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An advanced hybrid deep learning model for predicting total dissolved solids and electrical conductivity (EC) in coastal aquifers

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ENVIRONMENTAL SCIENCES EUROPE 2024年第1期36卷 20-20页

作者： Jamshidzadeh, Zahra Latif, Sarmad Dashti Ehteram, Mohammad Khozani, Zohreh Sheikh Ahmed, Ali Najah Sherif, Mohsen El-Shafie, Ahmed Univ Kashan Dept Civil Engn Kashan Iran Soran Univ Sci Res Ctr Erbil Kurdistan Regio Iraq Komar Univ Sci & Technol Coll Engn Civil Engn Dept Sulaimany Kurdistan Regio Iraq Semnan Univ Dept Water Engn Semnan Iran Alfred Wegener Inst Paleoclimate Dynam Grp Helmholtz Ctr Polar & Marine Res D-27570 Bremerhaven Germany Sunway Univ Sch Engn & Technol Petaling Jaya 47500 Malaysia United Arab Emirates Univ Civil & Environm Engn Dept Coll Engn Al Ain 15551 U Arab Emirates Univ Malaya UM Dept Civil Engn Fac Engn Kuala Lumpur 50603 Malaysia

For more than one billion people living in coastal regions, coastal aquifers provide a water resource. In coastal regions, monitoring water quality is an important issue for policymakers. Many studies mentioned that most of the conventional models were not accurate for predicting total dissolved solids (TDS) and electrical conductivity (EC) in coastal aquifers. Therefore, it is crucial to develop an accurate model for forecasting TDS and EC as two main parameters for water quality. Hence, in this study, a new hybrid deep learning model is presented based on Convolutional Neural Networks (CNNE), Long Short-Term Memory Neural Networks (LOST), and Gaussian Process Regression (GPRE) models. The objective of this study will contribute to the sustainable development goal (SDG) 6 of the united nation program which aims to guarantee universal access to clean water and proper sanitation. The new model can obtain point and interval predictions simultaneously. Additionally, features of data points can be extracted automatically. In the first step, the CNNE model automatically extracted features. Afterward, the outputs of CNNE were flattened. The LOST used flattened arrays for the point prediction. Finally, the outputs of the GPRE model receives the outputs of the LOST model to obtain the interval prediction. The model parameters were adjusted using the rat swarm optimization algorithm (ROSA). This study used PH, Ca + + , Mg2 + , Na + , K + , HCO3, SO4, and Cl- to predict EC and TDS in a coastal aquifer. For predicting EC, the CNNE-LOST-GPRE, LOST-GPRE, CNNE-GPRE, CNNE-LOST, LOST, and CNNE models achieved NSE values of 0.96, 0.95, 0.92, 0.91, 0.90, and 0.87, respectively. Sodium adsorption ratio, EC, magnesium hazard ratio, sodium percentage, and total hardness indices were used to evaluate the quality of GWL. These indices indicated poor groundwater quality in the aquifer. This study shows that the CNNE-LOST-GPRE is a reliable model for predicting complex phenomena. Therefore,

关键词： Water quality Sustainable development goal (SDG) Deep learning optimization algorithms Point prediction Interval prediction

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Prediction of one- and three-months yoga practices effect on chronic venous insufficiency based on machine learning classifiers

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EGYPTIAN INFORMATICS JOURNAL 2024年 27卷

作者： Han, Xue Hu, Nan Weifang Univ Coll Phys Educ Weifang 261061 Shandong Peoples R China Baicheng Normal Univ Coll Phys Educ Baicheng 137000 Jilin Peoples R China

