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检索条件"主题词=software algorithms"

共 12564 条记录，以下是1271-1280 订阅

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BuzzRacer: A Palm-sized Autonomous Vehicle Platform for Testing Multi-Agent Adversarial Decision-Making

BuzzRacer: A Palm-sized Autonomous Vehicle Platform for Test...

引用

IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Zhiyuan Zhang Panagiotis Tsiotras School of Aerospace Engineering Institute for Robotics and Intelligent Machines Georgia Institute of Technology Atlanta GA USA

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

We present BuzzRacer, a palm-sized autonomous vehicle platform suitable for multi-agent autonomous racing. BuzzRacer consists of two parts. First, a software framework with multiple racetrack environments, dynamic simulation, visualization, and control pipelines. Second, a miniature autonomous vehicle platform capable of 1g acceleration and 3.5m/s top speed. BuzzRacer is an open-source project currently used at Georgia Tech in a project-based robotics course and research projects for experimental validation and benchmarking of novel planning and control algorithms.

关键词： Location awareness Visualization software algorithms Pipelines software Sensor systems Planning Vehicle dynamics Autonomous vehicles Intelligent sensors

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Robustness of Random Walk on a Graph against Adversary Attacks

Robustness of Random Walk on a Graph against Adversary Attac...

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IEEE Annual International Computer software and Applications Conference (COMPSAC)

作者： Hiroki Kawamura Satoshi Shiina Han Nay Aung Hiroyuki Ohsaki Graduate School of Science and Technology Kwansei Gakuin University

ISBN: (数字)9798350376968

ISBN: (纸本)9798350376975

Random walk-based algorithms are frequently utilized to target node search and graph exploration in unknown graph structures. Unlike deterministic algorithms such as breadth-first search and depth-first search, target node search and graph exploration with random walk algorithms are expected to exhibit robustness against adversarial attacks because of their probabilistic nature. The characteristics of random walks when adversaries change the topology of the graph, known as adversarial attacks on random walks, have been just recently received attention. These attacks have been shown to significantly degrade the efficiency of target node search and graph exploration with random walks. However, questions regarding how robust random walks are against more realistic attacks persist. In this paper, we investigate adversarial attacks in the form of rewiring a limited number of links during target node search and graph exploration, particularly in scenarios where mobile agents employ random walk algorithms. The goal is to quantitatively determine how robust or vulnerable the conventional random walk algorithms are against link rewiring attacks. We consider three types of link rewiring attacks (centrality method, clustering method, and starting node method) and evaluate how they affect target node search (hitting time) and graph exploration time (cover time) of five major random walk algorithms on a graph - Simple Random Walk (SRW), Non-Backtracking Random Walk (NBRW), k-History random walk (k-History), Biased Random Walk (BiasedRW), and Vicinity Avoidance Random Walk (VARW) - in five graphs through simulation experiments.

关键词： Clustering methods software algorithms Mobile agents Clustering algorithms Probabilistic logic Approximation algorithms Robustness

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Automatic Surveillance of Accelerated Cooling Line Against Cyber-Attacks and Faults

Automatic Surveillance of Accelerated Cooling Line Against C...

引用

IEEE International Forum on Research and Technologies for Society and Industry Leveraging a better tomorrow (RTSI)

作者： Stefano Dettori Vincenzo Iannino Lorenzo Vannini Valentina Colla Francesco Ferraris TeCIP Institute Scuola Superiore Sant'Anna Pisa Italy Prisma Impianti S.P.A. Basaluzzo Italy

