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Integration of Refined Composite Multiscale Cross-Sample Entropy and Backpropagation Neural Networks for Structural Health Monitoring
(2020-02-01)
Lin, Tzu-Kang
;
Chen, Yu-Ching
;
土木工程學系
;
Department of Civil Engineering
This study developed a structural health monitoring (SHM) system based on refined composite multiscale cross-sample entropy (RCMCSE) and an artificial neural network for monitoring structures under ambient vibrations. RCMCSE was applied to enhance the reliability of entropy estimations. First, RCMCSE was implemented to extract damage features, and finite element analysis software was then used to generate training samples, which included stiffness reductions to achieve various damage patterns. A neural network model was constructed and trained using entropy values for these damage patterns. An experiment was conducted on a seven-story steel benchmark structure to validate the performance of the proposed system. Additionally, a confusion matrix was established to evaluate the performance of the proposed system. The results obtained for a scaled-down benchmark structure indicated that 89.8% of the floors were accurately classified, and 90% of the practical damaged floors were correctly diagnosed. The performance evaluation demonstrated that the proposed SHM system exhibited increased damage location accuracy.
Neural Network Based Luminance Variation Resistant Remote-Photoplethysmography for Driver's Heart Rate Monitoring
(2019-01-01)
Wu, Bing-Fei
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Chu, Yun-Wei
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Huang, Po-Wei
;
Chung, Meng-Liang
;
電機工程學系
The driver's physiological status has enormous value to public traffic safety and cannot be ignored nowadays. In addition, heart rate (HR) is one of the most important indicators of human's health status. When detecting the driver's HR, using traditional contact-type devices might bring about the driver's distraction or discomfort. On the contrary, the remote photoplethysmography (rPPG) technique is a better way to monitor a driver's HR in vehicle applications simply by using a web-camera without interfering the driver. Most of the rPPG studies intended to reduce the interference caused by facial motion or luminance changes in the indoor or controlled scenario, but there are relatively fewer discussions on outdoor scenarios. Consequently, the purpose of this paper is to enhance the rPPG technique to make it suitable for the outdoor driving scenarios and for monitoring the driver's HR in different weather conditions, including daytime and nighttime. We first utilize artificial neural network (ANN) and train multiple personalized ANN models for each driver. For predicting the drivers' HR beat more precisely, we propose the approach, adaptive neural network model selection (ANNMS), which adaptively selects a personalized ANN model based on different noise conditions. Our algorithm eliminates the effect of noises caused by the variations of facial luminance in eight outdoor driving scenarios. The proposed driver's HR beat monitoring system has been evaluated against the state-of-the-art rPPG techniques that are Chrominance signal-based (CHRO) and k-nearest neighbors-based (kNN) algorithms. Compared with the CHRO and kNN algorithms, the ANNMS reduces the mean absolute error from 14.71 bpm (CHRO) and 9.91 (kNN) to 4.51 bpm (ANNMS) and enhances the successrate-10, the probability in which the absolute error is below 10 bpm, from 44.1% (CHRO) and 56.3% (kNN) to 91.5% (ANNMS).
An Augmented Reality Question Answering System Based on Ensemble Neural Networks
(2017-01-01)
Chen, Chi-Hua
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Wu, Chen-Ling
;
Lo, Chi-Chun
;
Hwang, Feng-Jang
;
資訊管理與財務金融系註：原資管所+財金所
This paper proposes a classification algorithm based on ensemble neural networks. In the training phase, the proposed algorithm uses a random number of training data to develop multiple random artificial neural network (ANN) models until those ANN models converge. Those models with lower accuracy than the threshold are filtered out. The remaining highly accurate models will be used to predict the output in the testing phase. Meanwhile, the accuracy of ANN models is presented as a weighting value in the testing phase. In the testing phase, the testing data are loaded into the selected ANN models to predict the output class. The output values are multiplied by the corresponding weighting values of ANN models. Then the weighted average of the outputs can be obtained. Finally, the predicted output is converted into the predicted class. We design an augmented reality question answering system (AR-QAS) applying and implementing the proposed algorithm on mobile devices. AR-QAS offers an interactive user interface and automatically replies according to user's queries. By comparing with the logistic regression method and the ANN method, the experiment results demonstrate that the proposed algorithm offers the highest accuracy.
