• Proceedings of the Korean Information Science Society Conference
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• 2008.06c
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• pp.85-90
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• 2008

최근 유클리드 공간 상에서 효율적인 연속 k-최근접(k-Nearest Neighbors) 질의 처리를 위해 그리드 구조 기반의 많은 색인 기법들이 연구되었다. 하지만 기존 기법들은 k-최근접 객체들을 연산하기 위해 불필요한 셀을 접근하여 연산 자원을 낭비하거나 근접한 셀을 알아내는데 너무 큰 연산 비용을 초래한다. 그래서 본 논문에서는 한 셀과 주변 셀과의 거리 관계 패턴을 이용하여 k-최근접 질의 처리시 적은 연산비용과 적은 저장 공간을 사용하는 새로운 k-최근접 질의 처리 기법을 제안한다. 제안하는 기법은 k-최근접 질의 처리 시 거리 값을 기준으로 정렬된 거리 관계 패턴의 상대좌표를 순차적으로 적용하여 근접한 셀을 알아내기 때문에 O(n)의 셀 검색 비용이 요구된다. 또한 본 논문에서는 CPM[1]과 성능을 비교하여 제안하는 기법의 우수성을 입증한다.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2005.11a
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• pp.275-278
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• 2005

본 연구는 디스플레이 제품의 개발 및 생산과정에서 OSD 메뉴문자의 오류 유무를 검사하는 과정을 컴퓨터 비전기술을 사용하여 자동화하는 방법을 제안한다. 디스플레이 제품의 OSD 메뉴는 순차적인 제어과정을 통해서 제한된 디스플레이 영역에 여러 종류의 언어와 기호를 포함하는 형태로 출력된다. 기존의 제품개발 과정에서 이러한 메뉴 항목의 정확성을 검증하는 작업은 작업자의 육안에 의한 판단과 수작업에 의해 이루어지고 있는데, 이는 반복작업에 의한 집중력 저하 및 판단착오에 의한 오류의 가능성을 내재한다. 또한 작업자가 다양한 나라의 언어에 대한 문자형태와 기호표현의 특성을 이해하여야 하고, 검증작업 자체에 따르는 부수적인 시간과 노력을 필요로 한다. 이에 본 연구에서는 디스플레이 제품의 OSD 메뉴와 같이 특수한 구조를 갖는 문서영상에 대한 논리적인 구조분석을 통해서 연속적인 문서영상을 발생시키는 작업스케쥴러를 생성하고, 작업스케쥴러에 의해 순차적으로 발생된 영상문서에 대한 전처리, OSD 메뉴의 기하학적 구조분석 및 문자영역을 추출하는 방법과, 표준패턴 구축 및 원형정합에 의한 문자의 오류를 검증하는 방법과 오류를 관리하는 기법을 제안한다.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.10a
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• pp.269-271
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• 2022

In recent years, RNN networks with LSTM blocks have been used extensively in machine learning tasks that process sequential data. These networks have proven to be particularly good at sequential language processing tasks by being more able to accurately predict the next most likely word in a given sequence than traditional neural networks. This study trained an RNN / LSTM neural network on three different translations of 150 biblical Psalms - in both English and Korean. The resulting model is then fed an input word and a length number from which it automatically generates a new Psalm of the desired length based on the patterns it recognized while training. The results of training the network on both English text and Korean text are compared and discussed.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.39 no.3
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• pp.85-97
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• 2002

STUMPS has been widely used for built-in self-test of scan design with multiple scan chains. In the STUMPS architecture, there is very high correlation between the bit sequences in the adjacent scan chains. This correlation causes circuits lower the fault coverage. In order to solve this problem, an extra combinational circuit block(phase shifter) is placed between the LFSR and the inputs of STUMPS architecture despite the hardware overhead increase. This paper introduces an efficient test pattern generation technique and built-in self-test architecture for sequential circuits with multiple scan chains. The proposed test pattern generator is not used the input of LFSR and phase shifter, hence hardware overhead can be reduced and sufficiently high fault coverage is obtained. Only several XOR gates in each scan chain are required to modify the circuit for the scan BIST, so that the design is very simple.

