• Journal of the Optical Society of Korea
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• v.20 no.6
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• pp.704-715
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• 2016

In this paper, we explore a low-complex bandwidth allocation (BA) scheme with multi-level service guarantees in VLC-OFDM systems. Effective capacity theory, which evaluates wireless channel capacity from a novel view, is utilized to model the system capacity under delay QoS constraints of the link layer. Since intensity modulation of light is used in the system, problems caused by frequency selectivity can be neglected. Then, the BA problem can be formulated as an integer programming problem and it is further relaxed and transformed into a concave one. Lagrangian formulation is used to reformulate the concave problem. Considering the inefficiency of traditional gradient-based schemes and the demand for distributed implementation in local area networks, we localize the global parameters and propose a quasi-distributed quadratic allocation algorithm to provide two-level service guarantees, the first level is QoS oriented, and the second level is QoE oriented. Simulations have shown the efficient performance of the proposed algorithm. The users with more stringent QoS requirements require more subcarriers to guarantee their statistical delay QoS requirements. We also analyze the effect of subcarrier granularity on the aggregate effective capacity via simulations.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.62 no.1
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• pp.49-56
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• 2013

In this paper, for constrained optimization problem, one multi-objective optimization algorithm that ensures both performance robustness and constraint feasibility is proposed when uncertainties are involved in design variables. In the proposed algorithm, the gradient index of objective function assisted by design sensitivity with the help of finite element method is applied to evaluate robustness; the reliability calculated by the sensitivity-assisted Monte Carlo simulation method is used to assess the feasibility of constraint function. As a demonstration, the performance and numerical efficiency of the proposed method is investigated through application to the optimal design of TEAM problem 22--a superconducting magnetic energy storage system.

• Proceedings of the KIEE Conference
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• 1996.07b
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• pp.1266-1268
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• 1996

In this study an automatic roiling-coli labeling system using robot vision system and peripheral mechanism is proposed and implemented, which instead of the manual labor to attach labels Rolling-coils in a steel miil. The binary image process for the image processing is performed with the threshold, and the contour line is converted to the binary gradient which detects the discontinuous variation of brightness of rolling-coils. The moment invariants algorithm proposed by Hu is used to make it easy to recognize even when the position of the center are different from the trained data. The position error compensation algorithm of six degrees of freedom industrial robot manipulator is also developed and the data of the position of the center rolling-coils, which is obtained by floor mount camera, are transfered by asynchronous communication method. Therefore even if the position of center is changed, robot moves to the position of center and performs the labeling work successfully. Therefore, this system can be improved the safety and efficiency.

• Proceedings of the KIEE Conference
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• 2004.11a
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• pp.251-254
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• 2004

The aging diagnosis technique using partial discharge detection method detects partial discharge signals cause of power equipment failuer and able to forecast the aging state of insulation system through analysis algorithm, in this paper accumulates HFPD signal during constant scheduled cycles to build HFPD pattern and then analyzes HFPD pattern using statistical parameters and ${\Delta}F$ pattern. The 3D pattern is composed of detected signal frequency, amplitude and repeated number and the FRPDA(frequency resolved partial discharge analysis) technique is used in 3D pattern construction. The ${\Delta}F$ pattern shows variation characteristics of amplitude gradient of consecutive HFPD signal Pulses and able to classify discharge types-internal discharge, surface discharge and coronal discharge etc. Fractal mathematics applied to ${\Delta}F$ pattern quantification and neural networks is used in aging diagnostic algorithm.

• The KIPS Transactions:PartB
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• v.13B no.2 s.105
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• pp.83-88
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• 2006

Digital image processing algorithm was proposed to measure the area inside of an object image using angle-distance graph used to analyze the pattern of an object in the digital image processing techniques. The first angle-distance graph is generated from a point inside of an object area. The second angle-distance graphs are generated for the areas missed in the first graph by extracting the positions with large gradient in the first angle-distance graph. The order of the graph increases according to the complexity of an object pattern. Size of the area inside of an object boundary is measured by integrating square of distance multiplied by angle for each area from the hierarchical angie-distance graphs.

