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SSRN 2023年

作者： Li, Ziyu Yao, Yuncong Quan, Zhibin Qi, Lei Feng, Zhenhua Yang, Wankou School of Automation Southeast University Nanjing210096 China Key Lab of Measurement and Control of Complex Systems of Engineering Ministry of Education Southeast University Nanjing210096 China School of Computer Science and Engineering Southeast University Nanjing210096 China Department of Computer Science University of Surrey GuildfordGU2 7XH United Kingdom

LiDAR-based 3D detectors perform well on particular autonomous driving benchmarks, but may poorly generalize to other domains. Existing 3D domain adaptive detection methods usually require annotation-related statistics or continuous refinement of pseudo-labels. The former is not always feasible for practical applications, while the latter lacks sufficient accurate supervision. In this work, we propose a novel unsupervised domain adaptive framework, namely Adaptation Via Proxy (AVP), that explicitly leverages cross-domain relationships to generate adequate high-quality samples without any target statistics, thus mitigating domain shifts for existing LiDAR-based 3D detectors. Specifically, we first train the detector on the source domain with curriculum example mining to enhance its generalization capability. Then, we fine-tune the pre-trained detector on the instance-aware proxy that is a data collection with stronger supervision. The instance-aware proxy utilizes the profitable instances from the source domain to provide more robust supervision for optimizing the detector to overcome domain gaps. Note that AVP is architecture-agnostic thus it can be easily injected with any LiDAR-based 3D detectors. Extensive experiments on Waymo, nuScenes, KITTI and Lyft demonstrate the superiority of the proposed method over the state-of-the-art approaches for different adaptation scenarios. © 2023, The Authors. All rights reserved.

关键词： Object detection

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Distance- And velocity-based collision avoidance for time-varying formation control of second-order multi-agent systems

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IEEE Transactions on Circuits and systems II: Express Briefs 2021年第4期68卷 1253-1257页

作者： Pang, Zhong-Hua Zheng, Chang-Bing Sun, Jian Han, Qing-Long Liu, Guo-Ping Key Laboratory of Fieldbus Technology and Automation of Beijing North China University of Technology Beijing100144 China State Key Laboratory of Intelligent Control and Decision of Complex Systems School of Automation Beijing Institute of Technology Beijing100081 China School of Software and Electrical Engineering Swinburne University of Technology MelbourneVIC3122 Australia Department of Artificial Intelligence and Automation Wuhan University Wuhan430072 China

This brief addresses the time-varying formation control problem with collision avoidance for second-order multi-agent systems. By taking both distances and velocities between agents into account, a novel collision avoidance scheme is proposed on the basis of conventional distance-based artificial potential field methods. Compared with the latter, the proposed scheme can more accurately determine possible collision situations and thus significantly reduce possible collision areas, which consequently not only can reduce energy consumption, but also has as few adverse effects on formation maintenance as possible. Then a time-varying formation control protocol is designed so as to achieve zero steady-state position and velocity formation tracking errors without collision. Finally, theoretical analysis, numerical demonstration, as well as comparative simulation are provided to verify the effectiveness of the proposed method. © 2004-2012 IEEE.

关键词： Multi agent systems

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Assessment of the mineral potential of rocks using intelligent data analysis technologies 24

Assessment of the mineral potential of rocks using intellige...

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24th International Symposium INFOTEH-JAHORINA, INFOTEH 2025

作者： Tynchenko, Yadviga Tynchenko, Vadim Degtyareva, Ksenia Kalmykova, Ekaterina Kukartseva, Svetlana Bauman Moscow State Technical University Artificial Intelligence Technology Scientific and Education Center Moscow Russia Siberian Federal University School of Petroleum and Natural Gas Engineering Department of Technological Machines and Equipment of Oil and Gas Complex Krasnoyarsk Russia Reshetnev Siberian State University of Science and Technology Information-Control Systems Department Krasnoyarsk Russia Reshetnev Siberian State University of Science and Technology Department of Systems Analysis and Operations Research Krasnoyarsk Russia Bauman Moscow State Technical University Center for Continuing Education Moscow Russia Moscow Timiryazev Agricultural Academy Department of Applied Informatics Moscow Russia

ISBN: (纸本)9798331515799

This paper presents a study aimed at developing and evaluating a classification model for the mineral potential of geological layers using machine learning methods. The main goal was to create an algorithm capable of predicting the categories of mineral potential - low, medium and high - based on key geological characteristics such as ore content, density, depth, permeability and rock type. The results obtained demonstrated the overall accuracy of the model at 98%, with an almost ideal classification for the categories of medium and high mineral potential. The analysis of the significance of the features showed that the ore content and density have the greatest impact on the classification. The work also revealed certain difficulties in classifying low mineral potential, which is due to limited data or the intersection of characteristics between classes. These results highlight the importance of further research in feature selection and improving data balance. The findings demonstrate the effectiveness of using machine learning methods in geological research and their potential for automating mineral resource assessment processes. The developed methodology can be adapted to solve other problems in the field of geology, such as forecasting deposits and optimizing mining. © 2025 IEEE.

