• Proceedings of the Korean Society for Bioinformatics Conference
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• 2003.10a
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• pp.86-94
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• 2003

Electronically available biological literature has been accumulated exponentially in the course of time. So, researches on automatically acquiring knowledge from these tremendous data by text mining technology become more and more prosperous. However, most of the previous researches are technology oriented and are not well focused in practical extraction target, hence result in low performance and inconvenience for the bio-researchers to actually use. In this paper, we propose a more biology oriented target domain specific text mining system, that is, POSTECH bio-text mining system (POSBIOTM), for signal transduction pathway extraction, especially for G protein-coupled receptor (GPCR) pathway. To reflect more domain knowledge, we specify the concrete target for pathway extraction and define the minimal pathway domain ontology. Under this conceptual model, POSBIOTM extracts interactions and entities of pathways from the full biological articles using a machine learning oriented extraction method and visualizes the pathways using JDesigner module provided in the system biology workbench (SBW) [14]

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.3
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• pp.908-928
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• 2022

Multiple target tracking mainly focuses on tracking unknown number of targets in the complex environment of clutter and missed detection. The generalized labeled multi-Bernoulli (GLMB) filter has been shown to be an effective approach and attracted extensive attention. However, in the scenarios where the clutter rate is high or measurement-outliers often occur, the performance of the GLMB filter will significantly decline due to the Gaussian-based likelihood function is sensitive to clutter. To solve this problem, this paper presents a robust GLMB filter and smoother to improve the tracking performance in the scenarios with high clutter rate, low detection probability, and measurement-outliers. Firstly, a Student-T distribution variational Bayesian (TDVB) filtering technology is employed to update targets' states. Then, The likelihood weight in the tracking process is deduced again. Finally, a trajectory smoothing method is proposed to improve the integrative tracking performance. The proposed method are compared with recent multiple target tracking filters, and the simulation results show that the proposed method can effectively improve tracking accuracy in the scenarios with high clutter rate, low detection rate and measurement-outliers. Code is published on GitHub.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.7
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• pp.2328-2344
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• 2022

2D human pose estimation still faces difficulty in low-resolution images. Most existing top-down approaches scale up the target human bonding box images to the large size and insert the scaled image into the network. Due to up-sampling, artifacts occur in the low-resolution target images, and the degraded images adversely affect the accurate estimation of the joint positions. To address this issue, we propose a multi-resolution input feature fusion network for human pose estimation. Specifically, the bounding box image of the target human is rescaled to multiple input images of various sizes, and the features extracted from the multiple images are fused in the network. Moreover, we introduce a guiding channel which induces the multi-resolution input features to alternatively affect the network according to the resolution of the target image. We conduct experiments on MS COCO dataset which is a representative dataset for 2D human pose estimation, where our method achieves superior performance compared to the strong baseline HRNet and the previous state-of-the-art methods.

• Journal of Technology Innovation
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• v.19 no.1
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• pp.55-78
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• 2011

The social impact of science and technology is increasing, the meaning of technology assessment is not small in modern society, Specially, the technology assesment(TA) has been institutionalized and performed by Korean administration and its official results has reflected directly in the S&T policy. Therefore, the technology assesment is a device that prepare the negative effects of S&T and the social controversy in advance. To select the target technology of technology assessment will be done carefully in various perspective and concerned with the characteristics of technology assessment emphasizing social assessment, the institutional system of selecting one technology in each year and the contents and methods of evaluation that are determined in accordance with technology characteristics. However, the method of selecting target technology in TA is mostly peformed by qualitative discussion and vote rather than by reflecting various opinion and understanding TA until now. In this paper, we developed the indicator has to be considered to select target technology for TA by using factor analysis. Developed indicator is consist of five factor, such as the need for social consensus and the size of social/cultural impact etc., and we weight each factor by using Analytic Hierarchy Process(AHP). Futhermore, we show the example how to applicate directly these indicator and weights to select target technology and suggest institutional application in TA. Though using developed indicator in this paper, we expect to select appropriate technology for institutional TA and the application of TA results in society and public policy can be strengthened.

