Parallel-connected convolutional neural network with minority and majority feature extraction for the estimation of the remaining useful life of turbofans

The prediction of remaining useful life (RUL) plays a crucial role in assessing the condition of a machine before it completely fails, ensuring performance by the execution of preventive maintenance beforehand. Recently, various deep learning models have been frequently used for RUL estimation, and they have shown good performance. However, these deep learning models face several challenges such as inefficiency owing to the selection of complex preprocessing methods, overfitting owing to model complexity, and other unresolved issues. Therefore, this study proposes a new deep-learning-based approach to address these issues by constructing a novel structure that includes a simple preprocessing step, minority feature extraction module, and majority feature extraction module. First, it explains the relatively simple preprocessing and assumptions regarding the target data of an undefined training set. Second, we describe the design of a convolution-based model using minority and majority feature extraction modules created through 2D convolutional layers. This model can learn the relationships between minority and majority sensors over time. By connecting the modules in parallel, it aggregates various types of information using multiple features from a single dataset. Finally, we present various experiments on the proposed algorithm and compare it with the latest existing methods using the NASA C-MAPSS dataset.TRUEkcikci_cand

파노라마 방사선 사진에서 하악구치부의 임플란트 매식부위 평가시 치조정-하악관간 거리의 확대율에 영향을 미치는 요소에 관한 연구

Thesis (master`s)--서울대학교 대학원 :치의학과 치주과학전공,2001.Maste

Support Vector Machine Equalizer for Holographic Data Storage

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Regularization Parameter of Normalized Subband Adaptive Filter

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A Study on Mean-Square-Deviation Analysis of Normalized Subband Adaptive Filter-type Algorithm and the Performance Improvement

DoctorThis thesis proposes the mean-square-deviation (MSD) analysis of the normalized subband adaptive filter (NSAF)-type algorithm. The analysis provides useful guidelines to design the algorithm and allows the verification of the performance of the algorithm. In addition, the analysis gives us a way to improve the performance of the algorithm by varying step size or varying regularization parameter. Chapter 2 and 3 present the MSD analysis of the NSAF-type algorithm for long-length adaptive filter. Chapter 2 proposes a new approach to the MSD analysis of the NSAF algorithm by using the persistently exciting input and the practical assumption that the stopband attenuation of the prototype filter is high. Unlike the previous analysis, the proposed analysis is possible to be applied to the long-length adaptive filter such as the acoustic echo cancellation. The proposed analysis is also applied to a non-stationary model with a random walk of the optimal weight vector. The simulation results match with the theoretical results in both the transient-state and steady-state MSD. Chapter 3 proposes a general solution of steady-sate MSD analysis of the improved NSAF algorithm, which is based on the substitution of the past weight error vector in the weight error vector. The simulation shows that our theoretical results correspond closely with the computer simulation results in various environments. Chapter 4 and 5 improve the performance of the NSAF algorithm by using the MSD analysis. Chapter 4 describes a variable step size for the NSAF is derived by minimizing the MSD at each instant of time. The variable step size is presented in terms of error variance. Therefore, the proposed algorithm is capable of tracking in non-stationary environments. The simulation results show good tracking ability and low misalignment of the proposed algorithm in system identification. Chapter 5 proposes a variable regularization scheme for the NSAF is derived on the basis of the relationship between the weight-error vector and weight vector update, and by using the calculated MSD. The performance of the variable regularization algorithm is evaluated in terms of MSD. Our simulation results exhibit fast convergence and low steady-state MSD when using the proposed algorithm

Estimation of slag foaming height using slag gate image in electric arc furnace

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비접촉식 심전도 신호를 이용한 운전자 졸음 검출 시스템

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Adaptive combination with improved performance for sparse system

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Deep Learning-Based Pig Weight Estimation Algorithm Using Mobile Devices

This paper proposes a deep learning algorithm for estimating pig weight. The proposed algorithm estimates the weight of a pig using the point cloud obtained through a mobile device. The proposed model is based on the PointNet which is widely used in the point cloud data. Through the optimization of the PointNet, the proposed method not only improves the accuracy, but also reduces the computational complexity. The accuracy (82.4 %) of the proposed method was about 3 % higher than that of the conventional method (79.4 %). Also, the numbers of the trainable parameters for the PointNet and the proposed method were 3,114,771 and 150,554, respectively. That is, the proposed method used only 5 % of trainable parameters compared to the PointNet. The developed model makes it easier and faster to measure the weight of a pig than the conventional method.FALS