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Adaptive mutant particle swarm optimization based precise cargo airdrop of unmanned aerial vehicles

APPLIED SOFT COMPUTING 2022年 130卷

作者： Zhang, An Xu, Han Bi, Wenhao Xu, Shuangfei Northwestern Polytech Univ Sch Aeronaut Xian 710072 Peoples R China

Emerging unmanned system technologies and smart logistics have motivated logistics enterprises to use unmanned aerial vehicles (UAVs) for last-mile cargo delivery through the air so as to benefit from its flexibility and low cost. It is useful to deliver cargoes by airdrops from fixed-wing UAVs. However, wind and parameter errors of the UAV could make the cargo deviate from the desired landing point. To improve the accuracy of airdrop methods, this paper presents the Continuously Computed Release Point under conditions of strong wind (SW-CCRP) airdrop strategy to transport the cargo to a given position by a fixed-wing UAV. In this strategy, a set of differential equations are used to model the cargo motion and wind perturbations are considered. Based on the established motion model and the kinematic relationships between the cargo and the target position, the expected release point can be accurately predicted in real time. In order to satisfy the precision requirement, the precision assignment is studied to determine the permissible parameter error ranges. In view of that the conventional precision assignment methods are difficult to be applied in the airdrop system because of its complexity and strong nonlinearity, an adaptive mutant particle swarm optimization (AMPSO) algorithm is proposed to solve this problem, in which the adaptive inertia weight can balance the global and local search. In addition, the mutation factor of the AMPSO is able to avoid premature convergence and stagnation. Finally, two specific test scenarios are designed to validate the effectiveness of the proposed approaches.

关键词： Smart logistics Fixed-wing UAV Precision airdrop Adaptive mutant particle swarm optimization algorithm Precision assignment

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A Novel Data-Driven Method to Estimate Invisible Solar Power Generation: A Case Study in Taiwan 58

A Novel Data-Driven Method to Estimate Invisible Solar Power...

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58th IEEE/IAS Industrial and Commercial Power Systems Technical Conference Asia (IEEE I and CPS Asia)

作者： Thi Bich Phuong Nguyen Wu, Yuan-Kang Manh-Hai Pham Natl Chung Cheng Univ 168 Univ Rd Chiayi 62102 Taiwan Elect Power Univ 235 Hoang Quoc Viet Rd Hanoi Vietnam

ISBN: (纸本)9781665409186

As the penetration of photovoltaic (PV) solar generation increases, numerous residential and commercial solar PV systems without meters are being installed. The majority of these systems, however, are not monitored by power system operators. Therefore, the uncertainty of net load owing to these invisible solar power generation will raise additional challenges for power system operation. To reduce the above-mentioned impact, this work proposes a novel method to estimate the total solar power generation in a large region from a small representative subset. The proposed method is capable of capturing all relevant information that assists in the identification of representative subsets. Moreover, different optimization algorithms are utilized and evaluated to select the optimal number of clusters and representative subsets. As a case study, the power generation of 166 PV sites in Taiwan was collected and analyzed. The proposed method demonstrates a significant improvement in estimating the aggregated power generation compared to other existing studies.

关键词： Photovoltaic (PV) Invisible Solar Power Behind-the-Meter data dimension reduction hybrid method optimization algorithm

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Load Flow Assignments' Definition from Day-Ahead Electricity Market Interconnection Power Flows: A Study for Transmission Networks

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ENERGIES 2024年第6期17卷 1391页

作者： Fresia, Matteo Minetti, Manuela Procopio, Renato Bonfiglio, Andrea Lisciandrello, Giuseppe Orru, Luca Univ Genoa Dept Elect Elect Telecommun Engn & Naval Architect I-16145 Genoa Italy Terna SpA Rete Elettr Nazl I-00156 Rome Italy

The mass introduction of renewable energy sources (RESs) presents numerous challenges for transmission system operators (TSOs). The Italian TSO, Terna S.p.A., aims to assess the impact of inverter-based generation on system inertia, primary regulating energy and short-circuit power for the year 2030, characterized by a large penetration of these sources. The initial working point of the Italian transmission network has to be defined through load flow (LF) calculations before starting dynamical analyses and simulations of the power system. Terna 2030 development plan projections enable the estimation of active power generation and load for each hour of that year in each Italian market zone, as well as cross-zonal active power flows;this dataset differs from conventional LF assignments. Therefore, in order to set up a LF analysis for the characterization of the working point of the Italian transmission network, LF assignments have to be derived from the input dataset provided by Terna. For this purpose, this paper presents two methods for determining canonical LF assignments for each network bus, aligning with the available data. The methodologies are applied to a simplified model of the Italian network, but they are also valid for other transmission networks with similar topology and meet the future needs of TSOs. The methods are tested at selected hours, revealing that both approaches yield satisfactory results in terms of compliance with the hourly data provided.

