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检索条件"任意字段=International Conference on Engineering of Reconfigurable Systems and Algorithms"

共 89981 条记录，以下是421-430 订阅

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13th international Workshop of Advanced Manufacturing and Automation, IWAMA 2023

13th International Workshop of Advanced Manufacturing and Au...

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13th international Workshop of Advanced Manufacturing and Automation, IWAMA 2023

ISBN: (纸本)9789819706648

The proceedings contain 91 papers. The special focus in this conference is on Advanced Manufacturing and Automation. The topics include: A Short Survey of Evolutionary algorithms for Solving Many Objective Optimization Problems;the Application of Simulation in Facility Layout Design of an Industry 4.0 Factory;credibility Evaluation Software Design for Digital Twin systems of Spacecraft Agencies;stress Relaxation Characteristics Analysis and Storage Life Prediction of Butt Separation Pusher Spring;warehousing in the Context of Digital Supply Chain in the Oil and Gas Industry: Using Grounded Theory to Create Groundwork;warehousing in the Context of Digital Supply Chain in the Oil and Gas Industry: Towards Conceptualization and Groundwork;processing Method for Missing Data in Digital Twin System;reconfigurable Manufacturing systems: A Visual Simulation Approach in Matrix Manufacturing;path Planning for Unmanned Driving Based on Improved A* Algorithm;Analyzing the Impact of Metal Additive Manufacturing on SMEs Supply Chains Through Localization Optimization;digital Twin Method for Real-Time Stress Prediction Based on Surrogate Model;data Association Analysis on Critical Features Based on Different algorithms;research on Navigation of Multi-mobile Robot Formation Based on Leader-Follower Method;Effect of Grinding Passes on Hardened Layer Uniformity of HT250;associated Behavior Characteristics Mining of Psychological Crisis Based on Educational Data of College Students;a Network-Based Set Covering Model for Charging Station Location Problem: A Case Study in Norway;research on Multi-robot Cooperative Path Planning Technology Based on Leader-Follower;design and Characteristic Analysis of Ultrasonic Mass Transfer Device for Flotation;high-Speed Moving Target Detection Based on Background Modeling.

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Traffic Congestion Prediction in Smart Cities using Multilevel-Gated Recurrent Unit

Traffic Congestion Prediction in Smart Cities using Multilev...

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2024 international conference on Intelligent algorithms for Computational Intelligence systems, IACIS 2024

作者： Sravani, Busani Shreyas, A.V. Abbas, Haider Mohammed Chanti, Yerrolla Punitha, S. Malla Reddy Engineering College for Women Hyderabad India Nitte Meenakshi Institute of Technology Department of Civil Engineering Bengaluru India The Islamic University College of Technical Engineering Department of Computers Techniques Engineering Najaf Iraq Sr University School of Computer Science & Artificial Intelligence Warangal India Vinayaka Mission's Kirupananda Variyar Engineering College Department of Mathematics Salem India

ISBN: (纸本)9798350360660

Traffic Congestion (TC) is a main issue in smart cities because of increase in urbanization, as a result in financial losses. However, existing prediction algorithms failed to predict TC effectively due to dynamic changes of road network. To overcome this problem, a Multilevel-Gated Recurrent Unit (MGRU) is proposed for accurate prediction of traffic flow and congestion avoidance in smart cities. The main advantage of proposed M-GRU model is that it reduces errors while prediction by multilevel layers in softmax that passes information to adapt dynamic changes of road network. The data acquired based on weather conditions, number of vehicles improved the prediction process which utilized a few factors for prediction than previous prediction methods. Then the traffic density is estimated to predict TC on the road. Finally, the TC is predicted accurately by the proposed M-GRU model. The experimental results of the proposed method attained better results accuracy of 0.887, Mean Absolute Error (MAE) is 82.34 which is less than compared to existing method such as Convolutional Neural Network and Bidirectional-Long Short-Term Memory (Conv-Bi-LSTM). © 2024 IEEE.

关键词： Traffic congestion

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A Fast Scalable Hardware Priority Queue and Optimizations for Multi-Pushes

A Fast Scalable Hardware Priority Queue and Optimizations fo...

