• The Mathematical Education
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• v.48 no.2
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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.

• Journal of the Korea Society of Computer and Information
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• v.25 no.10
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• pp.51-58
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• 2020

The DDS discovery is a behind-the-scenes way in which DDS objects on different nodes find out each other in a same domain. If the DDS discovery takes a long time, the preparation time for DDS communication is also delayed. And if the DDS discovery between several nodes fails, DDS communication between nodes related to them would be also failed. This problems can be a big cause of overall system performance degradation. Therefore, the improvement of performance for the DDS discovery gives the effect that improves the performance of the entire system. In this paper, I propose an efficient new method which improves the performance and reduces the time of DDS discovery. I simulate both the origin and the new proposed method for DDS discovery, and I compare the result of performance. This result will help for improving a DDS discovery in a large-scale system.

• Journal of the Korea Society of Computer and Information
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• v.22 no.1
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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.

• Journal of Information Technology Services
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• v.13 no.1
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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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• 2008.02a
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• pp.669-673
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• 2008

The service discovery method is an important technology finding and offering users' desirable services in smart meeting spaces. Extensive researches of the service discovery methods are achieved: SSDP(simple service discovery protocol) of UPnP(universal plug & play) and so on. However, there are several limitations to satisfy the requirements of service discovery in smart meeting spaces. In this paper, the requirements of service discovery in smart meeting spaces are investigated and the service discovery method based on multi-agent system is proposed in the practical aspect. Additionally, we explore the possibilities of the proposed approach by implementing a couple of services belonging to the smart meeting spaces.

• Proceedings of the Korean Information Science Society Conference
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• 2007.10b
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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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• v.15 no.11
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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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• v.28 no.2
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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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• v.32 no.4
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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.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.38B no.5
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• pp.328-336
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• 2013

One of the important processes in device-to-device communication is the discovery of proximate devices. In order to perform precise discovery of devices, the distance information among all the device pairs should be gathered by each device sending a discovery signal in turn and the other devices receiving the signal. However, periodic discovery signal transmission by every device might require too long discovery period or too large resource for discovery. Above all, some discovery information might be lost due to several practical reasons and it may take substantial amount of time to obtain all the necessary information. In this paper, we propose a proximate-device-discovery method using partial discovery information in order to reduce the resource for discovery and support the cases in which some discovery information can be lost. We also discuss discovery probabilities with partial discovery information.