关键词： transmission system grid flexibilization load flow optimization algorithm

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OPTIONS attack detection in WSN using Optimized Multitask Multi-Attention Residual Shrinkage Convolutional Neural Network

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KNOWLEDGE-BASED SYSTEMS 2024年 300卷

作者： Selvi, S. Tamil Visalakshi, P. Kumar, M. Senthil SRM Inst Sci & Technol Dept Networking & Commun Kattankulathur 603203 Tamilnadu India SRM Valliammai Engn Coll Dept Cyber Secur Kattankulathur 603203 Tamilnadu India

The rapid increase in Internet users has made web applications essential for daily services, rendering them targets for various cyber-attacks like path traversal, zero-day attacks, and injection attacks. While traditional security measures can prevent many familiar attacks, they are often ineffective against OPTIONS attacks, which involve injecting malicious code via hyperlinks to obstruct user access to legitimate webpage content. To address this novel challenge, we propose the OAD-WSN-MMRCNN technique, leveraging an Optimized Multitask MultiAttention Residual Shrinkage Convolutional Neural Network for OPTIONS attack detection in Wireless Sensor Networks (WSNs). This approach begins by selecting a CPU parameters dataset for attack detection, followed by pre-processing with a Variational Bayesian-Based Maximum Correntropy Cubature Kalman Filter to remove redundant data. Key features such as handles, threads, processor, context switch, deferred procedure call (DPC), interrupt delta, CPU socket, and core are extracted using a variable velocity strategy particle swarm optimization algorithm. The MMRCNN, optimized with the Tyrannosaurus optimization algorithm, is then employed to detect normal and OPTIONS attacks. Implemented in Python, the efficacy of OAD-WSN-MMRCNN is evaluated using metrics such as energy consumption, target window, accuracy, precision, F-measure, recall, and CPU utilization. Experimental results demonstrate that OAD-WSN-MMRCNN outperforms existing techniques, achieving a 20 % improvement in detection accuracy and a 25 % reduction in energy consumption, highlighting its effectiveness and potential for enhancing web application cyber security.

关键词： OPTIONS attack Tyrannosaurus optimization algorithm Variable velocity strategy particle swarm optimization algorithm VBMCCKF WSN

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Mitigating Cyber Anomalies in Virtual Power Plants Using Artificial-Neural-Network-Based Secondary Control with a Federated Learning-Trust Adaptation

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ENERGIES 2024年第3期17卷 619页

作者： Taheri, Seyed Iman Davoodi, Mohammadreza Ali, Mohd Hasan Univ Memphis Dept Elect & Comp Engn Memphis TN 38152 USA

Virtual power plants (VPPs) are susceptible to cyber anomalies due to their extensive communication layer. FL-trust, an improved federated learning (FL) approach, has been recently introduced as a mitigation system for cyber-attacks. However, current FL-trust enhancements, relying solely on proportional-integral (PI), exhibit drawbacks like sensitivity to controller gain fluctuations and a slow response to sudden disturbances, and conventional FL-trust is not directly applicable to the non-independent and identically distributed (non-IID) datasets common in VPPs. To address these limitations, we introduce an artificial neural network (ANN)-based technique to adapt FL-trust to non-IID datasets. The ANN is designed as an intelligent anomaly mitigation control method, employing a dynamic recurrent neural network with exogenous inputs. We consider the effects of the most common VPP attacks, poisoning attacks, on the distributed cooperative controller at the secondary control level. The ANN is trained offline and tested online in the simulated VPP. Using MATLAB simulations on a HOMER-modeled VPP, the proposed technique demonstrates its superior ability to sustain normal VPP operation amidst cyber anomalies, outperforming a PI-based mitigation system in accuracy and detection speed.

