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Multi-Objective Optimization of Wavelet-Packet-Based Features in Pathological Diagnosis of Alzheimer Using Spontaneous Speech Signals

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IEEE ACCESS 2020年 8卷 112393-112406页

作者： Nasrolahzadeh, Mahda Haddadnia, Javad Rahnamayan, Shahryar Hakim Sabzevari Univ Dept Biomed Engn Sabzevar *** Iran Ontario Tech Univ Dept Elect Comp & Software Engn Oshawa ON L1G 0C5 Canada

Alzheimer's disease (AD) ranks among the main types of neurodegenerative disorders. Patients suffering AD should tackle serious problems since their language skills malfunction. The impact of such disorders is reflected by reduced quality and feature variation of spontaneous speech signals in speech analysis. This paper aims at assessing the variations of some specific types of these energy- and entropy-based features within the frequency range of the speech signals. In the approach followed, the wavelet-packet coefficients are utilized to extract the energy and entropy measures at every spectral sub-band in six successive levels of decomposition. However, the decomposition process conducts a set of high-dimensional feature vectors that is a challenging task for feature selection. This study suggests the application of a non-dominated sorting genetic algorithm-ii (NSGA-ii) for enhancing a group of the sub-band indexes of a wavelet-packet for which the extracted features lead to the highest diagnosis rate of the grouping of Alzheimer's and healthy individuals. The technique proposed here showed that the best overall classification results for both optimized entropy feature vs. energy are more noticeable in discriminating patients with AD from healthy subjects. It is also confirmed the significant impact of multi-objective feature selection on performance of classification (i.e., disease diagnosis) and, its conformity to the disordered nature of the biological signals could help diagnose AD in an efficient manner.

关键词： Alzheimer's disease Feature extraction Wavelet packets Entropy Pathology Alzheimer's disease spontaneous speech signal wavelet packet multi-objective optimization feature selection non-dominated sorting genetic algorithm-ii

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Multi-objective waste load allocation in river system under inflow uncertainty

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INTERNATIONAL JOURNAL OF ENVIRONMENTAL SCIENCE AND TECHNOLOGY 2021年第6期18卷 1549-1560页

作者： Farjoudi, S. Zare Moridi, A. Sarang, A. Shahid Beheshti Univ Dept Civil Water & Environm Engn Tehran Iran Univ Tehran Fac Environm Tehran Iran Univ British Columbia Dept Civil Engn Vancouver BC Canada

Discharging excessive pollution into rivers that exceeds their self-purification capacity decreases the quality of water and threatens the aquatic ecosystem. In such water systems, polluters and environmental protection agencies are involved parties that often have opposite interests. This research proposes a method to enhance the water quality of rivers while satisfying the interests of both parties. It allocates waste loads to polluters and requires them to pay the treatment cost in order to remove pollution. The proposed methodology employs a combination of QUAL2Kw and non-dominated sorting genetic algorithm-ii to minimize wastewater treatment costs and dissolved oxygen violation from the standard level. The river inflow uncertainty is considered by Latin hypercube sampling to give more real insights to decision makers through a stochastic approach. As a result, the Pareto sets act as strategies that can be used to meet the objectives, but they show the contradiction between parties' interests. According to the waste load criterion, this methodology reduces waste load from 145.5 to 79, 107.15 and 115 units for dry, normal and wet months. Also, it decreases treatment cost from the range of [160,000-180,000 $] to [100,000-130,000 $] considering the water quality and dischargers' interest. According to the results, considering river inflow uncertainty can increase the concentration of dissolved oxygen even more than the standard level, and point sources treat their wastewater more than non-point ones. Generally, it is a suitable tool to find solutions for minimizing the treatment cost in favor of dischargers and improving water quality.

