• 한국수학교육학회지시리즈A:수학교육
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• 제48권2호
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• pp.113-139
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• 2009

In this study, we apply the discovery method in the instruction of Permutation and Combination, and examine the effect upon the student's emotion after the instruction change. The research progressed through the instruction by the discovery method for two students of highschool Y. This research has been done for about one and half year from November 2006 to February 2008. We draw our research results through a series of processes consisted of videotaping a classroom activities, recording interview details and writing an observation diary, with the aim of the experimental instruction. In the end, we get to the conclusion that students showed a strong positive attitude on the discovery instructional method and that diverse discovery method has supplementary relation in classwork.

• 한국컴퓨터정보학회논문지
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• 제25권10호
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• pp.51-58
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• 2020

DDS 디스커버리는 같은 도메인을 사용하고, 다른 노드에 존재하는 DDS 객체를 찾는 과정이다. 한 노드에서 DDS 디스커버리 과정이 실패한다면, DDS 디스커버리가 실패한 노드에서 정상적인 DDS 데이터 송수신이 불가능하여 시스템 운용에 큰 영향을 미칠 수 있다. 그렇기 때문에, DDS 디스커버리의 성능을 향상시켜 DDS 디스커버리 과정을 빠르게 완료하고, 네트워크 부하와 컴퓨터 자원 사용량을 줄여서 DDS 디스커버리가 실패할 수 있는 가능성을 줄일 수 있다면 전체 시스템의 성능 향상에 큰 영향을 줄 수 있다. 본 논문에서는 대용량 시스템에서 DDS 디스커버리 시간과 네트워크 부하, 컴퓨터 자원 사용량을 줄일 수 있는 성능 향상을 위한 새로운 방법을 제안하고, 시험을 통하여 제안한 방법의 효율성을 증명한다.

• 한국컴퓨터정보학회논문지
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• 제22권1호
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• pp.23-32
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• 2017

We can obtain useful knowledge from data by using a subgroup discovery algorithm. Subgroup discovery is a rule model learning method that finds data subgroups containing specific information from data and expresses them in a rule form. Subgroups are meaningful as they account for a high percentage of total data and tend to differ significantly from the overall data. Subgroup is expressed with conjunction of only literals previously. So, the scope of the rules that can be derived from the learning process is limited. In this paper, we propose a method to increase expressiveness of rules through internal disjunctive representation of attribute values. Also, we analyze the characteristics of existing subgroup discovery algorithms and propose an improved algorithm that complements their defects and takes advantage of them. Experiments are conducted with the traffic accident data given from Busan metropolitan city. The results shows that performance of the proposed method is better than that of existing methods. Rule set learned by proposed method has interesting and general rules more.

• 한국IT서비스학회지
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• 제13권1호
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• pp.125-134
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• 2014

Device discovery for D2D (device-to-device) communication enables a device to discover other devices in order to initiate communication with them. Devices should perform the discovery phase using a small quantity of radio resource in a short time and be able to reduce the load of the base station. Legacy device discovery schemes have focused on discovering as many target devices as possible. However, it is not appropriate for peer-to-peer D2D communication scenario. Further, synchronization problems are an important issue for discovery signal transmission. This paper proposes a discovery method that one requesting device discovers a specific target for communication. Multiple antenna beamforming is employed for the synchronization between the base station and a target device. The proposal can reduce the load of the base station using the information that it already maintains and improve the reliability of the device discovery because two times of synchronizations using beamforming among the base station and devices can make the exact discovery of a target device with mobility possible.

• 한국HCI학회:학술대회논문집
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• 한국HCI학회 2008년도 학술대회 1부
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• pp.669-673
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• 2008

지능형 협업환경에서 사용자가 원하는 서비스를 제공받기 위해 선행되어야 할 기술은 사용자가 원하는 협업기능 요구사항에 따라 협업환경 내 서비스들을 적절히 발견하는 서비스 발견(service discovery) 기술이다. 일반적인 서비스 발견과 관련하여, UPnP(universal plug & play) 의 SSDP(simple service discovery protocol)을 비롯한 여러 종류의 서비스 발견 기술이 존재하나, 지능형 협업환경이 요구하는 서비스 발견 특정을 만족시키기에는 일부 제약 사항이 존재한다. 이에 본 논문에서는 지능형 협업환경에서 제공되어야 하는 서비스 발견 기술의 요구사항을 살펴보고, 이를 만족하기 위한 방법으로써 다중 에이전트 시스템 (multi-agent system,, MAS)을 적용한 실용적인 서비스 발견 방법을 제시한다. 또한 지능형 협업환경의 일부 서비스에 제안된 방법을 적용하여, 본 접근방법의 타당성을 구현 결과를 통해 확인하고자 한다.

