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4th International Conference on Machine Learning and Intelligent Communications, MLICOM 2019

作者： Li, Tong Song, Chunhe Liu, Yang Wang, Zhongfeng Yu, Shimao Su, Shanting Liaoning Electric Power Research Institute State Grid Liaoning Electric Power Co. Ltd. 110000 Shenyang China Key Laboratory of Networked Control Systems Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang110016 China Nanjing University of Aeronautics and Astronautics Nanjing China

ISBN: (纸本)9783030323875

For the diagnosis of large-scale local devices, the traditional centralized fault diagnosis systems are becoming incompetent to meet the requirement of real-time monitoring. This paper proposes the Distributed hierarchical Fault Diagnosis System (DFDS). Specifically, DFDS implements fault monitoring by an improved Sparse Auto-Encoder (SAE) on the monitor layer, classifies faults and identifies unknown faults by an improved random forest on the classification layer, learns new knowledge and updates the system on the decision layer. We apply DFDS in the laboratory data of Case Western Reserve University to verify the efficiency of the proposed system. The experimental results show that our method can accurately detect the fault and accurately identify the fault type. © 2019, ICST institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： Fault detection

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Preface: 11th International Conference on High-Performance Computing systems and Technologies in Scientific research, Automation of control and Production (HPCST-2021). May 21-22, 2021, Barnaul, Russia

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Journal of Physics: Conference Series 2021年第1期2142卷

作者： Jordan, Vladimir I. Shurina, Ella Tarasov, Ilya Filimonov, Nikolay Belozerskih, Vasiliy Faerman, Vladimir Kalachev, Alexander Pashnev, Vladimir Sedalischev, Viktor Sergeeva, Yana Shmakov, Igor Ulanov, Petr Abanin, Viktor Alontseva, Darya Avramchuk, Valeriy Beznosyuk, Sergey Filimonov, Alexander Gulyaev, Pavel Kadyrov, Ishembek Kalachev, Alexander Khmelev, Vladimir Fazilov, Shavkat Kosarev, Vladimir Mirzaev, Nomaz Kveglis, Lyudmilla Mescheryakov, Roman Nikitin, Aleksey Polyakov, Viktor Mikhailov, Leonid Prikhodko, Oleg Pronin, Sergey Saliev, Alisher Sedalischev, Viktor Titov, Vitaliy do Prado, Pedro Filipe Yakunin, Aleksey Altai State University Russia Novosibirsk State Technical University Russia Russian Technological University Russia V.A. Trapeznikov Institute of Control Sciences RAS Russia Tomsk State University of Control Systems and Radioelectronics Russia Biysk Technological Institute Russia Serikbayev East Kazakhstan Technical University Kazakhstan MIREA - Russian Technological University Russia Yugra State University Russia Kyrgyz National Agrarian University Kyrgyzstan Polzunov Altai State Technical University Russia Research Institute for the Development of Digital Technologies and Artificial Intelligence Uzbekistan Khristianovich Institute of Theoretical and Applied Mechanics SB RAS Russia University of Information Technologies n.a. al-Khorezmi Uzbekistan Amanzholov East Kazakhstan State University Kazakhstan V.A. Trapeznikov Institute of Control Sciences Russia Al-Farabi Kazakh National University Kazakhstan Kyrgyz Technical University Kyrgyzstan Southwest State University Russia Federal University of Espirito Santo Brazil

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LimeSoDa: A Dataset Collection for Benchmarking of Machine Learning Regressors in Digital Soil Mapping

