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检索条件"任意字段=21st Conference on System Modeling and Optimization"

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modeling and Simulation of a parabolic trough collector system for heating water under the Algerian Saharan climate condition: Tamanrasset area 2

Modeling and Simulation of a parabolic trough collector syst...

引用

2nd International conference on Energy Transition and Security, ICETS 2023

作者： Sarah, Regragui Maouedj, Rachid Yacine, Marif Lagouch, Aakila Benatiallaha, Ali Ahmed Draïa University Adrar Energy Environment and Information Systems Laboratory Adrar Algeria Renewable Energy Research Unit in the Saharan Environment Urerms Adrar Algeria New and Renewable Energy Development Université Kasdi Merbah Ouargla Adrar Algeria

ISBN: (纸本)9798350372182

this article evaluates the thermal performance of a solar water heating system utilizing a parabolic trough concentrator (PTC). The main goal is to simulate PTC operational parameters using a single-dimensional numerical heat exchange analysis. The receiver, glass envelope, and fluid were segmented and energy balances derived for each section. The partial differential equations that regulate the system were discretized into algebraic formulations and solved concurrently. This study utilized MATLAB programming to solve for direct normal irradiance (DNI) using the Capderou model. DNI was modeled for the equinoxes (March 21st and September 21st) and solstices (June 21st and December 21st) based on recorded data, in order to examine seasonal variations, and water is used as a heat transfer medium. The Capderou model was chosen to calculate the solar radiation for a clear sky condition. The results proved that the selected area has a large amount of radiation, which makes it suitable for the study of the basin. The results also show the obtained water temperature value, which makes the model suitable for the production of hot water. © 2023 IEEE.

关键词： Water temperature

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optimization design of FFE-DFE based on differential evolution algorithm 21

Optimization design of FFE-DFE based on differential evoluti...

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21st International conference on Optical Communications and Networks, ICOCN 2023

作者： Li, Jinjiang Zhou, Jiahao Ren, Xuecheng Liu, Jiaming Zhang, Jing Qiu, Kun University of Electronic Science and Technology of China School of Communication and Information Engineering Chengdu China

We propose the p-ADE to optimize the tap coefficients of FFE-DFE in the IM/DD system. The results show that the convergence of p-ADE is faster than DE. The p-ADE provides similar SNR as LMS algorithm. © 2023 IEEE.

ISBN: (纸本)9798350343502

关键词： optimization

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2024 IEEE/ACS 21st International conference on Computer systems and Applications, AICCSA 2024 - Proceedings

2024 IEEE/ACS 21st International Conference on Computer Syst...

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2024 IEEE/ACS 21st International conference on Computer systems and Applications, AICCSA 2024

ISBN: (纸本)9798331518240

The proceedings contain 105 papers. The topics discussed include: enhancing four-class motor imagery detection through advanced feature extraction techniques;are my answers medically accurate? answers reliability regarding questions over healthcare knowledge graphs;indoor geolocation demonstrator: enhancing efficiency through real-time tracking and AI optimization;a deep learning system for early detection of diabetic retinopathy;holistic design of economical and secure data centers for developing countries: a free integrative approach;new dynamic Bayesian network for time-to-event prediction;application of artificial intelligence techniques to detect water stress;utilizing multi-step loss for single image reflection removal;and streamlining disaster detection: a lightweight fusion architecture for low-resource deployment.

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optimization of system Parameter of Functionally Graded Sandwich Beams for Improved Damping Characteristics 1

引用

21st ISME International conference on Advances in Mechanical Engineering, ISME 2023

作者： Abhay Rao, A.G. Rao, Chaithanya Krishna Aniruddha, P.S. Chandrashekara, C.V. PES University Karnataka Bengaluru560085 India

ISBN: (数字)9789819736515

ISBN: (纸本)9789819736508

The technique of optimization is a popular tool among researchers across domains. optimization algorithms are widely used in structural dynamics to obtain the optimum system parameters for improved dynamic characteristics. Functionally graded materials (FGMs) are the center of attraction for engineers and researchers. They possess several advantages that are attractive in potential applications of aerospace, civil, and automotive sectors. Literature review indicates various research carried out in the interdisciplinary fields of FGM. A novel demonstration of optimization for sandwich beam structures is carried out in the present work. The optimization model is utilized in further research to better design and provide for efficient functioning of the system. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Sandwich structures

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system modeling for Smallest Optimizer Compiler system 21

System Modeling for Smallest Optimizer Compiler System

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21st International Computer conference on Wavelet Active Media Technology and Information Processing, ICCWAMTIP 2024