关键词： virtual power plant federated learning optimization algorithm power system distributed generation artificial neural network

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An adaptive weight search method based on the Grey wolf optimizer algorithm for skin lesion ensemble classification

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INTERNATIONAL JOURNAL OF IMAGING SYSTEMS AND TECHNOLOGY 2024年第2期34卷 e23049-e23049页

作者： Liu, Luzhou Zhang, Xiaoxia Xu, Zhinan Univ Sci & Technol Liaoning Sch Comp Sci & Software Engn Anshan Peoples R China Univ Sci & Technol Liaoning Sch Comp Sci & Software Engn Anshan 114051 Peoples R China

Skin cancer is a common type of malignant tumor that poses a serious threat to patients' lives and health, especially melanoma. It may spread to other body parts, resulting in serious complications and death. In the medical field, accurate identification of skin lesion images is crucial for diagnosing different diseases. However, due to the similarity between different skin lesions, it brings some challenges to medical diagnosis. In this paper, a novel Ensemble Learning Model (EL-DLOA) based on deep learning and optimization algorithms is proposed, which uses four different deep neural network architectures to generate confidence levels for classes, and optimization algorithms are used to integrate these confidence levels to make the final predictions. To ensure the model's accuracy and reliability, it is first trained using three different learning rates to find the best classification performance of the model. Then, a new search method based on the grey wolf optimization algorithm is proposed to enhance the grey wolf search efficiency. The method improves the search mechanism by changing the grey wolf's individual position through random perturbation or adaptive mutation, which solves the problem that the grey wolf algorithm is easy to fall into local optimum. Finally, four different ensemble strategies are used to reduce individual model bias in the classification process. The proposed model is trained and evaluated using the publicly available dataset HAM10000. The experimental results show that the improved grey wolf optimization algorithm effectively avoids the premature convergence problem and improves the search combination efficiency. Furthermore, in the ensemble methods, the adaptive weight average ensemble strategy effectively improves the classification performance, yielding accuracy, precision, recall, and F1 scores of 0.888, 0.837, 0.897, and 0.862, respectively. These metrics show varying degrees of improvement over the best performing single model. I

关键词： deep learning ensemble model optimization algorithm skin cancer skin lesion classification

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Optimizing sustainable energy integration: A novel approach using concentrated solar plant and hybrid power supply

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ELECTRIC POWER SYSTEMS RESEARCH 2024年 237卷

作者： Li, Mengyu Liu, Yunlong Li, Ji Li, Fei An, Yagang Gao, Xuezhe State Grid Hebei Mkt Serv Ctr Shijiazhuang 050000 Hebei Peoples R China State Grid Hebei Elect Power Supply Co Ltd Shijiazhuang 050000 Hebei Peoples R China

This study pioneers sustainable energy solutions amidst escalating demand and fossil fuel depletion. Its primary novelty lies in proposing an integrated energy system, encompassing a concentrated solar plant, thermal energy system, and hybrid power supply within the solar energy domain. This integration substantially enhances overall system performance. The architectural design integrates various energy conversion components, including wind turbines, energy storage devices, and electric heaters, fostering synergistic collaboration. The optimization of energy usage is a key focus, utilizing heating and electricity costs as pivotal signals for participation in an Integrated Demand Response program (IRD). The demand response model, encompassing both thermal and electric loads, incorporates a sophisticated price elasticity matrix. Addressing challenges associated with renewable energy intermittency, phased carbon emissions, and the advantages of demand response, this study introduces an advanced system operation optimization model to effectively curtail operational costs. In the realm of methodology, this research breaks new ground by proposing an enhanced optimization algorithm that ingeniously combines the Whale optimization algorithm (WOA) with the Wavelet Transform (WT). This novel amalgamation significantly enhances the algorithm's search capabilities, providing superior performance in tackling the complexities inherent in the optimization problem. The proposed model undergoes rigorous evaluation in a comprehensive study featuring diverse energy sources and multiple scenarios. The outcomes of this evaluation distinctly showcase substantial reductions in overall operational costs compared to conventional approaches. This multifaceted contribution positions the study at the forefront of endeavors to revolutionize energy systems for enhanced sustainability and efficiency.

