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bayesian-Based Hyperparameter optimization of 1D-CNN for Structural Anomaly Detection

SENSORS 2023年第11期23卷 5058-5058页

作者： Li, Xiaofei Guo, Hainan Xu, Langxing Xing, Zezheng Dalian Maritime Univ Coll Transportat Engn Dalian 116026 Peoples R China Univ Jinan Coll Informat Sci & Engn Jinan 250022 Peoples R China

With the rapid development of sensor technology, structural health monitoring data have tended to become more massive. Deep learning has advantages when handling big data, and has therefore been widely researched for diagnosing structural anomalies. However, for the diagnosis of different structural abnormalities, the model hyperparameters need to be adjusted according to different application scenarios, which is a complicated process. In this paper, a new strategy for building and optimizing 1D-CNN models is proposed that is suitable for diagnosing damage to different types of structure. This strategy involves optimizing hyperparameters with the bayesian algorithm and improving model recognition accuracy using data fusion technology. Under the condition of sparse sensor measurement points, the entire structure is monitored, and the high-precision diagnosis of structural damage is performed. This method improves the applicability of the model to different structure detection scenarios, and avoids the shortcomings of traditional hyperparameter adjustment methods based on experience and subjectivity. In preliminary research on the simply supported beam test case, the efficient and accurate identification of parameter changes in small local elements was achieved. Furthermore, publicly available structural datasets were utilized to verify the robustness of the method, and a high identification accuracy rate of 99.85% was achieved. Compared with other methods described in the literature, this strategy shows significant advantages in terms of sensor occupancy rate, computational cost, and identification accuracy.

关键词： structural anomaly detection 1-D convolutional neural network bayesian optimization algorithm decision-level fusion vibration signals

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Saw Blade Wear Identification Based on Data Enhancement and Feature Fusion

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IEEE ACCESS 2023年 11卷 123677-123687页

作者： Wang, Chengchao Wang, Xiangjiang Zeng, Chao Univ South China Coll Mech Engn Hengyang 421001 Peoples R China Univ South China Sch Nucl Sci & Technol Hengyang 421001 Peoples R China

In order to solve the problem of low accuracy of tool wear detection due to the poor quality of generated data under small sample problems, a deep learning model based on data enhancement and feature fusion is proposed. Firstly, in order to solve the problem that there is no quality evaluation standard in the training process of the traditional generative adversarial network (GAN), the K nearest neighbor algorithm is proposed to test the data generated by the GAN model for the second time. The improved GAN model can be automatically trained to get the optimal model according to the second test results. Secondly, in order to enhance the anti-interference effect of the model, a double-path parallel convolutional neural network (DPCNN) which combines with the characteristics of frequency domain and time-frequency domain is constructed to analyze the wear data. Furthermore, the hyperparameters of the model are optimized by bayesian optimization algorithm (BOA). Finally, the effectiveness of this method is verified in the saw blade wear detection experiment. The results show that the performance of this model is better than other models, and the accuracy rate in the experimental detection reaches 100%.

关键词： Data models Generative adversarial networks Convolutional neural networks Milling Feature extraction Monitoring Training Predictive control Aging Tools Product safety Saw blade wear identification feature fusion bayesian optimization algorithm double-path parallel convolution neural network generative adversarial networks

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Performance degradation prediction method of PEM fuel cells using bidirectional long short-term memory neural network based on bayesian optimization

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ENERGY 2023年 285卷

作者： Chen, Dongfang Wu, Wenlong Chang, Kuanyu Li, Yuehua Pei, Pucheng Xu, Xiaoming Univ Sci & Technol Beijing Sch Mech Engn Beijing 100083 Peoples R China Tsinghua Univ State Key Lab Automot Safety & Energy Beijing 100084 Peoples R China

Proton exchange membrane (PEM) fuel cell is the core equipment that can directly convert hydrogen energy into electricity. In the process of long-term operation, due to the aging of membrane electrode assembly and other components, the fuel cell performance gradually deteriorates. The voltage prediction of fuel cells is very important for performance and lifetime optimization. Long short-term memory neural network is one of the widely used prediction methods. Based on the prediction method of bidirectional long short-term memory neural network, the hyperparameters of the neural network model by bayesian optimization algorithm is optimized to improve the accuracy of fuel cell performance degradation prediction. When the sampling time interval is 25 min and the training set is 45 %, the root mean square error and the average absolute percentage error of the prediction results is reduced to 6.3 mV and 0.1245 %, respectively. Moreover, by analyzing the influence of different sampling time intervals and training set proportion on the prediction results, a data set that takes into accounts both efficiency and accuracy is obtained. The proposed method based on bayesian optimization can achieve more accurate performance degradation prediction.

