• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.11 no.4
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• pp.514-522
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• 2000

In this thesis, a two-dimensional active phased array antenna without phase shifter is studied for two-dimensional beam scanning. A designed two-dimensional oscillator-type active array antenna, radiation elements and the oscillator circuits were combined with via-hole and coupled by slot on the opposite ground plane. The operating characteristics are analyzed and experimentally demonstrated , The two-dimensional $4\times4$ elements were designed for the proper coupling strengths and coupling phases by adjusting the width, length and offset position of slot-lines. The fabricated active phased array antenna shows the beam shift characteristics capable of scanning from $-17^{\circ}$ to $18^{\circ}$ with respect to broadside in one dimension, from $-5^{\circ}$ to $10^{\circ}$ in two dimension. The experimental results show that it is possible to use the oscillator-type active phased array antenna as a two-dimensional planar array antenna.

• The KIPS Transactions:PartD
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• v.15D no.4
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• pp.531-540
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• 2008

An Adaptive Random Testing(ART) is one of test case generation algorithms, which was designed to get better performance in terms of fault-detection capability than that of Random Testing(RT) algorithm by locating test cases in evenly spreaded area. Two ART algorithms, such as Distance-based ART(D-ART) and Restricted Random Testing(RRT), had been indicated that they have significant drawbacks in computations, i.e., consuming quadratic order of runtime. To reduce the amount of computations of D-ART and RRT, iterative partitioning of input domain strategy was proposed. They achieved, to some extent, the moderate computation cost with relatively high performance of fault detection. Those algorithms, however, have yet the patterns of non-uniform distribution in test cases, which obstructs the scalability. In this paper we analyze the distribution of test cases in an iterative partitioning strategy, and propose a new method of input domain enlargement which makes the test cases get much evenly distributed. The simulation results show that the proposed one has about 3 percent of improvement in terms of mean relative F-measure for 2-dimension input domain, and shows 10 percent improvement for 3-dimension space.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.34 no.3
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• pp.219-228
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• 2010

A two-dimensional mathematical model was developed to predict the temperature and moisture-content profiles of a tumble dryer during infrared drying. The model is based on the movements of liquid water and moisture in the object and on the fluid and heat transfer in the drying air. The model was solved by the finite volume analysis for the fluid, temperature, and radiation intensity fields. After deriving the governing equations and developing the two-dimensional tumble dryer models, numerical investigations were carried out to examine the effects of various parameters such as the heater temperature and the heating patterns on the drying mechanism of the tumble dryer. All the results show that the drying time can be reduced by using the IR heater.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.16 no.2
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• pp.921-926
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• 2015

Embossed aluminum sheets were been used in heat insulation purpose for automative exhaust parts because of increasing their surface areas and stiffness reinforcement. However, there are many restrictions because of high rate of wrinkle occurrence on press working. We have performed the tensile and bending tests for embossed sheets to clarity its mechanical properties and springback characteristics. Embossed aluminum sheets showed a different flow stress after plastic yielding due to flattening the embossed cone shape. Above all, yield stress of parallel embossed specimen decreases while its diagonal one increases and the decrease of young's modulus in the embossed sheets contributes to the increase of springback amount.

• Journal of the Korea Society of Computer and Information
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• v.26 no.3
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• pp.35-42
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• 2021

In this paper, we propose a deep learning-based person re-identification method using a three-dimensional RGB-Depth Xtion2 camera considering joint coordinates and dynamic features(velocity, acceleration). The main idea of the proposed identification methodology is to easily extract gait data such as joint coordinates, dynamic features with an RGB-D camera and automatically identify gait patterns through a self-designed one-dimensional convolutional neural network classifier(1D-ConvNet). The accuracy was measured based on the F1 Score, and the influence was measured by comparing the accuracy with the classifier model (JC) that did not consider dynamic characteristics. As a result, our proposed classifier model in the case of considering the dynamic characteristics(JCSpeed) showed about 8% higher F1-Score than JC.

