• Structural Monitoring and Maintenance
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• v.6 no.3
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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.

• International Journal of Computer Science & Network Security
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• v.23 no.1
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• pp.1-10
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• 2023

Open surface water body extraction is gaining popularity in recent years due to its versatile applications. Multiple techniques are used for water detection based on applications. Different applications of Radar as LADAR, Ground-penetrating, synthetic aperture, and sounding radars are used to detect water. Shortwave infrared, thermal, optical, and multi-spectral sensors are widely used to detect water bodies. A stereo camera is another way to detect water and different methods are applied to the images of stereo cameras such as deep learning, machine learning, polarization, color variations, and descriptors are used to segment water and no water areas. The Satellite is also used at a high level to get water imagery and the captured imagery is processed using various methods such as features extraction, thresholding, entropy-based, and machine learning to find water on the surface. In this paper, we have summarized all the available methods to detect water areas. The main focus of this survey is on water detection especially in small patches or in small areas. The second aim of this survey is to detect water hazards for unmanned vehicles and off-sure navigation.

• Journal of Biomedical Engineering Research
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• v.24 no.1
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• pp.1-8
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• 2003

The job of long distance driving is likely to be fatiguing and requires long period alertness and attention, which make considerable demands of the driver. Driving fatigue contributes to driver related with accidents and fatalities. In this study, we investigated the relationship between the number of hours of driving and driving fatigue using heart rate variability(HRV) signal. With a more traditional measure of overall variability (standard deviation, mean, spectral values of heart rate). Nonlinear characteristics of HRV signal were analyzed using Approximate Entropy (ApEn) and Poincare plot. Five subjects drive the four passenger vehicle twice. All experiment number was 40. The test route was about 300Km continuous long highway circuit and driving time was about 3 hours. During the driving, measures of electrocardiogram(ECG) were performed at intervals of 30min. HRV signal, derived from the ECG, was analyzed using time, frequency domain parameters and nonlinear characteristic. The significance of differences on the response to driving fatigue was determined by Student's t-test. Differences were considered significant when a p value < 0.05 was observed. In the results, mean heart rate(HRmean) decreased consistently with driving time, standard deviation of RR intervals(SDRR), standard deviation of the successive difference of the RR intervals(SDSD) increased until 90min. Hereafter, they were almost unchanging until the end of the test. Normalized low frequency component $(LF_{norm})$, ratio of low to high frequency component (LF/HF) increased. We used the Approximate Entropy(ApEn), Poincare plot method to describe the nonlinear characteristics of HRV signal. Nonlinear characteristics of HRV signals decreased with driving time. Statistical significant is appeared after 60 min in all parameters.

• Journal of the Korean Chemical Society
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• v.55 no.4
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• pp.612-619
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• 2011

Lanthanide(III) complexes of 1,2-diphenyl-4-butyl-3,5-pyrazolidinedione(phenylbutazone, PB) have been synthesized and characterized by elemental analyses, molar conductance measurements, IR, UV-Vis. and NMR spectra. The spectral data reveal that the PB acts as a bidentate and mono-ionic ligand coordinating through both the carbonyl oxygens of the pyrazolidinedione ring. The molar conductance data suggest that the complexes are non-electrolytes. The thermal behaviour of the complexes was studied by TG and DTG in air atmosphere and the results provide information about dehydration, thermal stability and thermal decomposition. The final products are found to be the corresponding metal oxides. The thermodynamic parameters and kinetic parameters were evaluated for the dehydration and decomposition stages. The negative entropy values of the decomposition stages indicate that the activated complexes have a more ordered structure than the reactants and that the reactions are slower than normal. Based on these studies, the complexes have been formulated as $[Ln(PB)_3]{\cdot}5H_2O$(Ln=La and Ce) and $[Ln(PB)_3(H_2O)_2]{\cdot}2H_2O$(Ln=Pr, Nd and Sm).

• KSCE Journal of Civil and Environmental Engineering Research
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• v.36 no.5
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• pp.757-767
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• 2016

The power spectral density (PSD) for the road surface roughness on the bridges of minor roads in Wonju city and Hoengseong-gun, Gangwon-do is presented. To obtain the PSD, the road surface roughness on 18 different bridges with various superstructure type and span is measured by GPS at every 10 to 30cm interval. Assuming the PSD as the stationary normal probability process with zero mean value, the PSD of measured road surface roughness is obtained by applying the Maximum Entropy Method (MEM). A simple formula in evaluating the PSD of RC slab bridge, Rahmen bridge and PSC I-girder bridge which is applicable to the dynamic response analysis of bridges considering the road surface roughness is proposed. Using the calculated PSD curves, the road surface conditions on the 18 bridges are evaluated. The statistical relationship between the PSD and the IRI is presented by applying linear regression and correlation analysis.