关键词： PEM fuel cell Performance degradation prediction Long short -term memory neural network bayesian optimization algorithm Sampling time interval

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Deep learning-based prediction of delamination growth in composite structures: bayesian optimization and hyperparameter refinement

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PHYSICA SCRIPTA 2023年第10期98卷 106004-106004页

作者： Demircioglu, Ufuk Bakir, Halit Sivas Univ Sci & Technol Fac Engn & Nat Sci Dept Mech Engn Sivas Turkiye Sivas Univ Sci & Technol Fac Engn & Nat Sci Dept Comp Engn Sivas Turkiye

In this study, a novel method for detecting the growth of delamination in sandwich structures has been proposed. To this end, we suggested hybridizing the Deep Learning techniques (DL) and Finite Element Method (FEM) for predicting the growth of delamination in this structures. A dataset of simulated delamination growth under different delamination sizes has been produced using the FEM method. Then, a DL model has been trained using this dataset to precisely predict the growth of delamination. This study focused on predicting delamination growth using a tuned and optimized deep learning based regressor. Therefore, to find the ideal set of hyperparameters, the bayesian optimization algorithm has been used for selecting the best structure and enhancing the regressor performance. Afterward, the model was evaluated and multiple processes were conducted to improve its behavior and solve its stability and overfitting issues. Particularly, an inconsistency between validation loss and training loss has been initially detected in the behaviour of the model, which may indicate overfitting. To tackle this issue, dropout regularization has been added, which improved the consistency between the loss functions but results in less smooth convergence from the expectations. So, in a third study, dropout and L1 regularization has been combined to improve the stability of the model. This combination achieved a consistent and smooth convergence between the validation and training loss functions. The findings highlight the importance of hyperparameter optimization and regularization techniques in improving regression model performance. The study shows the efficiency of bayesian optimization in hyperparameter tuning and the iterative optimization of a regression model. Furthermore, the outcomes show that the suggested method can identify and predict delamination growth with high accuracy.

关键词： sandwich structure delamination growth deep learning bayesian optimization algorithm hyperparameter tuning

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Prediction of PM2.5 Concentration Based on bayesian optimization Random Forest

Prediction of PM2.5 Concentration Based on Bayesian Optimiza...

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第43届中国控制会议

作者： Yingji Deng Pengfei Zhi Wanlu Zhu Taize Sang Yuanjiang Li Jiangsu University of Science and Technology Jiangsu Daqo Cubicle-Type Substation Technology Co. Ltd.

ISBN: (数字)9789887581581

ISBN: (纸本)9798350366907

Nowadays, people pay more and more attention to air quality, and smog as a serious air pollution phenomenon has received greater attention. As the main culprit of haze phenomenon and an important indicator of air quality, it is of great significance to forecast PM2.5. Aiming at the prediction of PM2.5 concentration, this paper proposes a method of using random forest algorithm to build a regression prediction model and then predict PM2.5 concentration. This method mainly uses meteorological factors related to PM2.5(such as dew point, temperature, humidity, etc.) and historical data of PM2.5 concentration in the past three hours as input to build the model. Forecast future hourly PM2.5 concentrations. In this paper, the random forest model is successfully constructed, and the bayesian algorithm is used to optimize the random forest model, and the PM2.5 concentration is well predicted. At the same time, this paper also constructs the decision tree, support vector machine, intelligent gray Wolf algorithm optimized random forest model and Bayes algorithm optimized random forest model to compare. The superiority of bayesian algorithm in optimizing stochastic forest model is proved.

