• Proceedings of the Korean Society Of Semiconductor Equipment Technology
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• 2005.09a
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• pp.106-109
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• 2005

The ITO thin films for Top-Emitting Organic Light Emitting Devices (TOLEDs) were prepared on cell(LiF/Organic Layer/Bottom Electrode : ITO ) by FTS (Facing Targets Sputtering) system under different sputtering conditions which were varying gas pressure, input current and distance of target to target($D_{T-T}$). As a function of sputtering conditions, I-V characteristics of prepared ITO thin films on cell were measured by 4156A (HP). In the results, when the In thin films were deposited at $D_{T-T}$ 70mm and working pressure 1mTorr, the leakage current of ITO/cell was about 11[V] and 5E-6[$mA/cm^2$].

• The KIPS Transactions:PartD
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• v.10D no.3
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• pp.417-426
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• 2003

The problem of finding clusters in high dimensional data is well known in the field of data mining for its importance, because cluster analysis has been widely used in numerous applications, including pattern recognition, data analysis, and market analysis. Recently, a new framework, projected clustering, to solve the problem was suggested, which first select subdimensions of each candidate cluster and then each input point is assigned to the nearest cluster according to a distance function based on the chosen subdimensions of the clusters. We propose a new algorithm for subdimensional clustering of high dimensional data, each of the three major steps of which partitions the input points into several candidate clutters with proper numbers of points, filters the clusters that can not be useful in the next steps, and then merges the remaining clusters into the predefined number of clusters using a closeness function, respectively. The result of extensive experiments shows that the proposed algorithm exhibits better performance than the other existent clustering algorithms.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.41 no.6
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• pp.465-472
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• 2013

In this paper, we suggest how to determine the parachute deploy position for accurate landing of a UAV at a desired position. The 9-DOF dynamic modeling of UAV-parachute system is required to construct the proposed algorithm based on neural network nonlinear function approximation technique. The input and output data sets to train the neural network are obtained from simulation results using UAV-parachute 9-DOF model. The input data consist of the deploy position, UAV's velocity, and wind velocity. The output data consist of the cross range and down range of landing positions. So we predict the relative landing position from the current UAV position. The deploy position is then determined through distance compensations for the relative landing positions from the desired landing position. The deploy position is consistently calculated and updated.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.2
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• pp.225-230
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• 2020

Machine learning is an algorithm which learns a computer based on the data so that the computer can identify the trend of the data and predict the output of new input data. Machine learning can be classified into supervised learning, unsupervised learning, and reinforcement learning. Supervised learning is a way of learning a machine with given label of data. In other words, a method of inferring a function of the system through a pair of data and a label is used to predict a result using a function inferred about new input data. If the predicted value is continuous, regression analysis is used. If the predicted value is discrete, it is used as a classification. A result of analysis, no. 8 (5, 3.4, setosa), 27 (5, 3.4, setosa), 41 (5, 3.5, setosa), 44 (5, 3.5, setosa) and 40 (5.1, 3.4, setosa) in Table 3 were classified as the most similar Iris flower. Therefore, theoretical practical are suggested.

• The Journal of Fisheries Business Administration
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• v.45 no.1
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• pp.63-77
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• 2014

This study is to estimate the recent changes in total factor productivity of 15 Korean adjacent water fisheries based on Malmquist productivity indices. The study adopted both input and output oriented productivity measures utilizing a hyperbola distance function. In addition to this point, the study also calculated the 95% confidence interval for the various components of the productivities in order to access the statistical significance of estimates using 2000 times of re-sampling process through the smoothed bootstraping. The results of the study showed us that there was 18% reduction in the overall total factor productivity during the study period from 2007 to 2011, which turned out to be 5% of annual decrease in productivity. The study found that the main reason of this decrease in total productivity is about 22% downward shift of a fisheries production function due to recent conditions of a devastated fishing ground. When we evaluated the statistical significance of changes in technical efficiency combining both pure technical and scale efficiency based on the 95% confidence intervals, we could not find any evidence of changes in those components of total factor productivity. When we accessed the productivity of the each of 15 adjacent water fisheries methods, only the large danish seine fisheries showed us about 7% increase in productivity. Even though the large trawling and the large tow-boat trawling revealed no changes in productivity, all of the other 12 fisheries suffered the decreases in productivities.

