China

ISBN: (纸本)9798350382877

Given the substantial load fluctuations, pronounced stochasticity, and non-linearity influenced by factors like weather and temperature in power load forecasting, we present a short-term load forecasting model based on TDCI. This model introduces several enhancements to boost forecasting accuracy. Firstly, TDCI employs temporal decomposition, separating time-series data into seasonal and trend components, enabling the capture of inherent seasonal patterns and long-term trends that significantly impact load forecasting accuracy. Additionally, the incorporation of locality-sensitive hashing attention efficiently processes relevant temporal patterns, enhancing model performance while reducing computational complexity. Lastly, a channel independence approach is applied to each variable, allowing tailored processing based on individual feature characteristics. This adaptive feature handling ensures effective capture of distinct properties, thereby enhancing forecasting accuracy. Experimental evaluations on a real-world power load dataset demonstrate the superior forecasting precision of our proposed model compared to existing models. © 2023 IEEE.

关键词： Electric power plant loads

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Double-Graph Representation With Relational Enhancement for Emotion-Cause Pair Extraction

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IEEE Transactions on Neural Networks and Learning Systems 2025年第6期36卷 10859-10873页

作者： Ming Zhang Zhe Chen Vasile Palade Tao Lu Liya Wang Junchi Zhang Yanduo Zhang Hubei Provincial Key Laboratory of Intelligent Robot the School of Computer Science and Engineering and the School of Artificial Intelligence Wuhan Institute of Technology Wuhan China Institute of Information Technology Anhui Vocational College of Defense Technology Lu’An Anhui China Centre for Computational Science and Mathematical Modelling Coventry University Coventry U.K. Zhejiang Industry and Trade Vocational College Wenzhou China School of Artificial Intelligence Jianghan University Wuhan China Computer School Hubei University of Arts and Science Xiangyang China

The emotion-cause pair extraction (ECPE) task is to simultaneously extract emotions and causes as pairs (EC-pairs) from documents, which is important for natural language processing. Previous research tackled this task via a two-step approach, which first predicts separately the emotion and cause clauses, and then pairs them up by using a binary classifier. However, such a two-step approach may suffer from the possible propagation of errors, and it neglects the interaction between emotions and causes. In this article, an end-to-end double-graph method with relational enhancement (DGRE) is proposed to stimulate two relationship modes among clauses, i.e., semantic dependence and logical dependence. First, two united graph encoders are established to embed the semantic dependence into the representation of clauses and pairs. The first encoder is built on graph attention networks (GATs) for clause-level representation, the result of which is used by a relational graph convolutional network (RGCN) for the refinement of pair-level representation. Aiming to enhance the fitting ability of logical dependence, the emotion-type classification task is introduced into the multitask learning framework of GATs, which can effectively distinguish the logical relations between clauses according to their emotion types. Moreover, seven types of dependence relations have been designed for the node connections in RGCN, which emphasize the contextual interaction and clustering among neighboring nodes. Experiments on a benchmark Chinese corpus demonstrate that the proposed DGRE approach could effectively establish the communication mechanism between clauses and pairs from multiple perspectives, and comparisons with state-of-the-art (SOTA) models well validate its effectiveness.

关键词： Semantics Electronic mail Cause effect analysis Predictive models Context modeling Correlation Stacking Mutual information Learning systems Iterative methods

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Research on Location Algorithm of 5G Ceramic Filter Based on Machine Vision 16th

Research on Location Algorithm of 5G Ceramic Filter Based on...