• Journal of the Semiconductor & Display Technology
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• v.9 no.2
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• pp.111-116
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• 2010

In a large area magnetron sputtering system, which is under the influence of high heat load from the plasma, it is necessary to use the effective water cooling in order to maintain the proper deposition performance and the economic use of target materials. A series of three-dimensional numerical simulations are carried out on the simplified model of the large area magnetron sputtering system with the cooling plate that includes the U-shaped water channel. The analysis is focused on the effects of water channel geometry, cooling water flowrate, thermal conductivity of target material, and the degree of target erosion on the cooling performance of cooling plate, which is represented by the temperature distribution of target material.

• Journal of the Korea Institute of Military Science and Technology
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• v.22 no.4
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• pp.555-566
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• 2019

In order to prepare for the future warfare environment, which requires a faster operational tempo, it is necessary to utilize the fourth industrial revolution technology in the field of military operations. This study propose a methodology, 'machine learning based dynamic targeting', which can contribute to reduce required man-hour for dynamic targeting. Specifically, a decision tree algorithm is considered to apply to dynamic targeting process. The algorithm learns target prioritization patterns from JIPTL(Joint Integrated Prioritized Target List) which is the result of the deliberate targeting, and then learned algorithm rapidly(almost real-time) determines priorities for new targets that occur during ATO(Air Tasking Order) execution. An experiment is performed with artificially generated data to demonstrate the applicability of the methodology.

• Journal of the Semiconductor & Display Technology
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• v.21 no.3
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• pp.110-113
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• 2022

Non-playable game character plays an important role in improving the concentration of the game and the interest of the user, and recently implementation of NPC with reinforcement learning has been in the spotlight. In this paper, we estimate an AI target tracking method via reinforcement learning, and implement an AI-based tracking agency of specific target object with avoiding traps through Unity ML-Agents Toolkit. The implementation is built in Unity game engine, and simulations are conducted through a number of experiments. The experimental results show that outstanding performance of the tracking target with avoiding traps is shown with good enough results.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2020.11a
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• pp.33-36
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• 2020

Virtual try on is getting interested from researchers these days because its application in online shopping. But single pose virtual try on is not enough, customer may want to see themselves in different pose. Multiple pose virtual try on is getting input as customer image, an in-shop cloth and a target pose, it will try to generate realistic customer wearing the in-shop cloth with the target pose. We first generate the target segmentation layout using conditional generative network (cGAN), and then the in-shop cloth are warped to fit the customer body in target pose. Finally, all the result will be combine using a Resnet-like network. We experiment and show that our method outperforms stage of the art.

• Journal of Microbiology and Biotechnology
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• v.26 no.2
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• pp.394-401
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• 2016

Recently, the clustered regularly interspaced short palindromic repeats (CRISPR)-associated protein 9 (CRISPR/Cas9) system, a genome editing technology, was shown to be versatile in treating several antibiotic-resistant bacteria. In the present study, we applied the CRISPR/Cas9 technology to kill extended-spectrum beta-lactamase (ESBL)-producing Escherichia coli. ESBL bacteria are mostly multidrug resistant (MDR), and have plasmid-mediated antibiotic resistance genes that can be easily transferred to other members of the bacterial community by horizontal gene transfer. To restore sensitivity to antibiotics in these bacteria, we searched for a CRISPR/Cas9 target sequence that was conserved among >1,000 ESBL mutants. There was only one target sequence for each TEM- and SHV-type ESBL, with each of these sequences found in ~200 ESBL strains of each type. Furthermore, we showed that these target sequences can be exploited to re-sensitize MDR cells in which resistance is mediated by genes that are not the target of the CRISPR/Cas9 system, but by genes that are present on the same plasmid as target genes. We believe our Re-Sensitization to Antibiotics from Resistance (ReSAFR) technology, which enhances the practical value of the CRISPR/Cas9 system, will be an effective method of treatment against plasmid-carrying MDR bacteria.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.29 no.1
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• pp.68-76
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• 2018

Target prioritization is necessary for a multifunction radar(MFR) to track an important target and manage the resources of the radar platform efficiently. In this paper, we consider an artificial neural network(ANN) model that calculates the priority of the target. Furthermore, we propose a neural network learning algorithm based on the steepest descent method, which is more suitable for target prioritization by combining the conventional gradient descent method. Several simulation results show that the proposed scheme is much more superior to the traditional neural network model from analyzing the training data accuracy and the output priority relevance of the test scenarios.