• Korean Journal of Biological Psychiatry
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• v.9 no.1
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• pp.15-24
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• 2002

Even though alcoholism is a multi-factorial psychiatric disorder, it is reasonable to suppose that genetic factors play a substantial role in the manifestation of this disorder. Because alcohol is the reinforcing substance which manifests its effects through activation of the mesolimbic dopaminergic reward pathway of the brain, the gene encoding dopamine receptor subtypes can be a major natural candidate gene. Since 1990, many association studies have identified strong evidence implicating the dopamine D2 receptor(DRD2) gene in alcoholism, specifically TaqI A minor(A1) allele. Association studies have also been conducted on other dopamine receptor(DRD3 & DRD4) polymorphisms but the results have yet to be confirmed. Through a number of other approaches, each dopamine receptor gene has been investigated in association with different phenotypes in alcoholism, but further researches will be needed. In conclusion, studies in the past decade have shown that the TaqI A1 allele of the DRD2 gene is associated with alcoholism in various subject groups. Other dopamine receptor genes have since been added to the list but yet to be identified. Thus, the knowledge of these genes and their functional significance will enhance the understanding of the underlying biological mechanisms of alcoholism. Furthermore, it could lead to more helpful prevention and treatment approaches to alcoholism.

• Language and Information
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• v.15 no.2
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• pp.23-45
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• 2011

This paper proposes a statistical-based linguistic methodology for automatic mapping between large-scale heterogeneous languages resources for NLP applications in general. As a particular case, it treats automatic mapping between two large-scale heterogeneous Korean language resources: Sejong Semantic Classes (SJSC) in the Sejong Electronic Dictionary (SJD) and nouns in KorLex. KorLex is a large-scale Korean WordNet, but it lacks syntactic information. SJD contains refined semantic-syntactic information, with semantic labels depending on SJSC, but the list of its entry words is much smaller than that of KorLex. The goal of our study is to build a rich language resource by integrating useful information within SJD into KorLex. In this paper, we use both linguistic and statistical methods for constructing an automatic mapping methodology. The linguistic aspect of the methodology focuses on the following three linguistic clues: monosemy/polysemy of word forms, instances (example words), and semantically related words. The statistical aspect of the methodology uses the three statistical formulae ${\chi}^2$, Mutual Information and Information Gain to obtain candidate synsets. Compared with the performance of manual mapping, the automatic mapping based on our proposed statistical linguistic methods shows good performance rates in terms of correctness, specifically giving recall 0.838, precision 0.718, and F1 0.774.

• Proceedings of the Korean Society for Bioinformatics Conference
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• 2003.10a
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• pp.164-169
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• 2003

In this paper, we propose the integrated Bayesian network framework to reconstruct genetic regulatory networks from genome expression data. The proposed model overcomes the dimensionality problem of multivariate analysis by building coherent sub-networks from confined gene clusters and combining these networks via intermediary points. Gene Shaving algorithm is used to cluster genes that share a common function or co-regulation. Retrieved clusters incorporate prior biological knowledge such as Gene Ontology, pathway, and protein protein interaction information for extracting other related genes. With these extended gene list, system builds genetic sub-networks using Bayesian network with MDL score and Sparse Candidate algorithm. Identifying functional modules of genes is done by not only microarray data itself but also well-proved biological knowledge. This integrated approach can improve there liability of a network in that false relations due to the lack of data can be reduced. Another advantage is the decreased computational complexity by constrained gene sets. To evaluate the proposed system, S. Cerevisiae cell cycle data [1] is applied. The result analysis presents new hypotheses about novel genetic interactions as well as typical relationships known by previous researches [2].

• JSTS:Journal of Semiconductor Technology and Science
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• v.7 no.4
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• pp.221-228
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• 2007

In this paper, we propose a new symbolic simulation for scan chain diagnosis to solve the diagnosis resolution problem. The proposed scan chain fault simulation, called the SF-simulation, is able to analyze the effects caused by faulty scan cells in good scan chains. A new scan chain fault simulation is performed with a modified logic ATPG pattern. In this simulation, we consider the effect of errors caused by scan shifting in the faulty scan chain. Therefore, for scan chain diagnosis, we use the faulty information in good scan chains which are not contaminated by the faults while unloading scan out responses. The SF-simulation can tighten the size of the candidate list and achieve a high diagnosis resolution by analyzing fault effects of good scan chains, which are ignored by most previous works. Experimental results demonstrate the effectiveness of the proposed method.