关键词： Integrated learning Random forest PM2.5 concentration prediction bayesian optimization algorithm

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Hybrid intelligence modeling for estimating shear strength of FRP reinforced concrete members

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NEURAL COMPUTING & APPLICATIONS 2022年第9期34卷 7069-7079页

作者： Alam, Md Shah Sultana, N. Hossain, S. M. Zakir Islam, Mohammad S. Univ Bahrain Coll Engn Dept Civil Engn Zallaq Bahrain Imam Abdulrahman Bin Faisal Univ Coll Comp Sci & Informat Technol Dept Comp Sci Dammam Saudi Arabia Univ Bahrain Coll Engn Dept Chem Engn Zallaq Bahrain

The corrosion problem in the conventional steel reinforcement in concrete structures has diverted the researchers to explore alternative materials. As a substitute to replace the traditional steel bars in reinforced concrete structures, innovative reinforcement such as fiber reinforced polymer (FRP) rebars has been suggested. Consequently, different codes and guidelines have been proposed for forecasting the shear strength of FRP reinforced members using traditional empirical methods and neural networks. The current paper concentrates on the development of a hybrid intelligence model, namely an artificial neural network articulated with a bayesian optimization algorithm (ANN-BOA) for estimating the shear strength of these types of members without stirrups. Totally, 216 specimens, collected from the literature, were used in this analysis. The input parameters of the model were beam depth, the ratio of shear span and depth, effective reinforcement ratio, and concrete strength. The ANN hyperparameters (viz. neuron numbers in the hidden layer, learning rate) have been tuned automatically to get the best predictions. The estimated shear strengths are compared with the recent design provisions of Japan (JSCE), UK (BISE), Italy (CNR-DT 203), Canada (CHBDC, CSA S806), and the USA (ACI 440.1R). The results were also compared with a similar ANN model. It was observed that the predicted results using the proposed method are better than those of the other methods in terms of some statistical as well as performance measuring parameters with a maximum Pearson correlation coefficient (R) value of 0.97. These values are higher than the other investigated methods.

关键词： Hybrid intelligence Artificial neural network bayesian optimization algorithm Reinforced concrete Shear capacity Fiber reinforced polymer

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A novel remaining useful life prediction method based on multi-support vector regression fusion and adaptive weight updating

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ISA TRANSACTIONS 2022年 131卷 444-459页

作者： Li, Yuxiong Huang, Xianzhen Zhao, Chengying Ding, Pengfei Northeastern Univ Sch Mech Engn & Automat Shenyang 110819 Peoples R China Northeastern Univ Key Lab Vibrat & Control Aeroprop Syst Minist Educ China Shenyang 110819 Peoples R China

Remaining useful life prediction is of huge significance in preventing equipment malfunctions and reducing maintenance costs. Currently, machine learning algorithms have become hotspots in remain -ing useful life prediction due to their high flexibility and convenience. However, machine learnings require large amounts of data, and their prediction performance depends heavily on the selection of hyper-parameters. To overcome these shortcomings, a novel remaining useful life prediction method for small sample cases is proposed based on multi-support vector regression fusion. In the offline training phase, the fusion model is established, consisting of multiple support vector regression sub-models To obtain the optimal sub-model parameters, the bayesian optimization algorithm is applied and an improved optimization target is formulated with various metrics describing regression and prediction performance. In the online prediction phase, an adaptive weight updating algorithm based on dynamic time warping is developed to measure the fitness of each sub-model and determine the corresponding weight value. The C-MAPSS engine dataset is used to test the performance of the proposed method, along with some existing machine learning methods as comparison. The proposed method only requires 30% of the training data sample to achieve high accuracy, with a root mean square error of 14.98, which is superior to other state-of-the-art methods. The results demonstrate the superiority of the proposed method.(c) 2022 ISA. Published by Elsevier Ltd. All rights reserved.

关键词： Remaining useful life Small sample cases Support vector machine Parameters optimization bayesian optimization algorithm Adaptive updated weight

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Real-time data based thermal comfort prediction leading to temperature setpoint control

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JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING 2022年第9期14卷 12049-12060页

作者： Kumar, T. M. Sanjeev Kurian, Ciji Pearl Manipal Acad Higher Educ Manipal Inst Technol Dept Elect & Elect Engn Manipal India

The different thermal comfort indices such as Predictive Mean Vote (PMV), Standard Effective Temperature (SET), and Thermal Sensations (TS) have been used to predict occupants' thermal comfort in a building. The advances in the machine learning approach help overcome the challenges of predicting current traditional thermal indices in a real-time environment. The different indices have different types of data samples (continuous/labelled). Therefore, while considering the machine learning technique in developing the models of the predictive thermal indices, it is essential to select the vital features, the proper learning type, the algorithm, and the evaluation method to establish the models of the predictive thermal comfort. The main focus of this paper is on the development of the ML model and the evaluation technique that helps in selecting the best model in predicting the thermal indices. This work proposes the new neighbourhood-component-analysis bayesian-optimization-algorithm-based artificial-neural-network to develop a predictive model for the thermal indices. Here, we have proposed a regression-based model to predict PMV, SET and a classification-based model to predict 7-point TS. The statistical-testing results specify that the ANN model's performance is highly accurate and more reliable in predicting the thermal perception in a real-time environment. The performance of the selected model is validated using subjective measures. This prediction leads to the pre-emptive control of the setpoint temperature of the air-conditioning unit, hence resulting in energy efficiency and comfort.

