• The Transactions of the Korea Information Processing Society
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• v.1 no.1
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• pp.38-49
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• 1994

One of the most common problems encountered in the automatic translation of FORTRAN source code to VECTRAN is the occurrence of conditional transfer of control within loops. Transfers of control create control dependencies, in which the execution of a statement is dependent on the value of a variable in another statement. In this paper I propose algorithms involve an attempt to convert statements in the loop into conditional assignment statements that can be easily analyzed for data dependency, and this paper presents a simplification method for conditional assignment statement. Especially, I propose not only a method for simplifying boolean functions but extended method for n-state functions.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.31 no.4
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• pp.269-275
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• 2013

Recently, importance of pedestrian road network is getting emphasized as it is possible to provide mobile device users with both route guidance services and surrounding spatial information. However, it costs a tremendous amount of budget for generating and renovating pedestrian road network nationally, which hinder further advances of these services. Hence, algorithms extracting pedestrian road network automatically based on raster data are needed. On the other hand, road dataset generated from raster data usually has unnecessary vertices which lead to maintenance disutility such as excessive turns and increase in data memory. Therefore, this study proposed a method of selecting a proper threshold automatically for separate road entity using not only Douglas-Peucker algorithm but also road attribute data of digital map in order to remove redundant vertices, which maximizes line simplification efficiency and minimizes distortion of shape of roads simultaneously. As a result of the test, proposed method was suitable for automatic line simplification in terms of reduction ratio of vertices and accuracy of position.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.39C no.1
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• pp.17-27
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• 2014

As the importance of Cooperative Engagement Capability and network-centric warfare has been dramatically increasing, it is necessary to develop distributed tracking systems. Under the development of distributed tracking systems, it requires tracking filters and data fusion theory for nonlinear systems. Therefore, in this paper, the problem of nonlinear track fusion, which is suitable for distributed networks, is formulated, four algorithms to solve the problem of nonlinear track fusion are introduced, and performance of introduced algorithms are analyzed. It is a main problem of nonlinear track fusion that cross-covarinaces among multiple platforms are unknown. Thus, in order to solve the problem, two techniques are introduced; a simplification technique and a approximation technique. The simplification technique that help to ignore cross-covariances includes two algorithms, i.e. the sample mean algorithm and the Millman formula algorithm, and the approximation technique to obtain approximated cross-covariances utilizes two approaches, by using analytical linearization and statistical linearization based on the sigma point approach. In simulations, BCS fusion is the most efficient scheme because it reduces RMSE by approximating cross-covariances with low complexity.

• Journal of Korean Society of Transportation
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• v.14 no.1
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• pp.51-67
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• 1996

The optimal path-finding problem becomes complicated when multiple variables are simultaneously considered such as physical route length, degree of congestion, traffic capacity of intersections, number of intersections and lanes, and existence of free ways. Therefore, many researchers in various fields (management science, computer science, applied mathematics, production planning, satellite launching) attempted to solve the problem by ignoring many variables for problem simplification, by developing intelligent algorithms, or by developing high-speed hardware. In this research, an integration of expert system technique and case-based reasoning in high level with a conventional algorithms in lower level was attempted to develop an optimal path-finding system. Early application of experienced driver's knowledge and case data accumulated in case base drastically reduces number of possible combinations of optimal paths by generating promising alternatives and by eliminating non-profitable alternatives. Then, employment of a conventional optimization algorithm provides faster search mechanisms than other methods such as bidirectional algorithm and $A^*$ algorithm. The conclusion obtained from repeated laboratory experiments with real traffic data in Seoul metropolitan area shows that the integrated approach to finding optimal paths with consideration of various real world constraints provides reasonable solution in a faster way than others.

• Proceedings of the Korea Contents Association Conference
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• 2004.11a
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• pp.505-510
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• 2004

This paper suggests a new CT-image segmentation algorithm. This algorithm uses morphological filters and the watershed algorithms. The proposed CT-image segmentation algorithm consists of six parts: preprocessing, image simplification, feature extraction, decision making, region merging, and postprocessing. By combining spatial and temporal information, we can get more accurate segmentation results. The simulation results illustrate not only the segmentation results of the conventional scheme but also the results of the proposed scheme; this comparison illustrates the efficacy of the proposed technique. Furthermore, we compare the various medical images of the structuring elements. Indeed, to illustrate the improvement of coding efficiency in postprocessing, we use differential chain coding for the shape coding of results.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.36 no.2A
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• pp.100-107
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• 2011

