检索结果-内蒙古大学图书馆

Multimodal Renewable Energy Hybrid Supply optimization Model Based on Heterogeneous Cloud Wireless Access

IEEE ACCESS 2024年 12卷 78286-78303页

作者： Tian, Feng Wang, Hongjiang Jiang, He Zhao, Baida Shenyang Inst Engn Coll Informat Shenyang 110036 Peoples R China Shenyang Inst Engn Sch Renewable Energy Shenyang 110036 Peoples R China Shenyang Inst Engn Innovat & Entrepreneurship Educ Ctr Shenyang 110036 Peoples R China

With the increasing emphasis on environmental issues, the utilization of renewable energy has been recognized as a feasible solution to address the energy crisis and reduce environmental pollution. In view of this, this article proposes a multi-modal renewable energy hybrid power supply optimization model based on heterogeneous cloud wireless access. The model innovatively combines heterogeneous cloud wireless access technology and various intelligent optimization algorithms, including k-clustering algorithm, particle swarm optimization algorithm, and whale optimization algorithm, forming a hybrid optimization algorithm. In order to comprehensively evaluate the actual performance of the model, this study recruited 20 experts to provide detailed ratings on four core dimensions: cost-benefit ratio, reliability, robustness, and user satisfaction. The results showed that the model scored 95.1, 96.4, 95.6, and 96.2 in the four dimensions of cost-benefit ratio, reliability indicators, robustness, and user satisfaction, respectively. This series of significant data not only confirms the theoretical superiority of the model, but also demonstrates its strong potential and practical value in practical applications. In summary, this study provides a promising and innovative solution for the field of renewable energy supply.

关键词： optimization Clustering algorithms Heuristic algorithms Renewable energy sources Wireless communication Cloud computing whale optimization algorithms K-clustering algorithm heterogeneous cloud radio access particle swarm optimization algorithm WOA algorithm energy supply optimization

来源：评论

学校读者我要写书评

暂无评论

Hybridizing whale optimization Algorithm With Particle Swarm optimization for Scheduling a Dual-Command Storage/Retrieval Machine

引用

IEEE ACCESS 2023年 11卷 21264-21282页

作者： Hsu, Hsien-Pin Wang, Chia-Nan Natl Kaohsiung Univ Sci & Technol Dept Supply Chain Management Kaohsiung 81157 Taiwan Natl Kaohsiung Univ Sci & Technol Dept Ind Engn & Management Kaohsiung 807618 Taiwan

whale optimization algorithm (WOA) and particle swarm optimization (PSO) have been used individually usually. However, a separate use of them has a limitation. Hybridizing WOA with PSO is expected to evolve solutions better due to the cooperation between whales and seabirds. Developing such kind of model is the focus of this research. A framework has been further proposed to best utilize such hybridizations for developing simulation-based optimization approaches. The framework has the advantage of integrating metaheuristic, simulation, and optimization seamlessly. It can waive the rigorous and labor-intensive optimization procedure required for traditional simulation. In this research, simulation-based optimization approaches are used to deal with the dual-command block scheduling problem of a manufacturing firm's storage/retrieval (S/R) machine in an automated storage/retrieval system. The S/R machine is mainly used to store/retrieve stock-keeping units in an automated storage/retrieval system. Three simulation-based optimization approaches, Hybrid1 (WOA+PSO), Hybrid2 (WOA+PSO), and Hybrid3 (WOA+PSO), have been developed. To investigate their effectiveness, experiments have been conducted to compare them with their base models, WOA and PSO, as well as the genetic algorithm (GA) and PWOA. The PWOA is an abbreviation of a hybridization of PSO and WOA proposed in a previous study. The experimental results show that Hybrid3 (WOA+PSO) outperforms Hybrid2 (WOA+PSO), Hybrid1 (WOA+PSO), WOA, PSO, PWOA, and GA. The uses of techniques such as hybridization, Neighborhood heuristic, and adaptive movements of whales empower Hybrid3 (WOA+PSO) the most.

