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2022 International Conference Automatics and Informatics, ICAI 2022

作者： Kalandarov, Palvan I. Ubaydulayeva, Shakhnoza R. Gaziyeva, Rano T. Nikolov, Nikola N. Alexsandrova, Mariela I. National Research University Tashkent Institute of Irrigation and Agricultural Mechanization Engineers Department Automation and Control of Technology Process in Production Tashkent Uzbekistan Technical University of Varna Faculty of Computer Science and Automation Department of Automation Varna9010 Bulgaria

ISBN: (数字)9781665476256

ISBN: (纸本)9781665476256

The article deals with the choice of criteria for efficiency, applicability in measuring technology, analytical and construction, on the basis of which a methodology for assessing the quality of structures of measuring devices of moisture of materials of the agro-industrial complex is proposed. The standard deviation of the random error characterizing the accuracy of measurements is shown. A complex efficiency criterion is discussed, including the dominant particular parameters: accuracy, reliability, cost, allowing to solve the problem of optimal design of humidity control devices for the materials under consideration. As an optimal option, a differential measuring device is analyzed, which practically eliminates random and systematic errors and makes it possible to design a multiparameter measurement method that considers the features of a heterogeneous environment, such as bulk grain materials of the agro-industrial complex © 2022 IEEE.

关键词： Random errors

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An aircraft brake control algorithm with torque compensation based on RBF neural network

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Chinese Journal of Aeronautics 2024年第1期37卷 438-450页

作者： Ning BAI Xiaochao LIU Juefei LI Zhuangzhuang WANG Pengyuan QI Yaoxing SHANG Zongxia JIAO School of Automation Science and Electrical Engineering Beihang UniversityBeijing 100191China Science and Technology on Aircraft Control Laboratory Beihang UniversityBeijing 100191China Key Laboratory of Advanced Aircraft Systems(Beihang University) Ministry of Industry and Information TechnologyBeijing 100091China Research Institute for Frontier Science Beihang UniversityBeijing 100191China Ningbo Institute of Technology of Beihang University Ningbo 315800China Tianmushan Laboratory Xixi Octagon CityYuhang DistrictHangzhou 310023China

The wheel brake system of an aircraft is the key to ensure its safe landing and rejected takeoff.A wheel’s slip state is determined by the brake torque and ground adhesion torque,both of which have a large degree of *** is this nature that brings upon the challenge of obtaining high deceleration rate for aircraft brake *** overcome the disturbances caused by the above uncertainties,a braking control law is designed,which consists of two parts:runway surface recognition and wheel’s slip state *** runway surface recognition,the identification rules balancing safety and braking efficiency are defined,and the actual identification process is realized through recursive least square method with forgetting *** slip state tracking,the LuGre model with parameter adaptation and a brake torque compensation method based on RBF neural network are proposed,and their convergence are *** effectiveness of our control law is verified through simulation and ground *** in the experiments on the ground inertial test bench,compared to the improved pressure-biased-modulation(PBM)anti-skid algorithm,fewer wheel slips occur,and the average deceleration rate is increased by 5.78%,which makes it a control strategy with potential for engineering applications.

关键词： Aircraft landing Brakes Disturbance observer Neural networks Runway identification

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research on Intent Recognition Method for Non-Cooperative Satellite Based on LSTM Network

Research on Intent Recognition Method for Non-Cooperative Sa...

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2023 China automation Congress, CAC 2023

作者： Yihan, Liu Dong, Sun Han, Yan Quanrun, Mu Xiaochao, Liu Pengyuan, Qi School of Automation Science and Electrical Engineering Beihang University Beijing China Beijing Institute of Control Engineering National Key Laboratory of Space Intelligent Control Beijing100094 China Beijing Sun Wise Space Technology Ltd. Beijing100190 China Research Institute for Frontier Science Beihang University Beijing China

ISBN: (纸本)9798350303759

With the rapid advancements in space offensive and defensive countermeasure technologies, the scope of international confrontation has expanded beyond the traditional domains of sea, land, and air, reaching into the realm of space. In modern military warfare, the focus has shifted from solely physical and informational aspects to include the cognitive domain. Within this domain, intent recognition emerges as a crucial technology, offering tactical insights and decision-making guidance for future wars. This paper presents an intention recognition algorithm for space target behaviors, utilizing a long short-term memory (LSTM) network. By incorporating time series motion parameters and the relative relationships between enemy and friendly satellite, the algorithm identifies the tactical intentions behind space target orbital behaviors. Additionally, a space target attack and defense scenario simulation environment is established using MATLAB and C-W equations, enabling the creation of a dataset for space target recognition. Through the proposed method, the simulation effectively discerns the orbital behavior intentions of space targets, providing valuable guidance for battlefield decision-making. © 2023 IEEE.