关键词： Radioactive prospecting

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Behavioral Signal Processing with Machine Learning Based on FPGA 1st

Behavioral Signal Processing with Machine Learning Based on ...

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1st International Conference on systems and Information Sciences, ICCIS 2020

作者： Asanza, Víctor Sanchez, Galo Cajo, Ricardo Peláez, Enrique Facultad de Ingeniería en Electricidad y Computación FIEC Escuela Superior Politécnica del Litoral ESPOL Campus Gustavo Galindo Km 30.5 Vía Perimetral P.O. Box 09-01-5863 Guayaquil090150 Ecuador Research Group of Dynamical Systems and Control Department of Electromechanical Systems and Metal Engineering Ghent University Ghent9052 Belgium EEDT—Core Lab on Decision and Control Flanders Make Consortium Tech Lane Science Park 131 Ghent9052 Belgium

ISBN: (纸本)9783030591939

This paper focuses on analyzing health problems derived from a sedentary lifestyle. Studies seeking to improve physical activity have shown that a good incentive to increase physical activity requires social feedback, allowing the subject to keep motivated and competitive, along with a feedback of number of steps at the end of the day. This work describes the training and implementation of a neural network as an artificial intelligence model to predict the behavior of an individual, taking advantage of the flexibility provided by Field Programmable Gate Arrays (FPGAs). We propose the design of an edge computing system, analyzing the efficiency on power, area and computational performance. The results are presented through a display, making a comparison of the predicted and expected steps. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Edge computing

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Multi-Robot System for Autonomous Cooperative Counter-UAS Missions: Design, Integration, and Field Testing

Multi-Robot System for Autonomous Cooperative Counter-UAS Mi...

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IEEE International Workshop on Safety, Security, and Rescue Robotics (SSRR)

作者： Antonella Barisic Marlan Ball Noah Jackson Riley McCarthy Nasib Naimi Luca Strässle Jonathan Becker Maurice Brunner Julius Fricke Lovro Markovic Isaac Seslar David Novick Jonathan Salton Roland Siegwart Stjepan Bogdan Rafael Fierro Laboratory for Robotics and Intelligent Control Systems (LAR-ICS) Faculty of Electrical Engineering and Computing University of Zagreb Zagreb Croatia Electrical and Computer Engineering Department University of New Mexico Albuquerque NM USA ETH Zurich Autonomous Systems Lab (ASL) Zurich Switzerland Sandia National Laboratories Albuquerque New Mexico USA

ISBN: (纸本)9781665456814

With the rapid development of technology and the proliferation of uncrewed aerial systems (UAS), there is an immediate need for security solutions. Toward this end, we propose the use of a multi-robot system for autonomous and cooperative counter-UAS missions. In this paper, we present the design of the hardware and software components of different complementary robotic platforms: a mobile uncrewed ground vehicle (UGV) equipped with a LiDAR sensor, an uncrewed aerial vehicle (UAV) with a gimbal-mounted stereo camera for air-to-air inspections, and a UAV with a capture mechanism equipped with radars and camera. Our proposed system features 1) scalability to larger areas due to the distributed approach and online processing, 2) long-term cooperative missions, and 3) complementary multimodal perception for the detection of multirotor UAVs. In field experiments, we demonstrate the integration of all subsystems in accomplishing a counter-UAS task within an unstructured environment. The obtained results confirm the promising direction of using multi-robot and multi-modal systems for C-UAS.

关键词： Scalability Robot vision systems Autonomous aerial vehicles Cameras Software Safety Security

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The System of Electric Drive of the Dragging Camp During the Automation of the Technological Process of Drawing Steel Wire

The System of Electric Drive of the Dragging Camp During the...

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Advanced Technology (KhPIWeek), IEEE KhPI Week on

作者： Oksana Khrebtova Oleksandr Shapoval Oleg Khrebtov Mykhailo Rudych Maryna Babaryka Tatyana Kozlovs'ka Department of Systems of Automatic Control and Electric Drive Kremenchuk Mykhailo Ostrohradskyi National University Kremenchuk Ukraine Department of Mechanical Engineering Kremenchuk Mykhailo Ostrohradskyi National University Kremenchuk Ukraine Department of Jurisprudence Sciences Kremenchuk Mykhailo Ostrohradskyi National University Kremenchuk Ukraine Central Ukrainian National Technical University Kropyvnytskyi Ukraine KremenchukMykhailoOstrohradskyi National University Kremenchuk Ukraine Kremenchuk Flight College of Kharkiv National University of Internal Affairs Kremenchuk Ukraine

