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PREDICTION MODEL OF WAX DEPOSITION RATE BASED ON WOA-BPNN algorithm

FRONTIERS IN HEAT AND MASS TRANSFER 2022年 18卷

作者： Xiao, Rongge Zhuang, Qi Jin, Shuaishuai Jin, Wenbo Xian Shiyou Univ Coll Petr Engn Shaanxi Key Lab Adv Stimulat Technol Oil & Gas Re Xian 710065 Shaanxi Peoples R China

A model for predicting wax deposition rate in pipeline transportation is constructed to predict wax deposition in actual pipeline, which can provide decision support for the flow guarantee of waxy crude oil in pipeline transportation. This paper analyzes the working principle of Back Propagation Neural Networks (BPNN). Aiming at the problems of BPNN model, such as over learning, long training time, low generalization ability and easy to fall into local minimum, the paper proposes an improved scheme of using whale optimization algorithm (WOA) to optimize BPNN model (WOA-BPNN). Taking 38 groups of crude oil wax deposition experimental data in Huachi operation area as an example, the simulation calculation is carried out in MATLAB, and the Genetic algorithm optimized BPNN(GA-BPNN) and the non Optimized BP neural network are used as comparative models for comparative analysis. The results show that the Mean Relative Error (MRE) of WOA-BPNN model in predicting wax deposition rate is 2.72% and the coefficient of determination(R-2) is 0.9966, which are better than those of BPNN and GA-BPNN models. It is proved that WOA-BPNN model has higher accuracy and robustness in predicting wax deposition rate.

关键词： whale optimization algorithm Wax deposition rate BP Neural Network Prediction Model

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Classifying Heart Disease in Medical Data Using Deep Learning Methods

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Journal of Computer and Communications 2021年第1期9卷 66-79页

作者： T. Velmurugan U. Latha PG and Research Department of Computer Science D. G. Vaishnav College Chennai India

Recent days, heart ailments assume a fundamental role in the world. The physician gives different name for heart disease, for example, cardiovascular failure, heart failure and so on. Among the automated techniques to discover the coronary illness, this research work uses Named Entity Recognition (NER) algorithm to discover the equivalent words for the coronary illness content to mine the significance in clinical reports and different applications. The Heart sickness text information given by the physician is taken for the preprocessing and changes the text information to the ideal meaning, at that point the resultant text data taken as input for the prediction of heart disease. This experimental work utilizes the NER to discover the equivalent words of the coronary illness text data and currently uses the two strategies namely Optimal Deep Learning and whale optimization which are consolidated and proposed another strategy Optimal Deep Neural Network (ODNN) for predicting the illness. For the prediction, weights and ranges of the patient affected information by means of chosen attributes are picked for the experiment. The outcome is then characterized with the Deep Neural Network and Artificial Neural Network to discover the accuracy of the algorithms. The performance of the ODNN is assessed by means for classification methods, for example, precision, recall and f-measure values.

关键词： Named Entity Recognition algorithm Neural Network Methods whale optimization algorithm F-Measure Recall Precision

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基于元启发式算法的船舶动力装置随机建模与性能优化

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哈尔滨工程大学学报（英文版） 2023年第4期22卷 751-761页

作者： Monika Saini Bhavan Lal Patel Ashish Kumar Department of Mathematics&Statistics Manipal University JaipurJaipur-303007India

For the successful operation of any industry or plant continuous availability of power supply is *** of the large-scale plants established their power generation *** power plant having two generators is also fall in this *** this study,an effort is made to derive and optimize the availability of a marine power plant having two generators,one switch board and distribution *** this purpose,a mathematical model is proposed using Markov birth death process by considering exponentially distributed failure and repair rates of all the *** availability expression of marine power plant is *** algorithms namely dragonfly algorithm(DA),bat algorithm(BA)and whale optimization(WOA)are employed to optimize the availability of marine power *** is revealed that whale optimization algorithm outperforms over dragonfly algorithm(DA),and bat algorithm(BA)in optimum availability prediction and parameter *** numerical values of the availability and estimated parameters are appended as numerical *** derived results can be utilized in development of maintenance strategies of marine power plants and to carry out design modifications.

关键词： Markov process whale optimization algorithm Dragonfly algorithm Availability Marine power plant

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Estimation of Parameters in Fractional order Financial Chaotic system with Nature Inspired algorithms

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Procedia Computer Science 2020年 173卷 18-27页

作者： Sangeeta Gupta Gaurava Gautam Deepak Divyanshu Vats Pragya Varshney Smriti Srivastava Department of Instrumentation and Control Engineering Netaji Subhas Institute of Technology Dwarka Sec-3 New Delhi -110078 India

Chaotic system parameter modeling is one of the significant topics in the field of non-linear science and therefore has attracted the attention of many researchers in this field. This paper discusses parameter recognition of Fractional Order Financial Chaotic System by employing different metaheuristic algorithms. The implemented algorithms include Artificial bee colony, Grey wolf optimizer, whale optimization algorithm and Ant colony optimizer. Mean Square Error is employed as objective function to estimate parameters of system under consideration. Results of optimized parameters using the above-mentioned optimization are compared with each other and discussed in the paper. The overall outcome shows that whale optimization algorithm gives more accurate and robust results with higher convergence rate as compared with other algorithms.

