• Journal of Fisheries and Marine Sciences Education
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• v.17 no.1
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• pp.76-85
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• 2005

GIS (Geographic Information Systems) technology is a necessary function to support location based on service by using GPS in the mobile environment. These mobile systems have basic functional limitations such as a low rate of processing, limited memory capacity, and small screen size. Because of these limitations, most of the mobile systems require development of a reduced digital map to overcome problems with large-volume spatial data. In this paper, we suggest using the reduced digital map format in order to use location based on service in a PDA environment. The processing of the proposed data format consists of map generation, redefinition of layers, creating polygons, and format conversion. The proposed data format reduces the data size by about 98% comparing with DXF format based on the digital map of Busan.

• Transactions on Control, Automation and Systems Engineering
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• v.1 no.1
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• pp.62-68
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• 1999

FCM(Fuzzy Cognitive Map) is proposed for representing causal reasoning. Its structure allows systematic causal reasoning through a forward inference. Authors have already proposed a diagnostic system based on FCM to utilized to identify the true origin of fault by on-line pattern diagnosis. In FCM based fault diagnosis, Temporal Associative Memories (TAM) recall of FCM is utilized to identify the true origin of fault by on-line pattern match where predicted pattern sequences obtained from TAM recall of fault FCM models are compared with actually observed ones. In engineering processes, the propagation delays are induced by the dynamics of processes and may vary with variables involved. However, disregarding such propagation delays in FCM-based fault diagnosis may lead to erroneous diagnostic results. To solve the problem, a concept of FTCM(Fuzzy Time Cognitive Map) is introduced into FCM-based fault diagnosis in this work. Expecially, translation method of FTCM makes it possible to diagnose the fault for some discrete time. Simulation studies through two-tank system is carried out to verify the effectiveness of the proposed diagnostic scheme.

• Journal of Institute of Control, Robotics and Systems
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• v.17 no.1
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• pp.22-30
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• 2011

Plane detection is very important information for mission-critical of robot in 3D environment. A representative method of plane detection is Hough-transformation. Hough-transformation is robust to noise and makes the accurate plane detection possible. But it demands excessive memory and takes too much processing time. Iterative randomized Hough-transformation has been proposed to overcome these shortcomings. This method doesn't vote all data. It votes only one value of the randomly selected data into the Hough parameter space. This value calculated the value of the parameter of the shape that we want to extract. In Hough parameters space, it is possible to detect accurate plane through detection of repetitive maximum value. A common problem in these methods is that it requires too much computational cost and large number of memory space to find the distribution of mixed multiple planes in parameter space. In this paper, we detect multiple planes only via data sampling using Self Organizing Map method. It does not use conventional methods that include transforming to Hough parameter space, voting and repetitive plane extraction. And it improves the reliability of plane detection through division area searching and planarity evaluation. The proposed method is more accurate and faster than the conventional methods which is demonstrated the experiments in various conditions.

• Journal of the Korea Institute of Military Science and Technology
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• v.3 no.1
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• pp.116-126
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• 2000

Turbo codes are the most exciting and potentially important development in coding theory in recent years. They were introduced in 1993 by Berrou, Glavieux and $Thitimajshima,({(1)}$ and claimed to achieve near Shannon-limit error correction performance with relatively simple component codes and large interleavers. A required Eb/N0 of 0.7㏈ was reported for BER of $10^{-5}$ and code rate of $l/2.^{(1)}$ However, to implement the turbo code system, there are various important details that are necessary to reproduce these results such as AGC gain control, optimal wordlength determination, and metric rescaling. Further, the memory required to implement MAP-based turbo decoder is relatively considerable. In this paper, we confirmed the accuracy of these claims by computer simulation considering these points, and presented a optimal wordlength for Turbo code design. First, based on the analysis and simulation of the turbo decoder, we determined an optimal wordlength of Turbo decoder. Second, we suggested the MAP decoding algorithm based on sliding-window method which reduces the system memory significantly. By computer simulation, we could demonstrate that the suggested fixed-point Turbo decoder operates well with negligible performance loss.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.15 no.1
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• pp.81-88
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• 2004

