• Journal of Broadcast Engineering
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• v.15 no.1
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• pp.52-62
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• 2010

Recently, DVC (Distributed Video Coding) is attracting a lot of research works since this enables us to implement a light-weight video encoder by distributing the high complex tasks such as motion estimation into the decoder side. In order to improve the coding efficiency of the DVC, the existing works have been focused on the efficient generation of side information (SI) or the virtual channel modeling which can describe the statistical channel noise well. But, in order to improve the overall performance, this paper proposes a new scheme that can be implemented with simple bit operations without introducing complex operation. That is, the performance of the proposed scheme is enhanced by using the fact that the Wyner-Ziv (WZ) frame and side information are highly correlated, and by reducing the effect of virtual channel noise which tends to be clustered in some regions. For this aim, this paper proposes an efficient pixel-domain WZ (PDWZ) CODEC which effectively exploits the statistical redundancy by using the code conversion and Gray code, and then reduces the channel noise by using the bit interleaver. Through computer simulations, it is shown that the proposed scheme can improve the performance up to 0.5 dB in objective visual quality.

• Journal of Soil and Groundwater Environment
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• v.14 no.5
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• pp.18-28
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• 2009

Both feasibility and accuracy of lumped approach to group 12 organic compounds in mixtures into a fewer number of pseudocompounds in sorption processes were evaluated using mixtures containing organic compounds with various physicochemical properties and low-surface-area mineral sorbents. The lumped approach for sorption to simulated mineral sorbents was developed by cluster analysis from statistics. Using the lumped approach, the sorption estimated from both reduced number of pseudocompounds and their sorption parameters (i.e., $K_f$, n) can approximate sorption behavior of complex organic mixtures. Additionally, the pseudocompounds for various mixtures to different types of low-surface-area mineral sorbents can be estimated a priori from the physicochemical properties of organic compound (i.e., ${\gamma_w}^{sat}$). Therefore, the lumped approach may help to simplify the complex fate and transport model of organic contaminant mixtures, reduce experimental efforts, and yet provide results that are statistically identical for practical purposes. Further research is warranted to enhance the accuracy of lumped approach using the multiple regression analysis considering the H-bonding capacity, site concentrations, functional groups for mineral sorbents.

• Journal of the Korean Association of Geographic Information Studies
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• v.18 no.1
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• pp.120-134
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• 2015

InVEST Water Yield model developed by Natural Capital Project was applied for South Korea to assess domestic forest ecosystem's water provisioning services. The InVEST Water Yield model required 8 input dataset, including six spatial map data and two derived by coefficients. By running the model with relatively easy acquired and modified data, the result of domestic forest ecosystem's water provisioning services was 9,409,622,083 ton using the standard of the year 2011. The result showed similar patterns and distribution of rainfall in 2011, but showed difference when compared with existing researches spatially driven in nationwide statistical analysis results. This difference is assumed to occur with different model mechanism in spatial implementation and statistical analysis. So given that the model is currently still developing, applications should be taken on qualitative perspectives rather than on quantitative perspectives. Additionally, for advancing the application of InVEST water yield model, quantification of suitable input data and comparison using multi-modeling is required.

• KIPS Transactions on Software and Data Engineering
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• v.10 no.10
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• pp.391-398
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• 2021

Since the leased line is a structure that exclusively uses two connected areas for data transmission, a stable quality level and security are ensured, and despite the rapid increase in the number of switched lines, it is a line method that is continuously used a lot in companies. However, because the cost is relatively high, one of the important roles of the network operator in the enterprise is to maintain the optimal state by properly arranging and utilizing the resources of the network leased line. In other words, in order to properly support business service requirements, it is essential to properly manage bandwidth resources of leased lines from the viewpoint of data transmission, and properly predicting and managing leased line usage becomes a key factor. Therefore, in this study, various prediction models were applied and performance was evaluated based on the actual usage rate data of leased lines used in corporate networks. In general, the performance of each prediction was measured and compared by applying the smoothing model and ARIMA model, which are widely used as statistical methods, and the representative models of deep learning based on artificial neural networks, which are being studied a lot these days. In addition, based on the experimental results, we proposed the items to be considered in order for each model to achieve good performance for prediction from the viewpoint of effective operation of leased line resources.

• KEPCO Journal on Electric Power and Energy
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• v.6 no.4
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• pp.413-418
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• 2020

It is necessary to manage the prediction accuracy of the machine learning model to prevent the decrease in the performance of the grid network condition prediction model due to overfitting of the initial training data and to continuously utilize the prediction model in the field by maintaining the prediction accuracy. In this paper, we propose an automation technique for maintaining the performance of the model, which increases the accuracy and reliability of the prediction model by considering the characteristics of the power grid state data that constantly changes due to various factors, and enables quality maintenance at a level applicable to the field. The proposed technique modeled a series of tasks for maintaining the performance of the power grid condition prediction model through the application of the workflow management technology in the form of a workflow, and then automated it to make the work more efficient. In addition, the reliability of the performance result is secured by evaluating the performance of the prediction model taking into account both the degree of change in the statistical characteristics of the data and the level of generalization of the prediction, which has not been attempted in the existing technology. Through this, the accuracy of the prediction model is maintained at a certain level, and further new development of predictive models with excellent performance is possible. As a result, the proposed technique not only solves the problem of performance degradation of the predictive model, but also improves the field utilization of the condition prediction model in a complex power grid system.

