• Progress in Medical Physics
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• v.28 no.3
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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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• v.18 no.3
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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.

• Proceedings of the KSPS conference
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• 2004.05a
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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.

• Speech Sciences
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• v.11 no.2
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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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• v.12 no.2
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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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• v.7 no.4
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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.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.19 no.1
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• pp.1-8
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• 2019

Among the methods for extracting the most appropriate information from a large amount of log data, there is a method using inductive inference. In this paper, we use SVM (Support Vector Machine), which is an excellent classification method for inductive inference, in order to determine the ranking of intrusion logs in digital forensic analysis. For this purpose, the logs of the training log set are classified into intrusion logs and normal logs. The associated words are extracted from each classified set to generate a related word dictionary, and each log is expressed as a vector based on the generated dictionary. Next, the logs are learned using the SVM. We classify test logs into normal logs and intrusion logs by using the log set extracted through learning. Finally, the recommendation orders of intrusion logs are determined to recommend intrusion logs to the forensic analyst.

• Journal of Forest and Environmental Science
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• v.17 no.1
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• pp.62-73
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• 2001

This study was conducted to predict the tractive resistance for tree length logs being skidded by ground based logging machine. The mathematical models for predicting the tractive resistance of tree length log have been developed. The tractive resistance is expressed as a function of log weight, skidding coefficient, and ground gradient. The skidding coefficients for four species of Korean pine, Japanese larch, mongolian oak, and cork oak were determined under laboratory condition using universal testing machine and small soil bin, Three different tractive resistance models were applied to four species and compared with each other. The ratios (T/Wt) of skidding-line tensions to the skidding log weight increased linearly with increment in ground gradient. Semi-ground skidding generally required smaller tensions than ground skidding under given condition. Results of this study can be utilized as basic information for logging machine selection and power requirement of skidding winch.

• Journal of the Korea Society of Computer and Information
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• v.23 no.10
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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.

• Proceedings of the Acoustical Society of Korea Conference
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• 1984.12a
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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.