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检索条件"任意字段=4th International Conference on Optimization Learning Algorithms and Applications"

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Vehicle charging station location based on improved whale optimization algorithm 5

Vehicle charging station location based on improved whale op...

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5th international conference on Computer Engineering and Application (ICCEA)

作者： Li, Zhaodi Liu, Qingqing Yu, Kaixuan Henan Univ Dept Comp & Informat Engn Kaifeng Peoples R China Henan Light Ind Vocat Coll Dept Comp Sci & Art Zhengzhou Peoples R China State Key Lab Shield Machine & Boring Technol Zhengzhou Peoples R China

ISBN: (纸本)9798350386783;9798350386776

In order to better explore a method for solving the problem of electric vehicle charging station location planning, and improve the shortcomings of whale algorithm such as weak stability, sometimes slow convergence speed and easy to fall into local extremum, a whale optimization algorithm with quasi-opposition-based learning and enhanced search mechanism was proposed. then, the algorithm flow is given, and the CEC2017 test function set is simulated by six representative algorithms in multiple dimensions. the results show that the convergence speed, optimization accuracy and solution stability of the improved algorithm are significantly improved, and it has good convergence performance. In order to solve the problem of electric vehicle public charging station location and service area division, an improved whale algorithm and Voronoi diagram are used to jointly solve the location planning of charging station, so as to achieve the purpose of global optimization. the application results of the last example show that the improved whale algorithm can effectively solve the charging station location problem and has superior optimization performance.

关键词： Whale optimization algorithm Enhanced search Location of charging station

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Enhancing Mobile Robot Speed Control: PID Controller optimization with Bio-Inspired algorithms 4

Enhancing Mobile Robot Speed Control: PID Controller Optimiz...

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4th international conference on Expert Clouds and applications, ICOECA 2024

作者： Singh, Sangeeta Uma, H.R. Bhargav, H.K. Arya, Sujata Singh, Praveen Natrayan, L. JSS Academy of Technical Education Department of Electrical Engineering Uttar Pradesh Noida201 301 India BGS Institute of Technology Adichunchanagiri University Department of Computer Science and Engineering Karnataka Mandya571 448 India Basaveshwar Engineering College Department of Computer Science and Engineering Karnataka Bagalkot587 102 India Faculty of Law Viman Nagar Maharashtra Pune411 014 India Graphic Era Deemed to be University Department of Management Studies Uttarakhand Dehradun248002 India Saveetha School of Engineering SIMATS Department of Mechanical Engineering Tamil Nadu Chennai602105 India

ISBN: (纸本)9798350385793

this research study presents an investigation into the optimization of PID controllers using bio-inspired algorithms for controlling the speed of mobile robots. the study begins by identifying a mathematical model that accurately represents the dynamics of the DC motor used in robots. three different tuning methods, namely Ziegler-Nichols (ZN), Flower Pollination Algorithm (FPA), and Fish Swarm optimization (FSO) are then utilized to design the PID controllers. the primary objective is to enhance the speed control performance of mobile robots. To evaluate the effectiveness of the optimization methods, the set speed is given to each of the three controllers, and their performance is compared. the results demonstrate that the optimization techniques outperform the conventional ZN method in achieving the desired speed control. Among the two bio-inspired algorithms, FPA yields exceptional outcomes in terms of time characteristics. the comparison of time characteristics serves as a benchmark for evaluating the performance of the controllers. It reveals that bio-inspired optimization methods, especially FPA, significantly improve the speed control of mobile robots. the time characteristics, such as rise time, settling time, overshoot, peak time, and peak value, illustrate the superior performance achieved through the optimization process. © 2024 IEEE.

关键词： DC motors

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Enhancing Reliability in Wireless Sensor Networks through Cluster Based optimization 4

Enhancing Reliability in Wireless Sensor Networks through Cl...

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4th international conference of Science and Information Technology in Smart Administration, ICSINTESA 2024

作者： Hashim, Yaqin Jasim Abd Hasson, Saad Talib University of Babylon Iraq

ISBN: (纸本)9798350376111

this study delves into enhancing the reliability of Wireless Sensor Networks (WSNs) through the implementation of the Low-Energy Adaptive Clustering Hierarchy (LEACH) algorithm. WSNs play a vital role in various domains, including environmental monitoring and industrial automation, necessitating dependable data transmission. Initially, the research evaluates the reliability of WSNs without clustering, serving as a baseline. Subsequently, LEACH is applied to organize sensors into clusters, optimizing energy usage and network efficiency. the focus lies on improving reliability metrics, such as packet delivery ratio (PDR), which quantifies the proportion of successfully transmitted data packets. through simulation and analysis, the effectiveness of LEACH in enhancing reliability is thoroughly examined. the results demonstrate a significant enhancement in reliability, indicating a substantial reduction in packet loss and data inconsistency. this reliability enhancement is attributed to the intelligent clustering of sensors, which minimizes energy consumption and optimizes data routing. the findings underscore the importance of employing advanced algorithms like LEACH to bolster the reliability of WSNs, ensuring accurate and timely data delivery in critical applications. © 2024 IEEE.