ISBN: (数字)9798350384499

ISBN: (纸本)9798350384505

The study involved an analysis of methods and electromechanical equipment during the production of metal products using wire drawing machines. For manufacturing wires with a medium diameter (1.8 … 3.0 mm), the most promising approach is the use of wire drawing machines with a non-impact technological process, which has the potential for significant efficiency improvement through the optimization of power parameters and increasing the drawing speed. Increasing the efficiency of the wire drawing process is achievable through the modernization of existing electric drive systems, leading to a substantial increase in the overall drawing process efficiency. As for increasing the drawing speed, it can be accomplished by employing a more advanced electric drive control system. It has been proven that the electric drive based on the frequency converter - asynchronous motor is economically beneficial for use as the main electric drive in the wire drawing machine. An analysis of the operating modes of the wire drawing machine's electric drive using a mathematical model confirmed that the developed control principle provides speed regulation of wire drawing with an error that does not exceed the specified criteria. Thus, the technological process requirements are met in all operating modes.

关键词： Resistance Process control Motors Mathematical models Sensor systems Regulation Sensors Wire Frequency control Wire drawing

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Intelligent model for assessing the suitability of water based on its chemical and biological parameters

Intelligent model for assessing the suitability of water bas...

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International Symposium on INFOTEH-JAHORINA (INFOTEH)

作者： Yadviga Tynchenko Vadim Tynchenko Ksenia Degtyareva Georgy Kuznetsov Svetlana Kukartseva Artificial Intelligence Technology Scientific and Education Center Bauman Moscow State Technical University Moscow Russia Department of Technological Machines and Equipment of Oil and Gas Complex School of Petroleum and Natural Gas Engineering Siberian Federal University Krasnoyarsk Russia Information-Control Systems Department Reshetnev Siberian State University of Science and Technology Krasnoyarsk Russia Department of Applied Informatics Moscow Timiryazev Agricultural Academy Moscow Russia Department of Systems Analysis and Operations Research Reshetnev Siberian State University of Science and Technology Krasnoyarsk Russia Center for Continuing Education Bauman Moscow State Technical University Moscow Russia

ISBN: (数字)9798331515799

ISBN: (纸本)9798331515805

This paper presents a study on the development and testing of a machine learning model for classifying water according to its purpose, based on chemical and microbiological indicators. The aim of the study is to create an accurate and stable model that can effectively determine the categories of water — drinking, industrial, agricultural and technical — based on the analysis of its qualitative characteristics. The gradient boosting method was chosen as the algorithm for classification, which demonstrated high accuracy and reliability when working with multidimensional data. The study included the processing of a synthetic dataset containing information on water parameters such as calcium concentration, total bacterial content, phosphate levels, alkalinity, sulfates and oxidizability. The analysis of the correlation matrix made it possible to identify the relationships between the features, which made it possible to optimize the operation of the model and improve its accuracy. The quality of the model was tested using accuracy, completeness and F1-score metrics, which demonstrated almost perfect results for all categories. In conclusion, it is concluded that the proposed model can be successfully used to automate the analysis of water quality in ecology, industry and agriculture. The use of intelligent technologies opens up new opportunities for monitoring water resources, increasing the efficiency and reliability of quality control.

关键词： Measurement Analytical models Accuracy Correlation Biological system modeling Water quality Boosting Reliability Water resources Chemicals

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Maturity Model for AI in Smart Production Planning and control System 55

Maturity Model for AI in Smart Production Planning and Contr...

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55th CIRP Conference on Manufacturing systems, CIRP CMS 2022

作者： Colangelo, Eduardo Fries, Christian Hinrichsen, Theresa-Franziska Szaller, Ádám Nick, Gábor Fraunhofer Institute for Manufacturing Engineering and Automation IPA Nobelstr. 12 Stuttgart70569 Germany University of Stuttgart Institute of Industrial Manufacturing and Management IFF Allmandring 35 Stuttgart70569 Germany Institute for Computer Science and Control Kende str. 13-17. Budapest1111 Hungary Department of Manufacturing Science and Engineering Budapest University of Technology and Economics Budapest1111 Hungary EPIC InnoLabs Nonprofit Ltd. Kende str. 13-17. Budapest1111 Hungary

The utilization of Artificial Intelligence (AI) to improve processes constitutes a main subject for many enterprises. The area of Production Planning and control (PPC) possesses several functions that could profit from such approaches. However, manufacturing companies find themselves often limited in the application of these approaches. This paper concentrates on three elements to assist enterprises: 1) the clarification of what AI is (in the manufacturing context) and its application to the field of PPC;2) a review performed together with manufacturing enterprises in Germany and Hungary in order to understand the obstacles for the implementation of AI;and 3) the proposal of a maturity model to help enterprises understand where they are in regards to AI, as a way to help them create a roadmap to achieve their objectives. © 2022 The Authors. Published by Elsevier B.V.