关键词： Clean energy Demand response Hybrid power supply optimization algorithm Renewable energy Sustainable energy solutions

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Hybrid-scheduling-based sliding mode control for Markovian jump systems under transmission constraints

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JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS 2024年第5期361卷

作者： Zhang, Ying Chen, Bei Shanghai Univ Engn Sci Shanghai 201620 Peoples R China

This paper presents a new hybrid -scheduling -based design of sliding mode control for Markovian jump systems against transmission constraints. The hybrid scheduling scheme integrates stochastic communication protocol and event -triggering mechanism. At each transmission instant, a discrete -time Markovian chain is employed to orchestrate the stochastic transmission of multiple sensor nodes, by which only one sensor node has the priority to be evaluated whether it meets the triggering condition to access the network. This hybrid scheduling scheme aims to generate a sparser sampling pattern than the existing one while improving channel resource utilization. Based on which a feasible compensator and token -dependent sliding mode controller is constructed. Moreover, a genetic algorithm -based optimization problem is given to achieve a smaller convergent domain. Finally, a numerical example is shown to verify the present control strategy.

关键词： Communication scheduling protocol Event-triggering (ET) strategy Markovian jump systems (MJSs) optimization algorithm Sliding mode control (SMC)

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UAV Logistics and Distribution Path Planning in Urban Areas Based on Improved PSO and A* algorithms

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IEIE Transactions on Smart Processing & Computing 2025年第2期14卷 257-267页

作者： Li, Lei Peng, Huimin Ren, Xingxue Wang, Qianqian Henan Vocational University of Science and Technology Zhoukou 466000 China Henan Inland Port Logistics Information Engineering Technology Research Center Zhoukou 466000 China

Drones have shown enormous potential in urban logistics due to their efficiency and flexibility. However, traditional path planning methods such as particle swarm optimization and A* algorithm often find it difficult to meet both efficiency and safety when used alone. Therefore, this study proposes a new unmanned aerial vehicle logistics distribution path planning method. By adjusting parameters and optimizing search strategies, the particle swarm optimization algorithm utilizes the efficient pathfinding ability of A* algorithm to ensure its security. The results show that the obstacle avoidance success rate of the model is 94.85%, which is the best performance compared to other comparative algorithms and provides the shortest and smoothest path selection. This method demonstrates good path planning efficiency and stability, improving logistics and distribution capabilities in urban environments. This provides valuable reference for intelligent path planning and intelligent transportation systems.

关键词： A* algorithm Logistics optimization algorithm Particle swarm Path planning Unmanned aircraft

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An advanced optimization strategy for enhancing the performance of a hybrid pressure-swing distillation process in effective binary-azeotrope separation

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SEPARATION AND PURIFICATION TECHNOLOGY 2022年第PartB期282卷 120130-120130页

作者： Miao, Guang Zhuo, Kaisheng Li, Guoqing Xiao, Jing South China Univ Technol Key Lab Enhanced Heat Transfer & Energy Conservat Minist Educ Guangzhou 510640 Peoples R China South China Univ Technol Sch Chem & Chem Engn Guangzhou 510640 Peoples R China South China Univ Technol Sch Chem & Chem Engn Guangdong Prov Key Lab Green Chem Prod Technol Guangzhou 510640 Peoples R China

The separation of binary azeotropes with low cost and energy consumption is important and challenging for the production of chemicals. A hybrid heat-integrated pressure-swing distillation process (HHIPSD) with heat pump was proposed for the separation of both maximum-and minimum-boiling azeotropes in this work. Compared to the conventional fully heat-integrated pressure-swing distillation process (FHIPSD), the HHIPSD process showed lower total annual cost and higher thermodynamic efficiency in the separation of seven minimum-boiling azeotropes of benzene-ethanol, methanol-acetone, methanol-ethyl acetate (EA), ethanol-EA, acetonitrileethanol, isobutanol (IBA)/isobutyl acetate (IBAc) and diisopropyl ether (DIPE)-isopropyl alcohol (IPA), and two maximum-boiling azeotropes of water-ethylenediamine (EDA) and acetone-chloroform. For the best performance of HHIPSD process, robustness and algorithm optimization were studied in this paper. Scenario analysis method was proven to greatly enhance the convergence of HHIPSD models. The improved genetic algorithm (GA) was modified by particle-swarm optimization and pattern search method and validated by 13 benchmark functions. Meanwhile, different constraint handling methods were studied to ensure the better performance of the improve GA method. With the improvement of model robustness and optimization algorithm, the HHIPSD process achieved 21.6 % and 4.2 % decrease of TAC for water-EDA and methanol-acetone separation, respectively, compared to FHIPSD process. The proposed HHIPSD process together with the optimization strategy suggests the pressure-swing distillation technology can be highly effective in azeotrope separation.

关键词： Pressure-swing distillation Binary azeotropes Heat integration Robustness optimization optimization algorithm

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