Algorithms based on the QR decomposition of the equivalent space-time channel matrix have been proved useful in the detection of V-BLAST systems. Especially, the parallel detection (PD) algorithm offers ML approaching performance up to 4 transmit antennas with reasonable complexity. We show that when directly applied to STBCs, the PD algorithm may suffer a rather significant SNR degradation over ML detection, especially at high SNRs. However, simply extending the PD algorithm to allow p ${\geq}$ 2 candidate layers, i.e. p-PD, regains almost all the loss but only at a significant increase in complexity. Here, we propose a simplification to the p-PD algorithm specific to STBCs without a corresponding sacrifice in performance. The proposed algorithm results in significant complexity reductions for moderate to high order modulations.

• The Transactions of the Korea Information Processing Society
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• v.6 no.5
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• pp.1296-1302
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• 1999

In this paper, we propose an algorithms for the mounted PCB classification system using wavelet transform and ART2 neural network. The feature informations of a mounted PCB can be extracted from the coefficient matrix of wavelet transform adapted subband concept. As the preprocessing process, only the PCB area in the input image is extracted by histogram method and the feature vectors are composed of using wavelet transform method. These feature vectors are used as the input vector of ART2 neural network. In the experiment using 55 mounted PCB images, the proposed algorithm shows 100% classification rate at the vigilance parameter $\rho$=0.99. The proposed algorithm has some advantages of the feature extraction in the compressed domain and the simplification of processing steps.

• Speech Sciences
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• v.5 no.1
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• pp.89-103
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• 1999

It is relatively easy to develop a prototype of a Korean language processing system, but it is very difficult to make it an operational system. In this paper, we survey the current status and methodological issues of the Korean language processing systems such as morphological analyzer, parser and machine translator. In most cases, Korean language processing system easily comes to a dead-end state where its performance can not be improved any more. The reason is that it adopts a general algorithm covering similar problems as a whole because specific low-level problems are not clearly defined and their algorithms are unclear. So, when we add some restrictions to solve an individual linguistic problem, they are also applied to other linguistic phenomena as a side effect. It causes a critical problem that the improvement of the algorithm is very difficult. This paper proposes a 2-step paradigm, a divide-and-conquer method by the functional modularization, a simplification method, and an exception handling technique to develop an operational system that does not fall into a dead-end state.

• Proceedings of the Korean Association of Geographic Inforamtion Studies Conference
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• 2008.06a
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• pp.437-444
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• 2008

최근 유비쿼터스 시대를 맞아 건설 분야에서 이용되는 CAD 자료에서 GIS 자료로의 변환 및 융복합에 대한 요구가 증대되면서 상호변환을 위한 연구가 활발하게 진행되고 있다. 본 연구에서는 건설도면 CAD 데이터를 활용하여 수치지도의 건물데이터를 수정, 갱신하기 위한 방법론의 일환으로 건설도면의 건물외곽선을 추출하여 수치지도의 건물데이터 수준으로 일반화하는 선형 단순화 알고리즘을 비교 분석하였다. 선형 단순화 알고리즘은 Douglas-Peucker 알고리즘, Lang 알고리즘, Reumann-Witkam, Opheim 알고리즘을 적용하였으며 분석방법으로는 시각적 분석, 절점 수, 총길이, 면적 변화율 분석 그리고 각 절점이 수치지도 작성내규를 만족하는 비율을 이용하였다. 분석 결과 Douglas-Peycker 알고리즘이 시각적 측면과 절점 수 감소율 측면에서 상대적으로 우수한 단순화 결과를 보여주었으나 수치지도 작성내규 만족도 측면에서는 공통적으로 $50{\sim}60%$ 수준의 만족도를 보이고 있어 국내의 수치지도의 건물데이터를 작성하기 위한 단순화 기법으로는 한계가 있는 것으로 나타났으며 이를 만족시키기 위한 일반화 알고리즘의 개발이 필요하다고 판단된다.

• Electronics and Telecommunications Trends
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• v.34 no.2
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• pp.40-50
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• 2019

Considerable accuracy improvements in deep learning have recently been achieved in many applications that require large amounts of computation and expensive memory. However, recent advanced techniques for compacting and accelerating the deep learning model have been developed for deployment in lightweight devices with constrained resources. Lightweight deep learning techniques can be categorized into two schemes: lightweight deep learning algorithms (model simplification and efficient convolutional filters) in nature and transferring models into compact/small ones (model compression and knowledge distillation). In this report, we briefly summarize various lightweight deep learning techniques and possible research directions.