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Efficient Nonnegative Tensor Decomposition Using Alternating Direction Proximal Method of Multipliers

Chinese Journal of Electronics 2024年第5期33卷 1308-1316页

作者： Deqing WANG Guoqiang HU State Key laboratory of robotics Shenyang Institute of Automation Chinese Academy of Sciences Institutes for robotics and Intelligent Manufacturing Chinese Academy of Sciences Key laboratory of Marine robotics Liaoning Province College of artificial Intelligence Dalian Maritime University

Nonnegative CANDECOMP/PARAFAC（NCP） tensor decomposition is a powerful tool for multiway signal processing. The alternating direction method of multipliers（ADMM） optimization algorithm has become increasingly popular for solving tensor decomposition problems in the block coordinate descent framework. However,the ADMM-based NCP algorithm suffers from rank deficiency and slow convergence for some large-scale and highly sparse tensor data. The proximal algorithm is preferred to enhance optimization algorithms and improve convergence properties. In this study, we propose a novel NCP algorithm using the alternating direction proximal method of multipliers（ADPMM） that consists of the proximal algorithm. The proposed NCP algorithm can guarantee convergence and overcome the rank deficiency. Moreover, we implement the proposed NCP using an inexact scheme that alternatively optimizes the subproblems. Each subproblem is optimized by a finite number of inner iterations yielding fast computation speed. Our NCP algorithm is a hybrid of alternating optimization and ADPMM and is named *** experimental results on synthetic and real-world tensors demonstrate the effectiveness and efficiency of our proposed algorithm.

关键词： Nonnegative CANDECOMP/PARAFAC Alternating direction proximal method of multipliers Tensor decomposition Proximal algorithm Proximal algorithm Sparse regularization Sparse regularization Proximal algorithm Sparse regularization FACTORIZATION OPTIMIZATION ORGANIZATION ALGORITHMS MATRIX

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Grasp Detection with Hierarchical Multi-Scale Feature Fusion and Inverted Shuffle Residual

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Tsinghua Science and Technology 2024年第1期29卷 244-256页

作者： Wenjie Geng Zhiqiang Cao Peiyu Guan Fengshui Jing Min Tan Junzhi Yu State Key laboratory of Multimodal artificial Intelligence Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China School of artificial Intelligence University of Chinese Academy of SciencesBeijing 100049China Department of Advanced Manufacturing and robotics College of EngineeringPeking UniversityBeijing 100871China

Grasp detection plays a critical role for robot *** pixel-wise grasp detection networks with encoder-decoder structure receive much attention due to good accuracy and ***,they usually transmit the high-level feature in the encoder to the decoder,and low-level features are *** is noted that low-level features contain abundant detail information,and how to fully exploit low-level features remains ***,the channel information in high-level feature is also not well ***,the performance of grasp detection is *** solve these problems,we propose a grasp detection network with hierarchical multi-scale feature fusion and inverted shuffle *** low-level and high-level features in the encoder are firstly fused by the designed skip connections with attention module,and the fused information is then propagated to corresponding layers of the decoder for in-depth feature *** a hierarchical fusion guarantees the quality of grasp ***,an inverted shuffle residual module is created,where the high-level feature from encoder is split in channel and the resultant split features are processed in their respective *** such differentiation processing,more high-dimensional channel information is kept,which enhances the representation ability of the ***,an information enhancement module is added before the encoder to reinforce input *** proposed method attains 98.9%and 97.8%in image-wise and object-wise accuracy on the Cornell grasping dataset,respectively,and the experimental results verify the effectiveness of the method.