• Journal of Navigation and Port Research
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• v.43 no.1
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• pp.1-8
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• 2019

Identifying behavioral errors of seafarers that have led to marine accidents is a basis for research into prevention or mitigation of marine accidents. The purpose of this study is to estimate the optimal probability distribution function needed to model behavioral errors of crew members into three behaviors (i.e., Skill-, Rule-, Knowledge-based). Through use of behavioral data obtained from previous accidents, we estimated the optimal probability distribution function for the three behavioral errors and verified the significance between the probability values derived from the probability distribution function. Maximum Likelihood Estimation (MLE) was applied to the probability distribution function estimation and variance analysis (ANOVA) used for the significance test. The obtained experimental results show that the probability distribution function with the smallest error can be estimated for each of the three behavioral errors for eight types of marine accidents. The statistical significance of the three behavioral errors for eight types of marine accidents calculated using the probability distribution function was observed. In addition, behavioral errors were also found to significantly affect marine accidents. The results of this study can be applied to predicting marine accidents caused by behavioral errors.

• The Journal of Bigdata
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• v.3 no.2
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• pp.71-78
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• 2018

The study is based on machine learning techniques to increase the accuracy of the forest fire predictive model. It used 14 years of data from 2003 to 2016 in Gang-won-do where forest fire were the most frequent. To reduce weather data errors, Gang-won-do was divided into nine areas and weather data from each region was used. However, dividing the forest fire forecast model into nine zones would make a large difference between the date of occurrence and the date of not occurring. Imbalance issues can degrade model performance. To address this, several sampling methods were applied. To increase the accuracy of the model, five indices in the Canadian Frost Fire Weather Index (FWI) were used as derived variable. The modeling method used statistical methods for logistic regression and machine learning methods for random forest and xgboost. The selection criteria for each zone's final model were set in consideration of accuracy, sensitivity and specificity, and the prediction of the nine zones resulted in 80 of the 104 fires that occurred, and 7426 of the 9758 non-fires. Overall accuracy was 76.1%.

• Journal of the Korean Society of Earth Science Education
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• v.15 no.1
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• pp.62-75
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• 2022

In this study, experiential STEAM program using 3D printer was produced focusing on the content elements of 'solar' in the 2015 revised science curriculum, and in order to find out the effectiveness of the STEAM program, analyzed creative problem solving, STEAM attitude, and STEAM satisfaction by applying it to two middle school 77 students simple random sampled. The results of this study are as follows. First, a solar tactile model was produced using a 3D printer, and a program was developed to enable students to actively learn experience-oriented activities through visual impairment experiences. Second, in the response sample t-test by the difference in pre- and post-score of STEAM attitude tests, significant statistical test results were shown in 'interest', 'consideration', 'self-concept', 'self-efficacy', and 'science and engineering career choice' sub-factors except 'consideration' and 'usefulness / value recognition' sub-factors (p<.05). Third,, the STEAM satisfaction test conducted after the application of the 3D printer-based STEAM program showed that the average value range of sub-factors were 3.66~3.97, which improved students' understanding and interest in science subjects through the 3D printer-based STEAM program.

• Journal of Advanced Navigation Technology
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• v.27 no.6
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• pp.835-840
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• 2023

RFID(Radio Frequency Identification) systems are attracting attention as a key component of Internet of Things technology due to the cost and energy efficiency of application services. In order to use RFID technology in the IoT application service field, it is necessary to be able to store and manage various information for a long period of time as well as simple recognition between the reader and tag of the RFID system. And in order to read and write information to tags, a performance improvement technology that is strong and reliable in poor wireless channels is needed. In particular, in the UHF(Ultra High Frequency) RFID system, since multiple tags communicate passively in a crowded environment, it is essential to improve the recognition rate and transmission speed of individual tags. In this paper, Middleton's Class A impulsive noise model was selected to analyze the performance of the RFID system in an impulsive noise environment, and FM0 encoding and Miller encoding were applied to the tag to analyze the error rate performance of the RFID system. As a result of analyzing the performance of the RFID system in Middleton's Class A impulsive noise channel, it was found that the larger the Gaussian noise to impulsive noise power ratio and the impulsive noise index, the more similar the characteristics to the Gaussian noise channel.

• The Journal of the Acoustical Society of Korea
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• v.28 no.8
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• pp.697-705
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• 2009

An unsupervised (blind) method is proposed aiming at extracting rhythmic sources from commercial polyphonic music whose number of channels is limited to one. Commercial music signals are not usually provided with more than two channels while they often contain multiple instruments including singing voice. Therefore, instead of using conventional modeling of mixing environments or statistical characteristics, we should introduce other source-specific characteristics for separating or extracting sources in the under determined environments. In this paper, we concentrate on extracting rhythmic sources from the mixture with the other harmonic sources. An extension of nonnegative matrix factorization (NMF), which is called nonnegative matrix partial co-factorization (NMPCF), is used to analyze multiple relationships between spectral and temporal properties in the given input matrices. Moreover, temporal repeatability of the rhythmic sound sources is implicated as a common rhythmic property among segments of an input mixture signal. The proposed method shows acceptable, but not superior separation quality to referred prior knowledge-based drum source separation systems, but it has better applicability due to its blind manner in separation, for example, when there is no prior information or the target rhythmic source is irregular.