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检索条件"任意字段=International Workshop on Multilevel Optimization - Algorithms and Applications"

共 1022 条记录，以下是41-50 订阅

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RTK-B Enhancing Real-Time Kinematic (RTK) Positioning with Genetic algorithms and Long-Code GNSS Signals for Rapid Ambiguity Resolution and Improved Accuracy

RTK-B Enhancing Real-Time Kinematic (RTK) Positioning with G...

引用

international workshop on Advances in Civil Aviation Systems Development, ACASD 2023

作者： Petrovski, Ivan G. IP Solutions Satellite Applications Inc. Tokyo Japan

ISBN: (纸本)9783031380815

The proposed method in this paper synergizes genetic algorithms for carrier ambiguity resolution and the BGPS method for time ambiguity resolution of code observables, to ameliorate the RTK (Real-Time Kinematic) positioning process. By integrating these techniques, the RTK process initialization is expedited, and the positioning solution's accuracy is enhanced through the utilization of multiple observables within the same observation matrix. This methodology introduces supplementary constraints and redundancy, culminating in a more robust and reliable RTK solution. The method proposed herein employs advanced strategies from both carrier and code-based positioning methods to augment RTK positioning performance and holds potential for delivering more precise and dependable positioning solutions in diverse applications. It also allows to utilize the longer codes in QZSS LEX signals to offer advantages in code time ambiguity resolution, leading to improved positioning accuracy. Genetic algorithms are implemented to overcome the challenges posed by multiple local minima in the cost function optimization process, providing additional constraints and redundancy. The proposed approach demonstrates promising results, showing potential for enhancing the performance of multi-system, multi-frequency GNSS receivers in various positioning applications. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Genetic algorithms

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Selective Harmonic Elimination in multilevel Inverters with Fully Informed Search Algorithm

Selective Harmonic Elimination in Multilevel Inverters with ...

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Smart Systems for applications in Electrical Sciences (ICSSES), international Conference on

作者： Swaroop K P Srikanth Malladi Durga Prasad Garapati Omkar Koduri Sagiraju Dileep Kumar Varma SSSR Sarathbabu Duvvuri Department of Electrical & Electronics Engineering Shri Vishnu Engineering College for Women Bhimavaram (Autonomous) Bhimavaram India

ISBN: (数字)9798331507671

ISBN: (纸本)9798331507688

This research work presents a unique technique to Selective Harmonic Elimination (SHE) in Cascaded H-Bridge multilevel Inverters that employs the Fully Informed Search Algorithm (FISA). The approach seeks to maximize the reduction of lower-order harmonics while retaining the appropriate output voltage levels. FISA's performance is studied and compared to Particle Swarm optimization (PSO) and the Firefly algorithms. The methods are implemented and evaluated using MATLAB/Simulink, with key performance measures such as Total Harmonic Distortion (THD) and convergence behavior over different modulation indices. The results show that FISA surpasses both PSO and the Firefly Algorithm in terms of faster convergence, lower THD, and higher computational efficiency, giving it a reliable solution for SHE in multilevel inverters.

关键词： Total harmonic distortion Modulation Harmonic analysis multilevel inverters Search problems Smart systems Reliability Particle swarm optimization Convergence Switching circuits

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KDD-2023 workshop on Decision Intelligence and Analytics for Online Marketplaces 23

KDD-2023 Workshop on Decision Intelligence and Analytics for...

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29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD)

作者： Qin, Zhiwei (Tony) Song, Rui Ye, Jieping Zhu, Hongtu Jordan, Michael I. Lyft San Francisco CA 94107 USA Amazon Seattle WA USA Alibaba DAMO Acad Hangzhou Peoples R China Univ N Carolina Chapel Hill NC USA Univ Calif Berkeley Berkeley CA 94720 USA

ISBN: (纸本)9798400701030

Online marketplace is a digital platform that connects buyers (demand) and sellers (supply) and provides exposure opportunities that individual participants would not otherwise have access to. Online marketplaces exist in a diverse set of domains and industries, for example, rideshare (Lyft, DiDi, Uber), house rental (Airbnb), real estate (Beke), online retail (Amazon, Ebay), and food ordering and delivery (Doordash, Meituan). Besides academia, many companies and institutions are researching on topics specific to their particular domains. The fundamental mechanism of an online marketplace is to match supply and demand to generate transactions, with objectives considering service quality, participants experience, financial and operational efficiency. It is valuable to bring together researchers and practitioners from different application domains to discuss their experiences, challenges, and opportunities to leverage cross-domain knowledge. The goal of this workshop is to offer an opportunity to appreciate the diversity in applications, to draw connections to inform decision optimization across different industries, and to discover new problems that are fundamental to marketplaces of different domains. The previous version of this workshop at KDD-2022 was a tremendous success in terms of participation, technical contribution, and community interest. This updated version of the workshop is especially timely to cover the issues and algorithms pertinent to general online marketplaces, specific problems and applications arising from those diverse domains, as well as emerging topics such as competition and resilience to market condition shifts.

