• Journal of the Institute of Electronics Engineers of Korea TC
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• v.37 no.6
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• pp.404-404
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• 2000

The receiving responses of a bistatic GPR system are analyzed by using three-dimensional FDTD method and equivalent network model. The conventional delta-gap feed model may be inaccurate because of neglecting the impedance matching characteristics between the antenna and the transmission line. In this paper, the feed model is improved by considering the physical characteristics of the actual GPR. The actually received voltage is calculated by employing the equivalent network model in angular frequency-domain, which is composed by using the results of three-dimensional FDTD analysis for an actual bistatic GPR system. The validity of the presented model is assured by showing the convergence of the computed results to the measured data.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.37 no.6
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• pp.44-53
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• 2000

The receiving responses of a bistatic GPR system are analyzed by using three-dimensional FDTD method and equivalent network model. The conventional delta-gap feed model may be inaccurate because of neglecting the impedance matching characteristics between the antenna and the transmission line. In this paper, the feed model is improved by considering the physical characteristics of the actual GPR. The actually received voltage is calculated by employing the equivalent network model in angular frequency-domain, which is composed by using the results of three-dimensional FDTD analysis for an actual bistatic GPR system. The validity of the presented model is assured by showing the convergence of the computed results to the measured data.

• KIPS Transactions on Software and Data Engineering
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• v.10 no.3
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• pp.85-90
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• 2021

In this paper, fall detection using individual wearable devices for older people is considered. To design a low-cost wearable device for reliable fall detection, we present a comprehensive analysis of two representative models. One is a machine learning model composed of a decision tree, random forest, and Support Vector Machine(SVM). The other is a deep learning model relying on a one-dimensional(1D) Convolutional Neural Network(CNN). By considering data segmentation, preprocessing, and feature extraction methods applied to the input data, we also evaluate the considered models' validity. Simulation results verify the efficacy of the deep learning model showing improved overall performance.

• The Journal of Korea Robotics Society
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• v.18 no.2
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• pp.143-154
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• 2023

In this study, a fast motion planning method for the swing motion of a 6x6 wheel-legged robot to traverse large obstacles and gaps is proposed. The motion planning method presented in the previous paper, which was based on trajectory optimization, took up to tens of seconds and was limited to two-dimensional, structured vertical obstacles and trenches. A deep neural network based on one-dimensional Convolutional Neural Network (CNN) is introduced to generate keyframes, which are then used to represent smooth reference commands for the six leg angles along the robot's path. The network is initially trained using the behavioral cloning method with a dataset gathered from previous simulation results of the trajectory optimization. Its performance is then improved through reinforcement learning, using a one-step REINFORCE algorithm. The trained model has increased the speed of motion planning by up to 820 times and improved the success rates of obstacle crossing under harsh conditions, such as low friction and high roughness.

• Journal of the Korean Society for Precision Engineering
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• v.20 no.4
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• pp.112-117
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• 2003

This paper deals with the problem of synthesis of the 3-dimensional Grasp Planning. In previous studies the genetic algorithm has been used to find optimal grasping points, but it had a limitation such as the determination time of grasping points was so long. To overcome this limitation we proposed a new algorithm which employs the Neural Network. In the Neural network we chose input parameters based on the shape of the object and output parameters resulted from optimization with the GA method. In this study the GRNN method is employed, it has been trained by the result value of optimization method and it has been tested by known object. The algorithm is verified by computer simulation.

• Journal of ILASS-Korea
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• v.28 no.3
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• pp.119-125
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• 2023

In this research, a network model was developed to predict combustion instability in an annular gas turbine combustor (GT24) for power generation. The model consisted of various acoustic elements such as several ducts and area changes which could represent a real combustor with a complex geometry, applied mass, momentum, and energy equations to each element. In addition, a one-dimensional network model through a cylindrical coordinate system has been proposed to predict various acoustic modes. As a result of the analysis, the key resonant frequencies such as longitudinal, circumferential, and complex modes were derived from the EV combustor of GT24, and the reliability of the current model was verified through comparison with the 3D Helmholtz solver.

• Journal of Information Processing Systems
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• v.19 no.5
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• pp.576-589
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• 2023

To upgrade home style recommendations and user satisfaction, this paper proposes a personalized and optimized recommendation algorithm for interior design style based on local social network, which includes data acquisition by three-dimensional (3D) model, home-style feature definition, and style association mining. Through the analysis of user behaviors, the user interest model is established accordingly. Combined with the location-based social network of association rule mining algorithm, the association analysis of the 3D model dataset of interior design style is carried out, so as to get relevant home-style recommendations. The experimental results show that the proposed algorithm can complete effective analysis of 3D interior home style with the recommendation accuracy of 82% and the recommendation time of 1.1 minutes, which indicates excellent application effect.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.8 no.1
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• pp.15-22
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• 1990

The purpose of this study is to suggest possibility to analyze the three-dimensional positions of the whole surface of an object simultaneously and precisely by close-range photogrammetry. For this purpose, the geometry of network, namely imaging geometry and control configuration etc was considered, and then the whole surface of the object was analyzed by bundle adjustment through forma. lion of strip and block with which cover the whole surface of the object. As a result, we were able to prove possibility of the whole surface analysis of an object and to extract characteristics of accuracies in accordance with the number and configuration of control points. Also as desirable accuracies were able to be acquired even by employing configuration of only a few control point stationed on a limited surface, it is expected that the difficulties of control surveying will be able to be reduced considerably.

• Transactions of Materials Processing
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• v.27 no.3
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• pp.154-159
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• 2018

In the rectangular bar multi-stage drawing process, the cross-section dimensional accuracy of the rectangular bar varies depending on the aspect ratio and process conditions. It is very important to predict the dimensional error of the cross-section occurring in the multi-stage drawing process according to the aspect ratio of the rectangular bar and the half die angle of each pass. In this study, a process map for improving the dimensional accuracy according to the aspect ratio was derived in the drawing process of a rectangular bar. FE-simulation of the multi-stage shape drawing process was carried out with four types of rectangular bar. The results of the FE-simulation were trained to the nonlinear relationship between the shape parameters using an Artificial Neural Network (ANN), and the process maps were derived from them. The optimum half die angles were determined from the process maps on the dimensional accuracy. The validity of the suggested process map for aspect ratios 1.25~2:1 were verified through FE-simulation and experimentation.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.32 no.9C
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• pp.861-866
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• 2007

A clustering algorithm for Gaussian Probability Distribution Function (GPDF) data called Centroid Neural Network with a Bhattacharyya Kernel (BK-CNN) is proposed in this paper. The proposed BK-CNN is based on the unsupervised competitive Centroid Neural Network (CNN) and employs a kernel method for data projection. The kernel method adopted in the proposed BK-CNN is used to project data from the low dimensional input feature space into higher dimensional feature space so as the nonlinear problems associated with input space can be solved linearly in the feature space. In order to cluster the GPDF data, the Bhattacharyya kernel is used to measure the distance between two probability distributions for data projection. With the incorporation of the kernel method, the proposed BK-CNN is capable of dealing with nonlinear separation boundaries and can successfully allocate more code vector in the region that GPDF data are densely distributed. When applied to GPDF data in an image classification probleml, the experiment results show that the proposed BK-CNN algorithm gives 1.7%-4.3% improvements in average classification accuracy over other conventional algorithm such as k-means, Self-Organizing Map (SOM) and CNN algorithms with a Bhattacharyya distance, classed as Bk-Means, B-SOM, B-CNN algorithms.