关键词： optimization Techniques Fractional Calculus Fractional Order Fractional Order Financial Chaotic system whale optimization algorithm Ant colony optimization Grey wolf optimization Artificial bee colony optimization

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whale inspired algorithm based MPPT controllers for grid-connected solar photovoltaic system

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Energy Procedia 2019年 162卷 77-86页

作者： M.A. Ebrahim Adham Osama Khaled Mohamed Kotb Fahmy Bendary Department of Electrical Engineering Faculty of Engineering at Shoubra Benha University Cairo Egypt National Water Research Center ELqanater Elkhayrya 13411 Egypt

Over the past decades, meta-heuristic optimization techniques have become surprisingly very popular due to their flexibility and local optima avoidance capability. This paper uses the whale optimization algorithm (WOA), a swarm-based technique to tune the Proportional-Integral (PI) based Maximum Power Point Tracking (MPPT) controllers of a grid-connected solar Photovoltaic (PV) system. The results of the PI-based Incremental Conductance (IC) MPPT technique are compared with both the conventional incremental conductance and the Perturb & Observe (P&O) MPPT techniques. Various modes of the PI controller are used. I, PI and Fractional order PI (FOPI) gain parameters are determined using WOA. Performance indices are applied to estimate the best parameters of the PI controller. This paper aims to show the effect of using PI-based MPPT controllers on enhancing the performance of a 400-kW grid-connected PV system. Simulation results show the capability of PI-based MPPT controllers on improving the performance of the PV system. It demonstrates the superiority of FOPI controllers over the other modes in enhancing system performance. The proposed work is simulated using MATLAB SIMULINK.

关键词： Maximum Power Point Tracking Incremental conductance Perturb Observe whale optimization algorithm Proportional-Integral controller Photovoltaic

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Combined Regularized Discriminant Analysis and Swarm Intelligence Techniques for Gait Recognition

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SENSORS 2020年第23期20卷 6794-6794页

作者： Krzeszowski, Tomasz Wiktorowicz, Krzysztof Rzeszow Univ Technol Fac Elect & Comp Engn Al Powstancow Warszawy 12 PL-35959 Rzeszow Poland

In the gait recognition problem, most studies are devoted to developing gait descriptors rather than introducing new classification methods. This paper proposes hybrid methods that combine regularized discriminant analysis (RDA) and swarm intelligence techniques for gait recognition. The purpose of this study is to develop strategies that will achieve better gait recognition results than those achieved by classical classification methods. In our approach, particle swarm optimization (PSO), grey wolf optimization (GWO), and whale optimization algorithm (WOA) are used. These techniques tune the observation weights and hyperparameters of the RDA method to minimize the objective function. The experiments conducted on the GPJATK dataset proved the validity of the proposed concept.

关键词： gait recognition biometrics regularized discriminant analysis particle swarm optimization grey wolf optimization whale optimization algorithm

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Flood risk assessment of subway stations based on projection pursuit model optimized by whale algorithm: A case study of Changzhou, China

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INTERNATIONAL JOURNAL OF DISASTER RISK REDUCTION 2023年 98卷

作者： Ju, Weiyi Wu, Jie Cao, Haizhen Kang, Qingchun Ali, Shah Syed Salman Xing, Zhixiang Changzhou Univ Sch Safety Sci & Engn 21 Gehu Middle Rd Changzhou 213164 Jiangsu Peoples R China

With rapid modernization, traffic jams have become a part of people's daily lives. An increasing number of cities have begun to build subways to solve this problem. This study focuses on the flood risk analysis of subway stations, which is often overlooked. However, such disasters cause serious mass casualties and huge property losses. Therefore, this is of great concern. This study applies the method of projection pursuit model optimized by whale algorithm to evaluate the flood disasters of subway stations. The results showed that the flood risk level of 11 subway stations was very low, that of 21 subway stations was low, that of seven subway stations was moderate, and that of one subway station was high. Compared to the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) method, the application of the projection pursuit model based on the whale algorithm has higher precision and better global searchability. The main contribution of this study is to provide a new method and idea for the field of flood risk assessment of subway stations, and to provide a scientific model and reference for local rail companies. This study used the whale algorithm to optimize the projection pursuit model and applied it to the flood risk assessment of subway stations. This is an innovation in this field that has strong engineering application significance.

