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Dynamic Dense Graph Convolutional Network for Skeleton-based Human Motion Prediction

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arXiv 2023年

作者： Wang, Xinshun Zhang, Wanying Wang, Can Gao, Yuan Liu, Mengyuan the National Key Laboratory of General Artificial Intelligence Peking University Shenzhen Graduate School China the School of Intelligent Systems Engineering Sun Yat-sen University China the Multimedia Information Processing Laboratory the Department of Computer Science Kiel University Germany Peking University China the Unit of Computing Sciences CS Faculty of Information Technology and Communication Sciences ITC Tampere University Finland

Graph Convolutional Networks (GCN) which typically follows a neural message passing framework to model dependencies among skeletal joints has achieved high success in skeleton-based human motion prediction task. Nevertheless, how to construct a graph from a skeleton sequence and how to perform message passing on the graph are still open problems, which severely affect the performance of GCN. To solve both problems, this paper presents a Dynamic Dense Graph Convolutional Network (DD-GCN), which constructs a dense graph and implements an integrated dynamic message passing. More specifically, we construct a dense graph with 4D adjacency modeling as a comprehensive representation of motion sequence at different levels of abstraction. Based on the dense graph, we propose a dynamic message passing framework that learns dynamically from data to generate distinctive messages reflecting sample-specific relevance among nodes in the graph. Extensive experiments on benchmark Human 3.6M and CMU Mocap datasets verify the effectiveness of our DD-GCN which obviously outperforms state-of-the-art GCN-based methods, especially when using long-term and our proposed extremely long-term protocol. Copyright © 2023, The Authors. All rights reserved.

关键词： Message passing

Haptically-Enabled Robotic Teleoperation for Transcranial Magnetic Stimulation (TeleTMS)

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Haptically-Enabled Robotic Teleoperation for Transcranial Ma...

IEEE International Conference on systems, Man and Cybernetics

作者： Parham M. Kebria Saeid Nahavandi Peter Enticott Fernando Bello Institute for Intelligent Systems Research and Innovation (IISRI) Deakin University VIC Australia Swinburne University of Technology Australia Paulson School of Engineering and Applied Sciences Harvard University Allston MA USA Cognitive Neuroscience Unit Faculty of Health School of Psychology Deakin University Burwood VIC Australia Chelsea and Westminster Hospital Centre for Engagement and Simulation Science Imperial College London UK

Transcranial Magnetic Stimulation (TMS) is a non-invasive and painless technique used in both clinical trials and research on cortical activity and brain networks. TMS involves the use of an electromagnetic coil, which can induce powerful but brief magnetic pulses. When the coil is headed against the scalp, it can induce electrical activity in underlying brain tissue. For effective results, the TMS coil should be in appropriate contact with patients' scalp and positioned for consistent stimulation. In many cases, it requires researchers and clinicians to not only hold and position the coil on subjects' head, also to take care to ensure appropriate and consistent contact between the TMS coil and subject's scalp. This task is noticeably tiresome for the operators considering weight of the coil and a dense cable attached to it. On the other side, the patient or participant has to sit motionless; otherwise, the contact will be lost and the stimulation will have a reduced impact. In this paper, we propose and develop a haptically-enabled teleoperated robotic platform that removes all those limitations and burdensome from both TMS operators and patients/participants. The operator, through a haptic interface, remotely controls a robotic arm holding the coil. This system provides the operator with the sense of touch to feel the contact force between the coil and patient/participant's head. Therefore, operators and patients/participants do not need to be in the same location while conducting TMS, including the “motor thresholding” procedure. This will offer a huge benefit to the healthcare services in rural areas. Experimental evaluations carried out to demonstrate the effectiveness of the proposed robotic system.

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Exploiting the Intrinsic Neighborhood Semantic Structure for Domain Adaptation in EEG-based Emotion Recognition

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IEEE Transactions on Affective Computing 2025年

作者： Yang, Yi Wang, Ze Song, Yu Jia, Ziyu Wang, Boyu Jung, Tzyy-Ping Wan, Feng Macau University of Science and Technology Macao Centre for Mathematical Sciences Respiratory Disease AI Laboratory on Epidemic Intelligence and Medical Big Data Instrument Applications Faculty of Innovation Engineering 999078 China Tianjin University of Technology School of Electrical Engineering and Automation Tianjin Key Laboratory of New Energy Power Conversion Transmission and Intelligent Control Tianjin300384 China Chinese Academy of Sciences Beijing Key Laboratory of Brainnetome and Brain-Computer Interface and Brainnetome Center Institute of Automation Beijing100045 China Western University Department of Computer Science Brain Mind Institute LondonONN6A 3K7 Canada University of California at San Diego Swartz Center for Computational Neuroscience Institute for Neural Computation La Jolla CA92093 United States University of Macau Department of Electrical and Computer Engineering Faculty of Science and Technology China University of Macau Centre for Cognitive and Brain Sciences Centre for Artificial Intelligence and Robotics Institute of Collaborative Innovation 999078 China

