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2022 Chinese Automation Congress, CAC 2022

作者： Wang, Haibin Wang, Tian Zhang, Mengyi Zhang, Baochang Shi, Peng Snoussi, Hichem Beihang University School of Automation Science and Electrical Engineering Beijing China Nanjing University of Science and Technology Jiangsu Key Laboratory of Image and Video Understandingof Scoial Safety Nanjing China Institute of Artificial Intellgence Beihang University Beijing China Nanjing Tech University College of Electrical Engineering and Control Science Nanjing China Fujian Normal University School of Mathematics and Computer Science Fujian China University of Technology of Troyes Institute Charles Delaunay-LM2S Troyes France

ISBN: (纸本)9781665465335

Self-attention networks have achieved great success in traditional language processing, and have also made great progress in image classification and object detection. When analyzing the previous work, it is found that some point cloud information is lost in their feature extraction work, so we design a network to reduce the loss when extracting features. And through the feature extraction network - Gated Attention Unit instead of Transformer, to make the extraction of point cloud features more efficient. Based on Point Transformer, a new network structure named Point Gated Attention is used to classify point clouds. The overallacc on the dataset ModelNet40 can reach 91.4%, and the average accuracy can reach 93.9%. © 2022 IEEE.

关键词： Feature extraction

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Language-Conditioned Affordance-Pose Detection in 3D Point Clouds

arXiv

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arXiv 2023年

作者： Nguyen, Toan Vu, Minh Nhat Huang, Baoru Van Vo, Tuan Truong, Vy Le, Ngan Vo, Thieu Le, Bac Nguyen, Anh FPT Software AI Center Viet Nam Automation & Control Institute TU Wien Vienna Austria Imperial College London United Kingdom Department of Computer Science & Computer Engineering University of Arkansas United States Faculty of Mathematics and Statistics Ton Duc Thang University Ho Chi Minh City Viet Nam Department of Computer Science University of Science Viet Nam Department of Computer Science University of Liverpool United Kingdom

Affordance detection and pose estimation are of great importance in many robotic applications. Their combination helps the robot gain an enhanced manipulation capability, in which the generated pose can facilitate the corresponding affordance task. Previous methods for affodance-pose joint learning are limited to a predefined set of affordances, thus limiting the adaptability of robots in real-world environments. In this paper, we propose a new method for language-conditioned affordance-pose joint learning in 3D point clouds. Given a 3D point cloud object, our method detects the affordance region and generates appropriate 6-DoF poses for any unconstrained affordance label. Our method consists of an open-vocabulary affordance detection branch and a language-guided diffusion model that generates 6-DoF poses based on the affordance text. We also introduce a new high-quality dataset for the task of language-driven affordance-pose joint learning. Intensive experimental results demonstrate that our proposed method works effectively on a wide range of open-vocabulary affordances and outperforms other baselines by a large margin. In addition, we illustrate the usefulness of our method in real-world robotic applications. Our code and dataset are publicly available at https://***. © 2023, CC BY-NC-SA.

关键词： Robotics

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Decentralized Optimization of Coupled Power-Transportation Network considering Environment-aware User Behavior

Decentralized Optimization of Coupled Power-Transportation N...

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International Conference on computers, Communications, and Systems (ICCCS)

作者： Zhiao Cao Yinghua Han Qiang Zhao Jinkuan Wang Shuyi Zhao School of Control Engineering Northeastern University at Qinhuangdao Qinhuangdao China School of Computer and Communication Engineering Northeastern University at Qinhuangdao Qinhuangdao China College of Information Science and Engineering Northeastern University Shenyang China Faculty of business and law Taylor's University Subang Jaya Malaysia

The increasing popularity of electric vehicles (EVs) has led to a growing need for electrified transportation, which has further deepened the interconnectedness of power and transportation networks. To account for EV drivers' preferences in route selection, this paper proposes an environment-aware dispatch model that coordinates the integrated power and transportation network to achieve greater economic benefits and increased utilization of renewable energy. The model employs discrete choice models with an environmental factor to represent user behavior. The constructed user behavior models serve as the basis for the integrated optimal traffic-power flow (IOTPF) model, which aims to optimize the flow of both power and traffic while minimizing the total cost of EV travel and maximizing the use of renewable energy sources. The paper employs a decentralized optimization algorithm to solve the equilibrium flows of the IOTPF model. Numerical results illustrate how the environment-aware user behavior impacts travel costs and the use of renewable energy, demonstrating the effectiveness of the IOTPF model in coordinating the optimal strategy of the integrated power-transportation network.

