• The KIPS Transactions:PartB
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• v.13B no.3 s.106
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• pp.323-328
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• 2006

Proteins in a cell appear as spots in a two dimensional gel image which is used in protein analysis. The spots from the same protein are in near position when comparing two gel images. Finding out the different proteins between a normal tissue and a cancer one is important information in drug development. Automatic matching of gel images is difficult because they are made from biological experimental processes. This matching problem is known to be NP-hard. Neural networks are usually used to solve such NP-hard problems. Hopfield neural network is selected since it is appropriate to solve the gel matching. An energy function with location and distance parameters is defined. The two spots which make the energy function minimum are matching spots and they came from the same protein. The energy function is designed to reflect the topology of spots by examining not only the given spot but also neighborhood spots.

• The KIPS Transactions:PartB
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• v.16B no.4
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• pp.319-326
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• 2009

PSO(Particle Swarm Optimization) is an optimization algorithm in which simple particles search an optimal solution using shared information acquired through their own experiences. PSO applications are so numerous and diverse. Lots of researches have been made mainly on the parameter settings, topology, particle's movement in order to achieve fast convergence to proper regions of search space for optimization. In standard PSO, since each particle uses only information of its and best neighbor, swarm does not explore diverse regions and intended to premature to local optima. In this paper, we propose a new particle's movement strategy in order to explore diverse regions of search space. The strategy is that each particle moves according to relative weights of several better neighbors. The strategy of exploring diverse regions is effective and produces less local optimizations and accelerating of the optimization speed and higher success rates than standard PSO. Also, in order to raise success rates, we propose a strategy for checking whether swarm falls into local optimum. The new PSO algorithm with these two strategies shows the improvement in the search speed and success rate in the test of benchmark functions.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.17 no.6
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• pp.1462-1470
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• 2013

This paper proposes an SWAD-KNH(Sybil & Wormhole Attack Detection using Key, Neighbor list and Hop count) technique which consists of an SWAD(Sybil & Wormhole Attack Detection) module detecting an Worm attack and a KGDC(Key Generation and Distribution based on Cluster) module generating and an sense node key and a Group key by the cluster and distributing them. The KGDC module generates a group key and an sense node key by using an ECDH algorithm, a hash function, and a key-chain technique and distributes them safely. An SWAD module strengthens the detection of an Sybil attack by accomplishing 2-step key acknowledgement procedure and detects a Wormhole attack by using the number of the common neighbor nodes and hop counts of an source and destination node. As the result of the SWAD-KNH technique shows an Sybil attack detection rate is 91.2% and its average FPR 3.82%, a Wormhole attack detection rate is 90%, and its average FPR 4.64%, Sybil and wormhole attack detection rate and its reliability are improved.

• Journal of Internet Computing and Services
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• v.10 no.4
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• pp.1-11
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• 2009

Recently there has arisen concern in both the database community and the graphics society about data retrieval from large motion databases because the high dimensionality of motion data implies high costs. In this circumstance, finding an effective distance measure and an efficient query processing method for such data is a challenging problem. This paper presents an elaborate motion query processing system, SMoFinder (Similar Motion Finder), which incorporates a novel kinematic distance measure and an efficient indexing strategy via adaptive frame segmentation. To this end, we regard human motions as multi-linkage kinematics and propose the weighted Minkowski distance metric. For efficient indexing, we devise a new adaptive segmentation method that chooses representative frames among similar frames and stores chosen frames instead of all frames. For efficient search, we propose a new search method that processes k-nearest neighbors queries over only representative frames. Our experimental results show that the size of motion databases is reduced greatly (${\times}1/25$) but the search capability of SMoFinder is equal to or superior to that of other systems.

• The KIPS Transactions:PartB
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• v.15B no.3
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• pp.205-210
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• 2008

A new approach is proposed for restoration of corrupted regions and segmentation in natural text images. The challenge is to fill in the corrupted regions on the basis of color feature analysis by second order symmetric stick tensor. It is show how feature analysis can benefit from analyzing features using tensor voting with chromatic and achromatic components. The proposed method is applied to text images corrupted by manifold types of various noises. Firstly, we decompose an image into chromatic and achromatic components to analyze images. Secondly, selected feature vectors are analyzed by second-order symmetric stick tensor. And tensors are redefined by voting information with neighbor voters, while restore the corrupted regions. Lastly, mode estimation and segmentation are performed by adaptive mean shift and separated clustering method respectively. This approach is automatically done, thereby allowing to easily fill-in corrupted regions containing completely different structures and surrounding backgrounds. Applications of proposed method include the restoration of damaged text images; removal of superimposed noises or streaks. We so can see that proposed approach is efficient and robust in terms of restoring and segmenting text images corrupted.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.44 no.1
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• pp.17-25
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• 2007

