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SSRN 2023年

作者： Zhang, Ke Song, Chengqun Cheng, Jun Liu, Jiancheng Wang, Lei Hao, Fusheng Wu, Fuxiang Lou, Jie Dong, Qi CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China The Chinese University of Hong Kong 999077 Hong Kong China Nuclear Power Operations Co. Ltd China

Patrolling is crucial for maintaining the safety and security of real estate such as buildings, factories, and public areas. In general, a mobile patrol system (MPS) is most suited for circumstances when the environment is hazardous, difficult to reach, or where repetitive activities must be completed. MPS must overcome practical obstacles in order to provide thorough regional coverage and concentrated attention to high-risk regions. To address the drawbacks, a novel multi-target path updating and optimization method for MPS in nuclear power plants is proposed in this paper. The method leverages a Gaussian Mixture Model (GMM) to construct a risk heat map using environmental and security risk information. The high-risk points on the map are identified as targets for a single patrol path, which is dynamically updated to ensure that unpatrolled areas are considered during the next patrol. The static risk heat map is updated if any danger is detected. The multi-target Rapidly-Exploring Random Tree star (MT-RRT*) algorithm is used to obtain the patrol order and a feasible path, and then an efficient optimization approach is employed to generate a multi-target patrol path. The method has been shown to effectively achieve both comprehensive regional coverage and targeted monitoring of high-risk areas through multiple patrols. Although the proposed method is introduced from the scene of nuclear power plants, it can also be applied to the patrol of other scenes, e.g. military areas, hazardous waste sites, and industrial sites. © 2023, The Authors. All rights reserved.

关键词： Motion planning

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EEG Feature in Amputee with Five Projected Fingers: A Comparison Study

EEG Feature in Amputee with Five Projected Fingers: A Compar...

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IEEE International Conference on Robotics and Biomimetics

作者： Wenyuan Liang Changyu Qin Yuanzhe Dong Xi Tang Guanglin Li Peng Fang Sheng Bi Beijing Key Laboratory of Rehabilitation Technical Aids for Old-Age Disability National Research Center for Rehabilitation Technical Aids Beijing China Beijing Machine and Equipment Institute Beijing China CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems and Shenzhen Engineering Laboratory of Neural Rehabilitation Technology Shenzhen Institute of Advanced Technology Shenzhen China

ISBN: (纸本)9781665481106

The somatosensory ERP provides a tool to understand the tactile sensation mechanism for the projected finger. A total of four subjects (1 amputee & 3 able-bodied) were recruited. The tactile sensation is evoked via TENS. Through applying the stimulation electrodes on the target fingers, simultaneously the EEG data was recorded. Two major findings are concluded as follows. First, the somatosensory ERP of the early-stage in three groups are similar, and then it could infer that the projected finger may have a similar delivery pathway as the natural finger. Second, since three groups show similar cortical processing characteristics, it could infer that the projected finger evoke similar somatosensory neuro information as the natural finger. This tactile sensation mechanism study shows the uniformity between the natural finger and the natural finger.

关键词： Somatosensory Electrodes Biomimetics Fingers Robot sensing systems Electroencephalography

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Low-Light Enhancement Effect on Classification and Detection: An Empirical Study

arXiv

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arXiv 2024年

作者： Wu, Xu Lai, Zhihui Jie, Zhou Gao, Can Hou, Xianxu Zhang, Ya-Nan Shen, Linlin The Computer Vision Institute College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen518060 China Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University Shenzhen518060 China School of AI and Advanced Computing Xi’an Jiaotong-Liverpool University China The National Engineering Laboratory for Big Data System Computing Technology Shenzhen University SZU Branch Shenzhen Institute of Artificial Intelligence and Robotics for Society Guangdong Shenzhen518060 China

Low-light images are commonly encountered in real-world scenarios, and numerous low-light image enhancement (LLIE) methods have been proposed to improve the visibility of these images. The primary goal of LLIE is to generate clearer images that are more visually pleasing to humans. However, the impact of LLIE methods in high-level vision tasks, such as image classification and object detection, which rely on high-quality image datasets, is not well explored. To explore the impact, we comprehensively evaluate LLIE methods on these high-level vision tasks by utilizing an empirical investigation comprising image classification and object detection experiments. The evaluation reveals a dichotomy: While Low-Light Image Enhancement (LLIE) methods enhance human visual interpretation, their effect on computer vision tasks is inconsistent and can sometimes be harmful. Our findings suggest a disconnect between image enhancement for human visual perception and for machine analysis, indicating a need for LLIE methods tailored to support high-level vision tasks effectively. This insight is crucial for the development of LLIE techniques that align with the needs of both human and machine vision. Copyright © 2024, The Authors. All rights reserved.

