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18th CCF Conference on computer Supported Cooperative Work and Social Computing, ChineseCSCW 2023

作者： Wu, Jiahui Cai, Yuanzheng Wang, Tao Luo, Zhiming Shan, Senhua Li, Zuoyong College of Computer and Data Science Fuzhou University Fuzhou350108 China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control College of Computer and Control Engineering Minjiang University Fuzhou350108 China Department of Artificial Intelligence Xiamen University Xiamen361005 China Istrong Technology Limited Company Fuzhou350108 China

ISBN: (纸本)9789819996360

Compared with traditional natural images, remote sensing images (RSIs) typically have high resolution. The objects in the images are densely distributed, with heterogeneous orientation and large scale variation, even among objects of the same class. In recent years, object detection algorithms have made great strides in general images, but they are still difficult to meet the challenges that exist in RSIs. Therefore, we propose a foreground feature embedding network (FFE-Net) for object detection in RSIs. To better grasp the object features in RSIs, we design a foreground feature embedding module (FFEM) to learn the foreground features of the object. This is achieved by introducing an additional semantic segmentation branch and embedding the features in the classification and regression branches. Simultaneously, we propose a modified Gaussian function with focal loss (MGFFL) as a way to eliminate the extra background noise from soft labels, making the learned foreground features more robust. Our experimental results on two publicly available remote sensing image datasets, DOTA-v1.0 and HRSC2016, validate the effectiveness of FFE-Net. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Object detection

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Reliability Improvement Method for Precision Micro and Special Motors Based on Response Surface Methodology 14

Reliability Improvement Method for Precision Micro and Speci...

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14th International Conference on Reliability, Maintainability and Safety, ICRMS 2023

作者： Xu, Hongwu Fu, Liping Xiang, Gang Peng, Yu Pan, Weidi Wu, Heyuan Aerospace Product Engineering Research Institute China Academy of Aerospace Standardization and Product Assurance Beijing China China Aerospace Science and Technology Corporation Beijing China National Key Laboratory on Aerospace Intelligent Control Beijing Aerospace Automatic Control Institute Beijing China Engineering Technology Department Guizhou Aerospace Linquan Motor Control Ltd. GuiYang China

ISBN: (纸本)9798350329988

Currently, precision micro and special motors are essential basic products in the industry. They are key components widely used in platforms such as aerospace, aircraft, automobiles, and unmanned intelligence. This paper proposes a reliability improvement method for precision micro and special motors based on a response surface methodology. Firstly, we identify key design parameters that affect motor performance through sensitivity analysis methods. Then, the key design parameters are evenly divided into a 5x5 matrix. We conduct 100 Monte Carlo simulation analyses per group and obtain the probability density function of motor speed. Finally, a response surface functional relationship between reliability and key design parameters is established. The values of key design parameters corresponding to the maximum reliability are calculated and used to improve the reliability of the motor. © 2023 IEEE.

关键词： precision micro andspecialmotors reliability response surface sensitivity

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Acquisition and Monitoring of Environmental Parameters from High-altitude UAV Flight

Acquisition and Monitoring of Environmental Parameters from ...

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International Conference on System Theory, control, and Computing (ICSTCC)

作者： Marius Dima Dan Popescu Loretta Ichim Petrișor Valentin Pârvu Faculty of Automatic Control and Computer Science National University of Science and Technology Politehnica Bucharest Bucharest Romania Faculty of Aerospace Engineering National University of Science and Technology Politehnica Bucharest Bucharest Romania

ISBN: (数字)9798350364293

ISBN: (纸本)9798350364309

Unmanned aerial vehicles can improve short- term weather forecasting by acquiring information from weather sensors and other sensors. With this information, there is the possibility of making relevant maps like solar radiation, pollen, emissions, particles, and others. Another advantage of this acquisition system is the high rate of flights, compared to the classic measurements made with the weather balloon, which is launched twice a day. In addition to the advantages listed above, we discuss the multiplication factor of the acquired data, these systems being able to operate in various geographical locations.

关键词： Weather forecasting Data integration Autonomous aerial vehicles Prediction algorithms Particle measurements control systems Sensors Solar radiation Monitoring Payloads

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Applying Transformer-Based Neural Networks to Corpus Linguistic Data for Predictive Text Generation in Multilingual Environments 6

Applying Transformer-Based Neural Networks to Corpus Linguis...