关键词： Wireless sensor networks

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17th international conference on Combinatorial optimization and applications, COCOA 2024

17th International Conference on Combinatorial Optimization ...

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17th international conference on Combinatorial optimization and applications, COCOA 2024

ISBN: (纸本)9789819644445

the proceedings contain 14 papers. the special focus in this conference is on Combinatorial optimization and applications. the topics include: A Simple Algorithm for Scheduling Unit Jobs with Unknown Number of Machines;faster algorithms for Grid and Layered Drawings of Plane 3-Trees;reinforcement learning for Airline Continuous Dynamic Pricing;approximation algorithms for the Combination of Prize-Collecting Vertex Cover and Scheduling;randomized Mechanisms for Improved Approximation Ratios in Heterogeneous Two-Facility Location;comprehensive Multi-view Subspace Clustering with Global-and-Local Representation learning;on the Twin-width of Outerplanar Graphs;maximize an Approximate k-submodular Function under a Knapsack Constraint;a Short Proof and Experimental Study of the Approximation Algorithm for Label s-t Cut;risk-Embedded Scheduling optimization for a Virtual Power Plant Under Carbon Emission Trading Constraints;on the Constrained Steiner Strong Connectivity Augmentation Problem;competitive algorithms for Online Traveling Salesman Problem on a Semi-line;on the Inapproximability of Two-machine Open Shop Scheduling with Exact Delays;minimum Power Partial Cover with Fairness Constraint;differentially Private Counting Queries on Approximate Shortest Paths;semi-online Multiprocessor Scheduling with Known Largest Job Processing Time;acyclically Edge Color Triangle-free Toroidal Graphs in Δ+2 Colors;an Equally-Split Bin Packing Problem;the Location-routing Problem in the Pallet Pooling System Considering Carbon Emissions;scheduling Fully Parallel Jobs with Integer Units;UAV Target Tracking with Bandit-Based Data Fusion;fair Maximization of Monotone Submodular Functions in Data Streams;tournament Transitivity of Graphs;approximation Algorithm for Min-max Correlation Clustering Problem with Penalties;improved Approximation Algorithm for Individual Fairness k-Median;the Price of Fairness for Budget-Feasible EF1 Allocations;on the Min-max Heterogeneous Weighted Delivery P

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Direction of Machine learning Research in Obstetric Care in the Last 10 Years 4

Direction of Machine Learning Research in Obstetric Care in ...

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4th international conference on Emerging Smart Technologies and applications (eSmarTA)

作者： Maulana, Fairuz Iqbal Lestari, Dian Adi, Puput Dani Prasetyo Hamim, Miftahul Rahayu, Anita Widartha, Vandha Pradwiyasma Bina Nusantara Univ Sch Comp Sci Comp Sci Dept Jakarta 11480 Indonesia Airlangga Univ Fac Med Surabaya 60132 Indonesia Natl Res & Innovat Agcy Res Ctr Telecommun Bandung Indonesia Bina Nusantara Univ BINUS Business Sch Entrepreneurship Business Creat Jakarta 11480 Indonesia Pukyong Natl Univ Dept Informat Syst Busan South Korea

ISBN: (纸本)9798350354140;9798350354133

this study aims to use bibliometric analysis to examine how machine learning research trends in obstetric care have evolved over time. First, 829 publications from 2014 to 2023 were collected from the Web of Science database. the fundamental issues and characteristics of machine learning in obstetric care research were analyzed, such as annual publication contribution and focus, using theme analysis, concurrency analysis, and topic trend analysis. Second, productive objects, such as journals, authors, institutions, countries, regions, and Collaborative relationship mapping, are used to present who is leading the considerable attention in machine learning research in obstetric care. third, the citation structure of authors and journals was investigated, and references were cited. this Bibliometric analysis revealed an increasing annual publication trend, a shift in focus to machine learning in obstetric care, the dominance of authors from China, and an increasing number of international collaborations, indicating machine learning in obstetric care as a vibrant and growing field and need for further scientific enrichment. therefore, a comprehensive study on machine learning in obstetric care reviews the characteristics and trajectory of current research and helps scholars find suitable research entry points and conduct in-depth research.

关键词： machine learning obstetric bibliometric VOSviewer

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Federated learning for Resource Allocation in Maritime IoT Networks with Compressed Sensing 4

Federated Learning for Resource Allocation in Maritime IoT N...

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4th international conference on Communication Technology and Information Technology, ICCTIT 2024

作者： Liu, Jiwei Li, Baiyi Liu, Shutong Yang, Tingting Mao, Zhongyang Dalian Maritime University Navigation College Dalian China Naval Aviation University Department of Electronic and Information Engineering Yantai China

ISBN: (纸本)9798331528973

Maritime transportation and shipping are vital components of global economic development. In order to enhance shipping efficiency and ensure safety, a distributed machine learning architecture can be utilized to achieve it. Due to concerns about data privacy, many shipping data are not shared. Federated learning FL), as a form of distributed machine learning, offers the advantage of preserving privacy. the problem of communication efficiency and resource joint optimization in FL over communication networks has been studied, and proposed a communication resource allocation algorithm with compressed sensing. through experiments, we found that compared to the basic FL method with uniform resource allocation, the proposed RACS algorithm significantly improves the communication efficiency of wireless IoT network FL, as well as the learning performance in terms of training loss and testing accuracy. © 2024 IEEE.

