• Proceedings of the Korean Society of Soil and Groundwater Environment Conference
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• 1999.10a
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• pp.28-31
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• 1999

The purpose of this study is to see the effect of microbial population and dynamics of the indigeonous microorganisms on hydrocarbon contaminated areas. The microbial structures and activities to determine the microbial capabilities of the contaminated sites are very important for the remedial action technology selection. Throughout microbial studies on different conditions by ETS(Electron Transport System) and microbial activity analysis, it was found that aeration and water contents are the most important factors in this site remediation. According to test results, Burkholderia spp. was dominant species, and acclimation is also an important factor for the accerelated biodegradation.

• Wind and Structures
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• v.16 no.5
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• pp.477-490
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• 2013

This paper presents results of calculations performed according to our own semi-empirical mathematical model of critical vortex excitation. All calculations are carried out using own computer program, which allows the simulation of both the across-wind action caused by vortices and the lateral response of analysed structures. Vortex excitation simulations were performed in real time taking into account wind-structure interaction. Several structures of circular cross-sections were modelled using a FEM program and calculated under the action of critical vortex excitation. Six steel chimneys, six concrete chimneys and two concrete towers were considered. The method of selection and estimation of the experimental parameters describing the model are also presented. Finally, the results concerning maximum lateral top displacements of the structures are compared with available full-scale data for steel and concrete chimneys.

• Journal of the Korean Institute of Landscape Architecture
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• v.23 no.3
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• pp.94-105
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• 1995

Purposes of this study were to apply and examine that the importance-performance analysis(IPA) could be a useful park management tool. The IPA is an easily-applied technique developed in marketing field to measure attribute importance and performance. It displays them simultaneously on a 4-quardrant action grid; therefore, park managers can upgrade current services and address marketing strategies for various user segments. The children's grand park was selected as a study are. Two different types of questionaires were utilized for data analyses. They were collected from one respondent by on-site distribution or by mail survey, because attribute importance and performance should be rated before and after visiting a park, respectively. Then, total data was segmented systematically by 2 variables-age and benefit sought. Action grids from order to meet the needs of each segment. Several comments on both data collection methods and segment variables selection were described to improve the IPA in recreation settings for future researches.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.2
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• pp.929-944
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• 2019

Many critical applications require accurate real-time human action recognition. However, there are many hurdles associated with capturing and pre-processing image data, calculating features, and classification because they consume significant resources for both storage and computation. To circumvent these hurdles, this paper presents a recognition machine learning (ML) based system model which uses reduced data structure features by projecting real 3D skeleton modality on virtual 2D space. The MMU VAAC dataset is used to test the proposed ML model. The results show a high accuracy rate of 97.88% which is only slightly lower than the accuracy when using the original 3D modality-based features but with a 75% reduction ratio from using RGB modality. These results motivate implementing the proposed recognition model on an embedded system platform in the future.

• Journal of KIISE:Software and Applications
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• v.30 no.7_8
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• pp.672-680
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• 2003

A shopbot is a software agent whose goal is to maximize buyer´s satisfaction through automatically gathering the price and quality information of goods as well as the services from on-line sellers. In the response to shopbots´ activities, sellers on the Internet need the agents called pricebots that can help them maximize their own profits. In this paper we adopts Q-learning, one of the model-free reinforcement learning methods as a price-setting algorithm of pricebots. A Q-learned agent increases profitability and eliminates the cyclic price wars when compared with the agents using the myoptimal (myopically optimal) pricing strategy Q-teaming needs to select a sequence of state-action fairs for the convergence of Q-teaming. When the uniform random method in selecting state-action pairs is used, the number of accesses to the Q-tables to obtain the optimal Q-values is quite large. Therefore, it is not appropriate for universal on-line learning in a real world environment. This phenomenon occurs because the uniform random selection reflects the uncertainty of exploitation for the optimal policy. In this paper, we propose a Mixed Nonstationary Policy (MNP), which consists of both the auxiliary Markov process and the original Markov process. MNP tries to keep balance of exploration and exploitation in reinforcement learning. Our experiment results show that the Q-learning agent using MNP converges to the optimal Q-values about 2.6 time faster than the uniform random selection on the average.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.51 no.4
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• pp.160-166
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• 2002

Since conventional mobile robot control with one module has limitation to solve complex problems, there have been a variety of works on combining multiple modules for solving them. Recently, many researchers attempt to develop mobile robot controllers using artificial life techniques. In this paper, we develop a mobile robot controller using cellular automata based neural networks, where complex tasks are divided to simple sub-tasks and optimal neural structure of each sub-task is explored by genetic algorithm. Neural network modules are combined dynamically using the action selection mechanism, where basic behavior modules compete each other by inhibition and cooperation. Khepera mobile robot simulator is used to verify the proposed model. Experimental results show that complex behaviors emerge from the combination of low-level behavior modules.