arXiv

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arXiv 2025年

作者： Schmidinger, Jonas Vogel, Sebastian Barkov, Viacheslav Pham, Anh-Duy Gebbers, Robin Tavakoli, Hamed Correa, Jose Tavares, Tiago R. Filippi, Patrick Jones, Edward J. Lukas, Vojtech Boenecke, Eric Ruehlmann, Joerg Schroeter, Ingmar Kramer, Eckart Paetzold, Stefan Kodaira, Masakazu Wadoux, Alexandre M.J.-C. Bragazza, Luca Metzger, Konrad Huang, Jingyi Valente, Domingos S.M. Safanelli, Jose L. Bottega, Eduardo L. Dalmolin, Ricardo S.D. Farkas, Csilla Steiger, Alexander Horst, Taciara Z. Ramirez-Lopez, Leonardo Scholten, Thomas Stumpf, Felix Rosso, Pablo Costa, Marcelo M. Zandonadi, Rodrigo S. Wetterlind, Johanna Atzmueller, Martin Osnabrück University Joint Lab Artificial Intelligence and Data Science Osnabrück Germany Department of Agromechatronics Potsdam Germany Piracicaba Brazil The University of Sydney Sydney Institute of Agriculture Sydney Australia Mendel University in Brno Department of Agrosystems and Bioclimatology Brno Czech Republic Leibniz Institute of Vegetable and Ornamental Crops Next Generation Horticultural Systems Grossbeeren Germany Eberswalde University for Sustainable Development Landscape Management and Nature Conservation Eberswalde Germany Soil Science and Soil Ecology Bonn Germany Tokyo University of Agriculture and Technology Institute of Agriculture Tokyo Japan LISAH Univ. Montpellier AgroParisTech INRAE IRD L'Institut Agro Montpellier France Agroscope Field-Crop Systems and Plant Nutrition Nyon Switzerland University of Wisconsin-Madison Department of Soil Science Madison United States Federal University of Viçosa Department of Agricultural Engineering Viçosa Brazil Woodwell Climate Research Center Falmouth United States Academic Coordination Santa Maria Brazil Soil Department Santa Maria Brazil Division of Environment and Natural Resources Aas Norway University of Rostock Chair of Geodesy and Geoinformatics Rostock Germany Federal Technological University of Paraná Dois Vizinhos Brazil BÜCHI Labortechnik AG Data Science Department Flawil Switzerland Imperial College London Imperial College Business School London United Kingdom University of Tübingen Department of Geosciences Tübingen Germany University of Tübingen DFG Cluster of Excellence Machine Learning for Science’ Germany Bern University of Applied Sciences Competence Center for Soils Zollikofen Switzerland Simulation and Data Science Müncheberg Germany Federal University of Jataí Institute of Agricultural Sciences Jatai Brazil Federal University of Mato Grosso Instute of Agricultural and Environmental Scinces Sinop Brazil Department of Soil and Environment Skara

Digital soil mapping (DSM) relies on a broad pool of statistical methods, yet determining the optimal method for a given context remains challenging and contentious. Benchmarking studies on multiple datasets are needed to reveal strengths and limitations of commonly used methods. Existing DSM studies usually rely on a single dataset with restricted access, leading to incomplete and potentially misleading conclusions. To address these issues, we introduce an open-access dataset collection called Precision Liming Soil Datasets (LimeSoDa). LimeSoDa consists of 31 field- and farm-scale datasets from various countries. Each dataset has three target soil properties: (1) soil organic matter or soil organic carbon, (2) clay content and (3) pH, alongside a set of features. Features are dataset-specific and were obtained by optical spectroscopy, proximal- and remote soil sensing. All datasets were aligned to a tabular format and are ready-to-use for modeling. We demonstrated the use of LimeSoDa for benchmarking by comparing the predictive performance of four learning algorithms across all datasets. This comparison included multiple linear regression (MLR), support vector regression (SVR), categorical boosting (CatBoost) and random forest (RF). The results showed that although no single algorithm was universally superior, certain algorithms performed better in specific contexts. MLR and SVR performed better on high-dimensional spectral datasets, likely due to better compatibility with principal components. In contrast, CatBoost and RF exhibited considerably better performances when applied to datasets with a moderate number ( © 2025, CC BY-SA.

关键词： Adversarial machine learning

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Implementation of the hybrid technology for quadcopter motion control in a complex non-deterministic environment

Implementation of the hybrid technology for quadcopter motio...