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1st international conference on Smart Energy systems and Artificial Intelligence (SESAI)

作者： Collinson, Samuel Bai, Allan Sinnen, Oliver Univ Auckland Parallel & Reconfigurable Comp Lab Dept Elect Comp & Software Engn Auckland New Zealand

ISBN: (纸本)9798350364613;9798350364606

Priority queues (PQ) are an essential data structure for many-important algorithms. Hardware implementations of priority queues can accelerate such algorithms significantly. Usually a choice has to be made between very fast (fixed size) hardware PQs or scalable PQs. Fast hardware queues can provide single cycle push and pop operations, but are limited in size. Scalable hardware queues use embedded memory to achieve scalability, but thereby compromise on speed. This paper proposes a fast and scalable hardware priority queue that can be used for general purpose. It is based on a modular and flexible hybrid design, using a shift register queue combined with a heap-based queue. In the best case, i.e. when only the first queue is needed, push and pop operations only take a single cycle. In an experimental evaluation on a Xilinx FPGA we demonstrate that performance of the hybrid queue maintains a good balance between performance and scalability even for applications where the size of the working set of data can have large variance. We further propose an optimisation of the heap based queue to support workloads that repeat several consecutive push operations followed by a single pop operation, which is the workload, for instance, in state space search or ray-tracing.

关键词： heap hybrid hardware queues priority queue shift register

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Design and Development of a Microcontroller Based Solar PV Cooker 4

Design and Development of a Microcontroller Based Solar PV C...

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4th international conference on Ubiquitous Computing and Intelligent Information systems, ICUIS 2024

作者： Morake, Anita Samikannu, Ravi Akila, L. Karpagam, R. Rohini, R. Yahya, Abid Botswana International University of Science and Technology Department of Electrical and Communications Systems Engineering Palapye Botswana Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Ece Chennai India Periyar Maniammai Institute of Science and Technology Department of Electrical and Electronics Engineering Thanjavur India Easwari Engineering College Department of Electrical and Electronics Engineering Ramapuram India Vivekanandha College of Engineering for Women Department of Computer Science and Engineering Tiruchengode India

ISBN: (纸本)9798331529635

This study proposes an innovative approach to improve solar cooking systems by combining microcontroller technology and deep-learning techniques. The Solar-PV Microcontroller-based Cooker concept aims to provide a sustainable and eco-friendly cooking method by harnessing the sun's power. This innovative approach addresses global energy conservation and access to clean cooking options, particularly in areas where conventional methods contribute to deforestation and indoor air pollution. The system includes inputs for cooking duration and temperature monitoring, enhancing solar energy efficiency and user experience. The study investigates how deep learning algorithms can be incorporated into the microcontroller-based solar cooker system to enhance functionality and versatility. Cooking settings can be automatically adjusted to maximize energy consumption and cooking results by analyzing real-time data from sensors and human inputs. The hybrid approach combines the best features of deep learning with microcontroller technology, demonstrating the significance of renewable energy in a cleaner, more efficient future. The Solar-Pv cooker demonstrates the possibilities of fusing culinary technology with computer algorithms, showcasing the potential of renewable energy in a cleaner, more efficient future. © 2024 IEEE.

关键词： Indoor air pollution

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Renewable Energy Penetration Improving Method in Distribution Network based on Dynamic reconfigurable Battery Network 4

Renewable Energy Penetration Improving Method in Distributio...

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4th international conference on Smart Grid and Energy Internet, SGEI 2024

作者： Xiong, Ziyi Yang, Yi Zhou, Yanglin Zhou, Qi Ci, Song Department of Electrical Engineering Tsinghua University Beijing China State Key Laboratory of Power System Operation and Control Tsinghua University Beijing China State Grid Jiangsu Electric Power Co. Ltd. Electric Power Research Institute Nanjing China

ISBN: (纸本)9798350368314

High proportion of renewable energy brings great challenges to the stable operation of the distribution network. Distributed energy storage (DES), as an interactive Lithium-ion battery energy storage system, can effectively improve the grid connection and serve as a backup power source. Aiming at the DES system supporting the flexibility of distribution network, this paper proposes a new type of dynamic reconfigurable battery (DRBN) system and its efficient collaborative optimization control method with the power converter system, clarifies the operation control mechanism of the DRBN system of the CPS architecture, proposes a multi-objective optimization control strategy for the DRBN system based on the CPS architecture, and formulates a demonstration application and verification plan. ©2024 IEEE.

