• Journal of the Institute of Convergence Signal Processing
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• v.9 no.3
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• pp.195-200
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• 2008

To compress the moving picture data effectively, it is needed to reduce spatial and temporal redundancy of input image data. While motion estimation! compensation methods is effectively able to reduce temporal redundancy but it is increased computation complexity because of the prediction between frames. So, the study of algorithm for computation reduction and real time processing is needed. This paper is presenting quantizer effectively able to quantize DCT coefficient considering the human visual sensitivity. As quantizer that proposed DCT-based H.263 could make transmit more frame than TMN5 at a same transfer speed, and it could decrease the frame drop effect. And the luminance signal appeared the difference of $-0.3{\sim}+0.65dB$ in the average PSNR for the estimation of objective image quality and the chrominance signal appeared the improvement in about 1.73dB in comparision with TMN5. The proposed method reduces $30{\sim}31%$ compared with NTSS and $20{\sim}21%$ compared to 4SS in comparition of calculation quantity.

• Journal of the Ergonomics Society of Korea
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• v.30 no.6
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• pp.689-694
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• 2011

Objective: The aim of this study is to evaluate the fluctuation of signal amplitude during repetitive dynamic contraction based on surface electromyography(EMG). Background: The most previous studies were considered isometric muscle contraction and they were difference to smoothing window length by moving average filter. In practical, the human movement is dynamic state. Dynamic EMG signal which indicated as the nonstationary pattern should be analyzed differently compared with the static EMG signal. Method: Ten male subjects participated in this experiment, and EMG signal was recorded by biceps brachii, anterior/posterior deltoid, and upper/lower trapezius muscles. The subject was performed to repetitive right horizontal lifting task during ten cycles. This study was considered three independent variables(muscle, amplitude processing technique, and smoothing window length) as the within-subject experimental design. This study was estimated muscular activation by means of the linear envelope technique(LE). The dependent variable was set coefficient of variation(CV) of LE for each cycle. Results: The ANOVA results showed that the main and interaction effects between the amplitude processing technique and smoothing window length were significant difference. The CV value of peak LE was higher than mean LE. According to increase the smoothing window length, this study shows that the CV trend of peak LE was decreased. However, the CV of mean LE was analyzed constant fluctuation trend regardless of the smoothing window length. Conclusion: Based on these results, we expected that using the mean LE and 300ms window length increased reproducibility and signal noise ratio during repetitive dynamic muscle contraction. Application: These results can be used to provide fundamental information for repetitive dynamic EMG signal processing.

• Journal of KIISE:Databases
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• v.29 no.5
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• pp.381-392
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• 2002

This paper deals with the problem of shape-based retrieval in time-series databases. The shape-based retrieval is defined as the operation that searches for the (sub)sequences whose shapes are similar to that of a given query sequence regardless of their actual element values. In this paper, we propose an effective and efficient approach for shape-based retrieval of subsequences. We first introduce a new similarity model for shape-based retrieval that supports various combinations of transformations such as shifting, scaling, moving average, and time warping. For efficient processing of the shape-based retrieval based on the similarity model, we also propose the indexing and query processing methods. To verify the superiority of our approach, we perform extensive experiments with the real-world S&P 500 stock data. The results reveal that our approach successfully finds all the subsequences that have the shapes similar to that of the query sequence, and also achieves significant speedup up to around 66 times compared with the sequential scan method.

• Transactions of the Korean Society of Automotive Engineers
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• v.23 no.1
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• pp.130-138
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• 2015

Despite of recently strengthened vehicle emission regulations, NOx emissions are not decreased in urban areas because of discrepancies between certification emission test modes and real driving conditions. Thus, researches on RDE-LDV (Real-driving Emission-Light-duty Vehicle) have been conducted actively using PEMS (Portable Emissions Measurement Systems). In the present study, NOx emissions were measured for 5 Korean light duty vehicles for real driving conditions including city, combined, highway, and up-downhill test route. Emission characteristics were analyzed for averaged NOx emissions per unit driving distance of each driving test routes. Furthermore, MAW (Moving Average Window) method based on $CO_2$ emissions from WLTC, which will be supported for EU regulations, was utilized. It was revealed that DRs (deviation ratios) for diesel vehicles (i.e., 5.1 ~ 8.4) were greater than gasoline vehicles (less than 0.15). Especially DR of diesel vehicle for up-downhill test route was 8.4, which indicates severe NOx emissions.

• The Korean Journal of Applied Statistics
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• v.11 no.2
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• pp.335-350
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• 1998

X11ARIMA is established on the basis of X11 which is one of smoothing approach in time series area and this procedure was introduced by Bureau of Census of United States and developed by Dagum(1975). This procedure had been updated and adjusted by Dagum(1988) with 174 economic index of North America and has been used until nowadays. Recently, X12ARIMA procedure has been studied by William Bell et.al. (1995) and Chen. & Findly(1995) whose approaches adapt adjusting outliers, Trend-change effects, seasonal effect, arid Calender effect. However, both of these procedures were implemented for correct adjusting the economic index of North America. This article starts with providing some appropriate and effective ARIMA model for 102 indexes produced by national statistical office in Korea; which consists of production(21), shipping(27), stock(27), and operating rate index(21). And a reasonable smoothing method will be proposed to reflect the specificity of Korean economy using several moving average model. In addition, Sulnal(lunar happy new year) and Chusuk effects will be extracted from the indexes above and both of effects reflect contribution of lunar calender effect. Finally, we will discuss an alternative way to estimate holiday effect which is similar to X12ARIMA procedure in concept of using both of ARIMA model and Regression model for the best fitness.

