• The Journal of Korean Medicine Ophthalmology and Otolaryngology and Dermatology
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• v.23 no.1
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• pp.260-269
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• 2010

Background : Bojungikgitang(BJT) and Banhabaekchulchonmatang(BBT) are known to treat the tinnitus patients, which were registered Korean National Health Insurance coverage lists. Objective : Few studies have evaluated economic benefits of both herbal medicines. This research is to investigate the cost-effectiveness of Bojungikgitang(BJT) and Banhabaekchulchonmatang(BBT) in chronic tinnitus patients over nineteen years old. Method : We built the decision tree model of chronic tinnitus and executed the deterministic analysis and threshold sensitivity analysis based on randomized clinical trial. Effectiveness was measured in quality-adjusted life-years(QALYs), and costs were in 2009 KRW(South Korean Currency). The perspective is societal, time horizon is 10 weeks, and Korean willingness to pay threshold is assumed to 20,000,000KRW. Results : In the base case analysis, BJT treatment resulted is better outcomes as low cost, so BJT is dominant medicine and BBT is dominated. But both cost per QALYs (BJT is 3,120,339KWN per QALY, BBT is 3,505,780KWN per QALY) are lower than the threshold, that could be covered by Korean National Health Insurance(KNHI). Conclusion : This study results showed that BJT was more cost-effective than BBT treating tinnitus patients for 10 weeks, and the cost per QALYs of both alternatives were lower than Korean national threshold.

• Journal of the Korea Institute of Military Science and Technology
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• v.24 no.3
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• pp.281-292
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• 2021

With the development of deep learning technology, researchers and technicians keep attempting to apply deep learning in various industrial and academic fields, including the defense. Most of these attempts assume that the data are balanced. In reality, since lots of the data are imbalanced, the classifier is not properly built and the model's performance can be low. Therefore, this study proposes cost-sensitive learning as a solution to the imbalance data problem of image classification in the defense field. In the proposed model, cost-sensitive learning is a method of giving a high weight on the cost function of a minority class. The results of cost-sensitive based model shows the test F1-score is higher when cost-sensitive learning is applied than general learning's through 160 experiments using submarine/non-submarine dataset and warship/non-warship dataset. Furthermore, statistical tests are conducted and the results are shown significantly.

• Journal of the Earthquake Engineering Society of Korea
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• v.9 no.2 s.42
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• pp.17-27
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• 2005

This paper presents an integrated optimum design and a cost effectiveness evaluation method of a viscoelastically damped structural system. The criterion selected for the optimization is the minimization of the life?cycle cost which is a function of structural sizing variables and the amount of the viscoelastic damper. A genetic algorithm is used as a numerical searching technique in order to simultaneously find the optimum parameters of the integrated system. Optimal distributions of design variables according to various seismic characteristics are investigated by applying the proposed design method to a numerical example of a 10?story building structure. The cost effectiveness of viscoelastically damped structural system is also evaluated by comparing the life-cycle cost of the structure without viscoelastic dampers. The results show that the viscoelastic damper is effective in a region of low to moderate seismicity.

• Nuclear Engineering and Technology
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• v.51 no.3
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• pp.807-817
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• 2019

Airborne Gamma Ray Spectrometry (AGRS) with its important applications such as gathering radiation information of ground surface, geochemistry measuring of the abundance of Potassium, Thorium and Uranium in outer earth layer, environmental and nuclear site surveillance has a key role in the field of nuclear science and human life. The Broyden-Fletcher-Goldfarb-Shanno (BFGS), with its advanced numerical unconstrained nonlinear optimization in collaboration with Artificial Neural Networks (ANNs) provides a noteworthy opportunity for modern AGRS. In this study a new AGRS system empowered by ANN-BFGS has been proposed and evaluated on available empirical AGRS data. To that effect different architectures of adaptive ANN-BFGS were implemented for a sort of published experimental AGRS outputs. The selected approach among of various training methods, with its low iteration cost and nondiagonal scaling allocation is a new powerful algorithm for AGRS data due to its inherent stochastic properties. Experiments were performed by different architectures and trainings, the selected scheme achieved the smallest number of epochs, the minimum Mean Square Error (MSE) and the maximum performance in compare with different types of optimization strategies and algorithms. The proposed method is capable to be implemented on a cost effective and minimum electronic equipment to present its real-time process, which will let it to be used on board a light Unmanned Aerial Vehicle (UAV). The advanced adaptation properties and models of neural network, the training of stochastic process and its implementation on DSP outstands an affordable, reliable and low cost AGRS design. The main outcome of the study shows this method increases the quality of curvature information of AGRS data while cost of the algorithm is reduced in each iteration so the proposed ANN-BFGS is a trustworthy appropriate model for Gamma-ray data reconstruction and analysis based on advanced novel artificial intelligence systems.

