• The Journal of Korean Association of Computer Education
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• v.11 no.6
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• pp.95-102
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• 2008

In this paper through the implementation of cooperative learning using SDG, education puzzle actually applies to computer training. SDG(Single Display Groupware) which one computer display have a multi-input devices can work as a collaborative system. Learners are performing together through SDG-based cooperative learning system. SDG cooperative learning with a multi-input device is superior to traditional learning with individual. We have implementation of the puzzle game with this fact. This system through effective education and raising their children's education participation rate will be able to do.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.35S no.1
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• pp.80-87
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• 1998

In this study, a multi-element ultrasound transducer has been developed aiming at basic experiment of three-dimension endovascular ultrasound endscopy for clinical diagnos, and experimental results of two-dimensional object imaging in the water are presented by the ultrasound tranducer and neural network. Each ultrasound echo received by thirty-six angular transducer elements is inputed to the eural network, and then backpropagation is used as a learning algorithm. A three-layer artificial neural network is used for learning and imaging of targetw placed in front of the transducer. The object shape of imaging is restricted to rectangular shapes by considering experimental restraint conditions. As a result, rough visualization can be realized even for objects with unlearned shapes through the training by primitive patterns of a various sized rectangular targets.

• Journal of Electrical Engineering and Technology
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• v.5 no.3
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• pp.363-370
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• 2010

Load forecasting has always been essential to the operation and planning of power systems in deregulated electricity markets. Various methods have been proposed for load forecasting, and the neural network is one of the most widely accepted and used techniques. However, to obtain more accurate results, more information is needed as input variables, resulting in huge computational costs in the learning process. In this paper, to reduce training time in multi-layer perceptron-based short-term load forecasting, a graphics processing unit (GPU)-based computing method is introduced. The proposed approach is tested using the Korea electricity market historical demand data set. Results show that GPU-based computing greatly reduces computational costs.

• Journal of the Institute of Convergence Signal Processing
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• v.15 no.2
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• pp.37-41
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• 2014

The vector Taylor series (VTS) based method usually employs clean speech Hidden Markov Models (HMMs) when compensating speech feature vectors or adapting the parameters of trained HMMs. It is well-known that noisy speech HMMs trained by the Multi-condition TRaining (MTR) and the Multi-Model-based Speech Recognition framework (MMSR) method perform better than the clean speech HMM in noisy speech recognition. In this paper, we propose a method to use the noise-adapted HMMs in the VTS-based speech feature compensation method. We derived a novel mathematical relation between the train and the test noisy speech feature vector in the log-spectrum domain and the VTS is used to estimate the statistics of the test noisy speech. An iterative EM algorithm is used to estimate train noisy speech from the test noisy speech along with noise parameters. The proposed method was applied to the noise-adapted HMMs trained by the MTR and MMSR and could reduce the relative word error rate significantly in the noisy speech recognition experiments on the Aurora 2 database.

• Journal of the Korean Society for Precision Engineering
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• v.20 no.2
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• pp.146-153
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• 2003

Ultrasonic inspection of defects has been focused on the existence of defect in structural material and need has much time and expenses in inspecting all the coordinates (x, y) on material surface. Neural networks can have an application to coordinates (x, y) of defects by multi-point inspection method. Ultrasonic inspection modeling is optimized by neural networks Neural networks has trained training example of absolute and relative coordinate of defects, and defect pattern. This method can predict coordinates (x, y) of defects within engineering estimated mean error $\psi$.

• Journal of the Korean Society of Fisheries and Ocean Technology
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• v.49 no.2
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• pp.153-160
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• 2013

Control allocation is an important part of a system. It implements the function that map the desired command forces from the controller into the commands of the different actuators. In this paper, the authors present an approach for solving constrained control allocation problem in vessel system by using multi-parametric quadratic programming (mp-QP) algorithm. The goal of mp-QP algorithm applied in this study is to compute a solution to minimize a quadratic performance index subject to linear equality and inequality constraints. The solution can be pre-computed off-line in the explicit form of a piecewise linear (PWL) function of the generalized forces and constrains. The efficiency of mp-QP approach is evaluated through a dynamic positioning simulation for a vessel by using four tugboats with constraints about limited pushing forces and found to work well.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.5
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• pp.2590-2606
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• 2017

Generally, research of classical image classification algorithms assume that training data and testing data are derived from the same domain with the same distribution. Unfortunately, in practical applications, this assumption is rarely met. Aiming at the problem, a domain adaption image classification approach based on multi-sparse representation is proposed in this paper. The existences of intermediate domains are hypothesized between the source and target domains. And each intermediate subspace is modeled through online dictionary learning with target data updating. On the one hand, the reconstruction error of the target data is guaranteed, on the other, the transition from the source domain to the target domain is as smooth as possible. An augmented feature representation produced by invariant sparse codes across the source, intermediate and target domain dictionaries is employed for across domain recognition. Experimental results verify the effectiveness of the proposed algorithm.

• Journal of the Korea Safety Management & Science
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• v.14 no.3
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• pp.151-158
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• 2012

The objective of this paper is to propose a method of how to perform risk assessment in the early stage of defense research and development for the acquisition of weapon systems. An advanced method for risk assessment and its associated objective functions are developed first based on the concept of systems engineering. The developed method is then applied to carry out the analysis of alternatives in the trade-off environments. As a case study, the multi-purpose training ship is considered, where it is performed using the notions of technology readiness levels, degrees of difficulty, and technology need values to facilitate design space visualization and decision maker interaction. It is noted that decision makers can benefit from our approach as an improved risk assessment method in the context of multi-criteria decision making.

• Corrosion Science and Technology
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• v.11 no.6
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• pp.270-274
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• 2012

This study represents the result of corrosion monitoring on reinforced concrete specimens by means of multi-functional corrosion monitoring sensors. To confirm the effectiveness of the sensors, eight different kinds of condition were adopted. Test factors were corrosion potential, current, corrosion rate, resistivity, and temperature, which were monitored with the sensors. Through this study, judging corrosion of steel in concrete with single corrosion factor such as corrosion potential was difficult, because many other factors can have an influence on the reaction of corrosion. By using three different kinds of sensors, it could enhance the accuracy of corrosion monitoring.

• Proceedings of the Korean Nuclear Society Conference
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• 1998.05a
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• pp.833-837
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• 1998

This paper presents preliminary findings regarding a modeling framework under development for use in a multi-attribute decision model for advanced emergency operating procedures(EOPs). This model provides a means for optimal decision making strategy for advanced emergency operating procedures conceptualizing the dynamic coordination of responsibilities and information in the human system interactions with advanced reactor systems. For the purpose of evaluation of the applicability of this modeling framework, an empirical case study for a post-cooldown strategy during an steam generator tube rupture (SGTR) accident was carried out. As a result, it was found empirically that the multi-attribute decision model is a useful tool for establishing advanced EOPs that reduce the operator's cognitive and decision making burden during the accident mitigation process.