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检索条件"任意字段=4th International Conference on Data-Driven Optimization of Complex Systems, DOCS 2022"

共 109 条记录，以下是61-70 订阅

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Design Feature Selection and Classifiers for Hybrid Feature Selection-Particle Swarm optimization (HFS-PSO) 4

Design Feature Selection and Classifiers for Hybrid Feature ...

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4th international conference on Inventive Research in Computing Applications, ICIRCA 2022

作者： Siddamallappa, Kumar U Gandhewar, Nisarg Dr.A.P.J. Abdul Kalam University Department of Computer Science & Engineering Indore India

ISBN: (数字)9781665497077

ISBN: (纸本)9781665497077

In image classification, the feature selection process is critical for reducing overhead and improving accuracy. In light of the increasing growth of data, the concept of integrating classifiers with feature selection approaches has lately emerged as a new trend for boosting the performance of classification systems. there are several benchmark algorithms that use wrapper, filter, or hybrid approaches. these algorithms use a variety of strategies, ranging from basic search-based totally strategies to more complex nature-stimulated set of rules-based totally strategies. In this paper, a hybrid feature selection set of rules based on image classification is proposed. the suggested framework's performance was compared to many benchmark supervised and unsupervised feature choice techniques and found to be advanced. Moreover, the advised algorithm is much less computationally extensive and therefore took much less time to execute for the publicly available datasets used within the trials, which covered high-dimensional datasets. © 2022 IEEE.

关键词： Feature Selection

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Ship route optimization using hybrid physics-guided machine learning 4

Ship route optimization using hybrid physics-guided machine ...

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international Maritime and Port Technology and Development conference, MTEC 2022 and 4th international conference on Maritime Autonomous Surface Ships, ICMASS 2022

作者： JOrgensen, Ulrik Belingmo, Pauline ROstum Murray, Brian Berge, Svein Peder Pobitzer, Armin SINTEF Ocean AS Postbox 4762 Torgarden TrondheimNO-7465 Norway SINTEF Ålesund Norsk Maritimt Kompetansesenter Borgundvegen 340 ÅlesundNO-6009 Norway

this paper presents a method for energy efficient weather routing of a ferry in Norway. Historical operational data from the ferry and environmental data are used to develop two models that predict the energy consumption. the first is a purely data-driven linear regression energy model, while the second is as a hybrid model, combining physical models with data-driven models using machine learning techniques. With an established energy model, it is possible to develop a route optimization that proposes efficient routes with less energy usage compared to fixed speed and heading control. © Published under licence by IOP Publishing Ltd.

关键词： Energy utilization

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Energy optimization to Extend Network Lifetime for IoT based Wireless Sensor Networks 4

Energy Optimization to Extend Network Lifetime for IoT based...

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4th international conference on Smart systems and Inventive Technology, ICSSIT 2022

作者： Shreyas, J. Deepa, H. Udayaprasad, P.K. Chouhon, Dharmendra Srinidhi, N.N. Dilip Kumar, S.M. Creencia Technologies Private Limited Indiranagar Bangalore560038 India School of Computer Science and Engineering Reva University Bangalore India Dept. of Computer Science and Engineering University Visvesvaraya College of Engineering Bangalore India

ISBN: (纸本)9781665401180

the issue of extending the lifetime of Wireless Sensor Networks (WSN) designed for Internet of things (IoT) has been rigorously explored by researchers over the last two decades. IoT applications for real time are in demand for monitoring and complex analysis. In IoT, sensor nodes are powered by battery backup which is considered as energy constrain while performing various task. Maximizing the network lifetime of network nodes has become a challenging issue. In past researchers have focused on extending network lifetime of nodes through guarantee lifetime protocols, however some issues such as sensing coverage, scheduling and network connectivity are not been considered. therefore, in this work energy efficient routing scheme (EERS) is proposed for duty cycled nodes by considering sleep/awake mode and increased data rates to achieve higher packet delivery ratio and to extend network lifetime and optimize the energy consumption of the network. Sleep/awake modes for nodes are periodically assigned by sink node based on nodes residual energy, coverage area and total active time. © 2022 IEEE

关键词： Energy efficiency

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Research on key technologies of multi-agent game decision distributed learning system for agent equipment decision parameter optimization 4

Research on key technologies of multi-agent game decision di...