The rise of technology has heightened work demands, adversely impacting mental health and fitness. The COVID-19 pandemic exacerbates psychological stress, emphasizing the need for non-pharmacological interventions like yoga. Yoga positively influences the autonomic nervous system, benefiting cardio-respiratory health, metabolic efficiency, and conditions like Type-2 diabetes, Chronic Venous disease, and obesity. This study employs a dataset with 100 samples and 43 features related to Chronic Venous Insufficiency (CVI). Logistic and Random Forest classifiers are validated using K-fold cross-validation, with feature selection optimizing prediction accuracy. Hybrid models, enhanced with optimization algorithms, predict Venous Clinical Severity Score (VCSS) before, one, and three months after yoga practices. The Random Forest classifier, particularly RFGT, proves highly accurate in categorizing baseline severity and identifying Mild and Moderate CVI cases. RFGT demonstrated AUC score of 0.9072, 0.8714, 0.7709, and 0.7200 in Absent, Mild, Moderate, and Severe patient groups classification before yoga practices (VCSS-Pre). These values were 0.9158, 0.8644, 0.8142, and 0.6333 for VCSS-1 and reported as 0.9269, 0.8399, 0.7838, and 0.7500 for patients' classification in VCSS-3. Predicting VCSS scores before yoga intervention assists in categorizing participants for personalized care and efficient resource allocation. The RFC-based models, notably RFGT, show high accuracy in identifying baseline severity, enabling early intervention for high-risk individuals. These models, especially RFGT, perform well in classifying Mild and Moderate CVI cases, informing lifestyle modifications. Predicting VCSS-1 scores evaluates the short-term impact of yoga practices, identifying individuals requiring additional support. RFGT aids in personalized recommendations based on specific factors, crucial for severe conditions. Predicting VCSS-3 scores assesses the sustained impact over three month

关键词： Chronic venous insufficiency Cross validation Univariate selection Correlation matrix Classification methods optimization algorithms

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Optimizing the load curve through V2G technology for sustainable energy management

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ENERGY REPORTS 2025年 13卷 5848-5863页

作者： Majed Roy, Naruttam Kumar Ahmed, Anis Khulna Univ Engn & Technol Dept Elect & Elect Engn Khulna 9203 Bangladesh

This article investigates the potential of Electric Vehicles (EVs) to optimize load curves through Demand Side Management (DSM) by utilizing bidirectional charging capabilities enabled by Vehicle-to-Grid (V2G) technology. The primary objective is to decrease peak demand, reduce load curve fluctuations, and improve grid stability by strategically managing diversified EV charging and discharging landscapes based on their respective State-ofCharge. The simulation results show that utilizing Particle Swarm optimization (PSO) and Genetic Algorithm (GA) for DSM reduces the peak and base load variation by 43.52 % and 62.75 % and maximum load fluctuation by 38.85 % and 42.67 %, respectively, for bidirectional charging compared to unidirectional charging. Furthermore, our proposed EV-based DSM strategy was contrasted with the prevailing case that considers the existing vehicles adopting V2G technology. In our proposed illustration using GA and PSO, the reduction percentage for the peak and base load difference becomes 41.06 % and 2.91 % and the maximum load fluctuation reduces to around 48.28 % and 7.13 %, respectively, compared to the current scenario. The seasonal load variations have also been simulated for our proposed strategy to validate the work. The analysis suggests that the widespread use of EVs in demand response strategies could lead to a sustainable energy future in Bangladesh and globally.

关键词： Electric vehicles Demand side management optimization algorithms Genetic algorithm Particle swarm optimization Bidirectional charging State-of-charge

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Meta-heuristics optimization in electric vehicles-an extensive review

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RENEWABLE & SUSTAINABLE ENERGY REVIEWS 2022年第0期160卷 112285-112285页

