• 한국의학물리학회지:의학물리
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• 제28권3호
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• pp.129-133
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• 2017

The machine log files recorded by a scanning control unit in proton beam therapy system have been studied to be used as a quality assurance method of scanning beam deliveries. The accuracy of the data in the log files have been evaluated with a standard calibration beam scan pattern. The proton beam scan pattern has been delivered on a gafchromic film located at the isocenter plane of the proton beam treatment nozzle and found to agree within ${\pm}1.0mm$. The machine data accumulated for the scanning beam proton therapy of five different cases have been analyzed using a statistical method to estimate any systematic error in the data. The high-precision scanning beam log files in line scanning proton therapy system have been validated to be used for off-line scanning beam monitoring and thus as a patient-specific quality assurance method. The use of the machine log files for patient-specific quality assurance would simplify the quality assurance procedure with accurate scanning beam data.

• Journal of the Korean Data and Information Science Society
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• 제18권3호
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• pp.767-772
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• 2007

A kernel machine is proposed as an estimating procedure for the linear and nonlinear Poisson regression, which is based on the penalized negative log-likelihood. The proposed kernel machine provides the estimate of the mean function of the response variable, where the canonical parameter is related to the input vector in a nonlinear form. The generalized cross validation(GCV) function of MSE-type is introduced to determine hyperparameters which affect the performance of the machine. Experimental results are then presented which indicate the performance of the proposed machine.

• 대한음성학회:학술대회논문집
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• 대한음성학회 2004년도 춘계 학술대회 발표논문집
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• pp.239-242
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• 2004

An automatic pronunciation correction system provides users with correction guidelines for each pronunciation error. For this purpose, we propose a speech recognition system which automatically classifies pronunciation errors when Koreans speak a foreign language. In this paper, we also propose machine scoring methods for automatic assessment of pronunciation quality by the speech recognizer. Scores obtained from an expert human listener are used as the reference to evaluate the different machine scores and to provide targets when training some of algorithms. We use a log-likelihood score and a normalized log-likelihood score as machine scoring methods. Experimental results show that the normalized log-likelihood score had higher correlation with human scores than that obtained using the log-likelihood score.

• 음성과학
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• 제11권2호
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• pp.217-226
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• 2004

An automatic pronunciation correction system provides users with correction guidelines for each pronunciation error. For this purpose, we develop a speech recognition system which automatically classifies pronunciation errors when Koreans speak a foreign language. In this paper, we propose a machine scoring method for automatic assessment of pronunciation quality by the speech recognizer. Scores obtained from an expert human listener are used as the reference to evaluate the different machine scores and to provide targets when training some of algorithms. We use a log-likelihood score and a normalized log-likelihood score as machine scoring methods. Experimental results show that the normalized log-likelihood score had higher correlation with human scores than that obtained using the log-likelihood score.

• International journal of advanced smart convergence
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• 제12권2호
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• pp.17-24
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• 2023

Given the surge in the elderly population, and increasing in dementia cases, there is a growing interest in digital therapies that facilitate steady remote treatment. However, in the cognitive assessment of digital therapies through clinical trials, the absence of log data as an essential evaluation factor is a significant issue. To address this, we propose a solution of utilizing weighted derived variables based on high-importance variables' accuracy in log data utilization as an indirect cognitive assessment factor for digital therapies. We have validated the effectiveness of this approach using machine learning techniques such as XGBoost, LGBM, and CatBoost. Thus, we suggest the use of log data as a rapid and indirect cognitive evaluation factor for digital therapy users.

• International journal of advanced smart convergence
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• 제7권4호
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• pp.27-39
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• 2018

Since the global positioning system (GPS) has been included in mobile devices (e.g., for car navigation, in smartphones, and in smart watches), the impact of personal GPS log data on daily life has been unprecedented. For example, such log data have been used to solve public problems, such as mass transit traffic patterns, finding optimum travelers' routes, and determining prospective business zones. However, a real-time analysis technique for GPS log data has been unattainable due to theoretical limitations. We introduced a machine learning model in order to resolve the limitation. In this paper presents a new, three-stage real-time prediction model for a person's daily route activity. In the first stage, a machine learning-based clustering algorithm is adopted for place detection. The training data set was a personal GPS tracking history. In the second stage, prediction of a new person's transient mode is studied. In the third stage, to represent the person's activity on those daily routes, inference rules are applied.