关键词： machine vision

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SA-GCNN: Spatial Attention Based Graph Convolutional Neural Network for Pedestrian Trajectory Prediction

SA-GCNN: Spatial Attention Based Graph Convolutional Neural ...

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IEEE International Conference on Robotics and Biomimetics

作者： Xuesong Li Qieshi Zhang Wanting Wang Jian Tang Dong Liu Jun Cheng CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China Shenzhen College of Advanced Technology University of Chinese Academy of Sciences Beijing China The Chinese University of Hong Kong Hong Kong China Midea Group (Shanghai) Co. Ltd China

Accurate predicting the trajectories of moving pedestrians is a key technology in automatic driving system, which is challenging due to the complex interactions in pedestrians. Recent studies have shown that Spatio-Temporal (ST) graph has great ability to capture interactions between pedestrians. However, these methods neglect pedestrian’s limited vision and contains many invalid interactions. In order to tackle this issue, we proposed a Spatial Attention based Graph Convolutional Neural Network (SA-GCNN), which uses SA module to construct ST graph and focus on the most useful interactions. Meanwhile, SA-GCNN introduces temporal convolution module to capture temporal dependency between ST graphs. Moreover, the Graph Convolutional Network (GCN) and Temporal Convolution Network (TCN) are combined to extract graph features and decode multi-modal trajectories. Our model is trained on the widely-accepted benchmark datasets ETH and UCY. The empirical findings demonstrate that our SA-GCNN surpasses the performance of existing state-of-the-art methods used for comparison, suggests that our proposed model exhibits enhanced proficiency in capturing pedestrian interactions.

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TVEG: Model Selection of the Time-Varying Exponential Family Distributions Graphical Models

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IEEE Transactions on Network Science and Engineering 2025年

作者： Liu, Juan Mei, Guofeng Xia, Yuanqing Wu, Xiaoqun Lu, Jinhu Beijing Forestry University School of Technology Beijing100107 China Italy Zhongyuan University of Technology Henan 451191 China Wuhan University School of Mathematics and Statistics Wuhan430072 China Beihang University State Key Laboratory of Software Development Environment Beijing Advanced Innovation Center for Big Data and Brain Machine Intelligence School of Automation Science and Electrical Engineering Beijing100191 China

The undirected graphical model, a popular class of statistical model, offers a way to describe and explain the relationships among a set of variables. However, it remains a challenge to choose a certain graphical model to explain the relationships of variables adequately, especially when the relationships of variables are rewiring over time. This paper proposes the Time-Varying Exponential Family Distributions Graphical (TVEG) models, with time-varying structures and exponential family node-wise conditional distributions. TVEG models extend the scope of available graph models and can be applied to time-varying and exponential family distribution observation data in reality. We propose the Temporally Smoothed L1regularized exponential family graphical estimator (TSLEG), an estimator to infer the structure of TVEG from observations. We derive sufficient conditions for the TSLEG to recover the block partition and sparse pattern with high probability. We derive a message-passing optimization method to solve the TSLEG for time-varying Ising, Gaussian, exponential, and Poisson graphs based on the ADMM. The synthetic network simulations corroborate the theoretical analysis. Analysing of real data of stocks and the US Senate by the time-varying exponential model and Poisson model indicates the effectiveness and practicality of TVEG models. © 2025 IEEE. All rights reserved.

关键词： ADMM exponential family distributions Graphical model pseudo maximum likelihood estimation

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Short-Term and Mid-Term Population Projections Especially of Germany via WASD Neural Network: Theoretical Parts

Short-Term and Mid-Term Population Projections Especially of...

