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检索条件"机构=Control and Computer Engineering"

共 22325 条记录，以下是311-320 订阅

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Risk-Aware Real-Time Task Allocation for Stochastic Multi-Agent Systems under STL Specifications 63

Risk-Aware Real-Time Task Allocation for Stochastic Multi-Ag...

引用

63rd IEEE Conference on Decision and control, CDC 2024

作者： Engelaar, M.H.W. Zhang, Z. Vlahakis, E.E. Dimarogonas, D.V. Lazar, M. Haesaert, S. Netherlands Kth Royal Institute of Technology Division of Decision and Control Systems School of Electrical Engineering and Computer Science Sweden

ISBN: (纸本)9798350316339

This paper addresses the control synthesis of heterogeneous stochastic linear multi-agent systems with realtime allocation of signal temporal logic (STL) specifications. Based on previous work, we decompose specifications into subspecifications on the individual agent level. To leverage the efficiency of task allocation, a heuristic filter evaluates potential task allocation based on STL robustness, and subsequently, an auctioning algorithm determines the definitive allocation of specifications. Finally, a control strategy is synthesized for each agent-specification pair using tube-based model predictive control (MPC), ensuring provable probabilistic satisfaction. We demonstrate the efficacy of the proposed methods using a multi-shuttle scenario that highlights a promising extension to automated driving applications like vehicle routing. © 2024 IEEE.

关键词： Stochastic systems

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Characterization of Low Frequency Oscillations in Simple Hand Movements 46

Characterization of Low Frequency Oscillations in Simple Han...

引用

46th Annual International Conference of the IEEE engineering in Medicine and Biology Society, EMBC 2024

作者： Mongiardini, Elena Colamarino, Emma Toppi, J. Pichiorri, F. Mattia, D. Cincotti, F. Department of Computer Control and Management Engineering Sapienza University of Rome Italy Irccs Fondazione Santa Lucia Rome Italy

ISBN: (纸本)9798350371499

Recent studies, based on Local Field Potentials and source activity reconstruction from electroencephalographic (EEG) signals, conducted in animals and healthy individuals reported a power increase in the frequency range of 3-5 Hz over the cortical motor areas that accompanies the preparatory phase of movement. Such low frequency oscillations (LFOs) detected in the EEG of post-stroke patients under the same condition have been reported to show lower magnitude peak. In this work we investigate the LFOs activity in a sample of healthy participants performing the grasping and finger extension tasks with both hands separately. To characterize the LFOs in terms of temporal occurrence, we introduce a single-trial alignment around the LFOs peak. We also compared the tasks to determine if there are differences related to hand dominance and/or the type of task proposed. Our results suggest that there is a jitter in the occurrence of the LFOs power peak and that the side and the type of the experimental task have no influence on the LFOs activity. © 2024 IEEE.

关键词： Electroencephalography

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Signed expressions spotting and recognition in an assistive technology system

Signed expressions spotting and recognition in an assistive ...

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2023 International Conference on ENTERprise Information Systems, CENTERIS 2023 - International Conference on Project MANagement, ProjMAN 2023 - International Conference on Health and Social Care Information Systems and Technologies, HCist 2023

作者： Kapuscinski, Tomasz Department of Computer and Control Engineering Rzeszow University of Technology W. Pola 2 Rzeszow35-959 Poland

An algorithm for processing the continuous stream of incoming video data to detect and recognize signed expressions is proposed. The aim is to improve an assistive technology system. The method is based on the dynamic flow of expression models between a repository of inactive models and pools of active and completed models. When modeling signed expressions, so-called visemes are used. These are units smaller than words - the equivalent of phonemes known from speech recognition. Visemes are extracted by clustering descriptors of the holistic skeletons. The sequential-simultaneous nature of gestures is considered, and the modeling is carried out in four parallel channels related to the position and shape of both hands. The conditions for activation, deactivation, and recognition of the model as completed are formulated, assuming asynchronous changes in individual channels. The method is successfully tested on a demanding dataset recorded by potential users. © 2024 The Author(s).

