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TLBO optimization algorithm Based-Type2 Fuzzy Adaptive Filter for ECG Signals Denoising

TRAITEMENT DU SIGNAL 2020年第4期37卷 541-553页

作者： Ouali, Mohammed Assam Ghanai, Mouna Chafaa, Kheireddine Univ Msila Fac Technol Elect Dept LAAS Lab Msila 28000 Algeria Caen Univ GREYC Lab F-14000 Caen France Univ Batna 2 Elect Dept LAAAS Lab Fac Technol Batna 5000 Algeria

A novel type2-fuzzy adaptive filter is presented, which uses the concepts of type2-fuzzy logic, for electrocardiogram signals denoising. Type2-fuzzy adaptive filter is an information processor where both numerical and linguistic information are used as input-output pairs and fuzzy if-then rules, respectively. The proposed approach is based on an iterative procedure to achieve acceptable information extraction in the case where the statistical characteristics of the input-output signals are unknown. The proposed filter is presented as a dual-layered feedback system. Each layer has different function, the first layer being the type2-fuzzy autoregressive filter model. The second layer being responsible for training the membership function parameters. The second layer adjusts the type2-fuzzy adaptive filter parameters by using a teaching learning-based optimization algorithm (TLBO), which will allow the reaching of the required signal reconstruction by decreasing the criterion function. The proposed filter is validated and evaluated through some experimentations using the MIT-BIH ECGs databases where various artifacts were added to the ECGs signals;these included real and artificial noise. For comparison purposes, both model and non-model-based methods recently published are used. Furthermore, the effect of the proposed filter on the malformation of diagnostic features of the ECG was studied and compared with several benchmark schemes. The results show that the proposed method performs better denoising for all noise power levels and for a different criteria viewpoint.

关键词： ECG signal ECG denoising type-2 fuzzy logic optimization algorithm TLBO

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An optimization algorithm for individualized biomechanical analysis and simulation of tibia fractures

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JOURNAL OF BIOMECHANICS 2015年第6期48卷 1119-1124页

作者： Roland, M. Tjardes, T. Otchwemah, R. Bouillon, B. Diebels, S. Univ Saarland Chair Appl Mech D-66123 Saarbrucken Germany Univ Witten Herdecke Cologne Merheim Med Ctr Dept Trauma Surg Orthoped Surg & Sportstraumatol D-51109 Cologne Germany

An algorithmic strategy to determine the minimal fusion area of a tibia pseudarthrosis to achieve mechanical stability is presented. For this purpose, a workflow capable for implementation into clinical routine workup of tibia pseudarthrosis was developed using visual computing algorithms for image segmentation, that is a coarsening protocol to reduce computational effort resulting in an individualized volume-mesh based on computed tomography data. An algorithm detecting the minimal amount of fracture union necessary to allow physiological loading without subjecting the implant to stresses and strains that might result in implant failure is developed. The feasibility of the algorithm in terms of computational effort is demonstrated. Numerical finite element simulations show that the minimal fusion area of a tibia pseudarthrosis can be less than 90% of the full circumferential area given a defined maximal von Mises stress in the implant of 80% of the total stress arising in a complete pseudarthrosis of the tibia. (C) 2015 Elsevier Ltd. All rights reserved.

关键词： Patient-specific simulations Medical image segmentation Finite element method (FEM) Fractured tibia with implant optimization algorithm

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Complex ontology alignment for autonomous systems via the Compact Co-Evolutionary Brain Storm optimization algorithm

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ISA TRANSACTIONS 2023年 132卷 190-198页

作者： Xue, Xingsi Fujian Univ Technol Fujian Prov Key Lab Big Data Min & Applicat Fuzhou 350118 Fujian Peoples R China Fujian Univ Technol Intelligent Informat Proc Res Ctr Fuzhou 350118 Fujian Peoples R China