Developing a Conjunctive Use Optimization Model for Allocating Surface and Subsurface Water in an Off-Stream Artificial Lake System
(2016-08-01)
Pan, Chen-Che
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Chen, Yu-Wen
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Chang, Liang-Cheng
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Huang, Chun-Wei
;
土木工程學系
This work develops a rule curve-based conjunctive use management model for optimizing the operating rules for a lake-groundwater system with off-stream storage lakes. The proposed procedure is a simulation-optimization approach that embeds an Artificial Neural Network ( ANN) instead of a groundwater numerical model into a genetic algorithm ( GA). The direct physical exchange between lake water with groundwater is simulated using the ANN model, which is a reduced version of a full numerical model, MODFLOW with an LAK3 module. By applying the ANN model, the proposed procedure can reduce the computational burden that is induced by the nonlinear exchange. An operating rule-based optimal conjunctive use management model for the Gaopin Artificial lakes system in Taiwan was thus developed using the proposed framework. A set of optimal solutions involves rule curves and a discount ratio. Simulation results demonstrate that the embedded ANN model can accurately simulate the nonlinear exchange of a lake with groundwater. The embedded ANN model is less computationally complex than the numerical model. This work demonstrates a methodology for reducing the computational burden of the optimal conjunctive use management model that is associated with an internal nonlinear system by using the ANN reduced model. Specifically, the concept of, and results obtained using the developed operating rule-based model incorporating five artificial lakes and considering the nonlinear exchange of those lakes with the groundwater system provides a valuable practical reference for solving related conjunctive use problems.
On the influence of spread constant in radial basis networks for electrical impedance tomography
(2016-06-01)
Martin, Sebastien
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Choi, Charles T. M.
;
交大名義發表
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National Chiao Tung University
Electrical impedance tomography (EIT) is a non-invasive imaging technique. The main task of this work is to solve a non-linear inverse problem, for which several techniques have been suggested, but none of which gives a very high degree of accuracy. This paper introduces a novel approach, based on radial basis function (RBF) artificial neural networks (ANNs), to solve this problem, and uses several ANNs to obtain the best solution to the EIT inverse problem. ANNs have the potential to directly estimate the solution of the inverse problem with a high degree of accuracy. While different radial basis neural networks do not always perform well on different problems, they usually give good results on some specific problems. This paper evidences a strong correlation between the area of the target and the spread constant of the RBF network that gives the best reconstruction. A solution to automatically estimate the size of the target and pick the best neural network directly from voltage measurements is presented, making the reconstruction process automatic. By automatically selecting the best ANN for each specific set of voltage measurements, the proposed solution gives a more accurate reconstruction of both small and large targets.
On the influence of spread constant in radial basis networks for electrical impedance tomography
(2016-06)
Martin, Sebastien
;
Choi, Charles T. M.
;
交大名義發表
;
National Chiao Tung University
Electrical impedance tomography (EIT) is a non-invasive imaging technique. The main task of this work is to solve a non-linear inverse problem, for which several techniques have been suggested, but none of which gives a very high degree of accuracy. This paper introduces a novel approach, based on radial basis function (RBF) artificial neural networks (ANNs), to solve this problem, and uses several ANNs to obtain the best solution to the EIT inverse problem. ANNs have the potential to directly estimate the solution of the inverse problem with a high degree of accuracy. While different radial basis neural networks do not always perform well on different problems, they usually give good results on some specific problems. This paper evidences a strong correlation between the area of the target and the spread constant of the RBF network that gives the best reconstruction. A solution to automatically estimate the size of the target and pick the best neural network directly from voltage measurements is presented, making the reconstruction process automatic. By automatically selecting the best ANN for each specific set of voltage measurements, the proposed solution gives a more accurate reconstruction of both small and large targets.
