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Cross-modal data fusion and deep learning techniques enabled non-destructive fish vitality recognition during cryogenic waterless live transport

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Biosystems Engineering 2025年 257卷

作者： Zhu, Yanfei Zhang, Wenguan Zhang, Yongjun Zhang, Xiaoshuan Zhang, Mengjie College of Engineering China Agricultural University Beijing100083 China College of Animal Science and Technology China Agricultural University Beijing100193 China Sanya Research Institute of China Agricultural University Sanya572000 China School of Information Engineering Shandong Youth University of Political Science Jinan250103 China New Technology Research and Development Center of Intelligent Information Controlling in Universities of Shandong Jinan250103 China Smart Healthcare Big Data Engineering and Ubiquitous Computing Characteristic Laboratory in Universities of Shandong Jinan250103 China

Cryogenic waterless live transport (CWLT) is widely regarded as a green, efficient and low-carbon transportation strategy, which can ensure a certain degree of fish survival rates, enhance water resource utilisation and reduce transportation costs. This study focuses on the pearl gentian grouper, integrating multi-modal sensors, deep learning and cross-modal data fusion techniques for non-destructive vitality recognition during the live transport process. Changes in fish surface and physiological characteristics were captured based on multi-modal sensors. A cross-modal vitality recognition network (CmVRNet) was developed based on cross-modal data fusion and multi-input deep learning techniques. The enhanced whale optimisation algorithm based on multi-strategy fusion (EWOAm) was developed to optimise hyperparameters of the model (EWOAm-CmVRNet). The results showed that: (1) Cluster analysis classified physiological status of the grouper into four categories: high vitality (HV), medium vitality (MV), weak vitality (WV) and near death (ND), with significant differences between physiological data of fish with different vitality levels but the same size (P © 2025 IAgrE

关键词： Physiological models

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Outlier detection algorithm based on density and distance

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Journal of Physics: Conference Series 2021年第1期1941卷

作者： Ying Qu Fei Guo School of Economics and Management Hebei University of Science and Technology Shijiazhuang Hebei 050018 China Data Science and Intelligent Computing Research Center Hebei University of Science and Technology Shijiazhuang Hebei 050018 China

In order to solve the problem that the existing outlier detection algorithm is difficult to detect the one-dimensional integer data set with uneven frequency distribution and uniform distance distribution and low accuracy, the advantages of density outlier detection and distance outlier detection can be combined. An outlier detection algorithm DAD (Density and Distance) based on density and distance was proposed. In order to improve the possibility of outlier sample distance, the algorithm can define the weight distance; introduce the global density outlier factor combined with the weight distance as the relative distance, and use the cutting edge strategy to quickly cut the outliers based on the minimum spanning tree. Then, an artificial data set was used to test the algorithm. Experimental results showed that the algorithm had a good outlier detection accuracy in dealing with data sets with uneven frequency distribution and uniform distance distribution.

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Multi-robot Path Planning and Trajectory Smoothing

Multi-robot Path Planning and Trajectory Smoothing

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IEEE International Conference on Automation Science and Engineering (CASE)

作者： Hub Ali Gang Xiong Huaiyu Wu Bin Hu Zhen Shen Hongxing Bai School of Mechanical and Electrical Engineering University of Electronic Science and Technology of China State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China Guangdong Engineering Research Center of 3D Printing and Intelligent Manufacturing Cloud Computing Center Chinese Academy of Sciences Dongguan China Beijing Engineering Research Center of Intelligent Systems and Technology Institute of Automation Chinese Academy of Sciences China Hubei Jointown Technology Co. Ltd Artificial Intelligence Research Institute Wuhan China