关键词： grasp detection hierarchical multi-scale feature fusion skip connections with attention inverted shuffle residual

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Theaflavins ameliorate insulin resistance and anxiety-like behavior induced by propionate combined with high-fat diet

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Food Science and Human Wellness 2024年

作者： Huijuan Bai Qian Yang Xiang Zuo Qian Yu Qili Zhao Yifei Tian Yixin Xu Jiawei Du Xin Zhao Xizeng Feng College of life Science， State Key laboratory of Medicinal Chemical Biology， Nankai University Institute of robotics & Automatic Information System， College of artificial Intelligence， Nankai University

Insulin resistance is a key pathological process in the development of type II diabetes， which can also lead to depression， anxiety and other symptoms. With the accelerated pace of life， food with high-energy and additives is becoming more and more common in people's diet. Propionate is a naturally existing short-chain fatty acid salt that has been widely used as a food preservative， but studies have found that low concentrations of propionate can induce insulin resistance. Theaflavins (TFs) are characteristic components of black tea， which have the biological activity of hypoglycemic and lipid-lowering. In the present study， we used propionate combined with high-fat diet to induce insulin resistance in mice and treated them with theaflavins. The results showed that propionate could aggravate insulin resistance and anxiety induced by high-fat diet in mice. TFs alleviated insulin resistance and anxiety induced by propionate combined with high-fat diet by improving the disorder of metabolic gene expression and inflammation in the adipose and liver tissue， as well as neuroinflammation in the brain. Our study suggests that the food additive propionate combined with a high-fat diet may be harmful to organismal health. As a natural drug， TFs have the potential to alleviate insulin resistance induced by propionate combined with high-fat diet.

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Energy-efficient buffer and service rate allocation in manufacturing systems using hybrid machine learning and evolutionary algorithms

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Advances in Manufacturing 2024年第2期12卷 227-251页

作者： Si-Xiao Gao Hui Liu Jun Ota Key laboratory of Traffic Safety on Track of Ministry of Education Institute of Artificial Intelligence and Robotics(IAIR)School of Traffic and Transportation EngineeringCentral South UniversityChangsha410075People’s Republic of China Research into Artifacts Center for Engineering School of EngineeringThe University of TokyoTokyoJapan

Currently,simultaneous buffer and service rate allocation is a topic of interest in the optimization of manufacturing *** allocation problems have been solved previously to satisfy economic requirements;however,owing to the progress of green manufacturing,energy conservation and environmental protection have become increasingly ***,an energy-efficient approach is developed to maximize the throughput and minimize the energy consumption of manufacturing systems,subject to the total buffer capacity,total service rate,and predefined energy *** energy-efficient approach integrates the simulated annealing-non-dominated sorting genetic algorithm-II with the honey badger algorithm-histogram-based gradient boosting regression *** former algorithm searches for Pareto-optimal solutions of sufficient *** latter algorithm builds prediction models to rapidly calculate the throughput,energy consumption,and energy *** examples show that the proposed hybrid approach can achieve a better solution quality compared with previously reported ***,the prediction models can rapidly evaluate manufacturing systems with sufficient *** study benefits the multi-objective optimization of green manufacturing systems.

关键词： Energy-efficient allocation Multi-objective optimization Energy efficiency Energy consumption Machine learning

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BLE beacons for sample position estimation in a life science automation laboratory

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Transportation Safety and Environment 2024年第3期6卷 111-123页

作者： Haiping Wu Steffen Junginger Thomas Roddelkopf Hui Liu Kerstin Thurow Institute of artificial Intelligence&robotics(IAIR) Key Laboratory of Traffic Safety on Track of Ministry of EducationSchool of Traffic and Transportation EngineeringCentral South UniversityChangsha 410075HunanChina Institute of Automation(IAT) University of Rostock18119 RostockGermany Center of life Science Automation(celisca) University of Rostock18119 RostockGermany