• Solar Energy
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• v.17 no.3
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• pp.35-41
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• 1997

An experimental study was carried out in a vertical channel with square heat source by visualization equipment with laser apparatus. The image processing system consists of one commercial image board slit into a personal computer and 2-dimensional sheet light by Argon-Ion Laser with cylindrical lens and flow picture recording system. Instant simultaneous velocity vectors at whole field were measured by 2-D PIV system which adopted two-frame grey-level cross correlation algorithm. Heat source was uniform heat flux(5W). The obtained results show various flow patterns such as the kinetic energy distribution and the turbulent kinetic energy distribution.

• Journal of Korea Society of Industrial Information Systems
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• v.23 no.2
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• pp.1-9
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• 2018

In this paper, we propose a face recognition method which is not sensitive to illumination change and prevents false recognition of photographs. The proposed method uses infrared and depth images at the same time, solves sensitivity of illumination change by infrared image, and prevents false recognition of two - dimensional image such as photograph by depth image. Face detection method using infrared and depth images simultaneously and feature extraction and matching method for face recognition are realized. Simulation results show that accuracy of face recognition is increased compared to conventional methods.

• IEMEK Journal of Embedded Systems and Applications
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• v.17 no.4
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• pp.191-197
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• 2022

This study proposes an image-based Pose Intention Network (PIN) algorithm for rehabilitation via patients' intentions. The purpose of the PIN algorithm is for enabling an active rehabilitation exercise, which is implemented by estimating the patient's motion and classifying the intention. Existing rehabilitation involves the inconvenience of attaching a sensor directly to the patient's skin. In addition, the rehabilitation device moves the patient, which is a passive rehabilitation method. Our algorithm consists of two steps. First, we estimate the user's joint position through the OpenPose algorithm, which is efficient in estimating 2D human pose in an image. Second, an intention classifier is constructed for classifying the motions into three categories, and a sequence of images including joint information is used as input. The intention network also learns correlations between joints and changes in joints over a short period of time, which can be easily used to determine the intention of the motion. To implement the proposed algorithm and conduct real-world experiments, we collected our own dataset, which is composed of videos of three classes. The network is trained using short segment clips of the video. Experimental results demonstrate that the proposed algorithm is effective for classifying intentions based on a short video clip.

• Journal of the Korean Institute of Intelligent Systems
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• v.22 no.2
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• pp.135-140
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• 2012

The mirror neuron system in the cerebrum, which are handled by visual information-based imitative learning. When we observe the observer's range of mirror neuron system, we can assume intention of performance through progress of neural activation as specific range, in include of partially hidden range. It is goal of our paper that imitative learning is applied to 3D vision-based intelligent system. We have experiment as stereo camera-based restoration about acquired 3D image our previous research Using Optical flow, unscented Kalman filter. At this point, 3D input image is sequential continuous image as including of partially hidden range. We used Hidden Markov Model to perform the intention recognition about performance as result of restoration-based hidden range. The dynamic inference function about sequential input data have compatible properties such as hand gesture recognition include of hidden range. In this paper, for proposed intention recognition, we already had a simulation about object outline and feature extraction in the previous research, we generated temporal continuous feature vector about feature extraction and when we apply to Hidden Markov Model, make a result of simulation about hand gesture classification according to intention pattern. We got the result of hand gesture classification as value of posterior probability, and proved the accuracy outstandingness through the result.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.13 no.9
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• pp.1905-1912
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• 2009

Clustering results based on the FCM algorithm sometimes produces undesirable clustering result through data distribution in the clustered space because data is classified by comparison with membership degree which is calculated by the Euclidean distance between input vectors and clusters. Symmetrical measurement of clusters and fuzzy theory are applied to the classification to tackle this problem. The enhanced FCM algorithm has a low impact with the variation of changing distance about each cluster, middle of cluster and cluster formation. Improved hybrid network of applying FCM algorithm is proposed to classify patterns effectively. The proposed enhanced FCM algorithm is applied to the learning structure between input and middle layers, and normalized delta learning rule is applied in learning stage between middle and output layers in the hybrid network. The proposed algorithms compared with FCM-based RBF network using Max_Min neural network, FMC-based RBF network and HCM-based RBF network to evaluate learning and recognition performances in the two-dimensional coordinated data.