关键词： Long short-term memory

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Intelligent Accident Recognition and Notification System using Hybrid Deep Learning and IoT 4

Intelligent Accident Recognition and Notification System usi...

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4th International Conference on Soft Computing for Security Applications, ICSCSA 2024

作者： Sathish Kumar, P. Singh Rawat, Ramesh Shrimali, Manish Parikh, Swapnil Patil, Harshal Maranan, Ramya United Institute of Technology Department of Robotics & Automation Tamil Nadu Coimbatore641020 India Graphic Era Deemed to Be University Department of Computer Science & Engineering Uttarakhand Dehradun248 002 India Department of Computer Science and IT Rajasthan Udaipur313 003 India Parul Institute of Engineering and Technology Parul University Department of Computer Science and Engineering Gujarat 391760 India Symbiosis Institute of Technology Symbiosis International Deemed University Computer Science and Engineering Maharashtra Pune412 115 India Saveetha School of Engineering SIMATS Department of Research and Innovation Tamil Nadu Chennai602105 India

ISBN: (纸本)9798331515720

Ensuring the safety of individuals should be a top priority. Everyone wants to return home safely. Many riders continue to disregard safety constraints, increasing the chance of accidents that result in death. It is critical to incorporate the latest technology into road monitoring systems to make cities smarter and to ensure rider safety. For this research, Deep Learning (DL) and the Internet of Things (IoT) were chosen for effective road surveillance and alert systems. The primary goal of the study is to detect and inform riders about accidents that occur along their path and to provide details about the location of the detected accident. Additionally, the system provides the vehicle's speed. To identify automobile accidents in the footage, a hybrid DL model that includes Convolutional Neural Network-Bidirectional Long Short-Term Memory (CNN-Bi-LSTM) is suggested. For the model's training, both accident and non-Accident data were collected. The model is evaluated using three metrics: Accuracy, loss, and execution time. The next step is to upload the recommended model to the ThingSpeak cloud. A Graphical User Interface (GUI) is being created in the ThingSpeak cloud. The GUI displays the vehicle's speed, accident images, and the precise location of the accident scene using the Raspberry Pi, speed sensor, Pi-camera, and Global Positioning System (GPS) module. An LED is also integrated into the GUI to alert the user. A real-world test is conducted on the designed system. The proposed solution has many advantages and will ensure driver safety. © 2024 IEEE.

关键词： Graphical user interfaces

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Smartphone-based colorimetric detection of formaldehyde in the air

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Building Simulation 2024年第11期17卷 2007-2015页

作者： Meng Yang Jin Ye Tao Yu Ying Song Hua Qian Tianyi Liu Yang Chen Junqi Wang Shi-jie Cao Cong Liu School of Energy and Environment Southeast UniversityNanjing210096China School of Energy and Power Jiangsu University of Science and TechnologyZhenjiangJiangsu212100China Wuhan Second Ship Design and Research Institute Wuhan430205China Hubei Provincial Engineering and Technology Research Center for Food Quality and Safety Test Hubei Provincial Institute for Food Supervision and TestWuhan430075China Laboratory of Image Science and Technology the School of Computer Science and EngineeringSoutheast UniversityNanjingChina Jiangsu Provincial Joint International Research Laboratory of Medical Information Processing Southeast UniversityNanjingChina School of Cyber Science and Engineering Southeast UniversityNanjingChina Key Laboratory of Computer Network and Information Integration Southeast UniversityMinistry of EducationNanjingChina School of Architecture Southeast UniversityNanjingChina Jiangsu Province Engineering Research Center of Urban Heat and Pollution Control Southeast UniversityNanjingChina Global Centre for Clean Air Research(GCARE) University of SurreyGuildfordUK