Estimation of the sample position is essential for working process monitoring and management in the life science automation lab*** low-energy(BLE)beacons have the advantages of low price,small size and low energy consumption,which make them a promising solution for sample position estimation in the automated *** fingerprinting models have been proposed to achieve indoor localization with the received signal strength(RSS)***,most of the research depends on intensive beacon *** estimation,which depends entirely on one beacon,is more suitable for sample position estimation in large automated *** complexity of the life science automation laboratory environment brings challenges to the traditional path loss model(PLM),which is a widely used radio wave propagation model-based proximity estimation *** this paper,BLE sensing devices for sample position estimation are *** BLE beacon-based proximity estimation is discussed in the framework of machine learning,in which the support vector regression(SVR)is utilized to model the nonlinear relationship between the RSS data and distance,and the Kalman filter is utilized to decrease the RSS data *** experimental results over different environments indicate that the SVR outperforms the PLM significantly,and provides 1 m absolute errors for more than 95%of the testing *** Kalman filter brings benefits to stable distance *** from proximity-based sample position estimation,the proposed framework turned out to be effective in position estimation between parallel workbenches and position estimation on an automated workstation.

关键词： BLE beacon position estimation proximity estimation received signal strength(RSS) support vector regression(SVR) life science automation laboratory

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BLE beacon-based floor detection for mobile robots in a multi-floor automation laboratory

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Transportation Safety and Environment 2024年第2期6卷 114-121页

作者： Haiping Wu Hui Liu Thomas Roddelkopf Kerstin Thurow Institute of artificial Intelligence&robotics(IAIR) Key Laboratory of Traffic Safety on Track of Ministry of EducationSchool of Traffic and Transportation EngineeringCentral South UniversityChangshaHunan 410075China Center of life Science Automation(celisca) 18119 Rostoc kGerman y Institute of Automation(IAT) University Rostock18119 RostockGermany

As an important task of multi-floor localization,floor detection has elicited great *** infrastructures like Wi-Fi and Bluetooth Low Energy(BLE)play important roles in floor ***,most floor detection research tends to focus on data modelling but pays little attention to the data collection system,which is the basis of wireless infrastructure-based floor *** fact,the floor detection task can be greatly simplified with proper data collection system *** this paper,a floor detection solution is developed in a multi-floor life science automation lab.A data collection system consisting of BLE beacons,a receiver node and an Internet of Things(IoT)cloud is *** features of the BLE beacon under different settings are evaluated in detail.A mean filter is designed to deal with the fluctuation of the received signal strength indicator data.A simple floor detection method without a training process was implemented and evaluated in more than 100 floor detection *** time delay and floor detection accuracy under different settings are ***,floor detection is evaluated on the H20 multi-floor transportation *** sensor nodes are installed on the robot at different *** floor detection performance with different installation heights is *** experimental results indicate that the proposed floor detection method provides floor detection accuracy of 0.9877 to 1 with a time delay of 5s.

关键词： BLE beacon floor detection multi-floor automation transportation robot

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Correcting of the unexpected localization measurement for indoor automatic mobile robot transportation based on a neural network

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Transportation Safety and Environment 2024年第2期6卷 24-35页

作者： Jiahao Huang Steffen Jung inger Hui Liu Kerstin Thurow Center for life Science Automation(CELISCA) University of RostockRostock 18119Germany Institute of Automation University of RostockRostock 18119Germany Institute of artificial Intelligence&robotics(IAIR) School of Traffic&Transportation EngineeringCentral South UniversityChangsha 410075HunanChina

The increasing use of mobile robots in laboratory settings has led to a higher degree of laboratory ***,when mobile robots move in laboratory environments,mechanical errors,environmental disturbances and signal interruptions are *** can compromise the accuracy of the robot’s localization,which is crucial for the safety of staff,robots and the laboratory.A novel time-series predicting model based on the data processing method is proposed to handle the unexpected localization measurement of mobile robots in laboratory *** proposed model serves as an auxiliary localization system that can accurately correct unexpected localization errors by relying solely on the historical data of mobile *** experimental results demonstrate the effectiveness of this proposed method.