关键词： Artificial neural network bayesian optimization algorithm Feature selection Neighborhood component analysis Thermal comfort metrics Machine learning Temperature setpoint

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Lithium Battery State-of-Charge Estimation Based on a bayesian optimization Bidirectional Long Short-Term Memory Neural Network

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ENERGIES 2022年第13期15卷 4670-4670页

作者： Yang, Biao Wang, Yinshuang Zhan, Yuedong Kunming Univ Sci & Technol Fac Informat Engn & Automat Kunming 650500 Yunnan Peoples R China Kunming Univ Sci & Technol Yunnan Prov Key Lab Artificial Intelligence Kunming 650500 Yunnan Peoples R China Kunming Univ Sci & Technol Minist Educ Key Lab Unconvent Met Kunming 650093 Yunnan Peoples R China

State of charge (SOC) is the most important parameter in battery management systems (BMSs), but since the SOC is not a directly measurable state quantity, it is particularly important to use advanced strategies for accurate SOC estimation. In this paper, we first propose a bidirectional long short-term memory (BiLSTM) neural network, which enhances the comprehensiveness of information by acquiring both forward and reverse battery information compared to the general one-way recurrent neural network (RNN). Then, the parameters of this network are optimized by introducing a bayesian optimization algorithm to match the data characteristics of lithium batteries with the network topology. Finally, two sets of lithium battery public data sets are used to carry out experiments under different constant temperature and variable temperature environments. The experimental results show that the proposed model can effectively fit the actual measurement curve. Compared with traditional long short-term memory network (LSTM) and BiLSTM models, the prediction accuracy of the Bayes-BiLSTM model is the best, with a root mean square error (RMSE) within 1%, achieving a better ability for capturing long-term dependencies. Overall, the model exhibits high accuracy, adaptability, and generalization for the SOC estimation of batteries with different chemical compositions.

关键词： lithium battery state of charge bayesian optimization algorithm bidirectional long short-term memory neural network

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AI- and IoT-based hybrid model for air quality prediction in a smart city with network assistance

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IET NETWORKS 2022年第6期11卷 221-233页

作者： Kataria, Aman Puri, Vikram CSIR CSIO Imaging Av & Display Syst Chandigarh India Duy Tan Univ Ctr Virtualizat & Simulat Da Nang Vietnam

Air pollution is one of the biggest concerns in the world but it has not been paid much attention in developing countries. It is necessary to design models and methods to understand air pollution in developing countries to reduce the rate of pollution. This paper proposes an Internet of Things (IoT) and Artificial Intelligence (AI)-based hybrid model to predict the Air Quality Index (AQI) with a practical case study of the public data sets. The sensor node is deployed in the city to collect air quality data. Moreover, this sensor node connects to the cloud server for collecting data at the firebase real-time database through a WiFi/5G network embedded in the raspberry controller. Carbon monoxide (CO) and fine particular matter PM2.5 sensors are integrated within a sensor node to monitor the AQI of the regions. A Kalman fis also applied to remove unwanted noise from the data collected through the sensor node. Models namely Artificial Neural Network (ANN), Support Vector Machine (SVM), k-nearest neighbour (k-NN), Convolutional Neural Networks (CNN), Long Short Term Memory (LSTM), CNN-LSTM, ensemble model, and a proposed model, that is, CNN-LSTM-bayesian optimization algorithm (BOA) model, have been utilised to predict the AQI. The performance evaluation of models is done through statistical parameters, such as mean absolute error (MAE), root mean square error (RMSE), coefficient of determination (R-2), and accuracy score on two different public data sets and compared with the baseline models. The performance of the CNN-LSTM-BOA model is better than baseline models in terms of above-mentioned statistical parameters as the accuracy reported is more than 97 %.This study can help predict the Air Quality Index and provide sufficient time to generate warning signals in the location.

关键词： 5G Communication AQI bayesian optimization algorithm CNN IoT Kalman filter LSTM

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