• Journal of Biosystems Engineering
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• v.43 no.3
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• pp.219-228
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• 2018

Purpose: This study evaluated the feasibility of a near-infrared spectroscopy technique for the rancidity prediction of soybean oil. Methods: A near-infrared spectroscopy technique was used to evaluate the rancidity of soybean oils which were artificially deteriorated. A soybean oil sample was collected, and the acid values were measured using titrimetric analysis. In addition, the transmission spectra of the samples were obtained for whole test periods. The prediction model for the acid value was constructed by using a partial least-squares regression (PLSR) technique and the appropriate spectrum preprocessing methods. Furthermore, optimal wavelength selection methods such as variable importance in projection (VIP) and bootstrap of beta coefficients were applied to select the most appropriate variables from the preprocessed spectra. Results: There were significantly different increases in the acid values from the sixth days onwards during the 14-day test period. In addition, it was observed that the NIR spectra that exhibited intense absorption at 1,195 nm and 1,410 nm could indicate the degradation of soybean oil. The PLSR model developed using the Savitzky-Golay $2^{nd}$ order derivative method for preprocessing exhibited the highest performance in predicting the acid value of soybean oil samples. onclusions: The study helped establish the feasibility of predicting the rancidity of the soybean oil (using its acid value) by means of a NIR spectroscopy together with optimal variable selection methods successfully. The experimental results suggested that the wavelengths of 1,150 nm and 1,450 nm, which were highly correlated with the largest absorption by the second and first overtone of the C-H, O-H stretch vibrational transition, were caused by the deterioration of soybean oil.

• Korean Journal of Agricultural Science
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• v.41 no.3
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• pp.169-175
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• 2014

Objective of this study was to investigate effects of pre-processing method and number of sampling leaves on stability of the reflectance measurement for Chinese cabbage and kale leaves. Chinese cabbage and kale were transplanted and cultivated in a plant factory. Leaf samples of the kale and cabbage were collected at 4 weeks after transplanting of the seedlings. Spectra data were collected with an UV/VIS/NIR spectrometer in the wavelength region from 190 to 1130 nm. All leaves (mature and young leaves) were measured on 9 and 12 points in the blade part in the upper area for kale and cabbage leaves, respectively. To reduce the spectral noise, the raw spectral data were preprocessed by different methods: i) moving average, ii) Savitzky-Golay filter, iii) local regression using weighted linear least squares and a $1^{st}$ degree polynomial model (lowess), iv) local regression using weighted linear least squares and a $2^{nd}$ degree polynomial model (loess), v) a robust version of 'lowess', vi) a robust version of 'loess', with 7, 11, 15 smoothing points. Effects of number of sampling leaves were investigated by reflectance difference (RD) and cross-correlation (CC) methods. Results indicated that the contribution of the spectral data collected at 4 sampling leaves were good for both of the crops for reflectance measurement that does not change stability of measurement much. Furthermore, moving average method with 11 smoothing points was believed to provide reliable pre-processed data for further analysis.

• Proceedings of the Korea Information Processing Society Conference
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• 2015.10a
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• pp.1822-1825
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• 2015

본 논문에서는 운동 전후 또는 심장 질환과 관련 있는 사용자가 체중을 측정하면서 동시에 심전도 신호를 측정하여 운동 부하에 따른 심장의 활동 상태를 모니터링 할 수 있는 장치를 고안하였다. 이를 위한 방법으로 체중계에 수정된 바이폴라 금속전극을 적용하여 표준사지 측정법을 이용하여 심장활동 신호를 측정할 수 있는 방법을 제안하였다. 체중계에서 심전도를 측정하기 위해 기존의 Ag-AgCl 전극이 아닌 금속 판 형태의 전극을 사용하였으며 이를 위해 입력 임피던스의 설계를 브릿지 형의 AC-Coupling 회로를 통해 높은 CMRR이 유지되도록 설계하였다. 또한 시시각각 변화하는 노이즈를 제거하기 위해 Savitzky-golay filter를 사용하였으며 이를 통해 Baseline wandering 이 제거된 최종 심장활동 신호를 획득하였다. R-peak 검출을 통해 기준신호와의 심박수 및 Sensitivity의 비교평가를 수행하여 이 장치의 성능을 평가한 결과 심박 검출률의 평균 Sensitivity가 97.1%로 나타났다. 동잡음 제거에 대한 알고리즘이 보다 최적화 되어 최종 출력 신호의 안정성이 향상 된다면 체중계를 통한 심박 검출의 가능성과 그 유효성이 충분할 것으로 사료된다.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.44 no.3
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• pp.86-97
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• 2021

