• 한국정밀공학회지
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• 제31권11호
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• pp.1031-1040
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• 2014

In this paper, a new condition diagnosis algorithm for the lens injection molding process using various features extracted from cavity pressure, nozzle pressure and screw position signals is developed with the aid of probability neural network (PNN) method. A new feature extraction method is developed for identifying five (5), seven (7) and two (2) critical features from cavity pressure, nozzle pressure and screw position signals, respectively. The node energies extracted from cavity and nozzle pressure signals are also considered based on wavelet packet decomposition (WPD). The PNN method is introduced to build the condition diagnosis model by considering the extracted features and node energies. A series of the lens injection molding experiments are conducted to validate the model, and it is demonstrated that the proposed condition diagnosis model is useful with high diagnosis accuracy.

• Journal of Electrical Engineering and Technology
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• 제12권5호
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• pp.1955-1962
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• 2017

Induction motors are a workhorse for the industry. The condition monitoring and fault analysis are the main concern for the engineers. The bearing is one of the vital segment of the induction machine and the condition of the whole machine is decided based on the condition of the bearing. In the present paper, the vibration signal of the bearing has been used for the analysis. The first line of action is to perform a statistical analysis of the vibration signal which gives trends in signal. To get the location of a fault in the bearing the second action is to develop an index based on Wavelet Packet Transform node energy named as Bearing Damage Index (BDI). Further, Teager-Kaiser Energy Operator (TKEO) has been calculated from higher index value to get the envelope and finally Power Spectral Density (PSD) has been applied to identify the fault frequencies. A performance index has also been developed to compare the usefulness of the proposed method with other existing methods. The result shows that the strong amplitude of fault characteristics and its side bands help to decide the type of fault present in the recorded signal obtained from the bearing.

• 전자공학회논문지CI
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• 제38권2호
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• pp.16-26
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• 2001

본 논문에서는 주어진 필터와 구현 복잡도에 대하여 최대 코딩이득을 내는 부 주파수 분활을 가진 서브밴드 부호화기를 구축하는 고속 알고리듬을 제안한다. 이를 위하여 본 논문에서는 직교 기저 및 비 직교 기저와 임의의 부 주파수 분할에 대하여 적용할 수 있는 통합적인 코딩이득의 식을 유도한 다음, 부 주파수 대역수에 대하여 코딩이득이 단순 증가 함수임을 증명한다, 이를 바탕으로 복잡도에 대하여 최대 코딩이득을 내는 최적화 된 부 주파수 분할을 찾아내기 위하여 그 단순 증가 함수를 부 주파수 대역 수에 따른 왜곡 함수로 다룬다. 이 왜곡 함수을 목적함수로 두고 Lagrange 방법에 근거하여 최적화 된 해를 고속으로 제공하는 알고리듬을 개발한다.

• 한국IT서비스학회지
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• 제7권2호
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• pp.149-164
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• 2008

As the computing landscape is shifting to ubiquitous computing environments, there is increasingly growing the demand for a variety of device controls that react to user's implicit activities without excessively drawing user attentions. We developed an EMG-based car interface that enables the physically handicapped to drive a car using their functioning peripheral nerves. Our method extracts electromyogram signals caused by wrist movements from four places in the user's forearm and then infers the user's intent from the signals using multi-layered neural nets. By doing so, it makes it possible for the user to control the operation of car equipments and thus to drive the car. It also allows the user to enter inputs into the embedded computer through a user interface like an instrument LCD panel. We validated the effectiveness of our method through experimental use in a car built with the EMG-based interface.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제13권10호
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• pp.5078-5094
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• 2019

In recent years, the development of drone technologies has promoted the widespread commercial application of drones. However, the ability of drone to carry explosives and other destructive materials may bring serious threats to public safety. In order to reduce these threats from illegal drones, acoustic feature extraction and classification technologies are introduced for drone sound identification. In this paper, we introduce the acoustic feature vector extraction method of harmonic line association (HLA), and subband power feature extraction based on wavelet packet transform (WPT). We propose a feature vector extraction method based on combined HLA and WPT to extract more sophisticated characteristics of sound. Moreover, to identify drone sounds, support vector machine (SVM) classification with the optimized parameter by genetic algorithm (GA) is employed based on the extracted feature vector. Four drones' sounds and other kinds of sounds existing in outdoor environment are used to evaluate the performance of the proposed method. The experimental results show that with the proposed method, identification probability can achieve up to 100 % in trials, and robustness against noise is also significantly improved.

• Structural Monitoring and Maintenance
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• 제6권3호
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• pp.219-235
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• 2019

Scheduled inspections of common crossings are one of the main cost drivers of railway maintenance. Prognostics and health management (PHM) approach and modern monitoring means offer many possibilities in the optimization of inspections and maintenance. The present paper deals with data driven prognosis of the common crossing remaining useful life (RUL) that is based on an inertial monitoring system. The problem of scheduled inspections system for common crossings is outlined and analysed. The proposed analysis of inertial signals with the maximal overlap discrete wavelet packet transform (MODWPT) and Shannon entropy (SE) estimates enable to extract the spectral features. The relevant features for the acceleration components are selected with application of Lasso (Least absolute shrinkage and selection operator) regularization. The features are fused with time domain information about the longitudinal position of wheels impact and train velocities by multivariate regression. The fused structural health (SH) indicator has a significant correlation to the lifetime of crossing. The RUL prognosis is performed on the linear degradation stochastic model with recursive Bayesian update. Prognosis testing metrics show the promising results for common crossing inspection scheduling improvement.

