• Journal of the Korean Academy of Child and Adolescent Psychiatry
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• v.20 no.2
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• pp.61-67
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• 2009

Objectives: The purpose of this study was to examine the performance and electrophysiological characteristics of drug-naive children with attention-deficit/hyperactivity disorder(ADHD) during the Go/NoGo task. Methods: Twenty-three boys with ADHD and 18 age-matched normal boys were recruited at a child psychiatric outpatient clinic in Seoul. All subjects were assessed by the Kiddie Schedules for Affective Disorders and Schizophrenia Present and Lifetime version. The investigator also assessed all subjects using the ADHD Rating Scale-IV(ADHDRS). Event-related potentials were recorded from 8 scalp electrodes during the visual Go/NoGo task. Results: Children with ADHD showed a larger mean of standard deviation of response time during the Go/NoGo task than normal children. The temporal N200 and P300 amplitudes were larger in children with ADHD relative to controls. The parietal N200 and P300 latencies were more prolonged in children with ADHD compared to normal controls. Conclusion: These results suggest that psychotropic-naive children with ADHD may have more variable performance ability, more difficulty in discriminating visual stimuli, and slower information processing speed than their normal age-matched counterparts.

• Journal of KIISE:Software and Applications
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• v.33 no.10
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• pp.886-895
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• 2006

Fingerprint classification reduces the number of matches required in automated fingerprint identification systems by categorizing fingerprints into a predefined class. Support vector machines (SVMs), widely used in pattern classification, have produced a high accuracy rate when performing fingerprint classification. In order to effectively apply SVMs to multi-class fingerprint classification systems, we propose a novel method in which SVMs are generated with the one-vs-all (OVA) scheme and dynamically ordered with $na{\ddot{i}}ve$ Bayes classifiers. More specifically, it uses representative fingerprint features such as the FingerCode, singularities and pseudo ridges to train the OVA SVMs and $na{\ddot{i}}ve$ Bayes classifiers. The proposed method has been validated on the NIST-4 database and produced a classification accuracy of 90.8% for 5-class classification. Especially, it has effectively managed tie problems usually occurred in applying OVA SVMs to multi-class classification.

• Journal of The Korean Association For Science Education
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• v.24 no.5
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• pp.886-901
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• 2004

The purpose of this study was to document the changes in astronomical concepts for preservice and inservice elementary school teachers after being presented with the newly devised Earth Motions Centric Solar System Model. The subjects of the study were 31 preservice and 30 inservice elementary schools teachers in the Jeonbuk Province. First, the author investigated the naive theories of the subjects, and then, compared that data to the data obtained after their exposure to the model. The total number of items on the instrument for this study was 10. These items included questions about the motion of interior planets, the phases and sizes of interior planets, and the motion of exterior planets and comets. After analyzing the answers to the items before the experiment, the author was able to confirm the existence of the naive theories regarding astronomical phenomena. Also, after the experiment, the author was able to observe the conceptual change in thought of the preservice and inservice elementary school teachers. Results showed that learning through the new model had positive effects on the preservice and inservice elementary school teachers' conceptualization of the interior planets' motion, phases and sizes, and the exterior planets' motion.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.16 no.4
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• pp.1-12
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• 2017

Traffic accidents are caused by a combination of human factors, vehicle factors, and environmental factors. In the case of traffic accidents where rental cars are involved, the possibility and the severity of traffic accidents are expected to be different from those of other traffic accidents due to the unfamiliar environment of the driver. In this study, we developed a model to forecast the severity of rental car accidents by using Naive Bayes classifier for Busan, Gangneung, and Jeju city. In addition, we compared the prediction accuracy performance of two models where one model uses the variables of which statistical significance were verified in a prior study and another model uses the entire available variables. As a result of the comparison, it is shown that the prediction accuracy is higher when using the variables with statistical significance.

• Journal of Korean Elementary Science Education
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• v.22 no.1
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• pp.109-120
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• 2003

The purpose of this study is to observe, to analyze of the preservice teachers' naive theories about the change of season. And it is to find a instruction strategy which can solve problem about this. The general idea about the change of season is observed by the 3 methods which are simply explaining with words, explaining with pictures and models. The author is to find the similarity. difference and relationship which the preservice teachers have about the general idea about the change of season. The important changable primary factors, which can effect to the general Idea formation, are naturally dragged out through the observation of preservice teachers participation. For this study, 4 first year preservice teachers of one of national university of education are used. Before the interview. the author tries to form rapport with the preservice teachers. Experiment materials, pencil. paper, camcorder, digital recorder and interview note were used for the study with reflection of them just way they are. As the result of the interview. all of 4 preservice teachers had not being understand the concept about the change of season and the three ways of explanation methods were not matched each other, so it is revealed that the general Idea of the change of season, which the preservice teachers have, is not strongly formed. In spite of the repeated study of the change of season from elementary school to university, it has many problem about recognition of the general idea about the change of season which pre-elementary teachers have. Therefore it is needed to improve the experiment in elementary science text book and naive theories by the activity which is explaining the change of season in three dimension space. to prevent the naive theories which the preservice teachers may have.

