• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.17 no.4
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• pp.386-395
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• 2005

With the development of a living standard, the importance of indoor air conditioning system in all kinds of buildings and vehicles has increased. A lot of researches on energy losses in a duct and various kinds of flow pattern in branches or junctions have been carried out over many years, because the primary object of a duct system used in HVAC is to provide equal flow rate in the interior of each room by minimizing pressure drop. In this study, to get equal flow distribution in each branch, a blockage is applied to the rectangular duct system. The flow analysis for flow distribution of a rectangular duct with two branches was performed by CFD. By using SIMPLE algorithm and finite volume method, flow analysis is performed in the case of 3-D, incompressible, turbulent flow. Also, the standard $k-{\varepsilon}$ model and wall function method were used for analysis of turbulent fluid flow. The distribution diagrams of static pressure, velocity vector, turbulent energy and kinetic energy in accordance with variation of Reynolds number and blockages location in a rectangular duct show that flow distribution at duct outlets is improved by a blockage. In this rectangular duct system, mean velocity and flow rate distribution in two branch outlets are nearly constant regardless of variation of Reynolds number, and a flow pattern of the internal duct has a same tendency as well.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.44 no.6
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• pp.16-23
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• 2007

Changeable biometrics has been developed as a solution to problem of enhancing security and privacy. The idea is to transform a biometric signal or feature into a new one for the purposes of enrollment and matching. In this paper, we propose a changeable biometric system that can be applied to appearance based face recognition system. In the first step when using feature extraction, ICA(Independent Component Analysis) coefficient vectors extracted from an input face image are replaced randomly using their mean and variation. The transformed vectors by replacement are scrambled randomly and a new transformed face coefficient vector (transformed template) is generated by combination of the two transformed vectors. When this transformed template is compromised, it is replaced with new random numbers and a new scrambling rule. Because e transformed template is generated by e addition of two vectors, e original ICA coefficients could not be easily recovered from the transformed coefficients.

• The KIPS Transactions:PartC
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• v.10C no.7
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• pp.885-890
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• 2003

The Core Base Tree (CBT) multicast routing architecture is a multicast routing protocol for the internet. The CBT establishes a single shared tree for a multicast connection. The shared tree Is rooted at a center node called core. The location of the core may affect the cost and performance of the CBT. The core placement method requires the knowledge of the network topology In this Paper, we propose a simple and effective method for selecting the core. This method requires the distance vector information. in addition, we used results that calculated sample correlation coefficient. And then we select suitable routing algorithm according to member's arrangement states in muliticast group. we select core node that have minimum average cost or PIM-SM protocol is selected. The performance of this method is compared with several other methods by extensive simulations (i.e mean delay, maximum delay, and total cost). Our results shows that this method for Selecting Core is very effective.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.34 no.9C
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• pp.871-879
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• 2009

The performance variations of a spatio temporal smart antenna which is equipped at the basestation of CDMA cellular communication network due to the parametric change of multipath fading environment are studied in this paper. The smart antenna of interest employs space diversity based adaptive array structure in conjunction with rake receiver that has fingers the number of which is the same as that of multipath links. The beamforming is achieved via LMS(Least Mean Square) algorithm in which a reference signal is generated using decision directed formula. It has been shown by computer simulation that the performance of our smart antenna of interest depends significantly upon not only the degree of desired signal's DOA(Direction of Arrival)spread but the number of fingers of the rake receiver. The relative insensitivity of the smart antenna's performance on desired signal's delay spread has also been observed. Computer simulation has shown that the increase of the number of fingers brings in a nonlinear enhancement of the performance of our smart antenna. The renewal of weight vector in the beamforming procedure is taken place at post PN despread stage.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.28 no.9
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• pp.355-360
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• 2016

Continuing to the modeling of heating load, this paper, as the second part of consecutive works, presents LS-SVM (least square support vector machine) based model of winter time apartment hot water supply load in a district heating system, so as to be used in prediction of heating energy usage. Similar, but more severely, to heating load, hot water supply load varies in highly nonlinear manner. Such nonlinearity makes analytical model of it hardly exist in the literatures. LS-SVM is known as a good modeling tool for the system, especially for the nonlinear system depended by many independent factors. We collect 26,208 data of hot water supply load over a 13-week period in winter time, from 12 heat exchangers in seven different apartments. Then part of the collected data were used to construct LS-SVM based model and the rest of those were used to test the formed model accuracy. In modeling, we first constructed the model of district heating system's hot water supply load, using the unit heating area's hot water supply load of seven apartments. Such model will be used to estimate the total hot water supply load of which the district heating system needs to provide. Then the individual apartment hot water supply load model is also formed, which can be used to predict and to control the energy consumption of the individual apartment. The results obtained show that the total hot water supply load, which will be provided by the district heating system in winter time, can be predicted within 10% in MAPE (mean absolute percentage error). Also the individual apartment models can predict the individual apartment energy consumption for hot water supply load within 10% ~ 20% in MAPE.