• Journal of the Korea Institute of Military Science and Technology
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• v.14 no.2
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• pp.313-320
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• 2011

SPH(Smoothed Particle Hydrodynamics) is a gridless Lagrangian technique that is useful as an alternative numerical analysis method used to analyze high deformation problems as well as astrophysical and cosmological problems. In SPH, all points within the support of the kernel are taken as neighbours. The accuracy of the SHP is highly influenced by the method for choosing neighbours from all particle points considered. Typically a linked-list method or tree search method has been used as an effective tool because of its conceptual simplicity, but these methods have some liability in anisotropy situations. In this study, convex hull algorithm is presented as an improved method to eliminate this artifact. A convex hull is the smallest convex set that contains a certain set of points or a polygon. The selected candidate neighbours set are mapped into the new space by an inverse square mapping, and extract a convex hull. The neighbours are selected from the shell of the convex hull. These algorithms are proved by Fortran programs. The programs are expected to use as a searching algorithm in the future SPH program.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.11
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• pp.3674-3710
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• 2014

In this tutorial, we integrate the concept of cognitive radio technology into game theory and supermarket game theory to address the problem of resource allocation in multiuser multicarrier cognitive radio networks. In addition, multiuser multicarrier transmission technique is chosen as a candidate to study the resource allocation problem via game and supermarket game theory. This tutorial also includes various definitions, scenarios and examples related to (i) game theory (including both non-cooperative and cooperative games), (ii) supermarket game theory (including pricing, auction theory and oligopoly markets), and (iii) resource allocation in multicarrier techniques. Thus, interested readers can better understand the main tools that allow them to model the resource allocation problem in multicarrier networks via game and supermarket game theory. In this tutorial article, we first review the most fundamental concepts and architectures of CRNs and subsequently introduce the concepts of game theory, supermarket game theory and common solution to game models such as the Nash equilibrium and the Nash bargaining solution. Finally, a list of related studies is highlighted and compared in this tutorial.

• Journal of Ubiquitous Convergence Technology
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• v.1 no.1
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• pp.9-17
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• 2007

Feature based stereo matching is an effective way to perform 3D building reconstruction. However, in urban scene, the cluttered background and various building structures may interfere with the performance of building reconstruction. In this paper, we propose a novel method to robustly reconstruct buildings on the basis of rectangle regions. Firstly, we propose a multi-scale linear feature detector to obtain the salient line segments on the object contours. Secondly, candidate rectangle regions are extracted from the salient line segments based on their local information. Thirdly, stereo matching is performed with the list of matching line segments, which are boundary edges of the corresponding rectangles from the left and right image. Experimental results demonstrate that the proposed method can achieve better accuracy on the reconstructed result than pixel-level stereo matching.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.14 no.6
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• pp.79-89
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• 2004

Fingerprint identification consists of user enrollment phase storing user's fingerprint in a database and user identification phase making a candidate list for a given fingerprint. straightforward approach to perform the user identification phase is to scan the entire database sequentially, and takes times for large-scale databases. In this paper, we develop a hardware design which can perform the user identification phase in real-time. Our design employs parallel processing techniques and has been implemented on a PCI-based platform containing an FPGA and SDRAMs. Based on the performance evaluation, our hardware implementation can provide a scalability and perform the fingerprint identification in real-time.

• Journal of Information Processing Systems
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• v.17 no.3
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• pp.599-614
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• 2021

This paper proposes a digital signage system based on an intelligent recommendation model. The proposed system consists of a server and an edge. The server manages the data, learns the advertisement recommendation model, and uses the trained advertisement recommendation model to determine the advertisements to be promoted in real time. The advertisement recommendation model provides predictions for various products and probabilities. The purchase index between the product and weather data was extracted and reflected using correlation analysis to improve the accuracy of predicting the probability of purchasing a product. First, the user information and product information are input to a deep neural network as a vector through an embedding process. With this information, the product candidate group generation model reduces the product candidates that can be purchased by a certain user. The advertisement recommendation model uses a wide and deep recommendation model to derive the recommendation list by predicting the probability of purchase for the selected products. Finally, the most suitable advertisements are selected using the predicted probability of purchase for all the users within the advertisement range. The proposed system does not communicate with the server. Therefore, it determines the advertisements using a model trained at the edge. It can also be applied to digital signage that requires immediate response from several users.

• Journal of Korean Neurosurgical Society
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• v.66 no.4
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• pp.356-381
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• 2023

Although immunotherapy has been broadly successful in the treatment of hematologic malignancies and a subset of solid tumors, its clinical outcomes for glioblastoma are still inadequate. The results could be due to neuroanatomical structures such as the blood-brain-barrier, antigenic heterogeneity, and the highly immunosuppressive microenvironment of glioblastomas. The antitumor efficacy of endogenously activated effector cells induced by peptide or dendritic cell vaccines in particular has been insufficient to control tumors. Effector cells, such as T cells and natural killer (NK) cells can be expanded rapidly ex vivo and transferred to patients. The identification of neoantigens derived from tumor-specific mutations is expanding the list of tumor-specific antigens for glioblastoma. Moreover, recent advances in gene-editing technologies enable the effector cells to not only have multiple biological functionalities, such as cytokine production, multiple antigen recognition, and increased cell trafficking, but also relieve the immunosuppressive nature of the glioblastoma microenvironment by blocking immune inhibitory molecules, which together improve their cytotoxicity, persistence, and safety. Allogeneic chimeric antigen receptor (CAR) T cells edited to reduce graft-versus-host disease and allorejection, or induced pluripotent stem cell-derived NK cells expressing CARs that use NK-specific signaling domain can be a good candidate for off-the-shelf products of glioblastoma immunotherapy. We here discuss current progress and future directions for T cell and NK cell therapy in glioblastoma.