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Design and Analysis of Weather Parameters Using MEMS technology with Suitable Characteristics for Integration into a Single Chip for Environment Monitoring 3

Design and Analysis of Weather Parameters Using MEMS Technol...

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3rd International Conference on Smart Technologies and Systems for Next Generation Computing, ICSTSN 2024

作者： Hassan, Abdurrashid S. Juliet, Vimala A. Pius Agbulu, G. Joselin Retna Kumar, G. Raghavendra Reddy, N.V. Gunasekar, S. SRM Institute of Science and Technology Department of Electronics and Control/Instrumentation Engineering Kancheepuram Chennai. Tamil Nadu Kattankulathur India Department of Mechatronic Engineering School of Engineering Sciences and Technology Chinhoyi University of Technology Chinhoyi Zimbabwe Department of Electronics and Instrumentation Engineering SRM Institute of Science and Technology Kattankulathur Kancheepuram Tamil Nadu Chennai India RV Institute of Technology and Management Department of Mechanical Engineering Karnataka India Rajalakshmi Engineering College Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350391565

This study designs and simulates weather parameters using MEMS technology with suitable characteristics for integration into a single chip for environment monitoring. The sensing and heating filament of the temperature sensor was designed using a thin film platinum resistor, examining its ability to experience stability, increase in resistivity, accuracy, and free-contamination. The temperature sensing and heating element are based on platinum RTD (Resistance Temperature Detector) with meander shape and aluminum as the bonding pad connecting the sensor to the external circuit. A capacitive humidity sensor is implemented in consolidation with a piezoresistive pressure sensor with a square membrane. The capacitive humidity sensor with hole pattern at the upper plate and a polyimide serves as water absorbent sandwiched between the electrodes. The simulation analysis was conducted using finite element analysis in COMSOL Multiphysics version 6.0. It was observed that the temperature increases from -100oC to - 20oC, and 10oC to 50oC respectively as a result of deviation in relative humidity in the troposphere, and atmosphere. The average changes in capacitances were found at 0.0713/%RH, while the pressure was examined to vary from 1050hPa to 5hPa in a step of 200hPa with a sensitivity of 11.50mv/bar. © 2024 IEEE.

关键词： Humidity sensors

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Proposal of a novel peeling of nano-particle (PNP) process for localized material removal on a hard material surface by controllable magnetic fields 22

Proposal of a novel peeling of nano-particle (PNP) process f...

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22nd International Conference of the European Society for Precision Engineering and Nanotechnology, EUSPEN 2022

作者： Permpatdechakul, Thitipat Khajornrungruang, Panart Suzuki, Keisuke Kutomi, Shotaro Graduate School of Computer Science and System Engineering Mechanical Information Science and Technology Course Kyushu Institute of Technology Fukuoka Japan Department of Intelligent and Control Systems Avanced Mechanical Division Kyushu Institute of Technology Fukuoka Japan School of Computer Science and System Engineering Advanced Mechanical Course Kyushu Institute of Technology Fukuoka Japan

ISBN: (纸本)9781998999118

This article proposes a novel Peeling of Nano-Particle (PNP) process to locally remove material on a hard material surface using controllable magnetic fields. Fe3O4 particles in the size range of 50-100 nm in aqueous solution (pH value 11 adjusted by KOH) were magnetically controlled to adhere and remove from a 4H-SiC surface by two electromagnetic coils, which were sandwiched between the substrate. The particle controlling phenomenon on the surface was experimentally observed and verified by our developed apparatus applying an evanescent field. According to the results, particles could be controlled to approach and remove from the substrate surface with 12mT of magnetic field at the selective area on the substrate surface to realise the proposed PNP process. © European Society for Precision Engineering and Nanotechnology, Conference Proceedings - 22nd International Conference and Exhibition, EUSPEN 2022. All rights reserved.

关键词： Magnetic fields

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TSQ-2024: A Categorized Dataset of 2D LiDAR Images of Moving Dump Trucks in Various Environment Conditions 17

TSQ-2024: A Categorized Dataset of 2D LiDAR Images of Moving...