Due to the inherent non-stationarity and individual differences present in electroencephalogram (EEG) signals, developing a generalizable model that performs well on new subjects is challenging in EEG-based emotion recognition. Most existing domain adaptation (DA) methods typically mitigate these discrepancies by aligning the marginal distributions of domain feature representations. However, when there is a significant difference in the class-conditional distribution between domain features and labels, the domain-invariant features learned by aligning marginal distributions may have limited discriminative ability for unlabeled target instances or even prove counterproductive. To address this issue, we propose a Neighborhood Semantic Aware Learning-based Dynamic Graph Attention Convolution (NSAL-DGAT) approach that learns target semantic information by considering the inter-domain semantic topological structure, thereby improving classifier adaptation for target instances. Specifically, the proposed NSAL framework is designed to capitalize on the insight that after domain feature alignment, some target samples and their neighboring source samples exhibit similar semantics. By leveraging the neighborhood topological structure, we extract and incorporate semantic target features to train a more transferable classifier. Besides, we implement an entropy weighting mechanism to emphasize representative target semantic information, encouraging target instances to prioritize high-confidence individuals within the source neighborhood. We have conducted extensive experiments on the public SEED dataset and our collected the Hearing-Impaired EEG dataset (HIED). The experimental results underscore the efficacy of our proposed NSAL-DGAT approach, showcasing state-of-the-art accuracy in subject-dependent as well as subject-independent scenarios. The source code is available at https://***/YYingDL/NSAL-DGAT. © 2010-2012 IEEE.

关键词： Speech analysis

A Method for Demand Side Management in a Food Processing Plant Via Model Predictive Control

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SSRN

SSRN 2023年

作者： Wohlgenannt, Philipp Huber, Gerhard Rheinberger, Klaus Kolhe, Mohan Kepplinger, Peter Illwerke vkw Endowed Professorship for Energy Efficiency Research Center Energy Vorarlberg University of Applied Sciences Hochschulstrasse 1 Dornbirn6850 Austria Josef Ressel Centre for Intelligent Thermal Energy Systems Vorarlberg University of Applied Sciences Hochschulstrasse 1 Dornbirn6850 Austria Faculty of Engineering and Science University of Agder Jon Lilletuns vei 9 Grimstad4879 Norway

Particularly for the industrial sector, which globally demands the highest amount of electrical power, demand side management is viewed as a promising strategy to enhance energy efficiency and reduce energy consumption. This study proposes a demand side management method for an industrial food processing plant, utilizing a chilled water buffer as active thermal energy storage and the plant building as passive thermal energy storage. The plant building and production process are modeled using transient thermal energy balances and the demand side management problem is formulated as a linear program. Model predictive control is employed to manage uncertainties in the optimization process. A simulated case study of an Austrian food processing plant shows reductions in electrical power consumption by up to 18 %, electricity costs by up to 24 %, and peak load by up to 36 %, in three distinct optimization scenarios, respectively. Surprisingly, straightforward prediction methods, such as averaging historical data, yield nearly optimal outcomes for energy and cost savings. However, for peak load reduction, the study demonstrates the necessity of highly-accurate predictions, as basic forecasts in the model predictive control approach achieve only about a third of the reductions possible. © 2023, The Authors. All rights reserved.

关键词： Model predictive control

A Review of Multimodal Explainable Artificial Intelligence: Past, Present and Future

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arXiv 2024年

作者： Sun, Shilin An, Wenbin Tian, Feng Nan, Fang Liu, Qidong Liu, Jun Shah, Nazaraf Chen, Ping School of Computer Science and Technology Xi’an Jiaotong University Xi’an710049 China Ministry of Education Key Laboratory of Intelligent Networks and Network Security Xi’an Jiaotong University Xi’an710049 China Faculty of Electronic and Information Engineering Xi’an Jiaotong University Xi’an710049 China Shaanxi Province Key Laboratory of Big Data Knowledge Engineering Xi’an Jiaotong University Xi’an710049 China Institute for Future Transport and Cities Coventry University Priory Street CoventryCV1 5FB United Kingdom Department of Engineering University of Massachusetts Boston BostonMA02125 United States