关键词： Metaheuristics Manufacturing Genetic algorithms Job shop scheduling Analytical models Particle swarm optimization whale optimization algorithms whale optimization algorithm particle swarm optimization storage retrieval machine scheduling

来源：评论

学校读者我要写书评

暂无评论

Block size optimization for PoW consensus algorithm based blockchain applications by using whale optimization algorithm

引用

TURKISH JOURNAL OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCES 2022年第2期30卷 406-419页

作者： Aygun, Betul Arslan, Hilal Izmir Demokrasi Univ IIBF Management Informat Syst Izmir Turkey Ankara Yildirim Beyazit Univ Fac Engn & Nat Sci Dept Software Engn Ankara Turkey

Blockchain-based applications come up with cryptocurrencies, especially Bitcoin, introducing a distributed ledger technologies for peer-to-peer networks and essentially records the transactions in blocks containing hash value of the previous blocks. Block generation constitutes the basis of this technology, and the optimization of such systems is among the most crucial concerns. Determining either the block size or the number of transactions in the block brings out a remarkable problem that has been solved by the miners in recent years. First, higher block size results in higher transaction time, on the other hand, smaller block size has many disadvantages such as security, lower transaction fees, lower transaction numbers in a given time interval, which makes it unable to compete with other currency systems due to this bulky structure and higher block generation time. In this study, multiobjective optimization problem (OP) is proposed by minimizing block generation and transmission time. This multiobjective OP is transformed into a single OP by applying weighted sum method. To determine the optimal block size, particle swarm optimization (PSO) algorithm and whale optimization evolutionary algorithm (WOA) are employed. Although both algorithms have capability to reach optimum block size and corresponding time, WOA achieves better performance than PSO in terms of the convergence speed and output fluctuation. Moreover, analysis of the prediction of optimum block size is carried out under different weights which creates many optimization functions. Experimental results indicate that if higher weight is assigned to the transmission time, then block size decreases sharply. Furthermore, the experimental results reveal that design of the blockchain network and number of nodes in network profoundly affect the block size selection due to the time constraints.

关键词： Blockchain block size whale optimization algorithms particle swarm optimization

来源：评论

学校读者我要写书评

暂无评论

Ripple-Induced whale optimization Algorithm for Independent Tasks Scheduling on Fog Computing

引用

IEEE ACCESS 2024年 12卷 65736-65753页

作者： Khan, Zulfiqar Ali Aziz, Izzatdin Abdul Univ Teknol PETRONAS Dept Comp & Informat Sci Seri Iskandar 32610 Malaysia

Due to the revolution of Internet of Things (IoT), the amount of data generation has been redoubling, leading to higher latency and network traffic. This has resulted in delays in services and increased energy consumption of cloud servers. Fog computing tackles the issues associated with long geographical distance between end-users and cloud servers by extending service provision closer to the network edge, reducing latency and makespan, and optimizing energy consumption during workload execution. Instead of offloading all tasks to the cloud, delay-sensitive tasks are executed at fog nodes, while others are offloaded to the cloud. However, the resources at the fog layer are limited, posing a challenge for task scheduling in fog computing, particularly as a multi-objective optimization problem. Meta-heuristic algorithms have been potent to find an optimal solution for such problems within a reasonable amount of time. The whale optimization Algorithm (WOA) is a relatively new meta-heuristic algorithm that has received significant attention from researchers due to its impressive optimization characteristics. However, being an exploitation-oriented technique, it falls into local optima due to a lack of generating new solutions over time. Limited exploration capabilities also compromise the diversity of the solution space and prolong convergence time. Therefore, in this study, an enhanced Ripple-induced whale optimization Algorithm (RWOA) is proposed, utilizing ripple effects to schedule independent tasks in fog computing. It aims to minimize makespan and energy consumption while maximizing throughput in a fog-cloud infrastructure by improving poor solutions through substantial changes. Extensive simulations are performed to assess the effectiveness of the proposed algorithm. The proposed RWOA outperformed TCaS, HFSGA, MGWO, and WOAmM on two workload datasets: Random and NASA Ames iPSC. The statistical significance of the results is validated by the Friedman test and Wil

关键词： Task analysis Cloud computing Costs Edge computing Scheduling Energy consumption Delays whale optimization algorithms Task scheduling meta-heuristic whale optimization Algorithm (WOA) fog computing