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16th International Conference on Intelligent robotics and Applications, ICIRA 2023

作者： Zhu, Zuhao Long, Yi He, Long Lu, Yaoan Li, Jian Guo, Liangsheng College of Information Science and Engineering Northeastern University Liaoning Shenyang China Faculty of Robot Science and Engineering Northeastern University Liaoning Shenyang China Zhiyuan Research Institute Zhejiang Hangzhou China Guangdong Provincial Key Laboratory of Minimally Invasive Surgical Instruments and Manufacturing Technology Guangdong University of Technology Guangzhou China Department of Orthopaedics The Second Affiliated Hospital of Soochow University Suzhou China

ISBN: (纸本)9789819964796

In order to solve the problem of insufficient positioning accuracy in the detection process of ceramic filters, a high-precision positioning algorithm based on edge detection fitting is proposed. The algorithm combines hierarchical pyramid model to locate ceramic filter step by step and obtain ROI region. Secondly, according to the rough location results of the target area, combined with the improved Canny detection algorithm, the ceramic filter is sub-pixel extracted, and a hierarchical screening method is proposed to obtain the contour information of the circular hole, which improves the stability of the algorithm. The minimum circumscribed circle algorithm based on Tukey is used to fit the extracted sub-pixel contour by weight, and the center position of the circular hole is obtained. Finally, according to the geometric relationship between the center of the circular hole and the ceramic filter, the center coordinates and offset angle of the ceramic filter are solved. The results of the experiment demonstrate that the ceramic filter positioning algorithm described in this paper can accomplish accurate and precise positioning of ceramic filters, in which the center repeated positioning error is 1.86 pixels and the angle error is 0.1137°, which meets the requirements for the positioning accuracy of ceramic filters. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023.

关键词： computer vision

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EdgeMan: Ensuring Real-Time service for Containerized Edge Systems

EdgeMan: Ensuring Real-Time Service for Containerized Edge S...

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International Conference on Mobile Ad-hoc and Sensor Networks, MSN

作者： Wenzhao Zhang Wei Dong Geng Ren Yi Gao Zhejiang Key Lab of Service Robot College of Computer Science Zhejiang University Alibaba-Zhejiang University Joint Institute of Frontier Technologies Alibaba Group

Containers have emerged as a popular technology for edge computing platforms. Although there are varieties of container orchestration frameworks, e.g., Kubernetes to provide high-reliable services for cloud infrastructure, ensuring realtime service at the containerized edge systems (CESs) remains a challenge. In this paper, we propose Edgeman,a holistic edge service management framework for CESs, which consists of (1) a model-assisted event-driven lightweight online scheduling algorithm to provide request-level execution plans; (2) a bottleneck-metric-aware progressive resource allocation mechanism to improve resource efficiency. We then build a testbed that installed three containerized services with different latency sensitivities for concrete evaluation. Besides, we adopt real-world data traces from Alibaba and Twitter for large-scale emulations. Extensive experiments demonstrate that the deadline miss ratio of Edgemanis reduced 85.9% on average compared with existing methods in both industry and academia.

关键词： Industries Sensitivity Job shop scheduling Scheduling algorithms Social networking (online) Emulation Containers

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Tensor Based Sparse Estimation for RIS-Aided Massive MIMO 23

Tensor Based Sparse Estimation for RIS-Aided Massive MIMO

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23rd IEEE International Conference on Communication Technology, ICCT 2023

作者： Li, Weilai Wei, Li Huang, Chongwen Yang, Jun Yuen, Chau Zhang, Zhaoyang School of Electrical and Computer Engineering Georgia Institute of Technology Atlanta United States Xidian University State Key Laboratory of Integrated Service Networks Xi'an710071 China School of Electrical and Electronics Engineering Nanyang Technological University Singapore College of Information Science and Electronic Engineering Zhejiang University Hangzhou China Zte Corporation State Key Laboratory of Mobile Network and Mobile Multimedia Technology Shenzhen China

ISBN: (纸本)9798350325959

Reconfigurable intelligent surface (RIS) is intended as a potential technique for future 6-th Generation wireless communication system. However, acquiring accurate channel state information is challenging since the channel matrices are high-dimensional due to large number of reflective elements (REs) in RIS. In this paper, we investigate the problem of sparse channel estimation (CE) in massive multiple-input multiple-output (MIMO) system equipped with large RIS (IRIS), where there are only few REs that are active during CE phase. We propose a two-stage tensor based CE algorithm that exploits the merits of IRIS. Specifically, IRIS act as reflector in the first stage of CE. Then, we identify the condition for the IRIS to activate few specified REs in the second stage. Simulation results demonstrate that the proposed algorithm outperforms existing CE schemes. © 2023 IEEE.