Artificial intelligence (AI) has rapidly developed through advancements in computational power and the growth of massive datasets. However, this progress has also heightened challenges in interpreting the "black-box" nature of AI models. To address these concerns, eXplainable AI (XAI) has emerged with a focus on transparency and interpretability to enhance human understanding and trust in AI decision-making processes. In the context of multimodal data fusion and complex reasoning scenarios, the proposal of Multimodal eXplainable AI (MXAI) integrates multiple modalities for prediction and explanation tasks. Meanwhile, the advent of Large Language Models (LLMs) has led to remarkable breakthroughs in natural language processing, yet their complexity has further exacerbated the issue of MXAI. To gain key insights into the development of MXAI methods and provide crucial guidance for building more transparent, fair, and trustworthy AI systems, we review the MXAI methods from a historical perspective and categorize them across four eras: traditional machine learning, deep learning, discriminative foundation models, and generative LLMs. We also review evaluation metrics and datasets used in MXAI research, concluding with a discussion of future challenges and directions. A project related to this review has been created at https://***/ShilinSun/mxai_review. Copyright © 2024, The Authors. All rights reserved.

关键词： data fusion

Automatic Rail Component Detection Based on AttnConv-Net

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arXiv 2021年

作者： Wang, Tiange Zhang, Zijun Yang, Fangfang Tsui, Kwok-Leung The School of Data Science City University of Hong Kong Hong Kong The School of Intelligent Systems Engineering Sun Yat-Sen University Guangzhou510275 China The Grado Department of Industrial and Systems Engineering Virginia Polytechnic Institute State University BlacksburgVA24061 United States

The automatic detection of major rail components using railway images is beneficial to ensure the rail transport safety. In this paper, we propose an attention-powered deep convolutional network (AttnConv-net) to detect multiple rail components including the rail, clips, and bolts. The proposed method consists of a deep convolutional neural network (DCNN) as the backbone, cascading attention blocks (CAB), and two feed forward networks (FFN). Two types of positional embedding are applied to enrich information in latent features extracted from the backbone. Based on processed latent features, the CAB aims to learn the local context of rail components including their categories and component boundaries. Final categories and bounding boxes are generated via two FFN implemented in parallel. To enhance the detection of small components, various data augmentation methods are employed in training process. The effectiveness of the proposed AttnConv-net is validated with one real dataset and another synthesized dataset. Compared with classic convolutional neural network based methods, our proposed method simplifies the detection pipeline by eliminating the need of prior- and post-processing, which offers a new speed-quality solution to enable faster and more accurate image-based rail component detections. © 2021, CC BY.

关键词： Railroads

Digital Mahjong System: Towards Precise Cognitive Assessment with IoT Technologies 1

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24th International Conference on Human-Computer Interaction, HCII 2022

作者： An, Ning Hu, Enze Guo, Yanrui Yang, Jiaoyun Au, Rhoda Ding, Huitong School of Computer Science and Information Engineering Hefei University of Technology Hefei China Anhui Province Key Laboratory of Affective Computing and Advanced Intelligent Machine Hefei University of Technology Hefei China National Smart Eldercare International S&T Cooperation Base Hefei University of Technology Hefei China Intelligent Interconnected Systems Laboratory of Anhui Province Hefei University of Technology Hefei China Shenzhen Corecloud Innovation Technology Co. Ltd. Shenzhen China Key Laboratory of Knowledge Engineering with Big Data of Ministry of Education Hefei University of Technology Hefei China Department of Anatomy and Neurobiology Neurology and Framingham Heart Study Boston University School of Medicine Boston United States Department of Epidemiology Boston University School of Public Health Boston United States

ISBN: (数字)9783031179020

ISBN: (纸本)9783031179013

To the best of our knowledge, this paper is the first to apply IoT technologies to transform the popular Mahjong game into a Digital Mahjong System (DMS) for digitally performing cognitive assessments. People have started exploring digital cognitive assessment tools for better objectivity and simplification. However, most of these tools are not friendly for older adults. This paper aims to address this issue by integrating IoT technologies with Mahjong to make cognitive assessment simpler and more engaging for older adults. DMS has the following features: 1) integrating motion tracking devices into Mahjong tiles and transferring them into Digital Mahjong Tiles (DMTs), which can precisely capture their moving trajectory during the assessment, 2) using a Near Field Communication reader to configure DMT’s primary data, 3) supporting Over-The-Air Device Firmware Update that can be done remotely via Bluetooth, and 4) developing a charging base that can charge 16 DMTs simultaneously using Qi wireless charging standard. With further testing, the DMS has the potential to become a digital platform to implement and test various cognitive assessment tools integrating Mahjong elements. © 2022, Springer Nature Switzerland AG.