In most electronic imaging applications, image with high resolution(HR) are desired. HR means that pixel density within an image is high, and therefore HR image can offer more details that may be critical in various applications. Digital images that are captured by CCD and CMOS cameras usually have a very low resolution, which significantly limits the performance of image recognition systems. Image super-resolution techniques can be applied to overcome the limits of these imaging systems. Super-resolution techniques have been proposed to increase the resolution by combining information from multiple images. To techniques were consisted of the registration algorithm for estimation and shift, the nearest neighbor interpolation using weight of acquired frames and presented frames. In this paper, it is proposed the image interpolation techniques using the wavelet base function. This is applied to embody a correct edge image and natural image when expend part of the still image by applying the wavelet base function coefficient to the conventional Super-Resolution interpolation method. And the proposal algorithm in this paper is confirmed to improve the image applying the nearest neighbor interpolation algorithm, bilinear interpolation algorithm.,bicubic interpolation algorithm through the computer simulation.

• Journal of Korea Spatial Information System Society
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• v.6 no.2 s.12
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• pp.3-13
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• 2004

The TPR-tree exploits bounding rectangles based on the function of time in order to index moving objects. As time passes on, each edge of a BR expands with the fastest velocity vector. Since the expansion of the BR results in a serious overlaps between neighboring nodes, the performance of range query is getting worse. In this paper, we propose schemes to reorganize bounding rectangles of nodes. When inserting a moving object, we exploit a forced merging scheme to merge two overlapped nodes and re-split it. When deleting a moving object, we used forced reinsertion schemes to reinsert other objects of a node into a tree. The forced reinsertion schemes are classified into a deleted node reinsertion scheme and an overlapped nodes reinsertion scheme. The overlapped nodes reinsertion scheme outperforms the forced merging scheme and the deleted node reinsertion scheme in all experiments.

• Journal of the Korea Computer Graphics Society
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• v.7 no.1
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• pp.1-9
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• 2001

We present a scheme and a data structure that decompose the space into adaptive-sized cells to improve the visualization of soft objects. Soft objects are visualized through the evaluation of the field functions at every point of the space. According to the propsed scheme, the affecting soft objects for a point in the space is searched through the data structure called interval tree based on the bounding volume of the components, which represent a soft object whose defining primitive(skeleton) is a simple geometric object such as point or line segment. The bounding volume of each component is generated with respect to the radius of a local field function of the component, threshold value, and the relations between the components and other neighboring components. The proposed scheme can be used in many applications for soft objects such as modeling and rendering, especially in interactive modeling process.

• The Korean Journal of Applied Statistics
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• v.32 no.5
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• pp.671-681
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• 2019

Studies on predicting arrhythmia using machine learning have been actively conducted with increasing number of arrhythmia patients. Existing studies have predicted arrhythmia based on multivariate data of feature variables extracted from RR interval data at a specific time point. In this study, we consider that the pattern of the heart state changes with time can be important information for the arrhythmia prediction. Therefore, we investigate the usefulness of predicting the arrhythmia with multivariate time series data obtained by extracting and accumulating the multivariate vectors of the feature variables at various time points. When considering 1-nearest neighbor classification method and its ensemble for comparison, it is confirmed that the multivariate time series data based method can have better classification performance than the multivariate data based method if we select an appropriate time series distance function.

• The Transactions of the Korea Information Processing Society
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• v.4 no.10
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• pp.2521-2532
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• 1997

The next generation internet protocol, IPv6, have been developed by the IETF according to the requirements of enhancement of classic IP protocols to satisfy the lack of Internet address space as well as the support of multimedia applications. This paper presents an implementation of IPv6 protocols on the Windows NT kernel environment. In this work, we developed and also tested the basic functions, required for operating as an IPv6 host, such as IPv6 header processing, IPv6 address handling, control message processing, group membership processing and neighbor discovery functions. The implemented IPv6 protocol driver module is connected to the lower network interface card through NDIS, a standard network interface. And this driver module that operates within kernel, is implemented as it is connected to upper user applications and lower NDIS using dispatch and lower-edge functions. The developed IPv6 protocol driver can provide not only enhanced performance because it is implemented in kernel mode, but also convenience of usage to the application developers because it gives user interface as a dynamic link library.