关键词： Human computer interaction

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Multi-population Fruit Fly Optimization Algorithm with Genetic Operators for Multi-target Path Planning 1

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2nd International Conference on Cognitive Computation and Systems, ICCCS 2023

作者： Cheng, Ke Zhao, Qingjie Wang, Lei Liu, Wangwang Hu, Shichao Fang, Kairen School of Computer Science and Technology Beijing Institute of Technology Beijing China Beijing Institute of Control Engineering Beijing China

ISBN: (数字)9789819708857

ISBN: (纸本)9789819708840

Automatic path planning is very important for many applications such as robots exploring unknown environments and logistics delivery. In this paper, we propose a discrete multi-population fruit fly optimization algorithm with genetic operators, where a greedy strategy is used to obtain good initial population, 3-opt heuristic search simulating olfactory to make the algorithm achieve higher convergence accuracy, multiple population collaborative strategy simulating vision to avoid the algorithm falling into local optima, and the genetic mechanism of selection-crossover-mutation prompts the population easily getting the optimal solution. The proposed planning algorithm has fewer parameters to be adjusted and has the advantages of high accuracy and fast convergence. The experimental results prove that the proposed planning algorithm performs best compared with other several algorithms. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Motion planning

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Improvement Algorithm Analysis and Implementation of Plant Disease and Pest Recognition Based on YOLO 5

Improvement Algorithm Analysis and Implementation of Plant D...

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5th International Conference on computer Vision, Image and Deep Learning, CVIDL 2024

作者： Wei, Yongxiang Pan, Xin Jilin Institute of Chemical Technology School of Information and Control Engineering Jilin China Changchun Institute of Technology School of Computer Technology and Engineering Changchun China Changchun Institute of Technology School of Computer Technology and Engineering Jilin Changchun China

ISBN: (纸本)9798350373820

Agriculture is one of the main economic pillars in many countries and regions, contributing significantly to the stable development of the national economy. However, the growth rate of agriculture is currently declining rapidly, and plant diseases and pests are increasingly prevalent due to various factors such as climate and pollution. In response to the detection and recognition of plant diseases and pests, we propose introducing a multi-channel BiFormer module into the basic YOLOv8 target detection algorithm, which can effectively improve recognition accuracy. In experiments, we tested our method using a cashew dataset, and the results showed that our method outperforms traditional methods in terms of precision (P), recall rate (R), and mean average precision (mAP), indicating its high effectiveness. © 2024 IEEE.

关键词： Object detection

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Interpretable Roadside 2D Object Detection Based on Axial Attention 24

Interpretable Roadside 2D Object Detection Based on Axial At...

引用

24th IEEE/ACIS International Conference on computer and Information Science, ICIS 2024

作者： Wu, Fan Song, Shaojing Hu, Jinyan Shanghai Polytechnic University Faculty of Intelligent Manufacturing and Control Engineering Shanghai China College of Computer and Informational Engineering Shanghai Polytechnic University Shanghai China

ISBN: (纸本)9798350376647

Environmental perception plays a crucial role in the operation of autonomous vehicles. Recent studies have relied on single vehicle cameras for perception due to the lack of roadside view dataset, but this approach provides limited perception information. To address this limitation, there is a growing interest in studying roadside perception to complement vehicle-side perception information. However, existing methods face challenges in accurately recognizing and locating small objects or objects under occluding conditions in the roadside. And autonomous vehicles require millisecond-level decision-making for safe driving, yet many advanced object detection methods struggle to meet the need for high-speed reasoning. Additionally, deep learning models often make black-box decisions without providing explanations or understanding of their decision-making process. To tackle these issues, this study proposes an interpretable 2D object detection method for roadside based on the YOLO V8 with Axial Attention replacing the CNN network within the network architecture. The trained model is then input into HiResCAM to visualize the decision-making process of the roadside detection model. Experimental results on DAIR-V2X-I and Rope3D demonstrate that our proposed method not only improves mean Average Precision (mAP) but also enhances image processing speed by 1.5 to 2 times compared to existing methods. By visualizing the decision-making process of the roadside detection model, it make autonomous vehicle perception more transparent and credible. © 2024 IEEE.

关键词： Deep learning

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Constructive Function Approximation with Local Models 32

Constructive Function Approximation with Local Models

引用

32nd Mediterranean Conference on control and Automation, MED 2024

作者： Mavridis, Christos N. Johansson, Karl Henrik School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Division of Decision and Control Systems Stockholm Sweden

ISBN: (纸本)9798350395440

We introduce a constructive function approximation approach as a general tool, particularly useful in adaptive and data-driven methods for perception and control. The key idea is to estimate of a collection of simple local models as opposed to a single and complex regression model trained in the entire input space. We use principles from the Online Deterministic Annealing (ODA) optimization framework to construct an adaptive partition of the input space, which enables the introduction of local function approximation models within each subset of the partition. We show that both the partitioning and the local model training algorithms are stochastic approximation algorithms that operate online, and with the same observations, as part of a two-timescale stochastic approximation scheme. This process constitutes a heuristic method to gradually increase the complexity of the function approximation framework in a task-agnostic manner, giving emphasis to regions of the input space where the regression error is high. As a result this framework has inherent explainability properties, and is suitable for continuous learning applications where regression improvement without retraining from scratch is crucial. Simulation results illustrate the properties of the proposed approach. © 2024 IEEE.