• Proceedings of the ESK Conference
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• 1997.04a
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• pp.304-310
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• 1997

연세대학교 영동세브란스 병원에서 94년 요통환자 2천4백10명을 직업별로 분류한 결과, 주부가 56%, 학생이 13.4%를 차지했다고 발표했다. 특히 주부환자비율은 85-90년에 38.9%로 조사된 것에 비하면 주목할만한 증가세를 보였다. 이러한 주부요통증가의 원인 중 임상적으로 이미 확인된 것이 하이힐에 의한 것이다. 하이힐을 신을 경우 허리에 부담을 주고 요통을 유발할 수 있다는 것이다. 그러나, 이러한 임상적 가설에 대한 구체적인 연구나 검증이 이루어진 바 없어 하이힐이 허리에 어떤 영향을 주는지에 대해 확인할 수 없었다. 따라서, 본 연구에서는 하이힐이 허리에 미치는 영향에 대한 임상적 가설을 검증하고 영향 정도에 대한 구체적 수치를 제시하고자 한다. 이를 위해 5명의 신체 건강한 20대 초반의 여성들이 모집되었고, 하이힐의 굽높이를 독립변수로, 허리 근육 피로도를 종속 변수로 설정하여 하이힐의 굽높이가 허리 근육에 미치는 영향에 대해 조사하였다. 허리 근육 피로도는 Spectral EMG를 통해 분석하였고 정량화되었다. 측정된 자료를 통계 분석한 결과, 하이힐의 굽높이가 여성의 허리 근육에 유의하게 영향을 미침이 발견되었고, 우리 나라 여성에게 적합한 하이힐의 굽높이는 3-5cm 정도임이 밝혀졌다. 본 연구 결과는 하이힐의 디자인에 있어서 굽높이에 대한 추천치로 제안될 수 있으며 여성들의 하이힐로 인한 요통을 어느 정도 예방할 수 있어 여성 근로 손실을 줄이는 데 기여할 수 있다. 본 연구를 하이힐 굽형태나 충격흡수 등의 독립변수 요인을 추가하여 확대하면 하이힐 디자인에 응용하는데 더욱 유용하리라 생각된다. 없었다. 전신쾌적감은 약간 쾌적하게 나타났고 전신온냉감은 약간 따뜻하다라고 나타났으며 손가락끝의 동통감은 약간 아프다고쪽으로 나타났다.때문에 이를 디자인에 곧바로 적용시키기 어려운 점이 있다. 이에 본 연구는 기존의 바용성 평가를 위한 분석도구들이 갖는 문제 점들 해결하여 제품의 사용자 인터페이스 디자인 개발과정에서 활용할 수 있는 평가 분석도구를 개발하는 것을 목표로 한다. 이를 위해 첫째, 다양한 유형의 정보를 포함하는 비디오 정보를 선정하였따. 둘째, 데이터를 다양한 측면에서 추출할 수 있는 Data logger를 개발하였다. 셋째, 데이터를 시각적으로 정리하고 분석할 수 있는 도구를 제안한다. 마지막으로 인터페이스 디자인에서 여러 가지 디자인안을 도출해 내는 작업에 이용할 수 있는 종합화과정을 개발한다. 이러한 일련의 과정이 통합된 컴퓨터 시스템 안에서 이루어지도록 프로그램을 개발하여 정보의 유용성을 높일 수 있도록 한다.at the entropy index as a measurement of inter-business relatedness is not significant but technological relatedness index is significant. OLS estimates on pooled data were considerably different from FEM or REM estimates on panel data. By introducing interaction effect among the three variables for business portfolio properties, we obtai

• Bulletin of the Korean Chemical Society
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• v.11 no.4
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• pp.276-281
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• 1990

The equivalent conductance of univalent cation (potassium, silver, thallium and ammonium) perchlorates in methanol containing 18-membered crown ethers, 18-crown-6 (18C6) and 1,10-dithia-18-crown-6 (DT18C6) were measured at different temperatures. The equivalent conductances of ammonium perchlorate were increased by increasing content of DT18C6 exceptionally, due to more favorable solvations than complexations. From the equivalent conductance changes, the formation constants for 1:1 compmlexes have been determined, and the values of enthalpy and entropy changes have been calculated. The complexations of 18C6 and DT18C6 with the univalent cations under investigation are all exothermic and the ${\Delta}$S values are all negative and no considerable differences around 50 J/ (k mol). The selectivity order of 18C6 is $K^+ > Tl^+ > Ag^+ > NH_4^+$, while that of DT18C6 is $Ag^+ > Tl^+ > NH_4^+ > K^+$. By sulfur substitutions in 18C6 result in significant decrease in stability, but the stability of $Ag^+$-DT18C6 complex are $10^4$ times larger than those of $K^+$. This increase of stabilities for $Ag^+$-DT18C6 complex are primary due to the result of favorable exothermic heat of reaction between the polarizable soft cation and soft sulfur centers. In NMR experiment, the stepwise additions of cation perchlorates into crown ether solutions induced two major spectral changes. First, the resonance all shift down field and the cation induced shifts were linear up to 1:1 cation/crown ratio, above which no further changes were observed. On the basis of these results, it could be concluded that 1:1 complex is formed. Second, the magnitudes of cation induced shifts were different each other in same ligand. By addition of silver ion to the solution of DT18C6, the largest shift of proton peak near the sulfur atom was observed. These effects are also arisen from the results of covalent bonding between "soft-soft" interactions.