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6th IEEE International Conference on Emerging Smart Computing and Informatics, ESCI 2024

作者： Taufikin Orosoo, Myagmarsuren Rengarajan, Manikandan Fatma, Gulnaz Yuldashev, Dilyorjon Raj, I Infant IAIN Kudus Indonesia Mongolian National University of Education Mongolia Vel Tech Rangarajan Dr. Sagunthala R and D Institute of Science and Technology Tamil Nadu Chennai India University College Al Ardah Jazan University Jazan Saudi Arabia Turin Polytechnic University in Tashkent Automatic Control and Computer Engineering Department Tashkent Uzbekistan K. Ramakrishnan College of Engineering Department of CSE Tamil Nadu Trichy India

ISBN: (纸本)9798350306613

In the subject of corpus linguistics, this study explores the use of transformer-based neural networks to the problems of predictive text production in multilingual contexts. The Transformer architecture presents a viable framework for natural language processing problems because of its reputation for capturing complex patterns and long-range dependencies in sequential data. The research employs a two-phase approach, whereby the Transformer is first pre-Trained using a variety of multilingual corpora. This helps the model learn representations that are independent of language, which makes it easier for it to adapt to various linguistic circumstances. The model is then fine-Tuned using language-specific datasets to improve its ability to produce linguistically nuanced and contextually appropriate text. The study looks into how different model architectures, training methods, and hyperparameters affect how well the suggested multilingual predictive text generation system performs. Evaluation measures are used to evaluate the model's performance in several languages, including linguistic diversity measurements, BLEU scores, and perplexity. The overall accuracy, calculated across all languages, stands at 80.3%, indicating the model's robust cross-lingual generalization and its effectiveness in capturing diverse linguistic nuances The efficacy and generalization of Transformer-based models are improved across a wide range of languages to handle linguistic diversity. © 2024 IEEE.

关键词： Corpus Linguistics Cultural Context Integration Multilingual Text Generation Neural Language Models Transformer-Based Neural Networks

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Observer-Based control of Second-Order Multi-vehicle Systems in Bearing-Persistently Exciting Formations 63

Observer-Based Control of Second-Order Multi-vehicle Systems...

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63rd IEEE Conference on Decision and control, CDC 2024

作者： Tang, Zhiqi Fidan, Baris Johansson, Karl H. Martensson, Jonas Hamel, Tarek Kth Royal Institute of Technology Division of Decision and Control Systems School of Electrical Engineering and Computer Science StockholmSE-10044 Sweden University of Waterloo Mechanical and Mechatronics Engineering Department ON Canada I3S-UCA Cnrs France

ISBN: (纸本)9798350316339

This paper proposes an observer-based formation tracking control approach for multi-vehicle systems with second-order motion dynamics, assuming that vehicles' relative or global position and velocity measurements are unavailable. It is assumed that all vehicles are equipped with sensors capable of sensing the bearings relative to neighboring vehicles and only one leader vehicle has access to its global position. Each vehicle estimates its absolute position and velocity using relative bearing measurements and the estimates of neighboring vehicles received over a communication network. A distributed observer-based controller is designed, relying only on bearing and acceleration measurements. This work further explores the concept of the Bearing Persistently Exciting (BPE) formation by proposing new algorithms for bearing-based localization and state estimation of second-order systems in centralized and decentralized manners. It also examines conditions on the desired formation to guarantee the exponential stability of distributed observer-based formation tracking controllers. In support of our theoretical results, some simulation results are presented to illustrate the performance of the proposed observers as well as the observer-based tracking controllers. © 2024 IEEE.