• IEIE Transactions on Smart Processing and Computing
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• v.1 no.1
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• pp.8-16
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• 2012

An image obtained from a low light environment results in a low-exposure problem caused by non-ideal camera settings, i.e. aperture size and shutter speed. Of particular note, the multiple color-filter aperture (MCA) system inherently suffers from low-exposure problems and performance degradation in its image classification and registration processes due to its finite size of the apertures. In this context, this paper presents a novel method for the color enhancement of low-exposure images and its application to color shift model-based MCA system for image refocusing. Although various histogram equalization (HE) approaches have been proposed, they tend to distort the color information of the processed image due to the range limits of the histogram. The proposed color enhancement algorithm enhances the global brightness by analyzing the basic cause of the low-exposure phenomenon, and then compensates for the contrast degradation artifacts by using an adaptive histogram specification. We also apply the proposed algorithm to the preprocessing step of the refocusing technique in the MCA system to enhance the color image. The experimental results confirm that the proposed method can enhance the contrast of any low-exposure color image acquired by a conventional camera, and is suitable for commercial low-cost, high-quality imaging devices, such as consumer-grade camcorders, real-time 3D reconstruction systems, digital, and computational cameras.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.4
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• pp.1732-1750
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• 2016

Recently, Peer-to-Peer (P2P) live streaming assisted by the cloud computing has attracted considerable attention to improve the reliability of the P2P such as the resilience to peer churn and the shortage of upload capacity. The cost of cloud-assistance is comprised of the number of requests issued to the cloud and the amount of data fetched from the cloud. In this paper, we propose three techniques to reduce the cost of such a cloud-assistance.More concretely, in the proposed method, 1) each peer which lost its parent in the overlay can find a new parent by referring to the information registered in the cloud, 2) several peers which proactively fetch chunks from the cloud are dynamically invested, and 3) the number of requests issued to the cloud is reduced by allowing peers to fetch a collection of chunks using a single request. The performance of the proposed method is evaluated by simulation. The simulation results indicate that it reduces the cost of conventional scheme by 46% while guaranteeing the quality of live streaming service.

• Journal of the Korean Society of Safety
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• v.17 no.3
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• pp.30-35
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• 2002

The magnetic abrasive polishing is the useful method to finish using magnetic power of a magnet. It's not a long time this method was introduced to korea as one of precision finishing techniques. The magnetic abrasive polishing method is not so common for machine that it is not spreaded widely. The are rarely researcher in this field because of no-effectiveness of magnetic abrasive. The mechanism of this R&D is dealing with the dynamic state of magnet-abusive. This paper deals with mediocritizing magnetic polishing device into regular lathe and this experiment was conducted in order to get the best surface roughness at low cost. We need to continue the research on it. This paper contains the result of experiment to acquire the best surface roughness, not using the high-cost polishing material in processing. The average diameters of magnetic abrasive are the particles of 150$\mu\textrm{m}$, 250$\mu\textrm{m}$.

• IEMEK Journal of Embedded Systems and Applications
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• v.9 no.3
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• pp.173-182
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• 2014

The CRT monitor display system for SONAR signal that are commonly used in ships or naval vessels uses vector scanning method. Therefore the processing circuits of the system are complex. Also the purchase of parts is difficult as well as high-cost because the production had been shut down. FPGA-based embedded system is flexible to various digital applications because it can be able to simplify processing circuits and to make a easy customized design for end user, and it provides low-cost high-speed performance. In this paper, we describe an implementation of FPGA embedded system for real-time SONAR signal display using the triple buffering method to overcome some weakness of existing CRT system. Our system provides real-time acquisition and display capability of SONAR signal, and removes afterimage effect that is a critical problem of the system proposed in the preceding study.

• Proceedings of the KIPE Conference
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• 2005.07a
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• pp.750-753
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• 2005

This paper presents a method of detecting rotor position for single phase Switched Reluctance Motors(SRMs) using searchs coil. In the single phase SRM, mainly Hall effect sensors or photo interrupters have been used to detect the rotor position. But these sensors cause high cost and increase the volume of the motor, Furthermore when the motor is used in very hostile environment like high temperature or pressure, its reliability goes down. In this paper, low cost and robust characteristics of rotor position detection method are focused in order to compensate for disadvantage of existing sensors. Search coils wound around the stator pole are used for detection of the rotor position in single phase SRM. Rotor position detection is achieved through electromotive force patterns induced by time-varying flux linkage in the search coil. The validity of the method is verified by experimental results.

• The Transactions of the Korean Institute of Power Electronics
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• v.21 no.2
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• pp.144-149
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• 2016

This paper presents a power control method of high-speed single-phase BLDC motor. Most electric appliances require a power factor corrector (PFC) to mitigate grid current harmonics. However, the reactive components and power semiconductors in the PFC increase system cost and dimension. In this paper, a new motor drive system for a high-speed single-phase BLDC motor is proposed, which can decrease grid current harmonics without PFC by directly manipulating motor power and eliminating bulky electrolytic dc-link capacitor. Given that the proposed motor power control method does not require motor current controller, no current sensor is necessary. Moreover, the proposed algorithms can be easily implemented using a low-cost micro-controller. The effectiveness of the proposed power control method is verified by experiments.