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International Conference on control, Automation and systems ( ICCAS)

作者： D. Devitt R. Morozov M. Medvedev I. Shapovalov G. Konovalov Research & Development Institute of Robotics and Control Systems Southern Federal University Taganrog Russian Federation

ISBN: (纸本)9781538670804;9788993215168

This paper represents the results of development and research of the autonomous control system for the UAV. Existing control systems do not provide flight performance necessary for successful operations in non-deterministic environments. So, we propose hybrid intelligent technology based on unstable modes and the conception ofa virtual goal. The main feature of our approach is the efficient integration of global and local path planning providing smooth and quick correction of trajectories for the avoidance of obstacles detected by the quadrotor vision system. Moreover, our local planning approach gives the tool for passing areas with complex obstacles where other local path planning algorithms fail. The hardware-software architecture of the control system based on client-server model with the loose coupling in ROS is presented. The results of simulation and real test flights confirming the efficiency of the proposed technology are shown. We used the commercially available quadcopter and flight controller. That proves absence of necessity in special hardware tools for autonomous flight in environments with uncertainties.

关键词： Planning Trajectory Mobile robots Laser radar

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An Abnormal State Forecasting Method for Substation Equipment based on Improved LSTM

An Abnormal State Forecasting Method for Substation Equipmen...

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2019 IEEE Sustainable Power and Energy Conference, iSPEC 2019

作者： Li, Tong Yu, Shimao Song, Chunhe Yu, Tongwei Zeng, Peng Shen, Li State Grid Liaoning Electric Power Co. Ltd. Liaoning Electric Power Research Institute Shenyang China Shenyang Institute of Automation Chinese Academy of Sciences Key Laboratory of Networked Control Systems Shenyang China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China State Grid Liaoning Electric Power Co. Ltd. Shenyang China

ISBN: (纸本)9781728149301

Abnormal state accumulation over a long period will cause an electrical equipment fault. Therefore, Substation equipment state forecasting plays a vital role in smart grids. Fault forecasting method based on deep learning have been put forward in recent years, but they have deficiencies dealing with temporal features. This paper proposes an abnormal forecasting method for substation equipment based on improved LSTM, and temporal features of power equipment fault can be extracted effectively for a long-time span. The method reads secondary equipment information through SCADA system, then extracts feature to carry out deep learning training. In this paper, RNN and LSTM are firstly compared, and then the structure and algorithm of improved LSTM are given. This method increases coupling between forget gate and cell state compared with the standard LSTM structure, so as to make better use of information at previous moment. Meanwhile problems of network training and overfitting are discussed. Experiments are conducted to demonstrate that improved LSTM is capable of forecasting accurately the complex electric equipment state with a long forecasting horizon. The accuracy of abnormal state forecasting can reach more than 93% which has a better performance compared with many other methods. © 2019 IEEE.

关键词： SCADA systems

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The year 2020,a milestone in breaking the vicious cycle of poverty and illness in China

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Infectious Diseases of Poverty 2020年第1期9卷 1-8页

作者： Yun-Ping Wang Xiao-Nong Zhou China National Health Development Research Centre National Health Commission of ChinaWHO Collaborating Centre for Health Systems StrengtheningBeijing 100044China National Institute of Parasitic Diseases at Chinese Center for Diseases Control and Prevention Chinese Center for Tropical Diseases ResearchShanghai 200025China WHO Collaborating Centre for Tropical Diseases National Center for International Research on Tropical DiseasesMinistry of Science and TechnologyKey Laboratory of Parasite and Vector BiologyMinistry of HealthShanghai 200025China School of Global Health Chinese Center for Tropical Diseases ResearchJiatong University School of MedicineShanghai 200025China

