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Crack identification of Yunnan walnuts based on acoustic vibration and feature fusion

JOURNAL OF FOOD PROCESS ENGINEERING 2023年第11期46卷 e14422-e14422页

作者： Zhang, Hao Li, Lixia Tian, Liquan Zhang, Fujie Shi, Lei Wu, Wenqiang Zhan, Zicheng Kunming Univ Sci & Technol Fac Modern Agr Engn Kunming Peoples R China Key Lab Crop Harvesting Equipment Technol Zhejiang Jinhua Peoples R China Kunming Univ Sci & Technol Fac Modern Agr Engn Kunming 650500 Peoples R China

Cracks in walnuts during processing and storage can adversely affect their quality and cause economic losses. To achieve efficient identification of cracked walnuts, this study proposed a method of walnut crack identification based on acoustic vibration and feature fusion. First, the sound signals of intact and cracked walnuts were collected using an acoustic signal acquisition system, and 44 time-domain features, 13 frequency-domain features, and 768 Mel spectrogram features (the number of pixel frequencies corresponding to the gray-scale values of R, G, and B channels) of the sound signals were extracted. Then, the classification models of support vector machines (SVM), least squares support vector machines (LSSVM), and extreme learning machines (ELM) were established based on single class features data and fusion of different feature groups data respectively. The results indicated that the LSSVM model with the fusion of the three feature sets was optimal, with an accuracy of 85% in the testing set. Next, three feature selection methods were employed to reduce the dimensionality of the best fused feature data. Subsequently, the LSSVM classification model was established based on the feature selection data. Finally, arithmetic optimization algorithm (AOA), particle swarm optimization (PSO), and gray wolf optimization (GWO) were introduced to optimize the parameters c and delta(2) of the classification model. The results indicated that the best classification model was VISSA-IRIV-GWO-LSSVM, with 95% accuracy in the testing set. This study provides theoretical support for the research and development of online detection equipment for walnut crack in Yunnan Yangbi. Practical applications Cracks in walnut during harvesting, transportation and peeling may lead to economic losses and food safety problems. Aiming at the difficulty and low accuracy of crack identification in Yunnan walnut, this paper proposed a method for crack detection based on acoustic vibration identif

关键词： acoustic vibration identification feature fusion gray wolf optimization algorithm least squares support vector machine walnut cracks

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An improved sub-harmonic to harmonic ratio method for pitch estimation and Shadja detection

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CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE 2023年第7期35卷 e7604-e7604页

作者： Kumaraswamy, Balachandra Poonacha, P. G. BMS Coll Engn Dept Elect & Telecommun Engn Bengaluru India Int Inst Informat Technol CEEMS Bangalore India BMS Coll Engn Dept Elect & Telecommun Engn Bengaluru 560019 Karnataka India

In this paper, we have presented an improved Sub-Harmonic to Harmonic ratio (SHR) algorithm using the Genetic algorithm (GA) for pitch estimation of audio recordings of various ragas. Then we study the problem of Shadja identification using machine learning with the help of feature extraction and classification. The extraction of features from the raga signal is done with the help of statistical analysis of pitch estimation and these features are classified using a Neural Network (NN). Here, the training of NN is accomplished by gray wolf optimization (GWO) algorithm for, determining the weights. Performance of the proposed Sa detection algorithm is analyzed by comparing the developed NN using gray wolf optimization (GWO) models with the conventional models such as Levenberg Marquardt based NN (LM-NN), Gradient Descent based (GD-NN), Particle Swarm optimization based NN (PSO-NN) and FireFly based NN (FF-NN) in terms of positive and negative performance measures.

关键词： gray wolf optimization algorithm neural network pitch estimation raga Shadja detection sub-harmonic to harmonic ratio

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Application Big Data and Intelligent optimization algorithms on Teaching Evaluation Method for Higher Vocational Institutions

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EAI ENDORSED TRANSACTIONS ON SCALABLE INFORMATION SYSTEMS 2024年第5期11卷 13-13页

作者： Huang, Meijuan Anhui Business & Technol Coll Sch Informat Engn Hefei 231131 Anhui Peoples R China