来源：评论

学校读者我要写书评

暂无评论

Wide-Angle Scanning Thinned Phased Array Synthesis Based on Improved Multiobjective Beluga whale optimization Algorithm

引用

IEEE ANTENNAS AND WIRELESS PROPAGATION LETTERS 2024年第11期23卷 3511-3515页

作者： Zheng, Yuqing You, Chang Jiang Zhang, Naibo Zhu, Xueyong Ding, Yufeng He, Hao Univ Elect Sci & Technol China Chengdu 611731 Peoples R China UESTC Shenzhen Inst Adv Study Shenzhen 518110 Peoples R China China Elect Technol Grp Corp Beijing R&D Ctr Res Inst 54 Beijing 100070 Peoples R China

This letter proposes an algorithm that combines the improved multiobjective beluga whale optimization algorithm with the iterative Fourier transform (IFT) technique to optimize the sidelobe level (SLL) and directivity of the phased array. The algorithm is derived from the traditional beluga whale optimization algorithm, based on the framework of multiobjective optimization. By improving the traditional algorithm and combining with IFT technology and opposition-based learning strategy, the population diversity is enhanced, and the optimization ability of the algorithm is improved. By optimizing the array layout, the proposed method can effectively synthesize thinned planar phased arrays with low SLL and nondegraded directivity. Some specific examples are provided to confirm the effectiveness of the proposed method.

关键词： Phased arrays optimization Statistics Sociology Sorting whales whale optimization algorithms Beluga whale optimization (BWO) directivity phased array sidelobe level (SLL) thinned array synthesis

来源：评论

学校读者我要写书评

暂无评论

RLWOA-SOFL: A New Learning Model-Based Reinforcement Swarm Intelligence and Self-Organizing Deep Fuzzy Rules for fMRI Pain Decoding

引用

IEEE TRANSACTIONS ON AFFECTIVE COMPUTING 2024年第2期15卷 644-656页

作者： Anter, Ahmed M. Zhang, Zhiguo Egypt Japan Univ Sci & Technol E JUST Alexandria 21934 Egypt Beni Suef Univ Fac Comp & Artificial Intelligence Benisuef 62511 Egypt Harbin Inst Technol Sch Comp Sci & Technol Shenzhen 518055 Guangdong Peoples R China Peng Cheng Lab Shenzhen 518060 Peoples R China

Pain is highly subjective, so it is always desirable to develop objective pain assessment methods. Brain imaging techniques, such as functional magnetic resonance imaging (fMRI), have the potential to provide a physiological and quantitative pain assessment tool. However, the ultra-high-dimensional fMRI data and the nonlinear relationship between fMRI and pain greatly degrade the efficiency of fMRI-based pain decoding models. In this article, a novel pain decoding model is proposed based on the whale optimization algorithm (WOA), reinforcement learning (RL), and self-organizing fuzzy logic (SOFL), namely RLWOA-SOFL. The new non-linear WOA method incorporates RL and repository experiences (RE), which is based on a back-propagation neural network (BPNN) to map a set of agents states to appropriate actions, to extract and select features that are highly predictive of pain. More specifically, the proposed RLWOA is self-learning and self-optimizing so it can deal with the high-dimensional and complex fMRI data. On the other hand, to establish a fMRI-based pain decoding model, a novel SOFL method is proposed as a new type of deep fuzzy rule that can learn continuously from new data and identify prototypes to construct fuzzy rules. The proposed RLWOA-SOFL model is applied to real-world pain-evoked fMRI data, and the results show that the new model can decode pain intensity more accurately and can identify pain-related fMRI patterns more reliably. Therefore, the proposed RLWOA-SOFL model has great potential to evaluate the intensity of pain perception in clinical uses.

