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Optimal Test Point Placement Based on Fault Diagnosability Quantitative Evaluation

IEEE ACCESS 2022年 10卷 74495-74507页

作者： Shi, Xian-Jun Qin, Yu-Feng Zhao, Li Naval Aviat Univ Coll Coastal Def Force Yantai 264001 Peoples R China

The optimal test point placement problem in existing research results is mainly limited to the qualitative study of whether faults can be diagnosed without considering the difficulty of diagnosing faults. We proposed an optimal test point placement approach based on fault diagnosability quantitative evaluation to solve the above problem. First, the fault diagnosability is quantitatively evaluated based on the maximum mean discrepancy (MMD). Then, the problem of optimal test point placement is considered a multi objective optimization problem. The optimal test point set is solved using the multi objective sparrow search algorithm (MOSSA) based on the fault diagnosability quantitative evaluation results, considering limitations on the test point number, reliability, and cost. Finally, the proposed approach is used to optimize the placement of test points in the switching power supply system. The simulation results show that only three test points need to be placed to make the system meet the fault diagnosability requirements. Two test point placement schemes are obtained, which can be selected according to different practical requirements. The experimental results illustrate that the proposed approach can optimize the system test point placement while ensuring good fault diagnosability.

关键词： Fault diagnosability quantitative evaluation maximum mean discrepancy test point placement sparrow search algorithm

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Assistive Classification Modeling of Laryngeal Squamous Cell Carcinoma based on data expansion and SSA-SVM 4

Assistive Classification Modeling of Laryngeal Squamous Cell...

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4th International Symposium on Artificial Intelligence for Medicine Science (ISAIMS)

作者： Ren, Chunni Yu, Shuting Cui, Guimei Inner Mongolia Univ Sci & Technol Sch Informat Engn Key Lab Integrated Automat Proc Ind Baotou 014010 Peoples R China Peking Union Med Coll & Chinese Acad Med Sci Dept Otolaryngol Head & Neck Surg Peking Union Med Coll Hosp Beijing Peoples R China

ISBN: (纸本)9798400708138

Laryngeal squamous cell carcinoma (LSCC) is one of the major diseases causing death in the world, and early diagnosis of the disease is beneficial to later treatment. Aiming at the characteristics of small sample size and many variables of LSCC data, this paper proposes a prediction method of LSCC incidence risk based on data expansion and SSA-SVM (sparrow search algorithm Optimized Support Vector Machine). Firstly, the microbiota of patient samples was ranked in order of importance using the random forest feature algorithm to extract the features of typical microbiota;secondly, the basic features of patients and microbiota were combined to build a data expansion model based on CGAN (Conditional Generative Adversarial Network);and lastly, SSA was used to find the optimization of C and g of the SVM, and to build the prediction model for the risk of LSCC development by SSA-SVM. In this study, we compare the predictive results and evaluation metrics of multiple methods using actual patient data from a well-known tertiary hospital. The results show that the prediction accuracy of the prediction model based on data expansion and SSA-SVM is better than other methods, which verifies the feasibility of the proposed method.

关键词： laryngeal squamous cell carcinoma microbiota data expansion sparrow search algorithm support vector machine

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Coal permeability alteration prediction during CO2 geological sequestration in coal seams: a novel hybrid artificial intelligence approach

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GEOMECHANICS AND GEOPHYSICS FOR GEO-ENERGY AND GEO-RESOURCES 2022年第3期8卷 1-11页

作者： Yan, Hao Zhang, Jixiong Zhou, Nan Shi, Peitao Dong, Xiangjian China Univ Min & Technol Jiangsu Key Lab Coal Based Greenhouse Gas Control Xuzhou 221008 Jiangsu Peoples R China China Univ Min & Technol Sch Mines Xuzhou 221116 Jiangsu Peoples R China China Univ Min & Technol State Key Lab Coal Resources & Safe Min Xuzhou 221116 Jiangsu Peoples R China Univ Western Australia Dept Civil Environm & Min Engn Perth WA 6009 Australia