关键词： Latin hypercube sampling non-dominated sorting genetic algorithm-ii River water quality management Waste load allocation

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Multi-objective exergy-based optimization of continuous glycerol ketalization to synthesize solketal as a biodiesel additive in subcritical acetone

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ENERGY CONVERSION AND MANAGEMENT 2018年第Mar.期160卷 251-261页

作者： Aghbashlo, Mortaza Tabatabaei, Meisam Hosseinpour, Soleiman Rastegari, Hajar Ghaziaskar, Hassan S. Univ Tehran Fac Agr Engn & Technol Dept Mech Engn Agr Machinery Coll Agr & Nat Resources Karaj Iran AREEO ABRII Microbial Biotechnol Dept POB 31535-1897 Karaj Iran Biofuel Res Team BRTeam Karaj Iran Isfahan Univ Technol Dept Chem Esfahan Iran

This study was aimed at exergetically investigating and optimizing a continuous reactor applied to valorize glycerol into solketal as a biodiesel additive with subcritical acetone in the presence of Purolite PD206. The effects of reaction temperature (20-100 degrees C), acetone to glycerol molar ratio (15), feed flow rate (0.10.5 mL/min), pressure (1120 bar), and catalyst mass (0.52.5 g) were evaluated on the exergetic performance parameters of the reactor. In order to optimize the operating conditions of the reactor, adaptive neuro-fuzzy inference system (ANFIS) was coupled with non-dominated sorting genetic algorithm-ii (NSGA-ii). The ANFIS was applied to develop objective functions on the basis of the process parameters. The developed objective functions were then fed into the NSGA-ii to find the optimum operating conditions of the process by simultaneously maximizing universal and functional exergetic efficiencies and minimizing normalized exergy destruction. Overall, the process parameters significantly affected the exergetic performance of the reactor. The ANFIS approach successfully modeled the objective functions with a correlation coefficient higher than 0.99. The optimal ketalization conditions of glycerol were: reaction temperature = 40.66 degrees C, acetone to glycerol molar ratio = 4.97, feed flow rate = 0.49 mL/min, pressure = 42.31?bar, and catalyst mass = 0.50 g. These conditions could be applied in pilot- or industrial-scale reactors for converting glycerol into value-added solketal in a resource-efficient, cost-effective, and environmentally-friendly manner.

关键词： Adaptive neuro-fuzzy inference system Biodiesel additive Exergy analysis Glycerol conversion non-dominated sorting genetic algorithm-ii Solketal synthesis

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Portfolio selection of distributed energy generation projects considering uncertainty and project interaction under different enterprise strategic scenarios

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APPLIED ENERGY 2019年 236卷 444-464页

作者： Wu, Yunna Xu, Chuanbo Ke, Yiming Li, Xinying Li, Lingwenying North China Elect Power Univ Sch Econ & Management Beijing Peoples R China North China Elect Power Univ Beijing Key Lab New Energy & Low Carbon Beijing 102206 Peoples R China

Selecting a rational distributed energy generation (DEG) project portfolio is the key to achieving the strategic objectives of energy enterprises. The complexity associated with the selection of DEG project portfolio comes from uncertainties in decision-making environment, interactions between projects, and necessary alignment with the strategic objectives of enterprises. However, previous researches did not address the three issues simultaneously. To fill such gap, this study establishes a multi-criteria decision-making framework to select the optimal DEG project portfolio(s) under different strategic scenarios, where uncertainty and project interaction are considered. The framework consists of two stages. In the first phase, the weights of criteria are determined by the interval type-2 fuzzy analytic hierarchy process technique, and the strategic alignment indexes of each candidate distributed energy generation project are obtained using the interval type-2 fuzzy weighted averaging operator. In the second stage, considering the strategic interactions, a nonlinear 0-1 programming is formulated while satisfying the budget constraints, and the non-dominated sorting genetic algorithm-ii is utilized to obtain the optimal portfolio of DEG projects under different strategic scenarios. The proposed framework is applied in a case study to illustrate its suitability and effectiveness. The results show that the selected portfolios vary with the strategic objectives of enterprises. This research has practical applied value for project managers in project portfolio management.