作者： Yukun, Chen School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China

ISBN: (纸本)9798331519254

Compilers play a crucial role in translating high-level programming languages into asssembly or machine code. Many compilers either lack optimization capabilities, or too large and complex. This paper presents design of a Smallest Optimizer Compiler system (SOCS) based on the Kunpeng Server, which translates Compiler system (CSY) language to Intermediate Representation(IR), strongIR and static Single Assignment (SSA) format strongerIR, which combine LL VM IR and GCC's GIMPLE, using a Three Address Code (TAC) format for most situation. It's IR is lightweight while retaining modern features like SSA. The design based on LLVM IR ensures excellent compatibility, enabling seamless integration with LLVM's back-end to generate platform-specific assembly code. SOCS supports optimizations such as Constant Folding (CF), Algebraic Simplification and Re-association(ASR), Global Value Numbering (GVN), and Loop Invariant Code Motion (LICM). Functional and performance tests were processed on the Kunpeng Server. © 2024 IEEE.

关键词： Program compilers

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Online optimization Method for Virtually Coupled Train Operation Deviation from Reference Curve 21

Online Optimization Method for Virtually Coupled Train Opera...

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21st International conference on Networking, Sensing and Control, ICNSC 2024

作者： Lang, Yinghui Liu, Hongjie Liao, Yanli Li, Bosi Wang, Yihan Chai, Ming School of Automation and Intelligence Beijing Jiaotong University Beijing100044 China Beijing Jiaotong University National Engineering Research Center of Rail Transportation Operation and Control System Beijing100044 China Beijing Hollysys System Engineering Co. Ltd. Xi'an Branch Xi'an710061 China

ISBN: (纸本)9798350365221

Virtually coupled train set (VCTS) has become a popular research topic in the railway industry in recent years. It is considered one of the solutions to the uneven spatial and temporal distribution of passenger flow in urban rail transit. A VCTS consists of multiple train units (TUs). The overall operation of the VCTS relies on the coordinated efforts of each TU. A feasible approach involves pre-planning reference curves for the TUs, with the Automatic Train Operation (ATO) system tracking these curves. However, as the TUs operate at close intervals, the control strategy needs to ensure safety and precise control. Consequently, the reference curve of the TU is characterized by frequent changes in acceleration. As a result, the ATO system is prone to deviating from the curve. This paper builds a time-varying vehicle dynamics model and a controller performance model based on data learning. Using these models, we predict deviations and construct an online optimization model. A mesh adaptive direct search (MASD) algorithm is designed to solve for online adjustment curves, promptly correcting any deviations. Simulation experiments demonstrate the effectiveness of the proposed online optimization method when combined with PID and MPC alzorithms. © 2024 IEEE.

关键词： Urban transportation

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Advanced Power system optimization: Evaluation of Automatic Generation Control Techniques in Two-Area Networks 21

Advanced Power System Optimization: Evaluation of Automatic ...

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21st International Bhurban conference on Applied Sciences and Technology, IBCAst 2024

作者： Ayaz, Muhammad Baig, Dur-E-Zehra Hur Rizvi, Syed Muhammad Ejaz, Shazil Ul Haq, Muhammad Armughan Rathi, Laksh GIKI Faculty of Electrical Engineering Topi Swabi Pakistan Habib University Department of Electrical Engineering Karachi Pakistan

ISBN: (纸本)9798331516680

Automatic Generation Control (AGC) plays a critical role in ensuring frequency stability and power regulation in multi-area power systems. In this study, the application of intelligent techniques in AGC of a two-area power system was explored, and a comparative analysis of conventional AGC, Tie-Line Bias (TLB) Control, Particle Swarm optimization (PSO), and Genetic Algorithm (GA) was provided. The performance of each method is evaluated using metrics such as rise time, settling time, overshoot, peak time, and settling minimum and maximum values. The results demonstrated that GA emerged as the best-performing technique, offering the fastest rise time (1.7140e-06 s), shortest settling time (2.5346 s), and minimal overshoot (0.000693%). PSO also shows strong performance with a rise time of 0.0022 s and an overshoot of 0.0310%. Conventional AGC and TLB Control, while effective, exhibit longer settling times and higher overshoot compared to GA and PSO. These findings highlight the superiority of GA for AGC in two-area power systems, providing rapid and precise control with minimal deviations. This research suggests that intelligent control methods such as GA and PSO offer significant improvements over conventional techniques and have potential applications in real-world power systems. © 2024 IEEE.