关键词： Internet of things

Ultra-compact beam steering nanolasers

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arXiv 2024年

作者： Chen, Xinghong Gu, Mingxuan Tang, Jiankai Sang, Yungang Xiang, Bingrui Zhang, Kong Zhang, Guanjie Wang, Xingyuan Guo, Xuhan Zhou, Linjie Wu, Wengang Mao, Yifei School of Sensing Science and Engineering School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai200240 China SJTU-Pinghu Institute of Intelligent Optoelectronics Pinghu314200 China Institute of Microelectronics Chinese Academy of Sciences Beijing100029 China College of Mathematics and Physics Beijing University of Chemical Technology Beijing100029 China State Key Laboratory of Advanced Optical Communication Systems and Networks Department of Electronic Engineering Shanghai Jiao Tong University Shanghai200240 China State Key Laboratory of Advanced Optical Communication Systems and Networks Shanghai Key Lab of Navigation and Location Services Shanghai Institute for Advanced Communication and Data Science Department of Electronic Engineering Shanghai Jiao Tong University Shanghai200240 China National Key Laboratory of Science and Technology on Micro/Nano Fabrication School of Integrated Circuits Peking University Beijing100871 China Beijing Advanced Innovation Center for Integrated Circuits Beijing100871 China Frontiers Science Center for Nano-optoelectronics Peking University Beijing100871 China

The miniaturization and integration of beam steering devices have consistently been the focus of the field. Conventional methods alter the eigenmode of the optical cavity by regulating the refractive index. Due to the weak nonlinear effect of the optical system, the device must be sufficiently large to achieve sufficient light modulation. The effective method for miniaturizing beam steering devices currently in use is based on metasurfaces. However, this type of device necessitates the input of a laser source, which precludes the simultaneous generation and control of light in a single device. Here we propose a miniaturized beam steering device that employs mode selection between different bound states in the continuum (BIC) states through phase change material. The device is capable of simultaneously achieving both light generation and beam steering (33°) in a single device with a size of only 25 μm and with a low threshold of 8.9 kW cm-2. Furthermore, it is possible to achieve a significant degree of dynamic wavelength tunability, with a range extending up to 296 nm. This method achieves high-efficient regulation of light properties by dynamically controlling the system’s topological charge, circumventing the problem of weak nonlinearity in traditional methods. Furthermore, the integration of phase change materials with nanolasers enables the direct alteration of lasing properties, which provides a novel idea for dynamic light control. The device process scheme based on phase change materials is straightforward, direct, and highly compatible, which will be advantageous for its intended application. Copyright © 2024, The Authors. All rights reserved.

关键词： Phase change materials

A Spherical Harmonics-LSB-quantification Adaptive Watermarking Approach for 3D Multiresolution Meshes Security 19th

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A Spherical Harmonics-LSB-quantification Adaptive Watermarki...

19th International Conference on Computer Analysis of Images and Patterns, CAIP 2021

作者： Sayahi, Ikbel Jallouli, Malika Mabrouk, Anouar Ben Amar, Chokri Ben Mahjoub, Mohamed Ali LATIS Laboratory of Advanced Technology and Intelligent Systems Université de Sousse Ecole Nationale d’Ingénieurs de Sousse Sousse4023 Tunisia University of SFax Research Groups on Intelligent Machines Laboratory Private National Engineering School of Monastir Monastir Tunisia Department of Mathematics Higher Institute of Applied Mathematics and Computer Science University of Kairouan Kairouan3100 Tunisia Department of Mathematics Faculty of Science University of Tabuk Tabuk Saudi Arabia College of Computers and Information Technology Taif University Taif Saudi Arabia

ISBN: (纸本)9783030891305

Since the release of the first 3D watermarking algorithm in the last decade, 3D meshes applying such an algorithm have been growing up with a diversity of techniques during the embedding of information into meshes. The main objective is always to secure data shared by remote users. The originality of the present work is issued from the use of spherical harmonics to transform the mesh to the frequency domain to be watermarked, and the entropy concept to estimate the optimal order of the reconstruction. Our algorithm includes three parts. The first is the data pre-processing based on a combination of a signature generated by the well-known Secure Hash Algorithm, and a mesh description, subject to a convolutional error correcting code. The second step is the mesh treatment, consisting in extracting spherical harmonics coefficients to be watermarked. Finally, the third part consists in inserting data into coefficients using LSB and quantification method, and next to reconstruct the watermarked mesh. The experimentation of our approach has shown a high insertion rate, while maintaining the mesh quality. It is also robust against the most popular attacks. Our results showed that the present approach improves the existing works. © 2021, Springer Nature Switzerland AG.

关键词： Entropy