• Journal of Korea Multimedia Society
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• v.21 no.8
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• pp.952-959
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• 2018

Clustering is one of the most fundamental algorithms in machine learning. The performance of clustering is affected by the distribution of data, and when there are more data or more dimensions, the performance is degraded. For this reason, we use a stacked auto encoder, one of the deep learning algorithms, to reduce the dimension of data which generate a feature vector that best represents the input data. We use k-means, which is a famous algorithm, as a clustering. Sine the feature vector which reduced dimensions are also multi dimensional, we use the Euclidean distance as well as the cosine similarity to increase the performance which calculating the similarity between the center of the cluster and the data as a vector. A deep clustering networks combining a stacked auto encoder and k-means re-trains the networks when the k-means result changes. When re-training the networks, the loss function of the stacked auto encoder and the loss function of the k-means are combined to improve the performance and the stability of the network. Experiments of benchmark image ad document dataset empirically validated the power of the proposed algorithm.

• Journal of Korea Port Economic Association
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• v.22 no.4
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• pp.59-79
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• 2006

In this paper, we analyse the comparative competitiveness of the 10 major logistic hub cities in China. First, using the input distance function, we calculated the technical efficiencies and the opportunity costs of the transport infra structure investments. Then, based on not only these supply side factors but also demand side, the overall comparative competitiveness by cities are analyzed. Our main findings are as follows: early developed, larger cities such as Shanghai, Guangzhou, Shenzhen are technically efficient but their opportunity costs of the additional transport investments are higher than the other cities. We also found that overall competitiveness of these larger and leading logistic hub cities are dominant over the small and newly developed logistic cities.

• Journal of the Korean Institute of Intelligent Systems
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• v.15 no.7
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• pp.828-833
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• 2005

Kernel methods have shown to improve the performance of conventional linear classification algorithms for complex distributed data sets, as mapping the data in input space into a higher dimensional feature space(7). In this paper, we propose a fuzzy kernel K-nearest neighbor(fuzzy kernel K-NN) algorithm, which applies the distance measure in feature space based on kernel functions to the fuzzy K-nearest neighbor(fuzzy K-NN) algorithm. In doing so, the proposed algorithm can enhance the Performance of the conventional algorithm, by choosing an appropriate kernel function. Results on several data sets and segmentation results for real images are given to show the validity of our proposed algorithm.

• Journal of Power Electronics
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• v.19 no.4
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• pp.835-845
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• 2019

This paper presents a robust-optimal control strategy to improve the output-voltage error-tracking and control capability of a DC-DC boost converter. The proposed strategy employs an optimized Fractional-order Proportional-Integral (FoPI) controller that serves to eliminate oscillations, overshoots, undershoots and steady-state fluctuations. In order to significantly improve the error convergence-rate during a transient response, the FoPI controller is augmented with a pre-stage nonlinear error-modulator. The modulator combines the variations in the error and error-derivative via the signed-distance method. Then it feeds the aggregated-signal to a smooth sigmoidal control surface constituting an optimized hyperbolic secant function. The error-derivative is evaluated by measuring the output-capacitor current in order to compensate the hysteresis effect rendered by the parasitic impedances. The resulting modulated-signal is fed to the FoPI controller. The fixed controller parameters are meta-heuristically selected via a Particle-Swarm-Optimization (PSO) algorithm. The proposed control scheme exhibits rapid transits with improved damping in its response which aids in efficiently rejecting external disturbances such as load-transients and input-fluctuations. The superior robustness and time-optimality of the proposed control strategy is validated via experimental results.

• Nuclear Engineering and Technology
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• v.53 no.3
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• pp.967-973
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• 2021

In 2011, an earthquake and subsequent tsunami hit the Fukushima Daiichi Nuclear Power Plant, causing simultaneous accidents in several reactors. This accident shows us that if there are several reactors on site, the seismic risk to multiple units is important to consider, in addition to that to single units in isolation. When a seismic event occurs, a seismic-failure correlation exists between the nuclear power plant's structures, systems, and components (SSCs) due to their seismic-response and seismic-capacity correlations. Therefore, it is necessary to evaluate the multi-unit seismic risk by considering the SSCs' seismic-failure-correlation effect. In this study, a methodology is proposed to obtain the seismic-response-correlation coefficient between SSCs to calculate the risk to multi-unit facilities. This coefficient is calculated from a probabilistic multi-unit seismic-response analysis. The seismic-response and seismic-failure-correlation coefficients of the emergency diesel generators installed within the units are successfully derived via the proposed method. In addition, the distribution of the seismic-response-correlation coefficient was observed as a function of the distance between SSCs of various dynamic characteristics. It is demonstrated that the proposed methodology can reasonably derive the seismic-response-correlation coefficient between SSCs, which is the input data for multi-unit seismic probabilistic safety assessment.