关键词： Genetic algorithms

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Route optimization Algorithms for Faster E-Commerce Delivery 21

Route Optimization Algorithms for Faster E-Commerce Delivery

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21st OITS International conference on Information Technology, OCIT 2023

作者： Shah, Esha Kanakia, Janki Jadhav, Param Shah, Sachi Modi, Tvisha Singh, Artika MPSTME NMIMS Dept. of Computer Engineering Mumbai India

ISBN: (纸本)9798350358230

The increasing popularity of e-commerce has led to a surge in the number of deliveries, necessitating the development of efficient transport management systems to ensure the timely and accurate delivery of orders. This paper proposes a delivery management system for the shortest delivery route for products sold by e-commerce retail platforms in a sequential manner. This study evaluates three key algorithms-Traveling Salesman Problem, Genetic Algorithm, and Vehicle Routing Problem-To identify swift, practical solutions for vendors. The paper aims to enhance the delivery process by employing real-Time tracking and route optimization algorithms, efficiently reducing distances between destinations. Notably, no existing models adequately assist small vendors in product delivery. © 2023 IEEE.

关键词： Genetic algorithms

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Integrating Deep Learning for Comprehensive Detection and optimization of ECG-Based Arrhythmia Using Gaussian Function Model 21

Integrating Deep Learning for Comprehensive Detection and Op...

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21st IEEE International conference on Smart Communities: Improving Quality of Life using AI, Robotics and IoT, HONET 2024

作者： Boulkaboul, Sahar Baha, Meriem Slimani, Medjda Rihab CERIST Research Center Algiers Algeria Higher School of Computer Science Sidi Bel Abbes Algeria

ISBN: (纸本)9798350378078

The rapid advancement in deep learning has opened new avenues for the accurate detection and classification of ar-rhythmias using electrocardiogram (ECG) signals. This study fo-cuses on integrating deep learning techniques with Gaussian function models to enhance the detection of ECG-based arrhythmias. Traditional methods of arrhythmia detection often face challenges due to the complex and varied nature of ECG signals. By employing Gaussian function models, this work aims to create a more generalized representation of ECG beats, which can be effectively utilized by deep learning algorithms. The proposed approach combines the strengths of Gaussian modeling with advanced deep learning techniques, including Convolutional Neural Networks (CNN), Long Short-Term Memory (LstM) networks, Channel Attention Mechanism, and Transformer Encoders. These components collectively improve feature extraction, enhance contextual understanding, and refine the classification process, leading to a comprehensive and robust detection mechanism for various types of arrhythmias. Experimental results demonstrate that the integrated model significantly outperforms existing methods in terms of accuracy and computational efficiency, providing a promising tool for real-Time cardiac monitoring and early diagnosis of heart conditions. © 2024 IEEE.

关键词： Convolutional neural networks

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DRL-APNS: A Deep Reinforcement Learning-Powered Framework for Accurate Predictive Network Slicing Allocation 21

DRL-APNS: A Deep Reinforcement Learning-Powered Framework fo...

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IEEE 21st Consumer Communications and Networking conference (CCNC)

作者： Abo-eleneen, Amr Abdellatif, Alaa Awad Erbad, Aiman Mohamed, Amr Hamad Bin Khalifa Univ Coll Sci & Engn Ar Rayyan Qatar Qatar Univ Coll Engn Doha Qatar

ISBN: (纸本)9798350304572

Wireless networks have undergone significant advancements with the rapid progress in Software Defined Networks (SDN), Open RAN (O-RAN) and 5G technology. Among the most notable developments is the emergence of network slicing, which leverages the concept of virtual networks to separate the end-to-end network and computational resources into individual network slices per one or more services/tenants. However, determining the appropriate allocation of computational and network resources for each network slice to meet the specific requirements of each service remains a challenge, especially with continuously changing demands and varying Key Performance Indicators (KPIs) per service and the risk of insufficient or excessive resource allocation. To address these issues, this paper proposes an intelligent predictive framework based on Deep Reinforcement Learning (DRL). Our framework utilizes historical demand and KPI requirements to predict and reserve optimized network slices for multiple services in the future, considering unique constraints, dynamic pricing and under-provisioning. Using different datasets, we validated our system's effectiveness, scalability, and adaptiveness by comparing it with different baselines and state-of-the-art approaches. Our results affirm the efficiency of the proposed solution, showcasing a minimum cost reduction of 15% compared to different baselines and state-of-the-art solutions while incurring less than 2% additional resource consumption. Furthermore, our system demonstrates excellent scalability and adaptability across varying network conditions.

关键词： Deep reinforcement learning network slicing resource allocation optimization dynamic pricing

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