Marking the end of the five-year programme initiated by the Chinese Government to lift more than 70 million people out of poverty,the year 2020 is a *** alleviation has moved strongly forward in China and the major health indicators are now better than the average of all middle-and high-income ***,the dual burden of infectious and chronic diseases remains a challenge with respect to achieving the health target in the United Nations 2030 Agenda for sustainable development goals(SDGs).In 2015,about 44%of the poor population in China were impoverished by illness but already in 2018,multi-sectoral actions delivered by the Health-related Poverty Alleviation programme had reduced the number almost by *** the past three years 15 million poor people(98%of the poor population)with infectious and chronic diseases had been treated and taken care of thanks to financial support through multiple health insurance schemes and other governmental *** article discusses the lessons learnt with regard to health-related poverty alleviation in China with special reference to those still remaining impoverished by *** of the achievements reached and provision of basic needs to those still disadvantaged and in poor health will require a major improvement of accessibility to,and affordability of,health *** next step towards enhanced productivity and better living conditions will involve upgrading of the capacity of health professionals in the poor regions,promotion of coherent efforts in health-related poverty alleviation and rural revitalization *** an additional measure,data monitoring and research on health poverty alleviation should be strengthened as they are essential to generate the evidence and knowledge needed to support the move in the direction envisioned by the SDGs,and the new Healthy China 2030 programme.

关键词： Poverty alleviation Infectious diseases Poor population Health insurance Endemic diseases China Healthy China

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Monitoring dioxins and PCBs in eggs as sensitive indicators for environmental pollution and global contaminated sites and recommendations for reducing and controlling releases and exposure

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Emerging Contaminants 2022年第1期8卷 254-279页

作者： Jindrich Petrlik Lee Bell Joe DiGangi Serge Molly Allo'o Allo'o Gilbert Kuepouo Griffins Ochieng Ochola Valeriya Grechko Nikola Jelinek Jitka Strakova Martin Skalsky Yuyun Ismawati Drwiega Jonathan N.Hogarh Eric Akortia Sam Adu-Kumi Akarapon Teebthaisong Maria Carcamo Bjorn Beeler Peter Behnisch Claudia Baitinger Christine Herold Roland Weber International Pollutants Elimination Network(IPEN) GooteborgSweden Arnika e Toxics and Waste Programme Delnicka 13PragueCzech Republic National Toxics Network(NTN) PerthAustralia National SAICM Focal Point President of the Tenth Conference of the Parties to the Rotterdam ConventionMinistry of ForestryFisheries and EnvironmentLibrevilleGabon Centre de Recherche et d‘Education pour le Developpement(CREPD) YaoundeCameroon Centre for Environmental Justice and Development(CEJAD) NairobiKenya University of Chemistry and Technology Technicka 5PragueCzech Republic Arnika e Citizens'Support Center Delnicka 13PragueCzech Republic Nexus3 Foundation Denpasar80223Indonesia Department of Environmental Science Kwame Nkrumah University of Science and TechnologyKumasiGhana Ghana Atomic Energy Commission National Nuclear Research InstituteAccraRepublic of Ghana Chemicals Control and Management Centre Environmental Protection AgencyAccraGhana Ecological Alert and Recovery e Thailand(EARTH) NonthaburiThailand La Red de Accion en Plaguicidas y sus Alternativas para America Latina MontevideoUruguay BioDetection Systems BV(BDS) Science Park 4061098 XHAmsterdamthe Netherlands Bund für Umwelt und Naturschutz(BUND) Germany POPs Environmental Consulting D-73527Schw€abisch GmündGermany

This review compiles information on PCDD/F-and PCB-contaminated eggs from 20 years of global egg monitoring around emission sources in four continents conducted by the International Pollutants Elimination Network(IPEN)and Arnika as well as a compilation of data from scientific *** monitored 127 pooled egg samples including samples from 113 chicken flocks at potential PCDD/F-and PCB-contaminated sites around priority sources listed in the Stockholm Convention(*** incinerators,metal industries,cement plants,and open burning).99(88%)of pooled egg samples were above the EU maximum limits for PCDD/Fs(2.5 pg PCDD/F-TEQ/g fat)or the sum of PCDD/Fs and dioxinlike PCBs(5 pg PCDD/F-PCB-TEQ/g fat).Children consuming such eggs exceed the tolerable weekly intake(TWI).This demonstrates that close to 90%of these areas were not safe for the production of free-range *** out of the 113 egg samples(14%)were contaminated above 50 pg TEQ/g fat and exceeded the EU maximum limit more than 10 *** the 26 pooled egg samples around incinerators 24(92%)exceeded the limit with a mean of 43.1 pg TEQ/g fat(2.6e234 pg TEQ/g).All 21 egg samples around metal industries(4.4e112.6 pg TEQ/g fat)were above limits with mean concentration of 26.0 pg TEQ/g *** all 7 egg samples measured at e-waste recycling sites were above limits(mean 308 pg TEQ/g fat).In 58(51%)pooled egg samples the PCB-TEQ was above 5 pg TEQ/g fat exceeding the EU maximum limit with dioxin-like PCBs *** highlights the role of commercial PCBs for global contamination with dioxin-like *** was discovered that around metal industries,shredder plants,open burning sites of e-waste and dump sites,a high share of contamination was caused by *** clearly shows severe PCB release from the end-of-life management of PCB-containing equipment in developing *** highly contaminated eggs were found at many sites where plastic was *** highest contaminated egg sample ev

