• The Journal of the Korea institute of electronic communication sciences
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• v.9 no.1
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• pp.83-90
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• 2014

In this paper, we address the problem of designing and implementing an oil pump control system driven by a brushless DC (BLDC) motor. The proposed oil pump plays the role of providing fuel to the engine clutch and transmission of hybrid vehicles. Main consideration is given to enhancing response feature and accuracy of the oil pump by simplifying the controller that is driven by a BLDC motor under PWM voltage control, which is a standard control method for BLDC motors. The proposed control scheme also helps to increase efficiency and reliability of the oil pump system. To validate the performance of the proposed system, we conduct experiments on BLDC motor speed control and oil pump operations.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.1786-1790
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• 2004

The main objective of this paper is to analyze the performance of various force control algorithms in improving and adjusting the compliance of industrial robots in contact with their environment. Some of fundamental force control algorithms such as sensorless control, impedance control and hybrid position/force control are theoretically analyzed and simulated for various situations of an environment, and then a series of experiments using them were performed. In this paper, a control scheme to use position control in implementing the impedance control was investigated in order to nullify the effect of joint friction. The new reference trajectory is generated using contact force feedback and original desired trajectory. And an inner position control loop is designed to provide accurate position tracking for the new reference trajectory and good disturbance rejection. Experiments to insert a peg in a hole (so-called the peg-in-a-hole task) were performed with HILS (hardware-in-theloop simulation) system based on the results of the analyses and simulations on the characteristics of each control algorithm. The experiments showed that various force control methods improved the performance of robots in close contact with the environment by adjusting their compliance with respect to an arbitrary set of coordinates.

• Journal of Digital Convergence
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• v.10 no.8
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• pp.179-184
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• 2012

A car accident on a highway occurs a chain-reaction collision because of a vehicle's fast velocity. In order to prevent it, the accident vehicle should broadcast a safe message to its neighbors. If there are many neighbor nodes, a frame collision probability is high. To solve this, it was proposed for a system as a previous study to send a safe message without frame-collision using separating channels. However, the separation of multiple channels make feasibility low because of increasing hardware's development cost and complexity. In this paper, we proposes a TDMA-based MAC protocol using a single channel. As a result, we show the frame reception success rate of our protocol was almost the same as the previous protocol.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.67 no.3
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• pp.419-426
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• 2018

An useful and effective design method for the gas identification system is presented in this paper. The proposed gas identification system adopts hierarchical structure with two level rule base combining fuzzy sets with rough sets. At first, a hybrid genetic algorithm is used in grouping the array sensors of which the measured patterns are similar in order to reduce the dimensionality of patterns to be analyzed and to make rule construction easy and simple. Next, for low level identification, fuzzy inference systems for each divided group are designed by using TSK fuzzy rule, which allow handling the drift and the uncertainty of sensor data effectively. Finally, rough set theory is applied to derive the identification rules at high level which reflect the identification characteristics of each divided group. Thus, the proposed method is able to accomplish effectively dimensionality reduction as well as accurate gas identification. In simulation, we demonstrated the effectiveness of the proposed methods by identifying five types of gases.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.10
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• pp.5006-5022
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• 2017

Effect and robust detection of targets in infrared images has crucial meaning for many applications, such as infrared guidance, early warning, and video surveillance. However, it is not an easy task due to the special characteristics of the infrared images, in which the background clutters are severe and the targets are weak. The recent literature demonstrates that sparse representation can help handle the detection problem, however, the detection performance should be improved. To this end, in this text, a hybrid method based on local sparse representation and contrast is proposed, which can effectively and robustly detect the infrared targets. First, a residual image is calculated based on local sparse representation for the original image, in which the target can be effectively highlighted. Then, a local contrast based method is adopted to compute the target prediction image, in which the background clutters can be highly suppressed. Subsequently, the residual image and the target prediction image are combined together adaptively so as to accurately and robustly locate the targets. Based on a set of comprehensive experiments, our algorithm has demonstrated better performance than other existing alternatives.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.2
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• pp.600-619
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• 2015

We propose a cost-efficient hybrid algorithm for online location updates that efficiently combines feature point detection with the online trajectory-based sampling algorithm. Our algorithm is designed to minimize the average trajectory error with the minimal number of sample points. The algorithm is composed of 3 steps. First, we choose corner points from the map as sample points because they will most likely cause fewer trajectory errors. By employing the online trajectory sampling algorithm as the second step, our algorithm detects several missing and important sample points to prevent unwanted trajectory errors. The final step improves cost efficiency by eliminating redundant sample points on straight paths. We evaluate the proposed algorithm with real GPS trajectory data for various bus routes and compare our algorithm with the existing one. Simulation results show that our algorithm decreases the average trajectory error 28% compared to the existing one. In terms of cost efficiency, simulation results show that our algorithm is 29% more cost efficient than the existing one with real GPS trajectory data.

