• 한국통신학회논문지
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• 제41권11호
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• pp.1406-1409
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• 2016

본 논문에서는 송수신단 간 변조기법 및 채널 상태 값이 약속되지 않은 완벽한 블라인드 통신 상황에서 송신측의 변조방식을 알아내기 위해 성좌도 회전 및 확률밀도함수(probability density function : pdf)를 이용한 새로운 자율 변조 구분(Automatic modulation classification : AMC)기법과 경험적 신호 그룹화 알고리즘을 통해 채널 상태 값을 추정하는 방법을 제안한다. 평균제곱근 편차(Root mean square error : RMSE) 및 심볼 오류율(Symbol error rate : SER) 등의 모의실험을 통해 제안된 기법과 기존의 다른 기법간의 채널 상태와 변조 추정 능력을 비교 평가한다.

• 터널과지하공간
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• 제31권6호
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• pp.508-519
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• 2021

본 연구에서는 발파 시 사람과 주변 환경에 영향을 끼치는 발파진동(peak particle velocity, PPV)을 예측하는 모델을 개발하였다. PPV를 예측하기 위해 kNN(k-nearest neighbors), CART(classification and regression tree), SVR(support vector regression), PSO(particle swarm optimization)-SVR 알고리즘을 이용한 4가지 머신러닝 모델을 개발하고 상호 비교하였다. 머신러닝 모델을 훈련하기 위해 경상남도 창원시에 있는 욕망산을 연구지역으로 선정하고 1048개의 발파 데이터를 획득하였다. 발파 데이터는 천공장, 저항선, 공간격, 최대지발장약량, 비장약량, 총공수, 에멀전비율, 이격거리, PPV로 구성되었다. 훈련된 모델들의 성능을 평가하기 위한 지표 값으로 MAE(mean absolute error), MSE(mean squared error), RMSE(root mean squared error)를 사용하였다. 평가결과 PSO-SVR 모델이 MAE, MSE, RMSE가 각각 0.0348, 0.0021, 0.0458으로 가장 우수한 예측 성능을 나타냈다. 마지막으로 개발된 머신러닝 모델을 이용하여 주변 환경에 영향을 끼치는 정도를 예측하는 방법을 제시하였다.

• 한국환경과학회지
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• 제31권7호
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• pp.555-567
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• 2022

With the introduction of the tele-monitoring system (TMS) in South Korea, monitoring of the concentration of pollutants discharged from nationwide water quality TMS attachments is possible. In addition, the Ministry of Environment is implementing a smart sewage system program that combines ICT technology with wastewater treatment plants. Thus, many institutions are adopting the automatic operation technique which uses process operation factors and TMS data of sewage treatment plants. As a part of the preliminary study, a multilayer perceptron (MLP) analysis method was applied to TMS data to identify predictability degree. TMS data were designated as independent variables, and each pollutant was considered as an independent variables. To verify the validity of the prediction, root mean square error analysis was conducted. TMS data from two public sewage treatment plants in Chungnam were used. The values of RMSE in SS, T-N, and COD predictions (excluding T-P) in treatment plant A showed an error range of 10%, and in the case of treatment plant B, all items showed an error exceeding 20%. If the total amount of data used MLP analysis increases, the predictability of MLP analysis is expected to increase further.

• Journal of the Korean Data and Information Science Society
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• 제20권2호
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• pp.349-356
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• 2009

국내의 행정구역상 시군구 등과 같은 소지역에 있어서 실업률에 대한 남녀별 공통 상대위험도를 추정하는데, 추정방법으로 단순한 합동추정, 울프 방법에 기초한 가중추정과 잭나이프 추정들을 고려하고 이 추정 방법들의 효율성을 편의와 평균제곱오차의 개념을 통해서 비교하고자 한다. 이를 위해 2002년 12월 경기지역의 경제활동인구조사 자료를 이용하여 이 지역 내의 24개 시군단위 소지역들의 남녀별 실업률에 대한 상대위험도의 편의 및 평균제곱오차가 본 연구에서 제시된 추정절차에 의해 추정된다. 또한, 이들 추정치들의 안정성과 신뢰성은 상대편의와 상대오차제곱근을 통하여 비교된다. 추정결과 잭나이프 추정이 다른 두 추정들에 비해 매우 효율적임을 보였다.

• 한국정보과학회:학술대회논문집
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• 한국정보과학회 1998년도 가을 학술발표논문집 Vol.25 No.2 (2)
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• pp.467-468
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• 1998

Impulsive noise appears as black and/or white spots in an image. It is usually caused by errors during the image acquisition or transmission through communication channels. This paper presents a study on the impulsive noise reduction filter of digital image. A much more effective method for removing impulse noise is weighted median filtering. But it loses some information by changing center value with no condition. We propose some new technique to change center value with some conditions. In this paper, the performance of conditional weighted median filter is compared to the commonly used median filter, mean filter, max/min filter, and weighted median filter. A quantitative comparison is performed on MSE (Mean Square Error), RMSE (Root Mean Square Error), and SNR (Signal to Noise Ratio). Proposed conditional weighted median filter can yield better performance than regular filters.

