• 한국음향학회지
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• 제19권2호
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• pp.90-94
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• 2000

본 논문에서는 HMM 기반의 인식시스템에서 신뢰성이 낮은 입력패턴을 거절하기 위해 필요한 임계 문턱값을 효과적으로 추정하는 방법을 제안한다. Anti-model을 이용한 거절방식은 통계적 가설(statistical hypothesis)에 근거하여 주어진 입력에 대한 HMM과 anti-model 간의 유사도를 임계 문턱값과 비교하여 거절 여부를 결정한다. HMM은 각 클래스 별로 출력값의 편차가 심하게 나타나므로 일률적인 문턱값을 적용하는 것이 어렵다. 본 논문에서는 각 클래스 별로 HMM 생성을 위해 사용된 학습데이터를 이용하여 클래스에 종속적인 임계 문턱값을 추정하는 방법을 제안한다. 각 클래스에서의 임계 문턱값은 학습데이터에 대한 HMM과 anti-model 간의 유사도로부터 추정된다. 실험에서는 HMM 기반의 3차원 손 제스처 인식시스템에 대해 제안한 문턱값 추정방법을 적용하여 거절검사를 수행하였다. 실험 결과로부터 제안한 임계 문턱값 추정방법이 anti-model 구현 방식에 관계없이 적용이 가능하고 추정된 문턱값을 이용하여 부적절한 입력 패턴을 적절하게 거절할 수 있음을 확인할 수 있었다.

• 한국농공학회논문집
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• 제61권1호
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• pp.107-120
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• 2019

Reservoir water level data identify the current water storage of the reservoir, and they are utilized as primary data for management and research of agricultural water. For the reservoir storage management, Korea Rural Community Corporation (KRC) installed water level stations at around 1,600 agricultural reservoirs and has been collecting the water level data every 10 minutes. However, various kinds of outliers due to noise and erroneous problems are frequently appearing because of environmental and physical causes. Therefore, it is necessary to detect outlier and improve the quality of reservoir water level data to utilize the water level data in purpose. This study was conducted to detect and classify outlier and normal data using two different models including the threshold model and the artificial neural network (ANN) model. The results were compared to evaluate the performance of the models. The threshold model identifies the outlier by setting the upper/lower bound of water level data and variation data and by setting bandwidth of water level data as a threshold of regarding erroneous water level. The ANN model was trained with prepared training dataset as normal data (T) and outlier (F), and the ANN model operated for identifying the outlier. The models are evaluated with reference data which were collected reservoir water level data in daily by KRC. The outlier detection performance of the threshold model was better than the ANN model, but ANN model showed better detection performance for not classifying normal data as outlier.

• 한국경영과학회지
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• 제30권2호
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• pp.131-142
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• 2005

This paper proposes queueing models for a Web server system which is composed of an infinite-buffer main server and finite-buffer auxiliary server(s). The system is modeled by the level-dependent quasi-birth- death (QBD) process. Utilizing the special structure of the QBD, we convert the infinite level-dependent QBD into a finite level-independent QBD and compute the state probabilities. We then explore the operational characteristics of the proposed web-server models and draw some useful conclusions.

• Journal of the Korean Data and Information Science Society
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• 제18권1호
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• pp.211-218
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• 2007

Nonlinear ARMA model $X_n\;=\;h(X_{n-1},{\cdots},X_{n-p},e_{n-1},{\cdots},e_{n-p})+e_n$ is considered and easy-to-check sufficient condition for strict stationarity of {$X_n$} without some irreducibility or continuity assumption is given. Threshold ARMA(p, q) and momentum threshold ARMA(p, q) models are examined as special cases.

• Communications for Statistical Applications and Methods
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• 제3권2호
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• pp.249-273
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• 1996

This paper reviews and develops several statistical models for extreme values, based on threshold methodology. Extreme values of a time series are modeled in terms of tails which are defined as truncated forms of original variables, and Markov property is imposed on the tails. Tails of the generalized extreme value distribution and a multivariate extreme value distributively, of the tails of the series. These models are then applied to real ozone data series collected in the Chicago area. A major concern is given to detecting any possible trend in the extreme values.

• Communications for Statistical Applications and Methods
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• 제18권4호
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• pp.477-483
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• 2011

This article is concerned with a class of threshold-asymmetric GARCH models both for stationary case and for non-stationary case. We investigate large sample properties of estimators from QML(quasi-maximum likelihood) and QL(quasilikelihood) methods. Asymptotic distributions are derived and it is interesting to note for non-stationary case that both QML and QL give asymptotic normal distributions.

• 한국음향학회지
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• 제22권7호
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• pp.539-544
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• 2003

본 논문은MLLR (Maximum Likelihood Linear Regression)를 화자 적응시 과적응 방지를 위해 트리 구조에서 HHM 파라메타의 변환을 결정하는 점유 문턱값 (occupation threshold)의 영향을 감소하는 방법에 대해 기술한다. 데이터의 특징을 잘 나타내는 주성분 분석과 독립성분 분석을 통해 모델 혼합성분의 상관관계를 줄이고 상대적으로 데이터의 분포가 적은 축을 삭제함으로써 적은 적응데이터에 의한 과적응의 영향을 감소시켰다. 점유 문턱값을 작게 설정함으로써 변환함수의 수를 증가시켰을 경우, 기존의 MLLR 알고리즘은 과적응에 의해 화자 독립 모델보다 낮은 인식률을 나타내는 반면, 제안한 MLLR알고리즘은 화자 독립 모델의 성능에 비해 평균 2%이상 인식율 향상을 나타내었다.

• 한국항공운항학회지
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• 제29권4호
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• pp.45-51
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• 2021

A runway safety area (RSA) is defined as the surface surrounding the runway prepared or suitable for reducing the risk of damage to airplanes in the event of an undershoot, overshoot, or excursion from the runway. The Runway Stripe is a defined area including the runway stopway, if provided, intended firstly to reduce the risk of damage to aircraft running off a runway, and secondly, to protect aircraft flying over it during takeoff or landing operations. This study used 2 RSA analysis models; RSARA and LRSARA. The analysis utilizes historical data from the specific airport and allows to take into consideration specific operational conditions to which movements are subject, as well as the actual or planned RSA conditions in terms of dimensions, configuration, and boundaries defined by existing obstacles. This study applied the RSA and LRSA risk assessment models to a domestic airport that do not meet the criteria required by standards for aerodrome physical characteristics. The airport is considering a method to secure the runway strip standard through the displaced threshold. This study intends to confirm through quantitative risk estimation whether meeting facility standards through the runway displaced threshold leads to a positive change in risk mitigation.

• 농업과학연구
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• 제41권1호
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• pp.23-27
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• 2014

Potato late blight occurrence is caused by various environmental factors, and the progress can be regularly predicted so that several predictive models have been developed. The models predict the timing of the disease occurrence, but they do not include the methods of the disease control. Effective fungicide control, economic threshold, prediction models were investigated in the study to reflect on customized control modules for the model forecasting the occurrence of potato late blight.

• 농업과학연구
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• 제41권1호
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• pp.29-34
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• 2014

Phytophthora blight occurrence is caused by various environmental factors, and the progress can be regularly predicted so that several predictive models have been developed. The models predict the timing of the disease occurrence, but they do not include the methods of the disease control. Effective fungicide control, control threshold, prediction models were investigated in the study to reflect on customized control modules for the model forecasting the occurrence of Phytophthora blight on hot pepper.