The technology of CO2 geological storage while enhancing coalbed methane recovery has attracted significant attention. In this technology, the permeability alteration of coal after the injection of CO2 is directly related to the injectability of CO2, which is known as the CO2 geological storage effect. Currently, laboratory tests are often carried out to obtain coal permeability, however, this method has several drawbacks due to the complicated testing procedure, high cost and complex sample preparation. To efficiently forecast the permeability alteration of coal in the process of CO2 geological storage, this paper proposes an integrated new hybrid intelligent model of firefly algorithm (FA), sparrow search algorithm (SSA), and support vector machines (SVM). This FA-SSA-SVM hybrid intelligent model is trained and tested by a total of 154 data samples retrieved from the literature. The input variables include CO2 injection pressure, effective stress, coal rank, coal temperature and coal seam buried depth. The output variable is coal permeability. The evaluation indicators are R, MAE, RMSE, and MAPE. The results show that the FA-SSA-SVM prediction model have good potential for predicting the permeability alteration of coal during CO2 geological storage. In addition, by comparing and analysing the evaluation indicators among the FA-SSA-SVM, SSA-SVM and L-MRA models, the model FA-SSA-SVM shows the highest accuracy, while the L-MRA model has the lowest accuracy. These research results can provide important guidance for promoting and applying CO2 storage technology in coal seams.

关键词： Coal permeability CO2 geological storage Support vector machines sparrow search algorithm Intelligent prediction

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Prediction and analysis of key parameters of head deformation of hot-rolled plates based on artificial neural networks

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JOURNAL OF MANUFACTURING PROCESSES 2022年 77卷 282-300页

作者： Dong, Zishuo Li, Xu Luan, Feng Zhang, Dianhua Northeastern Univ State Key Lab Rolling & Automat Shenyang 110819 Peoples R China Northeastern Univ Sch Comp Sci & Engn Shenyang 110819 Peoples R China

In plate hot rolling, the prediction of head deformation of steel plates is crucial to improve the yield. In this paper, a novel prediction model based on neural networks for head deformation of medium-thick plates is proposed. The dataset of parameters of head deformation of medium-thick plates was established based on machine vision, and the artificial neural networks optimized by improved sparrow search algorithm (ISSA-ANN) was proposed to predict the irregular length and area of the head of finished medium-thick plates. During model parameters setting, mean error (MAE), root mean square error (RMSE), mean absolute percentage error (MAPE), and correlation coefficient (R) are used as the evaluation indexes. Results show that ISSA-ANN performs better than other models in this paper in predicting the head deformation of medium-thick plates. Meanwhile, the effect of key variables on head deformation is investigated and suggestions for improving the head shape of medium thick plates are given according to the influence of key variables of the model.

关键词： Hot-rolled plate Head deformation prediction Artificial neural network sparrow search algorithm Machine vision

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Prediction and parameter quantitative analysis of side orifice discharge coefficient based on machine learning

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WATER SUPPLY 2022年第12期22卷 8880-8892页

作者： Shen, Guiying Li, Shanshan Parsaie, Abbas Li, Guodong Cao, Dingye Pandey, Prashant Xian Univ Technol State Key Lab Ecohydraul Northwest Arid Reg China Xian 710048 Peoples R China Shahid Chamran Univ Ahvaz Fac Water Sci Engn Ahvaz Iran Rajkiya Engn Coll Dept Elect Engn Sonbhadra India

In this paper, the sparrow search algorithm is used to predict the discharge coefficient of the triangular side orifice for the first time. Dimensionless parameters influencing the size of Cd of side orifices are obtained as input values and discharge coefficient as output values of the model. The results show that the determination coefficient R2 is 0.973, the root means square error RMSE is 0.0122, and the average absolute percentage error is 0.010% in the testing phase. The model has high forecast accuracy, strong generalization ability and higher accuracy than other models and traditional empirical formulas. Quantitative analysis by Sobol's method shows that the ratio W/H of top orifice height to side orifice height, Fr of upstream Froude number, and ratio B/L of channel width to a bottom edge length of side orifice are the main factors influencing the discharge capacity of triangular side orifice. The first-order sensitivity coefficient and global sensitivity coefficient are 0.23, 0.11, 0.17 and 0.41, 0.39, 0.35 respectively.

关键词： discharge coefficient hydraulic characteristics quantitative analysis side orifice Sobol's method sparrow search algorithm

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Short-term industrial load forecasting based on Bi-LSTM optimized by SSA and Dropout 11

Short-term industrial load forecasting based on Bi-LSTM opti...