• Parasites, Hosts and Diseases
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• v.58 no.4
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• pp.467-473
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• 2020

Chinese edible frogs, Hoplobatrachus rugulosus, were examined to estimate the potential risks of human gnathostomiasis and sparganosis in Myanmar. A total of 20 frogs were purchased in a local market of Yangon and examined with naked eyes and the artificial digestion method after skin peeling in June 2018 and June 2019. Larvae of gnathostomes and Spirometra (=spargana) were detected in 15 (75.0%) and 15 (75.0%) frogs with average intensities of 10.5 and 6.3 larvae per infected frog, respectively. Gnathostome larvae were 2.75-3.80 (av. 3.30) mm long and 0.29-0.36 (0.33) mm wide. They had a characteristic head bulb with 4 rows of hooklets, a muscular long esophagus, and 2 pairs of cervical sac. The mean number of hooklets were 41, 44, 47, and 50 on the 1st, 2nd, 3rd, and 4th row, respectively. Collected spargana were actively moving, particularly with the scolex part, and have ivory-white color and variable in size. Conclusively, it has been first confirmed that Chinese edible frogs, H. rugulosus, are highly infected with larval gnathostomes and spargana in this study. Consuming these frogs is considered a potential risk of human gnathostomiasis and sparganosis in Myanmar.

• Journal of Korea Port Economic Association
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• v.37 no.1
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• pp.179-196
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• 2021

Port congestion rate at Busan Port has increased for three years. Port congestion causes container reconditioning, which increases the dockyard labor's work intensity and ship owner's waiting time. If congestion is prolonged, it can cause a drop in port service levels. Therefore, this study proposed an anomaly detection method using ARIMA(Autoregressive Integrated Moving Average) model with the daily volume data from 2013 to 2020. Most of the research that predicts port volume is mainly focusing on long-term forecasting. Furthermore, studies suggesting methods to utilize demand forecasting in terms of port operations are hard to find. Therefore, this study proposes a way to use daily demand forecasting for port anomaly detection to solve the congestion problem at Busan port.

• The Korean Journal of Applied Statistics
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• v.32 no.1
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• pp.69-81
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• 2019

We consider bivariate long range dependent (LRD) time series forecasting using a deep learning method. A long short-term memory (LSTM) network well-suited to time series data is applied to forecast bivariate time series; in addition, we compare the forecasting performance with bivariate fractional autoregressive integrated moving average (FARIMA) models. Out-of-sample forecasting errors are compared with various performance measures for functional MRI (fMRI) data and daily realized volatility data. The results show a subtle difference in the predicted values of the FIVARMA model and VARFIMA model. LSTM is computationally demanding due to hyper-parameter selection, but is more stable and the forecasting performance is competitively good to that of parametric long range dependent time series models.

• Journal of the Korea Society of Computer and Information
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• v.24 no.5
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• pp.149-156
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• 2019

The existing subtract image based intrusion detection system for CCTV digital images has a problem that it can not distinguish intruders from moving backgrounds that exist in the natural environment. In this paper, we tried to solve the problems of existing system by designing real - time intrusion detection system for CCTV digital image by combining subtract image based intrusion detection method and background learning artificial neural network technology. Our proposed system consists of three steps: subtract image based intrusion detection, background artificial neural network learning stage, and background artificial neural network evaluation stage. The final intrusion detection result is a combination of result of the subtract image based intrusion detection and the final intrusion detection result of the background artificial neural network. The step of subtract image based intrusion detection is a step of determining the occurrence of intrusion by obtaining a difference image between the background cumulative average image and the current frame image. In the background artificial neural network learning, the background is learned in a situation in which no intrusion occurs, and it is learned by dividing into a detection window unit set by the user. In the background artificial neural network evaluation, the learned background artificial neural network is used to produce background recognition or intrusion detection in the detection window unit. The proposed background learning intrusion detection system is able to detect intrusion more precisely than existing subtract image based intrusion detection system and adaptively execute machine learning on the background so that it can be operated as highly practical intrusion detection system.

• Journal of Biomedical Engineering Research
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• v.43 no.4
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• pp.280-289
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• 2022

In this paper, we present a QRS-complex detection algorithm to calculate an accurate heartbeat and clearly recognize irregular rhythm from ECG signals. The conventional Pan-Tompkins algorithm brings false QRS detection in the derivative when QRS and noise signals have similar instant variation. The proposed algorithm uses amplitude differences in 7 adjacent samples to detect QRS-complex which has the highest amplitude variation. The calculated amplitude is cubed to dominate QRS-complex and the moving average method is applied to diminish the noise signal's amplitude. Finally, a decision rule with a threshold value is applied to detect accurate QRS-complex. The calculated signals with Pan-Tompkins and proposed algorithms were compared by signal-to-noise ratio to evaluate the noise reduction degree. QRS-complex detection performance was confirmed by sensitivity and the positive predictive value(PPV). Normal ECG, muscle noise ECG, PVC, and atrial fibrillation signals were achieved which were measured from an ECG simulator. The signal-to-noise ratio difference between Pan-Tompkins and the proposed algorithm were 8.1, 8.5, 9.6, and 4.7, respectively. All ratio of the proposed algorithm is higher than the Pan-Tompkins values. It indicates that the proposed algorithm is more robust to noise than the Pan-Tompkins algorithm. The Pan-Tompkins algorithm and the proposed algorithm showed similar sensitivity and PPV at most waveforms. However, with a noisy atrial fibrillation signal, the PPV for QRS-complex has different values, 42% for the Pan-Tompkins algorithm and 100% for the proposed algorithm. It means that the proposed algorithm has superiority for QRS-complex detection in a noisy environment.