作者： Reddy, Aala Kalananda Vamsi Krishna Narayana, Komanapalli Venkata Lakshmi Vellore Inst Technol Sch Elect Engn Vellore 632014 Tamil Nadu India

optimization through meta-heuristics in electric vehicular (EV) transport has emerged as the key to improve the existing technologies and pave way for their mass deployment and revolutionize the current transport system while lowering greenhouse emissions. Range and cost have been the main aspects that continue to hinder the development of EVs. This paper provides a comprehensive outlook of the five major areas of optimization (over the last two decades) in EVs i.e., design optimization, energy management, optimal control, optimized charging/ discharging and routing with both single and multi-objective methods examined and analyzed. The mathematical modelling, formulation of objective functions and constraints are studied followed by an in-depth survey of the state-of-the-art publications in each category. Furthermore, a classification of the various analytical, conventional and nature-inspired optimization algorithms (swarm-intelligent, evolutionary and modern meta heuristics) based on their popularity is made with an analysis of their merits and demerits followed by a study of the various constraint handling techniques. Additionally, a literature survey of the advanced and improved variants of these meta-heuristics is also made providing a systematic reference for EV optimization with intelligent algorithms. Finally, a classification of the various simulatory platforms and driving cycles is provided to help the upcoming researchers gain insightfulness and expertise on the ongoing trends in the EV optimization domain.

关键词： Electric vehicles optimization algorithms Meta-heuristics Design optimization Energy management Control optimization Charging discharging optimization Route optimization

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Control Co-Design With Performance-Robustness Trade-Off Using Tube-Based Stochastic Model Predictive Control

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ASME Letters in Dynamic Systems and Control 2025年第3期5卷 031003页

作者： Tsai, Ying-Kuan Malak, Jr., Richard J. Department of Mechanical Engineering Northwestern University Evanston IL 60208 Department of Mechanical Engineering Texas A&M University College Station TX 77843

Control co-design (CCD) has been demonstrated to achieve superior solutions for closed-loop systems. However, limited work has addressed CCD problems under probabilistic disturbances. This article addresses this gap by formulating a finite-horizon optimal control problem with chance constraints and proposing a novel CCD approach. This approach integrates tube-based stochastic model predictive control with constraint-tightening techniques to optimize performance and robustness while preventing instability and infeasibility. A nested CCD framework is introduced, along with a constrained multi-objective optimization algorithm that enables the performance-robustness trade-off. A method for quantifying the robustness of closed-loop systems under stochastic disturbances is presented. The proposed CCD approach is demonstrated on a numerical example and an engineering case of the satellite attitude control system. Results show that CCD can generate more well-spread Pareto fronts that cannot be reached by other design strategies. This helps designers explore more potential solutions with different dynamic characteristics. Selected nondominated solution trajectories are visualized for qualitative comparisons. Future work will extend this to nonlinear applications.

关键词： Satellites Robustness Design constrained control Predictive control Pareto optimization predictive control for linear systems control co-design multi-objective optimization linear systems Tradeoffs optimization algorithms stochastic systems model predictive control algorithms uncertain systems

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optimization and Its Implementation Impact of Two-Modes Controller Fractional Approximation for Buck Converters

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MICROMACHINES 2022年第10期13卷 1600-1600页

作者： Sanchez, Allan G. S. Perez-Pinal, Francisco-Javier Espinosa-Calderon, Alejandro CONACYT Tecnol Nacl Mexico Celaya Celaya 38010 Mexico Tecnol Nacl Mexico Celaya Celaya 38010 Mexico Tecnol Nacl Mexico Reg Ctr Optimizat & Device Dev CRODE Celaya 38020 Mexico

Additional degrees of freedom in a fractional-order control strategy for power electronic converters are well received despite the lack of reliable tuning methods. Despite artificial/swarm intelligence techniques have been used to adjust controller parameters to improve more than one characteristic/property at the same time, smart tuning not always leads to realizable structures or reachable parameter values. Thus, adjustment boundaries to ensure controller viability are needed. In this manuscript the fractional-order approach is described in terms of El-Khazali biquadratic module, which produces the lowest order approximation, instead of using a definition. A two-modes controller structure is synthesize depending on uncontrolled plant needs and parameters are adjusted through particle swarm and genetic optimization algorithms for comparison. Two error-based minimization criteria are used to consider output performance into the process. Two restrictions complement the optimization scheme, one seeks to ensure desired robustness while the other prevents from synthesizing a high-gain controller. optimization results showed similarity between minima obtained and significant difference between parameters of those controller optimized without the proposed constraints was determined. Numerical and experimental results are provide to validate proposed approach effectiveness. Effective regulation, good tracking characteristic and robustness in the presence of load variations are the main results.