• Journal of the Korean Society for Advanced Composite Structures
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• v.6 no.1
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• pp.14-20
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• 2015

In this study, we propose an innovative lateral force distribution building system between tall buildings by utilizing the difference of moment of inertia, as the alternative design for highly integrated city area. Considering a tri-axial symmetric conditions and boundary conditions for the three-dimensional building structure system, a two-dimensional model is composed. In the proposed indeterminate structural model, important design variables are determined for obtaining minimum horizontal deflections, reactions and bending moments at the ground level of the buildings. Regarding a case of the provided two spatial structures connected to 4 buildings, the optimum location of middle located spatial structure is 45% from the top of the building, which minimize the end moments at the bottom of the buildings. In the considered verification examples, reduced drifts at the top location of the building systems are validated against static wind pressure loads and static earthquake loads. The suggested hybrid building system will improve the safety and reliability of the system due to the added internal truss-dome structures in terms of more than 30% reduced drift and vibration through the development of convergence of tall buildings and spatial structures.

• International Journal of Fluid Machinery and Systems
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• v.3 no.2
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• pp.150-159
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• 2010

Laidback fan shaped film-cooling hole is formulated numerically and optimized with the help of three-dimensional numerical analysis, surrogate methods, and the multi-objective evolutionary algorithm. As Pareto optimal front produces a set of optimal solutions, the trends of objective functions with design variables are predicted by hybrid multi-objective evolutionary algorithm. The problem is defined by four geometric design variables, the injection angle of the hole, the lateral expansion angle of the diffuser, the forward expansion angle of the hole, and the ratio of the length to the diameter of the hole, to maximize the film-cooling effectiveness compromising with the aerodynamic loss. The objective function values are numerically evaluated through Reynolds- averaged Navier-Stokes analysis at the designs that are selected through the Latin hypercube sampling method. Using these numerical simulation results, the Response Surface Approximation model are constructed for each objective function and a hybrid multi-objective evolutionary algorithm is applied to obtain the Pareto optimal front. The clustered points from Pareto optimal front were evaluated by flow analysis. These designs give enhanced objective function values in comparison with the experimental designs.

• ETRI Journal
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• v.40 no.4
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• pp.421-434
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• 2018

The application of deep neural networks (DNNs) to connect the world with cyber physical systems (CPSs) has attracted much attention. However, DNNs require a large amount of memory and computational cost, which hinders their use in the relatively low-end smart devices that are widely used in CPSs. In this paper, we aim to determine whether DNNs can be efficiently deployed and operated in low-end smart devices. To do this, we develop a method to reduce the memory requirement of DNNs and increase the inference speed, while maintaining the performance (for example, accuracy) close to the original level. The parameters of DNNs are decomposed using a hybrid of canonical polyadic-singular value decomposition, approximated using a tensor power method, and fine-tuned by performing iterative one-shot hybrid fine-tuning to recover from a decreased accuracy. In this study, we evaluate our method on frequently used networks. We also present results from extensive experiments on the effects of several fine-tuning methods, the importance of iterative fine-tuning, and decomposition techniques. We demonstrate the effectiveness of the proposed method by deploying compressed networks in smartphones.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.4
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• pp.1520-1542
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• 2020

Traditional software testing typically uses many physical resources to manually build various test environments, resulting in high resource costs and long test time due to limited resources, especially for small enterprises. Cloud computing can provide sufficient low-cost virtual resources to alleviate these problems through the virtualization of physical resources. However, the provision of various test environments and services for implementing software testing rapidly and conveniently based on cloud computing is challenging. This paper proposes a multilayer cloud testing model based on cloud computing and implements a hybrid cloud testing system based on virtual machines (VMs) and networks. This system realizes the automatic and rapid creation of test environments and the remote use of test tools and test services. We conduct experiments on this system and evaluate its applicability in terms of the VM provision time, VM performance and virtual network performance. The experimental results demonstrate that the performance of the VMs and virtual networks is satisfactory and that this system can improve the test efficiency and reduce test costs through rapid virtual resource provision and convenient test services.