As mechatronic systems have various, complex functions and require high performance, automatic fault detection is necessary for secure operation in manufacturing processes. For conducting automatic and real-time fault detection in modern mechatronic systems, multiple sensor signals are collected by internet of things technologies. Since traditional statistical control charts or machine learning approaches show significant results with unified and solid density models under normal operating states but they have limitations with scattered signal models under normal states, many pattern extraction and matching approaches have been paid attention. Signal discretization-based pattern extraction methods are one of popular signal analyses, which reduce the size of the given datasets as much as possible as well as highlight significant and inherent signal behaviors. Since general pattern extraction methods are usually conducted with a fixed size of time segmentation, they can easily cut off significant behaviors, and consequently the performance of the extracted fault patterns will be reduced. In this regard, adjustable time segmentation is proposed to extract much meaningful fault patterns in multiple sensor signals. By considering inflection points of signals, we determine the optimal cut-points of time segments in each sensor signal. In addition, to clarify the inflection points, we apply Savitzky-golay filter to the original datasets. To validate and verify the performance of the proposed segmentation, the dataset collected from an aircraft engine (provided by NASA prognostics center) is used to fault pattern extraction. As a result, the proposed adjustable time segmentation shows better performance in fault pattern extraction.

• Korean Journal of Agricultural Science
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• v.45 no.4
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• pp.823-837
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• 2018

The objective of this study was to evaluate the firmness and the sweetness index (SI) of tomatoes with a hyperspectral imaging (HSI) technique within the wavelength range of 1000 - 1550 nm. The hyperspectral images of 95 tomatoes were acquired with a push-broom hyperspectral reflectance imaging system, from which the mean spectra of each tomato were extracted from the regions of interest. The reference firmness and sweetness index of the same sample was measured and calibrated with their corresponding spectral data by partial least squares (PLS) regression with different preprocessing methods. The calibration model developed by PLS regression based on the Savitzky-Golay second-derivative preprocessed spectra resulted in a better performance for both the firmness and the SI of the tomatoes compared to models developed by other preprocessing methods. The correlation coefficients ($R_{pred}$) were 0.82, and 0.74 with a standard error of prediction of 0.86 N, and 0.63, respectively. Then, the feature wavelengths were identified using a model-based variable selection method, i.e., variable importance in projection, from the PLS regression analyses. Finally, chemical images were derived by applying the respective regression coefficients on the spectral image in a pixel-wise manner. The resulting chemical images provided detailed information on the firmness and the SI of the tomatoes. The results show that the proposed HSI technique has potential for rapid and non-destructive evaluation of firmness and the sweetness index of tomatoes.

• Korean Journal of Agricultural Science
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• v.49 no.1
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• pp.129-136
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• 2022

In this study, we aimed to develop a maturity classification model for tomatoes using hyperspectral imaging in the range of 400 - 1,000 nm. Fifty-seven tomatoes harvested in August and November of 2021 were used as the sample set, and hyperspectral data was extracted from the surfaces of these tomatoes. A combined method of SNV (standard normal variate) and SG (Savitzky-Golay) methods was used for the pre-processing of the hyperspectral data. In addition, the hyperspectral data were analyzed for all maturity stages and considering bandwidths with different FWHM (full width at half maximum) values of 2, 25, and 50 nm. The PCA (principal component analysis) method was used to analyze the principal components related to maturity stages for the tomatoes. As a result, 500 - 550 nm and 650 - 700 nm bands were found to be related to the maturity stages of tomatoes. In addition, PC1 and PC2 explained approximately 97% of the variance at all FWHM conditions and thus were used as input data for classification model training based on the SVM (support vector machine). The SVM models were able to classify tomato maturity into five stages (Green, Turning, Pink, Light red, and Red) with over 95% accuracy regardless of the FWHM condition. Therefore, it was considered that hyperspectral data with 50 nm FWHM and SVM is feasible for use in the classification of tomato maturity into five stages.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.424-424
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• 2023