关键词： fractional control DC-DC converters optimization algorithms particle swarm algorithm fractional-order implementation

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A Comprehensive Review of Microgrid Energy Management Strategies Considering Electric Vehicles, Energy Storage Systems, and AI Techniques

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PROCESSES 2024年第2期12卷 270页

作者： Khan, Muhammad Raheel Haider, Zunaib Maqsood Malik, Farhan Hameed Almasoudi, Fahad M. Alatawi, Khaled Saleem S. Bhutta, Muhammad Shoaib Islamia Univ Bahawalpur Dept Elect Engn Bahawalpur 63100 Pakistan Abu Dhabi Polytech Dept Electromech Engn Abu Dhabi 13232 U Arab Emirates Univ Tabuk Fac Engn Dept Elect Engn Tabuk 47913 Saudi Arabia Guilin Univ Aerosp Technol Sch Automobile Engn Guilin 541004 Peoples R China

The relentlessly depleting fossil-fuel-based energy resources worldwide have forbidden an imminent energy crisis that could severely impact the general population. This dire situation calls for the immediate exploitation of renewable energy resources to redress the balance between power consumption and generation. This manuscript confers about energy management tactics to optimize the methods of power production and consumption. Furthermore, this paper also discusses the solutions to enhance the reliability of the electrical power system. In order to elucidate the enhanced reliability of the electrical system, microgrids consisting of different energy resources, load types, and optimization techniques are comprehensively analyzed to explore the significance of energy management systems (EMSs) and demand response strategies. Subsequently, this paper discusses the role of EMS for the proper consumption of electrical power considering the advent of electric vehicles (EVs) in the energy market. The main reason to integrate EVs is the growing hazards of climate change due to carbon emissions. Moreover, this paper sheds light on the growing importance of artificial intelligence (AI) in the technological realm and its incorporation into electrical systems with the notion of strengthening existing smart grid technologies and to handle the uncertainties in load management. This paper also delineates the different methodologies to effectively mitigate the probability of facing cyber-attacks and to make the smart grids invulnerable.

关键词： energy storage system demand-side management renewable energy resources microgrid smart grid optimization algorithms electric vehicles artificial intelligence

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Techno-economic optimization of a hybrid energy system with limited grid connection in pursuit of net zero carbon emissions for New Zealand

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e-Prime - Advances in Electrical Engineering, Electronics and Energy 2024年 8卷

作者： Hill, Daniel Tito, Shafiqur Rahman Walmsley, Michael Hedengren, John Department of Chemical Engineering Brigham Young University Provo 84602 UT United States Ahuora Centre for Smart Energy Systems University of Waikato Hamilton 3240 New Zealand

As New Zealand moves towards net zero carbon emissions by 2050, multiple factors must be considered including increased electrical load due to electrification, variability of renewable energy generators, required storage capacity, system economics and limitations on grid transmission capacity. Complex and region-specific interactions between the various design choices involved are likely to require an understanding of a range of optimal and near-optimal designs for proposed micro-grid systems as opposed to a single optimal point. This work develops a novel multiscale optimization algorithm for optimization from a univariate capacity optimization approach to a multivariate one. This enhanced algorithm is applied to a grid-connected hybrid energy system consisting of local wind and solar generation, battery storage, and a limited grid connection for industrial and residential loads. This analysis is repeated for current 2023 and forecasted net zero 2050 grid conditions. Development of local generation allows for a 36.8% reduction of levelized cost of electricity in the 2023 case and a 38.6% reduction in the 2050 case. This results in a projected reduction of 19920 tonnes of CO₂/yr. The algorithm and methodology developed are broadly applicable to optimization of next-generation energy grids. © 2024 The Author(s)