关键词： Artificial Intelligence Maturity Model Production Planning and control Smart Production

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Plate Shape Prediction Based on Data-Driven in Roll Quenching Process

Plate Shape Prediction Based on Data-Driven in Roll Quenchin...

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Chinese Automation Congress (CAC)

作者： Liu Hu Luefeng Chen Jie Hu Min Wu Witold Pedrycz Kaoru Hirota School of Automation China University of Geosciences Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems and the Engineering Research Center of Intelligent Technology for Geo-Exploration School of Automation Ministry of Education China University of Geosciences Wuhan China Department of Electrical and Computer Engineering University of Alberta Edmonton Canada Systems Research Institute Polish Academy of Sciences Warsaw Poland The Department of Computer Engineering Istinye University Sariyer/Istanbul Turkey Tokyo Institute of Technology Yokohama Japan The Tokyo Institute of Technology Tokyo Japan

In the process of steel plate production, predicting the plate shape is of great significance for producing high-quality and consistently stable plate shapes. This paper presents a model that predicts both the defect types and flatness of the plate, providing theoretical support for setting process parameters in roller quenching production. First, the parameters of the quenching process are analyzed to identify their characteristics. Then, the K-Means clustering algorithm and correlation analysis are employed to process the quenching process parameters. A gradient boosting decision tree (GBDT) model is used to predict the defect types and flatness of the steel plates. Finally, industrial production data is utilized for experimental validation. The obtained experimental results verify the reliability of the proposed method.

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Spatiotemporal pattern of ENSO-induced modulation on landscape fires over Pacific Rim from 2001 to 2020

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Advances in Climate Change Research 2024年第6期15卷 1057-1066页

作者： Fang-Zhou LI Ning-Ran LI Martin J.WOOSTER Wen-Shi LIN Tao XUE Tong ZHU State Key Joint Laboratory of Environment Simulation and Pollution Control College of Environmental Sciences and EngineeringPeking UniversityBeijing 100871China State Environmental Protection Key Laboratory of Atmospheric Exposure and Health Risk Management and Center for Environment and HealthPeking UniversityBeijing 100871China School of Science Swinburne University of TechnologyMelbourne VIC 3122Australia Leverhulme Centre for Wildfires Environment&SocietyDepartment of GeographyKing's College LondonLondon WC2R 2LSUK NERC National Centre for Earth Observation Department of GeographyKing's College LondonLondon WC2R 2LSUK School of Atmospheric Sciences Sun Yat-sen UniversitySouthern Marine Science and Engineering Guangdong Laboratory(Zhuhai)Zhuhai 519000China Guangdong Province Key Laboratory for Climate Change and Natural Disaster Studies Sun Yat-sen UniversityZhuhai 519000China Institute of Reproductive and Child Health National Health Commission Key Laboratory of Reproductive Health/Department of Epidemiology and BiostatisticsMinistry of Education Key Laboratory of Epidemiology of Major Diseases(PKU)School of Public HealthPeking University Health Science CentreBeijing 100191China Research Station of Alpine Ecology Environment and Health Tibet UniversityLhasa 850000China

While El Niño-Southern Oscillation(ENSO)modulates landscape fire activities across the Pacific Rim,the detailed patterns and mode-specific variations of these effects remain poorly *** study aims to address that gap by systematically assessing the spatiotemporal variations in burned areas from 2001 to 2020 during different modes of ENSO,namely,Eastern Pacific(EP)El Niño,EP La Niña,Central Pacific(CP)El Niño and CP La Niñ*** evaluate the modulation patterns using classical correlation and cross-spectrum analyses,focusing on five hotspot regions:Temperate North America(TENA),Southern Hemisphere South America(SHSA),Northern Hemisphere South America(NHSA),Equatorial Asia(EQAS)and Australia and New Zealand(AUST).El Niño and La Niña exhibit asymmetrical opposite effects on burned areas,as *** findings reveal spatial non-uniformity in the impact of ENSO on landscape fires,with La Niña enhancing burned areas in TENA and SHSA,whereas El Niño predominantly affects AUST,EQAS and *** and EP ENSO events differ in intensity and their large-scale circulation *** differences cause notable variations in the regional burned *** vapour redistribution emerges as the key *** study emphasises the heterogeneity in ENSO modes in regulating landscape fires,providing insights for future landscape fire risk assessment and prevention efforts under climate change.

关键词： ENSO mode Landscape fire Burned area Pacific Rim

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