Adverse impacts of exposure to formaldehyde on human health significantly increases attention in monitoring formaldehyde concentrations in the *** formaldehyde detection methods typically rely on large and costly instruments and requires high skills of expertise,preventing it from being widely accessible to *** study introduced a novel approach utilizing smartphone-based colorimetric *** of green channel signals of digital images by a smartphone successfully capture variation of purple color of 4-amino-3-hydrazino-5-mercapto-1,2,4-triazol solution,which is proportional to formaldehyde *** is because that green and purple are complimentary color pairs.A calibration curve was established between green channel signals and formaldehyde concentrations,with a correlation coefficient of *** limit of the smartphone-based method is 0.008 mg/m^(3).Measurement errors decrease as formaldehyde concentrations increase,with median relative errors of 34%,17%,and 6%for concentration ranges of 0–0.06 mg/m^(3),0.06–0.12 mg/m^(3),and 0.12–0.35 mg/m^(3),*** method replaced scientific instrumentation with ordinary items,greatly reducing cost and operation *** would provide an opportunity to realize onsite measurements for formaldehyde by occupants themselves and increase awareness of air quality for better health protection.

关键词： indoor air quality VOC RGB color identification machine vision

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Fully Actuated System Approaches: Theory and Applications

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Journal of Systems Science & Complexity 2022年第2期35卷 437-440页

作者： WU Ai-Guo ZHOU Bin HOU Mingzhe ZHANG Ying Harbin Institute of Technology(Shenzhen) Shenzhen 518055China HIT Wuhu Robot Technology Research Institute Wuhu 241008China The Center for Control Theory and Guidance Technology Harbin Institute of TechnologyHarbin 150001China School of Mechanical Engineering and Automation Harbin Institute of Technology(Shenzhen)Shenzhen 518055China

Since 1960s,the first-order state space approach has been dominant in analysis and design of control *** a state-space model of a dynamical system,the state variable is ***,some problems relevant to state estimation and the seeking of state solution can be conveniently ***,the control variable is the core in the design of controllers.

关键词： state conveniently seeking

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Editorial: The Second International Workshop on Artificial Intelligence Systems in Education - AIxEDU 2

Editorial: The Second International Workshop on Artificial I...

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2nd International Workshop on Artificial INtelligent Systems in Education, AIxEDU 2024

作者： Casalino, Gabriella Limongelli, Carla Schicchi, Daniele Taibi, Davide Temperini, Marco Department of Computer Science University of Bari Aldo Moro Bari Italy Department of Civil Computer Science and Aeronautical Technologies Engineering Roma Tre University Rome Italy Institute for Educational Technology Italian National Research Council CNR-ITD Palermo Italy Department of Computer Control and Management Engineering Sapienza University of Rome Rome Italy

The Second International Workshop on Artificial Intelligence Systems in Education (AIxEDU) marks its 2024 edition in conjunction with the 23rd International Conference of the Italian Association for Artificial Intelligence (AIxIA), held from 25-28 November 2024 in Bolzano, Italy. This workshop brings together Artificial Intelligence and Education experts to explore the latest innovations and best practices in applying AI to educational contexts. Participants discuss recent trends, research initiatives, and emerging developments in AI technologies that aim to enhance teaching methodologies, support personalized learning, and address pressing educational challenges. The workshop emphasizes a multidisciplinary approach to understanding how AI systems can promote inclusion, improve learning outcomes, and empower educators while addressing ethical considerations and ensuring equitable access to education aligned with global sustainable development goals. © 2024 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

关键词： Adversarial machine learning

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Reinforcement Fuzzy Q-Learning Incorporated with Genetic Kinematics Analysis for Self-organizing Holonomic Motion control of Six-Link Stewart Platforms

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International Journal of Fuzzy Systems 2023年第3期25卷 1239-1255页

作者： Huang, Hsu-Chih Xu, Sendren Sheng-Dong Chen, Yu-Xiang Chen, Chien-Ming Department of Electrical Engineering National Ilan University Yilan260007 Taiwan Graduate Institute of Automation and Control National Taiwan University of Science and Technology Taipei106335 Taiwan Advanced Manufacturing Research Center National Taiwan University of Science and Technology Taipei106335 Taiwan