关键词： two-sided marketplace decision intelligence

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An ASGC-Surrogate Model Based Yield optimization for Passive Microwave Devices with Differential Evolution Algorithm

An ASGC-Surrogate Model Based Yield Optimization for Passive...

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IEEE MTT-S international Microwave workshop Series on Advanced Materials and Processes for RF and THz applications (IMWS-AMP)

作者： Han, Yujie Li, Ping Shanghai Jiao Tong Univ Minist Educ China Res Design & Electromagnet Comp Key Lab Shanghai Peoples R China

ISBN: (纸本)9781665478342

The performance of microwave devices is affected by many uncertainties during the manufacturing process. Proper design with least influence from these disturbances is required for optimal yield. At the design stage of microwave devices, the electromagnetic (EM) based simulation methods are accurate and comprehensive, but it suffers from expensive computational cost. For yield optimization, highly efficient optimization algorithms are necessary. In this article, a global surrogate model is constructed on the basis of the adaptive hierarchical sparse grid collocation (ASGC) algorithm to significantly reduce the number of sampling points. The differential evolution algorithm combined with the ASGC-based surrogate model is proposed to decrease the total simulation time. Finally, to validate the accuracy and efficiency of the proposed algorithm, several representative examples are presented.

关键词： Adaptive hierarchical sparse grid collocation (ASGC) algorithm differential evolution (DE) algorithm microwave filters manufacturing uncertainties yield optimization

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Exact Combinatorial optimization with Temporo-Attentional Graph Neural Networks

Exact Combinatorial Optimization with Temporo-Attentional Gr...

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5th international workshop on Learning with Imbalanced Domains - Theory and applications / European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD)

作者： Seyfi, Mehdi Banitalebi-Dehkordi, Amin Zhou, Zirui Zhang, Yong Huawei Technol Canada Co Ltd Shenzhen Peoples R China

ISBN: (纸本)9783031434204;9783031434211

Combinatorial optimization finds an optimal solution within a discrete set of variables and constraints. The field has seen tremendous progress both in research and industry. With the success of deep learning in the past decade, a recent trend in combinatorial optimization has been to improve state-of-the-art combinatorial optimization solvers by replacing key heuristic components with machine learning (ML) models. In this paper, we investigate two essential aspects of machine learning algorithms for combinatorial optimization: temporal characteristics and attention. We argue that for the task of variable selection in the branch-and-bound (B&B) algorithm, incorporating the temporal information as well as the bipartite graph attention improves the solver's performance. We support our claims with intuitions and numerical results over several standard datasets used in the literature and competitions. (Code is available at: https://***/develop/aigallery/notebook/detail?id=047c6cf2-8463-40d7-b92f- 7b2ca998e935.)

关键词： Combinatorial optimization Graph Neural Networks Temporal Attention Mixed Integer Linear Program

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Evolutionary Algorithm Based Approach to Reduce Total Harmonic Distortion for Symmetrical 7-Level Inverter

Evolutionary Algorithm Based Approach to Reduce Total Harmon...

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Power, Electrical, and Electronics and Industrial applications (PEEIACON), IEEE international Conference on

作者： Rony Tota Lima Tasnim Shafiqul Mutasim Akash Masudur Rahman Dept. of Electrical and Electronic Engineering Hajee Mohammad Danesh Science and Technology University Dinajpur Bangladesh

ISBN: (数字)9798331517984

ISBN: (纸本)9798331517991

In this research, some computational approaches based on biological evolutionary theory have been employed to diminish the total harmonic distortion (THD) of a cascaded 7-level -inverter system. Two evolutionary strategies, the GA (Genetic Algorithm) and PSO (Particle Swarm optimization) are utilized to resolve the transcendental equations of SHE PWM for a 7-level symmetrical inverter voltage waveform. This study compares the lower-order harmonics and THD reduction performance of GA and PSO with another recent work of Bio-Inspired Intelligent Algorithm Based SHE in multilevel Inverter. The THD values of that paper using GA and PSO were reduced in this research and also the individual 3rd, 5th, and 7th order harmonics have been reduced using GA and 3rd, 5th order harmonics have been reduced using PSO. This study depicts the use of evolutionary algorithms for SHE- PWM in multilevel inverters.

关键词： Total harmonic distortion Switches Voltage Pulse width modulation Power system harmonics Harmonic analysis multilevel inverters Real-time systems Particle swarm optimization Genetic algorithms

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9th IEEE international workshop on Automatic Performance Tuning (iWAPT 2024)

9th IEEE International Workshop on Automatic Performance Tun...

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IEEE international Symposium on Parallel and Distributed Processing workshops and Phd Forum (IPDPSW)

作者： Valero Lara Pedro

ISBN: (数字)9798350364606

ISBN: (纸本)9798350364613

iWAPT (international workshop on Automatic Performance Tuning) is a series of workshops that focus on research and techniques related to performance sustainability issues. The series provides an opportunity for researchers and users of automatic performance tuning (AT) technologies to exchange ideas and experiences acquired when applying such technologies to improve the performance of algorithms, libraries, and applications; in particular, on cutting edge computing platforms. Topics of interest include performance modeling; adaptive algorithms; autotuned numerical algorithms; libraries and scientific applications; empirical compilation; automated code generation; frameworks and theories of AT and software optimization; autonomic computing; and context-aware computing.