ISBN: (数字)9781728169040

ISBN: (纸本)9781728169057

In this paper we consider a problem in task execution for multi-robot trajectory planning with collision avoidance in a shared working environment. Consider two or more robots generating trajectories towards their respective goal positions. The collision may occur if their trajectory coordinates are intersecting at a point or follow the same path segment simultaneously. The central planner is introduced to control robot motion in the collision state and to reduce the complexity of the multi-robot path planning system. The global path for every robot is generated by the $\mathrm{A}^{*}$ algorithm in a grid-based environment. The path has presented a sequence of optimal grid numbers and later transformed into Cartesian coordinates for smooth trajectory generation. The central planner takes an optimal grid sequence for every robot to analyze the collision state according to its cost value. It regenerates the trajectories to minimize the complexity cost value and replaces the previous trajectory based on minimum cost value. In the collision state, the central planner allows one robot at a time to pass along the conflict path segment and hold others in queue at a safety offset distance until the previous robot passes safely. The algorithm has been applied to robots working in a shared environment in complex maps and the simulations is performed with MATLAB to calculate the efficiency of this approach for handling collision states in a multi-robot path planning system.

关键词： Robot kinematics Trajectory Collision avoidance Task analysis Resource management

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Comparative Study of Four Speed Controllers of Brushless DC Motors for Industrial Applications

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IFAC-PapersOnLine 2020年第5期53卷 59-64页

作者： Tariku Sinshaw Tamir Gang Xiong Zhen Shen Xiaoyan Gong Sheng Liu Ehtisham Lodhi Li Wan Xisong Dong State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing 100190 China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing 100049 China Guangdong Engineering Research Center of 3D Printing and Intelligent Manufacturing Cloud Computing Center Chinese Academy of Sciences Donggguan 523808 China Beijing Engineering Research Center of Intelligent Systems and Technology Institute of Automation Chinese Academy of Sciences Beijing 100190 China Ten Dimensions (Guangdong) Technology Co. Ltd. Foshan 528225 China

Direct current (DC) motors are one of the most important kind of motors and are widely used in robotic and industrial applications. Recently, there have been significant efforts to develop direct current (DC) motors in an attempt to control speed of motors. However, conventional controlling approaches perform undesirably in terms of stability and quick response. Therefore, this paper presents a hybrid intelligent controller configuration for optimized speed control of brushless direct current (BLDC) motors in a factory supervisory control data acquisition (SCADA) system. We compare this hybrid intelligent controller with a conventional PID controller, fuzzy logic controller (FLC), and artificial neural network model reference controller (ANNMRC) in MATLAB , and the results show that the hybrid (neuro-fuzzy) controller performs superior in terms of stability, speed trajectory tracking capability, fast response, and simplicity for implementation.

关键词： BLDC motor fuzzy logic controller (FLC) artificial neural network model reference controller (ANNMRC) hybrid (neuro-fuzzy) control

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Lessons Learned from Assessing Trustworthy AI in Practice

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Digital Society 2023年第3期2卷 1-25页

作者： Vetter, Dennis Amann, Julia Bruneault, Frédérick Coffee, Megan Düdder, Boris Gallucci, Alessio Gilbert, Thomas Krendl Hagendorff, Thilo van Halem, Irmhild Hickman, Eleanore Hildt, Elisabeth Holm, Sune Kararigas, Georgios Kringen, Pedro Madai, Vince I. Wiinblad Mathez, Emilie Tithi, Jesmin Jahan Westerlund, Magnus Wurth, Renee Zicari, Roberto V. Computational Vision and Artificial Intelligence Lab Goethe University Frankfurt Frankfurt Am Main Germany Z-Inspection® Initiative Venice Italy Health Ethics and Policy Lab ETH Zurich Zurich Switzerland Strategy and Innovation Careum Foundation Zurich Switzerland Philosophie Departement Collège André-Laurendeau Montréal Canada École Des Médias Université du Québec À Montréal Montréal Canada Department of Medicine Division of Infectious Diseases and Immunology New York University Grossman School of Medicine New York City USA Department of Computer Science University of Copenhagen Copenhagen Denmark Digital Life Initiative Cornell Tech New York City USA Cluster of Excellence “Machine Learning: New Perspectives for Science” University of Tuebingen Tuebingen Germany School of Law University of Bristol Bristol UK Center for the Study of Ethics in the Professions Illinois Institute of Technology Chicago USA Department of Business Management and Analytics Arcada University of Applied Sciences Helsinki Finland Department of Food & Resource Economics University of Copenhagen Copenhagen Denmark Department of Physiology Faculty of Medicine University of Iceland Reykjavik Iceland QUEST Centre for Responsible Research Berlin Institute of Health Charité Universitätsmedizin Berlin Berlin Germany Faculty of Computing Engineering and the Built Environment School of Computing and Digital Technology Birmingham City University Birmingham UK Parallel Computing Labs Intel Santa Clara USA School of Economics Innovation and Technology Kristiania University College Oslo Norway Data Science Graduate School Seoul National University Seoul South Korea

