• Journal of Korea Water Resources Association
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• v.47 no.3
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• pp.285-295
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• 2014

We applied the ISPSO-GLUE method, which integrates the Isolated-Speciation-based Particle Swarm Optimization (ISPSO) with the Generalized Likelihood Uncertainty Estimation (GLUE) method, to the uncertainty analysis of the Topography Model (TOPMODEL) and compared its performance with that of the GLUE method. When we performed the same number of model runs for the both methods, we were able to identify the point where the performance of ISPSO-GLUE exceeded that of GLUE, after which ISPSOGLUE kept improving its performance steadily while GLUE did not. When we compared the 95% uncertainty bounds of the two methods, their general shapes and trends were very similar, but those of ISPSO-GLUE enclosed about 5.4 times more observed values than those of GLUE did. What it means is that ISPSOGLUE requires much less number of parameter samples to generate better performing uncertainty bounds. When compared to ISPSO-GLUE, GLUE overestimated uncertainty in the recession limb following the maximum peak streamflow. For this recession period, GLUE requires to find more behavioral models to reduce the uncertainty. ISPSO-GLUE can be a promising alternative to GLUE because the uncertainty bounds of the method were quantitatively superior to those of GLUE and, especially, computationally expensive hydrologic models are expected to greatly take advantage of the feature.

• Analytical Science and Technology
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• v.25 no.5
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• pp.292-299
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• 2012

GC/MS has been utilized for many applications due to great resolution and reproducibility, which made it possible to build up the database of mass spectrum, while HS-SPME has the advantage of solventfree extraction of volatile compounds. The combination of these two methods, HS-SPME GC/MS, enabled many scientific applications with various possibilities. In this study, the analysis of trail pheromone excreted from live Camponotus japonicus with the feature of solvent-free extraction was carried out and the optimization for this analysis was performed. The major compounds detected were n-decane, n-undecane, and n-tridecane. Optimization for the best detection of these hydrocarbons was processed in the point of SPME parameter (selection of fiber, extraction temperature, extraction time, etc.). The advantage of the analysis of live sample is to analyze phenomenon right after it is excreted by ants. But the experimental process has restriction of extraction temperature and time because of the analysis of live ants. Establishing the process of HS-SPME GC/MS applied to live samples shown in this study can be a breakthrough for the ecofriendly and ethical research of live things.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.40 no.4
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• pp.700-708
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• 2015

A software birthmark is the set of characteristics of a program which can be used to identify the program. Many researchers have studied on detecting theft of java programs using some birthmarks. In case of Android apps, code obfuscation techniques are used to protect the apps against reverse-engineering and tampering. However, attackers can also use the obfuscation techniques in order to conceal a stolen program. A birthmark (feature) of an app can be alterable by code obfuscations. Therefore, it is necessary to detect Android app theft based on the birthmark which is resilient to code obfuscation. In this paper, we propose an effective Android app birthmark and app theft detection through the proposed birthmark. By analyzing some obfuscation tools, we have first selected parameter and the return types of methods as an adequate birthmark. Then, we have measured similarity of target apps using the birthmarks extracted from the apps, where some target apps are not obfuscated and the others obfuscated. The measurement results show that our proposed birthmark is effective for detecting Android app theft even though the apps are obfuscated.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.48 no.1
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• pp.47-54
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• 2011

The remote control mobile robot is the robot accomplishing a task according to the orders giving by a user through departed communication system using a joystick. Basically, to supply a lot of information, as this type of robot uses visual information, the user can check the transmitted information by eyes and give orders to the robot. But the weak point of this type of robot is that it has a possibility to come into a collision with an obstacle not be seen to the user because of the communication delay occurring in a communication system and dead zone happening in visual information. To solve the problem, in this paper, we try to suggest a system applying a fuzzy control system to the robot to avoid collision with an obstacle by an immediate order of the user. The fuzzy control system has better performance than any other existing control methods in the change of noise and parameter. And it is more efficient than any other since it solves easy the complexity of the system analysis occurring because of the nonlinear feature of the mobile robot system. In this paper, we made experiments how the mobile robot controlled by the fuzzy control system avoids an obstacle, tracks the path and avoids the obstacle in the path, to prove the performance and to check the evaluation and the application possibility of the fuzzy control system.