关键词： mobile robots laboratory automation indoor localization neural network

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Towards robustness and generalization of point cloud representation:A geometry coding method and a large-scale object-level dataset

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Computational Visual Media 2024年第1期10卷 27-43页

作者： Mingye Xu Zhipeng Zhou Yali Wang Yu Qiao Guangdong-Hong Kong-Macao Joint laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institute of Advanced TechnologyChinese Academy of SciencesShenzhen518000China University of Chinese Academy of Sciences Beijing065001China Shanghai AI laboratory Shanghai200001China SIAT Branch Shenzhen Institute of Artificial Intelligence and Robotics for SocietyShenzhen518000China Alibaba DAMO Academy Hangzhou242332China

Robustness and generalization are two challenging problems for learning point cloud *** tackle these problems,we first design a novel geometry coding model,which can effectively use an invariant eigengraph to group points with similar geometric information,even when such points are far from each *** also introduce a large-scale point cloud dataset,*** consists of 184 categories and 51,915 synthetic objects,which brings new challenges for point cloud classification,and provides a new benchmark to assess point cloud cross-domain ***,we perform extensive experiments on point cloud classification,using ModelNet40,ScanObjectNN,and our PCNet184,and segmentation,using ShapeNetPart and *** method achieves comparable performance to state-of-the-art methods on these datasets,for both supervised and unsupervised *** and our dataset are available at https://***/MingyeXu/PCNet184.

关键词： geometry coding self-supervised learning point cloud classification segmentation 3D analysis

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Human-robot object handover:Recent progress and future direction

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Biomimetic Intelligence & robotics 2024年第1期4卷 1-13页

作者： Haonan Duan Yifan Yang Daheng Li Peng Wang Institute of Automation Chinese Academy of SciencesBeijing 100190China School of artificial Intelligence University of Chinese Academy of SciencesBeijing 100049China Centre for artificial Intelligence and robotics Hong Kong Institute of Science and InnovationChinese Academy of SciencesHong Kong 999077China

Human-robot object handover is one of the most primitive and crucial capabilities in human-robot *** is of great significance to promote robots to truly enter human production and life scenarios and serve human in numerous *** progressions in the field of human-robot object handover have been made by *** article reviews the recent literature on human-robot object *** this end,we summarize the results from multiple dimensions,from the role played by the robot(receiver or giver),to the end-effector of the robot(parallel-jaw gripper or multi-finger hand),to the robot abilities(grasp strategy or motion planning).We also implement a human-robot object handover system for anthropomorphic hand to verify human-robot object handover *** review aims to provide researchers and developers with a guideline for designing human-robot object handover methods.

关键词： Human-robot interactions Object handover

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A deep convolutional neural network for diabetic retinopathy detection via mining local and long-range dependence

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CAAI Transactions on Intelligence Technology 2024年第1期9卷 153-166页

作者： Xiaoling Luo Wei Wang Yong Xu Zhihui Lai Xiaopeng Jin Bob Zhang David Zhang Shenzhen Key laboratory of Visual Object Detection and Recognition Harbin Institute of TechnologyShenzhenChina Peng Cheng laboratory ShenzhenChina Shenzhen Institute of artificial Intelligence and robotics for Society ShenzhenChina College of Big Data and Internet Shenzhen Technology UniversityShenzhenChina The Department of Computer and Information Science University of MacaoMacaoMacaoChina The Chinese University of Hong Kong(Shenzhen) ShenzhenChina

Diabetic retinopathy(DR),the main cause of irreversible blindness,is one of the most common complications of *** present,deep convolutional neural networks have achieved promising performance in automatic DR detection *** convolution operation of methods is a local cross-correlation operation,whose receptive field de-termines the size of the local neighbourhood for ***,for retinal fundus photographs,there is not only the local information but also long-distance dependence between the lesion features(*** and exudates)scattered throughout the whole *** proposed method incorporates correlations between long-range patches into the deep learning framework to improve DR ***-wise re-lationships are used to enhance the local patch features since lesions of DR usually appear as *** Long-Range unit in the proposed network with a residual structure can be flexibly embedded into other trained *** experimental results demon-strate that the proposed approach can achieve higher accuracy than existing state-of-the-art models on Messidor and EyePACS datasets.

关键词： image classification medical image processing pattern recognition

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