• Journal of Digital Convergence
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• v.14 no.1
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• pp.171-179
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• 2016

The energy consumption problem in wireless sensor networks is investigated. The problem is to expend as little energy as possible receiving and transmitting data, because of constrained battery. In this paper, in order to extend the lifetime of the network, we proposed a location-based clustering algorithm for wireless sensor network with skewed-topology. The proposed algorithm is to deploy multiple child nodes at the sink to avoid bottleneck near the sink and to save energy. Proposed algorithm can reduce control traffic overhead by creating a dynamic cluster. We have evaluated the performance of our clustering algorithm through an analysis and a simulation. We compare our algorithm's performance to the best known centralized algorithm, and demonstrate that it achieves a good performance in terms of the life time.

• The KIPS Transactions:PartD
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• v.13D no.3 s.106
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• pp.347-356
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• 2006

A video stream can be represented by a sequence of data points in a multidimensional space. In this paper, we introduce a trend vector that approximates values of data points in a sequence and represents the moving trend of points in the sequence, and present a pattern similarity matching method for data sequences using the trend vector. A sequence is partitioned into multiple segments, each of which is represented by a trend vector. The query processing is based on the comparison of these vectors instead of scanning data elements of entire sequences. Using the trend vector, our method is designed to filter out irrelevant sequences from a database and to find similar sequences with respect to a query. We have performed an extensive experiment on synthetic sequences as well as video streams. Experimental results show that the precision of our method is up to 2.1 times higher and the processing time is up to 45% reduced, compared with an existing method.

• Journal of KIISE:Databases
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• v.36 no.1
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• pp.22-29
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• 2009

The World Wide Web has emerged as largest media which provides even a single user to market their products and publish desired information; on the other hand the user can access what kind of information abundantly enough as well. As a result web holds large amount of related information distributed over multiple web pages. The current search engines search for all the entered keywords in a single webpage and rank the resulting set of web pages as an answer to the user query. But this approach fails to retrieve the pair of web pages which contains more relevant information for users search. We introduce a new search paradigm which gives different weights to the query keywords according to their order of appearance. We propose a new arc weight measure that assigns more relevance to the pair of web pages with alternate keywords present so that the pair of web pages which contains related but distributed information can be presented to the user. Our measure proved to be effective on the similarity search in which the experimentation represented the e~arc ranking measure outperforming the conventional ones.

• Proceedings of the Korean Information Science Society Conference
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• 2004.04b
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• pp.541-543
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• 2004

능동적 학습(active learning)은 제한된 시간과 인력으로 가능한 정확도가 높은 분류기(classifier)를 생성하기 위하여, 훈련집합에 추가할 예제 즉 문의예제(query example)의 선정과 확장된 훈련집합으로 다시 학습하는 과정을 반복하여 수행한다. 능동적 학습의 핵심은 사용자에게 카테고리(category) 부여를 요청할 문의예제를 선정하는 과정에 있다. 효과적인 문의예제를 선정하기 위하여 다양한 방안들이 제안되었으나, 이들은 매 문의단계마다 하나의 문의예제를 선정하는 경우에 가장 적합하도록 고안되었다. 능동적 학습이 복수의 예제를 사용자에게 문의할 수 있다면, 사용자는 문의예제들을 서로 비교해 가면서 작업할 수 있으므로 카테고리 부여작업을 보다 빠르고 정확하게 수행할 수 있을 것이다. 또한 충분한 인력을 보유한 상황에서는, 카테고리 부여작업을 병렬로 처리할 수 있어 전반적인 학습시간의 단축에 큰 도움이 될 것이다. 하지만, 각 예제의 문의예제로써의 적합 정도를 추정하면 유사한 예제들은 서로 비슷한 수준으로 평가되므로, 기존의 방안들을 복수의 문의예제 선정작업에 그대로 적용할 경우, 유사한 예제들이 문의예제로 동시에 선정되어 능동적 학습의 효율이 저하되는 현상이 나타날 수 있다. 본 논문에서는 특정 예제를 문의예제로 선정하면 이와 일정 수준이상 유사한 예제들은 해당 예제와 함께 문의예제로 선정하지 않음으로써, 이러한 문제점을 극복할 수 있는 방안을 제안한다. 제안한 방안을 문서분류 문제에 적용해 본 결과 기존 문의예제 선정방안으로 복수 문의예제를 선정할 때 발생할 수 있는 문제점을 상당히 완화시킬 있을 뿐 아니라, 복수의 문의예제를 선정하더라도 각 문의 단계마다 하나의 예제를 선정하는 경우에 비해 큰 성능의 저하가 없음을 실험적으로 확인하였다./$m\ell$로 나타났다.TEX>${HCO_3}^-$ 이온의 탈착은 서서히 진행되었다. R＆D investment increases are directly not liked to R＆D productivities because of delays and side effects during transition periods between different stages of technology development. Thus, It is necessary to develope strategies in order to enhance efficiency of technological development process by perceiving the switching pattern. 기여할 수 있을 것으로 기대된다. 것이다.'ity, and warm water discharges from a power plant, etc.h to the way to dispose heavy water adsorbent. Through this we could reduce solid waste products and the expense of permanent disposal of radioactive waste products and also we could contribute nuclear power plant run safely. According to the result we could keep the best condition of radiation safety super vision and we could help people believe in safety with Radioactivity wastes control for harmony with Environ