댐, 저수지 등 수자원 시스템분석 시 가장 기초가 되는 유입량 자료는 실측 수위(저수량)와 방류량을 역산하여 산정된다. 이 중 댐 수위는 수표면 진동으로 인해 변동이 크며, 특히, 급격한 수위 변화가 발생하는 홍수기에는 수위-저수량 변환 시 큰 오차가 발생하여 유입량 진동이 더욱 커지게 된다. 하지만 홍수기 저수지 운영 효과 분석 등 관련 연구를 위해서는 시간 간격이 짧은 10분 또는 1시간 단위의 유입량 자료가 필요함에 따라 관련 연구 수행 시 이동평균법(Moving Average) 등을 통해 실측 유입량 자료를 보정하여 사용하는 것이 일반적이다. 데이터 평활화를 위해 이동평균법을 적용하면 데이터의 변동을 효과적으로 줄일 수는 있지만 실측자료와 비교하였을 때 첨두 유입량이 큰 폭으로 감소하거나, 첨두 유입량 발생시간이 지체되는 문제가 발생한다. 본 연구에서는 저수지 유입량과 같이 변동이 큰 수문자료의 평활화를 위해 가우시안 가중 이동평균법(Gaussian-weighted moving average technique), 사비츠키-골레이 필터링기법(Savitzky-Golay filtering technique) 등 필터링 기법을 댐 유입량 보정에 적용하고, 이에 따른 효과를 분석하고자 하였다. 이를 위해 2020년 8월에 발생한 홍수사상을 대상으로 충주댐, 합천댐 등 다목적댐 유입량 자료를 수집하고, 보정을 수행하였다. 필터링 기법의 적용 효과 분석을 위해서는 실측자료와 이동평균법을 적용하여 보정한 결과와 비교하였고, 추가적으로 비교적 변동이 작은 일 단위 유입량 자료와의 양적 비교를 진행하였다. 그 결과 이동평균법을 적용하였을 때보다 필터링 기법을 적용하였을 때 실측자료와의 양적 차이가 작고, 첨두 유입량 및 첨두 유입 발생시간에서도 차이를 큰 폭으로 감소시킬 수 있는 것으로 확인되었다.

• Proceedings of the Korea Technical Association of the Pulp and Paper Industry Conference
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• 2011.10a
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• pp.255-269
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• 2011

The purpose of study has to analyze with non destructive method for researching the tool that could be measured with the status of record written on Hanji speedily. Because the original record should be destructed for analyzing with previous method in the case of the paper record, it was to develop the tool based on non destructive method for overcoming such limit. The study was used with FT NIR (Fourier transform NIR) for analyzing the Hanji for being written and preserved. The FT NIR spectrometer that of NIR spectrometer has the better performance of precision and accuracy than dispersive NIR spectrometer was used. Also the wavelength of FT-NIR was measured with 12,500 to 4,000 $cm^{-1}$, and the integrating sphere as diffuse reflectance type was used for analyzing Hanji. The moisture and acidity (pH) of chemical factors as quality evaluated factor of Hanji was studied for the correlation of NIR spectrum. And then The NIR spectrum was pretreated for showing the coefficients of optimum correlation. MSC and First derivative of Savitzky - Golay was used as pretreated method, and the coefficients of optimum correlation were shown by PLSR(Partial least square regression). And the coefficients of optimum correlation were calculated by PLSR(Partial least square regression). The correlation coefficients of acidity had 0.92 on NIR spectra without pretreatment. Also the SEP of acidity was 0.24. And then The NIR spectra with pretreatment would have more good correlation coefficients ($R^2=0.98$) and more good SEP(=019) on acidity. Therefore the data of correlation coefficients ($R^2$) and SEP with pretreatment was shown to be superior. And NIR spectra data of first derivative had best linearity on the correlation coefficients ($R^2=0.99$) and also SEP(=0.45) was superior. Therefore the correlation coefficients and SEP of first derivative had better than those of NIR spectra of no pretreatment. As such result, it was possible to evaluate the record status of Hanji speedily with integrated sphere and NIR analyzer as non destructive method.