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第40届中国控制会议

作者： Zhiwen Yuan Siyuan Feng Chengxu Ye Yunong Zhang School of Computer Science and Engineering Sun Yat-sen University Guangdong Key Laboratory of Modern Control Technology Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education School of Computer Science Qinghai Normal University

Population has a direct impact on the demands of consumption, resource, and employment. Population factors, such as size and growth rate, are crucial in making decisions and plans of trade, environment protection, resource allocation and human resource development. Thus, population projection is an essential part of strategy development for companies as well as governments, and draws the attention of many researchers. Some projection methods have been developed, but most of the popular ones require a vast knowledge in demography and only can be operated by skilled professionals. In this paper, as an improvement, we theoretically develop a regression model and a subtrend model of WASD（weights and structure determination）neural network in a simultaneous manner to make population projections especially of Germany in the mid term and near ***, we aim at utilizing the learning capabilities of the WASD neural-network models with single input and multiple inputs, having been very popular recently, to propose these easy-used and high accuracy models for verifications and projections via later computer experiments.

关键词： Germany population Projections Neural network WASD（weights and structure determination） Multiple inputs

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Simultaneous q-Space Sampling Optimization and Reconstruction for Fast and High-fidelity Diffusion Magnetic Resonance Imaging

arXiv

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arXiv 2024年

作者： Yang, Jing Cheng, Jian Li, Cheng Fan, Wenxin Zou, Juan Wu, Ruoyou Wang, Shanshan Paul C. Lauterbur Research Center for Biomedical Imaging Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Guangdong Shenzhen China University of Chinese Academy of Sciences Beijing China State Key Laboratory of Software Development Environment Beihang University Beijing China Peng Cheng Laboratory Guangdong Shenzhen China Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application Guangdong China Key Laboratory of Data Science and Intelligent Computing Institute of International Innovation Beihang University Yuhang District Hangzhou China

Diffusion Magnetic Resonance Imaging (dMRI) plays a crucial role in the noninvasive investigation of tissue microstructural properties and structural connectivity in the in vivo human brain. However, to effectively capture the intricate characteristics of water diffusion at various directions and scales, it is important to employ comprehensive q-space sampling. Unfortunately, this requirement leads to long scan times, limiting the clinical applicability of dMRI. To address this challenge, we propose SSOR, a Simultaneous q-Space sampling Optimization and Reconstruction framework. We jointly optimize a subset of q-space samples using a continuous representation of spherical harmonic functions and a reconstruction network. Additionally, we integrate the unique properties of diffusion magnetic resonance imaging (dMRI) in both the q-space and image domains by applying l1-norm and total-variation *** experiments conducted on HCP data demonstrate that SSOR has promising strengths both quantitatively and qualitatively and exhibits robustness to noise. Copyright © 2024, The Authors. All rights reserved.

关键词： Magnetic resonance imaging

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Tactile grasp stability classification based on graph convolutional networks

Tactile grasp stability classification based on graph convol...

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2021 IEEE International Conference on Real-Time computing and Robotics, RCAR 2021

作者： Mi, Tingting Que, Dashun Fang, Senlin Zhou, Zhenning Ye, Chaoxiang Liu, Chengliang Yi, Zhengkun Wu, Xinyu School of Information Engineering Wuhan University of Technology Wuhan430070 China Shenzhen Institute of Advanced Technology CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen518055 China Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China Shenzhen Key Laboratory of Smart Sensing and Intelligent Systems Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China SIAT Branch Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen518055 China

ISBN: (纸本)9781665436786

One of the challenges for robots to grasp unknown objects is to predict whether objects will fall at the beginning of grasping. Evaluating robotic grasp state accurately and efficiently is a significant step to address this issue. In this paper, based on the different fusion approaches of multi-sensor tactile signals, we propose two novel methods based on Graph Convolution Network (GCN) for robotic stability classification. Specifically, we propose two deep learning methods including GCN based on data-level fusion (GCN-DF) and GCN based on feature-level fusion (GCN-FF). We explore the optimal parameters for transforming sensor signals into a graph structure. Furthermore, we verify the effectiveness of the proposed methods on the BioTac Grasp Stability (BiGS) dataset. The experimental results prove that the proposed approaches achieve higher classification accuracy than Support Vector machine (SVM) and Long Short-Term Memory (LSTM). © 2021 IEEE.