Turbo code has been adopted in the 3GPP standard, since its performance is very close to the Shannon limit. However, the turbo decoder requires a lot of computations and the amount of the memory increases as the block size of turbo codes becomes larger. In order to reduce the complexity of the turbo decoder, the Log-MAP, the Max-Log-MAP and the sliding window algorithm have been proposed. In this paper, the performance of turbo codes adopted in the 3GPP standard is analyzed by using the floating point and the fixed point implementation. The efficient decoding method is also proposed. It is shown that the BER performance of the proposed method is close to that of the Log-MAP algorithm.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.21 no.9
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• pp.1731-1737
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• 2017

K Nearest Neighbor Join (KNN Join) is a simple yet effective method in machine learning. It is widely used in small dataset of the past time. As the number of data increases, it is infeasible to run this model on an actual application by a single machine due to memory and time restrictions. Nowadays a popular batch process model called MapReduce which can run on a cluster with a large number of computers is widely used for large-scale data processing. Hadoop is a framework to implement MapReduce, but its performance can be further improved by a new framework named Spark. In the present study, we will provide a KNN Join implement based on Spark. With the advantage of its in-memory calculation capability, it will be faster and more effective than Hadoop. In our experiments, we study the influence of different factors on running time and demonstrate robustness and efficiency of our approach.

• Journal of Intelligence and Information Systems
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• v.5 no.2
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• pp.79-97
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• 1999

Knowledge management (KM) is emerging as a robust management mechanism with which an organization can remain highly intelligent and competitive in a turbulent market. Organization knowledge is at the heart of KM success. As a vehicle of acquiring organizational knowledge in a distributed decision-making environment, we applied a fuzzy cognitive map (FMM) technique and proved its effectiveness in a distributed knowledge management environment. Our approach was applied to the financial statement analysis problem, yielding a robust result.

• Korean Journal of Microbiology
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• v.24 no.1
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• pp.12-17
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• 1986

A new computer algorithm is described to order the restriction fragments of plasmid DNA which has been cleaved with several restriction endonucleases in single or double digestions rapidly with realistic error rates. The permutation and high weight on small fragments methods construct all logical circular map solutions. The program is written in Apple BASIC and run on an Apple II plus microcomputer with 64K memory. Several examples are presented which indicate the high efficiency of the profram in construction possible restriction map for YEp24.

• Journal of Korean Institute of Industrial Engineers
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• v.28 no.2
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• pp.162-170
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• 2002

Detecting straight lines in an image is frequently required for various machine vision applications such as restoring CAD drawings from scanned images and object recognition. The standard Hough transform has been dominantly used to that purpose. However, massive storage requirement and low precision in estimating line parameters due to the quantization of parameter space are the major drawbacks of the Hough transform technique. In this paper, to overcome the drawbacks, an iterative algorithm based on a self-organizing map is presented. The self-organizing map can be adaptively learned such that image points are clustered by prominent lines. Through the procedure of the algorithm, a set of lines are sequentially detected one at a time. The algorithm can produce highly precised estimates of line parameters using very small amount of storage memory. Computational results for synthetically generated images are given. The promise of the algorithm is also demonstrated with its application to two natural images of inserts.

• International Journal of Internet, Broadcasting and Communication
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• v.9 no.1
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• pp.9-17
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• 2017

This paper describes a process of developing commercial real time image recognition system with company. In this paper we will make a system that is combining an empirical kernel map method and conjugate least squares support vector machine in order to represent images in a low-dimensional subspace for real time image recognition. In the traditional approach calculating these eigenspace models, known as traditional PCA method, model must capture all the images needed to build the internal representation. Updating of the existing eigenspace is only possible when all the images must be kept in order to update the eigenspace, requiring a lot of storage capability. Proposed method allows discarding the acquired images immediately after the update. By experimental results we can show that empirical kernel map has similar accuracy compare to traditional batch way eigenspace method and more efficient in memory requirement than traditional one. This experimental result shows that proposed model is suitable for commercial real time image recognition system.