• Magazine of the Korean Society of Agricultural Engineers
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• v.31 no.4
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• pp.31-49
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• 1989

It is of the most urgent necessity to get hydrological time series of long duration for the establishment of rational design and operation criterion for the Agricultural hydraulic structures. This study was conducted to select best fitted frequency distribution for the monthly runoff and to simulate long series of generated flows by multi-season first order Markov model with comparison of statistical parameters which are derivated from observed and sy- nthetic flows in the five watersheds along Geum river basin. The results summarized through this study are as follows. 1. Both two parameter gamma and two parameter lognormal distribution were judged to be as good fitted distributions for monthly discharge by Kolmogorov-Smirnov method for goodness of fit test in all watersheds. 2. Statistical parameters were obtained from synthetic flows simulated by two parameter gamma distribution were closer to the results from observed flows than those of two para- meter lognormal distribution in all watersheds. 3. In general, fluctuation for the coefficient of variation based on two parameter gamma distribution was shown as more good agreement with the observed flow than that of two parameter lognormal distribution. Especially, coefficient of variation based on two parameter lognormal distribution was quite closer to that of observed flow during June and August in all years. 4. Monthly synthetic flows based on two parameter gamma distribution are considered to give more reasonably good results than those of two parameter lognormal distribution in the multi-season first order Markov model in all watersheds. 5. Synthetic monthly flows with 100 years for eack watershed were sjmulated by multi- season first order Markov model based on two parameter gamma distribution which is ack- nowledged to fit the actual distribution of monthly discharges of watersheds. Simulated sy- nthetic monthly flows may be considered to be contributed to the long series of discharges as an input data for the development of water resources. 6. It is to be desired that generation technique of synthetic flow in this study would be compared with other simulation techniques for the objective time series.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.8
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• pp.3295-3311
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• 2020

With the wide application of hyperspectral images, it becomes more and more important to compress hyperspectral images. Conventional recursive least squares (CRLS) algorithm has great potentiality in lossless compression for hyperspectral images. The prediction accuracy of CRLS is closely related to the correlations between the reference bands and the current band, and the similarity between pixels in prediction context. According to this characteristic, we present an improved CRLS with adaptive band selection and adaptive predictor selection (CRLS-ABS-APS). Firstly, a spectral vector correlation coefficient-based k-means clustering algorithm is employed to generate clustering map. Afterwards, an adaptive band selection strategy based on inter-spectral correlation coefficient is adopted to select the reference bands for each band. Then, an adaptive predictor selection strategy based on clustering map is adopted to select the optimal CRLS predictor for each pixel. In addition, a double snake scan mode is used to further improve the similarity of prediction context, and a recursive average estimation method is used to accelerate the local average calculation. Finally, the prediction residuals are entropy encoded by arithmetic encoder. Experiments on the Airborne Visible Infrared Imaging Spectrometer (AVIRIS) 2006 data set show that the CRLS-ABS-APS achieves average bit rates of 3.28 bpp, 5.55 bpp and 2.39 bpp on the three subsets, respectively. The results indicate that the CRLS-ABS-APS effectively improves the compression effect with lower computation complexity, and outperforms to the current state-of-the-art methods.

• Journal of Advanced Navigation Technology
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• v.23 no.1
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• pp.69-76
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• 2019

In this paper, a design method for an enhanced-sensitivity microwave sensor based on microstrip defected ground structure was studied for the permittivity measurement of planar dielectric substrates. The proposed sensor was designed by modifying the ridge structure of an H-shaped aperture into the shape of a capacitor symbol. The sensitivity of the proposed sensor was compared with that of a conventional sensor based on a double-ring complementary split ring resonator(DR-CSRR). Two sensors were designed and fabricated on a 0.76-mm-thick RF-35 substrate so that the transmission coefficient would resonate at 1.5 GHz in the absence of the substrate under test. Five types of taconic substrates with a relative permittivity ranging from 2.17 to 10.2 were selected asthe substrate under test. Experiment results show that the sensitivity of the proposed sensor, which is measured by the shift in the resonant frequency of the transmission coefficient, is 1.31 to 1.62 times higher than that of the conventional DR-CSRR-based sensor.

• The Journal of the Acoustical Society of Korea
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• v.28 no.4
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• pp.401-407
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• 2009

In this paper, we propose a novel method for the cross-correlation based double-talk detection (DTD), which employing the Gaussian Mixture Model (GMM) in the frequency domain. The proposed algorithm transforms the cross correlation coefficient used in the time domain into 16 channels in the frequency domain using the discrete fourier transform (DFT). The channels are then selected into seven feature vectors for GMM and we identify three different regions such as far-end, double-talk and near-end speech using the likelihood comparison based on those feature vectors. The presented DTD algorithm detects efficiently the double-talk regions without Voice Activity Detector which has been used in conventional cross correlation based double-talk detection. The performance of the proposed algorithm is evaluated under various conditions and yields better results compared with the conventional schemes. especially, show the robustness against detection errors resulting from the background noises or echo path change which one of the key issues in practical DTD.

