• 융합보안논문지
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• 제21권2호
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• pp.67-79
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• 2021

전훈이란 군사용어로서 전투발전분야의 교육과 현실에서 문제점이 확인되거나 개선이 필요한 요소를 찾아서 미래의 발전을 도모하는 모든 활동이다. 이 논문에서는 전훈활동을 추진하는데 드러나는 문제점, 즉 분석시 장기간 소요, 예산 문제, 전문가 필요성 등을 해결하고자 실제 사례를 제시하고 인공지능 분석 추론기술을 적용하는 데 초점을 맞춘다. 이미 실용화되어 사용 중인, 인지 컴퓨팅 관련 기술을 활용한 인공지능 법률자문 서비스가 전훈의 문제점을 해결하는데 가장 적합한 사례로 판단했다. 이 논문은 인공지능을 활용한 지능형 전훈분석 추론기술의 효과적인 적용방안을 제시한다. 이를 위해, 전훈분석 정의 및 사례, 인공지능의 머신러닝으로 진화, 인지 컴퓨팅 등 이론적 배경을 살펴보고, 새롭게 제안한 L²-OODA 앙상블 알고리즘을 이용해 국방분야 신기술에 적용함으로써 현존전력 개선 및 최적화를 구현하는데 기여하고자 한다.

• 국제학술발표논문집
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• The 8th International Conference on Construction Engineering and Project Management
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• pp.463-481
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• 2020

Cutter suction dredgers (CSDs) are widely used in various dredging constructions such as channel excavation, wharf construction, and reef construction. During a CSD construction, the main operation is to control the swing speed of cutter to keep the slurry concentration in a proper range. However, the slurry concentration cannot be monitored in real-time, i.e., there is a "time-lag effect" in the log of slurry concentration, making it difficult for operators to make the optimal decision on controlling. Concerning this issue, a solution scheme that using real-time monitored indicators to predict current slurry concentration is proposed in this research. The characteristics of the CSD monitoring data are first studied, and a set of preprocessing methods are presented. Then we put forward the concept of "index class" to select the important indices. Finally, an ensemble learning algorithm is set up to fit the relationship between the slurry concentration and the indices of the index classes. In the experiment, log data over seven days of a practical dredging construction is collected. For comparison, the Deep Neural Network (DNN), Long Short Time Memory (LSTM), Support Vector Machine (SVM), Random Forest (RF), Gradient Boosting Decision Tree (GBDT), and the Bayesian Ridge algorithm are tried. The results show that our method has the best performance with an R² of 0.886 and a mean square error (MSE) of 5.538. This research provides an effective way for real-time predicting the slurry concentration of CSDs and can help to improve the stationarity and production efficiency of dredging construction.

• 한국인터넷방송통신학회논문지
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• 제18권3호
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• pp.77-82
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• 2018

본 논문은 가변 스텝 크기를 가지는 MSAG-GMMA(modified Stop-and-Go generalized multi modulus algorithm) 적응 블라인드 등화 알고리즘의 성능 분석에 관한 것이다. 제안한 알고리즘은 등화 계수 갱신 식에서 고정 스텝 크기에 결정지향 알고리즘의 오차신호의 크기를 곱하여 오차크기에 따라서 스텝 크기가 변하도록 하였다. 또한 결정지향 알고리즘의 오차신호의 크기를 판단하여 어느 임계값 이상에서는 정상상태로의 빠른 수렴 속도를 유지하도록 스텝 크기가 고정인 값을 가지는 MSAG-GMMA가 동작하고, 미만일 때는 스텝 크기가 가변되는 MSAG-GMMA가 동작하도록 하였다. 제안한 알고리즘의 성능을 평가하기 위하여 성능 지수로 앙상블 ISI, 앙상블-평균 MSE, 그리고 등화기의 출력으로 얻어지는 등화 후 신호점도를 사용하였다. 모의실험을 통하여 제안한 알고리즘이 MMA, GMMA, 그리고 MSAG-GMMA보다 빠른 수렴 속도와 정상상태에서 작은 잔류 오차를 가짐을 확인하였다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제8권1호
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• pp.196-212
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• 2014

Facial expression recognition is an important part in emotional interaction between human and machine. This paper proposes a facial expression recognition approach based on multi-classifier fusion with stacking algorithm. The kappa-error diagram is employed in base-level classifiers selection, which gains insights about which individual classifier has the better recognition performance and how diverse among them to help improve the recognition accuracy rate by fusing the complementary functions. In order to avoid the influence of the chance factor caused by guessing in algorithm evaluation and get more reliable awareness of algorithm performance, kappa and informedness besides accuracy are utilized as measure criteria in the comparison experiments. To verify the effectiveness of our approach, two public databases are used in the experiments. The experiment results show that compared with individual classifier and two other typical ensemble methods, our proposed stacked ensemble system does recognize facial expression more accurately with less standard deviation. It overcomes the individual classifier's bias and achieves more reliable recognition results.

• 응용통계연구
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• 제13권2호
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• pp.415-425
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• 2000

붓스트랩 표본추출과 pairwise coupling의 알고리즘을 결합한 새로운 분류 알고리즘을 제안하고, 이를 선형판별분석과 2차 판별분석에 적용하였다. 그리고 새로운 분류 알고리즘의 정확도를 비교하기위해 널리 사용되는 waveform 자료 등을 분석한 후, 그 결과를 기존 분류 방법과 비교하였다.

