• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
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• v.8 no.3
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• pp.57-67
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• 2018

The ensemble is a unified approach used for getting better performance by using multiple algorithms in machine learning. In this paper, we introduce boosting and bagging, which have been widely used in ensemble techniques, and design a method using support vector regression, radial basis function network, Gaussian process, and multilayer perceptron. In addition, our experiment was performed by adding a recurrent neural network and MOHID numerical model. The drifter data used for our experimental verification consist of 683 observations in seven regions. The performance of our ensemble technique is verified by comparison with four algorithms each. As verification, mean absolute error was adapted. The presented methods are based on ensemble models using bagging, boosting, and machine learning. The error rate was calculated by assigning the equal weight value and different weight value to each unit model in ensemble. The ensemble model using machine learning showed 61.7% improvement compared to the average of four machine learning technique.

• Journal of the Korea Society of Computer and Information
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• v.28 no.4
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• pp.13-20
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• 2023

In this paper, the actual data of VOC reduction facilities was analyzed through a model that detects and predicts data anomalies. Using the USAD model, which shows stable performance in the field of anomaly detection, anomalies in real-time data are detected and sensors that cause anomalies are searched. In addition, we propose a method of predicting and warning, when abnormalities that time will occur by predicting future outliers with an auto-regressive model. The experiment was conducted with the actual data of the VOC reduction facility, and the anomaly detection test results showed high detection rates with precision, recall, and F1-score of 98.54%, 89.08%, and 93.57%, respectively. As a result, averaging of the precision, recall, and F1-score for 8 sensors of detection rates were 99.64%, 99.37%, and 99.63%. In addition, the Hamming loss obtained to confirm the validity of the detection experiment for each sensor was 0.0058, showing stable performance. And the abnormal prediction test result showed stable performance with an average absolute error of 0.0902.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.26 no.3D
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• pp.461-467
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• 2006

To detect individual vehicular speed, double loop detection technique has been widely used. This paper investigates four methodologies to measure individual speed using only a single loop sensor in a traveling lane. Two methods developed earlier include estimating the speed by means of (Case 1) the slop of inductance wave form generated by the sensor and (Case 2) the average vehicle lengths. Two other methods are newly developed through this study, which are estimations by measuring (Case 3) the mean of wheelbases using the sensor installed traversal to the traveling lane and (Case 4) the mean of wheel tracks by the sensor installed diagonally to the traveling lane. These four methodologies were field-tested and their accuracy of speed output was compared statistically. This study used Equality Coefficient and Mean Absolute Percentage Error for the assessment. It was found that the method (Case 1) was best accurate, followed by method (Case 4), (Case 2), and (Case 3).

• Journal of Korea Multimedia Society
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• v.7 no.8
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• pp.1025-1034
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• 2004

This paper proposes an adaptive motion estimation algorithm using the temporal continuity of motion. We set up a squared global search region (GSR), which basically corresponds to the search region of FSA, and non-squared adaptive local search regions (LSRs), the positions for which are predicted by the motion vectors of the temporal neighbor blocks, are constructed in the GSR. The previous frame blocks that possibly have effects on the current block are to be the temporal neighbor blocks. Because motion estimation is only performed in the areas made by LSRs, we can estimate motion more correctly and reduce processing time. Experimental results show that the proposed method can enhance visual qualities with significant reductions of complexity by reducing search regions, when compared to the conventional methods.

• The Journal of Korean Association of Computer Education
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• v.14 no.4
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• pp.63-70
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• 2011

Recommender systems, which predict and recommend items that may possibly draw users' interests, have been applied in various fields as e-commerce systems are widespread. Collaborative filtering, one of the major methodologies of recommender systems, recommends either items similar to those preferred by the user, or items preferred by the other similar user. Therefore, two problems determine its performance; one is correct estimation of similarity and the other is predicting the real rating of the recommended item. This study addresses the latter problem. Previous studies predict the real rating based on the mean of the ratings, but this study proposes a prediction based on the range of the ratings and investigates its performance through experiments. As a result, it is demonstrated that the proposed method improves the mean absolute error significantly, compared to the previous method.