Autonomous Systems (ASs) that work in the open, dynamic environment are required to share their data entities and semantics to implement the co-operations. Typically, AS's data schemas and semantics are described via ontology. Since ASs need to maintain their autonomy and conceptual specificity, their ontologies might define one concept with different terms or in different contexts, which yields the heterogeneity issue and hampers their co-operations. An effective solution is to establish a set of data entity's correspondences through the Ontology Alignment (OA). Sine the simple correspondence of one-to-one style lacks expressiveness and cannot completely cover different types of heterogeneity, ASs' co-operations require using the complex correspondence of one-to-many or many-to-may style. Inspired by the success of applying the Brain Storm optimization algorithm (BSO) to solve diverse complex optimization problems, this work proposes a Compact Co-Evolutionary BSO (CCBSO) to face the challenge of aligning AS ontologies. In particular, the AS ontology aligning problem is formally defined, a hybrid confidence measure for distinguishing the simple and complex correspondences is proposed, and a problem-specific CCBSO is presented. The experiment tests CCBSO's performance on different AS ontology aligning tasks, which consist of two simple ontology aligning tasks and one complex ontology aligning track. The experimental results show that CCBSO outperforms the state-of-the-art ontology aligning techniques on all simple and complex ontology aligning tasks.(c) 2022 ISA. Published by Elsevier Ltd. All rights reserved.

关键词： Autonomous system Complex ontology alignment Compact Co-Evolutionary Brain Storm optimization algorithm Hybrid confidence measure

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Performance prediction of the PEMFCs based on gate recurrent unit network optimized by improved version of prairie dog optimization algorithm

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INTERNATIONAL JOURNAL OF HYDROGEN ENERGY 2023年第69期48卷 26951-26963页

作者： Liu, Jie Zhang, Shubo Druzhinin, Zumrat Chongqing Creat Vocat Coll Sch Artificial Intelligence Chongqing 402160 Peoples R China Tajik Tech Univ Dushanbe Tajikistan

In this article, the black box dynamic model is presented for forecasting the performance of the PEM (Proton-exchange membrane) fuel cell (FC). An optimized deep artificial neural network has been used to build the experimental nonlinear model of the polymer membrane FC series that functions with hydrogen and oxygen. This research investigates predictability for a gate recurrent unit (GRU) optimized by a modified Prairie Dog Optimizer in PEMFCs. The results obtained have been validated by applying a case study and then a comparison is conducted among the outcomes of the offered technique and 2 other published methods: modified relevance vector machine and Lattice Gated Recurrent Unit (LGRU). The voltage clearly changes significantly, as demonstrated by simulations, even though the FC is handled with a low starting temperature and current. Also, the voltage point distribution has become more concentrated when the current and temperature are high. In both the training and prediction phases, the MAPE is reduced to approximately 0.0043 and 0.0047, respectively, showing that the proposed GRU technique produces superior prediction results when the operational settings approach the optimum operating conditions. According to simulations, the proposed IPDO/GRU with a 0.004 root mean square has the least error, followed by the mRVM and GRU with 0.009 and 0.010 root mean square values. The outcomes show that using the offered procedure does provide the finest verification of the empirical data. & COPY;2023 Hydrogen Energy Publications LLC. Published by Elsevier Ltd. All rights reserved.

关键词： PEMFC Performance prediction Gate recurrent unit Improved version of prairie dog optimization algorithm

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The application of hybrid cuckoo search-grey wolf optimization algorithm in optimal parameters identification of solid oxide fuel cell

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INTERNATIONAL JOURNAL OF HYDROGEN ENERGY 2022年第9期47卷 6200-6216页

作者： Bai, Qiuchan Li, Hao Huaiyin Inst Technol Fac Automat Huaian 223003 Peoples R China Hebei Agr Univ Coll Mech & Elect Engn Baoding 071001 Hebei Peoples R China

In the present study, dependable and accurate variables of solid oxide fuel cell (SOFC) are identified using an accurate and remarkable convergence pace optimization algorithm, socalled cuckoo search grey wolf optimization (CSGWO). The mentioned hybrid algorithm is acquired from the integration of cuckoo search (CS) and grey wolf optimization (GWO) algorithms. To confirm the CSGWO performance, it is compared with well-known optimization algorithms. The proposed CSGWO hybrid algorithm is applied for a 5-kW physically-based dynamic tubular stack. Along with this, the modeling is performed for different temperatures and pressures. The results of the CSGWO integrated algorithm reveal the supremacy of the considered method compared to other algorithms with the lowest values of MSE and corroborate precision, robustness, and excellent convergence pace compared to different optimization algorithms. The statistical results revealed that the developed CSGWO algorithm has the lowest MSE values by 1.3%, 0.1%, 0.3%, 0.1%, and 0% for the operational pressures of 1, 2, 3, 4, and 5 atm, respectively. All in all, the experimental records confirm that the CSGWO integrated algorithm could be introduced as a favorable substitute to the SOFC models' parameter estimation. (c) 2021 Hydrogen Energy Publications LLC. Published by Elsevier Ltd. All rights reserved.

