• Journal of the Institute of Electronics and Information Engineers
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• v.54 no.8
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• pp.26-33
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• 2017

Vehicle communication can facilitate efficient coordination among vehicles on the road and enable future vehicular applications such as vehicle safety enhancement, infotainment, or even autonomous driving. In the $3^{rd}$ Generation Partnership Project (3GPP), many studies focus on long term evolution (LTE)-based vehicle communication. Because vehicle speed is high enough to cause severe channel distortion in vehicle-to-vehicle (V2V) environments. We can utilize channel estimation methods to approach a reliable vehicle communication systems. Conventional channel estimation schemes can be categorized as least-squares (LS), decision-directed channel estimation (DDCE), spectral temporal averaging (STA), and smoothing methods. In this study, we propose a smart channel estimation scheme in LTE-based V2V environments. The channel estimation scheme, based on an LTE uplink system, uses a demodulation reference signal (DMRS) as the pilot symbol. Unlike conventional channel estimation schemes, we propose an adaptive smoothing channel estimation scheme (ASCE) using quadratic smoothing (QS) of the pilot symbols, which estimates a channel with greater accuracy and adaptively estimates channels in data symbols. In simulation results, the proposed ASCE scheme shows improved overall performance in terms of the normalized mean square error (NMSE) and bit error rate (BER) relative to conventional schemes.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.33 no.12A
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• pp.1225-1232
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• 2008

In this paper, we describe a compensation method of diurnal effect which is one of the factors giving large effect on the performance when using ground-wave signals like Loran-C for a backup and substitute navigation system of global satellite navigation system such as GPS, and currently many researches of the topics are doing in USA and in Europe. In order to compensate diurnal effect, we find periodic frequency components by using the Least Square Spectral Analysis (LSSA) method at first and then compensate the effect by subtracting the estimated compensation signal, obtained by using the estimated amplitude and phase of the individual frequency component, from the original signal. In this paper, we propose a simple compensation algorithm and analysis the performance through simulations. From the results, it is observed that the amplitude and phase can be estimated with under 5 % and 0.17 % in a somewhat poor receiving situation with 0 dB Signal to Noise Ratio (SNR). Also, we analyze the obtainable performance improvement after compensation by using the measured Loran-C data. From the results, it is observed that we can get about 22 % performance improvement when a moving average with 5 minutes interval is employed.

• Journal of the Korean Geographical Society
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• v.51 no.4
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• pp.509-523
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• 2016

The soil prediction can provide quantitative soil information for sustainable mountainous ecosystem management. Visible near infrared spectroscopy, one of soil prediction methods, has been applied to predict several soil properties with effective costs, rapid and nondesctructive analysis, and satisfactory accuracy. Spectral preprocessing is a essential procedure to correct noisy spectra for visible near infrared spectroscopy. However, there are no attempts to evaluate various spectral preprocessing methods. We tested 5 different pretreatments, namely continuum removal, Savitzky-Golay filter, discrete wavelet transform, 1st derivative, and 2nd derivative to predict soil carbon(C) and nitrogen(N). Partial least squares regression was used for the prediction method. The total of 153 soil samples was split into 122 samples for calibration and 31 samples for validation. In the all range, absorption was increased with increasing C contents. Specifically, the visible region (650nm and 700nm) showed high values of the correlation coefficient with soil C and N contents. For spectral preprocessing methods, continuum removal had the highest prediction accuracy(Root Mean Square Error) for C(9.53mg/g) and N(0.79mg/g). Therefore, continuum removal was selected as the best preprocessing method. Additionally, there were no distinct differences between Savitzky-Golay filter and discrete wavelet transform for visual assessment and the methods showed similar validation results. According to the results, we also recommended Savitzky-Golay filter that is a simple pre-treatment with continuum removal.

• The Journal of the Korea Contents Association
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• v.6 no.12
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• pp.89-95
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• 2006

The performance of blind source separation(BSS) using independent component analysis (ICA) declines significantly in a reverberant environment. A post-processing method proposed in this paper was designed to remove the residual component precisely. The proposed method used modified NLMS(normalized least mean square) filter in frequency domain, to estimate cross-talk path that causes residual cross-talk components. Residual cross-talk components in one channel is correspond to direct components in another channel. Therefore, we can estimate cross-talk path using another channel input signals from adaptive filter. Step size is normalized by input signal power in conventional NLMS filter, but it is normalized by sum of input signal power and error signal power in modified NLMS filter. By using this method, we can prevent misadjustment of filter weights. The estimated residual cross-talk components are subtracted by non-stationary spectral subtraction. The computer simulation results using speech signals show that the proposed method improves the noise reduction ratio(NRR) by approximately 3dB on conventional FDICA.