关键词： Distributed energy source Project portfolio Multi-criteria decision-making Interval type-2 fuzzy analytic hierarchy process Interval type-2 fuzzy weighted averaging non-dominated sorting genetic algorithm-ii

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Joint Power and 3D Trajectory Optimization for UAV-Enabled Wireless Powered Communication Networks With Obstacles

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IEEE TRANSACTIONS ON COMMUNICATIONS 2023年第4期71卷 2364-2380页

作者： Pan, Hongyang Liu, Yanheng Sun, Geng Fan, Junsong Liang, Shuang Yuen, Chau Jilin Univ Coll Comp Sci & Technol Changchun 130012 Peoples R China Singapore Univ Technol & Design Engn Prod Dev EPD Pillar Singapore 487372 Singapore Jilin Univ Key Lab Symbol Computat & Knowledge Engn Minist Educ Changchun 130012 Peoples R China Northeast Normal Univ Sch Informat Sci & Technol Changchun 130024 Peoples R China

Unmanned aerial vehicle (UAV)-enabled wireless powered communication networks (WPCNs) are promising technologies in 5G/6G wireless communications, while there are several challenges about UAV power allocation and scheduling to enhance the energy utilization efficiency, considering the existence of obstacles. In this work, we consider a UAV-enabled WPCN scenario that a UAV needs to cover the ground wireless devices (WDs). During the coverage process, the UAV needs to collect data from the WDs and charge them simultaneously. To this end, we formulate a joint-UAV power and three-dimensional (3D) trajectory optimization problem (JUPTTOP) to simultaneously increase the total number of the covered WDs, increase the time efficiency, and reduce the total flying distance of UAV so as to improve the energy utilization efficiency in the network. Due to the difficulties and complexities, we decompose it into two sub optimization problems, which are the UAV power allocation optimization problem (UPAOP) and UAV 3D trajectory optimization problem (UTTOP), respectively. Then, we propose an improved non-dominated sorting genetic algorithm-ii with K-means initialization operator and Variable dimension mechanism (NSGA-ii-KV) for solving the UPAOP. For UTTOP, we first introduce a pretreatment method, and then use an improved particle swarm optimization with Normal distribution initialization, genetic mechanism, Differential mechanism and Pursuit operator (PSO-NGDP) to deal with this sub optimiza-tion problem. Simulation results verify the effectiveness of the proposed strategies under different scales and settings of the networks.

关键词： Wireless powered communication networks unmanned aerial vehicle energy consumption non-dominated sorting genetic algorithm-ii particle swarm optimization.

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Multi-objective exergoeconomic and exergoenvironmental optimization of continuous synthesis of solketal through glycerol ketalization with acetone in the presence of ethanol as co-solvent

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RENEWABLE ENERGY 2019年第Jan.期130卷 735-748页

作者： Aghbashlo, Mortaza Hosseinpour, Soleiman Tabatabaei, Meisam Rastegari, Hajar Ghaziaskar, Hassan S. Univ Tehran Coll Agr & Nat Resources Fac Agr Engn & Technol Dept Mech Engn Agr Machinery Karaj Iran AREEO ABRII Microbial Biotechnol Dept POB 31535-1897 Karaj Iran Biofuel Res Team BRTeam Karaj Iran Isfahan Univ Technol Dept Chem Esfahan Iran

This work was aimed at conducting a multi-objective exergoeconomic and exergoenvironmental optimization of continuous synthesis of solketal through glycerol ketalization with acetone in the presence of ethanol as co-solvent and Purolite PD206 as catalyst. Exergoeconomic and exergoenvironmental performance parameters of the reactor were computed and discussed comprehensively after writing and solving their balance equations based on the experimental data. The effects of process parameters viz. ketalization temperature (T), acetone/glycerol molar ratio (X), feed flow rate (F), reaction pressure (P), and catalyst quantity (C) on the exergy-based variables were investigated in detail. The optimization process was performed based on minimizing two more important exergetic parameters, i.e., cost and environmental per unit of exergy for the product. To this end, an elaborated coupled version of adaptive neuro-fuzzy inference system (ANFIS) and non-dominated sorting genetic algorithm-ii (NSGA-ii) was employed. The ANFIS approach was used for modeling the process, while the NSGA-ii was applied for finding the optimum operating conditions of the reactor. According to the results obtained, the ANFIS approach successfully predicted both objective parameters with an R-2 higher than 0.99. The optimum ketalization conditions for solketal synthesis in the developed reactor corresponded to T = 35.1 degrees C, X = 4.5, F = 0.4 mL/min, P = 26.7 bar, and C = 2.2 g, leading to the cost and environmental impact per unit of exergy for the product of 5032.9 USD/GJ and 143.9 mPts/GJ, respectively. (C) 2018 Elsevier Ltd. All rights reserved.