关键词： Acceleration measurement

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A Comparative Study of Compressive Sensing Algorithms for Hyperspectral Imaging Reconstruction

arXiv

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arXiv 2024年

作者： Justo, Jon Alvarez Lupu, Daniela Orlandić, Milica Necoara, Ion Johansen, Tor Arne Department of Electronic Systems Norwegian University of Science and Technology Norway Department of Automatic Control and Systems Engineering University Politehnica Bucharest Romania Department of Engineering Cybernetics Norwegian University of Science and Technology Norway

Hyperspectral Imaging comprises excessive data consequently leading to significant challenges for data processing, storage and transmission. Compressive Sensing has been used in the field of Hyperspectral Imaging as a technique to compress the large amount of data. This work addresses the recovery of hyperspectral images 2.5× compressed. A comparative study in terms of the accuracy and the performance of the convex FISTA/ADMM in addition to the greedy gOMP/BIHT/CoSaMP recovery algorithms is presented. The results indicate that the algorithms recover successfully the compressed data, yet the gOMP algorithm achieves superior accuracy and faster recovery in comparison to the other algorithms at the expense of high dependence on unknown sparsity level of the data to recover. Copyright © 2024, The Authors. All rights reserved.

关键词： Compressed sensing

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Ethical Considerations in Explainable AI: Balancing Transparency and User Privacy in English Language-based Virtual Assistants 5

Ethical Considerations in Explainable AI: Balancing Transpar...

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5th International Conference on Intelligent Communication Technologies and Virtual Mobile Networks, ICICV 2024

作者： Alijoyo, Franciskus Antonius Sandya Sneha Sri, S. Alapati, Purnachandra Rao Yuldashev, Dilyorjon Ponni Valavan, M. Center for Risk Management and Sustainability Indonesia School of Business and Information Technology Stmik Likmi Bandung Indonesia Krishna University Andhra Pradesh India Prasad v Potluri Siddhartha Institute of Technology Andhra Pradesh Vijayawada Kanuru India Turin Polytechnic University in Tashkent Automatic Control and Computer Engineering Department Tashkent Uzbekistan K.Ramakrishnan College of Engineering Trichy Department of Artificial Intelligence and Data Science Tamil Nadu India

ISBN: (纸本)9798350385649

English Language-Based Virtual Assistants (ELB-VAs) are AI-powered systems designed to comprehend and respond to user queries in the English language, exemplified by virtual assistants like Siri or Alexa. The need for balancing transparency and user privacy in ELB-VAs is paramount due to their pervasive integration into daily life. Ensuring transparency imbues user trust, while safeguarding privacy addresses ethical concerns associated with personal data. Existing methods involve clear privacy policies, user-controlled data sharing settings, and encryption. However, drawbacks include user confusion and potential biases. To address these limitations, this study proposes a novel approach. Methodologically, it integrates pre-processing techniques such as lowercasing and tokenization, coupled with a Natural Language Understanding model. This model undergoes intent and entity recognition training, enhancing accuracy, and incorporates privacy-aware response generation, ensuring informative yet privacy-conscious interactions. The implementation of the study's results is carried out using Python tools, showcasing improved metrics and response times. This approach contributes to a more transparent and privacy-respecting user experience, aligning with evolving ethical norms and setting the stage for advancements in ELB-VA technology. This comprehensive exploration bridges existing gaps, emphasizing the ethical imperative of user-centric and privacy-aware AI interactions in ELB- VAs. The proposed NLU model exhibits a substantial increase in accuracy compared to other methods, with an impressive accuracy value of 99.1%• On average, it outperforms the Random Forest and Decision Tree models by 15.7 percentage points, highlighting its superior predictive capabilities in the evaluated task. This comprehensive exploration aligns with evolving ethical norms and establishes a foundation for future advancements in ELB-VA technology. © 2024 IEEE.

关键词： Transparency

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Tensor and Minimum Connected Dominating Set based Confident Information Coverage Reliability Evaluation for IoT

IEEE Transactions on Sustainable Computing

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IEEE Transactions on Sustainable Computing 2024年第3期10卷 547-561页

作者： Xiao, Ziheng Zhu, Chenlu Feng, Wei Liu, Shenghao Deng, Xianjun Lu, Hongwei Yang, Laurence T. Park, Jong Hyuk Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology Hubei Wuhan430074 China Artificial Intelligence and Intelligent Transportation Joint Technical Center of HUST and Hubei Chutian Intelligent Transportation Company Ltd United States Network and Industrial Control Information Security Technology Department China Nuclear Power Operation Technology Company Ltd. 1011 Xiongchu Avenue Hubei Wuhan430070 China The Department of Computer Science and Engineering Seoul National University of Science and Technology Seoul01811 Korea Republic of