• 한국정보과학회:학술대회논문집
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• 한국정보과학회 2007년도 가을 학술발표논문집 Vol.34 No.2 (B)
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• pp.17-21
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• 2007

Drug discovery is a long process with a low rate of successful new therapeutic discovery regardless of the advances in information technologies. Identification of candidate proteins is an essential step for the drug discovery and it usually requires considerable time and efforts in the drug discovery. The drug discovery is not a logical, but a fortuitous process. Nevertheless, considerable amount of information on drugs are accumulated in UniProt, NCBI, or DrugBank. As a result, it has become possible to try to devise new computational methods classifying drug target candidates extracting the common features of known drug target proteins. In this paper, we devise a method for drug target protein classification by using weighted feature summation and Support Vector Machine. According to our evaluation, the method is revealed to show moderate accuracy $85{\sim}90%$. This indicates that if the devised method is used appropriately, it can contribute in reducing the time and cost of the drug discovery process, particularly in identifying new drug target proteins.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제15권11호
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• pp.3935-3949
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• 2021

With the rapid development of Internet and big data technology, various online social network platforms have been established, producing massive information every day. Hot topic discovery aims to dig out meaningful content that users commonly concern about from the massive information on the Internet. Most of the existing hot topic discovery methods focus on a single network data source, and can hardly grasp hot spots as a whole, nor meet the challenges of text sparsity and topic hotness evaluation in cross-network scenarios. This paper proposes a novel hot topic discovery method across social network based on an im-proved LDA model, which first integrates the text information from multiple social network platforms into a unified data set, then obtains the potential topic distribution in the text through the improved LDA model. Finally, it adopts a heat evaluation method based on the word frequency of topic label words to take the latent topic with the highest heat value as a hot topic. This paper obtains data from the online social networks and constructs a cross-network topic discovery data set. The experimental results demonstrate the superiority of the proposed method compared to baseline methods.

• East Asian mathematical journal
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• 제28권2호
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• pp.197-213
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• 2012

In this paper we study Soltan & Meidman's method that is able to be used in mathematical discovery. We analyze Soltan & Meidman's book "Tozdestva i Neravenstva v Treugolike" that is published in Moldova Republic. In this work we formulate Soltan & Meidman's method related with discovery of triangle's various equalities, and use the method to discovery mathematical equalities. As a result we suggest some new mathematical equalities related with triangle's sides and its proof.

• Bulletin of the Korean Chemical Society
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• 제32권4호
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• pp.1237-1240
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• 2011

In this study, we have developed a ligand-based in-silico prediction model to classify chemical structures into hERG blockers using Bayesian and random forest modeling methods. These models were built based on patch clamp experimental results. The findings presented in this work indicate that Laplacian-modified naive Bayesian classification with diverse selection is useful for predicting hERG inhibitors when a large data set is not obtained.

• 한국통신학회논문지
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• 제38B권5호
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• pp.328-336
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• 2013

디바이스 간 직접통신을 할 때 중요한 절차 중 하나는 근거리에 위치한 디바이스들을 발견하는 것이다. 정확한 디바이스 발견을 위해서는 모든 디바이스들이 한 번 이상 발견 신호를 전송하고 다른 디바이스들이 그 신호를 수신함으로써 모든 디바이스들 사이의 거리 정보를 파악해야 한다. 그러나 디바이스 발견을 위해서 주기적으로 모든 디바이스들이 한 번 이상 신호를 전송한다면 디바이스 신호 전송 주기가 너무 길어지거나 디바이스 발견을 위한 자원을 많이 사용해야 하는 문제가 발생할 수 있다. 또한 여러 실제적인 요인에 의해서 일부 정보들이 손실될 수 있으며 모든 필요한 정보를 얻는데 너무 많은 시간이 소요될 수 있다. 본 논문에서는 디바이스 발견을 위한 자원을 줄이고 발견 정보 손실이 있는 경우를 지원하기 위하여 일부 정보만을 활용하여 근거리에 있는 디바이스를 발견하는 방법을 제안한다. 또한 일부 발견 정보만이 있을 때의 발견 확률에 대해서 논한다.