关键词： Convolution

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Vehicle Trajectories Prediction via Nearest Neighbor Historical Behavior

Vehicle Trajectories Prediction via Nearest Neighbor Histori...

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IEEE International Conference on Robotics and Biomimetics

作者： Wanting Wang Qieshi Zhang Xuesong Li Jian Tang Dong Liu Jun Cheng CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China Shenzhen College of Advanced Technology University of Chinese Academy of Sciences Beijing China The Chinese University of Hong Kong Hong Kong China Midea Group (Shanghai) Co. Ltd China

Trajectory prediction is crucial in enabling autonomous driving systems to make informed decisions, plan appropriate paths, and enhance traffic safety and efficiency. It remains an immensely challenging task due to complex interaction between vehicles, the difficulty of extracting traffic rules information, and the dynamic changes in traffic flow. Existing methods model the interactions among vehicles or extract traffic flow density features, but overlook the effects of neighboring vehicles’ movements and interactions, which contain traffic rules and the influence of surrounding traffic conditions. To achieve this, we propose a new method taking into account neighboring vehicles’ dynamic behaviors and history, allowing for a more comprehensive understanding of the traffic environment. Firstly, a novel dual-stream nearest vehicle attention mechanism method is proposed that leverages the historical state and position of the neighbors’ vehicle and captures its features. Secondly, in order to effectively encode these features, we recombine them by the multi-head attention mechanism. Lastly, in order to fusion these features and other inputs, we extract and combine the relationships between them by a self-attention mechanism. Our approach not only outperforms other methods in evaluation metrics but also demonstrates excellent results in real-world scenarios.

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Performance Evaluation of Deep Learning Techniques on Classifying Commercial Sensor-based Auditory Brainstem Response 16

Performance Evaluation of Deep Learning Techniques on Classi...

引用

16th International Conference on Sensing Technology, ICST 2023

作者： Huang, Kaiyang Wang, Xin He, Yuchao Ji, Junyu Xu, Yangjie Jia, Desheng Zhao, Chunrui Huang, Jinzhu Tao, Yuan Pan, Hongguang Li, Guanglin Chen, Shixiong Guangzhou University of Chinese Medicine School of Medical Information Engineering Guangzhou China Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Cas Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen China Reliability and Trust University of Luxembourg Interdisciplinary Centre for Security Luxembourg Luxembourg Shenzhen Children's Hospital Department of Otolaryngology Shenzhen China The First Dongguan Affiliated Hospital of Guangdong Medical University Department of Endocrine Dongguan China Peking University Shenzhen Hospital Department of Otorhinolaryngology Shenzhen China

ISBN: (纸本)9798350395341

To objectively evaluate the health condition of the auditory pathway, Auditory Brainstem Response (ABR) is used in the clinic. The clinical diagnosis is concluded by the audiologist according to the morphology and characteristic waves of ABR through manual screening. It is a time-consuming and subjective task that not only reduces the efficiency of the physicians but also introduces subjective error which might cause an incorrect diagnosis. Therefore, it is necessary to propose an objective method that can solve the abovementioned problems. Deep Learning (DL) technique develops fast and is widely used in many fields, including biomedical engineering. Hence, DL might help with the objective classification of ABR and release the physicians from the heavy workload. In this paper, we evaluated the classification performance of three commonly used DL methods, namely EEGNet, DeepConvNet and ShallowConvNet, on several metrics. The results showed that on the task of classifying normal and abnormal ABR signals, EEGNet had the best performance, with the accuracy of 0.9995 ± 0.0011, the precision of 0.9995 ± 0.0011, the recall of 0.9995 ± 0.0011, the F1-score of 0.9995 ± 0.0011, and the AUC of 1.0000 ± 0.0000. It is believed that DL can help with objective ABR classification and release the physicians' workload. © 2023 IEEE.

关键词： Biomedical engineering

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