• The Journal of Korean Institute of Communications and Information Sciences
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• v.30 no.3C
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• pp.147-152
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• 2005

In this paper, we proposed a method for compressing the static video test patterns in high quality to test the picture quality in DTV. In our method, we use the fact that the generated bits and average quantization value have almost identical distribution characteristics per each GOP and we propose a new target bit allocation method suitable for compressing the static test pattern while the target bit allocation method in MPEG-2 TM5 is suitable for the moving picture. The proposed target bit allocation method is to maintain the high quality video continuously by using the normalized complexities which are updated or maintained by means of picture qualities at each GOP. Experiment result showed that the test pattern stream encoded by MPEG-2 software with the proposed algorithm had a stable bit rate and good video quality during the decoding process.

• Fashion & Textile Research Journal
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• v.14 no.2
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• pp.277-285
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• 2012

This research studied how to develop tight upper sportswear from 3D scan data considering fabric stretch property. Subjects were five Korean men of average figure in their 20's. Scanning was done for ten postures via vitus smart/pro(Techmath LTD). Analyzing from 3D scan data, more than 70% of the upper body surface showed surface change rate under 20%. It was shoulder and under arm side part that showed most noticeable body surface change when moving. A parametric model with convex surface was generated and flattened onto the plane, resulting 2D pattern. The error rate occurring in the process of 3D to 2D conversion was 0.2% for outline and 0.13% for area, respectively. Thirteen kinds of stretchable fabrics in the market were collected for this study. Stretch property was in the range of 16.0~58.2% for wale direction; 23.1~78.4% for course. Based on wear trial test, four fabrics were chosen for making the 1st experimental garment and finally one fabric was chosen for the 2nd one, which was developed applying 4 kinds of crosswise reduction rate on 2D pattern: 0, 5, 10, and 15%. Through wear trial test and garment pressure measurement, experimental garment applied with 10% pattern reduction rate was evaluated as most comfortable and considerable.

• Wind and Structures
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• v.19 no.3
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• pp.339-352
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• 2014

This paper discusses the appropriate duration for basic gust wind speeds in wind loading codes and standards, and in wind engineering generally. Although various proposed definitions are discussed, the 'moving average' gust duration has been widely accepted internationally. The commonly-specified gust duration of 3-seconds, however, is shown to have a significant effect on the high-frequency end of the spectrum of turbulence, and may not be ideally suited for wind engineering purposes. The effective gust durations measured by commonly-used anemometer types are discussed; these are typically considerably shorter than the 'standard' duration of 3 seconds. Using stationary random process theory, the paper gives expected peak factors, $g_u$, as a function of the non-dimensional parameter ($T/{\tau}$), where T is the sample, or reference, time, and ${\tau}$ is the gust duration, and a non-dimensional mean wind speed, $\bar{U}.T/L_u$, where $\bar{U}$ is a mean wind speed, and $L_u$ is the integral length scale of turbulence. The commonly-used Durst relationship, relating gusts of various durations, is shown to correspond to a particular value of turbulence intensity $I_u$, of 16.5%, and is therefore applicable to particular terrain and height situations, and hence should not be applied universally. The effective frontal areas associated with peak gusts of various durations are discussed; this indicates that a gust of 3 seconds has an equivalent frontal area equal to that of a tall building. Finally a generalized gust response factor format, accounting for fluctuating and resonant along-wind loading of structures, applicable to any code is presented.

• Journal of Korea Water Resources Association
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• v.34 no.6
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• pp.701-711
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• 2001

Forecasting of water quality variation is not an easy process due to the complicated nature of various water quality factors and their interrelationships. The objective of this study is to test the applicability of neural network models to the forecasting of the water quality at Gongju station in Geum River. This is done by forecasting monthly water qualities such as DO, BOD, and TN, and comparing with those obtained by ARIMA model. The neural network models of this study use BP(Back Propagation) algorithm for training. In order to improve the performance of the training, the models are tested in three different styles ; MANN model which uses the Moment-Adaptive learning rate method, LMNN model which uses the Levenberg-Marquardt method, and MNN model which separates the hidden layers for judgement factors from the hidden layers for water quality data. the results show that the forecasted water qualities are reasonably close to the observed data. And the MNN model shows the best results among the three models tested

• The KIPS Transactions:PartB
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• v.11B no.3
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• pp.247-256
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• 2004

In this paper, we propose the prediction search algorithm for block matching using the temporal/spatial correlation of the video sequence and the renter-biased property of motion vectors The proposed algorithm determines the location of a better starting point for the search of an exact motion vector using the point of the smallest SAD(Sum of Absolute Difference) value by the predicted motion vector from the same block of the previous frame and the predictor candidate pint in each search region and the predicted motion vector from the neighbour blocks of the current frame. And the searching process after moving the starting point is processed a adaptive search pattern according to the magnitude of motion vector Simulation results show that PSNR(Peak-to-Signal Noise Ratio) values are improved up to the 0.75dB as depend on the video sequences and improved about 0.05∼0.34dB on an average except the FS (Full Search) algorithm.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.15 no.2
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• pp.167-173
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• 2015

