• Radiation Oncology Journal
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• v.17 no.2
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• pp.151-157
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• 1999

Purpose : By sequencing the Erpressed Sequence Tags of human 걸ermal papilla CDNA library, we identified a clone named K872 of which the expression decreased during differentiation of HL6O cell line. Materials and Methods : K872 plasmid DNA was isolated according to QIA plasmid extraction kit (Qiagen GmbH, Germany). The nucleotide sequencing was performed by Sanger's method with K872 plasmid DNA. The most updated GenBank EMBL necleic acid banks were searched through the internet by using BLAST (Basic Local Alignment Search Tools) program. Nothern bots were performed using RNA isolated from various human tissues and cancer cell lines. The gene expression of the fusion protein was achieved by His-Patch Thiofusicn expression system and the protein product was identified on SDS-PAGE. Results : K872 clone is 1006 nucleotides long, and has a coding region of 675 nucleotides and a 3' non-coding region of 280 nucleotides. The presumed open reading frame starting at the 5' terminus of K872 encodes 226 amino acids, including the initiation methionine residue. The amino acid sequence deduced from the open reading frame of K872 shares $70\%$, identity with that of rat glutathione 5-transferase kappa 1 (rGSTKl). The transcripts were expressed in a variety of human tissues and cancer cells. The levels of transcript were relatively high in those tissues such as heart, skeletal muscle, and peripheral blood leukocyte. It is noteworthy that K872 was found to be abundantly expressed in coloreetal cancer and melanoma cell lines. Conclusion : Homology search result suggests that K872 clone is the human homolog of the rGSTK1 which is known to be involved in the resistance of cytotoxic therapy. We propose that meticulous functional analysis should be followed to confirm that.

• Journal of KIISE
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• v.43 no.11
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• pp.1233-1244
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• 2016

In the IoT network environments for LLNs(Low power and Lossy networks), IPv6 Routing Protocol for Low Power and Lossy networks(RPL) has been proposed by IETF(Internet Engineering Task Force). The goal of RPL is to create a directed acyclic graph, without loops. As recommended by the IETF standard, RPL route recovery mechanisms in the event of a failure of a node should avoid loop, loop detection, DIO Poisoning. In this process, route recovery time and control message might be increased in the sub-tree because of the repeated route search. In this paper, we suggested RPL route recovery method to solve the routing overhead problem in the sub-tree during a loss of a link in the RPL routing protocol based on IoT wireless networks. The proposed method improved local repair process by utilizing a route that could not be selected as the preferred existing parents. This reduced the traffic control packet, especially in the disconnected node's sub tree. It also resulted in a quick recovery. Our simulation results showed that the proposed RPL local repair reduced the recovery time and the traffic of control packets of RPL. According to our experiment results, the proposed method improved the recovery performance of RPL.

• Journal of the Korean Institute of Telematics and Electronics C
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• v.35C no.12
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• pp.85-98
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• 1998

This paper proposes a new design method of neuro-FLC by the Lamarckian co-adaptation scheme that incorporates the backpropagation learning into the GA evolution in an attempt to find optimal design parameters (fuzzy rule base and membership functions) of application-specific FLC. The design parameters are determined by evolution and learning in a way that the evolution performs the global search and makes inter-FLC parameter adjustments in order to obtain both the optimal rule base having high covering value and small number of useful fuzzy rules and the optimal membership functions having small approximation error and good control performance while the learning performs the local search and makes intra-FLC parameter adjustments by interacting each FLC with its environment. The proposed co-adaptive design method produces better approximation ability because it includes the backpropagation learning in every generation of GA evolution, shows better control performance because the used COG defuzzifier computes the crisp value accurately, and requires small workspace because the optimization procedure of fuzzy rule base and membership functions is performed concurrently by an integrated fitness function on the same fuzzy partition. Simulation results show that the Lamarckian co-adapted FLC produces the most superior one among the differently generated FLCs in all aspects such as the number of fuzzy rules, the approximation ability, and the control performance.

• Journal of KIISE:Software and Applications
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• v.36 no.8
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• pp.623-632
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• 2009

In this paper, we propose a pulmonary nodule registration for the tracking of lung nodules in sequential CT scans. Our method consists of following five steps. First, a translational mismatch is corrected by aligning the center of optimal bounding volumes including each segmented lung. Second, coronal maximum intensity projection(MIP) images including a rib structure which has the highest intensity region in baseline and follow-up CT series are generated. Third, rigid transformations are optimized by normalized average density differences between coronal MIP images. Forth, corresponding nodule candidates are defined by Euclidean distance measure after rigid registration. Finally, template matching is performed between the nodule template in baseline CT image and the search volume in follow-up CT image for the nodule matching. To evaluate the result of our method, we performed the visual inspection, accuracy and processing time. The experimental results show that nodules in serial CT scans can be rapidly and correctly registered by coronal MIP-based rigid registration and local template matching.