关键词： PCDD/PCDF dl-PCBs PBDD/PBDF Contaminated sites Monitoring Egg DR CALUX

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Chattering-free fixed-time sliding mode control for bilateral teleoperation under unknown time-varying delay via disturbance and state observers

Advanced Control for Applications: Engineering and Industria...

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Advanced control for Applications: Engineering and Industrial systems 2020年第4期2卷

作者： Abadi, Ali Soltani Sharif Hosseinabadi, Pooyan Alinaghi Mekhilef, Saad Ordys, Andrzej Department of Electrical Engineering Faculty of Engineering Yazd University Yazd Iran Faculty of Engineering University of Malaya Kuala Lumpur Malaysia School of Software and Electrical Engineering Faculty of Science Engineering and Technology Swinburne University of Technology Melbourne Australia Center of Research Excellence in Renewable Energy and Power Systems King Abdulaziz University Jeddah Saudi Arabia The Institute of Automatic Control and Robotics Faculty of Mechatronics Warsaw University of Technology Warsaw Poland

This paper proposes a new chattering-free Fixed-time Observer-Based Sliding Mode control (FOBSMC) scheme for synchronization of two bilateral teleoperations under unknown time-varying delay. The system is subjected to unknown disturbances which are estimated in a fixed time by utilizing the Disturbance Observer-based SMC scheme. The fixed-time State Observer-based SMC is designed to estimate the unmeasured velocity state, while the position state is assumed to be available. Fixed-time stability method is used to provide the system convergence time independently of initial states. A FOBSMC scheme is first designed for master system of bilateral teleoperation under unknown time-varying delay to track the environmental torque by the human operating torque in the presence of time delay in the communication channel, as well as estimating the unknown disturbances and unmeasured states. A FOBSMC scheme is designed to synchronize slave system to master system in the presence of time delay in the communication channel, as well as estimating disturbances and states. The stability analysis is performed for closed-loop system of combined controller-observer by using Lyapunov theory. The efficacy of the proposed method to fulfill a good transparency and stability under time-varying communication delays is tested by performing numerical simulation in Simulink/MATLAB. © 2020 John Wiley & Sons Ltd.

关键词： Time delay

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Correction to: MR beyond diagnostics at the ESMRMB annual meeting: MR theranostics and intervention

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Magma (New York, N.Y.) 2024年第5期37卷 947-948页