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Weighted Fusion of Multiple Classifiers for Human Activity Recognition

Weighted Fusion of Multiple Classifiers for Human Activity R...

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International Conference on Information Fusion

作者： Kezhu Zuo Xinde Li Jean Dezert Yilin Dong School of Cyber Science and Engineering Southeast University Nanjing China Key Laboratory of Measurement and Control of CSE School of Automation Southeast University Nanjing China The French Aerospace Lab ONERA DTIS/MIDL Palaiseau France College of Computer Science and Engineering Shanghai Maritime University Shanghai China

Human Activity Recognition (HAR) based on wear-able device has become a hot topic of research due to its wide range of applications in health-care, fitness and smart homes. However, the classification of some activities with similar sensor readings, such as standing and sitting, is usually more challenging for the design of efficient activity recognition algorithms. Considering the inconsistent performance of different classifiers, which can provide information complementary for individual classifier, we propose a novel multi-classifier fusion method based on belief functions (BFs) theory for HAR. Specifically, at first, four classifiers are trained using time-domain and frequency-domain features to obtain basic belief assignments (BBA) of activity, respectively. Then, three assessment criteria are utilized to evaluate the reliability of the classifiers and a scoring matrix is constructed. Next, the algorithm of Belief Function based the Technique for Order Preference by Similarity to Ideal Solution (BF-TOPSIS) is employed to calculate the weighting coefficients for each classifier. Finally, the discounting and Dempster’s rules are adopted to combine the multiple classifiers and further decision making. Several experiments were conducted to illustrate the performance of the proposed method using the UCI smartphone dataset, and the results show that the proposed method is more accurate than the state-of-art methods.

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Efficient Wideband Beamforming with Null Broadening Using Fusion Convolutional Block

IEEE Transactions on Green Communications and Networking

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IEEE Transactions on Green Communications and Networking 2025年

作者： Liu, Fulai Li, Chenglong Feng, Liwen Gao, Yajie Shi, Baozhu Du, Ruiyan Lab of Electromagnetic Environment Cognition and Control Utilization Hebei Key Laboratory of Marine Perception Network and Data Processing 066004 China Northeastern University School of Computer Science and Engineering Shenyang 110819 China Hebei University College of Mathematics and Information Science Baoding 071002 China

To meet the development requirements of bandwidth and efficiency in future 6G wireless communications, an efficient wideband beamforming (WB) algorithm is proposed based on fusion convolutional block neural network (FCBNN) in this paper, named as WB-FCBNN algorithm. The presented algorithm can significantly enhance the real-time performance while obtaining a superior output signal to interference plus noise ratio (SINR) of WB. Firstly, the algorithm utilizes null broadening algorithm with interference plus noise covariance matrix (INCM) reconstruction to generate labels, avoiding the influence of interference direction of arrival (DOA) estimation errors. Then, a novel FCBNN framework is constructed to enhance the real-time performance and the output SINR. Specifically, a fusion convolutional block (FCB) is introduced into the FCBNN framework, which can fuse the asymmetric convolution and the standard convolution into one convolution. In this way, the network parameters will be reduced, thus the computational complexity of the network can be reduced. Furthermore, the FCBNN model can be trained via the label. Finally, the well-trained FCBNN model can quickly predict the near-optimal weight vector of WB. Simulation results demonstrate that the presented algorithm can effectively increase the real-time performance of WB, and still has a satisfactory SINR in the environment of high SNR and low snapshot. © 2017 IEEE.

关键词： Array signal processing deep learning wideband beamforming

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Infrastructure-Free Smartphone Indoor Localization Using Room Acoustic Responses 21

Infrastructure-Free Smartphone Indoor Localization Using Roo...