This paper contributes to the development of reinforcement fuzzy Q-learning incorporated with genetic kinematics analysis for self-organizing holonomic motion control of six-link Stewart platforms. The fuzzy theory is integrated with the Q-learning algorithm to develop a hybrid reinforcement learning strategy. Both the actions and the Q-functions in the classical Q-learning algorithm are inferred by means of fuzzy rules. The proposed hybrid reinforcement learning is then applied to self-organizing holonomic motion control of parallel Stewart platforms for industrial applications. After the kinematics analysis using a geometrical approach and genetic algorithm (GA)-based Newton–Raphson formulation, this study develops a practical control scheme that combines fuzzy Q-learning and proportional–integral–differential (PID) control scheme, called FQPID. In the proposed FQPID holonomic motion control method, the PID control gains are online-tuned via the fuzzy Q-learning computation process. An experimental Stewart platform consisting of six servo motors, adjustable links, and two platforms is constructed to illustrate the effectiveness of the presented methods. The experimental results are compared with some existing control methods using the same evaluation criterion. Through the experimental results and comparative works, the superiority of the proposed FQPID control scheme is presented and discussed. © 2023, The Author(s) under exclusive licence to Taiwan Fuzzy Systems Association.

关键词： Reinforcement learning

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Cross-Modality Image Transformation Using Generative Adversarial Network 24th

Cross-Modality Image Transformation Using Generative Adversa...

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24th Conference on Man-Machine-Environment System engineering, MMESE 2024

作者： Liu, Haoting Yu, Shiqi Hou, Qingwen Chen, Shuai Guo, Kuiyuan Wang, Xu Li, Qing Beijing Engineering Research Center of Industrial Spectrum Imaging School of Automation and Electrical Engineering University of Science and Technology Beijing Beijing100083 China Research Institute of Trustworthy Autonomous Systems Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China CATARC Technology Co. Ltd. Shenzhen518118 China

ISBN: (纸本)9789819771387

A cross-modality image transformation method using the Generative Adversarial Network (GAN) is proposed in this paper. First, the preprocessing methods are performed to the original image data. The computational steps include adjusting image size and suppressing image noises. Second, a kind of GAN is constructed. Both the generator network and discriminator network are created. Third, the GAN is trained and the back propagation optimization is performed continuously. Finally, the trained GAN can be used for data enhancement. To test the effects of computational steps above, the image transformation from the visible light image to other noise-contaminated image is performed. Lots of experiments have illustrated the correctness of our method. © Beijing KeCui Man-Machine-Environment System engineering Technology research Academy 2024.

关键词： Generative adversarial networks

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Evolution of Constellation Reconstruction for Enhanced Regional Communication Capability

Evolution of Constellation Reconstruction for Enhanced Regio...

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2023 China automation Congress, CAC 2023

作者： Dong, Sun Yihan, Liu Han, Yan Xiaochao, Liu Pengyuan, Qi School of Automation Science and Electrical Engineering Beihang University Beijing China Beijing Institute of Control Engineering National Key Laboratory of Space Intelligent Control Beijing100094 China Beijing Sun Wise Space Technology Ltd. Beijing100190 China Research Institute for Frontier Science Beihang University Beijing China

ISBN: (纸本)9798350303759

In the era of global personal communication and the rapid progress of small satellite technology, low-orbit constellations have emerged as a focal point for countries worldwide. These constellations, primarily employed for communication purposes, offer advantages such as enhanced signal strength and shorter revisit cycles due to their proximity to the Earth's surface. However, the high deployment cost associated with the large number of satellites in low-orbit constellations poses a significant challenge. As launch costs continue to decline and the number of satellites in constellations increases, there is a growing interest in leveraging these constellations during times of war or sudden disasters in specific regions. This entails designing communication-enhanced constellations tailored to the targeted area and formulating an active configuration evolution plan that facilitates seamless transition from the existing constellation to the desired configuration. The objective of this paper is to propose a scheme for optimizing constellation parameters to enhance communication capabilities within a specific region. Additionally, an active configuration evolution scheme with minimal fuel consumption is introduced to effectively address emergency scenarios. © 2023 IEEE.

关键词： Orbits

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