• Proceedings of the International Union of Geodesy And Geophysics Korea Journal of Geophysical Research Conference
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• 2003.05a
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• pp.19-19
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• 2003

본 연구에서는 국립해양조사원의 '해양2000호'를 통해 1996년과 1997년에 측정한 동해 지역의 중력자료에 대한 자료 처리를 하였다. 효과적인 자료처리를 위해 선상중력자료 처리에 필요한 각종 보정 절차와 문제점 등을 알아보았으며, 선상자료와 해면고도계자료의 비교 및 이를 통한 선상자료의 검증을 실시하였다. 선상중력자료는 측정과 처리 과정에 있어 여러 사항을 고려하여야 한다. 즉, 육상중력기점을 이용한 절대중력으로의 환산 문제, 선박 항해 위치의 부정확성에 기인하는 문제 및 중력계의 기계적 특성과 중력 측정이 이루어질 때의 해상 조건에 의한 영향 등으로 선상중력자료에 나타나는 여러 오차를 최소화하여야 한다. 선상중력자료로부터 각종 지구중력장 연구에 필요한 중력이상을 계산하기 위해 선상중력 측정시 기인되는 각종 요인의 오차를 고려한 효과적인 보정이 이루어져야 한다. 즉, 선상중력계의 기계변이 보정, 탐사선에 대한 위치 자료의 획득 및 필터링, 그리고 탐사선의 이동으로 인한 Eotvos 효과의 정확한 계산 및 보정이 필요하고, 선상중력계의 기계적 특성에 의해 나타나는 시간지연에 대한 보정도 필요하다. 또한 이러한 보정을 통해 계산한 중력 이상에서 각 교점의 오차를 보정하는 교정오차 보정도 실시하여야 한다. 특히, 탐사선의 이동으로 인한 지구자전 각속도의 상대적인 증감의 효과로 나타나는 Eotvos 효과의 영향은 선상중력자료의 정확도에 가장 큰 영향을 미친다. 이의 정확한 계산 및 보정을 위해서는 정확한 위치정보가 필요하며 본 연구에서는 이를 위해 GPS 항해정보에 대한 Kalman 필터를 실시하였고, Eotvos 효과에 대해 Savitzky-Golay 필터를 적용하여 최적의 Eotvos 보정을 시도하였다. 본 연구에서 계산된 동해지역의 중력이상에 대한 대력적인 범위는 경도 129° - 133°이고 위도 35° - 38.3° 부근이다. 이 지역에 대한 고도이상은 최소 -42.46 mGal에서 최고 161.13 mGal사이에 분포하며, 고도이상의 평균은 14.450 mGal이다. 또한 Bouguer 이상은 최소 -l5.09 mGal에서 최고 218.61 mGal이고 이의 평균은 82.681 mGal이다. 그리고 동해지역의 선상중력 측정지역에서 선상자료에 의한 중력이상과 Altimeter 자료에 의한 고도이상의 전반적인 윤곽은 비슷하면서도 일부 작은 이상의 차이가 나타났으며, 지형자료와 비교하여 보면 Altimeter에 의한 결과보다 선상측정에 의한 결과가 더욱 잘 일치하고 있어 본 연구에서 계산한 선상자료의 타당성을 알 수 있다. 고도이상의 차이는 최소 -25.94 mGal에서 최대 85.33 mGal의 차이를 보이며 차이의 평균은 3.517 mGal, RMS는 6.774 meal이다. 이는 비교적 큰 차이로 선상측정자료의 중요성과 필요성을 단적으로 나타내고 있다.

• Korean Journal of Environmental Biology
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• v.39 no.4
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• pp.581-589
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• 2021

Using visible-near infrared(Vis-NIR) spectra combined with machine learning methods, the feasibility of quick and non-destructive classification of Convolvulaceae species was studied. The main aim of this study is to classify six Convolvulaceae species in the field in different geographical regions of South Korea using a handheld spectrometer. Spectra were taken at 1.5 nm intervals from the adaxial side of the leaves in the Vis-NIR spectral region between 400 and 1,075 nm. The obtained spectra were preprocessed with three different preprocessing methods to find the best preprocessing approach with the highest classification accuracy. Preprocessed spectra of the six Convolvulaceae sp. were provided as input for the machine learning analysis. After cross-validation, the classification accuracy of various combinations of preprocessing and modeling ranged between 43.4% and 98.6%. The combination of Savitzky-Golay and Support vector machine methods showed the highest classification accuracy of 98.6% for the discrimination of Convolvulaceae sp. The growth stage of the plants, different measuring locations, and the scanning position of leaves on the plant were some of the crucial factors that affected the outcomes in this investigation. We conclude that Vis-NIR spectroscopy, coupled with suitable preprocessing and machine learning approaches, can be used in the field to effectively discriminate Convolvulaceae sp. for effective weed monitoring and management.