Building artificial intelligence (AI) systems that adhere to ethical standards is a complex problem. Even though a multitude of guidelines for the design and development of such trustworthy AI systems exist, these guidelines focus on high-level and abstract requirements for AI systems, and it is often very difficult to assess if a specific system fulfills these requirements. The Z-Inspection® process provides a holistic and dynamic framework to evaluate the trustworthiness of specific AI systems at different stages of the AI lifecycle, including intended use, design, and development. It focuses, in particular, on the discussion and identification of ethical issues and tensions through the analysis of socio-technical scenarios and a requirement-based framework for ethical and trustworthy AI. This article is a methodological reflection on the Z-Inspection® process. We illustrate how high-level guidelines for ethical and trustworthy AI can be applied in practice and provide insights for both AI researchers and AI practitioners. We share the lessons learned from conducting a series of independent assessments to evaluate the trustworthiness of real-world AI systems, as well as key recommendations and practical suggestions on how to ensure a rigorous trustworthiness assessment throughout the lifecycle of an AI system. The results presented in this article are based on our assessments of AI systems in the healthcare sector and environmental monitoring, where we used the framework for trustworthy AI proposed in the Ethics Guidelines for Trustworthy AI by the European Commission’s High-Level Expert Group on AI. However, the assessment process and the lessons learned can be adapted to other domains and include additional frameworks.

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Ordinal Optimization for Optimal Orientation Problems in 3D Printing

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IFAC-PapersOnLine 2020年第5期53卷 97-102页

作者： Can Luo Gang Xiong Zhishuai Li Zhen Shen Li Wan MengChu Zhou Fei-Yue Wang State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China Guangdong Engineering Research Center of 3D Printing and Intelligent Manufacturing Cloud Computing Center Chinese Academy of Sciences Donggguan China Ten Dimensions (Guangdong) Technology Co. Ltd. Foshan China Macau University of Science and Technology Taipa Macau

Compared with a traditional manufacturing process, 3D printing has advantages of performance and cost in personalized customization and has been applied in many fields. The problem of 3D model orientation optimization is a crucial one in practice. In this paper, based on the mathematical relationship between model orientation and printing time, surface quality, and supporting area, the model orientation problem is transformed into a multi-objective optimization problem with goal of minimizing printing time, surface quality, and supporting area. Ordinal Optimization (OO) is not only applicable to problems with random factors, but also to solve complex deterministic problems. The model orientation is a complex deterministic problem. We solve it with OO in this paper and use linear weighting to convert the multi-objective optimization problem into single-objective one. Finally, we compare the experimental results of solving 3D model orientation problems solved by OO and Genetic Algorithm (GA). The results show that OO requires less calculation time than GA while achieving comparable performance.

