• Journal of the Korea Academia-Industrial cooperation Society
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• v.13 no.5
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• pp.2133-2141
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• 2012

The Purpose of this study was to report magnetic resonance imaging(MRI) changes of six cases of Lumbar herniated disc which was treated with spinal decompression therapy, oriental medical therapy. 'Disc heights' were measured on sagittal view of $T_2$-weighted MRI. Size of the herniated disc was measured by MRI and 'disc herniations index'. The grading system and algorithm for 'disc degeneration' were based on MRI signal intensity, disc structure, distinction between nucleus and anulus, and disc height. Data for disc height, disc herniations index and disc degeneration grade were collected before and after the treatment together with calculation from the MRI. Case studies were showed that decompression therapy for the herniated disc has an positive effect on disc herniations index, disc regeneration, not disc heights. The clinical evidence for the use of decompression in herniated disc remains inconclusive because of limited researches. Further trials, which give attention to these areas, are needed before any firm conclusions may be made.

• Journal of KIISE:Computing Practices and Letters
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• v.7 no.6
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• pp.724-732
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• 2001

In this paper, a new method of feature extraction of major objects to represent an image using Vector Quantization(VQ) is proposed. The principal features of the image, which are used in a content-based image retrieval system, are color, texture, shape and spatial positions of objects. The representative color and texture features are extracted from the given image using VQ(Vector Quantization) clustering algorithm with a general feature extraction method of color and texture. Since these are used for content-based image retrieval and searched by objects, it is possible to search and retrieve some desirable images regardless of the position, rotation and size of objects. The experimental results show that the representative feature extraction time is much reduced by using VQ, and the highest retrieval rate is given as the weighted values of color and texture are set to 0.5 and 0.5, respectively, and the proposed method provides up to 90% precision and recall rate for 'person'query images.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.11
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• pp.3876-3895
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• 2014

Recently, Heterogeneous Network (HetNet) with Coordinated Multi-Point (CoMP) scheme is introduced into Long Term Evolution-Advanced (LTE-A) systems to improve digital services for User Equipments (UEs), especially for cell-edge UEs. However, Radio Resource Management (RRM), including Resource Block (RB) scheduling and Power Allocation (PA), in this scenario becomes challenging, due to the intercell cooperation. In this paper, we investigate the RRM problem for downlink transmission of HetNet system with Joint Processing (JP) CoMP (both joint transmission and dynamic cell selection schemes), aiming at maximizing weighted sum data rate under the constraints of both transmission power and backhaul capacity. First, joint RB scheduling and PA problem is formulated as a constrained Mixed Integer Programming (MIP) which is NP-hard. To simplify the formulation problem, we decompose it into two problems of RB scheduling and PA. For RB scheduling, we propose an algorithm with less computational complexity to achieve a suboptimal solution. Then, according to the obtained scheduling results, we present an iterative Karush-Kuhn-Tucker (KKT) method to solve the PA problem. Extensive simulations are conducted to verify the effectiveness and efficiency of the proposed algorithms. Two kinds of JP CoMP schemes are compared with a non-CoMP greedy scheme (max capacity scheme). Simulation results prove that the CoMP schemes with the proposed RRM algorithms dramatically enhance data rate of cell-edge UEs, thereby improving UEs' fairness of data rate. Also, it is shown that the proposed PA algorithms can decrease power consumption of transmission antennas without loss of transmission performance.

• The Journal of the Korea Contents Association
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• v.15 no.11
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• pp.1-9
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• 2015

Emotion recognition and regular controls are concentrated interest in computer studies to emotional changes. Thus, the quantified by objective assessment methods are essential for application of color sensibility computing situations. In this paper, it is applied to a digital color image emotion emotional computing calculations numbered recognized as one representation. Emotional computing research approach consists of a color attribute to the image recognition focused sensibility and emotional attributes of color is the color, brightness and saturation separated by. Computes the sensitivity weighted according to the score and the percentage increase or decrease in the sensitivity property tone applied to emotional expression. Sensitivity calculation is free-degree (X), and calculates the tension (Y-axis). And free-level (X-axis) coordinate of emotion, which is located the intersection of the tension (Y-axis) as a sensitivity point. The emotional effect of the Russell coordinates are utilizing the core (Core Affect). Tue numbers represent the size and sensitivity in the emotional relationship between emotional point location and quantified by computing the color sensibility.

• The Bulletin of The Korean Astronomical Society
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• v.36 no.2
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• pp.129.1-129.1
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• 2011

We have applied an advanced multi-aperture indexing photometry and sophisticated de-trending method to existing Taiwanese-American Occultation Survey (TAOS) data sets. TAOS, a wide-field ($3^{\circ}{\times}3^{\circ}$) and rapid photometry (5Hz) survey, is designed to detect small objects in the Kuiper Belt. Since TAOS has fast and multiple exposures per zipper mode image, point spread function (PSF) varies in a given image. Selecting appropriate aperture among various size apertures allows us to reflect these variations in each light curve. The survey data turned out to contain various trends such as telescope vibration, CCD noise, and unstable local weather. We select multiple sets of stars using a hierarchical clustering algorithm in such a way that the light curves in each cluster show strong correlations between them. We then determine a primary trend (PT) per cluster using a weighted sum of the normalized light curves, and we use the constructed PTs to remove trends in individual light curves. After removing the trend, we can get each synthetic light curve of star that has much higher signal-to-noise ratio. We compare the efficiency of the synthetic light curves with the efficiency of light curves made by previous existing photometry pipelines. Our photometric method is able to restore subtle brightness variation that tends to be missed in conventional aperture photometric methods, and can be applied to other wide-field surveys suffering from PSF variations and trends. We are developing an analysis package for the next generation TAOS survey (TAOS II) based on the current experiments.

