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检索条件"任意字段=Scientific Conference on Mechatronics Engineering and Computer Science"

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Obstacle detection system based on light markers grid deformation 3

Obstacle detection system based on light markers grid deform...

引用

3rd International conference on Optics, computer Applications, and Materials science, CMSD 2023

作者： Verkhovtsev, Daniil O. Popinako, Yaroslav V. Nikitenko, Mikhail S. Khudonogov, Danila Yu. Kizilov, Sergey A. Federal State Budgetary Scientific Institution the Federal Research Center of Coal Coal Chemistry of Siberian Branch Russian Academy of Science 18 Sovetsky ave Kemerovo Russia

ISBN: (纸本)9781510674486

The article considers autonomous vehicle control system using obstacle detection method based on machine vision using light markers on motion path. The data gaining method for optimization and calculating image is proposed. The specific feature of realizing the aim is light markers grid deformation consideration. The software realization of obstacle detection based on light markers grid geometric deformation is demonstrated. © 2024 SPIE. All rights reserved.

关键词： computer vision

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Unveiling computer Chess Evolution: Can Machine Learning Detect Historical Trends? 23rd

Unveiling Computer Chess Evolution: Can Machine Learning Det...

引用

23rd IFIP TC 14 International conference on Entertainment Computing, ICEC 2024

作者： Borghesi, Andrea Ciancarini, Paolo Di Iorio, Angelo Moro, Gianluca Department of Computer Science and Engineering University of Bologna Bologna Italy

ISBN: (纸本)9783031743528

computer Chess and AI are deeply intertwined, addressing similar research issues and proposing solutions that intersect. Even from a historical viewpoint, these two areas are strongly connected. This article presents a historical overview of computer Chess research segmented in four seasons. Our research goal is to report an experiment of using cutting-edge machine learning tools to extract keywords and topics from a large set of scientific articles on computer Chess, aiming at singling out the characterizing differences among the seasons. Moreover, we investigate the relationships between topics across seasons and their evolution. Although the seasons can be identified by clear milestones, we observed a lack of distinct boundaries between their topics. Instead, some issues recur across different seasons, albeit adjusted to new contexts, tools, and technologies. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Adversarial machine learning

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A Novel Approach of Particle Swarm and ANT Colony Optimization for Task Scheduling in Cloud 14

A Novel Approach of Particle Swarm and ANT Colony Optimizati...

引用

14th International conference on Cloud Computing, Data science and engineering, Confluence 2024

作者： Kaliappan, Seeniappan Paranthaman, V. Kamal, M.D. Raj Sudhakar, A.V.V. Muthukannan, M. Kcg College of Technology Department of Mechatronics Engineering Karapakkam Tamil Nadu Chennai600097 India Adhi College of Engineering and Technology Department of Mechanical Engineering Tamilnadu Chennai India Velammal Institute of Technology Department of Mechanical Engineering Tamil Nadu Chennai601204 India Sr University Department of Electrical and Electronics Engineering Telangana Warangal1506371 India Kcg College of Technology Department of Civil Engineering Karapakkam Chennai600097 India

ISBN: (纸本)9798350344837

'The cloud' is shorthand for a cloud-based computer infrastructure that helps make other technologies possible. The workload is split among several cloud-based, virtual computers according to some factors. However, cloud computing (CC) provides options for dealing with issues like job scheduling, such as improved task scheduling. To begin, we present a Dynamic Weighted Round-Robin strategy for more efficient cloud-based task allocation. Because of its efficiency in optimizing schedules in light of finite resources, jobs' relative importance, and available time, the SPMOA approach comes highly recommended. Second, a heuristic method called Hybrid Particle Swarm Parallel Ant Colony Optimization is introduced to solve the problem of task execution latency in SPMOA-based task scheduling. In conclusion, we enhance cloud-based task scheduling by creating a fuzzy logic framework for MLOA. Both the inertial mass of the PSO and the pheromone trails left by the P ACO are modified using fuzzy methods. Fuzzy Hybrid Particle Swarm Parallel Ant Colony Optimization is the optimal method for scheduling tasks in the cloud. The strategy reduces downtime due to waiting and procedures while simultaneously increasing output and minimizing inefficiency. © 2024 IEEE.

关键词： Ant colony optimization

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Wildfire Spread Prediction Using Attention Mechanisms in U-NET

Wildfire Spread Prediction Using Attention Mechanisms in U-N...

