• Journal of the Institute of Electronics Engineers of Korea SD
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• v.42 no.12
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• pp.79-90
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• 2005

In this paper, we propose a new clustered reconfigurable interconnect network (CRIN) BIST to improve the embedding probabilities of random-pattern-resistant-patterns. The proposed method uses a scan-cell reordering technique based on the signal probabilities of given test cubes and specific hardware blocks that increases the embedding probabilities of care bit clustered scan chain test cubes. We have developed a simulated annealing based algorithm that maximizes the embedding probabilities of scan chain test cubes to reorder scan cells, and an iterative algorithm for synthesizing the CRIN hardware. Experimental results demonstrate that the proposed CRIN BIST technique achieves complete fault coverage with lower storage requirement and shorter testing time in comparison with the conventional methods.

• Journal of the Korean Geographical Society
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• v.46 no.3
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• pp.366-381
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• 2011

A competitive location problem in discrete space is computationally difficult to solve in general because of its combinatorial feature. In this paper, we address an alternative method for solving competitive location problems in discrete space, particularly employing deterministic allocation. The key point of the suggested method is to reducing the number of predefined potential facility sites associated with the size of problem by utilizing geometric concepts. The suggested method was applied to the existing broadband marketplace with increasing competition as an application. Specifically, we compared computational results and spatial configurations of two different sized problems: the problem with the original potential sites over the study area and the problem with the reduced potential sites extracted by a GIS-based geometric algorithm. The results show that the competitive location model with the reduced potential sites can be solved more efficiently, while both problems presented the same optimal locations maximizing customer capture.

• The Transactions of the Korea Information Processing Society
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• v.4 no.11
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• pp.2769-2779
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• 1997

This Paper Proposes an efficient method for improving the training performance of the neural networks using a hybrid of conjugate gradient backpropagation algorithm and dynamic tunneling backpropagation algorithm The conjugate gradient backpropagation algorithm, which is the fast gradient algorithm, is applied for high speed optimization. The dynamic tunneling backpropagation algorithm, which is the deterministic method with tunneling phenomenon, is applied for global optimization. Conversing to the local minima by using the conjugate gradient backpropagation algorithm, the new initial point for escaping the local minima is estimated by dynamic tunneling backpropagation algorithm. The proposed method has been applied to the parity check and the pattern classification. The simulation results show that the performance of proposed method is superior to those of gradient descent backpropagtion algorithm and a hybrid of gradient descent and dynamic tunneling backpropagation algorithm, and the new algorithm converges more often to the global minima than gradient descent backpropagation algorithm.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.1804-1809
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• 2003

In a large-scale chemical plant, there are scheduling problems in inventory and packing process although production process is stabilized. The profit of the plant is restricted by these problems. In order to improve these problems, integrated scheduling model, which is concerned with whole processes from production to shipment, has been developed in this paper. In this model, decision variables are production sequence, silo allocation, amounts of bulk shipment and packing amounts. In case of a real plant, it is hard to solve by deterministic methods because there are too many decision variables to solve. In this paper, genetic algorithm is presented to solve a PVC process scheduling model within an hour with PCs.

• Proceedings of the IEEK Conference
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• 2000.09a
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• pp.399-402
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• 2000

In this paper, we present a regularized mixed norm multichannel image restoration algorithm. The problem of multichannel restoration using both within- and between- channel deterministic information is considered. For each channel a functional which combines the least mean squares (LMS), the least mean fourth(LMF), and a smoothing functional is proposed, We introduce a mixed norm parameter that controls the relative contribution between the LMS and the LMF, and a regularization parameter that defines the degree of smoothness of the solution, both updated at each iteration according to the noise characteristics of each channel. The novelty of the proposed algorithm is that no knowledge of the noise distribution for each channel is required, and the parameters mentioned above are adjusted based on the partially restored image.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.24 no.2 s.173
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• pp.290-302
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• 2000

In this paper, we study on the identification of discrete-time state-space model for robust control of tandem cold mill, using a MOESP(MIMO output-error state-space model identification) algorithm based on subspace method. It is shown that the identified model is well adapted to input-output data sets, which are obtained from nonlinear mathematical equations of tandem cold mill. Furthermore, deterministic H$\infty$ norm bounds on uncertainties including modeling errors and disturbances are quantitatively identified in the frequency domain. Finally, the results give a basic idea to determine weighting functions included in formulating some robust control problems of tandem cold mill.

• Journal of Korea Multimedia Society
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• v.9 no.12
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• pp.1572-1579
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• 2006

An usual acoustic emission(AE) event has two widely characterized parameters in time domain, peak amplitude and event duration. But noise in AE measuring may disturb the signals with its parameters and aggrandize the signal incertitude. Experiment activity of detection of the nick inside of porcelain with AE was made and study on AE signal processing with statistic be presented in this paper in order to pick-up information expected from the signal with noise. Effort is concentrated on developing a novel arithmetic to improve extraction of the characteristic from stochastic signal and to enhance the voracity of detection. The main purpose discussed in this paper is to treat with signals on amplitudes with statistic mutuality and power density spectrum in frequency domain, and farther more to select samples for neural networks training by means of least-squares algorithm between real measuring signal and deterministic signals under laboratory condition. By seeking optimization with the algorithm, the parameters representing characteristic of the porcelain object are selected, while the stochastic interfere be weakened, then study for detection on neural networks is developed based on processing above.

• Structural Monitoring and Maintenance
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• v.1 no.4
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• pp.427-449
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• 2014

A hybrid optimization method for the identification of state-space models is presented in this study. Hybridization is succeeded by combining the advantages of deterministic and stochastic algorithms in a superior scheme that promises faster convergence rate and reliability in the search for the global optimum. The proposed hybrid algorithm is developed by replacing the original stochastic mutation operator of Evolution Strategies (ES) by the Levenberg-Marquardt (LM) quasi-Newton algorithm. This substitution results in a scheme where the entire population cloud is involved in the search for the global optimum, while single individuals are involved in the local search, undertaken by the LM method. The novel hybrid identification framework is assessed through the Monte Carlo analysis of a simulated system and an experimental case study on a shear frame structure. Comparisons to subspace identification, as well as to conventional, self-adaptive ES provide significant indication of superior performance.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2006.11a
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• pp.693-696
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• 2006

Global optimization is a method for finding more reliable models in various fields, such as financial engineering, pattern recognition, process optimization. In this study, we compare and analyze the performance of the state-of-the-art global optimization techniques, which include Genetic Algorithm (DE,SCGA), Simulated Annealing (ASA, DSSA, SAHPS), Tabu & Direct Search (DTS, DIRECT), Deterministic (MCS, SNOBIT), and Trust-Region algorithm. The test functions for the experiments are Benchmark problems in Hedar & Fukushima (2004), which are evaluated with respect to efficiency and accuracy. Through the experiment, we analyse the computational complexity of the methods and finally discuss the pros and cons of them.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.1032-1035
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• 1996

While Ethernet and TCP/IP are the most widely used protocol, for Real-time system, it is not applicable because it doesn't guarantee the deterministic transmission time. Furthermore, the TCP acknowledgement scheme and sliding window algorithm enforce to collide packets. Although various Collision-Free CSMA protocol was presented, it is very difficult to implement in well known OS(UNIX, WilidowsNT) because we have to modify network kernel. This paper presents another transmission protocol based on modified UDP. The colliding probability can be minimized by avoiding successive packet transmission and decreasing competition duration. The proposed algorithm can be used for the soft real-time industrial automation network.