关键词： DC distribution systems

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Group Selection Beamforming for RIS-Aided V2X Communication systems

Group Selection Beamforming for RIS-Aided V2X Communication ...

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international conference on Communication Technology (ICCT)

作者： Chenxu Wang Youyun Xu School of Communication and Information Engineering Nanjing University of Posts and Telecommunications Nanjing China National Engineering Research Center of Communications and Networking Nanjing China

ISBN: (数字)9798350363760

ISBN: (纸本)9798350363777

reconfigurable intelligent surface is a promising technology for enhancing wireless communication in various scenarios. Despite its promise, utilizing reconfigurable intelligent surface for communication services poses non-convex optimization challenges. In this paper, we propose a hybrid beamforming algorithm for reconfigurable intelligent surface aided mmWave vehicle-to-everything communication systems to maximize the system’s sum rate. The proposed algorithm is an iterative process developed jointly using coati optimization algorithm, group selection and codebook compression for reconfigurable intelligent surface aided beamforming. The algorithm first selects the reconfigurable intelligent surface phase shift matrix, then groups the vehicular user equipments and utilizes codebook compression algorithm to compute the beamforming matrix. The algorithm runs until it finds the maximum sum rate. Simulation results have verified the effectiveness of the proposed algorithm as compared to different benchmark schemes.

关键词： Wireless communication Correlation Array signal processing Simulation reconfigurable intelligent surfaces Iterative algorithms Millimeter wave communication Optimization Compression algorithms Vehicle-to-everything

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Ensemble Learning algorithms for Solar Power Prediction in Saudi Arabia: A Data-Driven Approach

Ensemble Learning Algorithms for Solar Power Prediction in S...

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2024 ASU international conference in Emerging Technologies for Sustainability and Intelligent systems, ICETSIS 2024

作者： Hossain, Mohammad Kamal Arifuzzaman, Md Seliaman, Mohamed Elhasan Rahman, Arifur Sarker, Debasish Altammar, Hussain Dhahran31261 Saudi Arabia Civil and Environmental Engineering King Faisal University Al Hofuf Saudi Arabia King Faisal University Department of Information Systems Al Hofuf Saudi Arabia Dhahran31261 Saudi Arabia Mechanical Engineering College of Engineering and Technology International University of Business Agriculture and Technology Bangladesh Mechanical Engineering King Faisal University Al Hofuf Saudi Arabia

ISBN: (纸本)9798350372229

This paper explores into Saudi Arabia's global leadership in renewable energy, particularly its solar initiatives. The study employs a detailed analysis of input variables, including time, temperature, wind speed, humidity, and air pressure, forming the basis for a predictive model focused on Umax (voltage). Rigorous data analysis establishes the reliability of findings, paving the way for further exploration into the models' inner workings. The paper concludes by highlighting the significance of the research for stakeholders, offering nuanced insights into Umax variations and optimizing solar power generation on a global scale. © 2024 IEEE.

关键词： Energy Efficiency Environmental Impact Machine Learning algorithms Sustainability

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Improving Accuracy in Network Intrusion Detection via Machine Learning algorithms: An In-Depth Examination

Improving Accuracy in Network Intrusion Detection via Machin...

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2024 international conference on Distributed systems, Computer Networks and Cybersecurity, ICDSCNC 2024

作者： Suriya Prakash, J. Trishlaa, S. Soniya, R. Lakshmanan, V. Kiran, S. Department of Cse Ramnagara India Rajarajeshwari College of Engineering Department of Artificial Intelligence and Machine Learning Bengaluru India Sri Eshwar College of Engineering Department of Computer Science and Engineering Coimbatore India Nitte Meenakshi Institute of Technology Department of Mathematics Bengaluru India

ISBN: (纸本)9798350375442

Intrusion detection is essential for securing computer networks by monitoring and analyzing activity to detect and respond to unauthorized access and malicious behavior. By examining network traffic and system logs, intrusion detection systems (IDS) help protect data integrity and availability, enabling swift responses to potential security incidents. In an ever-evolving cyber threat landscape, IDS plays a critical role in maintaining network security. This research investigates the performance of various machine learning algorithms for classification tasks using a dataset from Kyoto collected on December 31st, 2015. The algorithms explored include Naive Bayes, K-Nearest Neighbors (KNN), Extreme Gradient Boosting (XGBoost), AdaBoost, and CatBoost. Training and testing the model with different data splits (80/20, 70/30, 60/40) revealed that AdaBoost, LGBM, XGBoost, Decision Tree, Gradient Boosting, and Random Forest consistently delivered the highest accuracy. The study concludes that selecting the appropriate algorithms and data splits is crucial for enhancing IDS effectiveness, providing valuable insights for future research. Explore the code behind this study at https://***/Trishlaa07/Kyoto_2015_Dec_day31 © 2024 IEEE.