引用

2023 International conference on Electrical, computer, Communications and mechatronics engineering, ICECCME 2023

作者： Shah, Kamen Pantoja, Maria California Polytechnic State University Dept. Computer Science and Software Engineering San Luis Obispo United States

ISBN: (纸本)9798350322972

Global warming has increased large-scale natural disasters at an alarmingly greater frequency. These natural disasters affect humans and the ecosystems that all species rely upon for food and shelter. A significant byproduct of global warming is the emergence of mega-fires, which are wildfires that burn more than 100,000 acres. Mega fires have exhibited the capacity to destroy entire ecosystems and urban areas. Researchers forecast a significant increase in wildfires during the next decade. This is a fundamentally growing problem that mandates modern solutions. Currently, simulation models are employed to predict wildfire propagation and aid firefighters in suppressing them. However, these methods are computationally expensive and often inaccurate. A recent advance in this area is the usage of neural networks (NN), which are systematically more accurate and computationally efficient. This paper focuses on predicting wildfire spread using NN with an attention mechanism to improve spatial recognition and overall performance in neural networks for wildfire spread prediction. We train different models on 12 distinct observational variables derived from the Google Earth Engine catalog. Evaluation is conducted with accuracy, Dice coefficient score, ROC-AUC, and F1-score. Results show that when augmenting segmentation models with attention mechanisms, the attention component improves feature suppression and recognition, improving overall performance. Furthermore, we use ensemble modeling to reduce bias and variation, leading to more consistent and accurate predictions. The architecture presented in this research achieved a ROC-AUC score of 86.2% and an accuracy of 82.1% when inferencing on wildfire propagation at 30-minute intervals. © 2023 IEEE.

关键词： Forecasting

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Improvement of PRM algorithm based on lazy collision idea 7

Improvement of PRM algorithm based on lazy collision idea

引用

7th International conference on computer Information science and Application Technology, CISAT 2024

作者： Gong, Wenbin Li, Hongbing Long, Boxuan Su, Yu Jiang, Yutao Gu, Yu Chongqing Three Gorges University College of Electronic and Information Engineering Chongqing Wanzhou China ChongqingThree Gorges University College of Computer Science and Engineering Chongqing Wanzhou China Changchun University of Science and Technology College of Computer Science and Engineering Jilin Changchun China

ISBN: (纸本)9798350375107

Aiming at the problems of PRM algorithm, such as the lack of orientation in the selection of sampling points, the excessive number of sampling points, and the long convergence time of the algorithm, an improved probabilistic map algorithm (Restricted sampling area and Lazy collision, RL-PRM) based on the restricted sampling area and Lazy collision is proposed. The algorithm generates random sampling points in an elliptical area near the main axis of the map endpoint, and adopts the lazy collision strategy to generate the initial path. After judging whether the initial path traverses the interior of the obstacle, if there is a traversal situation, additional nodes are generated on the outside of the convex edge of the obstacle and added to the roadmap G(V, E), the collision nodes and the connecting lines are deleted, and the path is queried again to obtain the final path. Simulation results show that compared with the PRM∗ algorithm and PRM-Astar algorithm, the RL-PRM algorithm has a faster convergence speed and a shorter convergence path, and can still find the effective optimal path quickly while reducing the number of sampling points to a large extent. © 2024 IEEE.

关键词： Mobile robots

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Sentiment Analysis of Bitcoin data by tweets through Naive Bayes

Sentiment Analysis of Bitcoin data by tweets through Naive B...

引用

2022 International conference on Electrical, computer, Communications and mechatronics engineering, ICECCME 2022

作者： Padmalatha, E. Sheral, Sailekya Dhanush, K. Samveeth, S. Datta, B.Ruchi Krishna, K.Tarun CBIT Department of Computer Science Telangana Hyd India University of Exeter Department of Computer Science Exeter United Kingdom GITAM University Department of AI & ML Telangana Hyd India Deparment of DS & AI ICFAI University Telangana Hyd India

ISBN: (数字)9781665470957

ISBN: (纸本)9781665470957

Bitcoin, as one of the most popular cryptocurrencies, is notorious for its concerning volatility on any given day, which leads to people having mixed feelings about it. This paper examines the opinions of people all over the world using data gathered from the social media platform Twitter, to decide if investing in bitcoin is a good idea for future. The opinions are analyzed and classified using the Bernoulli and Multinomial Naive Bayes model through sentiment analysis, the models implemented yielded 87 and 86 percent of accuracies respectively. Furthermore, the variation in sentiment over time is comparedto the variation in bitcoin price over the same period. © 2022 IEEE.

关键词： Bitcoin

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Machine Learning for DoS Attack Detection in IoT Systems 19

Machine Learning for DoS Attack Detection in IoT Systems

引用

19th International conference on Future Networks and Communications, FNC 2024 / 21st International conference on Mobile Systems and Pervasive Computing, MobiSPC 2024 / 14th International conference on Sustainable Energy Information Technology, SEIT 2024

作者： Kikissagbe, Brunel Rolack Adda, Mehdi Célicourt, Paul Tchappihaman, Igor Najjar, Amro Mathematics Computer Science and Engineering Departement University of Quebec RimouskiQC Canada National Institute of Scientific Research QC Canada University of Luxembourg Luxembourg

This study focuses on enhancing DoS attack detection in IoT systems through a Machine Learning approach that combines class balancing, feature selection, and optimized classifiers. Utilizing the Edge IIoT dataset, we applied SMOTE and Random Undersampling for class balance and employed DNN, Random Forest, and PCA for feature selection. We evaluated six technique combinations across four classifiers (DNN, SVM, XGBoost, and Random Forest), finding that certain combinations notably improve detection efficiency and accuracy. This research contributes to IoT security by offering an effective methodology for DoS attack detection, setting a foundation for further advances in IoT system protection against security threats. © 2024 The Authors. Published by Elsevier B.V.