关键词： Pain Functional magnetic resonance imaging Decoding whales Feature extraction Brain modeling whale optimization algorithms Reinforcement learning swarm intelligence self-organizing deep fuzzy rules functional magnetic resonance imaging and pain decoding

来源：评论

学校读者我要写书评

暂无评论

Rapid Prediction of Cutterhead Torque in Hard-Rock Tunneling Using IEWOA-TSVD-ITELM

引用

IEEE ACCESS 2024年 12卷 88658-88680页

作者： Li, Long Liu, Zaobao Shandong Technol & Business Univ Sch Management Sci & Engn Yantai 264005 Shandong Peoples R China Northeastern Univ Coll Resources & Civil Engn Key Lab Minist Educ Safe Min Deep Met Mines Shenyang 110819 Peoples R China Northeastern Univ Key Lab Liaoning Prov Deep Engn & Intelligent Tech Shenyang 110819 Peoples R China

Predicting cutterhead torque is essential for optimizing TBM construction strategies and minimizing jamming risks. This study presents a novel hybrid model (IEWOA-TSVD-ITELM), developed using data from 7635 tunneling cycles from the Yinsong Project, to enhance cutterhead torque prediction accuracy. The EWOA enhances its global search capability by introducing new position updating and adaptive adjustment strategies (IEWOA). In addition, by leveraging the Softsign function for the nonlinear transformation of the expected output matrix of the TELM, a third hidden layer is added to enhance the feature extraction capabilities (ITELM), whereas truncated singular value decomposition (TSVD) is employed to reduce the noise in the output matrix of the third hidden layer of the ITELM (TSVD-ITELM). Furthermore, the IEWOA optimized the number of neurons and randomly generated weights and biases in the TSVD-ITELM. This study comprehensively evaluates and compares six optimization algorithms using 25 standard test functions. Additionally, the IEWOA-TSVD-ITELM is compared with eight classical machine learning models. This study examines the impact of different timing lengths of the rising phase and rock mass grades on model performance. The results demonstrate the outstanding performance of the IEWOA as an optimization algorithm. The IEWOA-TSVD-ITELM model achieves an R-2 value of 0.644 on the test set, with an MAE of 326.623 and an RMSE of 435.821, outperforming the other algorithms. Increasing the timing length from 30 to 60 s reduces the MAE and RMSE by 11.82% and 9.56%, respectively, but the gains diminish when the timing length increases from 60 to 90 s.

关键词： Torque Prediction algorithms Predictive models Tunneling Rocks optimization Adaptation models whale optimization algorithms Extreme learning machines Machine learning TBM cutterhead torque prediction whale optimization algorithm truncated singular value decomposition two-hidden-layer extreme learning machine extreme hybrid model

来源：评论

学校读者我要写书评

暂无评论

A Business Model for Resource Sharing in Cell-Free UAVs-Assisted Wireless Networks

引用

IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2022年第8期71卷 8839-8852页

作者： Tun, Yan Kyaw Park, Yu Min Tra Huong Thi Le Han, Zhu Hong, Choong Seon Kyung Hee Univ Dept Comp Sci & Engn Yongin 17104 South Korea Kyung Hee Univ Elect & Informat Convergence Engn Dept Yongin 17104 South Korea Univ Houston Dept Elect & Comp Engn Houston TX 77004 USA

Unmanned aerial vehicles (UAVs) are widely deployed to enhance the wireless network capacity and to provide communication services to mobile users beyond the infrastructure coverage. Recently, with the help of a promising technology called network virtualization, multiple service providers (SPs) can share the infrastructures and wireless resources owned by the mobile network operators (MNOs). Then, they provide specific services to their mobile users using the resources obtained from MNOs. However, wireless resource sharing among SPs is challenging as each SP wants to maximize their utility/profit selfishly while satisfying the QoS requirement of their mobile users. Therefore, in this paper, we propose joint users association and wireless resource sharing problem in the cell-free UAVs-assisted wireless networks with the objective of maximizing the total network utility of the SPs while ensuring QoS constraints of their mobile users and the resource constraints of the UAVs deployed by MNOs. To solve the proposed mixed-integer non-convex problem, we decompose the proposed problem into two subproblems: users association, and resource sharing problems. Then, a two-sided matching algorithm is deployed in order to solve users association problem. We further deploy the whale optimization and Lagrangian relaxation algorithms to solve the resource sharing problem. Finally, extensive numerical results are provided in order to show the effectiveness of our proposed algorithm.