作者： Milan Hájek Ulrich Flögel Adriana A S Tavares Lucia Nichelli Aneurin Kennerley Thomas Kahn Jurgen J Futterer Aikaterini Fitsiori Holger Grüll Nandita Saha Felipe Couñago Dogu Baran Aydogan Maria Eugenia Caligiuri Cornelius Faber Laura C Bell Patrícia Figueiredo Joan C Vilanova Francesco Santini Ralf Mekle Sonia Waiczies Department of Diagnostic and Interventional Radiology Institute for Clinical and Experimental Medicine Prague Czech Republic. Experimental Cardiovascular Imaging Institute for Molecular Cardiology Heinrich Heine University Düsseldorf Düsseldorf Germany. Centre for Cardiovascular Sciences and Edinburgh Imaging University of Edinburgh Edinburgh UK. Sorbonne Université Inserm CNRS UMR S 1127 Paris Brain Institute ICM Paris France. Department of Neuroradiology AP-HP Pitié-Salpêtrière Hospital Paris France. Department of Sports and Exercise Science Institute of Sport Manchester Metropolitan University Manchester UK. Department of Biology University of York York UK. Department of Diagnostic and Interventional Radiology University of Leipzig Leipzig Germany. Minimally Invasive Image-Guided Intervention Center (MAGIC) Department of Medical Imaging Radboudumc Nijmegen The Netherlands. Unit of Diagnostic and Interventional Neuroradiology Diagnostic Department University Hospitals of Geneva Geneva Switzerland. Institute of Diagnostic and Interventional Radiology Faculty of Medicine University Hospital of Cologne University of Cologne Cologne Germany. Department of Chemistry Faculty of Mathematics and Natural Sciences University of Cologne Cologne Germany. Max-Delbrück-Centrum Für Molekulare Medizin (MDC) Berlin Ultrahigh Field Facility Berlin Germany. Charité-Universitätsmedizin Berlin Berlin Germany. Department of Radiation Oncology Hospital Universitario San Francisco de Asís Hospital Universitario Vithas La Milagrosa GenesisCare 28010 Madrid Spain. A.I. Virtanen Institute for Molecular Sciences University of Eastern Finland Kuopio Finland. Neuroscience Research Center Department of Medical and Surgical Sciences Università Degli Studi "Magna Graecia" Catanzaro Italy. Translational Research Imaging Center (TRIC) Clinic of Radiology University of Münster Münster Germany. Early Clinical Development Genentech Inc. South San Francisco USA. Institute for S

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Integration of MRI and somatosensory evoked potentials facilitate diagnosis of spinal cord compression

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Scientific Reports 2023年第1期13卷 1-12页

作者： Sun, Shu-Pin Phang, Chun-Ren Tzou, Shey-Cherng Chen, Chang-Mu Ko, Li-Wei International Ph.D. Program in Interdisciplinary Neuroscience (UST) College of Biological Science and Technology National Yang Ming Chiao Tung University 734 Engineering Bldg. 5 1001 Daxue Road Hsinchu 30010 Taiwan Department of Medical Research National Taiwan University Hospital Hsin-Chu Branch Hsinchu 300 Taiwan Institute of Molecular Medicine and Bioengineering National Yang Ming Chiao Tung University Hsinchu 300 Taiwan Department of Surgery College of Medicine and Hospital National Taiwan University No. 7 Zhongshan South Road Taipei 10002 Taiwan Center for Intelligent Drug Systems and Smart Bio-devices (IDS2B) College of Biological Science and Technology National Yang Ming Chiao Tung University Hsinchu 300 Taiwan Institute of Electrical and Control Engineering Department of Electronics and Electrical Engineering National Yang Ming Chiao Tung University Hsinchu 300 Taiwan Department of Biomedical Science and Environment Biology and the Drug Development and Value Creation Research Center Kaohsiung Medical University Kaohsiung 807 Taiwan

This study aimed to integrate magnetic resonance imaging (MRI) and related somatosensory evoked potential (SSEP) features to assist in the diagnosis of spinal cord compression (SCC). MRI scans were graded from 0 to 3 according to the changes in the subarachnoid space and scan signals to confirm differences in SCC levels. The amplitude, latency, and time–frequency analysis (TFA) power of preoperative SSEP features were extracted and the changes were used as standard judgments to detect neurological function changes. Then the patient distribution was quantified according to the SSEP feature changes under the same and different MRI compression grades. Significant differences were found in the amplitude and TFA power between MRI grades. We estimated three degrees of amplitude anomalies and power loss under each MRI grade and found the presence or absence of power loss occurs after abnormal changes in amplitude only. For SCC, few integrated approach combines the advantages of both MRI and evoked potentials. However, integrating the amplitude and TFA power changes of SSEP features with MRI grading can help in the diagnosis and speculate progression of SCC. © 2023, The Author(s).

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