关键词： Federated learning

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Exploring the Capacitated Vehicle Routing Problem Using the Power of Machine learning: A Literature Review 7th

Exploring the Capacitated Vehicle Routing Problem Using the ...

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7th international conference on Logistics Operations Management (GOL)

作者： EL Jaouhari, Manal Bencheikh, Ghita Bencheikh, Ghizlane Moulay Ismail Univ FS Comp Sci & Applicat Meknes Morocco CESI Engn Sch LINEACT Assat France Moulay Ismail Univ FSJES Meknes Morocco

ISBN: (纸本)9783031686337;9783031686344

the Capacitated Vehicle Routing Problem is a classic logistics optimization challenge for which numerous approaches are employed to address it. A prevalent strategy involves employing machine learning for predicting demand, leveraging historical data to accurately forecast customer needs. Furthermore, reinforcement learning techniques are employed to optimize vehicle routes, adapting them dynamically in response to changing traffic conditions and new customer requests. Hybrid models combining traditional optimization algorithms with machine learning have shown promise in improving the quality of solutions. Dive into this state-of-the-art compilation, we have meticulously gathered and synthesized related studies to provide a thorough overview and deliver readers an insightful comprehension of the latest statement of the CVRP.

关键词： CVRP Metaheuristic Machine learning Reinforcement learning

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Design and optimization of Double Square FSS for Sub 6G applications Using ML algorithms 4

Design and Optimization of Double Square FSS for Sub 6G Appl...

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4th IEEE international conference on Electrical, Computer, and Energy Technologies, ICECET 2024

作者： Golla, Pranav Sairam, S.D. Kumar, D. Sriram National Institute of Technology Dept. of Electronics and Communication Engineering Tamil Nadu Tiruchirappalli India

ISBN: (纸本)9798350395914

Frequency Selective Surfaces (FSS) play a very critical role now-a-days in electromagnetic engineering, which enables precise control of electromagnetic wave propagation for various applications. this article explores the integration of Machine learning (ML) algorithms to optimize the structure to achieve enhanced performance and efficiency for Sub 6G applications (6 GHz and 2.4 GHz). the Double Square Frequency Selective Surface (DS-FSS) consists of two distinct layers of square shaped periodic patterns. the substrate used is RT Duroid 5880 which has a dielectric constant of 2.2 with a width of 1.6 mm for better characteristics. the periodicity of the unit cell is 20 mm x 20 mm which makes it isotropic and polarization insensitive. this configuration provides greater flexibility in tailoring the overall frequency response compared to single layer FSS. A comprehensive data set (approx. 5000) has been prepared by simulating the electromagnetic responses from the Equivalent Circuit Model (ECM) which are fed to the ML algorithm to extract the features and build the regression model and to estimate the optimal parameters. the ML algorithms used are Linear Regression and Random Forest. these are then validated against the electromagnetic simulations using HFSS Software. Two stop bands, one at 2.4 GHz and the other at 6.3 GHz and one pass band at 3.8 GHz has been achieved. Shielding Effectiveness of about 49 dB is achieved at 2.4 GHz and about 57 dB is achieved at 6.3 GHz. the angular stability of 0° to 80°, with a stable bandwidth, is also achieved by the proposed design which is better than the existing works. © 2024 IEEE.

关键词： Random forests

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A New Multi-objective optimization Model for Optimal Configuration of CNNs 4th

A New Multi-objective Optimization Model for Optimal Configu...

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4th international conference on Digital Technologies and applications (ICDTA)

作者： Boufssasse, Ali Hssayni, El Houssaine Joudar, Nour-Eddine Ettaouil, Mohamed Sidi Mohamed Ben Abdellah Univ Dept Math FST Fez Fes Morocco Mohammed V Univ Rabat ENSIAS Rabat Morocco

ISBN: (纸本)9783031686528;9783031686535

In recent years, convolutional neural networks (CNNs) are compute-intensive learning models with growing applicability in a variety of real-world problems. However, determining an optimal CNN architecture is still unclear and needs mathematical development. Furthermore, most current CNN models are primarily crafted in a specific manner. this study introduces a novel multi-objective optimization model to find optimal hyperparameters related to CNN architecture. Our proposal simultaneously minimizes both cross-entropy loss and a related CNN complexity function. this model enables the fine control of each neuron's contribution within the CNN's fully connected layers through a set of associated decision variables. To solve the resulting model, we opt for the evolutionary multi-objective algorithm (NSGA-II). Extensive experiment on three benchmark datasets MNIST, SVHN, and NORB demonstrate the effectiveness of our proposed approach.

关键词： CNN NSGA-II Muli-objective optimization image classification Pareto front

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17th international conference on Combinatorial optimization and applications, COCOA 2024

17th International Conference on Combinatorial Optimization ...

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17th international conference on Combinatorial optimization and applications, COCOA 2024

ISBN: (纸本)9789819644476

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