• Journal of KIISE:Software and Applications
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• v.30 no.3_4
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• pp.316-325
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• 2003

A user preference prediction method using an exiting collaborative filtering technique has used the nearest-neighborhood method based on the user preference about items and has sought the user's similarity from the Pearson correlation coefficient. Therefore, it does not reflect any contents about items and also solve the problem of the sparsity. This study suggests the preference prediction system using the similarity weight granted Bayesian estimated value and the associative user clustering to complement problems of an exiting collaborative preference prediction method. This method suggested in this paper groups the user according to the Genre by using Association Rule Hypergraph Partitioning Algorithm and the new user is classified into one of these Genres by Naive Bayes classifier to slove the problem of sparsity in the collaborative filtering system. Besides, for get the similarity between users belonged to the classified genre and new users, this study allows the different estimated value to item which user vote through Naive Bayes learning. If the preference with estimated value is applied to the exiting Pearson correlation coefficient, it is able to promote the precision of the prediction by reducing the error of the prediction because of missing value. To estimate the performance of suggested method, the suggested method is compared with existing collaborative filtering techniques. As a result, the proposed method is efficient for improving the accuracy of prediction through solving problems of existing collaborative filtering techniques.

• Journal of the Korea Computer Graphics Society
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• v.16 no.1
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• pp.29-35
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• 2010

In this paper, we introduce a novel entropy coding method to improve the JPEG image compression standard. JPEG is one of the most widely used image compression methods due to its high visual quality for the compression ratio, and especially because of its high efficiency. Based on the observation that the blocks of data fed to the entropy coder usually contain consecutive sequences of numbers with small magnitudes including 0, 1, and -1, we separate those sequences from the data and encode them using a method dedicated to those values. We further improve the compression ratio based on the fact that this separation makes the lengths of blocks much shorter. In our experiment, we show that the proposed method can outperform the JPEG standard preserving its visual characteristics.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.10 no.7
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• pp.1188-1195
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• 2006

This paper presents an efficient design method of 2-D state-space digital filter based on an improved branch-and -bound algorithm. The resultant 2-D state-space digital filters whose coefficients are represented as the sum of two power-of-two terms, are attractive for high-speed operation and simple implementation. The feasibility of the proposed method is demonstrated by several experiments. The results show that the approximation error and group delay characteristic of the resultant filters are similar to those of the digital filters which designed in the continuous coefficient space.

• Structural Monitoring and Maintenance
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• v.7 no.2
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• pp.109-124
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• 2020

Benefiting from the massive monitoring data collected by the Structural health monitoring (SHM) system, scholars can grasp the complex environmental effects and structural state during structure operation. However, the monitoring data is often missing due to sensor faults and other reasons. It is necessary to study the recovery method of missing monitoring data. Taking the structural temperature monitoring data of Nanjing Dashengguan Yangtze River Bridge as an example, the long short-term memory (LSTM) network-based recovery method for missing structural temperature data is proposed in this paper. Firstly, the prediction results of temperature data using LSTM network, support vector machine (SVM), and wavelet neural network (WNN) are compared to verify the accuracy advantage of LSTM network in predicting time series data (such as structural temperature). Secondly, the application of LSTM network in the recovery of missing structural temperature data is discussed in detail. The results show that: the LSTM network can effectively recover the missing structural temperature data; incorporating more intact sensor data as input will further improve the recovery effect of missing data; selecting the sensor data which has a higher correlation coefficient with the data we want to recover as the input can achieve higher accuracy.

• The Korean Journal of Applied Statistics
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• v.24 no.6
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• pp.1077-1094
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• 2011

We accomplish clustering analyses for yeast cell cycle microarray expression data. To reflect the characteristics of a time-course data, we screen the genes using the test statistics with Fourier coefficients applying a FDR procedure. We compare the results done by model-based clustering, K-means, PAM, SOM, hierarchical Ward method and Fuzzy method with the yeast data. As the validity measure for clustering results, connectivity, Dunn index and silhouette values are computed and compared. A biological interpretation with GO analysis is also included.

• The Korean Journal of Community Living Science
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• v.21 no.2
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• pp.191-200
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• 2010

The purpose of this study was to research the differences between gender roles and creativity. This study was done based on 178 pre-service teachers who were from the Department of Early Childhood Education in Universities around the Busan area. The researchers have collected statistical data by questioning pre-service teachers about creative thinking tests, creative personality tests, and gender role identification awareness tests. The data was interpreted by the Paerson's Simple Product-moment Correlation Coefficient method, the one-way ANOVA method, and the $Sch\acute{e}ffe$ Post-hoc comparison method. According to this study, the group perceived of high androgyny type group showed the highest level in important factors of creative thinking, such as fluency, elaborateness, ness, and openness. This result meant that the more a pre-service teacher was aware of the identity of gender roles, the more she or he thought creatively. Additionally, the acceptance of authority, an element of the creative personality factor, showed the highest level in a high feminity type group. On the other hand, self confidence, inquisitiveness, and disciplined imagination showed the highest level in a group which perceived the identity of androgyny type roles.