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17th International Conference on Machine Vision, ICMV 2024

作者： Kokhan, V.V. Konyushenko, I.D. Bocharov, D.A. Seleznev, I.O. Nikolaev, I.P. Nikolaev, D.P. Institute for Information Transmission Problems Russian Academy of Sciences Moscow Russia Moscow Institute of Physics and Technology Dolgoprudny Russia GLAVKARIER LLC Borovsk Russia Federal Research Center "Computer Science and Control" Russian Academy of Sciences Moscow Russia Smart Engines Service LLC Moscow117312 Russia

ISBN: (纸本)9781510688278

LiDARs are powerful and common sensors used for complex 3D environment analysis tasks. They are widely utilized as data providers in industrial measurements, robotics and unmanned technologies. Due to physical properties of such sensors they suffer from environmental heterogenity resulting in distortions of obtained measurements because of natural clutter such as cloud dust, snow and fog. Nevertheless the problem of robust clutter filtering algorithms is of significant importance, we highlight the lack of LiDAR measurements datasets containing natural clutter. The current study presents a novel publicly available and expertly annotated 2D LiDAR measurements dataset TSQ-2024 that contains 120 measurements of passing trucks under natural conditions including sand dust clutter data. The proposed dataset was utilized in the task of truck body segmentation. The experiments section provides the performance evaluation of the proposed truck body segmentation algorithm on the TSQ-2024 dataset which is also published along with the data. © 2025 SPIE.

关键词： Radar clutter

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Distributed Prescribed-Time Observer for Nonlinear Systems in Block-Triangular Form

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IEEE control Systems Letters 2025年 9卷 222-227页

作者： Vincent de Heij M. Umar B. Niazi Karl H. Johansson Saeed Ahmed Jan C. Willems Center for Systems and Control and the Engineering and Technology Institute Groningen Faculty of Science and Engineering University of Groningen Groningen The Netherlands Division of Decision and Control Systems Digital Futures KTH Royal Institute of Technology Stockholm Sweden Department of Electrical Engineering and Computer Science Laboratory for Information and Decision Systems Massachusetts Institute of Technology Cambridge MA USA

This letter proposes a design of a distributed prescribed-time observer for nonlinear systems representable in a block-triangular observable canonical form. Using a weighted average of neighbor estimates exchanged over a strongly connected digraph, each observer estimates the system state despite the limited observability of local sensor measurements. The proposed design guarantees that distributed state estimation errors converge to zero at a user-specified convergence time, irrespective of observers’ initial conditions. To achieve this prescribed-time convergence, distributed observers implement time-varying local output injection gains that monotonically increase and approach infinity at the prescribed time. The theoretical convergence is rigorously proven and validated through numerical simulations, where some implementation issues due to increasing gains have also been clarified.

关键词： Observers Convergence Nonlinear systems Symmetric matrices Observability Directed graphs control systems Vectors Linear systems Electronic mail

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Adaptive path planning control of snake-like robot based on reinforcement tracking learning 6

Adaptive path planning control of snake-like robot based on ...

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6th IEEE International Conference on Intelligent Computing and Signal Processing, ICSP 2021

作者： Zhang, Xiaojun Institute of Computer Control Technology Huali College Guangdong University of Technology Guangzhou China

ISBN: (纸本)9780738143705

The path adaptive planning control of snake-like robot is influenced by uncertain disturbance of environment variables and joint mechanism components, which leads to poor stability of path planning parameter output. In order to improve the stability of path adaptive planning control of snake-like robot, a path adaptive planning control method of snake-like robot based on reinforcement tracking learning is proposed. The kinematics model of snake-like robot path adaptive planning is constructed, constraint parameter modeling of snake-like robot path adaptive planning control is carried out, path distribution parameters of snake-like robot are collected by using sensitive components such as attitude sensor information output instrument and accelerometer, and path spatial distribution parameters of snake-like robot are fused by using enhanced tracking learning method and input into time control actuator. Aiming at the error of unknown disturbance in path planning parameter control of snake-like robot, attitude parameter fusion and inertia error correction are carried out by using enhanced parameter fusion learning method, so as to realize stable control of snake-like robot path adaptive planning. Simulation results show that this method has good stability in path adaptive planning control of snake-like robot, strong positioning and tracking ability of attitude parameters, and improves the robustness of snake-like robot moving forward. © 2021 IEEE.