• Journal of the Korean Institute of Intelligent Systems
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• v.15 no.4
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• pp.400-405
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• 2005

This paper analyzes the performance of a face recognition algorithm using the eigenfaces method. In the absence of robust personal recognition schemes, a biometric recognition system has essentially researched to improve their shortcomings. A face recognition system in biometries is widely researched in the field of computer vision and pattern recognition, since it is possible to comprehend intuitively our faces. The proposed system projects facial images onto a feature space that effectively expresses the significant variations among known facial images. The significant features are known as 'eigenfaces', because they are the eigenvectors(principal components) of the set of faces. The projection operation characterizes an individual face by a weighted sum of the eigenface features, and to recognize a particular face it is necessary only to compare these weights to those of known individuals. In order to analyze the performance of the system, we develop a face recognition system by using Harvard database in Harvard Robotics Laboratory. We present the recognition rate according to variations on the lighting condition, numbers of the employed eigenfaces, and existence of a pre-processing step. Finally, we construct a rejection curve in order to investigate the practicability of the recognition method using the eigenfaces.

• Journal of the Society of Naval Architects of Korea
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• v.49 no.6
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• pp.461-468
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• 2012

In this study, a Cartesian-grid method based on finite volume approach is applied to simulate the ship motions in large amplitude waves. Fractional step method is applied for pressure-velocity coupling and TVD limiter is used to interpolate the cell face value for the discretization of convective term. Water, air, and solid phases are identified by using the concept of volume-fraction function for each phase. In order to capture the interface between air and water, the tangent of hyperbola for interface capturing (THINC) scheme is used with weighed line interface calculation (WLIC) method which considers multidimensional information. The volume fraction of solid body embedded in the Cartesian grid system is calculated using a level-set based algorithm, and the body boundary condition is imposed by a volume weighted formula. Numerical simulations for the two-dimensional barge type model and Wigley hull in linear waves have been carried out to validate the newly developed code. To demonstrate the applicability for highly nonlinear wave-body interactions such as green water on the deck, numerical analysis on the large-amplitude motion of S175 containership is conducted and all computational results are compared with experimental data.

• Journal of Computational Design and Engineering
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• v.2 no.3
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• pp.157-164
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• 2015

This paper discusses an optimization-based approach for the design of a product platform for industrial three-axis linear-type robots, which are widely used for handling objects in manufacturing lines. Since the operational specifications of these robots, such as operation speed, working distance and orientation, weight and shape of loads, etc., will vary for different applications, robotic system vendors must provide various types of robots efficiently and effectively to meet a range of market needs. A promising step toward this goal is the concept of a product platform, in which several key elements are commonly used across a series of products, which can then be customized for individual requirements. However the design of a product platform is more complicated than that of each product, due to the need to optimize the design across many products. This paper proposes an optimization-based fundamental framework toward the design of a product platform for industrial three-axis linear-type robots; this framework allows the solution of a complicated design problem and builds an optimal design method of fundamental features of robot frames that are commonly used for a wide range of robots. In this formulation, some key performance metrics of the robot are estimated by a reducedorder model which is configured with beam theory. A multi-objective optimization problem is formulated to represent the trade-offs among key design parameters using a weighted-sum form for a single product. This formulation is integrated into a mini-max type optimization problem across a series of robots as an optimal design formulation for the product platform. Some case studies of optimal platform design for industrial three-axis linear-type robots are presented to demonstrate the applications of a genetic algorithm to such mathematical models.

• Journal of KIISE
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• v.42 no.12
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• pp.1611-1622
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• 2015

Although Android systems provide a permission-based access control mechanism and demand a user to decide whether to install an app based on its permission list, many users tend to ignore this phase. Thus, an improved method is necessary for users to intuitively make informed decisions when installing a new app. In this paper, with regard to the permission-based access control system, we present a novel approach based on a machine-learning technique in order to support a user decision-making on the fly. We apply the K-NN (K-Nearest Neighbors) classification algorithm with necessary weighted modifications for malicious app classification, and use 152 Android permissions as features. Our experiment shows a superior classification result (93.5% accuracy) compared to other previous work. We expect that our method can help users make informed decisions at the installation step.

• Journal of the Korean Institute of Intelligent Systems
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• v.13 no.6
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• pp.635-642
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• 2003

Reinforcement learning got successful results in a lot of applications such as control and scheduling. Various function approximation methods have been studied in order to improve the learning speed and to solve the shortage of storage in the standard reinforcement learning algorithm of Q-Learning. Most function approximation methods remove some special quality of reinforcement learning and need prior knowledge and preprocessing. Fuzzy Q-Learning needs preprocessing to define fuzzy variables and Local Weighted Regression uses training examples. In this paper, we propose a function approximation method, Fuzzy Q-Map that is based on on-line fuzzy clustering. Fuzzy Q-Map classifies a query state and predicts a suitable action according to the membership degree. We applied the Fuzzy Q-Map, CMAC and LWR to the mountain car problem. Fuzzy Q-Map reached the optimal prediction rate faster than CMAC and the lower prediction rate was seen than LWR that uses training example.