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2023 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2023

作者： Akbari, Behzad Zhu, Haibin Pan, Ya-Jun Dalhousie University Advanced Control and Mechatronics Laboratory Department of Mechanical Engineering HalifaxNS Canada Nipissing University Collaborative Systems Laboratory Department of Computer Science and Mathematics North BayON Canada

ISBN: (纸本)9798350337020

Trust evaluation and trust establishment play crucial roles in the management of trust within a multi-agent system. When it comes to collaboration systems, trust becomes directly linked to the specific roles performed by agents. The Role-Based Collaboration (RBC) methodology serves as a framework for assigning roles that facilitate agent collaboration. Within this context, the behavior of an agent with respect to a role is referred to as a process role. This research paper introduces a role engine that incorporates a trust establishment algorithm aimed at identifying optimal and reliable process roles. In our study, we define trust as a continuous value ranging from 0 to 1. To optimize trustworthy process roles, we have developed a consensus-based Gaussian Process Factor Graph (GPFG) tool. Our simulations and experiments validate the feasibility and efficiency of our proposed approach with autonomous robots in unsignalized intersections and narrow hallways. © 2023 IEEE.

关键词： Gaussian process factor graph Multi-Agent Systems (MASs) Role-based collaboration Trust management systems

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A Multi-Layer Long Short-Term Memory Neural Network on Battery State-of-Health Estimation with Transfer Learning 3

A Multi-Layer Long Short-Term Memory Neural Network on Batte...

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3rd IEEE International Conference on Industrial Electronics for Sustainable Energy Systems, IESES 2023

作者： Tian, Jilun Zhang, Jiusi Jiang, Yuchen Luo, Hao Huang, Congsheng Chow, Mo-Yuen Harbin Institute of Technology Department of Control Science and Engineering Harbin150001 China National Taipei University of Technology Department of Electrical Engineering Taipei10608 Taiwan North Carolina State University Department of Electrical and Computer Engineering RaleighNC27606 United States

ISBN: (纸本)9798350324754

Due to the important part of batteries in industrial systems, its safety analysis has causes widespread attention from researchers, and its effective maintenance decision-making is needed. Data-driven state-of-health (SOH) estimation can provide useful information by monitoring historical data during the aging process, but it can be failed in the cross-domain scenarios due to the different data distributions. To tackle this issue, we propose a long short-term memory (LSTM) neural network with an additional fully connected dense as a basic predictor, and apply the pretrain and fine-tuning training algorithm to realize the high-performance prediction. By validated in two real-world datasets, we find that blindly expanding the training set may have a negative impact on model accuracy, and the proposed TL-LSTM can achieve a great performance under the cross-domain tasks. © 2023 IEEE.

关键词： Long short-term memory

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A Foreground Feature Embedding Network for Object Detection in Remote Sensing Images 18th

A Foreground Feature Embedding Network for Object Detection...

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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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Decentralized Blockchain-based Platform for Managing and Issuing Academic Certificates

Decentralized Blockchain-based Platform for Managing and Iss...

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

作者： Ciocan Stefan-Robert Cristian Nicolae Butincu Department of Computer Science Faculty of Automatic Control and Computer Engineering "Gheorghe Asachi" Technical University of Iasi Romania

ISBN: (数字)9798350364293

ISBN: (纸本)9798350364309

This paper presents a blockchain-based platform for managing and issuing academic certificates that features security, transparency, non-repudiation and traceability. Relying on the security provided by Ethereum blockchain and using asymmetrical cryptography in certificate generation, combined with certificate revocation mechanisms, the proposed platform offers a reliable, decentralized and secure solution that can be used by academic institutions to streamline the management of academic certificates and reduce bureaucracy.

关键词： Smart contracts Non-repudiation Information age control systems Fraud Reliability Certification

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Algorithmic approach for audio segmentation

Algorithmic approach for audio segmentation

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

作者： Vasile-Silviu Sarmasanu Cristian Nicolae Butincu Vasile Ion Manta Andrei Stoleru Department of Computer Science Faculty of Automatic Control and Computer Engineering "Gheorghe Asachi" Technical University of Iasi Romania

ISBN: (数字)9798350364293

ISBN: (纸本)9798350364309

This paper presents an algorithmic approach for segmenting news broadcasts produced by television stations. The proposed approach uses advanced signal processing techniques to extract and classify audio segments from Romanian TV news broadcasts. By using derivative of 1-dimensional Gaussian filter and binary classification models, this paper aims to automatically identify and classify news segments into categories such as studio news, field news, commercials and music breaks. The primary objective of this paper is to develop a segmentation methodology optimized for reduced computational memory and quick processing. By leveraging efficient computational techniques, the news segmentation can be performed in a timely manner without compromising accuracy. This optimization is crucial for real-time or near-real-time applications within TV networks, facilitating tasks such as content monitoring and analysis.