关键词： Error correction

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Improving Classification Robustness for Noisy Texts with Robust Word Vectors

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Journal of Mathematical Sciences (United States) 2023年第4期273卷 605-613页

作者： Malykh, V. Lyalin, V. St. Petersburg Department of Steklov Mathematical Institute RAS St. Petersburg Russian Federation Moscow Institute of Physics and Technology Moscow Region Dolgoprudny Russian Federation Institute for Systems Analysis Federal Research Center “Computer Science and Control” of Russian Academy of Sciences Moscow Russian Federation

Text classification is a fundamental task in natural language processing, and with a huge and rapidly growing body of research devoted to it. However, there has been little work on investigating noise robustness for the developed approaches. In this work, we are bridging this gap, introducing results on noise robustness testing of modern text classification architectures for English and Russian languages. We benchmark the CharCNN and SentenceCNN models and introduce a new model, called RoVe (Robust Vectors), that we show to be the most robust to noise. © 2023, Springer Nature Switzerland AG.

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MulDoor: A Multi-target Backdoor Attack Against Federated Learning System

MulDoor: A Multi-target Backdoor Attack Against Federated Le...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Li, Xuan Wu, Longfei Guan, Zhitao Du, Xiaojiang Aitsaadi, Nadjib Guizani, Mohsen North China Electric Power University School of Control and Computer Engineering China Fayetteville State University Department of Mathematics and Computer Science Fayetteville United States Stevens Institute of Technology Department of Electrical and Computer Engineering HobokenNJ United States Université Paris-Saclay Uvsq David Versailles France Machine Learning Department United Arab Emirates

ISBN: (纸本)9798350351255

In recent years, with the development of wireless communication networks, federated learning (FL) has been widely deployed in distributed scenarios as a privacy-preserving machine learning paradigm. Due to its inherent features, FL shows vulnerability to backdoor attacks. In a backdoor attack, an adversary manipulates the global model's output by compromising the model of one or multiple participants. Existing backdoor attacks are constrained to outputting a single specified target label during the inference phase, limiting the adversary's flexibility to alter the model's output when different target labels are required. In this paper, we study the multi-target attack scenario within the federated learning context, where the adversary aims to manipulate the global model to output various specified labels by inserting different types of triggers. To effectively insert multiple backdoors simultaneously without reducing the attack's effectiveness, we propose MulDoor, a novel multi-target backdoor attack scheme. MulDoor incorporates the concept of supervised contrastive learning to learn the discrepancies among different types of triggers and mitigate interference between them. The experimental results demonstrate that MulDoor achieves better attack effectiveness compared to existing backdoor attacks in a multi-target backdoor attack setting. © 2024 IEEE.

关键词： Contrastive Learning

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SRG Analysis of Lur’e Systems and the Generalized Circle Criterion

arXiv

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arXiv 2024年

作者： Krebbekx, Julius P.J. Tóth, Roland Das, Amritam Control Systems Group Department of Electrical Engineering Eindhoven University of Technology Netherlands Systems and Control Lab HUN-REN Institute for Computer Science and Control Budapest Hungary

Scaled Relative Graphs (SRGs) provide a novel graphical frequency-domain method for the analysis of nonlinear systems. However, we show that the current SRG analysis suffers from some pitfalls that limit its applicability in analysing practical nonlinear systems. We overcome these pitfalls by modifying the SRG of a linear time invariant operator, combining the SRG with the Nyquist criterion, and apply our result to Lur’e systems. We thereby obtain a generalization of the celebrated circle criterion, which deals with broader class of nonlinearities, and provides (incremental) L2-gain performance bounds. We illustrate the power of the new approach on the analysis of the controlled Duffing oscillator. © 2024, CC BY.

