• Journal of Biomedical Engineering Research
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• v.19 no.2
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• pp.119-128
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• 1998

This paper describes a filtering algorithm which employs apriori information of SPECT lesion detectability potential for the filtering of degraded projection images prior to the backprojection reconstruction. In this algorithm, we determined m minimum detectable lesion sized(MDLSs) by assuming m object contrasts uniformly-chosen in the range of 0.0-1.0, based on a signal/noise model which provides the capability potential of SPECT in terms of physical factors. A best estimate of given projection image is attempted as a weighted combination of the subimages from m optimal filters whose design is focused on maximizing the local S/N ratios for the MDLS-lesions. These subimages show relatively larger resolution recovery effect and relatively smaller noise reduction effect with the decreased MDLS, and the weighting on each subimage was controlled by the difference between the subimage and the maximum-resolution-recovered projection image. The proposed filtering algoritym was tested on SPECT image reconstruction problems, and produced good results. Especially, this algorithm showed the adaptive effect that approximately averages the filter outputs in homogeneous areas and sensitively depends on each filter strength on contrast preserving/enhancing in textured lesion areas of the reconstructed image.

• The Korean Journal of Physiology and Pharmacology
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• v.16 no.6
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• pp.405-411
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• 2012

The spontaneous axon regeneration of damaged neurons is limited after spinal cord injury (SCI). Recently, mesenchymal stem cell (MSC) transplantation was proposed as a potential approach for enhancing nerve regeneration that avoids the ethical issues associated with embryonic stem cell transplantation. As SCI is a complex pathological entity, the treatment of SCI requires a multipronged approach. The purpose of the present study was to investigate the functional recovery and therapeutic potential of human MSCs (hMSCs) and polymer in a spinal cord hemisection injury model. Rats were subjected to hemisection injuries and then divided into three groups. Two groups of rats underwent partial thoracic hemisection injury followed by implantation of either polymer only or polymer with hMSCs. Another hemisection-only group was used as a control. Behavioral, electrophysiological and immunohistochemical studies were performed on all rats. The functional recovery was significantly improved in the polymer with hMSC-transplanted group as compared with control at five weeks after transplantation. The results of electrophysiologic study demonstrated that the latency of somatosensory-evoked potentials (SSEPs) in the polymer with hMSC-transplanted group was significantly shorter than in the hemisection-only control group. In the results of immunohistochemical study, ${\beta}$-gal-positive cells were observed in the injured and adjacent sites after hMSC transplantation. Surviving hMSCs differentiated into various cell types such as neurons, astrocytes and oligodendrocytes. These data suggest that hMSC transplantation with polymer may play an important role in functional recovery and axonal regeneration after SCI, and may be a potential therapeutic strategy for SCI.

• Journal of Korea Technology Innovation Society
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• v.12 no.1
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• pp.189-211
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• 2009

The importance of searching the weak signal has been increasingly recognized to cope with rapidly changing circumstances as an environmental analysis technique. This study proposed the NEST process for the searching for the weak signal. The NEST (New & Emerging Signals of Trends) is a micro environmental analysis process based on both quantitative and qualitative method. For this, the weak signal Searching Board is developed and traditional methods as global monitoring, trend analysis, brainstorming and delphi method are implemented to NEST. The NEST process is consists of three stage modules; the global monitoring stage in search of seeds information related to the environmental change, the weak signal analysis stage using the weak signal Tracking Board, and the delphi valuation stage for objectifying the final result. The NEST provides the weak signal of the promising technology which can bring new paradigm and the Up-Coming Trends which can lead new trend in the future. These outputs can be used to select promising technology from firm level to national level. The NEST system can be effectively operated as well as in small group so that small and medium innovative firms can develop and execute their own NEST process individually.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.8
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• pp.64-71
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• 2017

A damage estimation method is presented for jacket-type support structure of offshore wind turbine using a change of modal properties due to damage and committee of neural networks for effective structural health monitoring. For more practical monitoring, it is necessary to monitor the critical and prospective damaged members with a limited number of measurement locations. That is, many data channels and sensors are needed to identify all the members appropriately because the jacket-type support structure has many members. This is inappropriate considering economical and practical health monitoring. Therefore, intensive damage estimation for the critical members using a limited number of the measurement locations is carried out in this study. An analytical model for a jacket-type support structure which can be applied for a 5 MW offshore wind turbine is established, and a training pattern is generated using the numerical simulations. Twenty damage cases are estimated using the proposed method. The identified damage locations and severities agree reasonably well with the exact values and the accuracy of the estimation can be improved by applying the committee of neural networks. A verification experiment is carried out, and the damage arising in 3 damage cases is reasonably identified.

• Smart Media Journal
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• v.5 no.2
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• pp.15-23
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• 2016

In this paper we propose the depth-poselets using body-part-poses and also propose the method to recognize the gesture. Since the gestures are composed of sequential poses, in order to recognize a gesture, it should emphasize to obtain the time series pose. Because of distortion and high degree of freedom, it is difficult to recognize pose correctly. So, in this paper we used partial pose for obtaining a feature of the pose correctly without full-body-pose. In this paper, we define the 16 gestures, a depth image using a learning image was generated based on the defined gestures. The depth poselets that were proposed in this paper consists of principal three-dimensional coordinates of the depth image and its depth image of the body part. In the training process after receiving the input defined gesture by using a depth camera in order to train the gesture, the depth poselets were generated by obtaining 3D joint coordinates. And part-gesture HMM were constructed using the depth poselets. In the testing process after receiving the input test image by using a depth camera in order to test, it extracts foreground and extracts the body part of the input image by comparing depth poselets. And we check part gestures for recognizing gesture by using result of applying HMM. We can recognize the gestures efficiently by using HMM, and the recognition rates could be confirmed about 89%.