• 한국인터넷방송통신학회논문지
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• 제20권4호
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• pp.101-106
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• 2020

현재 의료 현장에서 초음파 진단은 과거 청진기와 같다고 할 수 있다. 그러나 초음파의 특성상 검사자의 숙련도에 따라 결과 예측이 불확실하다는 단점을 가진다. 따라서 본 논문에서는 이런 문제를 해결하기 위해 딥러닝 기술을 기반으로 초음파 검사 중 간병변 탐지의 정확도를 높이고자 한다. 제안 논문에서는 CNN 모델과 앙상블 모델을 이용해 병변 분류의 정확도 비교 실험하였다. 실험결과 CNN 모델에서 분류 정확도는 평균 82.33%에서 앙상블모델의 경우 평균 89.9%로 약 7% 높은 것을 확인하였다. 또한 앙상블 모델이 평균 ROC커브에서도 0.97로 CNN모델보다 약 0.4정도 높은 것을 확인하였다.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2018년도 학술발표회
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• pp.155-155
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• 2018

Uncertainty in flood forecasting using a coupled meteorological and hydrological model is arisen from various sources, especially the uncertainty comes from the inaccuracy of Quantitative Precipitation Forecasts (QPFs). In order to improve the capability of flood forecast, the uncertainty estimation and mitigation are required to perform. This study is conducted to investigate and reduce such uncertainty. First, ensemble QPFs are generated by using Monte - Carlo simulation, then each ensemble member is forced as input for a hydrological model to obtain ensemble streamflow prediction. Likelihood measures are evaluated to identify feasible member. These members are retained to define upper and lower limits of the uncertainty interval and assess the uncertainty. To mitigate the uncertainty for very short lead time, a blending method, which merges the ensemble QPFs with radar-based rainfall prediction considering both qualitative and quantitative skills, is proposed. Finally, blending bias ratios, which are estimated from previous time step, are used to update the members over total lead time. The proposed method is verified for the two flood events in 2013 and 2016 in the Yeonguol and Soyang watersheds that are located in the Han River basin, South Korea. The uncertainty in flood forecasting using a coupled Local Data Assimilation and Prediction System (LDAPS) and Sejong University Rainfall - Runoff (SURR) model is investigated and then mitigated by blending the generated ensemble LDAPS members with radar-based rainfall prediction that uses McGill algorithm for precipitation nowcasting by Lagrangian extrapolation (MAPLE). The results show that the uncertainty of flood forecasting using the coupled model increases when the lead time is longer. The mitigation method indicates its effectiveness for mitigating the uncertainty with the increases of the percentage of feasible member (POFM) and the ratio of the number of observations that fall into the uncertainty interval (p-factor).

• 대한산업공학회지
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• 제27권1호
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• pp.47-53
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• 2001

In this paper, we compare the classification performances of both ensemble and clustering algorithms (Data Bagging, Variable Selection Bagging, Parameter Combining, Clustering) to logistic regression in consideration of various characteristics of input data. Four factors used to simulate the logistic model are (1) correlation among input variables (2) variance of observation (3) training data size and (4) input-output function. In view of the unknown relationship between input and output function, we use a Taguchi design to improve the practicality of our study results by letting it as a noise factor. Experimental study results indicate the following: When the level of the variance is medium, Bagging & Parameter Combining performs worse than Logistic Regression, Variable Selection Bagging and Clustering. However, classification performances of Logistic Regression, Variable Selection Bagging, Bagging and Clustering are not significantly different when the variance of input data is either small or large. When there is strong correlation in input variables, Variable Selection Bagging outperforms both Logistic Regression and Parameter combining. In general, Parameter Combining algorithm appears to be the worst at our disappointment.

• 한국정보과학회논문지:소프트웨어및응용
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• 제36권2호
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• pp.109-121
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• 2009

사전정보는 클러스터링 성능을 유도할 수 있는 요인이지만, 활용 방법에 따라 차이는 발생한다. 특히, 사전정보를 초기 중심으로 활용할 때, 사전정보 간 유사도에 대해 고려하는 것이 필요하다. 레이블이 같더라도 낮은 유사도를 갖는 사전정보로 인해 초기 중심 설정 시 문제가 발생할 수 있기 때문에, 이들을 구분하여 활용하는 방법이 필요하다. 따라서 본 논문은 낮은 유사도를 갖는 사전정보를 구분하여 문제를 해결하는 방법을 제시한다. 또한 유사도에 의해 구분된 사전정보는 다양하게 활용함으로써 생성되는 다양한 클러스터링 결과를 연관규칙에 기반하여 앙상블 함으로써 통합된 하나의 분석 결과를 도출하여 클러스터링 분석 성능을 더욱 개선시킬 수 있다.

• Journal of Electrical Engineering and Technology
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• 제7권1호
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• pp.115-123
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• 2012

A video analysis system used to detect events in video streams generally has several processes, including object detection, object trajectories analysis, and recognition of the trajectories by comparison with an a priori trained model. However, these processes do not work well in a complex environment that has many occlusions, mirror effects, and/or shadow effects. We propose a new approach to a context-aware video surveillance system to detect predefined contexts in video streams. The proposed system consists of two modules: a feature extractor and a context recognizer. The feature extractor calculates the moving energy that represents the amount of moving objects in a video stream and the stationary energy that represents the amount of still objects in a video stream. We represent situations and events as motion changes and stationary energy in video streams. The context recognizer determines whether predefined contexts are included in video streams using the extracted moving and stationary energies from a feature extractor. To train each context model and recognize predefined contexts in video streams, we propose and use a new ensemble classifier based on the AdaBoost algorithm, DAdaBoost, which is one of the most famous ensemble classifier algorithms. Our proposed approach is expected to be a robust method in more complex environments that have a mirror effect and/or a shadow effect.