INTRODUCTION: The optimization of the teaching evaluation system, as an essential part of teaching reform in higher vocational colleges and universities, is conducive to the development of higher vocational colleges and universities' disciplines, making the existing teaching more standardized. OBJECTIVES: Aiming at the problems of inefficiency, incomplete index system, and low assessment accuracy in evaluation methods of higher vocational colleges and universities. METHODS: Proposes a teaching evaluation method for higher vocational colleges and universities with a big data mining algorithm and an intelligent optimization algorithm. Firstly, the teaching evaluation index system of higher vocational colleges and universities is downgraded and analyzed by using principal component analysis;then, the random forest hyperparameters are optimized by the grey wolf optimization algorithm, and the teaching evaluation model of higher vocational colleges and universities is constructed;finally, the validity and stability of the proposed method is verified by simulation experimental analysis. RESULTS: The results show that the proposed method improves the accuracy of the evaluation model. CONCLUSION: Solves the problems of low evaluation accuracy, incomplete system, and low efficiency of teaching evaluation methods in higher vocational colleges.

关键词： teaching evaluation in higher education institutions random forest gray wolf optimization algorithm principal component analysis approach

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Optimal Operation of Distributed Generations Considering Demand Response in a Microgrid Using GWO algorithm

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Computer Systems Science & Engineering 2023年第10期47卷 809-822页

作者： Hassan Shokouhandeh Mehrdad Ahmadi Kamarposhti William Holderbaum Ilhami Colak Phatiphat Thounthong Department of Electrical Engineering Semnan UniversitySemnanIran Department of Electrical Engineering Jouybar BranchIslamic Azad UniversityJouybarIran School of Science Engineering and EnvironmentUniversity of SalfordSalfordUK Department of Electrical and Electronics Engineering Faculty of Engineering and ArchitecturesNisantasi UniversityIstanbulTurkey Renewable Energy Research Centre(RERC) Department of Teacher Training in Electrical EngineeringFaculty of Technical EducationKing Mongkut’s University of Technology North Bangkok1518Pracharat 1 RoadBangsueBangkok10800Thailand

The widespread penetration of distributed energy sources and the use of load response programs,especially in a microgrid,have caused many power system issues,such as control and operation of these networks,to be *** control and operation of many small-distributed generation units with different performance characteristics create another challenge for the safe and efficient operation of the *** this paper,the optimum operation of distributed generation resources and heat and power storage in a microgrid,was performed based on real-time pricing through the proposed gray wolf optimization(GWO)algorithm to reduce the energy supply cost with the *** generation resources such as solar panels,diesel generators with battery storage,and boiler thermal resources with thermal storage were used in the studied ***,a combined heat and power(CHP)unit was used to produce thermal and electrical energy *** the simulations,in addition to the gray wolf algorithm,some optimization algorithms have also been *** the results of 20 runs for each algorithm confirmed the high accuracy of the proposed GWO *** results of the simulations indicated that the CHP energy resources must be managed to have a minimum cost of energy supply in the microgrid,considering the demand response program.

关键词： Microgrid demand response program cost reduction gray wolf optimization algorithm

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Program source code comprehension by module clustering using combination of discretized gray wolf and genetic algorithms

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ADVANCES IN ENGINEERING SOFTWARE 2022年第0期173卷

作者： Arasteh, Bahman Abdi, Mohammad Bouyer, Asgarali Istinye Univ Fac Engn & Nat Sci Dept Software Engn Istanbul Turkey Islamic Azad Univ Dept Comp Engn Tabriz Branch Tabriz Iran Azarbaijan Shahid Madani Univ Dept Comp Engn Tabriz Iran Istinye Univ Istanbul Turkey

Maintenance is a critical and costly phase of software lifecycle. Understanding the structure of software will make it much easier to maintain the software. Clustering the modules of software is regarded as a useful reverse engineering technique for constructing software structural models from source code. Minimizing the connections between produced clusters, maximizing the internal connections within the clusters, and maximizing the clustering quality are the most important objectives in software module clustering. Finding the optimal software clustering model is regarded as an NP-complete problem. The low success rate, limited stability, and poor modularization quality are the main drawbacks of the previous methods. In this paper, a combination of gray wolf optimization algorithm and genetic algorithms is suggested for efficient clustering of software modules. An extensive series of experiments on 14 standard benchmarks have been conducted to evaluated the proposed method. The results illustrate that using the combination of gray wolf and genetic algorithms to the softwaremodule clustering problem increases the quality of clustering. In terms of modularization quality and convergence speed, proposed hybrid method outperforms the other heuristic approaches.