关键词： Flood disaster Subway stations whale optimization algorithm Projection pursuit model Random weight

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Air pollutant prediction based on ARIMA-WOA-LSTM model

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ATMOSPHERIC POLLUTION RESEARCH 2023年第6期14卷

作者： Luo, Jun Gong, Yaping Zhejiang Ocean Univ Dept Ocean Engn Equipment Zhoushan 316022 Peoples R China

The problem of air pollution has always plagued people's lives, and the management of air pollution cannot be achieved without the prediction and assessment of the concentration of various pollutants. In this paper, we propose a method to accurately predict air pollutants with the aim of ensuring the efficiency of air pollution management. The proposed ARIMA-WOA-LSTM model uses ARIMA to extract the linear part of air pollution data and output the nonlinear part, while the WOA-LSTM model is used to predict the nonlinear part, where the whale algorithm is used to find the perfect hyperparameters for the LSTM, and the objectives of the search include the number of neurons, model learning rate and batch length. To prove the excellence of the model developed in the article ARIMA-WOA-LSTM is compared with ARIMA-LSTM, CEEMDAN-WOA-LSTM, WOA-LSTM, ARIMA, and LSTM. The results show that ARIMA-WOA-LSTM performs better than other models in three aspects: pollutant prediction accuracy, model prediction accuracy, and prediction stability;the combined model also performs much better than the single model in the three aspects;The whale algorithm is excellent for the search of the five hyperparameters in the LSTM, which is important for the error reduction of the model. ARIMA-WOA-LSTM model has high reference for air pollution management.

关键词： ARIMA-WOA-LSTM Combined model Air pollution prediction whale optimization algorithm

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RCS reduction effect based on transparent and flexible polarization conversion metasurface arrays

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RESULTS IN PHYSICS 2023年 52卷

作者： Zhen, Qiutong Mao, Zhoutao Cui, Jiayi Guo, Meijia Chernogor, Leonid F. Jin, Zhejun Zheng, Yu Liu, Tian Qingdao Univ Coll Elect & Informat Qingdao 266071 Peoples R China Kharkov Natl Univ Dept Space Radio Phys UA-61022 Kharkiv Ukraine

In recent years, radar cross section (RCS) reduction techniques based on electromagnetic absorption materials have become a hot research topic in the field of electromagnetic stealth. This paper proposes a transparent conformal encoded metasurface based on polarization rotation units. The metasurface array is optimized using the whale optimization algorithm to construct a 1-bit polarization rotation encoded unit array. The proposed metasurface array exhibits good RCS reduction and electromagnetic wave polarization conversion performance in the frequency range of 13.2-20.7 GHz. The RCS reduction effect of the metasurface array is effective at different angles and polarization states. The metasurface is composed of transparent and flexible materials, namely ITO, PET, and PVC plastic. It exhibits conformal properties while maintaining a high physical transmittance of up to 83.6%. The RCS reduction performance of the metasurface is studied under different curvatures and demonstrates good conformal scattering characteristics. The RCS variation of ITO material compared to traditional metallic materials was also analyzed. It was found that ITO with low surface resistance is an effective means to broaden the RCS bandwidth. This makes it suitable for theoretical research and the design of electromagnetic stealth for electronic devices such as radars and antennas with different curvatures.

关键词： Transparent and flexible metasurface Polarization conversion Radar cross section whale optimization algorithm

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Optimal Weighted Logarithmic Transformation Converted HMOG Features for Automatic Smart Phone Authentication

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INTERNATIONAL JOURNAL OF MOBILE COMPUTING AND MULTIMEDIA COMMUNICATIONS 2022年第1期13卷

作者： Vinod, P. R. Anitha, A. Noorul Islam Ctr Higher Educ Kumaracoil Tamil Nadu India

This paper intends to develop an automatic behavior-based smart phone authentication model by three major phases: feature extraction, weighted logarithmic transformation, and classification. Initially, from the data related to the touches/gesture of the smartphone user, hand movement, orientation, and grasp (HMOG), features are extracted with the aid of grasp resistance and grasp stability. These extracted features are mapped within the particular range by normalizing HMOG. These normalized data are multiplied with the weights followed by logarithmic transformation in the weighted logarithmic transformation phase. As a novelty, the decision-making process related to the logarithmic and weight selection is based on the improved optimization algorithm, called modified threshold-based whale optimization algorithm (MT-WOA). The final feature vectors are fed to DBN for recognizing the authorized users. Finally, a performance-based evaluation is performed between the MT-WOA+DBN and the existing models in terms of various relevant performance measures.

关键词： HMOG Modified Threshold Function Smart Phone Authentication Weighted Logarithmic Transformation whale optimization algorithm

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