• 천문학회보
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• 제43권2호
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• pp.45.3-46
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• 2018

Oscillations in a sunspot are easily detected through the Doppler velocity observation. Although the sunspot oscillations look erratic, the wavelet analysis show that they consist of successive wave packets which have strong power near three or five minutes. Previous studies found that 3-min oscillation at the chromosphere is a visual pattern of upward propagating acoustic waves along the magnetic field lines. Resent multi-height observations help this like vertical study, however, we also focus on horizontal facet to extend three dimensional understand of sunspot waves. So, we investigate a fragmented sunspot expected to have complex wave profiles according to the positions in the sunspot observed by the Fast Imaging Solar Spectrograph. We choose 4 points at different umbral cores as sampling positions to determine coherence of oscillations. The sets of cross-correlation with three and five minutes bandpass filters during a single wave packet reveal interesting results. Na I line show weak correlations with some lags, but Fe I and Ni I have strong correlations with no phase difference over the sunspots. It is more remarkable at Ni I line with 3-min bandpass that all sets of cross-correlation look like the autocorrelation. We can interpret this as sunspot oscillations occur spontaneously over a sunspot at photosphere but not at chromosphere. It implies a larger or deeper origin of 3-min sunspot oscillation.

• Smart Structures and Systems
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• 제32권1호
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• pp.61-81
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• 2023

This study presents an ensemble learning based Bayesian model updating approach for structural damage diagnosis. In the developed framework, the structure is initially decomposed into a set of substructures. The autoregressive moving average (ARMAX) model is established first for structural damage localization based structural motion equation. The wavelet packet decomposition is utilized to extract the damage-sensitive node energy in different frequency bands for constructing structural surrogate models. Four methods, including Kriging predictor (KRG), radial basis function neural network (RBFNN), support vector regression (SVR), and multivariate adaptive regression splines (MARS), are selected as candidate structural surrogate models. These models are then resampled by bootstrapping and combined to obtain an ensemble model by probabilistic ensemble. Meanwhile, the maximum entropy principal is adopted to search for new design points for sample space updating, yielding a more robust ensemble model. Through the iterations, a framework of surrogate ensemble learning based model updating with high model construction efficiency and accuracy is proposed. The specificities of the method are discussed and investigated in a case study.

• The Journal of Advanced Prosthodontics
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• 제4권2호
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• pp.61-71
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• 2012

PURPOSE. To compare the changes in the occlusal vertical dimension, activity of masseter muscles and biting force after insertion of immediate denture constructed with conventional, tooth-supported and Implant-supported immediate mandibular complete denture. MATERIALS AND METHODS. Patients were selected and treatment was carried out with all the three different concepts i.e, immediate denture constructed with conventional (Group A), tooth-supported (Group B) and Implant-supported (Group C) immediate mandibular complete dentures. Parameters of evaluation and comparison were occlusal vertical dimension measured by radiograph (at three different time intervals), Masseter muscle electromyographic (EMG) measurement by EMG analysis (at three different positions of jaws) and bite force measured by force transducer (at two different time intervals). The obtained data were statistically analyzed by using ANOVA-F test at 5% level of significance. If the F test was significant, Least Significant Difference test was performed to test further significant differences between variables. RESULTS. Comparison between mean differences in occlusal vertical dimension for tested groups showed that it was only statistically significant at 1 year after immediate dentures insertion. Comparison between mean differences in wavelet packet coefficients of the electromyographic signals of masseter muscles for tested groups was not significant at rest position, but significant at initial contact position and maximum voluntary clench position. Comparison between mean differences in maximum biting force for tested groups was not statistically significant at 5% level of significance. CONCLUSION. Immediate complete overdentures whether tooth or implant supported prosthesis is recommended than totally mucosal supported prosthesis.

• Smart Structures and Systems
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• 제20권2호
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• pp.175-180
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• 2017

The load-carrying capacity and structural behavior of concrete-filled steel tube (CFST) structures is highly influenced by the grouting compactness in the steel tube. Due to the invisibility of the grout in the steel tube, monitoring of the grouting progress in such a structure is still a challenge. This paper develops an active sensing approach with combined piezoceramic-based smart aggregates (SA) and piezoceramic patches to monitor the grouting compactness of CFST bridge structure. A small-scale steel specimen was designed and fabricated to simulate CFST bridge structure in this research. Before casting, four SAs and two piezoceramic patches were installed in the pre-determined locations of the specimen. In the active sensing approach, selected SAs were utilized as actuators to generate designed stress waves, which were detected by other SAs or piezoceramic patch sensors. Since concrete functions as a wave conduit, the stress wave response can be only detected when the wave path between the actuator and the sensor is filled with concrete. For the sake of monitoring the grouting progress, the steel tube specimen was grouted in four stages, and each stage held three days for cement drying. Experimental results show that the received sensor signals in time domain clearly indicate the change of the signal amplitude before and after the wave path is filled with concrete. Further, a wavelet packet-based energy index matrix (WPEIM) was developed to compute signal energy of the received signals. The computed signal energies of the sensors shown in the WPEIM demonstrate the feasibility of the proposed method in the monitoring of the grouting progress.