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19th ACM Conference on Embedded Networked Sensor Systems, SenSys 2021

作者： Guo, Dongfang Luo, Wenjie Gu, Chaojie Wu, Yuting Song, Qun Yan, Zhenyu Tan, Rui Singtel Cognitive and Artificial Intelligence Lab for Enterprises Nanyang Technological University Singapore College of Control Science and Engineering Zhejiang University China ERI-N IGS School of Computer Science and Engineering Nanyang Technological University Singapore Department of Information Engineering Chinese University of Hong Kong Hong Kong

ISBN: (纸本)9781450390972

Smartphone indoor location awareness is increasingly demanded by a variety of mobile applications. The existing solutions for accurate smartphone indoor localization rely on additional devices or pre-installed infrastructure (e.g., dense WiFi access points, Bluetooth beacons). In this demo, we present EchoLoc, an infrastructure-free smartphone indoor localization system using room acoustic response to a chirp emitted by the phone. EchoLoc consists of a mobile client for echo data collection and a cloud server hosting a deep neural network for location inference. EchoLoc achieves 95% accuracy in recognizing 101 locations in a large public indoor space and a median localization error of 0.5 m in a typical lab area. Demo video is available at https://***/5si0Cq6LzT4. © 2021 Owner/Author.

关键词： Smartphones

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Automatic Detection of Lumbar Disc Herniation Using YOLOv7

Automatic Detection of Lumbar Disc Herniation Using YOLOv7

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

作者： Prisilla, Ardha Ardea Pusparani, Yori Chang, Wen-Thong Liau, Ben-Yi Jan, Yih-Kuen Ardhianto, Peter Lin, Chih-Yang Lung, Chi-Wen Asia University Taichung Department of Digital Media Design Taiwan LaSalle College Jakarta Department of Fashion Design Jakarta Indonesia Budi Luhur University Department of Visual Communication Design Jakarta Indonesia Department of Computer Science and Information Engineering Taichung Taiwan Feng Chia University Department of Automatic Control Engineering Taichung Taiwan Asia University Department of Creative Product Design Taichung Taiwan Soegijapranata Catholic University Department of Visual Communication Design Semarang Indonesia National Central University Department of Mechanical Engineering Taoyuan Taiwan

ISBN: (纸本)9798350324174

The detection of lumbar disc herniation (LDH) through magnetic resonance imaging (MRI) poses a challenge due to the various shapes, sizes, angles, and regions associated with bulges, protrusions, extrusions, and sequestrations. One potential solution is using deep learning methods to identify lumbar abnormalities in MRI images automatically. The YOU ONLY LOOK ONCE (YOLO) model series has gained popularity for training deep learning algorithms for real-time biomedical image detection. This study aims to assess the performance of the latest YOLOv7 in detecting LDH across different regions of the lumbar intervertebral disc. The analysis revealed that YOLOv7 exhibits a poor performance and low detection rate of LDH across the L1-L2, L2-L3, L3-L4, L4-L5, and L5-S1 regions. © 2023 IEEE.

关键词： Magnetic resonance imaging

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Energy Consumption Prediction for Manufacturing in Industrial IoT Based on Heterogeneous GNN

Energy Consumption Prediction for Manufacturing in Industria...

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IEEE International Conference on Communications in China (ICCC)

作者： Xuan Su Mingyan Li Qihao Li Chao Chen Songtao Guo Xu Wang College of Mechanical and Vehicle Engineering Chongqing University Chongqing P. R. China College of Computer Science Chongqing University Chongqing P. R. China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai P. R. China College of Communication Engineering Jilin University Changchun P. R. China

ISBN: (数字)9798350378412

ISBN: (纸本)9798350378429

Energy consumption prediction plays a crucial role in green manufacturing for industry, providing a theoretical foundation for energy-saving planning, workshop scheduling, and process optimization. However, owing to the processing complexity of mechanical products and the multiplicity of production information, previous methods can only achieve prediction for individual devices, which are quite restrictive to collaborative manufacturing planning. To deal with product-level energy management, this paper first proposes a heterogeneous manufacturing association graph (HetMG) which contains various manufacturing elements including product projects, decomposed orders and processing devices. Then, with HetMG, an energy consumption prediction method, HetEP, is introduced based on heterogeneous graph neural network (GNN), which incorporates Relational Graph Convolutional Networks (R-GCNs), and Long Short-Term Memory (LSTM) to mine the spatial-temporal relation of heterogeneous elements during the manufacturing process. Moreover, Graph Attention Network (GAT) is utilized in HetEP to adaptively learn the graph-structured data so that informative representations of each element can be obtained. The experimental results based on a manufacturing dataset from a generator factory demonstrate that the proposed HetEP yields superior prediction performance compared to the device-oriented prediction methods in terms of prediction accuracy.