• The Bulletin of The Korean Astronomical Society
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• v.38 no.1
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• pp.36.2-36.2
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• 2013

The SPICA (SPace Infrared Telescope for Cosmology & Astrophysics) project is a next-generation infrared space telescope optimized for mid- and far-infrared observation with a cryogenically cooled 3m-class telescope. The focal plane instruments onboard SPICA will enable us to resolve many astronomical key issues from the formation and evolution of galaxies to the planetary formation. The FPC-S (Focal Plane Camera - Sciecne) is a near-infrared instrument proposed by Korea as an international collaboration. Owing to the capability of both low-resolution imaging spectroscopy and wide-band imaging with a field of view of $5^{\prime}{\times}5^{\prime}$, it has large throughput as well as high sensitivity for diffuse light compared with JWST. In order to strengthen advantages of the FPC-S, we propose the studies of probing population III stars by the measurement of cosmic near-infrared background radiation and the star formation history at high redshift by the discoveries of active star-forming galaxies. In addition to the major scientific targets, to survey large area opens a new parameter space to investigate the deep Universe. The good survey capability in the parallel imaging mode allows us to study the rare, bright objects such as quasars, bright star-forming galaxies in the early Universe as a way to understand the formation of the first objects in the Universe, and ultra-cool brown dwarfs. Observations in the warm mission will give us a unique chance to detect high-z supernovae, ices in young stellar objects (YSOs) even with low mass, the $3.3{\mu}$ feature of shocked circumstance in supernova remnants. Here, we report the current status of SPICA/FPC project and its extragalactic sciences.

• The Transactions of the Korea Information Processing Society
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• v.4 no.7
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• pp.1749-1758
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• 1997

This paper suggests the development of dynamic forecasting model for short-term power demand based on Radial Basis Function Network and Pal's GLVQ algorithm. Radial Basis Function methods are often compared with the backpropagation training, feed-forward network, which is the most widely used neural network paradigm. The Radial Basis Function Network is a single hidden layer feed-forward neural network. Each node of the hidden layer has a parameter vector called center. This center is determined by clustering algorithm. Theatments of classical approached to clustering methods include theories by Hartigan(K-means algorithm), Kohonen(Self Organized Feature Maps %3A SOFM and Learning Vector Quantization %3A LVQ model), Carpenter and Grossberg(ART-2 model). In this model, the first approach organizes the load pattern into two clusters by Pal's GLVQ clustering algorithm. The reason of using GLVQ algorithm in this model is that GLVQ algorithm can classify the patterns better than other algorithms. And the second approach forecasts hourly load patterns by radial basis function network which has been constructed two hidden nodes. These nodes are determined from the cluster centers of the GLVQ in first step. This model was applied to forecast the hourly loads on Mar. $4^{th},\;Jun.\;4^{th},\;Jul.\;4^{th},\;Sep.\;4^{th},\;Nov.\;4^{th},$ 1995, after having trained the data for the days from Mar. $1^{th}\;to\;3^{th},\;from\;Jun.\;1^{th}\;to\;3^{th},\;from\;Jul.\;1^{th}\;to\;3^{th},\;from\;Sep.\;1^{th}\;to\;3^{th},\;and\;from\;Nov.\;1^{th}\;to\;3^{th},$ 1995, respectively. In the experiments, the average absolute errors of one-hour ahead forecasts on utility actual data are shown to be 1.3795%.

• Geophysics and Geophysical Exploration
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• v.6 no.4
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• pp.199-206
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• 2003