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11th International Conference on Information Technology - IoT and Smart City (ITIoTSC)

作者： Ying, Zhangchi Xu, Haiyang Zhou, Yang Wu, Xuan He, Dong State Grid Zhejiang Elect Power Co Ltd Informat & Commun Branch Hangzhou 310000 Peoples R China State Grid Yiwu Power Supply Co Yiwu 322000 Zhejiang Peoples R China Zhejiang Univ Coll Comp Sci & Technol Hangzhou 310030 Peoples R China

ISBN: (纸本)9798350328356;9798350328349

Accurate prediction of industrial load is a crucial step in the development of smart grids. Industrial load forecasting is fundamentally a time series forecasting problem. Traditional time series forecasting models struggle to accurately predict complex industrial load changes due to their nonlinearity, time series nature, and unstationarity. Neural network models, with their robust self-learning capabilities, can effectively process industrial load data. However, these models are prone to overfitting and uncertainty issues arising from manual parameter adjustments based on experience during training. Moreover, the inability to handle bidirectional data propagation causes conventional neural network models to lose essential load data characteristics and interrelated data information. In this paper, we propose an SSA-Dropout-Bi-LSTM prediction model based on a sliding window. First, the Bi-directional Long Short-Term Memory (Bi-LSTM) neural network theory is employed to facilitate bidirectional information transfer. Dropout technique is utilized to decrease the model's overfitting degree. The sparrow search algorithm (SSA) is further used to search for the optimal hyperparameters of the Dropout-Bi-LSTM model. The model's parameter search uncertainty is reduced by dynamically adjusting parameters through the machine learning algorithm, thereby enhancing the neural network model's generalization ability. We conducted load forecasting for six industrial users in Zhejiang province. The proposed model's Mean Absolute Percentage Error (MAPE) on the test set averages at 3.75%, an improvement compared to other combinations of LSTM models (4.17%-5.37%), and a significant enhancement compared to RNN (7.22%) and GRU (5.94%). The mean coefficient of determination (R2) of the proposed model is 94.34%, which is considerably higher than RNN (87.58%) and GRU (90.28%). In comparison, the proposed model demonstrates higher prediction accuracy and better model fitting effects.

关键词： short-term industrial load forecasting sparrow search algorithm Bi-LSTM SSA-Dropout-Bi-LSTM Optimal hyperparameters

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Study of the Sine-SSA-BP Model in Classification of Cognitive Impairment by Eye Movement Features for Patients with Epilepsy The Sine-SSA-BP Model in Classification for epilepsy 4

Study of the Sine-SSA-BP Model in Classification of Cognitiv...

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4th International Symposium on Artificial Intelligence for Medicine Science (ISAIMS)

作者： Wei, Xiaojie Zhang, Huangyemin Wen, Shirui Zhu, Guangpu Huang, Kailing Hu, Bingliang Wang, Quan Feng, Li Chinese Acad Sci Key Lab Spectral Imaging Technol Xian Inst Opt & Precis Mech Xian Peoples R China Univ Chinese Acad Sci Beijing Peoples R China Cent South Univ Dept Neurol Xiangya Hosp Changsha Peoples R China

ISBN: (纸本)9798400708138

Most studies reported that patients with epilepsy could suffer from attention dysfunction and other social cognitive impairment but there were few studies on automatic detection for patients with epilepsy of cognitive impairment based on eye-tracking technology. The current study aimed to explore objective and nontraumatic method of assisting in the detection of patients with epilepsy of cognitive impairment. Thirty-seven patients with epilepsy of cognitive impairment and twenty-nine healthy controls performed the Attention Network Test (ANT) based on eye-tracking technology. The random forest algorithm combined with the principal component analysis was applied to extract the main eye-tracking features, and then the back propagation neural network model was carried out to identify patients with epilepsy of cognitive impairment. To improve the accuracy of the classification model, the sparrow search algorithm (SSA) with Sine chaotic mapping was used to optimize the initial weight and threshold of the back propagation (BP) network. The results showed that compared with the BP network accuracy of 60.00% and the BP network optimized by the SSA accuracy of 90.45%, the BP network optimized by SSA with Sine chaotic mapping had the higher classification accuracy of 96.67%. It proved that Sine-SSA-BP based on eye-tracking features can facilitate early detection for patients with epilepsy of cognitive impairment.

关键词： Epilepsy Cognitive impairment Eye-tracking Sine chaotic mapping sparrow search algorithm Back propagation neural network

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Research on energy consumption prediction of public buildings based on improved support vector machine 35

Research on energy consumption prediction of public building...