• Journal of Sasang Constitutional Medicine
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• v.23 no.2
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• pp.139-152
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• 2011

1. Objectives: This research was proposed to find out the selection and form of addition and subtraction of the Sasang prescription. 2.Methods: The related contents of the pathology and the medical action of a Sasang disease were selected in "Donguisusebowon- Sasangchobongyun, -Gabobon, -Shinchukbon(東醫壽世保元-四象草本卷, -甲午本, -辛丑本)", "Donguisasangsinfyun(東醫四象新編)". 3.Results and Conclusions: 1) The change and development of the Sasang prescription. 2) The selection of drugs are caused by immunity of the ordinary times and a class of present disease. 3) Addition and subtraction of the Sasang prescription can divide 4-type.

• Smart Media Journal
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• v.9 no.1
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• pp.23-29
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• 2020

This paper presents an approach for dynamic hand gesture recognition by using algorithm based on 3D Convolutional Neural Network (3D_CNN), which is later extended to 3D Residual Networks (3D_ResNet), and the neural network based key frame selection. Typically, 3D deep neural network is used to classify gestures from the input of image frames, randomly sampled from a video data. In this work, to improve the classification performance, we employ key frames which represent the overall video, as the input of the classification network. The key frames are extracted by SegNet instead of conventional clustering algorithms for video summarization (VSUMM) which require heavy computation. By using a deep neural network, key frame selection can be performed in a real-time system. Experiments are conducted using 3D convolutional kernels such as 3D_CNN, Inflated 3D_CNN (I3D) and 3D_ResNet for gesture classification. Our algorithm achieved up to 97.8% of classification accuracy on the Cambridge gesture dataset. The experimental results show that the proposed approach is efficient and outperforms existing methods.

• The Journal of Information Systems
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• v.12 no.1
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• pp.145-158
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• 2003

Workflow is the automation of a business process, in whole or part, during which documents, information or tasks are passed from one participant to another for action, according to a set of procedural rules. The software selection problem is made difficult by the multiplicity of competing products and the lack of expertise and experience of users in the methods of software evaluation. Although the selection process for workflow is similar to that proposed for the acquisition of any software packages, differences arise in their evaluation criteria and choice methods. In this paper, we suggest the Analytic Hierarchy Process(AHP) method for selecting workflow software. The AHP is an intuitively easy method for formulating and analyzing decisions. It was developed to solve a specific class of problems that involves prioritization of potential alternate solutions. We showed how to evaluate 9 commercial workflow products by deciding the relative importance of the main criteria in the AHP model. We utilized the evaluation data for criteria ready suggested by specialist groups. Our methodology will be helpful to those who are going to adopt a best workflow product in their organizations. Although the criteria and their evaluation scores regarding workflow products are suggested, it is not easy to apply them to a real case and get solutions without a model.

• Journal of the East Asian Society of Dietary Life
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• v.15 no.2
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• pp.235-245
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• 2005

This study was conducted to investigate the eating out behavior and its factors in restaurant selection by students in Incheon area. Self administered questionnaires were collected from 297 students. Statistical data analysis was completed using a SPSS v. 10.0 program. The results are summarized as follows: The average age, weight, height, BMI and male and female students were $24.31,\;67.96\cal{kg},\;174.93\cal{cm},\;22.17\;;\;20.55,\;51.99\cal{kg},\;165.44\cal{cm},\;19.00$ reflectively. About $54.2\%$ of male students and $38.0\%$ of female students responded to eat out at least once a day. And lunch was most frequent meal for eating out. About $57\%$ of students responded to have eat at restaurants around campus, and $19.5\%$ of students used the university foodservice system. Korean-style food was most favorite dish when they ate out. Major source of restaurants information was recommended action by friends or family. But the use of internet or magazine was negligible. The factor of restaurants choice was in order 'taste', 'price', 'atmosphere' and 'hygienic'.