关键词： Frequency domain analysis

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Probabilistic Tube-based control Synthesis of Stochastic Multi-Agent Systems under Signal Temporal Logic 63

Probabilistic Tube-based Control Synthesis of Stochastic Mul...

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63rd IEEE Conference on Decision and control, CDC 2024

作者： Vlahakis, Eleftherios E. Lindemann, Lars Sopasakis, Pantelis Dimarogonas, Dimos V. Kth Royal Institute of Technology Division of Decision and Control Systems School of Electrical Engineering and Computer Science Stockholm10044 Sweden University of Southern California Viterbi School of Engineering Department of Computer Science Los AngelesCA90089 United States Queen's University School of Electronics Electrical Engineering and Computer Science Northern Ireland Belfast United Kingdom

ISBN: (纸本)9798350316339

We consider the control design of stochastic discrete-time linear multi-agent systems (MASs) under a global signal temporal logic (STL) specification to be satisfied at a predefined probability. By decomposing the dynamics into deterministic and error components, we construct a probabilistic reachable tube (PRT) as the Cartesian product of reachable sets of the individual error systems driven by disturbances lying in confidence regions (CRs) with a fixed probability. By bounding the PRT probability with the specification probability, we tighten all state constraints induced by the STL specification by solving tractable optimization problems over segments of the PRT, and relax the underlying stochastic problem with a deterministic one. This approach reduces conservatism compared to tightening guided by the STL structure. Additionally, we propose a recursively feasible algorithm to attack the resulting problem by decomposing it into agent-level subproblems, which are solved iteratively according to a scheduling policy. We demonstrate our method on a ten-agent system, where existing approaches are impractical. © 2024 IEEE.

关键词： Stochastic systems

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Active Detection technology of 3-Dimension Artificial Intelligence Robot Based on Visual Positioning

Active Detection Technology of 3-Dimension Artificial Intell...

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2021 IEEE International Conference on computer Science, Electronic Information Engineering and Intelligent control technology, CEI 2021

作者： Feng, ZhiHong Institute of computer control technology Guangzhou Huali College Guangzhou China

ISBN: (纸本)9780738146492

Camera repositioning is a key module in SLAM system, which is of great significance to ensure the robustness of positioning and mapping of mobile robots in complex scenes, and is also one of the core technologies of augmented reality and automatic driving. In order to improve the active detection and spatial positioning ability of 3D artificial intelligence robot with self-adaptive posture, an active detection and spatial positioning method of 3D artificial intelligence robot with self-adaptive posture based on visual positioning and Monte Carlo estimation is proposed. The method comprises the following steps of: constructing a dynamic environment characteristic heat map ray projection recognition model of the posture self-adaptive 3D artificial intelligence robot, collecting scene data in the process of edge characteristic detection of the movement track of the posture self-adaptive 3D artificial intelligence robot by adopting a characteristic heat map ray projection method, and establishing a Monte Carlo parameter estimation model for global positioning detection of the movement track distribution of the posture self-adaptive 3D artificial intelligence robot;Based on visual positioning and Monte Carlo parameter estimation method, the edge feature detection and active detection parameter positioning of 3D artificial intelligence robot with self-adaptive posture adjustment are realized. Through 3D visual positioning and maximum likelihood estimation parameter identification, the motion space planning and active detection of 3D artificial intelligence robot with self-adaptive posture adjustment are realized. Combined with visual SLAM method, the active detection model of 3D artificial intelligence robot is constructed to improve the control stability. Tests show that this method has good adaptability in active detection and spatial positioning of 3D artificial intelligence robot, and has strong obstacle avoidance ability. © 2021 IEEE.

关键词： Augmented reality

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