• Journal of Plant Biotechnology
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• v.43 no.2
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• pp.213-222
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• 2016

This study aims to establish a system for the rapid discrimination of Zoysia species using metabolite fingerprinting of FT-IR spectroscopy combined with multivariate analysis. Whole cell extracts from leaves of 19 identified Zoysia japonica, 6 identified Zoysia sinica, and 38 different unidentified Zoysia species were subjected to Fourier transform infrared spectroscopy (FT-IR). PCA (principle component analysis) and PLS-DA (partial least square discriminant analysis) from FT-IR spectral data successfully divided the 25 identified turf grasses into two groups, representing good agreement with species identification using molecular markers. PC (principal component) loading values show that the $1,100{\sim}950cm^{-1}$ region of the FT-IR spectra are important for the discrimination of Zoysia species. A dendrogram based on hierarchical clustering analysis (HCA) from the PCA and PLS-DA data of turf grasses showed that turf grass samples were divided into Zoysia japonica and Zoysia sinica in a species-dependent manner. PCA and PLS-DA from FT-IR spectral data of Zoysia species identified and unidentified by molecular markers successfully divided the 49 turf grasses into Z. japonica and Z. sinica. In particular, PLS-DA and the HCA dendrogram could mostly discriminate the 47 Z. japonica grasses into two groups depending on their origins (mountainous areas and island area). Considering these results, we suggest that FT-IR fingerprinting combined with multivariate analysis could be applied to discriminate between Zoysia species as well as their geographical origins of various Zoysia species.

• KSBB Journal
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• v.21 no.1 s.96
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• pp.59-67
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• 2006

2D spectrofluorometer produces many spectral data during fermentation processes. The fluorescence spectra are analyzed using chemometric methods such as principal component analysis (PCA), principal component regression (PCR) and partial least square regression (PLS). Analysis of the spectral data by PCA results in scores and loadings that are visualized in score-loading plots and used to monitor a few fermentation processes by S. cerevisae and recombinant E. coli. Two chemometric models were established to analyze the correlation between fluorescence spectra and process variables using PCR and PLS, and PLS was found to show slightly better calibration and prediction performance than PCR.

• Journal of the Korean Society for Nondestructive Testing
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• v.34 no.1
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• pp.31-37
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• 2014

The aim of this study was the non-destructive evaluation of bloody eggs using VIS/NIR spectroscopy. The bloody egg samples used to develop the sorting mode were produced by injecting chicken blood into the edges of egg yolks. Blood amounts of 0.1, 0.7, 0.04, and 0.01 mL were used for the bloody egg samples. The wavelength range for the VIS/NIR spectroscopy was 471 to 1154 nm, and the spectral resolution was 1.5nm. For the measurement system, the position of the light source was set to $30^{\circ}$, and the distance between the light source and samples was set to 100 mm. The minimum exposure time of the light source was set to 30 ms to ensure the fast sorting of bloody eggs and prevent heating damage of the egg samples. Partial least squares-discriminant analysis (PLS-DA) was used for the spectral data obtained from VIS/NIR spectroscopy. The classification accuracies of the sorting models developed with blood samples of 0.1, 0.07, 0.04, and 0.01 mL were 97.9%, 98.9%, 94.8%, and 86.45%, respectively. In this study, a novel nondestructive sorting technique was developed to detect bloody brown eggs using spectral data obtained from VIS/NIR spectroscopy.

• Korean Journal of Agricultural Science
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• v.39 no.1
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• pp.117-123
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• 2012

Pathogenic fungi and bacteria such as Pectobacterium atrosepticum, Clavibacter michiganensis subsp. sepedonicus, Verticillium albo-atrum, and Rhizoctonia solani were the major microorganism which causes diseases in seed potato during postharvest process. Current detection method for disease-infected seed potato relies on human inspection, which is subjective, inaccurate and labor-intensive method. In this study, a reflectance spectroscopy was used to classify sound and disease-infected seed potatoes with the spectral range from 400 to 1100 nm. Partial least square discriminant analysis (PLS-DA) with various preprocessing methods was used to investigate the feasibility of classification between sound and disease-infected seed potatoes. The classification accuracy was above 97 % for discriminating disease seed potatoes from sound ones. The results show that Vis/NIR reflectance method has good potential for non-destructive sorting for disease-infected seed potatoes.

• Korean Journal of Food Science and Technology
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• v.36 no.4
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• pp.527-530
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• 2004

Adulteration of sesame oil using near infrared (NIR) spectroscopy was determined. Vegetable oils including sesame oil were scanned on the NIR spectrophotometer at 400-2500 nm. Partial least square (PLS) was applied on the standardized full NIR spectral data. Discriminant analysis with PLS is adequate for determination of sesame oil adulteration, except with decreasing adulteration rate. Designing of quality control system, which uses NIR spectroscopy to measure adulteration level of sesame oil is thus possible, although more work is required to give acceptable accuracy level.

• Journal of The Korean Society of Grassland and Forage Science
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• v.34 no.3
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• pp.214-218
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• 2014

This study was conducted to assess the feasibility of near-infrared reflectance spectroscopy (NIRS) as a rapid and reliable method for the estimation of crude protein (CP) fractions in forage legume mixtures (sudangrass and pea mixture, and kidney bean and potato mixture). A total of 178 samples were collected and their spectral reflectance obtained in the range of 400~2,500 nm. Of these, 50 samples were selected for calibration and validation, and 35 samples were used for calibration of the data set, and the modified partial least square regression (MPLSR) analysis was performed. The correlation coefficient ($r^2$) and the standard error of cross-validation (SECV) of the calibration models in the CP fractions, A, B1, B2, B3, and C, were 0.94 (1.05), 0.92 (0.74), 0.96 (0.95), 0.91 (0.42), and 0.83 (0.38), respectively. Fifteen samples were used for equation validation, and the $r^2$ and the standard error of prediction (SEP) were 0.87 (1.45), 0.91 (0.49), 0.94 (1.13), 0.36 (0.96), and 0.74 (0.67), respectively. This study showed that NIRS could be an effective tool for the rapid and precise estimation of CP fractions in forage legume mixtures.