关键词： Heuristic methods

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Underwater Target Detection Based on Dual Inversion Dehazing and Multi-Scale Feature Fusion Network 17th

Underwater Target Detection Based on Dual Inversion Dehazing...

引用

17th China Conference on Wireless Sensor Networks, CWSN 2023

作者： Wei, Lian-Suo Ma, Long-Yu Huang, Shen-Hao School of Information Engineering Suqian University Jiangsu Suqian223800 China College of Computer and Control Engineering Qiqihar University Heilongjiang Qiqihar161006 China

ISBN: (纸本)9789819710096

In response to the insufficient small-scale feature extraction capabilities of traditional SSD object detection algorithms, which lead to missed detections and false alarms in the detection of small underwater targets, this paper proposes a new underwater object detection algorithm. The algorithm first uses a Dual Reverse Dehazing (DRDM) module for image enhancement. Then, on the basis of the backbone network of the SSD algorithm, a channel attention mechanism and feature extraction convolution are introduced to construct a SE-Feature-Enhancement Module (SFEM) to integrate high-resolution shallow feature maps and deep feature maps with high semantic information. This fusion method preserves the context information of the feature maps, learns the necessary features, and suppresses redundant information, thereby improving the network’s learning ability. The experimental results show that the proposed algorithm achieves a detection accuracy of 94.62% on the Wilder_fish_new dataset. Compared with the current commonly used SSD (VGG) and SSD (Mobilenetv2) algorithms, the proposed algorithm improves the accuracy by 2.7 and 3.13 percentage points, respectively. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Feature extraction

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Improving Driving Behavior: A Blockchain-Based Gamification System 6

Improving Driving Behavior: A Blockchain-Based Gamification ...

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6th Distributed Ledger Technology Workshop, DLT 2024

作者： Butera, Alberto Romani, Noemi Gatteschi, Valentina Department of Computer and Control Engineering Politecnico di Torino Corso Duca degli Abruzzi 24 Turin10129 Italy

In an era of increased vehicles usage, traffic accidents caused by improper driving behavior have also risen. Therefore, there is a growing need to improve road safety by promoting a correct driving style and counteracting accidents with innovative strategies. This paper presents the implementation of a decentralized system, based on blockchain, that utilizes gamification principles to incentivize safe driving. The proposed solution aims to reduce the number of traffic accidents, through gamification, and could serve as a tool for insurance companies to create customized insurance policies based on users' driving behavior. The system leverages on a reward mechanism that incentivizes the most careful drivers with tokens, creating a virtuous circle that benefits all people, drivers and non-drivers alike. The article proposes a working prototype platform and describes its implementation details, demonstrating its potential to incentivize a prudent driving style and contributing to a future where good driving is the norm, rather than the exception. © 2024 Copyright for this paper by its authors.

关键词： Gamification

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Efficient-Learning Grasping and Pushing in Dense Stacking via Mask Function and Pixel Overlap Rate 7

Efficient-Learning Grasping and Pushing in Dense Stacking vi...

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7th International Symposium on Autonomous Systems, ISAS 2024

作者： Lian, Jie Jiang, Juncheng Qiu, Chaochao Pan, Qinghui Dong, Yongxiang Wang, Zhao Wang, Dong Dalian University of Technology School of Control Science and Engineering Dalian China University of Science and Technology Liaoning Shool of Computer Science and Software Engineering China

ISBN: (纸本)9798350363173

In the field of robotic grasping, grasping tightly stacked objects is a formidable challenge. The presence of non-target objects significantly increases the risk of grasping failure. To solve this problem, the aim is to take action while grasping, reduce clutter, and effectively complete grasping tasks. Based on deep reinforcement learning, the Efficient Learning Grasping(ELG) framework is proposed that integrates pushing actions alongside grasping to efficiently pick up objects from cluttered environments with reduced training time. However, in the process of grasping, the agent is rewarded only when the grasp is successful, so the data collection is extremely inefficient and spends a lot of time on the training of grasping strategy. In our work, two strategies are proposed to improve the training efficiency. First, two mask functions are introduced as prior knowledge to enable the agent to focus on more meaningful regions while ignoring some unnecessary regions, aiming at effective learning. Second, The Pixel Overlap Rate (POR) is introduced to quantify the environment clutter level, and set a new push action reward function based on the POR. The implementation of the above ideas in a simulation environment and transferred the learned models to the real world and verified them in practice. Compared with existing method, our method improves the training efficiency of the initial algorithm while having a higher success rate of grasping. Detailed grasping video at https://***/CqtSvaVl60M. © 2024 IEEE.

关键词： Reinforcement learning

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