• Journal of KIISE:Computer Systems and Theory
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• v.31 no.10
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• pp.593-602
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• 2004

A new surface reconstruction scheme for approximating the surface from a set of unorganized 3D points is proposed. Our method, called shrink-wrapped boundary face (SWBF) algorithm, produces the final surface by iteratively shrinking the initial mesh generated from the definition of the boundary faces. Proposed method surmounts the genus-0 spherical topology restriction of previous shrink-wrapping based mesh generation technique, and can be applicable to any kind of surface topology. Furthermore, SWBF is much faster than the previous one since it requires only local nearest-point-search in the shrinking process. According to experiments, it is proved to be very robust and efficient for mesh generation from unorganized points cloud.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.4
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• pp.2038-2057
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• 2017

Gaussian learning is a new technology in the computational intelligence area. However, this technology weakens the learning ability of a particle swarm and achieves a lack of diversity. Thus, this paper proposes a vector Gaussian learning strategy and presents an effective approach, named particle swarm optimization based on vector Gaussian learning. The experiments show that the algorithm is more close to the optimal solution and the better search efficiency after we use vector Gaussian learning strategy. The strategy adopts vector Gaussian learning to generate the Gaussian solution of a swarm's optimal location, increases the learning ability of the swarm's optimal location, and maintains the diversity of the swarm. The method divides the states into normal and premature states by analyzing the state threshold of the swarm. If the swarm is in the premature category, the algorithm adopts an inertia weight strategy that decreases linearly in addition to vector Gaussian learning; otherwise, it uses a fixed inertia weight strategy. Experiments are conducted on eight well-known benchmark functions to verify the performance of the new approach. The results demonstrate promising performance of the new method in terms of convergence velocity and precision, with an improved ability to escape from a local optimum.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.41 no.5
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• pp.165-173
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• 2004

Since the conventional image segmentation methods using mathematical morphology tend to yield over-segmented results, they normally need postprocess which merges small regions to obtain larger ones. To solve this over-segmentation problem without postprocess had to increase size of structuring element used marker extraction. As size of structuring element is very large, edge of region segments incorrectly. Therefore, this paper selectively applies structuring element of mathematical morphology to improve performance of image segmentation and classifies input image into texture region, edge region and simple region using averaged local variance and image gradient. Proposed image segmentation method removes the cause for over-segmentation of image as selectively applies size of structuring element to each region. Simulation results show that proposed method correctly segment for pixel region of similar luminance value and more correctly search texture region and edge region than conventional methods.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.32 no.5C
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• pp.496-512
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• 2007

In this paper, we propose a novel JPEG-2000 compressed image retrieval system using feature vector extracted through partial entropy decoding. Main idea of the proposed method is to utilize the context information that is generated during entropy encoding/decoding. In the framework of JPEG-2000, the context of a current coefficient is determined depending on the pattern of the significance and/or the sign of its neighbors in three bit-plane coding passes and four coding modes. The contexts provide a model for estimating the probability of each symbol to be coded. And they can efficiently describe texture images which have different pattern because they represent the local property of images. In addition, our system can directly search the images in the JPEG-2000 compressed domain without full decompression. Therefore, our proposed scheme can accelerate the work of retrieving images. We create various distortion and similarity image databases using MIT VisTex texture images for simulation. we evaluate the proposed algorithm comparing with the previous ones. Through simulations, we demonstrate that our method achieves good performance in terms of the retrieval accuracy as well as the computational complexity.

• The KIPS Transactions:PartD
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• v.19D no.4
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• pp.299-306
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• 2012

To fulfill the complex user requirement, composition web service comprised of existing services is considered from the efficient and reusable point of view instead of making entirely new web service. However, with the growing the number of web services which provide the same functionality but differ in quality value, the service composition becomes a decision problem on which component services should be selected such that end-to-end QoS constraints by the client and overall QoS of the composition service are satisfied. QoS of service aspects is a determinant factor for selecting the services, since the performance of the composed service is determined by the performance of the involved component web service. In this paper, hybrid genetic algorithm is presented to select component services to take part in the QoS-aware composition. The local search method is used to be combined with the genetic algorithm to improve the individuals (component service) in population as well as composed service. The paper also presents a set of experiments conducted to evaluate the efficiency of selection algorithm using the real web service data.

• Journal of the Korean Institute of Intelligent Systems
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• v.16 no.2
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• pp.131-137
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• 2006

In swarm robot systems, each robot must behaves by itself according to the its states and environments, and if necessary, must cooperates with other robots in order to carry out a given task. Therefore it is essential that each robot has both learning and evolution ability to adapt the dynamic environments. In this paper, the new learning and evolution method based on reinforcement learning having delayed reward ability and distributed genetic algorithms is proposed for behavior learning and evolution of collective autonomous mobile robots. Reinforcement learning having delayed reward is still useful even though when there is no immediate reward. And by distributed genetic algorithm exchanging the chromosome acquired under different environments by communication each robot can improve its behavior ability. Specially, in order to improve the performance of evolution, selective crossover using the characteristic of reinforcement learning is adopted in this paper. we verify the effectiveness of the proposed method by applying it to cooperative search problem.