关键词： Design optimization Dispatch optimization Hybrid energy systems optimization algorithms

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Hybrid whale optimization algorithm-Levy flight approach for multilevel thresholding image segmentation

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JOURNAL OF ELECTRONIC IMAGING 2022年第5期31卷 051420-051420页

作者： Shivahare, Basu Dev Gupta, Sanjai Kumar Dr APJ Abdul Kalam Tech Univ Dept Comp Sci & Engn Lucknow Uttar Pradesh India Bundelkhand Inst Engn & Technol Jhansi Dept Comp Sci & Engn Jhansi Uttar Pradesh India

Determining the optimal threshold for multilevel image segmentation is a stimulating task. Image segmentation has many applications, such as content-based image retrieval, medical imaging, object detection, object recognition, machine vision, etc. In multilevel thresholding, multiple thresholds are used to segment complex images, and the images are segmented into multiple levels to extract meaningful information for further image analysis. Where threshold number is small, p-tile, Otsu, moment preserving, and entropy thresholding methods attain good accuracy. These classical thresholding methods are time-consuming, computation expensive, and unable to produce good segmentation accuracy with increased threshold numbers. To overcome these problems, the classical thresholding methods are utilized as objective functions with nature-inspired metaheuristic algorithms such as whale optimization algorithm (WOA), simplified swarm optimization (SSO), sine cosine algorithm (SCA), BAT algorithm, WOA-thresholding, and black widow optimization (BWO) to determine optimal multiple thresholds. Nature-inspired metaheuristic algorithms are widely used to search for an optimal solution for global optimization problems. WOA is widely used to find the optimal solution in search space. WOA suffers from entrapment into local optima due to premature convergence behavior. We introduce hybridization of WOA with Levy flight trajectory and named as hybrid whale optimization algorithm-Levy flight (HWOAL), which is utilized to find optimal multiple thresholds for multilevel image segmentation. Levy flight trajectory is utilized to increase diversity in the swarm population. The efficacy of HWOAL is tested on 23 benchmark optimization functions (F1 to F23) and compared with WOA, SSO, SCA, and BAT algorithms. Experiment arms that HWOAL is efficient and makes WOA faster, enhances the ability of exploitation and exploration phase, and can avoid getting stuck into local optima. The segmentation perform

关键词： black widow optimization differential evolution Levy flight multilevel image segmentation optimization algorithms whale optimization algorithm

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Distributed optimization of Clique-Wise Coupled Problems via Three-Operator Splitting

arXiv

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arXiv 2023年

作者： Watanabe, Yuto Sakurama, Kazunori Department of Electrical and Computer Engineering University of California San Diego San DiegoCA92093 United States Department of System Innovation Graduate School of Engineering Science Osaka University 1-3 Machikaneyama Toyonaka Osaka560-8531 Japan

This study explores distributed optimization problems with clique-wise coupling via operator splitting and how we can utilize this framework for performance analysis and enhancement. This framework extends beyond conventional pairwise coupled problems (e.g., consensus optimization) and is applicable to broader examples. To this end, we first introduce a new distributed optimization algorithm by leveraging a clique-based matrix and the Davis-Yin splitting (DYS), a versatile three-operator splitting method. We then demonstrate that this approach sheds new light on conventional algorithms in the following way: (i) Existing algorithms (NIDS, Exact diffusion, diffusion, and our previous work) can be derived from our proposed method;(ii) We present a new mixing matrix based on clique-wise coupling, which surfaces when deriving the NIDS. We prove its preferable distribution of eigenvalues, enabling fast consensus;(iii) These observations yield a new linear convergence rate for the NIDS with non-smooth objective functions. Remarkably our linear rate is first established for the general DYS with a projection for a subspace. This case is not covered by any prior results, to our knowledge. Finally, numerical examples showcase the efficacy of our proposed approach. Copyright © 2023, The Authors. All rights reserved.

关键词： optimization algorithms

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