• KIPS Transactions on Computer and Communication Systems
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• v.3 no.1
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• pp.1-6
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• 2014

Most of the previous algorithms focus on computing the convex hull for a set of points. In this paper, we present a method for approximating the convex hull for a set of spheres with various radii in discrete space. Computing the convex hull for a set of spheres is a base technology for many applications that study structural properties of molecules. We present a voxel map data structures, where the molecule is represented as a set of spheres, and corresponding algorithms. Based on CUDA programming for using the parallel architecture of GPU, our algorithm takes less than 40ms for computing the convex hull of 6,400 spheres in average.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.21 no.10
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• pp.1551-1560
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• 1997

This study is devoted to the detection of the 3-dimensional point obstacles on the plane by using accumulated scan line images. The proposed accumulating only one scan line allow to process image at real time. And the change of motion of the feature in image is small because of the short time between image frames, so it does not take much time to track features. To obtain recursive optimal obstacles position and robot motion along to the motion of camera, Kalman filter algorithm is used. After using Kalman filter in case of the fixed environment, 3-dimensional obstacles point map is obtained. The position and motion of moving obstacles can also be obtained by pre-segmentation. Finally, to solve the stereo ambiguity problem from multiple matches, the camera motion is actively used to discard mis-matched features. To get relative distance of obstacles from camera, parallel stereo camera setup is used. In order to evaluate the proposed algorithm, experiments are carried out by a small test vehicle.

• Korean Journal of Cognitive Science
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• v.14 no.2
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• pp.15-25
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• 2003

The Monkey and Banana Problem is an example commonly used for illustrating simple problem solving. It can be solved by conventional approaches, but this requires a procedural aspect when inferences are processed, and this fact works as a limitation condition in solving complex problems. However, if we use DNA computing methods which are naturally able to realize massive parallel processing. the Monkey and Banana Problem can be solved effectively without weakening the fundamental aims above. In this paper, we design a method of representing the problem using DNA molecules, and show that various solutions are generated through computer-simulations based on the design. The simulation results are obviously interesting in that these are contrary to the fact that the Prolog program for the Monkey and Banana Problem, which was implemented from the conventional point of view, gives us only one optimal solution. That is, DNA computing overcomes the limitations of conventional approaches.

• Journal of KIISE:Computer Systems and Theory
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• v.29 no.12
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• pp.648-654
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• 2002

The Virtual Interface Architecture(VIA) is an industry standard for communication over system area networks(SANs). M-VIA is a software implementation of VIA technology on Linux. In this paper, we implemented the M-VIA on an AceNIC Gigabit Ethernet by developing a new AceNIC driver for the M-VIA. We analyzed the M-VIA data segmentation processes. When a Gigabit Ethernet MTU is larger than 1514 bytes, M-VIA data segmentation size leaves much room for improvement. So we experimented with various MTU and M-VIA data segmentation size and compared the performances.

• Journal of the Korean Institute of Intelligent Systems
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• v.2 no.1
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• pp.17-32
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• 1992

The multi-layer neural network that has broadly been utilized in designing the controller of robot manipulator possesses the desirable characteristics of learning capacity, by which the uncertain variation of the dynamic parameters of robot can be handled adaptively, and parallel distributed processing that makes it possible to control on real-time. However the error back propagation algorithm that has been utilized popularly in the learning of the multi-layer neural network has the problem of its slow convergence speed. In this paper, an approach to improve the convergence speed is proposed using the fuzzy logic that can effectively handle the uncertain and fuzzy informations by linguistic level. The effectiveness of the proposed algorithm is demonstrated by computer simulation of PUMA 560 robot manupulator.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.5 no.4
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• pp.756-765
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• 2001

An effective dexterous motion control method of redundant robot manipulators based on neural optimization network is proposed to satisfy multi-criteria such as singularity avoidance, minimizing energy consumption, and avoiding physical limits of actuator, while performing a given task. The method employs a neural optimization network with parallel processing capability, where only a simple geometric analysis for resolved motion of each joint is required instead of computing of the Jacobian and its pseudo inverse matrix. For dexterous motion, a joint geometric manipulability measure(JGMM) is proposed. JGMM evaluates a contribution of each joint differential motion in enlarging the length of the shortest axis among principal axes of the manipulability ellipsoid volume approximately obtained by a geometric analysis. Redundant robot manipulators is then controlled by neural optimization networks in such a way that 1) linear combination of the resolved motion by each joint differential motion should be equal to the desired velocity, 2) physical limits of joints are not violated, and 3) weighted sum of the square of each differential joint motion is minimized where weightings are adjusted by JGMM. To show the validity of the proposed method, several numerical examples are illustrated.

• KIPS Transactions on Computer and Communication Systems
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• v.7 no.4
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• pp.99-102
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• 2018

Caffe is a neural net learning software which is widely used in academic researches. The GPU memory capacity is one of the most important aspects of designing neural net architectures. For example, many object detection systems require to use less than 12GB to fit a single GPU. In this paper, we extended Caffe to allow to use more than 12GB GPU memory. To verify the effectiveness of the extended software, we executed some training experiments to determine the learning efficiency of the object detection neural net software using a PC with three GPUs.

• The KIPS Transactions:PartA
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• v.9A no.1
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• pp.37-44
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• 2002

Most researches of transforming sequential programs into parallel programs have been based on the loop structure transformation method. However, most programs have implicit interprocedure parallelism. This paper suggests a way of extracting parallelism from the loops with procedure calls using the data dependency elimination method. Most parallelization of the loop with procedure calls have been conducted for extracting parallelism from the uniform code. In this paper, we propose interprocedural transformation, which can be apply to both uniform and nonuniform code. We show the examples of uniform, nonuniform, and complex code parallelization. We then evaluated the performance of the various transformation methods using the CRAY-T3E system. The comparison results show that the proposed algorithm out-performs other conventional methods.

• Journal of KIISE:Computer Systems and Theory
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• v.29 no.2
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• pp.57-64
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• 2002

We implemented two fast PC clusters using fast PCs and high speed network. First. we built the first generation of 16 PC cluster and have used it for real-time operation at Cheju Regional Meteorological Office. Next, we built the second generation of 16PC with dual CUs cluster which was efficiently improved based on performance analysis of the first generation of cluster. In this research we also analyzed performance of two different clusters, which have different CPUs and communication devices using the parallel model MM5 which has been used for the real-time weather forecasting.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.23 no.4
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• pp.388-393
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• 2019

As a fiiting method to data with outliers, RANSAC(RANdom SAmple Consensus) based algorithm is widely used in fitting of line, circle, ellipse, etc. CUDA is currently most widely used GPU with massive parallel processing capability. This paper proposes an efficient CUDA implementation of multiple planes fitting using RANSAC with 3d points data, of which one set of 3d points is used for one plane fitting. The performance of the proposed algorithm is demonstrated compared with CPU implementation using both artificially generated data and real 3d heights data of a PCB. The speed-up of the algorithm over CPU seems to be higher in data with lower inlier ratio, more planes to fit, and more points per plane fitting. This method can be easily applied to a wide variety of other fitting applications.