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Particle Swarm optimization with Time-Varying Acceleration Coefficients in multilevel Image Thresholding

Particle Swarm Optimization with Time-Varying Acceleration C...

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international Conference on Computer applications and Industrial Electronics (ICCAIE)

作者： Emir Turajlic Faculty of Electrical Engineering University of Sarajevo Sarajevo Bosnia and Herzegovina

ISBN: (数字)9798350348798

ISBN: (纸本)9798350348804

multilevel image thresholding based on the exhaustive search for the optimal thresholds is computationally expensive. To overcome this drawback this paper investigates the use of the particle swarm optimization (PSO) algorithms with time-varying acceleration coefficients in multilevel image thresholding. Specifically, two multilevel image thresholding methods based on Kapur's entropy and PSO algorithm with time-varying acceleration coefficients are considered. The two methods use different strategies to vary cognitive and social acceleration coefficients within the PSO algorithm. The considered thresholding methods are assessed on five test images. The multilevel image thresholding performance is assessed for varying numbers of thresholds. The performance of the methods under consideration is compared to that of the thresholding method based on the PSO algorithm with constant acceleration coefficients. The experimental results show that the thresholding methods based on the PSO algorithm with time-varying acceleration coefficients can be successfully used to obtain image thresholds across different test images.

关键词： Industrial electronics Thresholding (Imaging) Life estimation Color Gray-scale Entropy Particle swarm optimization

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Compressing Wav2vec2 for Embedded applications 33

Compressing Wav2vec2 for Embedded Applications

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33rd IEEE international workshop on Machine Learning for Signal Processing, MLSP 2023

作者： Ludwig, Oswaldo Claes, Tom Cerence Inc. Guldensporenpark 32 Merelbeke9820 Belgium

ISBN: (纸本)9798350324112

Wav2vec2 self-supervised multilingual training learns speech units common to multiple languages, leading to better generalization capacity. However, Wav2vec2 is larger than other E2E ASR models such as the Conformer ASR. Therefore, the objective of this work is to reduce the Wav2vec footprint by pruning lines from the intermediate dense layers of the encoder block, since they represent about two thirds of the encoder parameters. We apply Genetic algorithms (GA) to solve the combinatorial optimization problem associated with pruning, which means running many copies of the Wav2vec2 decoder in parallel using multiprocessing on a computer grid, so an effort was made to optimize the GA for good performance with few CPUs. The experiments show a small absolute word error rate damage of 0.21% (1.26% relative) for a pruning of 40% and compare this value with those of the usual L1-norm pruning and model restructuring by singular value decomposition. © 2023 IEEE.

关键词： Genetic algorithms

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2nd international Conference on Forthcoming Networks and Sustainability in the AIoT Era, FoNeS-AIoT 2024

2nd International Conference on Forthcoming Networks and Sus...

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2nd international Conference on Forthcoming Networks and Sustainability in the AIoT Era, FoNeS-AIoT 2024

ISBN: (纸本)9783031628801

The proceedings contain 69 papers. The special focus in this conference is on Forthcoming Networks and Sustainability in the AIoT Era. The topics include: Finding the Flies in the Sky Using Advanced Deep Learning algorithms;selective Harmonic Elimination in multilevel Inverters Using the Bonobo optimization Algorithm;optimizing Solar Energy Efficiency Through Automatic Solar Tracking Systems;instant High Starting Current Protection System for Induction Motor Based Reducing the Starting Speed;robot Hand-Controlled by Gyroscope Sensor Using Arduino;human Activity Recognition Using Convolutional Neural Networks;cyberbullying Detection for Urdu Language Using Machine Learning;A Cybersecurity Procedure to Vulnerabilities Classification of Windows OS Based on Feature Selection and Machine Learning;a Survey of Next Words Prediction Models;blockchain-Based Secured Estate Property Registration System: Baghdad Estate Registration as a Case Study;a Mobile Robot with an Autonomous and Custom-Designed Control System;A Web-Based Disease Prediction System Using Machine Learning algorithms and PCA;enhancing IoT Device Security: A Comparative Analysis of Machine Learning algorithms for Attack Detection;Enhancing Facial Recognition Accuracy and Efficiency Through Integrated CNN, PCA, and SVM Techniques;a Survey of Machine Learning Assistance in Seismic Interpretation;designing and Simulation of Three Phase Grid-Connected Photovoltaic System;Creating Synthetic Test Data by Generative Adversarial Networks (GANs) for Mobile Health (mHealth) applications;deep Neural Networks for Fetal Health Monitoring Through Cardiography Data Analysis;Detecting BGP Routing Anomalies Using Machine Learning: A Review;calculation of Bit-Error Probability for Direct-Sequence Spread-Spectrum Communications with Multiple-Access Interference of Rayleigh Distribution;determining the Digits of Turkish Sign Languages Using Deep Learning Techniques.

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