Internet of Things (IoT) reliability evaluation contributes to the sustainable computing and enhanced stability of the network. Previous algorithms usually evaluate the reliability of IoT by enumenating the states of nodes and networks, which are difficult to handle IoT with hundreds of nodes because the computational cost. In this paper, a novel algorithm, TMCRA, is proposed to evaluate the reliability of IoT in complex network environment, which consider both coverage and connectivity. For coverage, TMCRA employs the Confident Information Coverage (CIC) model to divide the target area into independent grids and calculates the coverage rate. In terms of connectivity, TMCRA forming the Virtual Backbone Network (VBN) based on two proposed methods: TMA and MGIN, and evaluate connectivity by analyzing the VBN rather than the whole network. The TMA and MGIN are two algorithms for constructing Minimum Connected Dominant Sets (MCDS), which are suitable for different scale networks. Finally, based on the data of coverage and connectivity, TMCRA utilizes tensors for the unified modeling and representation of network structure, and calculates IoT reliability based on the tensors. Simulations are carried out for various sizes of IoT to show the advantages and effectiveness of the proposed approach in reliability evaluation. © 2016 IEEE.

关键词： Data reliability

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Optimizing High Resolution Wide Field of View Optical Design for Long Wave Infrared Objective

Optimizing High Resolution Wide Field of View Optical Design...

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Electrical engineering (ICEENG), 12th International Conference on

作者： Hossam Aboalia Sherif Hussein Alaaeldin Mahmoud Control Systems Department Optoelectronics and Automatic Military Technical College Cairo Egypt Artificial Intelligence Department Computer Engineering Military Technical College Cairo Egypt

ISBN: (数字)9798350343427

ISBN: (纸本)9798350343434

Optical system is a basic component of infrared (IR) imaging systems that affect the image quality of the observed field of view (FOV). Responsible for collecting IR radiation from the scene and projecting it onto detector pixels. The optical system's characteristics, such as lens material, focal length, and optical diameter, critically impact the amount of transmitted IR radiation. The design process aims to eliminate and limit aberrations to enhance performance. Increasing spatial resolution and widening the FOV are crucial for image quality improvement. In this study, we present a unique high-resolution wide FOV optical design for an infrared objective operating in the long-wave infrared (LWIR) band $(\mathbf{8}-\mathbf{12}\ \boldsymbol{\mu} \mathbf{m})$ . Utilizing the time-tested Cooke triplet structure as the foundation, we enhance its capabilities through the implementation of a splitting methodology. This approach allows for improved control over aberrations and enhances the optical design's overall performance. Ansys Zemax OpticStudio software was employed to optimize radii of curvatures and thicknesses based on the proposed merit function operands. Our concept makes use of a small pixel size $(\mathbf{15}\ \mu \mathbf{m})$ to achieve an expansive FOV of 18° along with a high spatial resolution of 31 cycles/mm. This makes the design a highly suitable candidate for diverse remote sensing observation applications.

关键词： Integrated optics Image quality Optical design Optical detectors Optical imaging Size measurement Software

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Data-Enabled Finite control-Set Model Predictive control of a Grid-Tied Inverter

Data-Enabled Finite Control-Set Model Predictive Control of ...

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2023 IEEE Energy Conversion Congress and Exposition, ECCE 2023

作者： Wu, Wenjie Qiu, Lin Li, Hui Liu, Xing Du, Chao Fang, Youtong Zhejiang University College of Electrical Engineering Hangzhou China Florida State University Department of Electrical and Computer Engineering TallahasseeFL United States Shaanxi University of Science & Technology College of Electrical and Control Engineering Xian China

ISBN: (纸本)9798350316445

This paper presents a novel predictive control architecture for power converters that addresses the challenges of model mismatch and parameter sensitivity in the finite control-set model predictive control (FCS-MPC) framework. The proposed method employs a dynamic-linearization-based approach, which replaces the detailed model used in the FCS-MPC controller with a virtual equivalent data model, creating a data-enabled FCS-MPC architecture. By relying solely on input-output data, the proposed method exhibits considerable robustness against parameter perturbations while retaining the attractive features of the conventional FCS-MPC method. The effectiveness of the proposed design is demonstrated through comparative simulations results on a two-level grid-tied inverter. © 2023 IEEE.

关键词： Grid-tied inverter Model predictive control (MPC) Robustness

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