• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• v.29 no.1
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• pp.457-463
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• 2005

The use of the internet to facilitate commerce among companies promises vast benefits. Lots of e-marketplaces are building for several industries such as chemistry, airplane, and automobile industries. This study proposed new B2B EC business model for the shipping industry which concerns relatively massive fixed assets to be fully utilized. To be successful the proposed model gives participants to support useful information. To do this the expert system is constructed as the hybrid prediction system of neural network (NN) and memory based reasoning (MBR) with self-organizing map (SOM) and knowledge augmentaton technique using qualitative reasoning (QR). The expert system supports participants useful information coping with dynamic market environment. with this transportation companies are induced to participate in the proposed e-marketplace and helped for exchanges easily. Also participants would utilize their assets fully through B2B exchanges.

• Molecules and Cells
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• v.37 no.12
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• pp.841-850
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• 2014

Polycomb group (PcG) proteins are conserved chromatin regulators involved in the control of key developmental programs in eukaryotes. They collectively provide the transcriptional memory unique to each cell identity by maintaining transcriptional states of developmental genes. PcG proteins form multi-protein complexes, known as Polycomb repressive complex 1 (PRC1) and Polycomb repressive complex 2 (PRC2). PRC1 and PRC2 contribute to the stable gene silencing in part through catalyzing covalent histone modifications. Components of PRC1 and PRC2 are well conserved from plants to animals. PcG-mediated gene silencing has been extensively investigated in efforts to understand molecular mechanisms underlying developmental programs in eukaryotes. Here, we describe our current knowledge on PcG-mediated gene repression which dictates developmental programs by dynamic layers of regulatory activities, with an emphasis given to the model plant Arabidopsis thaliana.

• Proceedings of the KSPS conference
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• 2007.05a
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• pp.327-329
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• 2007

This paper presents a FSN-based LVCSR system and it's application to the speech TV program guide. Unlike the most popular statistical language model-based system, we used FSN grammar based on the graph theory-based FSN optimization algorithm and knowledge-based advanced word boundary modeling. For the memory and latency efficiency, we implemented the dynamic pruning scheduling based on the histogram of active words and their likelihood distribution. We achieved a 10.7% word accuracy improvement with 57.3% speedup.

• Transactions of the Society of Information Storage Systems
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• v.1 no.2
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• pp.132-136
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• 2005

It is widely accepted that the reading mechanism of Super-RENS(super-resolution near field structure) and Super-ROM(super-resolution read only memory) is closely related with non-linear temperature dependent material properties such as refractive indices, phase change. Furthermore, the dynamic change of the temperature distribution also an essential part of reading mechanism of Super-RENS/ROM. Therefore, the knowledge of the temperature distribution as a function a time is one of the important keys to reveal the physics of reading mechanism in Super-RENS/ROM. We calculated time-dependent temperature distribution in a 3-dimensional Super-ROM disk structure when moving laser beam is irradiated. With a help of commercial software FEMLAB which employed finite element method, we simulated the temperature distribution of ROM structure whose pit diameter is 120-nm with 50-nm depth. Energy absorption by moving laser irradiation, time variations of heat transfer processes, heat fluxes, heat transfer ratios, and temperature distributions of the complicate 3-dimensional ROM structure have been obtained.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.52 no.12
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• pp.671-678
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• 2003

This paper proposes an expert system to generate automatically the test table of test system which can highly enhance the quality and productivity of product by inspecting quickly and accurately the defect device on the electronic circuit board tested. The expert system identifies accurately the tested components and the circuit patterns by tracing automatically the connectivity of circuit from electronic circuit database. And it generates automatically the test table to detect accurately the missing components, the misplaced components, and the wrong components for analog components such as resistance, coil, condenser, diode, and transistor, based on the experience knowledge of veteran expert. It is implemented in C computer language for the purpose of the implementation of the inference engine using the dynamic memory allocation technique, the interface with the electronic circuit database and the hardware direct control. And, the validity of the builded expert system is proved by simulating for a typical electronic board model.

• Proceedings of the KIEE Conference
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• 1998.11b
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• pp.514-516
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• 1998

A Mobile Robot should be able to navigate safely in the workspaces without any additional human's helps. In this paper, a method to generate the safe path to avoid the unknown obstacles using the pre-knowledge of the workspaces was proposed. For the efficiency of the algorithm, it is proposed to model the obstacles as the line segments in numerical map, which can reduce the required memory size and give the simple forms. To make the environments map, we used the Hough transform and the sonar measurements is converted to the set of line segments by Hough transform. In this algorithm, the subgoals are generated to avoid the obstacles until a mobile robot arrives the final position using the proposed environmental model.

• Journal of Navigation and Port Research
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• v.29 no.4
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• pp.349-355
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• 2005

The use of the internet to facilitate commerce among companies promises vast benefits. Lots of e-marketplaces are building for several industries such as chemistry, airplane, and automobile industries. This study provides the new B2B EC business model for the shipping industry which concerns relatively massive fixed assets to be fully utilized. To be successful the proposed model gives participants useful information. To do this the expert system is constructed with the hybrid prediction system of neural network (NN) and memory based reasoning (MBR) with self-organizing map (SOM) and knowledge augmentation technique using qualitative reasoning (QR). The expert system supports participants useful information coping with dynamic market environment. with this shipping companies are induced to participate in the proposed e-marketplace and helped for exchanges easily. Also participants would utilize their assets fully through B2B exchanges.

• Electronics and Telecommunications Trends
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• v.34 no.2
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• pp.40-50
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• 2019

Considerable accuracy improvements in deep learning have recently been achieved in many applications that require large amounts of computation and expensive memory. However, recent advanced techniques for compacting and accelerating the deep learning model have been developed for deployment in lightweight devices with constrained resources. Lightweight deep learning techniques can be categorized into two schemes: lightweight deep learning algorithms (model simplification and efficient convolutional filters) in nature and transferring models into compact/small ones (model compression and knowledge distillation). In this report, we briefly summarize various lightweight deep learning techniques and possible research directions.

• Annals of Clinical Neurophysiology
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• v.21 no.2
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• pp.102-104
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• 2019

Transient global amnesia is a syndrome of temporary loss of short-term memory and is not accompanied by any other neurological deficit. Diffusion-weighted imaging is useful to improve the diagnostic accuracy of transient global amnesia. We report a 68-year-old woman with multiple lesions on diffusion-weighted imaging in the right corpus callosum and left hippocampus. To the best of our knowledge, this is the first case of a diffusion-weighted imaging lesion in the body portion of the corpus callosum.

• Molecules and Cells
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• v.44 no.6
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• pp.392-400
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• 2021

It has been more than a year since severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) first emerged. Many studies have provided insights into the various aspects of the immune response in coronavirus disease 2019 (COVID-19). Especially for antibody treatment and vaccine development, humoral immunity to SARS-CoV-2 has been studied extensively, though there is still much that is unknown and controversial. Here, we introduce key discoveries on the humoral immune responses in COVID-19, including the immune dynamics of antibody responses and correlations with disease severity, neutralizing antibodies and their cross-reactivity, how long the antibody and memory B-cell responses last, aberrant autoreactive antibodies generated in COVID-19 patients, and the efficacy of currently available therapeutic antibodies and vaccines against circulating SARS-CoV-2 variants, and highlight gaps in the current knowledge.