关键词： Adaptive neuro-fuzzy inference system Continuous glycerol ketalization Cost and environmental per unit of exergy for the product Exergoeconomic and exergoenvironmental analyses non-dominated sorting genetic algorithm-ii Solketal synthesis

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Boosting hydropower generation of mixed reservoirs for reducing carbon emissions by using a simulation-optimization framework

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HYDROLOGY RESEARCH 2024年第2期55卷 144-160页

作者： He, Yanfeng Guo, Shenglian Zhou, Yanlai Zhu, Di Chen, Hua Xiong, Lihua Liu, Jie Xu, Chong-Yu Wuhan Univ State Key Lab Water Resources Engn & Management Wuhan 430072 Peoples R China Power China Chengdu Engn Corp Ltd Chengdu 610072 Peoples R China Technol Innovat Ctr Hydropower Wind Solar & Energy Chengdu 610072 Peoples R China Chengdu Univ Sch Architecture & Civil Engn Chengdu 610106 Peoples R China Univ Oslo Dept Geosci POB 1047 Blindern N-0316 Oslo Norway

The optimization operation of reservoir seasonal flood-limited water levels (FLWLs) can counterbalance hydropower generation and flood prevention in the flood season. This study proposes a multi-objective optimization operation model to optimize the reservoir seasonal FLWLs for synergistically maximizing hydropower benefits and reducing flood risk. The mixed reservoirs composed of the Three Gorges Reservoir and six other reservoirs located in the upstream Yangtze River of China constitute the case study. The results showed that (1) compared with the annual FLWL scheme, the best solutions without lowering flood prevention standards could increase the seasonal FLWL values of the seven reservoirs from 1.39 to 6.51 m in the pre-flood season, from 0.13 to 1.53 m in the main-flood season, and from 0.76 to 4.36 m in the post-flood season;(2) the proposed seasonal FLWL schemes without increasing flood prevention risk could facilitate the joint operation of the mixed reservoirs to achieve 868 million kW h (5.1% improvement) in average hydroelectricity production during the flood season, meanwhile reducing 681 million kg CO2 emissions accordingly. The results support that the proposed methods can boost hydropower production to benefit China's national tactics in accomplishing peak carbon dioxide emissions before 2030.

关键词： hydropower sustainability multi-objective optimization non-dominated sorting genetic algorithm-ii reservoir operation Yangtze River

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Multi-objective optimization based on hyperparameter random forest regression for linear motor design

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INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS 2022年第10期13卷 2929-2942页

作者： Wen, Cheng Zhao, Qiankai Li, Mingye Liu, Jingna Li, Mingwei Zhao, Xingqiao Shijiazhuang Tiedao Univ Sch Elect & Elect Shijiazhuang 050043 Hebei Peoples R China Hebei Polytech Inst Sch Network & Commun Shijiazhuang 050091 Hebei Peoples R China

Existing method of multi-objective optimization for linear motors provides poor consideration for model robustness, thus, the models of linear motor which are implemented to multi-objective optimization may not be optimal. A multi-objective optimization design combined Random Forest after hyperparameter optimization and non-dominated sorting genetic algorithm-ii (NSGA-ii) for Double-sided Linear Flux Switching Permanent Magnet motor (DLFSPMs) is proposed. The average thrust and the thrust ripple which generated by operation of DLFSPMs are selected as objectives. A machine learning algorithm, Random Forest (RF), is introduced to establish the regression models between structural parameters and performances of DLFSPMs. Furthermore, in order to improve the stability and accuracy of the regression models, a hyperparameter optimization, which is called Bayesian Optimization and HyperBand (BOHB), is proposed to search for the best hyperparametric configuration to obtain predicted performance of DLFSPMs. Moreover, the proposed BOHB-RF model is compared with the Bayesian optimization-RF (BO-RF) model and the HyperBand-RF (HB-RF) model to verify the advantages of BOHB-RF model. Then, NSGA-ii is adopted to design for multi-objective optimization of DLFSPMs to calculate Pareto front of DLFSPMs performances based on BOHB-RF models. Finally, the results of finite element analysis (FEA) prove the effectiveness and feasibility for proposed modeling and optimizing method.