关键词： Digital Manufacturing Genetic Algorithm intelligent Optimization Machine Learning Ordinal Optimization Orientation Optimization 3D Printing

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The Prediction of the Epidemic Trend of COVID-19 Using Neural Networks

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IFAC-PapersOnLine 2020年第5期53卷 857-862页

作者： Jing Yang Zhen Shen Xisong Dong Xiuqin Shang Wei Li Gang Xiong J. Yang is with the State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing 100190 China and also with College of Information Sciences & Technology Beijing University of Chemical Technology Beijing 100029 China. Z. Shen is with the State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing 100190 China and also with the Intelligent Manufacturing Center Qingdao Academy of Intelligent Industries Qingdao Shandong 266113 China. X. Dong X. Shang and W. Li are with the State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing 100190 China. G. Xiong is with Beijing Engineering Research Center of Intelligent Systems and Technology Institute of Automation Chinese Academy of Sciences Beijing 100190 China and also with the Guangdong Engineering Research Center of 3D Printing and Intelligent Manufacturing Cloud Computing Center Dongguan Guangdong 523808 China.

In this paper, a BP neural network and an LSTM network are applied respectively to the prediction of Coronavirus Disease 2019 (COVID-19) in Wuhan, China and South Korea. The methods do not require specific theories of modelling and the predicted values can be obtained as long as the conventional parameters are set. The mean absolute percentage error (MAPE) of all the experiments are below 5% and the values of the determinable coefficient R are all larger than 0.9. The experiments show that the models can fit the actual values well and make relatively accurate predictions. As of March 29, 2020, the cumulative number of confirmed cases in Wuhan is expected to reach 50,068 using BP neural networks and 49,972 using LSTM network, respectively. As of April 13, 2020, the cumulative number of confirmed cases in South Korea is expected to reach 8,862 using BP neural networks and 8,716 using LSTM network, respectively. The models of neural networks are effective in predicting the trend of the COVID-19 epidemic, which is meaningful to prevent and control the epidemic.

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COVID-19 Infection Percentage Estimation from Computed Tomography Scans: Results and Insights from the International Per-COVID-19 Challenge

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Sensors 2024年第5期24卷 1557页

作者： Bougourzi, Fares Distante, Cosimo Dornaika, Fadi Taleb-Ahmed, Abdelmalik Hadid, Abdenour Chaudhary, Suman Yang, Wanting Qiang, Yan Anwar, Talha Breaban, Mihaela Elena Hsu, Chih-Chung Tai, Shen-Chieh Chen, Shao-Ning Tricarico, Davide Chaudhry, Hafiza Ayesha Hoor Fiandrotti, Attilio Grangetto, Marco Spatafora, Maria Ausilia Napoli Ortis, Alessandro Battiato, Sebastiano Institute of Applied Sciences and Intelligent Systems National Research Council of Italy Lecce73100 Italy Laboratoire LISSI University Paris-Est Creteil Paris Vitry sur Seine94400 France Department of Computer Science and Artificial Intelligence University of the Basque Country UPV/EHU Manuel Lardizabal 1 San Sebastian20018 Spain IKERBASQUE Basque Foundation for Science Bilbao48011 Spain UMR 8520 Universite Polytechnique Hauts-de-France Université de Lille CNRS Valenciennes59313 France Sorbonne Center for Artificial Intelligence Sorbonne University of Abu Dhabi P.O. Box 38044 Abu Dhabi United Arab Emirates College of Computer Science and Technology Taiyuan University of Technology Taiyuan030024 China School of Computing National University of Computer and Emerging Sciences Islamabad44000 Pakistan Faculty of Computer Science Alexandru Ioan Cuza University Iasi700506 Romania Institute of Data Science National Cheng Kung University No. 1 University Rd. East Dist. Tainan City701 Taiwan Dipartimento di Informatica Universita degli Studi di Torino Corso Svizzera 185 Torino10149 Italy Department of Mathematics and Computer Science University of Catania Catania95125 Italy