Characteristics of the static shift are discussed by comparing the three-dimensional MT inversion with/without static shift parameterization. The galvanic distortion by small-scale shallow feature often leads severe distortion in inverted resistivity structures. The new inversion algorithm is applied to four numerical data sets contaminated by different amount of static shift. In real field data interpretations, we generally do not have any a-priori information about how much the data contains the static shift. In this study, we developed an algorithm for finding both Lagrangian multiplier for smoothness and the trade-off parameter for static shift, simultaneously in 3-D MT inversion. Applications of this inversion routine for the numerical data sets showed quite reasonable estimation of static shift parameters without any a-priori information. The inversion scheme is successfully applied to all the four data sets, even when the static shift does not obey the Gaussian distribution. Allowing the static shift parameters have non-zero degree of freedom to the inversion, we could get more accurate block resistivities as well as static shifts in the data. When inversion does not consider the static shift as inversion parameters (conventional MT inversion), the block resistivities on the surface are modified considerably to match possible static shift. The inhomogeneous blocks on the surface can generate the static shift at low frequencies. By those mechanisms, the conventional 3-D MT inversion can reconstruct the resistivity structures to some extent in the deeper parts even when moderate static shifts are in the data. As frequency increased, however, the galvanic distortion is not frequency independent any more, and thus the conventional inversion failed to fit the apparent resistivity and phase, especially when strong static shift is added. Even in such case, however, reasonable estimation of block resistivity as well as static shift parameters were obtained by 3-D MT inversion with static shift parameterization.

• The Journal of the Acoustical Society of Korea
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• v.40 no.2
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• pp.139-147
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• 2021

This paper introduces and implements a Sound Event Detection (SED) system based on weakly-supervised learning where only part of the data is labeled, and analyzes the effect of parameters. The SED system estimates the classes and onset/offset times of events in the acoustic signal. In order to train the model, all information on the event class and onset/offset times must be provided. Unfortunately, the onset/offset times are hard to be labeled exactly. Therefore, in the weakly-supervised task, the SED model is trained by "strongly labeled data" including the event class and activations, "weakly labeled data" including the event class, and "unlabeled data" without any label. Recently, the SED systems using the mean-teacher model are widely used for the task with several parameters. These parameters should be chosen carefully because they may affect the performance. In this paper, performance analysis was performed on parameters, such as the feature, moving average parameter, weight of the consistency cost function, ramp-up length, and maximum learning rate, using the data of DCASE 2020 Task 4. Effects and the optimal values of the parameters were discussed.

• Journal of the Korea Society of Computer and Information
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• v.27 no.12
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• pp.59-68
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• 2022

This paper presents a method for 1:1 verification by comparing the similarity between the given real product image and the drawing image. The proposed method combines two existing CNN-based deep learning models to construct a Siamese Network. After extracting the feature vector of the image through the FC (Fully Connected) Layer of each network and comparing the similarity, if the real product image and the drawing image (front view, left and right side view, top view, etc) are the same product, the similarity is set to 1 for learning and, if it is a different product, the similarity is set to 0. The test (inference) model is a deep learning model that queries the real product image and the drawing image in pairs to determine whether the pair is the same product or not. In the proposed model, through a comparison of the similarity between the real product image and the drawing image, if the similarity is greater than or equal to a threshold value (Threshold: 0.5), it is determined that the product is the same, and if it is less than or equal to, it is determined that the product is a different product. The proposed model showed an accuracy of about 71.8% for a query to a product (positive: positive) with the same drawing as the real product, and an accuracy of about 83.1% for a query to a different product (positive: negative). In the future, we plan to conduct a study to improve the matching accuracy between the real product image and the drawing image by combining the parameter optimization study with the proposed model and adding processes such as data purification.

• 한국체육학회지인문사회과학편
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• v.57 no.2
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• pp.279-289
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• 2018

The purpose of this study is to investigate features of leisure and recreation scholarship study in The Korean Journal of physical education based on co-authors network and topic modeling through using Word Cloud and LDA Topic Modeling(Latent Dirichlet Allocation). The data collected for this study are 2,697 papers published online from January 2008 to March 2017 on the Korean journal of physical education. Respectively ordered analysis targets are the major author, author of correspondence, co-author 1, co-author 2, co-author n in related document to explore studies' trends using the 369 documents. As a result, the co-author network analysis result found that 451 were linked to the research network, on average researchers had 1.52 relationships and the average distance between researchers was 2.33. The Representative author's concentration of connection was ranked high in the order of the following, Lee. K. M., Hwang. S. H., H., Lee. C. S., and proximity centers were shown in Seo K. B., Han. J. H., Kim. K. J. Finally, parameter-centric features appeared in order of Lee. C. W. and Seo. K. B. was most actively connected between the researchers of the leisure-related academic papers. Future research needs discussions among scholars regarding the trend and direction of future leisure research.