Extraction of Finger-end points is one of the most process for user multi-commands in the Hand-Gesture interface technology. However, most of previous works use the geometric and morphological method for extracting a finger-end points. Therefore, this paper proposes the method of user finger-end points extraction that is motivated a ultrared diffused illumination, which is used for the user commands in the multi-touch display device. Proposed air-mouse is worked by the quantity state and moving direction of extracted finger-end points. Also, our system includes a basic mouse event, as well as the continuous command function for expending a user multi-gesture. In order to evaluate the performance of the our proposed method, after applying to the web browser application as a command device. As a result, the proposed method showed the average 90% success-rate for the various user-commands.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.49 no.4
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• pp.23-31
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• 2012

This paper proposes a motion estimation-based super resolution algorithm to restore input low-resolution images of large movement into a super-resolution image. It is difficult to find the sub-pixel motion estimation in images of large movement compared to typical experimental images. Also, it has disadvantage which have high computational complexity to find reference images and candidate images using general motion estimation method. In order to solve these problems for the traditional two-dimensional motion estimation using the proposed registration threshold that satisfy the conditions based on the reference image is determined. Candidate image with minimum weight among the best candidates for super resolution images, the restoration process to proceed with to find a new image registration algorithm is proposed. According to experimental results, the average PSNR of the proposed algorithm is 31.89dB and this is better than PSNR of traditional super-resolution algorithm and it also shows improvement of computational complexity.

• Journal of Korea Port Economic Association
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• v.31 no.1
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• pp.69-84
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• 2015

The purpose of this study is to forecast the seaborne trade volume during January 1994 to December 2014 using the multiplicative seasonal autoregressive integrated moving average (ARIMA) along with intervention factors and an artificial neural network (ANN) model. Diagnostic checks of the ARIMA model were conducted using the Ljung-Box Q and Jarque-Bera statistics. All types of ARIMA process satisfied the basic assumption of residuals. The ARIMA(2,1,0) $(1,0,1)_{12}$ model showed the lowest forecast error. In addition, the prediction error of the artificial neural network indicated a level of 5.9% on hidden layer 5, which suggests a relatively accurate forecasts. Furthermore, the ex-ante predicted values based on the ARIMA model and ANN model are presented. The result shows that the seaborne trade volume increases very slowly.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.39 no.1
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• pp.25-30
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• 2016

It is critical to forecast the maximum daily and monthly demand for power with as little error as possible for our industry and national economy. In general, long-term forecasting of power demand has been studied from both the consumer's perspective and an econometrics model in the form of a generalized linear model with predictors. Time series techniques are used for short-term forecasting with no predictors as predictors must be predicted prior to forecasting response variables and containing estimation errors during this process is inevitable. In previous researches, seasonal exponential smoothing method, SARMA (Seasonal Auto Regressive Moving Average) with consideration to weekly pattern Neuron-Fuzzy model, SVR (Support Vector Regression) model with predictors explored through machine learning, and K-means clustering technique in the various approaches have been applied to short-term power supply forecasting. In this paper, SARMA and intervention model are fitted to forecast the maximum power load daily, weekly, and monthly by using the empirical data from 2011 through 2013. $ARMA(2,\;1,\;2)(1,\;1,\;1)_7$ and $ARMA(0,\;1,\;1)(1,\;1,\;0)_{12}$ are fitted respectively to the daily and monthly power demand, but the weekly power demand is not fitted by AREA because of unit root series. In our fitted intervention model, the factors of long holidays, summer and winter are significant in the form of indicator function. The SARMA with MAPE (Mean Absolute Percentage Error) of 2.45% and intervention model with MAPE of 2.44% are more efficient than the present seasonal exponential smoothing with MAPE of about 4%. Although the dynamic repression model with the predictors of humidity, temperature, and seasonal dummies was applied to foretaste the daily power demand, it lead to a high MAPE of 3.5% even though it has estimation error of predictors.

• The Transactions of the Korea Information Processing Society
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• v.7 no.2S
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• pp.638-648
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• 2000

This paper introduces a new technique for block is matching motion estimation. Since the temporal correlation of the image sequence, the motion vector of a block is highly related to the motion vector of the same coordinate block in the previous image frame. If we can obtain useful and enough information from the motion vector of the same coordinate block of the previous frame, the total number of search points used to find the motion vector of the current block may be reduced significantly. Using that idea, an efficient predicted direction search algorithm (PDSA) for block matching algorithm is proposed. Based on the direction of the blocks of the two successive previous frames, if the direction of the to successive blocks is same, the first search point of the proposed PDSA is moved two pixels to the direction of the block. The searching process after moving the first search point is processed according to the fixed search patterns. Otherwise, full search is performed with search area $\pm$2. Simulation results show that PSNR values are improved up to the 3.4dB as depend on the image sequences and improved about 1.5dB on an average. Search times are reduced about 20% than the other fast search algorithms. Simulation results also show that the performance of the PDSA scheme gives better subjective picture quality than the other fast search algorithms and is closer to that of the FS(Full Search) algorithm.