• Journal of Korean Medicine Rehabilitation
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• v.30 no.2
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• pp.47-66
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• 2020

Objectives The purpose of this study is to evaluate the bone healing effect of Dohongsamul-tang (Taohongsiwu-tang; DH) on femur fractured mice. Methods Mice were randomly divided into 4 groups (naive, control, positive control and DH). All groups except naive group were subjected to bone fracture on both hind limb femurs. Naive group received no treatment at all. Control group was fed with normal saline, and positive control group was orally medicated with tramadol. DH-treated group was orally medicated with DH. We analysed the levels of BMP2, COX2, Col2a1, Sox9, Runx2, and Osterix genes on 3, 7 and 14 days after fracture. Alkaline phosphatase, aspartate aminotransferase, alanine aminotransferase, blood urea nitrogen, creatinine, total cholesterol, and triglyceride levels were measured for safety assessment. Results In morphological, histological analysis, callus formation process of DH-treated group was faster than the control group. BMP2, Sox9 gene expression were significantly increased at 7 days after fracture compared to the control group. COX2, Col2a1 gene expression were significantly increased at 14 days after fracture compared to the control group. Total cholesterol was significantly increased by DH at 3 days. Triglyceride was significantly decreased by DH at 3, 7 days after fracture compared to the control group. Conclusions Dohongsamul-tang promoted bone healing process after fracture by stimulating the bone regeneration factors. And DH shows no hepatotoxicity, nephrotoxicity and serum lipid abnormality. In conclusion, it seems that DH helps to promote fracture regeneration after bone fracture by regulating gene expressions related to bone repair.

• Journal of the Korea Society for Simulation
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• v.20 no.4
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• pp.139-147
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• 2011

There is a growing tendency toward a vehicle demand and a utilization of traffic information systems. Due to various kinds of traffic information systems and increasing of communication data, the traffic information service requires a very high IT infrastructure. A cloud computing environment is an essential approach for reducing a IT infrastructure cost. And the traffic information service needs a provisioning scheduling method for managing a resource. So we propose a provisioning scheduling with conditional probability inference (PSCPI) for the traffic information service on cloud environment. PSCPI uses a naive bayse inference technique based on a status of a virtual machine. And PSCPI allocates a job to the virtual machines on the basis of an availability of each virtual machine. Naive bayse based PSCPI provides a high throughput and an high availability of virtual machines for real-time traffic information services.

• Journal of Cadastre & Land InformatiX
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• v.49 no.1
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• pp.65-84
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• 2019

This study attempted to predict citizens' emotions regarding mortgage rates using machine learning algorithms. To accomplish the research purpose, I reviewed the related literature and then set up two research questions. To find the answers to the research questions, I classified emotions according to Akman's classification and then predicted citizens' emotions on mortgage rates using six machine learning algorithms. The results showed that AdaBoost was the best classifier in all evaluation categories. However, the performance level of Naive Bayes was found to be lower than those of other classifiers. Also, this study conducted a ROC analysis to identify which classifier predicts each emotion category well. The results demonstrated that AdaBoost was the best predictor of the residents' emotions on home mortgage rates in all emotion categories. However, in the sadness class, the performance levels of the six algorithms used in this study were much lower than those in the other emotion categories.

• Proceedings of the Korea Information Processing Society Conference
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• 2013.11a
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• pp.1294-1297
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• 2013

본 논문은 기계학습을 통하여 특허문서를 국제 특허 분류(IPC) 기준에 따라 자동으로 분류하는 시스템에 관한 연구로 잠재 의미 색인 기법을 이용하여 분류의 성능을 높일 수 있는 방법을 제안하기 위한 연구이다. 종래 특허문서에 관한 IPC 자동 분류에 관한 연구가 단어 매칭 방식의 색인 기법에 의존해서 이루어진바가 있으나, 현대 기술용어의 발생 속도와 다양성 등을 고려할 때 특허문서들 간의 관련성을 분석하는데 있어서는 단어 자체의 빈도 보다는 용어의 개념에 의한 접근이 보다 효과적일 것이라 판단하여 잠재 의미 색인(LSI) 기법에 의한 분류에 관한 연구를 하게 된 것이다. 실험은 단어 매칭 방식의 색인 기법의 대표적인 자질선택 방법인 정보획득량(IG)과 카이제곱 통계량(CHI)을 이용했을 때의 성능과 잠재 의미 색인 방법을 이용했을 때의 성능을 SVM, kNN 및 Naive Bayes 분류기를 사용하여 분석하고, 그중 가장 성능이 우수하게 나오는 SVM을 사용하여 잠재 의미 색인에서 명사가 해당 용어의 개념적 의미 구조를 구축하는데 기여하는 정도가 어느 정도인지 평가함과 아울러, LSI 기법 이용시 최적의 성능을 나타내는 특이값의 범위를 실험을 통해 비교 분석 하였다. 분석결과 LSI 기법이 단어 매칭 기법(IG, CHI)에 비해 우수한 성능을 보였으며, SVM, Naive Bayes 분류기는 단어 매칭 기법에서는 비슷한 수준을 보였으나, LSI 기법에서는 SVM의 성능이 월등이 우수한 것으로 나왔다. 또한, SVM은 LSI 기법에서 약 3%의 성능 향상을 보였지만 Naive Bayes는 오히려 20%의 성능 저하를 보였다. LSI 기법에서 명사가 잠재적 의미 구조에 미치는 영향은 모든 단어들을 내용어로 한 경우 보다 약 10% 더 향상된 결과를 보여주었고, 특이값의 범위에 따른 성능 분석에 있어서는 30% 수준에 Rank 되는 범위에서 가장 높은 성능의 결과가 나왔다.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.18 no.2
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• pp.97-105
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• 2008

The various mutations of the malicious codes are fast generated on the network. Also the behaviors of them become intelligent and the damage becomes larger step by step. In this paper, we suggest the method to select the useful measures for the detection of the codes. The method has the advantage of shortening the detection time by using header data without payloads and uses connection data that are composed of TCP/IP packets, and much information of each connection makes use of the measures. A naive estimator is applied to the probability distribution that are calculated by the histogram estimator to select the specific measures among 80 measures for the useful detection. The useful measures are then selected by using relative entropy. This method solves the problem that is to misclassify the measure values. We present the usefulness of the proposed method through the result of the detection experiment using the detection patterns based on the selected measures.