关键词： massive MIMO RIS Sparse channel estimation tensor decomposition

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Simultaneous Communication and Localization for Double-RIS Aided Multi-UE ISAC Systems 23

Simultaneous Communication and Localization for Double-RIS A...

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23rd IEEE International Conference on Communication Technology, ICCT 2023

作者： Gan, Xu Huang, Chongwen Yang, Zhaohui Zhong, Caijun Chen, Xiaoming He, Jiguang Zhang, Zhaoyang Guo, Qinghua Yuen, Chau Debbah, Merouane College of Information Science and Electronic Engineering Zhejiang University China Xidian University State Key Laboratory of Integrated Service Networks China Technology Innovation Institute Abu Dhabi United Arab Emirates School of Electrical Computer and Telecommunications Engineering University of Wollongong Australia School of Electrical and Electronics Engineering Nanyang Technological University Singapore Khalifa University of Science and Technology United Arab Emirates

ISBN: (纸本)9798350325959

Reconfigurable intelligent surface (RIS) has great potential to improve the performance of integrated sensing and communication (ISAC) systems, especially in scenarios where line-of-sight paths between the base station and users are blocked. However, the spectral efficiency of RIS-aided ISAC uplink transmissions may be drastically reduced by the heavy burden of pilot overhead for realizing sensing capabilities. In this paper, we tackle this bottleneck by proposing a superimposed symbol scheme, which superimposes sensing pilots onto data symbols over the same time-frequency resources. Specifically, a low-complexity simultaneous communication and localization algorithm is proposed, which employs the unitary approximate message passing in the Bayesian learning framework for initial angle estimate, followed by iterative refinements through reduced-dimension matrix calculations. Moreover, the sparse code multiple access technology is incorporated into this iterative framework for accurate data detection which also facilitates localization. Numerical results show that the proposed superimposed symbol-based scheme empowered by the developed algorithm can achieve centimeter-level localization and provide an effective throughput improvement over 143%. © 2023 IEEE.

关键词： Bayesian learning Integrated sensing and communication (ISAC) reconfigurable intelligent surface (RIS) superimposed coding

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Efficient Medical Image Segmentation Based on Knowledge Distillation

arXiv

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arXiv 2021年

作者： Qin, Dian Bu, Jia-Jun Liu, Zhe Shen, Xin Zhou, Sheng Gu, Jing-Jun Wang, Zhi-Hua Wu, Lei Dai, Hui-Fen Zhejiang Provincial Key Laboratory of Service Robot College of Computer Science Zhejiang University Ningbo Research Institute School of Software Technology Zhejiang University The Fourth Affiliated Hospital Zhejiang University School of Medicine

Recent advances have been made in applying convolutional neural networks to achieve more precise prediction results for medical image segmentation problems. However, the success of existing methods has highly relied on huge computational complexity and massive storage, which is impractical in the real-world scenario. To deal with this problem, we propose an efficient architecture by distilling knowledge from well-trained medical image segmentation networks to train another lightweight network. This architecture empowers the lightweight network to get a significant improvement on segmentation capability while retaining its runtime efficiency. We further devise a novel distillation module tailored for medical image segmentation to transfer semantic region information from teacher to student network. It forces the student network to mimic the extent of difference of representations calculated from different tissue regions. This module avoids the ambiguous boundary problem encountered when dealing with medical imaging but instead encodes the internal information of each semantic region for transferring. Benefited from our module, the lightweight network could receive an improvement of up to 32.6% in our experiment while maintaining its portability in the inference phase. The entire structure has been verified on two widely accepted public CT datasets LiTS17 and KiTS19. We demonstrate that a lightweight network distilled by our method has non-negligible value in the scenario which requires relatively high operating speed and low storage usage. Copyright © 2021, The Authors. All rights reserved.

关键词： Semantics

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