• 한국환경과학회지
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• 제22권5호
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• pp.581-589
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• 2013

Surface Image Velocimetry(SIV) is an instrument to measure water surface velocity by using image processing techniques. Since SIV is a non-contact type measurement method, it is very effective and useful to measure water surface velocity for steep mountainous streams, such as streams in Jeju island. In the present study, a surface imaging velocimetry system was used to calculate the flow rate for flood event due to a typhoon. At the same time, two types of electromagnetic surface velocimetries (electromagnetic surface current meter and Kalesto) were used to observe flow velocities and compare the accuracies of each instrument. The comparison showed that for velocity distributions root mean square error(RMSE) was 0.33 and R-squared was 0.72. For discharge measurements, root mean square error(RMSE) reached 6.04 and R-squared did 0.92. It means that surface image velocimetry could be used as an alternative method for electromagnetic surface velocimetries in measuring flood discharge.

• 한국통신학회논문지
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• 제23권6호
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• pp.1471-1481
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• 1998

본 논문에서는 비트 플레인 영생(bit-plane image)에서의 정합을 이용하여 디지털 영상 안정화를 수행하는 새로운 기법을 제안한다. 제안된 기법에서는 기존의 전역 탐색 알고리즘을 하나의 비트 플레인으로부터 추출한 이진 영상에 적용함으로써 움직임 추정에 소요되는 계산량을 줄이면서도 기존 방식들보다 우수한 웅직임 추정 성능을 갖도록 하였다. 더욱이 제안된 기법은 조도 변화를 고려하여 비트 플레인을 선정하는데 있어서 적응 기법을 적용함으로써 견고한 성능을 갖도록 하였다. Brute-force 방식을 기준으로 RMSE(root mean square error)를 이용하여 제안하는 방식과 기존의 방식들을 비교하였고, 실험 결과에서 제안하는 방식이 카메라의 움직임 벡터를 기존 방식들보다 정확하게 검출하고 있음을 확인하였다. 또한 제안하는 방식은 기존 방식들에 비해서 연산량이 작고, 보다 간단한 방식으로 디지털 영상 안정화를 수행하고 있다.

• Wind and Structures
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• 제32권2호
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• pp.161-167
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• 2021

In this study, the artificial neural network (ANN) method was used for estimating the monthly mean wind speed of Sivas, in the central part of Turkey. Eighteen years of wind speed data obtained from nine measurement stations during the period of 2000-2017 at 10 m height was used for ANN analysis. It was found that mean absolute percentage error (MAPE) ranged from 3.928 to 6.662, mean bias error (MBE) ranged from -0.089 to -0.003, while root mean square error (RMSE) ranged from 0.050 to 0.157 and R2 ranged from 0.86 to 0.966. ANN models provide a good approximation of the wind speed for all measurement stations, however, a tendency to underestimate is also obvious.

• Membrane and Water Treatment
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• 제9권5호
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• pp.363-371
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• 2018

In this work, it was investigated the usability of artificial bee colony (ABC) and genetic algorithm (GA) in modeling adsorption of Co(II) onto drinking water treatment sludge (DWTS). DWTS, obtained as inevitable byproduct at the end of drinking water treatment stages, was used as an adsorbent without any physical or chemical pre-treatment in the adsorption experiments. Firstly, DWTS was characterized employing various analytical procedures such as elemental, FT-IR, SEM-EDS, XRD, XRF and TGA/DTA analysis. Then, adsorption experiments were carried out in a batch system and DWTS's Co(II) removal potential was modelled via ABC and GA methods considering the effects of certain experimental parameters (initial pH, contact time, initial Co(II) concentration, DWTS dosage) called as the input parameters. The accuracy of ABC and GA method was determined and these methods were applied to four different functions: quadratic, exponential, linear and power. Some statistical indices (sum square error, root mean square error, mean absolute error, average relative error, and determination coefficient) were used to evaluate the performance of these models. The ABC and GA method with quadratic forms obtained better prediction. As a result, it was shown ABC and GA can be used optimization of the regression function coefficients in modeling adsorption experiments.

• 한국항해학회지
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• 제21권3호
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• pp.67-74
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• 1997

This paper described the method and the result of making a dynamic fiber optic gyrocompass measuring the heading angles of ships by processing the output signal from a constant rotating fiber optic sensor and also showed the measurement to test the performance of our system. Considerig an economical view we designed and ordered a cheap medium grade fiber densors increased not fiber length but the diameter of a fiber sensing loop. The scale factor and noise was 267mV/deg/s and 2 deg/hr/$\sqrt{Hz}(1{\sigma})$, respectively. We made the dynamic fiber optic gyrocompass by this sensor. We measured the heading angles in an arbitrary direction to evaluate the accuracy of our system and the root mean square error was $0.4^\circ$. Moreover, we measured the angles ineach direction of $45^\circ$. successive rotation to know whether this system has distoritions in a specific direction or not and the root mean square error in this case was $0.5^\circ$.