COVID-19 analysis from medical imaging is an important task that has been intensively studied in the last years due to the spread of the COVID-19 pandemic. In fact, medical imaging has often been used as a complementary or main tool to recognize the infected persons. On the other hand, medical imaging has the ability to provide more details about COVID-19 infection, including its severity and spread, which makes it possible to evaluate the infection and follow-up the patient’s state. CT scans are the most informative tool for COVID-19 infection, where the evaluation of COVID-19 infection is usually performed through infection segmentation. However, segmentation is a tedious task that requires much effort and time from expert radiologists. To deal with this limitation, an efficient framework for estimating COVID-19 infection as a regression task is proposed. The goal of the Per-COVID-19 challenge is to test the efficiency of modern deep learning methods on COVID-19 infection percentage estimation (CIPE) from CT scans. Participants had to develop an efficient deep learning approach that can learn from noisy data. In addition, participants had to cope with many challenges, including those related to COVID-19 infection complexity and crossdataset scenarios. This paper provides an overview of the COVID-19 infection percentage estimation challenge (Per-COVID-19) held at MIA-COVID-2022. Details of the competition data, challenges, and evaluation metrics are presented. The best performing approaches and their results are described and discussed. © 2024 by the authors.

关键词： COVID-19

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Implementation of the parallel mean shift-based imagesegmentation algorithm on a GPU cluster

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International Journal of Digital Earth 2019年第3期12卷 328-353页

作者： Fang Huang Yinjie Chen Li Li Ji Zhou Jian Tao Xicheng Tan Guangsong Fana School of Resources&Environment University of Electronic Science and Technology of ChinaChengduPeople’s Republic of China Institute of Remote Sensing Big Data Big Data Research CenterUniversity of Electronic Science and Technology of ChinaChengduPeople’s Republic of China School of Construction&Management Engineering Xihua UniversityChengduPeople’s Republic of China Texas A&M Engineering Experiment Station and High Performance Research Computing Texas A&M UniversityCollege StationTXUSA International School of Software Wuhan UniversityWuhanPeople’s Republic of China

The mean shift image segmentation algorithm is very computationintensive. To address the need to deal with a large number of remotesensing (RS) image segmentations in real-world applications, this studyhas investigated the parallelization of the mean shift algorithm on asingle graphics processing unit (GPU) and a task-scheduling methodwith message passing interface (MPI)+OpenCL programming model on aGPU cluster platform. This paper presents the test results of the parallelmean shift image segmentation algorithm on Shelob, a GPU clusterplatform at Louisiana State University, with different datasets andparameters. The experimental results show that the proposed parallelalgorithm can achieve good speedups with different configurations andRS data and can provide an effective solution for RS image processingon a GPU cluster.

关键词： Mean shift algorithm GPU cluster task scheduling MPI OpenCL

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基于MeerKAT和FAST探索球状星团:脉冲星偏振研究（英文）

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Science Bulletin 2025年第10期 1568-1571页

作者：张蕾 Federico Abbate 李菂 Andrea Possenti Matthew Bailes Alessandro Ridolfi Paulo C.C.Freire Scott M.Ransom 张永坤郭猛尼盟萌胡佳乐冯毅王培张洁支启军 National Astronomical Observatories Chinese Academy of Sciences Centre for Astrophysics and Supercomputing Swinburne University of Technology INAF-Osservatorio Astronomico di Cagliari Max-Planck-Institut für Radioastronomie New Cornerstone Science Laboratory Department of AstronomyTsinghua University ARC Center of Excellence for Gravitational Wave Discovery (OzGrav) Swinburne University of Technology Faculty of Physics University of Bielefeld National Radio Astronomy Observatory National Supercomputing Center in Jinan Qilu University of Technology Jinan Institute of Supercomputing Technology Research Center for Intelligent Computing Platforms Zhejiang Laboratory Institute for Astronomy School of PhysicsZhejiang University College of Physics and Electronic Engineering Qilu Normal University Guizhou Provincial Key Laboratory of Radio Astronomy and Data Processing Guizhou Normal University

Magnetic fields are pervasive throughout the Universe. They are integral to a wide array of astrophysical processes that span various physical scales and field strengths. The Galactic magnetic field,in particular, holds significant importance in shaping the evolution of our Galaxy. However, our understanding of its behavior on small scales remains poor, especially when considering its penetration into the Galactic halo [1].

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