关键词： Adversarial machine learning

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Enhanced and Automatic Defect Detection in Thermography using Stacked Denoising Autoencoder 5

Enhanced and Automatic Defect Detection in Thermography usin...

引用

5th IEEE Global conference for Advancement in Technology, GCAT 2024

作者： Vesala, G.T. Ghali, V.S. Lakshmi, A. Vijaya Wang, Fei Yerneni, Naga Prasanthi Malla Reddy University School of Engineering Department of Computer Science Engineering Hyderabad India Koneru Lakshmaiah Education Foundation Department of Electronics and Communication Engineering Vijayawada India Amrita Sai Institute of Science and Technology Department of Electronics and Communication Engineering Paritala India Harbin Institute of Technology School of Mechatronics Engineering Harbin China

ISBN: (纸本)9798350376685

Active infrared thermography (AIRT) with frequency-modulated stimuli has emerged as a feasible and cost-effective non-destructive testing method for checking a variety of materials, with improved fault identification and depth resolution. However, assessing this non-stationary thermal response while reducing noise and inhomogeneous backgrounds is difficult in FMT. Conventional thermographic data analysis techniques use signal or image processing methods and supervised learning models, which are known for linear representation or feature extraction of thermographic data. This paper describes a stacked denoising autoencoder (SDAE) in frequency modulated thermography (FMT) that extracts non-linear information from temporal thermal profiles to improve flaw detection in a steel structure. The SDAE trains on temporal thermal profiles in order to generate denoised profiles at the other end. Furthermore, the latent space in the middle of the SDAE decreases dimensionality while highlighting flaws. Finally, the latent space of SDAE and local outlier factor (LOF) were coupled to form SDAE-LOF, which provided automatic fault detection in unsupervised learning passion. Experimentation results on a mild steel structure show an improvement in defect signature by reducing noise in temporal thermal profiles, and the proposed SDAE-LOF achieved an AUC of 0.81 in automatic defect detection. © 2024 IEEE.

关键词： Thermography (imaging)

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Advancing scientific Research in computer science by ChatGPT and LLaMA—A Review

Advancing Scientific Research in Computer Science by ChatGPT...

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International conference on Intelligent Manufacturing and Energy Sustainability, ICIMES 2023

作者： Hassan, Esraa Bhatnagar, Roheet Shams, Mahmoud Y. Faculty of Artificial Intelligence Kafrelsheikh University Kafrelsheikh33516 Egypt Department of Computer Science and Engineering Manipal University Jaipur Rajasthan Jaipur India

ISBN: (纸本)9789819967735

ChatGPT and LLaMA AI Modelling are advancing computer science scientific research in several ways. We discuss the capabilities of ChatGPT and LLaMA to advance computer science research in this review. Whereas LLaMA is an automated dialogue system that can respond to questions about scientific subjects, ChatGPT is an NLP model that can create dialogues. The authors contend that by offering scientists more effective methods for data collection and analysis, these two technologies have the potential to fundamentally alter the way they conduct research. According to the authors, ChatGPT and LLaMA could be used to speed up data collection and analysis and give researchers more accurate findings. Additionally, they propose that similar technologies may be employed to develop virtual assistants for scientists, enabling them to easily access pertinent data and take better decisions. Additionally, they think that these technologies could be utilized to enhance inter-scientist communication, facilitating more productive collaboration. There are many benefits of employing LLaMA and ChatGPT in scientific research. These innovations may shorten the time required for data gathering and analysis while also giving researchers more precise findings. These might also help scientists communicate more effectively with one another, which would improve collaboration. Using these tools for scientific study does have certain restrictions. In the real world, it is unknown, for instance, how trustworthy the outcomes produced by ChatGPT and LLaMA would be. Furthermore, it's not obvious how long it would take to train these models before they could be put to good use in a research environment. Prior to putting AI-based systems into practice in the real world, it is important to consider the ethical issues surrounding their use for scientific study. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Data acquisition

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Modeling of Energy Flows in Microgrids 31

Modeling of Energy Flows in Microgrids

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2023 31st National conference with International Participation, TELECOM 2023

作者： Stanchev, Plamen Vacheva, Gergana Hinov, Nikolay Klimenta, Dardan Technical University of Sofia Faculty of Computer Systems and Technologies Departement of Information Techonology in Industry Sofia Bulgaria Technical University of Sofia Faculty of Electronic Engineering and Technologies Departement of Power Electronics Sofia Bulgaria University of Priština in Kosovska Chair of Power Engineering Faculty of Technical Science Kosovska Mitrovica Mitrovica Serbia

ISBN: (纸本)9798350303292

In this article, a microgrid model with the possibility of adding renewable energy sources is considered. The considered grid is radial and in it the dues of the lines between the individual nodes have been added and the values of the loads have been set. According to a certain methodology, the necessary cross-sections and type of wires are calculated and designed. Different energy sources and energy storage elements can be easily connected to the implemented scheme. For this reason, it is suitable for study by students and doctoral students for a better presentation of the basic principles in the distribution of energy flows in low voltage grids. © 2023 IEEE.

关键词： Renewable energy

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