• The Transactions of the Korea Information Processing Society
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• v.4 no.10
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• pp.2553-2561
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• 1997

In this paper, a hardware architecture to calculate straight-line Hough transform algorithm for image recognition is suggested. This processor consists of the filtering module for gradient calculation and the HT calculation module, and the angle information are stored in memory table. For the suggested architecture, firstly, algorithm simulation is executed using C language to confirm the operation and to decide the precision of calculation, and secondly, architecture simulation is executed using VHDL language for the total blocks. According to C & VHDL simulation results, it is confirmed that the calculated data value is similarly obtained and the calculation defference is decreased as image clarity and bits increase.

• Computers and Concrete
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• v.20 no.6
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• pp.627-634
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• 2017

Predicting the compressive strength of concrete is important to assess the load-carrying capacity of a structure. However, the use of blended cements to accrue the technical, economic and environmental benefits has increased the complexity of prediction models. Artificial Neural Networks (ANNs) have been used for predicting the compressive strength of ordinary Portland cement concrete, i.e., concrete produced without the addition of supplementary cementing materials. In this study, models to predict the compressive strength of blended cement concrete prepared with a natural pozzolan were developed using regression models and single- and 2-phase learning ANNs. Back-propagation (BP), Levenberg-Marquardt (LM) and Conjugate Gradient Descent (CGD) methods were used for training the ANNs. A 2-phase learning algorithm is proposed for the first time in this study for predictive modeling of the compressive strength of blended cement concrete. The output of these predictive models indicates that the use of a 2-phase learning algorithm will provide better results than the linear regression model or the traditional single-phase ANN models.

• Journal of Korean Society on Water Environment
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• v.37 no.5
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• pp.335-343
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• 2021

Water quality prediction is essential for the proper management of water supply systems. Increased suspended sediment concentration (SSC) has various effects on water supply systems such as increased treatment cost and consequently, there have been various efforts to develop a model for predicting SSC. However, SSC is affected by both the natural and anthropogenic environment, making it challenging to predict SSC. Recently, advanced machine learning models have increasingly been used for water quality prediction. This study developed an ensemble machine learning model to predict SSC using the XGBoost (XGB) algorithm. The observed discharge (Q) and SSC in two fields monitoring stations were used to develop the model. The input variables were clustered in two groups with low and high ranges of Q using the k-means clustering algorithm. Then each group of data was separately used to optimize XGB (Model 1). The model performance was compared with that of the XGB model using the entire data (Model 2). The models were evaluated by mean squared error-ob servation standard deviation ratio (RSR) and root mean squared error. The RSR were 0.51 and 0.57 in the two monitoring stations for Model 2, respectively, while the model performance improved to RSR 0.46 and 0.55, respectively, for Model 1.

• The Journal of Korea Robotics Society
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• v.16 no.2
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• pp.137-146
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• 2021

When controlling manipulator, degree of freedom is lost in singularity so specific joint velocity does not propagate to the end effector. In addition, control problem occurs because jacobian inverse matrix can not be calculated. To avoid singularity, we apply Deep Deterministic Policy Gradient(DDPG), algorithm of reinforcement learning that rewards behavior according to actions then determines high-reward actions in simulation. DDPG uses off-policy that uses 𝝐-greedy policy for selecting action of current time step and greed policy for the next step. In the simulation, learning is given by negative reward when moving near singulairty, and positive reward when moving away from the singularity and moving to target point. The reward equation consists of distance to target point and singularity, manipulability, and arrival flag. Dual arm manipulators hold long rod at the same time and conduct experiments to avoid singularity by simulated path. In the learning process, if object to be avoided is set as a space rather than point, it is expected that avoidance of obstacles will be possible in future research.

• Journal of KIBIM
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• v.12 no.1
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• pp.55-61
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• 2022

Recently, demand for designing own space is increasing as the rapid growth of home furnishing market. However, there is a limitation that it is not easy to compare the style between before construction view and after view. This study aims to translate real image into another style with GAN model learned with interior images. To implement this, first we established style criteria and collected modern, natural, and classic style images, and experimented with ResNet, UNet, Gradient penalty concept to CycleGAN algorithm. As a result of training, model recognize common indoor image elements, such as floor, wall, and furniture, and suitable color, material was converted according to interior style. On the other hand, the form of furniture, ornaments, and detailed pattern expressions are difficult to be recognized by CycleGAN model, and the accuracy lacked. Although UNet converted images more radically than ResNet, it was more stained. The GAN algorithm allowed us to represent results within 2 seconds. Through this, it is possible to quickly and easily visualize and compare the front and after the interior space style to be constructed. Furthermore, this GAN will be available to use in the design rendering include interior.