关键词： Linear motor Multi-objective optimization Random forest Bayesian optimization and HyperBand non-dominated sorting genetic algorithm-ii

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A novel laparoscopic surgical robot (LSR) based on double-leg ultrasonic motor (DUM)

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JOURNAL OF INTELLIGENT MATERIAL SYSTEMS AND STRUCTURES 2023年第11期34卷 1276-1288页

作者： Lu, Qing Sun, Zhijun Yan, He Zhang, Jiacheng Zhang, Jialiang Yang, Jianlin Nanjing Univ Aeronaut & Astronaut State Key Lab Mech & Control Mech Struct 29 Yudao St Nanjing 210016 Peoples R China

Magnetic anchor and guidance system has been widely used in laparoendoscopic single-site surgery with the benefit of small wound, wide field of vision (FoV), and convenience for repeated positioning. This paper introduces a novel three DoF laparoscopic surgical robot (LSR) based on double-leg ultrasonic motor (DUM) which features small dimension, fast response, and high positional precision. Piezoelectric ceramic (PZT) plates can excite longitudinal and bending coupled vibration of DUM and the two phase in-plane three order bending vibration can form traveling wave to drive DUM rotor. DUM stator is optimized by non-dominated sorting genetic algorithm-ii for good consistency of two working mode frequencies and negligible influence of adjacent interference modes on working modes, which improves motion stability and driving efficiency. The mechanical characteristic of DUM under different chamfer parameters has been studied through comparative experiments and the design of robot prototype is completed further. For clockwise and counterclockwise rotation, the maximum no-load rotary speeds are 333.75 and 335.77 rpm, and the maximum output torques are 2 and 1.6 N mm. The experimental platform for FoV measurement is built to verify that LSR can adjust the posture to obtain suitable surgical FoV, indicating that the application of DUM in LSR has significant advantages and great potential.

关键词： Laparoscopic surgical robot double-leg ultrasonic motor longitudinal and bending coupled vibration in-plane traveling wave non-dominated sorting genetic algorithm-ii

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Proposal and Comparative Study of Evolutionary algorithms for Optimum Design of a Gear System

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IEEE ACCESS 2020年 8卷 3482-3497页

作者： Mendez, Maximo Rossit, Daniel A. Gonzalez, Begona Frutos, Mariano Univ Las Palmas de Gran Canaria ULPGC Inst Univ SIANI Las Palmas Gran Canaria 35001 Spain Univ Nacl Sur Dept Engn RA-8000 Bahia Blanca Buenos Aires Argentina INMABB CONICET RA-8000 Bahia Blanca Buenos Aires Argentina IIESS CONICET RA-8002 Bahia Blanca Buenos Aires Argentina

This paper proposes a novel metaheuristic framework using a Differential Evolution (DE) algorithm with the non-dominated sorting genetic algorithm-ii (NSGA-ii). Both algorithms are combined employing a collaborative strategy with sequential execution, which is called DE-NSGA-ii. The DE-NSGA-ii takes advantage of the exploration abilities of the multi-objective evolutionary algorithms strengthened with the ability to search global mono-objective optimum of DE, that enhances the capability of finding those extreme solutions of Pareto Optimal Front (POF) difficult to achieve. Numerous experiments and performance comparisons between different evolutionary algorithms were performed on a referent problem for the mono-objective and multi-objective literature, which consists of the design of a double reduction gear train. A preliminary study of the problem, solved in an exhaustive way, discovers the low density of solutions in the vicinity of the optimal solution (mono-objective case) as well as in some areas of the POF of potential interest to a decision maker (multi-objective case). This characteristic of the problem would explain the considerable difficulties for its resolution when exact methods and/or metaheuristics are used, especially in the multi-objective case. However, the DE-NSGA-ii framework exceeds these difficulties and obtains the whole POF which significantly improves the few previous multi-objective studies.

关键词： Differential evolution evolutionary computation gear train optimization genetic algorithms mechanical engineering multi-objective evolutionary algorithms non-dominated sorting genetic algorithm-ii

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