关键词： Representation learning Performance evaluation Energy consumption Green manufacturing Job shop scheduling Prediction methods Production facilities

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Triple IRS-Aided Communications: Row-Column Sparsity Enhanced Bayesian Tensor Learning for Channel Estimation

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IEEE Transactions on Communications 2025年

作者： Hu, Limei Shao, Xiaodan Qiu, Tingzhi Chen, Feng Cheng, Lei Wu, Qingqing Southwest University College of Artificial Intelligence College of Electronic and Information Engineering Brain-Inspired Computing and Intelligent Control of Chongqing Key Laboratory National and Local Joint Engineering Laboratory of Intelligent Transmission and Control Technology Chongqing 400715 China University of Waterloo Department of Electrical and Computer Engineering Waterloo N2L 3G1 ON Canada Zhejiang University College of Information Science and Electronic Engineering Hangzhou 310058 China Shanghai Jiao Tong University Department of Electronic Engineering Shanghai China

Channel acquisition presents a major challenge in deploying intelligent reflecting surfaces (IRS) aided communication systems, due to massive reflective elements that create a complex multi-path channel and increase channel dimensions. For an IRS-aided communication system, complete channel includes three parts: From the users to IRS, from the IRS to BS, and from the BS back to the IRS. Thus, a generalized multi-IRS cascaded communication system with three cascaded IRSs is considered. Unfortunately, existing channel estimation methods focus on single or double IRS cascades, which is not applicable to the case of triple cascaded IRS channel estimation directly. In this paper, we study the uplink channel estimation for triple cascaded IRSs aided single-user single-input single-output (SISO) systems. Specifically, the triple IRS cascaded channel is typically sparse. It permits us to characterize the channel estimation as a problem of sparse matrix recovery. Then, the sparse learning is explored to achieve robust channel estimation with limited training overhead. Particularly, the sparse channel matrices of the cascaded triple IRS channels have a common row-column block sparsity structure. However, a unique challenge lies in characterizing and enhancing such a common row-column sparsity. To tackle this issue, we apply a random matrix prior to promote the common row-column-wise sparsity of the channel matrix, and then an efficient Bayesian tensor inference algorithm is proposed to estimate the IRS channel. Finally, simulation results confirm that the proposed scheme outperforms traditional counterparts in terms of accuracy. © 1972-2012 IEEE.

关键词： Bayesian learning common row-column sparsity tensor modeling Triple intelligent reflecting surface (IRS)

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HISTORIAE, history of socio-cultural transformation as linguistic data science. A humanities use case 3

HISTORIAE, history of socio-cultural transformation as lingu...

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3rd Conference on Language, Data and Knowledge, LDK 2021

作者： Armaselu, Florentina Apostol, Elena-Simona Khan, Anas Fahad Liebeskind, Chaya McGillivray, Barbara Truică, Ciprian-Octavian Oleškevičiene, Giedre Valūnaite University of Luxembourg Luxembourg Department of Computer Science and Engineering Faculty of Automatic Control and Computer University Politehnica of Bucharest Romania Institute for Computational Linguistics «A. Zampolli» National Research Council of Italy Pisa Italy Department of Computer Science Jerusalem College of Technology Israel Theoretical and Applied Linguistics Faculty of Modern and Medieval Languages and Linguistics University of Cambridge United Kingdom The Alan Turing Institute London United Kingdom Department of Computer Science and Engineering Faculty of Automatic Control and ComputerUniversity Politehnica of Bucharest Romania Institute of Humanities Mykolas Romeris University Vilnius Lithuania

ISBN: (纸本)9783959771993

The paper proposes an interdisciplinary approach including methods from disciplines such as history of concepts, linguistics, natural language processing (NLP) and Semantic Web, to create a comparative framework for detecting semantic change in multilingual historical corpora and generating diachronic ontologies as linguistic linked open data (LLOD). Initiated as a use case (UC4.2.1) within the COST Action Nexus Linguarum, European network for Web-centred linguistic data science, the study will explore emerging trends in knowledge extraction, analysis and representation from linguistic data science, and apply the devised methodology to datasets in the humanities to trace the evolution of concepts from the domain of socio-cultural transformation. The paper will describe the main elements of the methodological framework and preliminary planning of the intended workflow. © Florentina Armaselu, Elena-Simona Apostol, Anas Fahad Khan, Chaya Liebeskind, Barbara McGillivray, Ciprian-Octavian Truică, and Giedre Valūnaite Oleškevičien e;licensed under Creative Commons License CC-BY 4.0

关键词： Linguistics

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