ISBN: (数字)9798350362138

ISBN: (纸本)9798350362145

Faults and cyber-attacks on industrial facilities have several common aspects. Both can severely damage equipment and plants, and harm the health of workers or even the environment. Both can create economic damage due to production stops or damage to connected equipment and facilities. To make the plant robust to cyber-attacks, a risk assessment must be firstly performed, by evaluating the vulnerability of the process, the equipment most susceptible to failure or intrusions, and related effects on production, and the eventual risks for process operators. Secondly, the industrial control system must be improved to increase its resilience against anomalies and intrusion, through ad-hoc algorithms implementing fault and cyber-attacks detection methodologies. This work proposes a solution for automatic detection of faults and attacks in accelerated cooling lines, an important process in steelworks. The detection system is composed of a physical computational platform and a software application for monitoring the state of the system, reacting to anomalies and attacks and sending alarms to operators. Detection algorithms exploit plant models, banks of extended Kalman filters and classification algorithms based on One Class Support Vector machines. The algorithms and software have been tested in real-time through a Hardware In the Loop approach with successful and encouraging results.

关键词： Cooling software algorithms Production Industrial facilities software Real-time systems Classification algorithms Kalman filters Cyberattack Testing

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Bottom-Up Cracks Detection in Road Pavement in Artificial Neural Network

Bottom-Up Cracks Detection in Road Pavement in Artificial Ne...

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software, Systems and Information Technology (SSITCON), International Conference on

作者： V Sunil Kumar Laith H. Alzubaidi G. P. Ramesh M. V Sreenath T. Gayathri Department of Artificial Intelligence & Machine Learning Nitte Meenakshi Institute of Technology Bengaluru India Department of Computers Techniques The Islamic University of Babylon Babylon Iraq Department of Electronics and Communication Engineering St. Peter’s Institute of Higher Education and Research (SPIHER) Chennai India Department of Information Science and Engineering Nitte Meenakshi institute of technology Bangalore India Department of Computer Science and Engineering School of Engineering Dayananda Sagar University Bangalore India

ISBN: (数字)9798350352931

ISBN: (纸本)9798350352948

In recent years, road crack detection is a significant task for detecting surface defects, road safety and road maintenance. But there is lack of approach that able to detect the concealed cracks especially bottom-up cracks, and this is challenging due to poor continuity of cracks, various crack’s widths, and low contrast among cracks and the surrounding pavement. The Artificial Neural Network (ANN) algorithm is proposed for detecting and classifying the structural health status of bottom-up crack in road pavements, which allows detecting surface defects and failures. The approach aims at collection of these signatures by utilizing acoustic-sensors, located at roadside, and also classification of SHS in pavements over the ANN approaches. The various Machine Learning (ML) classifiers such as Multilayer Perceptron (MLP), 2-Dimensional Convolutional Neural Network (2D-CNN), Support Vector Classifier (SVC) and Random Forest Classifier (RFC) are utilized as well as compared for the analysis of crack in road pavement. The experimental results illustrate the probability of associating with great accuracy $92.7 \%$ of MLP, $96.7 \%$ of 2D-CNN, $92.5 \%$ of RFC and ${9 9 . 3 \%}$ of SVC respectively.

关键词： Accuracy Roads software algorithms Static VAr compensators software systems Vectors Road safety Classification algorithms Convolutional neural networks Surface cracks

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Lightweight fire detection algorithm based on LSCD-FasterC2f-YOLOv8

Lightweight fire detection algorithm based on LSCD-FasterC2f...

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Big Data & Artificial Intelligence & software Engineering (ICBASE), International Conference on

作者： Bo Yin Department of Mechanical and Electronic Engineering GuiZhou Communications Polytechnic University Gui Yang China

ISBN: (数字)9798331506612

ISBN: (纸本)9798331506629

In this paper, a lightweight fire detection algorithm based on LSCD-FasterC2f-YOLOv8 is proposed to optimize the real-time and accuracy of traditional target detection models in fire scenarios to meet the requirements of efficient monitoring in complex environments. Addressing YOLOv8’s deployment hurdles on limited devices, it innovates in three ways: 1) Introducing FasterC2f, an efficient C2f alternative, cuts parameters and complexity via dimensional reduction. 2) A lightweight LSCD detection head boosts efficiency and accuracy, leveraging shared convolutions to reduce parameters and enhance feature fusion, ensuring precision in challenging fires. 3) Rigorously comparing multiple IoU loss functions optimizes bounding box regression, enhancing detection without extra computation. The experimental results show that the proposed LSCD-FasterC2f-YOLOv8 algorithm achieves excellent performance in the fire detection task, not only achieving significant lightweight in model volume and computational quantity, but also maintaining even higher detection accuracy comparable to the original YOLOv8.