• Smart Media Journal
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• v.8 no.4
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• pp.58-63
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• 2019

The growth of the mobile device market has changed the education market and led to the quantitative growth of various media education. In particular, smart devices, which have better interaction than existing PCs or consoles, can develop more user-friendly content, allowing various types of educational content and inducing changes in traditional education methods for consumers. Although many researchers recently suggest viable development methods or marketing elements of contents, development companies, and developers, until now, merely rely on the human senses. Therefore, it is necessary to study the actual user's smart-device based usability and experience environment. This study aims to propose an intuitive statistical processing method for analyzing the usability of game-type educational contents in terms of form, for popular games that have been released as a basis for analyzing the user experience environment. In particular, because the game industry has a sufficient number of similar examples, it is possible to conduct research based on big data and to use them for immediate decision-making between multiple co-developers through the analysis method proposed by the research. It is expected to become an analytical model that can communicate with other industries because it is effective in securing data sources.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.1
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• pp.169-177
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• 2020

In South Korea, the results of R&D in science and technology are submitted to the National Science and Technology Information Service (NTIS) in reports that have Korea national science and technology standard classification codes (K-NSCC). However, considering there are more than 2000 sub-categories, it is non-trivial to choose correct classification codes without a clear understanding of the K-NSCC. In addition, there are few cases of automatic document classification research based on the K-NSCC, and there are no training data in the public domain. To the best of our knowledge, this study is the first attempt to build a highly performing K-NSCC classification system based on NTIS report meta-information from the last five years (2013-2017). To this end, about 210 mid-level categories were selected, and we conducted preprocessing considering the characteristics of research report metadata. More specifically, we propose a convolutional neural network (CNN) technique using only task names and keywords, which are the most influential fields. The proposed model is compared with several machine learning methods (e.g., the linear support vector classifier, CNN, gated recurrent unit, etc.) that show good performance in text classification, and that have a performance advantage of 1% to 7% based on a top-three F1 score.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.1
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• pp.190-196
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• 2020

FRACAS(Failure Reporting, Analysis and Corrective Action System) has been applied in various industries to improve the reliability of the systems. FRACAS is effective in improving reliability by repeating failure analysis, proper corrective action, and result verification for identified failures. However, FRACAS has many limitations in terms of process, data collection and management to be integrated into the existing development environment. In the domestic defense industry, studies on the development of FRACAS system and process improvement have been conducted to solve the difficulties of applying FRACAS, but most of them are concentrated in the operation/maintenance phase. Since FRACAS should be conducted in consideration of TLCSM(Total Life Cycle System Management), it is necessary to study the reference architecture so that FRACAS can be applied from the early design phase. In this paper, we studied the TLCSM-based integrated architecture considering the system life cycle phases, FRACAS closed-loop process, and FRACAS essentials in order to effectively apply FRACAS throughout the life cycle of defense systems. The proposed architecture was used as a reference model for FRACAS in a shipboard combat system.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.5
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• pp.723-730
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• 2016

Solar cells in a PV module are connected in series and parallel to produce a higher voltage and current. The PV module has performance characteristics depending on solar radiation and temperature. In addition, the PV system causes power loss by special situations, including the shadows of the surrounding environment, such as nearby buildings and trees. In other words, an increase in power loss and a decrease in life cycle can occur because of the partial shadow and hot-spot effect. Therefore, this paper proposes the optimal configuration algorithm of a bypass diode to improve the output of a PV module and one of a PV array to minimize the loss of the PV array. In addition, this paper presents a model of a PV module and PV array based on the PSIM S/W. The simulation results confirmed that the proposed optimal configuration algorithms are useful tools for improving the performance of PV system.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2010.05a
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• pp.871-874
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• 2010

Needs for Smart Grid development are increasing all over the world as a solution to its problem according to depletion of energy resources, climatic and environmental rapidly change and growing demand for electrical power. Especially decentralized power is attracting world's attention. In this mood a new era for a unit scale of decentralized power environment is on its way in building. However there is a problem to have to be solved in the uniformity of power quality because the amount of power generated from renewable energy resources such as wind power and solar light is very sensitive to climate fluctuation. And thus this paper tries to suggest an energy management method on basis of real time monitoring for meteorological data. In the current situation of lacking in USN-based killer application in Smart Grid field, this paper proposes the USN-based DER management system which collects the meteorological data and control power system througout utilizing wireless sensor network technique this business. This communication technique is regarded to be efficient in aspects of installation cost and tits maintenance cost. The proposed EMS model embodies the method for predicting the power generation by monitoring and analyzing the climatic data and controling the efficient power distribution between the renewable energy and the existing power. The ultimate goal of this paper is to provide the technological basis for achieving zero-energy house.