• 한국인터넷방송통신학회논문지
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• 제19권1호
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• pp.1-8
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• 2019

대량의 로그 자료로부터 가장 적합한 정보를 추출하기 위한 방법 중 귀납 추리를 이용한 방법이 있다. 본 논문에서는 디지털 포렌식 분석에서 침입 흔적 로그의 순위를 결정하기 위하여 귀납 추리를 이용한 방법 중 분류에 있어서 우수한 SVM(Support Vector Machine)을 이용한다. 이를 위하여, 훈련 로그 집합의 로그 데이터를 침입 흔적 로그와 정상 로그로 분류한다. 분류된 각 집합으로부터 연관 단어를 추출하여 연관 단어 사전을 생성하고, 생성된 사전을 기반으로 각 로그를 벡터로 표현한다. 다음으로, 벡터로 표현된 로그를 SVM을 이용하여 학습하고, 학습된 로그 집합을 기반으로 테스트 로그 집합을 정상 로그와 침입 흔적 로그로 분류한다. 최종적으로, 포렌식 분석가에게 침입 흔적 로그를 추천하기 위하여 침입 흔적 로그의 추천 순위를 결정한다.

• Journal of Forest and Environmental Science
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• 제17권1호
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• pp.62-73
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• 2001

본 연구는 지면끌기집재에 사용되는 기계에 의해 견인되는 견인목의 견인저항을 예측하기 위해 견인목의 중량, 견인저항계수, 지면의 경사 등의 함수로 표현된 수학적 모델들을 개발하였다. 또한 만능재료시험기와 토양조를 이용한 실험실조건에서 4개 수종(잣나무, 일본잎갈나무, 신갈나무, 굴참나무)의 견인저항계수를 산출하였다. 산출한 견인저항계수와 가상 조건을 이용하여 개발된 3가지의 수학적 견인저항 모델에 적용하였다. 그 결과 견인목 중량에 대한 견인저항력의 비(T/Wt)는 지면의 경사가 증가할수록 전형적으로 증가하였으며, 반지면끌기집재가 지면끌기집재보다 견인저항력이 더 작게 나타났다. 본 연구의 결과는 집재작업기계의 선정과 집재윈치의 동력요구량 산정엔 기본적인 자료로 활용할 수 있을 것이다.

• 한국컴퓨터정보학회논문지
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• 제23권10호
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• pp.11-21
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• 2018

In this paper we propose a novel log template discovery algorithm which achieves high quality of discovered log templates through iterative log filtering technique. Log templates are the static string pattern of logs that are used to produce actual logs by inserting variable values during runtime. Identifying individual logs into their template category correctly enables us to conduct automated analysis using state-of-the-art machine learning techniques. Our technique looks at the group of logs column-wise and filters the logs that have the value of the highest proportion. We repeat this process per each column until we are left with highly homogeneous set of logs that most likely belong to the same log template category. Then, we determine which column is the static part and which is the variable part by vertically comparing all the logs in the group. This process repeats until we have discovered all the templates from given logs. Also, during this process we discover the custom patterns such as ID formats that are unique to the application. This information helps us quickly identify such strings in the logs as variable parts thereby further increasing the accuracy of the discovered log templates. Existing solutions suffer from log templates being too general or too specific because of the inability to detect custom patterns. Through extensive evaluations we have learned that our proposed method achieves 2 to 20 times better accuracy.

• 한국음향학회:학술대회논문집
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• 한국음향학회 1984년도 추계학술발표회 논문집
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• pp.66-69
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• 1984

This paper is a study on the Endpoint Detection for Korean Speech Recognition. In speech signal process, analysis parameter was classification from Zero Crossing Rate(Z.C.R), Log Energy(L.E), Energy in the predictive error(Ep) and fundamental Korean Speech digits, /영/-/구/ are selected as date for the Recognition of Speech. The main goal of this paper is to develop techniques and system for Speech input ot machine. In order to detect the Endpoint, this paper makes choice of Log Energy(L.E) from various parameters analysis, and the Log Energy is very effective parameter in classifying speech and nonspeech segments. The error rate of 1.43% result from the analysis.