关键词： Source code comprehension Software modules clustering Modularization quality gray wolf optimization algorithm Cohesion Coupling

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Modified Tuned Liquid Dampers for Seismic Protection of Buildings Considering Soil-Structure Interaction Effects

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IRANIAN JOURNAL OF SCIENCE AND TECHNOLOGY-TRANSACTIONS OF CIVIL ENGINEERING 2020年第1期44卷 339-354页

作者： Kamgar, Reza Gholami, Fatemeh Sanayei, Hamed Reza Zarif Heidarzadeh, Heisam Shahrekord Univ Dept Civil Engn POB 88186-3414 Shahrekord Iran

Considering the effect of soil-structure interaction in dynamic analysis of structures can change their responses. It is generally assumed that the structure is located on a rigid foundation and the flexibility effect of the soil is not considered. Researches on the soil-structure interaction show that the dynamic response of the structures located on a soft and flexible soil is completely different from the dynamic response of the same structure located on a stiff soil. In this paper, the effect of the soil-structure interaction on the response of a single-degree-of-freedom system (Nagasaki airport tower) that is controlled by a modified tuned liquid damper is investigated. The soil effect is modeled using an approximate cone method based on the semi-infinite boundary conditions. First, the governing equations for describing the fluid sloshing obtained with shallow water wave theory are solved by Lax's finite-difference scheme. Then, the dynamic equilibrium equations for a structure controlled with a modified tuned liquid damper are obtained by considering the effect of soil-structure interaction using Lagrange's method. These equations are solved numerically by Newmark's method. The controlled structural responses are calculated in different time steps and compared with the responses of the uncontrolled structure. Results show that the seismic design of the modified tuned liquid damper system can be more effective to reduce the structural responses. Also, this system can reduce efficiently the maximum responses of the structures considering soil-structure interaction effect during a near-fault earthquake.

关键词： optimization Soil-structure interaction Modified tuned liquid damper gray wolf optimization algorithm Seismic protection

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A Fault Diagnosis Method Based on GWO-DELM for Aeroengine Gas Path

A Fault Diagnosis Method Based on GWO-DELM for Aeroengine Ga...

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第34届中国控制与决策会议

作者： Jianguo Cui Qian Yu Xiao Cui Liying Jiang Mingyue Yu Wenyou Du Dong Liu Jinglin Wang School of Automation Shenyang Aerospace University Model Balance and Wind Tunnel Equipment Department 5 AVIC Aerodynamics Research Institute Aviation Key Laboratory of Science and Technology on Fault Diagnosis and Health Management

Aiming at the problem of complex working mechanism of aeroengine gas path system and difficulty in effective fault diagnosis in actual work,a new fault diagnosis method of aeroengine gas path based on gray wolf optimization Deep Extreme Learning Machine(GWO-DELM) is ***,analyze a large amount of monitoring data of a certain type of aeroengine gas path components,and sort out the health data and fault sample data ***,create the DELM fault diagnosis model by the health data and fault sample data set of the aeroengine gas circuit *** reduce the influence of artificially setting network parameters on the diagnosis results,the gray wolf optimization algorithm(GWO) is used to optimize the DELM network parameters,and the optimal DELM fault diagnosis model GWO-DELM is ***,the GWO-DELM fault diagnosis model is used to study the fault diagnosis verification technology of the aeroengine air circuit system,and the diagnosis results of the ELM,DELM and Multilayer Kernel Extreme Learning Machine(ML-KELM) fault diagnosis models are *** result shows that the fault diagnosis accuracy of the proposed GWO-DELM fault diagnosis model is 96.0%,which is significantly higher than that of the ELM model of 88.0%,the DELM model of 92.0% and the ML-KELM model of 94.0%,the effectiveness of the proposed method is verified,and it has a good application prospect.

关键词： Deep Extreme Learning Machine gray wolf optimization algorithm Fault Diagnosis Aeroengine

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Short-term photovoltaic power prediction on modal reconstruction: A novel hybrid model approach

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SUSTAINABLE ENERGY TECHNOLOGIES AND ASSESSMENTS 2021年 45卷 101048-101048页

作者： Liu, Zhi-Feng Luo, Shi-Fan Tseng, Ming-Lang Liu, Han-Min Li, Lingling Mashud, Abu Hashan Md Hebei Univ Technol State Key Lab Reliabil & Intelligence Elect Equip Tianjin 300130 Peoples R China Hebei Univ Technol Key Lab Electromagnet Field & Elect Apparat Relia Tianjin 300130 Peoples R China Northeastern Univ Coll Engn Boston MA 02115 USA Asia Univ Inst Innovat & Circular Econ Taichung Taiwan China Med Univ China Med Univ Hosp Dept Med Res Taichung Taiwan Univ Kebangsaan Malaysia Fac Econ & Management Bangi Malaysia ZhangJiakou Wind Photovolta & Energy Storage Demo Zhangjiakou 075061 Peoples R China Hajee Mohammad Danesh Sci & Technol Univ Dept Math Dinajpur 5200 Bangladesh