关键词： Resource management Wireless networks Virtualization Quality of service Games whale optimization algorithms Interference Two-sided matching game whale optimization Lagrangian relaxation mobile network operators (MNOs) unmanned aerial vehicles (UAVs) service providers (SPs) user association resource sharing

来源：评论

学校读者我要写书评

暂无评论

Dependency Structure-Based Rules Using Root Node Technique for Explicit Aspect Extraction From Online Reviews

引用

IEEE ACCESS 2023年 11卷 65117-65137页

作者： Mishra, Pallavi Panda, Sandeep Kumar ICFAI Fdn Higher Educ Fac Sci & Technol IcfaiTech Dept Comp Sci & Engn Hyderabad 501203 India ICFAI Fdn Higher Educ Fac Sci & Technol IcfaiTech Dept Artificial Intelligence & Data Sci Hyderabad 501203 India

Aspect-based sentiment analysis (ABSA) includes aspect extraction and sentiment analysis on those extracted aspects. This paper is mainly focused on the explicit aspect extraction from product based online customer reviews. Many research studies on explicit aspect extractions have adopted dependency rule-based techniques but limited their focus to only nouns and noun phrases as potential aspects. Moreover, extraction of multiple-aspects and multi-word aspects from online reviews are also not addressed by previous research studies. In this paper, we have proposed a Dependency structure-based rules using ROOT Node (DS-RN) technique using spaCy dependency parser to extract nouns, noun phrases, verbs, verb phrases in addition to single word, multiple aspects and multi word aspect extractions from customer review datasets. In our proposed methodology, 30 new dependency-based rules are formulated and implemented on 5 different product-based datasets. This study is based on the pattern analysis of dependency structures of review sentences to develop dependency-based rules for explicit aspect extraction. The proposed approach also incorporated lexicon-based pruning techniques to remove irrelevant aspects and retain correct aspects. The performance results on 5 different product-based customer review datasets demonstrate that our proposed DS-RN approach outperforms all other state-of-the-art baseline works with averaged value of precision as 87%, recall with 97% and 91% as F1-score.

关键词： Sentiment analysis Feature extraction Task analysis Batteries Electronic commerce whale optimization algorithms Periodic structures Aspects dependency structure-based rule explicit aspect extraction opinion lexicon ROOT node sentiment analysis

来源：评论

学校读者我要写书评

暂无评论

A Novel Computational Intelligence-Based Parameter Extraction of UAV-Integrated Photovoltaic System

引用

IEEE ACCESS 2024年 12卷 97664-97677页

作者： Saeedinia, Mohammadhosein Hashemi, Behzad Cretu, Ana-Maria Taheri, Shamsodin Univ Quebec Outaouais Dept Comp Sci & Engn Gatineau PQ J8X 3X7 Canada

The precise modeling of unmanned aerial vehicle (UAV)-integrated photovoltaic (PV) systems is central for their implementation on UAVs, especially due to their continuous movement. Accurate modeling is essential for developing control methods, conducting performance studies, and designing the whole system considering PV system parameters. This original study presents a novel modeling procedure for comprehensive modeling of UAV-integrated PV modules using multi-input multi-output (MIMO) and multi-input single-output (MISO) machine learning models. These models are developed based on a historical environmental dataset and a randomly generated dataset representing flight conditions. Both datasets get processed during the preparation phase. In this phase, two sets of empirically derived group of modified equations (GME) of the PV module are utilized. In addition, the whale optimization Algorithm (WOA) is employed to optimize one set of GME containing PV system five unknown parameters. Moreover, the Dogleg Trust Region Algorithm (DTRA) is used as an unconstrained problem solver in each loop of WOA to solve a system of equations consisting of the unknown parameters. Finally, the MIMO and MISO models will be trained with the processed dataset. The proposed approach predicts UAV-integrated PV module behavior under diverse flight conditions, interprets panel movements, and is validated through experiments.

关键词： Mathematical models Autonomous aerial vehicles Computational modeling Prediction algorithms optimization Predictive models Photovoltaic systems Machine learning whale optimization algorithms UAV-integrated photovoltaic machine learning moving PV UAV flying PV whale optimization algorithm (WOA)

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：