关键词： Solid modeling Accuracy Computational modeling software algorithms Object detection Feature extraction Real-time systems Detection algorithms Monitoring software engineering

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Partial Shading Effect Minimization in a PV Array using Shading Behaviour based Adaptive Reconfiguration Algorithm

Partial Shading Effect Minimization in a PV Array using Shad...

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Intelligent Computing and Sustainable Innovations in Technology (IC-SIT), International Conference on

作者： Pankaj Verma Gurinder Singh Abinash Singh University Centre for Research and Development Chandigarh University Mohali Punjab India Electronics and Communication Engineering Chandigarh University Mohali Punjab India

ISBN: (数字)9798350369175

ISBN: (纸本)9798350369182

During the partial shading in a PV array, the modules of the same array receive a different level of irradiance, this phenomenon is known as the partial shading. Partial shading deeply effects the maximum available power of PVs, moreover, this results in the multiple maximums on the power versus voltage curves. The partial shading on PV array can be dispersed by physically relocating or rearranging the arrays of the modules. In this paper, the partial shading pattern incident on the PV array is investigated to develop the physical reconfiguration algorithm for PV modules. In this algorithm, the irradiance value of each module is compared to locate the partially shaded modules, further these modules are arranged in one column to increase the power output. The shading behaviour based adaptive reconfiguration algorithm (SBARA) is applied on a $3 \times 3$ array configuration and is tested for three different case scenarios. Three different partial shading patterns are simulated on the array using the MATLAB software, SBARA is called in each case to reconfigure the modules, the results are obtained by analyzing the power vs voltage curve of PV array.

关键词： Technological innovation software packages software algorithms Adaptive arrays Voltage Minimization MATLAB Power generation

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Chip Surface Character Detection System Based on Machine Vision

Chip Surface Character Detection System Based on Machine Vis...

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Computer Science, Electronic Information Engineering and Intelligent Control Technology (CEI), IEEE International Conference on

作者： Cheng Liao Chunlian Zhan Weiheng He Han Gao Peisong Zhou School of Optics and Electronic Technology China Jiliang University Hang Zhou China Haining Academy of Integrated Circuit and Advanced Manufacturing Jia Xing China

ISBN: (数字)9798350378542

ISBN: (纸本)9798350378559

A machine vision based chip surface character detection system has been designed to address the defects caused by characters on the chip surface in actual production. The system includes an image acquisition device and software processing algorithms. Firstly, extract the region of interest (ROI) of the chip character by inputting a template image. Then, the template character image is segmented into individual characters using the connected domain labeling method, and a template character library is established. Secondly, based on the template matching method, obtain the best matching position coordinates and extract the geometric features of the characters. Finally, characters are detected based on the number of connected domains, area and perimeter features, Hu moment, centroid coordinates, and edge contours. The experimental results show that the method used can effectively segment characters affected by defects and accurately detect the presence of defects on the chip surface characters.

关键词： software algorithms Object segmentation Production Feature extraction software Libraries Labeling Surface treatment Defect detection Intelligent control

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Learning to Explore Indoor Environments using Autonomous Micro Aerial Vehicles

Learning to Explore Indoor Environments using Autonomous Mic...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Yuezhan Tao Eran Iceland Beiming Li Elchanan Zwecher Uri Heinemann Avraham Cohen Amir Avni Oren Gal Ariel Barel Vijay Kumar GRASP Laboratory University of Pennsylvania Philadelphia PA USA Hebrew University of Jerusalem Jerusalem Israel Technion - Israel Institute of Technology Haifa Israel