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35th Chinese Control and Decision Conference (CCDC)

作者： Yang, Liyao Ma, Hongyan Zhang, Yingda Li, Shengyan Beijing Univ Civil Engn & Architecture Sch Elect & Informat Engn Beijing Peoples R China Beijing Univ Civil Engn & Architecture Beijing Key Lab Intelligent Proc Bldg Big Data Inst Distributed Energy Storage Safety Big Data Sch Elect & Informat Engn Beijing Peoples R China

ISBN: (纸本)9798350334722

An improved support vector machine model for energy consumption prediction is proposed to achieve efficient energy saving in buildings. In this paper, firstly, a gray correlation analysis model is used to measure the correlation between temperature, humidity, sunlight and other factors and building energy consumption. Secondly, the sparrow search algorithm (SSA) is introduced to optimize the penalty coefficient c and kernel function parameter g of the support vector machine, and then the SSA-SVM energy consumption prediction model is established. Finally, the experimental results are analyzed by comparing with the data derived from the support vector machine prediction (SVM) model and BP neural network energy consumption prediction. The experimental results show that compared with SVM and BP neural network, the prediction results of SSA-SVM model perform better in error index, indicating that the energy consumption prediction of SSA-SVM model has higher prediction accuracy;the maximum relative error of SSA-SVM prediction model is 0.0514, and the maximum relative errors of the other two models are greater than 0.55, indicating that SSA-SVM model has a higher degree of reliability.

关键词： sparrow search algorithm Energy Consumption Prediction Support Vector Machine Public Building

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Intelligent Fault Diagnosis of Rotating Machines Based on Wavelet Time-Frequency Diagram and Optimized Stacked Denoising Auto-Encoder

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IEEE SENSORS JOURNAL 2022年第17期22卷 17139-17150页

作者： Jia, Ning Cheng, Yao Liu, Yunyang Tian, Youyuan Chongqing Univ Technol Chongqing 400050 Peoples R China

When a stacked denoising auto-encoder (SDAE) manually sets several parameters, the gradient of neuron weight becomes dispersed, reducing the ability to retrieve sensitive fault feature information from a bearing vibration signal under multiple working conditions and strong noise. A bearing health monitoring and defect diagnostic model based on variational mode decomposition (VMD) combined with continuous wavelet transform (CWT) and SDAE optimized by sparrow search algorithm (SSA) is presented to tackle this problem. The wavelet time-frequency diagram is obtained by VMD and CWT, which maps the fault characteristic information to different local positions in time and scale, and then the wavelet time-frequency diagrams are input into the SDAE for in-depth training. To achieve the ideal structure of SDAE and increase the feature extraction capabilities of SDAE for weak signals, SSA is utilized for the global combination and adaptive selection of several SDAE parameters. The bearing failure diagnostic model based on VMD-CWT-SSA-SDAE outperforms BPNN, SVM, the traditional SDAE, GA-SDAE, PSO-SDAE, and SSA-DBN in diagnosis accuracy, generalization performance, and anti-noise performance when tested on various data sets.

关键词： Feature extraction Time-frequency analysis Fault diagnosis Continuous wavelet transforms Wavelet transforms Optimization Vibrations Rotating machines fault diagnosis the wavelet time-frequency diagram sparrow search algorithm stacked denoising auto-encoder

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Enterprise Performance Prediction Based on FASSA Elman Neural Network 3

Enterprise Performance Prediction Based on FASSA Elman Neura...

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3rd IEEE International Conference on Electrical Engineering, Big Data and algorithms, EEBDA 2024

作者： Wang, Enhua Claro M. Recto Academy of Advanced Studies Lyceum of the Philippines University Manila Manila Philippines

ISBN: (纸本)9798350380989

The Elman Neural Network, known for its distinctive architecture with recurrent connections, is a tool for dynamic modeling and capturing temporal dependencies. To enhance its capabilities, this paper introduces an innovative optimization approach, addressing challenges related to random weight initialization. We propose an enhanced Elman Neural Network optimization strategy using the sparrow search algorithm (SSA), recognizing its global search capabilities and improved precision. To overcome SSA's drawbacks, a novel Firefly Optimized sparrow search algorithm is introduced, integrating the Firefly algorithm to enhance optimization complexity. The FA-SSA optimized Elman Neural Network is applied to predict enterprise performance, showcasing benefits in capturing temporal dynamics and intricate patterns. In summary, the proposed Firefly Optimized sparrow search algorithm offers a robust solution for systematic weight and threshold optimization, enhancing the Elman Neural Network's performance. © 2024 IEEE.

关键词： Elman Neural Network Firefly algorithm performance prediction sparrow search algorithm

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