• Journal of the Korea Society of Computer and Information
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• v.13 no.5
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• pp.255-261
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• 2008

RFID(Radio Frequency IDentification) is a technology that automatically identifies objects containing the electronic tags by using radio frequency. In RFID system, the reader needs the anti collision algorithm for fast identifring all of the tags in the interrogation zone. This Paper proposes the tree based TPAE(Tag Prediction Algorithm using Extra bit) algorithm to arbitrate the tag collision. The proposed algorithm can identify tags without identifring all the bits in the tag ID. The reader uses the extra bit which is added to the tag ID and if there are two collided bits or multiple collided bits, it checks the extra bit and grasps the tag IDs concurrently. In the experiment, the proposed algorithm had about 50% less query iterations than query tree algorithm and binary search algorithm regardless of the number of tags and tag ID lengths.

• Journal of Korea Society of Industrial Information Systems
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• v.28 no.5
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• pp.31-39
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• 2023

The evolution of Natural Language Processing (NLP) and the rise of large language models (LLM) like ChatGPT have paved the way for specialized question-answering (QA) systems tailored to specific domains. This study outlines a system harnessing the power of LLM in conjunction with document search algorithms to interpret and address user inquiries using documents from the Korea Atomic Energy Research Institute (KAERI). Initially, the system refines multiple documents for optimized search and analysis, breaking the content into managable paragraphs suitable for the language model's processing. Each paragraph's content is converted into a vector via an embedding model and archived in a database. Upon receiving a user query, the system matches the extracted vectors from the question with the stored vectors, pinpointing the most pertinent content. The chosen paragraphs, combined with the user's query, are then processed by the language generation model to formulate a response. Tests encompassing a spectrum of questions verified the system's proficiency in discerning question intent, understanding diverse documents, and delivering rapid and precise answers.

• Journal of KIISE:Computer Systems and Theory
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• v.27 no.7
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• pp.639-649
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• 2000

In the relational database systems, the join operation is one of the most time-consuming query operations. Many parallel join algorithms have been developed to reduce the execution time. Multiple hash join algorithm using allocation tree is one of most efficient ones. However, it may have some delay on the processing each node of allocation tree, which is occurred in tuple-probing phase by the difference between one page reading time of outer relation and the processing time of already read one. In this paper, to solve the performance degrading problem by the delay, we develop a join algorithm using the concept of 'synchronization of page execution time' for multiple hash joins. We reduce the processing time of each nodes in the allocation tree and improve the total system performance. In addition, we analyze the performance by building the analytical cost model and verify the validity of it by various performance comparison with previous method.

• Journal of KIISE:Databases
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• v.35 no.4
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• pp.307-318
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• 2008

One of the primary issues confronting XML message brokers is the difficulty associated with processing a large set of continuous XPath queries over incoming XML seams. This paper proposes a novel system designed to present an effective solution to this problem. The proposed system transforms multiple XPath queries before their run-time into a new region-based data structure, called an XP-table, by sharing their common constraints. An XP-table is matched with a stream relation (SR) transformed from a target XML stream by a SAX parser. This arrangement is intended to minimize the runtime workload of continuous query processing. Also, system performance is estimated and verified through a variety of experiments, including comparisons with previous approaches such as YFilter and LazyDFA. The proposed system is practically linear- scalable and stable for evaluating a set of XPath queries in a continuous and timely fashion.

• Journal of KIISE:Databases
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• v.33 no.4
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• pp.409-422
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• 2006

In general, there can be many reusable intermediate results due to the overlapped windows and periodic execution intervals among Multiple Continuous Queries (MCQ) on data streams. In this regard, we propose an efficient greedy algorithm for a global query plan construction, called GAGPC. GAGPC first decides an execution cycle and finds the maximal Set(s) of Related execution Points (SRP). Next, GAGPC constructs a global execution plan to make MCQ share common join-fragments with the highest benefit in each SRP. The algorithm suggests that the best plan of the same continuous queries may be different according to not only the existence of common expressions, but the size of overlapped windows related to them. It also reflects to reuse not only the whole but partial intermediate results unlike previous work. Finally, we show experimental results for the validation of GAGPC.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.40 no.1
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• pp.19-29
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• 2003

This paper presents a novel hybrid classifier for face recognition with artificially generated virtual training samples. We utilize both the nearest neighbor approach in feature angle space and a connectionist model to obtain a synergy effect by combining the results of two heterogeneous classifiers. First, a classifier called the nearest feature angle (NFA), based on angular information, finds the most similar feature to the query from a given training set. Second, a classifier has been developed based on the recall of stored frontal projection of the query feature. It uses a frontal recall network (FRN) that finds the most similar frontal one among the stored frontal feature set. For FRN, we used an ensemble neural network consisting of multiple multiplayer perceptrons (MLPs), each of which is trained independently to enhance generalization capability. Further, both classifiers used the virtual training set generated adaptively, according to the spatial distribution of each person's training samples. Finally, the results of the two classifiers are combined to comprise the best matching class, and a corresponding similarit measure is used to make the final decision. The proposed classifier achieved an average classification rate of 96.33% against a large group of different test sets of images, and its average error rate is 61.5% that of the nearest feature line (NFL) method, and achieves a more robust classification performance.