Robot control optimization using reinforcement learning
(1998-03-01)
Song, KT
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Sun, WY
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電控工程研究所
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Institute of Electrical and Control Engineering
Conventional robot control schemes are basically model-based methods. However, exact modeling of robot dynamics poses considerable problems and faces various uncertainties in task execution. This paper proposes a reinforcement learning control approach for overcoming such drawbacks. An artificial neural network (ANN) serves as the learning structure, and an applied stochastic real-valued (SRV) unit as the learning method. Initially, force tracking control of a two-link robot arm is simulated to verify the control design. The simulation results confirm that even without information related to the robot dynamic model and environment states, operation rules for simultaneous controlling force and velocity are achievable by repetitive exploration. Hitherto, however, an acceptable performance has demanded many learning iterations and the learning speed proved too slow for practical applications. The approach herein, therefore, improves the tracking performance by combining a conventional controller with a reinforcement learning strategy. Experimental results demonstrate improved trajectory tracking performance of a two-link direct-drive robot manipulator using the proposed method.
Applying robust multi-response quality engineering for parameter selection using a novel neural-genetic algorithm
(2003-01-01)
Li, TS
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Su, CT
;
Chiang, TL
;
工業工程與管理學系
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Department of Industrial Engineering and Management
This study presents a neural-genetic algorithm to solve the selection problem of manufacturing process parameters. The proposed algorithm is a combination of artificial neural network (ANN) and genetic algorithms (GAs). In addition, the neural network is used to formulate a fitness function for predicting the value of the response based on the parameter settings. GAs then take the fitness function from the trained neural network to search for the optimal parameter combination. Owing to the most of manufactured products have more than one quality characteristic and the quality characteristics are generally correlated with each other, this study also proposes a desirability function to obtain a compromise, composite solution. A case study of how the silicon manufacturing process parameters are selected offline demonstrates the effectiveness of the proposed approach. (C) 2002 Elsevier Science B.V. All rights reserved.
Due-date assignment in wafer fabrication using artificial neural networks
(2004-05-01)
Sha, DY
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Hsu, SY
;
工業工程與管理學系
;
Department of Industrial Engineering and Management
Due-date assignment (DDA) is the first important task of shop floor control in wafer fabrication. Due-date related performance is impacted by the quality of the DDA rules. Assigning order due dates and timely delivering the goods to the customer will enhance customer service and competitive advantage. A new methodology for lead-time prediction, artificial neural network (ANN) prediction is considered in this work. An ANN-based DDA rule combined with simulation technology and statistical analysis is developed. Besides, regression-based DDA rules for wafer fabrication are modelled as benchmarking. Whether neural networks can outperform conventional and regression-based DDA rules taken from the literature is examined. From the simulation and statistical results, ANN-based DDA rules perform a better job in due-date prediction. ANN-based DDA rules have a lower tardiness rate than the other rules. ANN-based DDA rules have better sensitivity and variance than the other rules. Therefore, if the wafer fab information is not difficult to obtain, the ANN-based DDA rule can perform better due-date prediction. The SFM_sep and JIQ in regression-based and conventional rules are better than the others.
The development of a real-time flooding operation model in the Tseng-Wen Reservoir
(2014)
Chen, Yu-Wen
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Tsai, Jul-Pin
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Chang, Liang-Cheng
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Ho, Chih-Chao
;
Chen, You-Cheng
Typhoon events occur frequently in Taiwan resulting in flood-related disasters. A well-operated reservoir can reduce the severity of a disaster. This study incorporates a genetic algorithm, a river hydraulic model, an artificial neural network and a simulation model of Tseng-Wen Reservoir to propose a real-time flooding operation model. The model includes two parts: an optimal flooding operation model (OFOM) and a reservoir inflow forecasting. Given an inflow condition, the OFOM is run based on the safety of the dam structure, reservoir flooding operation rule, and minimization of the downstream loss due to flood. A simple and robust model for reservoir inflow forecasting, which automatically chooses the most similar event from a typhoon event database as the future inflow, is developed. This study compares the model results with the real operations during Typhoons Sepat, Krosa, Kalmaegi, Fung-wong, Sinlaku, and Jangmi. This study compares the performances of the proposed model with the practical operation operated by the management center of Tseng-Wen Reservoir. The proposed model indicates shorter flooding duration in the downstream area. For example, the flood durations of the model output are 4 and 3 hours shorter during Typhoon Krosa and Sinlaku, respectively, than the practical operations.

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