The contribution is to propose a novel hybrid model based on modal reconstruction to predict short-term photovoltaic (PV) power. PV power generation large-scale grid connection causes the impact on the power system due to the instability and intermittence, and PV curtailment measures are taken to reduce the impact from voltage fluctuation. Accurate forecast is necessary to make a reasonable generation plan. A novel hybrid forecasting model is proposed, and an enhanced gray wolf optimization algorithm is proposed to improve the convergence ability and to solve the influence of extreme learning machine random parameters. The proposed algorithm has stronger convergence stability and higher convergence accuracy compared with the existing algorithms. The ensemble empirical mode decomposition algorithm is used to decompose the PV power under different weather conditions. The complexity of each component is calculated by the sample entropy, and the components are reconstructed to reduce the computational cost of forecasting models. The results revealed that mean absolute percentage error and root mean square error of the proposed model are smaller than 3% and 0.36 under various weather conditions. Meanwhile, the determination coefficient of the proposed model is more than 98%.

关键词： Photovoltaic power forecasting Extreme learning machine gray wolf optimization algorithm Modal reconstruction Ensemble empirical mode decomposition

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Weak fault feature extraction for polycrystalline diamond compact bit based on ensemble empirical mode decomposition and adaptive stochastic resonance

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MEASUREMENT 2021年 178卷 109304-109304页

作者： Gao, Kangping Xu, Xinxin Li, Jiabo Jiao, Shengjie Shi, Ning Changan Univ Natl Engn Lab Highway Maintenance Equipment Xian 710064 Peoples R China Henan Gaoyuan Maintenance Technol Highway Co Ltd Xinxiang 453003 Henan Peoples R China

Aiming at the problem of weak early fault signals of rotating machinery, a feature extraction method combining ensemble empirical mode decomposition (EEMD) and adaptive stochastic resonance (ASR) is proposed. First, the original vibration signal is decomposed into a series of intrinsic mode functions (IMF) through EEMD, and the main IMF components are selected using the correlation coefficient and the root mean square principle. Next, the selected components are reconstructed and used as the input of the ASR system based on the gray wolf algorithm. Finally, to evaluate the performance of ASR, a new evaluation index-weighted power spectrum kurtosis (WPSK) is defined. The results of experimental analysis on the simulation signals and vibration signals are used to verify the feasibility and superiority of the proposed method. Compared with the GA-SR, the proposed method increases the WPSK value by 34.5% when detecting weak signals of worn polycrystalline diamond compact bits.

关键词： Weak signal detection Feature extraction Adaptive stochastic resonance Ensemble empirical mode decomposition gray wolf optimization algorithm Polycrystalline diamond compact bit

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Fault Location of Active Distribution Network Based on Improved gray wolf algorithm

Fault Location of Active Distribution Network Based on Impro...

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Chinese Automation Congress (CAC)

作者： Li, Zheng Zhang, Xiao Song, Qiang Wu, Na Shandong Univ Sci & Technol Coll Elect Engn & Automat Qingdao Peoples R China State Grid Shandong Elect Power Co Jining Power Supply Co Jining Peoples R China

ISBN: (纸本)9781728176871

As the scale of the power grid continues to expand and a large number of distributed power sources are connected, the difficulty of locating faults is increasing. The gray wolf optimization algorithm is simple in principle, ease to implement and has good global performance, so it is widely used, but the algorithm also has the defects of low optimization accuracy and easy to fall into the local optimal solution. Based on the gray wolf algorithm based on dynamic update weights, this paper proposes an improved gray wolf optimization algorithm. The algorithm first improved the C strategy to balance the global exploration and local development capabilities of the algorithm;then the sine and cosine global optimization strategy was used to guide and update the position again to reduce the premature probability and improve the algorithm optimization accuracy;finally, the adaptive local optimization strategy was used to select the optimal solution through neighborhood comparison to avoid the algorithm falling into the local optimal solution. The gray wolf optimization algorithm before and after the improvement is used to simulate and analyze the fault section location, which further verifies the correctness and superiority of the algorithm.

关键词： Active distribution network fault location gray wolf optimization algorithm sine and cosine self-adaption

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