• Journal of Korean Society of Transportation
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• v.25 no.4
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• pp.69-77
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• 2007

This study presents comparison results for pedestrian accident reconstruction models representing the relationship between collision speed and horizontal distance that a body travels while falling and sliding. A set of 49 reliable pedestrian accident cases are applied to compare the existing reconstruction models. In addition, the authors investigate the effects of a set of parameters associated with the effects of the frontal shape of a vehicle on the horizontal distance a pedestrian travels while falling and sliding. It has been revealed that the length of the bumper is the most dominant factor to affect the horizontal distance of pedestrian travel after collision. Further analyses utilizing more accident data need to conducted to develop a more accurate and reliable reconstruction model.

• Proceedings of the Korea Water Resources Association Conference
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• 2007.05a
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• pp.1383-1386
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• 2007

하절기 고탁수로 문제가 되고 있는 임하호에 원활한 탁수배제를 위하여 선택취수시설이 설치되어 2006년 운영되었다. 본 연구는 이러한 선택취수시설에 대한 효과를 분석하기 위하여 CE-QUAL-W2 모형을 이용하여 호내의 탁수 분포를 분석하고 보정된 모형을 이용하여 선택취수와 표면취수(표층${\sim}7\;m$)에 따른 탁수의 저감효과에 대한 분석을 수행하였다. 임하댐 유입부의 탁도 자동 측정 시스템에서 측정된 수온 및 탁도자료를 입력자료로 사용하였고, 모형의 호내의 4개지점의 수심별 자료에 대하여 보정을 실시하였다. 보정결과 관측값과 비교에서 절대 평균 오차(RMSE)는 5.2로 산정되었으며 수심별 수온 및 탁도 분포와 최고 탁도층이 발생하는 수심을 적절히 모의하였다. 선택취수시설을 적용한 경우 표면취수의 경우와 비교해서 100 NTU 이상의 고탁수층에 대해서는 (1) 호내의 탁수량이 $35\;Mm^3$이상이 저감되는 것으로 나타났고, (2) 고탁수의 방류량이 3배이상 증대되는 것으로 나타났다. 선택취수는 표면취수에 비해 고탁수층의 배제에 효과적이며 취수구의 위치 변경등 지속적인 관리를 통하여 효율적인 운영이 가능할 것으로 판단된다. 또한, 효과분석 결과를 바탕으로 시설에 대한 운영방안을 수립할 수 있을 것으로 기대된다.

• Journal of the Korean Society of Hazard Mitigation
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• v.9 no.2
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• pp.87-93
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• 2009

The labyrinth weir can be defined that the plane shape of overflow part is not straight line and is a kind of weir having overflow length increased by changing its plane shape. Recently, the labyrinth weir can be widely applied to various hydraulic facilities such as dam spillway, irrigation facilities, and canal structures by increasing precipitation. This study was performed to analyze the hydraulic characteristics according to triangle labyrinth weir using hydraulic model experiments and finally estimate the discharge coefficients for triangle labyrinth weirs. The formulae of discharge coefficient provided in this study, which make it feasible to calculate the overflow rate by a coefficient of correlation. sum of residuals, MAPE(Mean Absolute Percentage Error), are expected to be widely applied to design of hydraulic facilities such as dam spillway and irrigation system.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.42 no.1
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• pp.214-217
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• 2017

In this paper, we propose a sleep efficiency measurement algorithm based on IR-UWB radar sensor in distance. Among the vital signs which can be measured by the IR-UWB radar sensor such as breathing rate, heartbeat rate, and movement, we analyzed correlation between the movement and the sleep efficiency, and based on the result, we propose a sleep efficiency measurement algorithm. In order to verify the performance of the proposed algorithm, we applied the algorithm to three polysomnography patients in hospitals and obtained the performance of an average absolute error within 3.9%.

• The Journal of Korean Association of Computer Education
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• v.14 no.5
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• pp.97-104
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• 2011

Collaborative filtering is a methodology to recommend websites by obtaining data and opinions from the other users with similar tastes. During the past few years, this method has been used in various fields such as books, food, and movies in e-commerce systems. This study addresses the computation of similarity between users to determine items to be recommended in collaborative filtering systems. Previous studies measured similarity between users by treating each user's ratings independently without considering the distribution of the user's ratings. In contrast, this study measures similarity by utilizing position and rank information of each rating in the range of the user's ratings. The result of the experiments on the real datasets demonstrated that the proposed method improves the mean absolute error significantly, compared to the previous methods, especially when the predetermined range of ratings is large.