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4th international Signal Processing, Communications and Engineering Management conference, ISPCEM 2024

作者： Yu, Xie Yu, Changzhou Liu, Siyuan Yang, Guang Xu, Haoran An, Bo Jiangsu WebAce Intelligent Industry Innovation Center CO. LTD. Nanjing211135 China

ISBN: (纸本)9798331528676

the optimization of decision parameters for equipment in the Intelligent agent domain is becoming increasingly paramount, driven by advancements in modern technology and the intricacies of battlefield environments. Multi-agent game theory, by simulating the interactions and confrontations of various agents under diverse scenarios, can authentically replicate the complex dynamic relationships inherent in the battlefield. Meanwhile, distributed learning systems demonstrate unique advantages in managing vast data sets and intricate computations. the synergy of these two approaches not only enhances the accuracy and timeliness of decision-making but also propels the innovation and advancement of intelligent agent technology. the transition from theory to practical application encompasses numerous critical technologies, including the construction of multi-agent game models, algorithm design, and the establishment of distributed architectures, all aimed at achieving efficient and precise optimization of intelligent agent equipment decision parameters. ©2024 IEEE.

关键词： Adversarial machine learning

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datalog-2.0 2022 - Proceedings of the 4th international Workshop on the Resurgence of datalog in Academia and Industry, co-located with the 16th international conference on Logic Programming and Nonmonotonic Reasoning, LPNMR 2022

Datalog-2.0 2022 - Proceedings of the 4th International Work...

引用

4th international Workshop on the Resurgence of datalog in Academia and Industry, datalog-2.0 2022

the proceedings contain 21 papers. the topics discussed include: novel techniques in datalog optimization;certain answers of extensions of conjunctive queries by datalog and first-order rewriting;querying data exchange settings beyond positive queries;new perspectives for fuzzy datalog;model checking verification of multilayer perceptrons in datalog: a many-valued approach with typicality;towards bridging traditional and smart contracts with datalog-based languages;datalog-based reasoning with heuristics over knowledge graphs;a rule-based constraint language for event streams;a formal comparison between datalog-based languages for stream reasoning;towards better understanding of the performance and design of datalog systems;and intelligent agents and complex event processing to enhance patient monitoring.

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Fusion data-Assisted optimization of Holographic System 4

Fusion Data-Assisted Optimization of Holographic System

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

作者： Zhang, Hong Electronic Technology Co. Ltd 201620 China

ISBN: (纸本)9798350385793

the quest for authentic three-dimensional (3D) imaging has driven significant advances in holographic projection technology. the proposed model explores the integration of computational holography and data fusion methods to enhance holographic display systems. this study presents a novel holographic system combined with a sophisticated data fusion model to improve overall functionality and performance. the proposed system utilizes computational holography to generate robust and reproducible holographic representations that overcome the limitations of physical objects. By synergizing data fusion techniques with holographic imaging, our system maximizes efficiency and adaptability in various applications. through design, implementation, and optimization, this thesis provides a comprehensive overview of our innovative holographic system and its integration with data fusion, contributing to the advancement of holographic display technologies. © 2024 IEEE.

关键词： Image fusion

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Scheduling Big data Workflows in the Cloud under Budget Constraints 4

Scheduling Big Data Workflows in the Cloud under Budget Cons...

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4th IEEE international conference on Big data (Big data)

作者： Mohan, Aravind Ebrahimi, Mandi Lu, Shiyong Kotov, Alexander Wayne State Univ Detroit MI 48202 USA

ISBN: (纸本)9781467390057

Big data is fast becoming a ubiquitous term in both academia and industry and there is a strong need for new data-centric workflow tools and techniques to process and analyze large-scale complex datasets that are growing exponentially. On the other hand, the unbound resource leasing capability foreseen in the cloud facilitates data scientists to wring actionable insights from the data in a time and cost efficient manner. In the data-centric workflow environment, scheduling data processing tasks onto appropriate resources are often driven by the constraints provided by the users. Enforcing a constraint while executing the workflow in the cloud adds a new optimization challenge on how to meet the objective while satisfying the given constraint. In this paper, we propose a new Big data woRkflow schEduler uNder budgeT conStraint known as BARENTS that supports high-performance workflow scheduling in a heterogeneous cloud computing environment with a single objective to minimize the workflow makespan under a provided budget constraint. Our case study and experiments show the competitive advantages of our proposed scheduler. the proposed BARENTS scheduler is implemented in a new release of dataVIEW, one of the most usable big data workflow systems in the community.