关键词： Solid oxide fuel cell Unknown parameters Parameter estimation Hybrid cuckoo search-grey wolf optimization algorithm

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Fennec Fox optimization: A New Nature-Inspired optimization algorithm

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IEEE ACCESS 2022年 10卷 84417-84443页

作者： Trojovska, Eva Dehghani, Mohammad Trojovsky, Pavel Univ Hradec Kralove Fac Sci Dept Math Hradec Kralove 50003 Czech Republic

This paper proposes a new nature-based metaheuristic algorithm called Fennec Fox optimization (FFA), mimicking two natural behaviors of the animal Fennec Fox in nature. Concretely, Fennec's digging ability and escape strategy from wild predators were the fundamental inspiration for the proposed FFA. The mathematical model of FFA is presented in two phases based on imitating these two behaviors. First, the efficiency of FFA was evaluated in the optimization of sixty-eight standard benchmark functions and four engineering design problems. Second, FFA performance is challenged against eight well-known optimization algorithms. The optimization results show that FFA perfectly balances exploration and exploitation in searching for the global optimum. Hence, FFA can provide suitable solutions to optimization problems. The comparison of results indicates the superiority of FFA in most objective functions over competitor algorithms in providing the optimal solution.

关键词： optimization Behavioral sciences Metaheuristics Search problems Benchmark testing Mathematical models Linear programming Fennec fox optimization nature-inspired optimization algorithm metaheuristic exploration exploitation

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Tool path optimization algorithm of spatial cam flank milling based on NURBS surface

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JOURNAL OF THE BRAZILIAN SOCIETY OF MECHANICAL SCIENCES AND ENGINEERING 2018年第4期40卷 1-8页

作者： Hu, Dongfang Guo, Jianwei Henan Univ Sci & Technol Sch Mechatron Engn Collaborat Innovat Ctr Machinery Equipment Adv Mf Luoyang 471003 Peoples R China

To solve the problems of approximation error for data point discretization and great fluctuation of cutter path ruled surface, a tool path optimization algorithm of spatial cam flank milling based on NURBS surface is proposed in this paper. On the basis of the expression of the theoretical cutter axis surface, a theory derivation method of isometric surface is put forward based on the expansion line of the spatial cam profile, using cutter axis surface as the working surface of the spatial cam, and the work profile can be obtained from initial design requirements of the spatial cam. According to the geometrical properties of the cutter axis surface, the data sampling grid is generated by defining the adaptive algorithm of curvature for curved surface. To get the valid data points, the discrete mesh generated on self-adaption is planned by the maximum bending degree point-selection method. Taking advantage of these data points, the NURBS ruled surface is constructed to approximate the theoretical cutter axis surface and the NURBS cutter axis ruled surface was being refactored by leastsquare method. Combined with theoretical CNC machining error model, the algorithm validity is proved by numerical calculation of the example and simulation experiment results.

关键词： Spatial cam Flank milling optimization algorithm Least-square method NURBS surface

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A Finite Difference Method and Effective Modification of Gradient Descent optimization algorithm for MHD Fluid Flow Over a Linearly Stretching Surface

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Computers, Materials & Continua 2020年第2期62卷 657-677页

作者： Yasir Nawaz Muhammad Shoaib Arif Mairaj Bibi Javeria Nawaz Abbasi Umer Javed Amna Nazeer Stochastic Analysis&Optimization Research Group Department of MathematicsAir UniversityPAF Complex E-9Islamabad44000Pakistan Department of Mathematics Comsats UniversityChak Shahzad CampusIslamabad44000Pakistan Department of Electrical and Computer Engineering Comsats UniversityIslamabadWah CampusWah Cantt47040Pakistan