• Journal of the Korean Institute of Intelligent Systems
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• v.21 no.6
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• pp.730-736
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• 2011

In this paper, we designed the models for pattern classification which can reflect the latest trend in time series. It has been shown that fusion models based on statistical and AI methods are superior to traditional ones for the pattern classification model supporting decision making. Especially, the hit rates of pattern classification models combined with fuzzy theory are relatively increased. The statistical SVM models combined with fuzzy membership function, or the models combining neural network and FCM has shown good performance. BPN, PNN, FNN, FCM, SVM, FSVM, Decision Tree, Time Series Analysis, and Regression Analysis were used for pattern classification models in the experiments of this paper. The economical indices DB with time series properties of the financial market(Korea, KOSPI200 DB) and the electrocardiogram DB of arrhythmia patients in hospital emergencies(USA, MIT-BIH DB) were used for data base.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.37 no.11
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• pp.1397-1405
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• 2013

The optimal rib pattern design of B-pillar trim considering occupant head protection can be determined by two methods. One is the conventional approximate optimization method that uses the statistical design of experiments (DOE) and response surface method (RSM). Generally, approximated optimum results are obtained through the iterative process by trial-and-error. The quality of results strongly depends on the factors and levels assigned by a designer. The other is a methodology derived from previous work by the authors, called the sequential design of experiments (SDOE), to reduce the trial-and-error procedure and to find an appropriate condition for using artificial neural network (ANN) systematically. An appropriate condition is determined from the iterative process based on the analysis of means. With this new technique and ANN, it is possible to find an optimum design accurately and efficiently.

• The Journal of Bigdata
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• v.5 no.1
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• pp.55-63
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• 2020

This study proposed an approach of clustering collective users' activities of location-based social networks using check-in data of Foursquare users in Seoul. In order to cluster the collective activities, we generated sequential rules of the activities using sequential rule mining, and then constructed activity networks based on the rules. We analyzed the activity networks to identify network structure and hub activities, and clustered the activities within the networks. Unlike previous studies that analyzed activity transition patterns of location-based social network users, this study focused on analyzing the structure and clusters of successive activities. Hubs and clusters of activities with the approach proposed in this study can be used for location-based services and marketing. They could also be used in the public sector, such as infection prevention and urban policies.

• Journal of Korea Spatial Information System Society
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• v.11 no.1
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• pp.127-136
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• 2009

This paper proposes an on-line mining algorithm of moving trajectory patterns in RFID data streams considering changing characteristics over time and constraints of single-pass data scan. Since RFID, sensor, and mobile network technology have been rapidly developed, many researchers have been recently focused on the study of real-time data gathering from real-world and mining the useful patterns from them. Previous researches for sequential patterns or moving trajectory patterns based on stream data have an extremely time-consum ing problem because of multi-pass database scan and tree traversal, and they also did not consider the time-changing characteristics of stream data. The proposed method preserves the sequential strength of 2-lengths frequent patterns in binary relationship table using the time-evolving graph to exactly reflect changes of RFID data stream from time to time. In addition, in order to solve the problem of the repetitive data scans, the proposed algorithm infers candidate k-lengths moving trajectory patterns beforehand at a time point t, and then extracts the patterns after screening the candidate patterns by only one-pass at a time point t+1. Through the experiment, the proposed method shows the superior performance in respect of time and space complexity than the Apriori-like method according as the reduction ratio of candidate sets is about 7 percent.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.44 no.11
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• pp.82-88
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• 2007

In this paper, we present a pattern comparison algorithm for reducing fault candidate lists. The number of fault candidates determines the total fault simulation time. To decrease the total fault diagnosis time, the reduction of the number of fault candidates is essential. Critical path tracing determines fault candidate lists detected by a set of tests using a backtracing algorithm starting at the primary outputs of a circuit. The proposed algorithm reduces fault candidates comparing failing patterns with good patterns during critical path tracing process. As we reduce all fault candidates of the circuit to more accurately suspected fault candidates, we can greatly reduce fault simulation time. The proposed algorithm greatly increases simulation speed than that of a conventional backtracing method. The proposed algorithm is applicable to both combinational and sequential circuits. Experimental results on ISCAS#85 and ISCAS#89 benchmark circuits showed fault candidates are pruned and fault diagnosis time is also decreased in proportion to fault candidate decrease.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.40 no.11
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• pp.54-61
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• 2003

In this paper, an efficient non-scan design-for-testability (DFT) method for controller circuits is proposed. The proposed method always guarantees a short test pattern generation time and complete fault efficiency. It has a lower area overhead than full-scan and other non-scan DFT methods and enables to apply test patterns at-speed. The proposed method also shortens the test application time through a test pattern re-ordering procedure. The efficiency of the proposed method is demonstrated using well known MCNC'91 FSM benchmark circuits.