• Korean Journal of Remote Sensing
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• v.40 no.1
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• pp.93-101
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• 2024

Accurate field crop classification is essential for various agricultural applications, yet existing methods face challenges due to diverse crop types and complex field conditions. This study aimed to address these issues by combining support vector machine (SVM) models with multi-seasonal unmanned aerial vehicle (UAV) images, texture information extracted from Gray Level Co-occurrence Matrix (GLCM), and RGB spectral data. Twelve high-resolution UAV image captures spanned March-October 2021, while field surveys on three dates provided ground truth data. We focused on data from August (-A), September (-S), and October (-O) images and trained four support vector classifier (SVC) models (SVC-A, SVC-S, SVC-O, SVC-AS) using visual bands and eight GLCM features. Farm maps provided by the Ministry of Agriculture, Food and Rural Affairs proved efficient for open-field crop identification and served as a reference for accuracy comparison. Our analysis showcased the significant impact of hyperparameter tuning (C and gamma) on SVM model performance, requiring careful optimization for each scenario. Importantly, we identified models exhibiting distinct high-accuracy zones, with SVC-O trained on October data achieving the highest overall and individual crop classification accuracy. This success likely stems from its ability to capture distinct texture information from mature crops.Incorporating GLCM features proved highly effective for all models,significantly boosting classification accuracy.Among these features, homogeneity, entropy, and correlation consistently demonstrated the most impactful contribution. However, balancing accuracy with computational efficiency and feature selection remains crucial for practical application. Performance analysis revealed that SVC-O achieved exceptional results in overall and individual crop classification, while soybeans and rice were consistently classified well by all models. Challenges were encountered with cabbage due to its early growth stage and low field cover density. The study demonstrates the potential of utilizing farm maps and GLCM features in conjunction with SVM models for accurate field crop classification. Careful parameter tuning and model selection based on specific scenarios are key for optimizing performance in real-world applications.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.2
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• pp.348-369
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• 2024

With the advancement of Industry 4.0 and Industrial Internet of Things (IIoT), manufacturing increasingly seeks automation and intelligence. Temperature and vibration monitoring are essential for machinery health. Traditional abnormal state detection methodologies often overlook the intricate frequency characteristics inherent in vibration time series and are susceptible to erroneously reconstructing temperature abnormalities due to the highly similar waveforms. To address these limitations, we introduce synergistic, end-to-end, unsupervised Frequency-Time Domain Memory-Enhanced Autoencoders (FTD-MAE) capable of identifying abnormalities in both temperature and vibration datasets. This model is adept at accommodating time series with variable frequency complexities and mitigates the risk of overgeneralization. Initially, the frequency domain encoder processes the spectrogram generated through Short-Time Fourier Transform (STFT), while the time domain encoder interprets the raw time series. This results in two disparate sets of latent representations. Subsequently, these are subjected to a memory mechanism and a limiting function, which numerically constrain each memory term. These processed terms are then amalgamated to create two unified, novel representations that the decoder leverages to produce reconstructed samples. Furthermore, the model employs Spectral Entropy to dynamically assess the frequency complexity of the time series, which, in turn, calibrates the weightage attributed to the loss functions of the individual branches, thereby generating definitive abnormal scores. Through extensive experiments, FTD-MAE achieved an average ACC and F1 of 0.9826 and 0.9808 on the CMHS and CWRU datasets, respectively. Compared to the best representative model, the ACC increased by 0.2114 and the F1 by 0.1876.

• Journal of the Institute of Electronics and Information Engineers
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• v.50 no.8
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• pp.83-93
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• 2013

By comparing Wiener's MMSE on stochastic signal transmission with Shannon's mutual information first proved by C.E. Shannon in terms of information theory, connections between two approaches were investigated. What Wiener wanted to see in signal transmission in noisy channel is to try to capture fundamental limits for signal quality in signal estimation. On the other hands, Shannon was interested in finding fundamental limits of signal quantity that maximize the uncertainty in mutual information using the entropy concept in noisy channel. First concern of this paper is to show that in deriving limits of Shannon's point to point fundamental channel capacity, Shannon's mutual information obtained by exploiting MMSE combiner and Wiener filter's MMSE are interelated by integro-differential equantion. Then, At the meeting point of Wiener's MMSE and Shannon's mutual information the upper bound of spectral efficiency and the lower bound of energy efficiency were computed. Choosing a proper lattice-type code of a mod-${\Lambda}$AWGN channel model and MMSE estimation of ${\alpha}$ confirmed to lead to the fundamental Shannon capacity limits.