• Journal of Mechanical Science and Technology
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• v.18 no.7
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• pp.1121-1130
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• 2004

The Genetic Algorithm (GA), an optimization technique based on the theory of natural selection, has proven to be a relatively robust means of searching for global optimum. It converges to the global optimum point without auxiliary information such as differentiation of function. In the case of a complex problem, the GA involves a large population number and requires a lot of computing time. To improve the process, this research used parallel processing with several personal computers. Parallel process technique is classified into two methods according to subpopulation's size and number. One is the fine-grained method (FGM), and the other is the coarse-grained method (CGM). This study selected the CGM as a parallel process technique because the load is equally divided among several computers. The given design domain should be reduced according to the degree of feasibility, because mechanical system problems have constraints. The reduced domain is used as an initial design domain. It is consistent with the feasible domain and the infeasible domain around feasible domain boundary. This parallel process used the Message Passing Interface library.

• The Transactions of the Korea Information Processing Society
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• v.6 no.5
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• pp.1219-1224
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• 1999

This paper derived Montgomery's parallel algorithms for modular multiplication based on Walter's and Iwamura's method, and compared data dependence graph of each parallel algorithm. Comparing the result, Walter's parallel algorithm has small computational index in data dependence graph, so it is selected and used to computed spatial and temporal pipelining diagrams with each projection direction for designing expansible bit-level systolic array. We also evaluated internal operation of proposed expansible systolic array C++ language.

• Journal of Industrial Technology
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• v.10
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• pp.15-20
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• 1990

A parallel processing algorithm for discrete Kalman filter, which is one of the most commonly used filtering technique in modern control, signal processing, and communication, is proposed. Previously proposed parallel algorithms to decrease the number of computations needed in the Kalman filter are the hierachical structures by distributed processing of measurements, or the systolic structures to disperse the computational burden. In this paper, a new parallel Kalman filter employing a structure similar to recursive doubling is proposed. Estimated values of state variables by the new algorithm converge with two times faster data processing speed than that of the conventional Kalman filter. Moreover it maintains the optimality of the conventional Kalman filter.

• Proceedings of the IEEK Conference
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• 2003.07e
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• pp.2367-2370
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• 2003

A fast pattern classification algorithm with Cellular Parallel Processing Network-based dynamic programming is proposed. The Cellular Parallel Processing Networks is an analog parallel processing architecture and the dynamic programming is an efficient computation algorithm for optimization problem. Combining merits of these two technologies, fast Pattern classification with optimization is formed. On such CPPN-based dynamic programming, if exemplars and test patterns are presented as the goals and the start positions, respectively, the optimal paths from test patterns to their closest exemplars are found. Such paths are utilized as aggregating keys for the classification. The pattern classification is performed well regardless of degree of the nonlinearity in class borders.

• Proceedings of the KIEE Conference
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• 1990.07a
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• pp.64-67
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• 1990

A parallel processing algorithm for discrete Kalman filter, which is one of the most commonly used filtering technique in modern control, signal processing, and communication. is proposed. Previously proposed parallel algorithms to decrease the number of computations needed in the Kalman filter are the hierachical structures by distributed processing of measurements, or the systolic structures to disperse the computational burden. In this paper, a new parallel Kalman filter employing a structure similar to recursive doubling is proposed. Estimated values of state variables by the new algorithm converge with two times faster data processing speed than that of the conventional Kalman filter. Moreover it maintains the optimality of the conventional Kalman filter.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.4
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• pp.1679-1691
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• 2016

Successive interference cancellation (SIC) is an effective means of multi-packet reception to combat interference at the physical layer. We investigate the joint optimization issue of channel access and power control for capacity maximization in SIC-enabled wireless networks. We propose a new interference model to characterize the sequential detection nature of SIC. Afterward, we formulize the joint optimization problem, prove it to be a nondeterministic polynomial-time-hard problem, and propose a novel approximation approach based on the genetic algorithm (GA). Finally, we discuss the design and parameter setting of the GA approach and validate its performance through extensive simulations.

• The Transactions of the Korea Information Processing Society
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• v.2 no.5
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• pp.701-710
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• 1995

A thinning algorithm is a very important procedure in order to increase recognition rate in the character recognition. This paper is the study of a parallel thinning algorithm available for the recognition of various characters, and it proposes the parallel thinning algorithm using the 8-neighbors connectivity value. Characteristics of the proposed algorithm are easiness of implementation of parallelism, the result of thinning is perfectly-8 connectivity and represented by numeric information. The proposed algorithm is very suitable for characters having many curve segments such as English, Japanese etc. Performance evaluation was performed by the measure of similarity to reference skeleton.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.67 no.5
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• pp.656-662
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• 2018

Pattern matching algorithm is widely used in many application fields such as bio-informatics, intrusion detection, etc. Among many string matching algorithms, KMP (Knuth-Morris-Pratt) algorithm is commonly used because of its fast execution time when using large texts. However, the processing speed of KMP algorithm is also limited when the text size increases significantly. In this paper, we propose a high throughput parallel KMP algorithm considering CPU-GPU memory hierarchy based on OpenCL in GPGPU (General Purpose computing on Graphic Processing Unit). We focus on the optimization for the allocation of work-times and work-groups, the local memory copy of the pattern data and the failure table, and the overlapping of the data transfer with the string matching operations. The experimental results show that the execution time of the optimized parallel KMP algorithm is about 3.6 times faster than that of the non-optimized parallel KMP algorithm.

• Proceedings of the Korea Information Processing Society Conference
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• 2011.04a
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• pp.156-157
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• 2011

In this work, we study the checkpoint/restart problem for message-passing parallel applications running on cloud computing environment. This is a new direction which arises from the trend of enabling the applications to run on the cloud computing environment. The main objective is to propose an efficient checkpoint algorithm for message-passing parallel applications considering communications with external systems. We further implement the novel algorithm by modifying gSOAP and OpenMPI (the open source libraries) which support service calls and checkpoint message-passing parallel programs, especially. The simulation showed that additional costs to the executing and checkpointing application of the algorithm are negligible. Ultimately, the algorithm supports efficiently the checkpoint/restart service for message-passing parallel applications, that send requests to external services.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.65 no.4
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• pp.666-672
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• 2016

Recently the fingerprint is widely used as one of biometrics to improve the security of financial mobile applications, because of its user convenience and high recognition rate. However, in order to apply fingerprint algorithms to finance and security applications, the recognition rate and processing speed of the fingerprint algorithms have to be improved further. In this paper, we propose the parallel fingerprint enhancement algorithm on general-purpose computing on graphics processing unit (GPGPU) using OpenCL. We discuss the analysis of the parallelism in the fingerprint algorithm as well as the exploration of optimization parameters of the parallel fingerprint algorithm to improve the performance. The experimental results showed that the execution of parallel fingerprint enhancement algorithm on GPGPUs was accelerated from 29.4 upto 69.2 times compared with the execution of the original one on the host CPUs.