关键词： TV Content management Accuracy Computational modeling Signal processing algorithms Signal processing Filtering algorithms Real-time systems Classification algorithms Reliability

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CPU-GPU Parallelism Applied to Quality-Diversity Volume Rendering

CPU-GPU Parallelism Applied to Quality-Diversity Volume Rend...

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

作者： Cristian Nicolae Butincu Marius Gavrilescu Department of Computer Science Faculty of Automatic Control and Computer Engineering "Gheorghe Asachi" Technical University of Iasi Romania

ISBN: (数字)9798350364293

ISBN: (纸本)9798350364309

This paper presents the performance analysis and the most significant design decisions taken to improve the runtime performance of a quality-diversity volume rendering framework, focusing on using parallel and distributed computing to optimize specific stages within the framework pipeline. After considering the benefits and trade-offs of several aspects like minimization of execution time, portability and hardware independence, we developed a hybrid CPU-GPU parallel computing model that increased the overall efficiency. Using this model, we achieved significant improvement in runtime speed for critical components of the framework compared to the sequential implementation. Additionally, this model can be applied in a wide range of scenarios, where the performance improvements provided by GPU processing can be complemented by CPU parallelization, with better runtime results.

关键词： Solid modeling Runtime Computational modeling Volume measurement Pipelines Performance gain Parallel processing Feature extraction Rendering (computer graphics) Hardware

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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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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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Leveraging Large Language Models for Automatic Smart Contract Generation

Leveraging Large Language Models for Automatic Smart Contrac...

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IEEE Annual International computer Software and Applications Conference (COMPSAC)

作者： Emanuele Antonio Napoli Fadi Barbàra Valentina Gatteschi Claudio Schifanella Department of Control and Computer Engineering Politecnico di Torino Turin ITALY Department of Computer Science University of Turin Turin ITALY

ISBN: (数字)9798350376968

ISBN: (纸本)9798350376975

In the rapidly evolving landscape of blockchain technology, smart contracts stand as pivotal instrument for automating and enforcing digital agreements. However, their creation often necessitates specialized programming skills, hindering broader adoption and accessibility. This paper proposes a pipeline that leverages the capabilities of Large Language Models (LLMs) to automate the generation of smart contracts. By harnessing the natural language understanding and generation capabilities of LLMs, our approach aims to make accessible smart contract development to people that are not familiar with this task. The proposed pipeline employs the CO-STAR methodology to optimize prompt creation for high-quality outputs. Moreover, in order to assess the correctness and reliability of the generated smart contracts, we leverage on Slither, one of the most cutting-edge vulnerability detection tools. Furthermore, we propose a benchmarking suite based on metrics such as compilability, vulnerabilities, and presence of comments, among the others, in order to evaluate the effectiveness of the pipeline in terms of consistency of generated smart contracts, LLM's temperature effect, and prompt selection. The results show that our pipeline is able to produce 98.1% of compilable smart contracts, the temperature value has negligible effect on the generated smart contracts, and the CO-STAR methodology produces valuable and consistent outputs with low-impact vulnerabilities.

关键词： Measurement Large language models Instruments Smart contracts Pipelines Programming Software

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A 3d-Pointcloud Based Semi-Automatic Private Dataset Generation Method for Specific 6d Pose Estimation Tasks

SSRN

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SSRN 2024年

作者： Lou, Yuanlong Fu, Dongxiang Tian, Ying Department of control science and Engineering School of Optical-Electrical and Computer Engineering University of Shanghai for Science and Technology Shanghai200093 China

Deep learning algorithms often require high-quality datasets, but public datasets can be less effective for specific applications like visual robotic arm grasping. Generating private datasets traditionally involves time-consuming manual work or is limited by equipment constraints. To address this problem, a 3D-pointcloud based semi-automatic private dataset generation method for specific object pose detection task is proposed, which has broadly adaptive performance that can be done with only one binocular camera. Firstly, the RGBD data is used to automatically generate labeled point clouds. Then, the labels, which contain the target category and position information as well as the pose information are automatically generated from the point cloud. Only the denoising need simple manual processing, the rest of the steps are operated by the algorithm, so it is called semi-automatic method. Compared to manual labeling, experiments show this semi-automatic method significantly reduces time while maintaining accuracy. © 2024, The Authors. All rights reserved.

关键词： Object detection

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