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

In this paper, we address the challenge of exploring unknown indoor environments using autonomous aerial robots with Size Weight and Power (SWaP) constraints. The SWaP constraints induce limits on mission time requiring efficiency in exploration. We present a novel exploration framework that uses Deep Learning (DL) to predict the most likely indoor map given the previous observations, and Deep Reinforcement Learning (DRL) for exploration, designed to run on modern SWaP constraints neural processors. The DL-based map predictor provides a prediction of the occupancy of the unseen environment while the DRL-based planner determines the best navigation goals that can be safely reached to provide the most information. The two modules are tightly coupled and run onboard allowing the vehicle to safely map an unknown environment. Extensive experimental and simulation results show that our approach surpasses state-of-the-art methods by 50-60% in efficiency, which we measure by the fraction of the explored space as a function of the trajectory length.

关键词： Program processors Simulation software algorithms Full stack Robot sensing systems Indoor environment Space exploration

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Mutation Testing Reinvented: How Artificial Intelligence Complements Classic Methods

Mutation Testing Reinvented: How Artificial Intelligence Com...

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International Conference on Computer Science and Engineering (UBMK)

作者： Serhat Uzunbayir Kaan Kurtel Department of Software Engineering Izmir University of Economics Izmir Turkiye

ISBN: (数字)9798350365887

ISBN: (纸本)9798350365894

Mutation testing is a widely accepted method for assessing the effectiveness of software test suites. It focuses on evaluating how well a test suite can identify deliberately introduced faults, known as mutations, in the code, helping to reveal potential vulnerabilities. Traditional mutation testing approaches, however, often encounter significant issues such as high computational demands and limited fault detection range. Recently, there has been an increasing interest in integrating artificial intelligence (AI) into mutation testing to address these challenges. AI can enhance mutation testing by incorporating intelligent algorithms and automating various tasks. It can analyze the codebase, identify key program components, and strategically select mutation operators that are more likely to detect faults. This paper explores the fundamental concepts of mutation testing, relevant research, and innovations in combining AI with mutation testing, focusing on the challenges and the most effective models currently available. By integrating AI, mutation testing has seen improvements in both fault detection and computational efficiency.

关键词： Fault diagnosis software testing Technological innovation Fault detection software algorithms software systems Computational efficiency software reliability Artificial intelligence Testing

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DC Optimal Power Flow in Unit Commitment Using Quantum Computing: An ADMM Approach

DC Optimal Power Flow in Unit Commitment Using Quantum Compu...

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Annual North-American Power Symposium

作者： Madan Rana Magar Dilip Pandit Duong Nguyen Nga Nguyen Electrical Engineering and Computer Science University of Wyoming Laramie WY

ISBN: (数字)9798331521035

ISBN: (纸本)9798331521042

This paper introduces an approach to solve unit commitment-based DC optimal power flow using the quantum-based alternating direction method of multipliers optimizer. The substantial interconnection of current generation and loads and the widespread incorporation of renewable energy have significantly altered and increased the complexity of the grid. As a result, efficient planning and operation of the power grid have become central focuses. This paper proposes an advanced method to solve DC-optimal power flow via quantum heuristic approaches leveraging the power of both quantum and classical computing. This is achieved by splitting the procedure to decompose a mixed binary quadratic problem into a quadratic unconstrained binary optimization sub-problem which is solved using a quantum approximate optimization algorithm. Following this, a continuous convex-constrained sub-problem is solved with a classical optimization solver. Within the DC-OPF, the startup cost and shutdown cost of each generator are incorporated in addition to the general cost function with the generator's cost coefficients. The DC-OPF is solved using the IBM Qiskit software development kit on an IEEE-14 bus system. The results obtained are then validated with the results obtained from classical methods using MATPOWER.

关键词： Renewable energy sources Quantum computing Costs software algorithms Approximation algorithms Generators Planning Optimization Load flow software development management

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