关键词： Scheduler BARENTS Big data

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An Operation with Crossover and Mutation of MPSO Algorithm 4th

An Operation with Crossover and Mutation of MPSO Algorithm

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4th international conference on Smart Vehicular Technology, Transportation, Communication and Applications, VTCA 2021

作者： Zhong, Yuxin Chen, Yuxin Yang, Chen Meng, Zhenyu Fujian Provincial Key Lab of Big Data Mining and Applications Fujian University of Technology Fuzhou China

ISBN: (纸本)9789811640384

As an efficient and simple optimization algorithm, particle swarm optimization (PSO) has been widely applied to solve various real optimization problems in expert systems. However, avoiding premature convergence and balancing the global exploration and local exploitation capabilities of the PSO remains an open issue. To overcome these drawbacks and strengthen the ability of PSO in solving complex optimization problems, a modified PSO using adaptive strategy called MPSO is proposed, although MPSO has achieved excellent performance, and its convergence and stability are still some defects. In this paper, we presented a new variant of MPSO algorithm which can explore the search space deeper than the previous method, and better performance can be achieved under CEC2013 test suite. © 2022, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Expert systems

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Health Prognostics for Lithium-ion Battery Based on Hybrid data-driven Method 4

Health Prognostics for Lithium-ion Battery Based on Hybrid D...

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4th international conference on Smart Power and Internet Energy systems (SPIES)

作者： Ma, Yan Shan, Ce Hu, Yunfeng Chen, Hong Jilin Univ State Key Lab Automot Simulat & Control Dept Control Sci & Engn Changchun Peoples R China Jilin Univ Dept Control Sci & Engn Changchun Peoples R China Tongji Univ New Energy Automot Engn Ctr Shanghai Peoples R China

ISBN: (纸本)9781665489577

Accurate State of health prediction of lithium-ion battery provides a guarantee for the safe driving of electric vehicles. However, battery aging is a long-term complex process accompanied by capacity self-regeneration phenomenon, which also brings challenges to accurately prediction of SOH. therefore, a novel SOH prediction method based on mode decomposition and hybrid machine learning is proposed in this paper. the original capacity data is decomposed by complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) to obtain the trend item and detail item, which indicates the long-term degradation trend and self-regeneration effect. After that, long short-term memory(LSTM) and support vector regression (SVR) are employed for prediction of trend item and detail item respectively. the final SOH prediction is obtained by accumulating the prediction results of the trend item and the detail item. the effectiveness of the proposed method is verified by 2 different datasets. the prediction error of the proposed method is under 2%, which is less than the compared methods. the prediction results of different dataset show good accuracy, which indicates that the proposed method has high robustness, good accuracy, and applicability.

关键词： Lithium-ion battery mode decomposition long short-term memory support vector regression capacity regeneration

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Real-Time optimization of Power Plant Boiler Based on Pattern Matching and Neural Networks 4

Real-Time Optimization of Power Plant Boiler Based on Patter...

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4th international conference on High Performance Big data and Intelligent systems, HDIS 2022

作者： Lin, Xueru Wu, Yanling Lin, Xiaojie Zhong, Wei College of Energy Engineering Zhejiang University Hangzhou China Zhejiang Engipower Technology Co. Ltd. Hangzhou China Changzhou Industrial Technology Research Institute Zhejiang University Changzhou China Changzhou Engipower Technology Co. Ltd. Changzhou China

ISBN: (数字)9781665491440

ISBN: (纸本)9781665491440

Using machine models to characterize the operational experience of complex systems, instead of manual optimization decisions, has become an advanced solution for optimizing the operation of various power plants. A new modeling framework based on pattern matching and neural networks is proposed to improve the effectiveness and real-Time performance of coal-fired boiler operations in power plants. A three-layer optimization mechanism of attention mechanism parameters, interval frequency method, and the regulation minimum is introduced to ensure the effectiveness of the pattern matching scheme. Furthermore, we build the neural networks-based agent model that characterizes pattern matching steps for online applications. the case results show that the proposed pattern matching optimization model can effectively find the optimal boiler operation scheme;the similarity of working conditions and operation is more than 95%;the boiler efficiency can be increased by 2.15% in practice;the mean square error of the trained agent model is within 0.54%. Compared with the traditional methods, this method avoids the influence of generalization error caused by the optimization solver algorithm and improves the boiler efficiency while having high reliability and calculation response performance. © 2022 IEEE.

关键词： Pattern matching

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