关键词： Network intrusion

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Autonomous Navigation For TurtleBot3 Robots in Gazebo Simulation Environment

Autonomous Navigation For TurtleBot3 Robots in Gazebo Simula...

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2024 international conference on Innovation and Intelligence for Informatics, Computing, and Technologies, 3ICT 2024

作者： Tamang, Madhav Theeng Maheriya, Darshana Sharif, Mhd Saeed Sutharssan, Thamo Intelligent Technologies Research Group School of Architecture Computing and Engineering University of East London London United Kingdom Department of Engineering University of East London London United Kingdom

ISBN: (纸本)9798331533137

The fast-paced growth in the field of robotics has driven the creation of autonomous navigation systems that are necessary for robots to work autonomously in diverse environments. This research targets TurtleBot3, an immensely popular robotic platform that is known for its affordability and adaptability. The objective of TurtleBot3 is to improve the capabilities within the Gazebo simulation environment, an open-source robotic simulator that provides a realistic virtual environment for testing and developing navigation algorithms for robotics. The combination of intricate sensor technologies, leading-edge control systems, and innovative artificial intelligence strategies is the core of this research, enabling autonomous navigation in complicated surroundings. The initial focus of the study is the application and utilization of path planning algorithms, such as A* (A Star) and Rapidly exploring Random Tree (RRT) and using Simultaneous Localization and Mapping (SLAM) algorithms. Results from simulations demonstrate that the A* algorithm achieved a path accuracy rate of over 95% in static environments, while the RRT algorithm proved effective in dynamic, multi-dimensional spaces. The aim of this analysis is to enhance autonomous navigation algorithms using the Gazebo simulation environment. Conducting tests in a virtual setting minimizes the risks and costs compared to real-time testing. The outcomes of this research are anticipated to advance the field of robotics and hold significant value for applications in autonomous systems, including engineering, urban mobility, and domestic assistance. ©2024 IEEE.

关键词： Robot Operating System

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Spatial Attention Mechanism Integrated Ensemble Machine Learning for Real Time Prediction and Classification in Precision Agriculture

Spatial Attention Mechanism Integrated Ensemble Machine Lear...

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2024 international conference on Intelligent algorithms for Computational Intelligence systems, IACIS 2024

作者： Adnan, Myasar Mundher Vijay Kumar, Bura Madhura, G.K. Mohammed, G. Das, Abhijeet The Islamic University Engineering College of Technical Engineering Department of Computers Techniques Najaf Iraq Sr University School of Computer Science and Artificial Intelligence Warangal India Nitte Meenakshi Institute of Technology Department of Artificial Intelligence and Machine Learning Bengaluru India Ahl Al Bayt University Karbala Iraq Department of Civil Engineering Bhubaneswar India

ISBN: (纸本)9798350360660

Generating spatial and temporal features for real time prediction and classification in precision agriculture is challenging due to inaccurate estimation of features from the remote sensing images. Using Geographical Information System (GIS) and systematic data regarding spatial and temporal data for monitoring and management of agricultural activities. The proposed research integrated GIS and used satellite image processing with Ensemble Machine Learning (EML) algorithms. Initially, the remote sensing data is pre-processed with normalization technique to remove noise. The pre-processed data is given as input to the Improved Speeded Up Robust Features (ISURF) for fast computation and identification of relevant features. The EML algorithms such as Random Forest (RF), Support Vector Machine (SVM), and Artificial Neural Network (ANN) are used as a prediction and classification models considering the ISURF features as input. Additionally, the Spatial Attention Mechanism (SAM) is integrated with the EML algorithm to particularly focus on the spatial features of the remote sensing data. The experimental results showed that the proposed SAM-EML achieved precise prediction and classification with 98.30% accuracy, and 97.19% precision when compared to the existing methods Random Forest Regression (RFR) and RF-SVM. © 2024 IEEE.

关键词： Adversarial machine learning

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