Present contribution is concerned with the construction and application of a numerical method for the fluid flow problem over a linearly stretching surface with the modification of standard Gradient descent algorithm to solve the resulted difference *** flow problem is constructed using continuity,and Navier Stoke equations and these PDEs are further converted into boundary value problem by applying suitable similarity transformations.A central finite difference method is proposed that gives third-order accuracy using three grid *** stability conditions of the present proposed method using a Gauss-Seidel iterative procedure is found using Von-Neumann stability criteria and order of the finite difference method is proved by applying the Taylor series on the discretised *** comparison of the presently modified optimisation algorithm with the Gauss-Seidel iterative method and standard Newton’s method in optimisation is also *** can be concluded that the presently modified optimisation algorithm takes a few iterations to converge with a small value of the parameter contained in it compared with the standard descent algorithm that may take millions of iterations to *** present modification of the steepest descent method converges faster than Gauss-Seidel method and standard steepest descent method,and it may also overcome the deficiency of singular hessian arise in Newton’s method for some of the cases that may arise in optimisation problem(s).

关键词： Magnetohydrodynamic exact finite difference scheme optimization algorithm stability&convergence

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An enhanced Harris Hawk optimization algorithm for parameter estimation of single, double and triple diode photovoltaic models

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SOFT COMPUTING 2022年第15期26卷 7233-7257页

作者： Ramadan, Abdelhady Kamel, Salah Korashy, Ahmed Almalaq, Abdulaziz Luis Dominguez-Garcia, Jose Aswan Univ Fac Engn Dept Elect Engn Aswan 81542 Egypt Univ Hail Engn Coll Dept Elect Engn Hail 55476 Saudi Arabia IREC Catalonia Inst Energy Res Jardins Dones Negre S-N Barcelona 08930 Spain

Due to the rapid development of photovoltaic (PV) system and spreading of its application, the accuracy of modeling of solar cells, as the main and basic element of PV systems, is gaining relevance. In this paper, an Enhanced Harris Hawk optimization algorithm (EHHO) is proposed and applied for estimating the required parameters of different PV models in an effective and accurate way. Harris Hawk algorithm (HHO) is based on Hawks ways in hunting and catching their preys. The HHO utilizes two phases including exploration and exploitation. The main purpose of proposed enhancement is to improve the second phase of HHO. This enhancement is performed on the exploration phase by fluctuating toward or outward the best optimal solution using sine and cosine functions. Both conventional and proposed algorithms are applied for single, double and triple diode PV models. In order to test the applicability and robustness of proposed algorithm, it is applied for estimating the parameters of different real PV systems and compared with other recent optimization algorithms. The results show that the proposed EHHO is more accurate than conventional HHO and other algorithms.

关键词： Photovoltaic (PV) optimization algorithm Harries hawk and single Double and triple diode models

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Development of a WWTP influent characterization method for an activated sludge model using an optimization algorithm

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MEMBRANE WATER TREATMENT 2018年第3期9卷 155-162页

作者： You, Kwangtae Kim, Jongrack Pak, Gijung Yun, Zuwhan Kim, Hyunook UnU Inc Seoul 08390 South Korea Korea Univ Dept Environm Engn Sejong 30019 South Korea Univ Seoul Dept Energy & Environm Syst Engn Seoul 02504 South Korea

Process modeling with activated sludge models (ASMs) is useful for the design and operational improvement of biological nutrient removal (BNR) processes. Effective utilization of ASMs requires the influent fraction analysis (IFA) of the wastewater treatment plant (WWTP). However, this is difficult due to the time and cost involved in the design and operation steps, thereby declining the simulation reliability. Harmony Search (HS) algorithm was utilized herein to determine the relationships between composite variables and state variables of the model IWA ASM1. Influent fraction analysis was used in estimating fractions of the state variables of the WWTP influent and its application to 9 wastewater treatment processes in South Korea. The results of influent Ss and Xs+X-BH, which are the most sensitive variables for design of activated sludge process, are estimated within the error ranges of 8.9-14.2% and 3.8-6.4%, respectively. Utilizing the chemical oxygen demand (COD) fraction analysis for influent wastewater, it was possible to predict the concentrations of treated organic matter and nitrogen in 9 full scale BNR processes with high accuracy. In addition, the results of daily influent fraction analysis (D-IFA) method were superior to those of the constant influent fraction analysis (C-IFA) method.

关键词： ASMs COD influent fraction analyzer (IFA) Harmony Search (HS) optimization algorithm WWTPs

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