• Journal of The Korean Astronomical Society
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• v.51 no.5
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• pp.143-153
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• 2018

We present the first results of the invariant point (IVP) coordinates of the KVN Ulsan and Tamna radio telescopes. To determine the IVP coordinates in the geocentric frame (ITRF2014), a coordinate transformation method from the local frame, in which it is possible to survey using the optical instrument, to the geocentric frame was adopted. The least-square circles are fitted in three dimensions using the Gauss-Newton method to determine the azimuth and elevation axes in the local frame. The IVP in the local frame is defined as the mean value of the intersection points of the azimuth axis and the orthogonal vector between the azimuth and elevation axes. The geocentric coordinates of the IVP are determined by obtaining the seven transformation parameters between the local frame and the east-north-up (ENU) geodetic frame. The axis-offset between the azimuth and elevation axes is also estimated. To validate the results, the variation of coordinates of the GNSS station installed at KVN Ulsan was compared to the movement of the IVP coordinates over 9 months, showing good agreement in both magnitude and direction. This result will provide an important basis for geodetic and astrometric applications.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• v.1
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• pp.141-144
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• 2006

The deviations in the injection orbital parameters, resulting from launcher dispersions, need to be estimated and used for autonomous satellite operations. For the proposed small satellite mission of the university there will be two GPS receivers onboard the satellite to provide the instantaneous orbital state to the onboard data handling system. In order to meet the power requirements, the satellite will be sun-tracking whenever there is no imaging operation. For imaging activities, the satellite will be maneuvered to nadir-pointing mode. Due to such different modes of orientation the geometry for the GPS receivers will not be favorable at all times and there will be instances of poor geometry resulting in no output from the GPS receivers. Onboard the satellite, the orbital information should be continuously available for autonomous switching on/off of various subsystems. The paper presents the strategies to make use of small arcs of data from GPS receivers to compute the mean orbital parameters and use the updated orbital parameters to calculate the position and velocity whenever the same is not available from GPS receiver. Thus the navigation message from the GPS receiver, namely the position vector in Earth-Centered-Earth-Fixed (ECEF) frame, is used as measurements. As for estimation, two techniques - (1) batch least squares method, and (2) Kalman Filter method are used for orbit estimation (in real time). The performance of the onboard orbit estimation has been assessed based on hardware based multi-channel GPS Signal simulator. The results indicate good converge even with short arcs of data as the GPS navigation data are generally very accurate and the data rate is also fast (typically 1Hz).

• Journal of the Korea Institute of Information and Communication Engineering
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• v.13 no.10
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• pp.2163-2170
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• 2009

We present an automated, energy minimized-based method for Lung parenchyma segmenting Chest Computed Tomography(CT) datasets. Deformable model is used for energy minimized segmentation. Quantitative knowledge including expected volume, shape of Chest CT provides more feature constrain to diagnosis or surgery operation planning. Segmentation subdivides an lung image into its consistent regions or objects. Depends on energy-minimizing, the level detail image of subdivision is carried. Segmentation should stop when the objects or region of interest in an application have been detected. The deformable model that has attracted the most attention to date is popularly known as snakes. Snakes or deformable contour models represent a special case of the general multidimensional deformable model theory. This is used extensively in computer vision and image processing applications, particularly to locate object boundaries, in the mean time a new type of external force for deformable models, called gradient vector flow(GVF) was introduced by Xu. Our proposed algorithm of deformable model is new external energy of GVF for exact segmentation. In this paper, Clinical material for experiments shows better results of proposal algorithm in Lung parenchyma segmentation on Chest CT.

• Journal of Physiology & Pathology in Korean Medicine
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• v.20 no.1
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• pp.224-228
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• 2006

The pattern identification of exterior-interior syndrome and cold-heat syndrome is one of the diagnostic methods using most frequently in Oriental medicine. There was no systematic studies analyzing the characteristics of the 'exterior-interior and cold-heat' between healthy and disease group. In this study, cold-heat pattern, blood pressure, pulse rate, height and weight are recorded from 100 healthy subjects and 196 disease subjects with age ranging from 30 to 59 years. To analyze the differences between healthy and disease group, we used the descriptive statistics. And linear regression function, linear support vector machine and bayesian classifier were used for distinguishing healthy group from disease group. The score of both exterior-heat and interior-cold in healthy group is higher than the score in disease group. This means that if one belongs to the disease group, his(or her) exterior gets cold and his interior gets hot. And also, these result have no relevance to age. But, the attempt to classify healthy group from disease group with a exterior-interior and cold-heat and other vital signs did not have good performance. It mean that even though they have a different trend each other, only these kinds of information couldn't classify healthy group and disease group.

• Journal of Biomedical Engineering Research
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• v.40 no.2
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• pp.62-67
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• 2019

The purpose of this study was to evaluate the performance of deep neural network model in order to determine whether there is a risk factor for coronary artery disease based on the cardiac variation parameter. The study used unidentifiable 297 data to evaluate the performance of the model. Input data consists of heart rate parameters, which are SDNN (standard deviation of the N-N intervals), PSI (physical stress index), TP (total power), VLF (very low frequency), LF (low frequency), HF (high frequency), RMSSD (root mean square of successive difference) APEN (approximate entropy) and SRD (successive R-R interval difference), the age group and sex. Output data are divided into normal and patient groups, and the patient group consists of those diagnosed with diabetes, high blood pressure, and hyperlipidemia among the various risk factors that can cause coronary artery disease. Based on this, a binary classification model was applied using Deep Neural Network of deep learning techniques to classify normal and patient groups efficiently. To evaluate the effectiveness of the model used in this study, Kernel SVM (support vector machine), one of the classification models in machine learning, was compared and evaluated using same data. The results showed that the accuracy of the proposed deep neural network was train set 91.79% and test set 85.56% and the specificity was 87.04% and the sensitivity was 83.33% from the point of diagnosis. These results suggest that deep learning is more efficient when classifying these medical data because the train set accuracy in the deep neural network was 7.73% higher than the comparative model Kernel SVM.