• The Journal of Engineering Geology
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• v.28 no.4
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• pp.741-753
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• 2018

Local drought in South Korea has recently increased interest in the efficient use of groundwater and then induces a growing need to introduce artificial recharge of groundwater that stores water in sedimentary layer. In order to evaluate the potential artificial recharge sites in the alluvial basins in Chungcheongnamdo province, an AHP (Analytical hierarchy process) model consisting of three primary and seven secondary factors was developed in this study. In the AHP model, adding candidate sites changes final evaluation score through a mathematical calculation process. By contrast ANN (Artificial neural network) model always provides an unchanged score for each candidate area. Therefore, the score can be used as a selection criterion for artificial recharge sites. It is concluded that the possibility of artificial recharge is relatively low if the score of the ANN model is less than about 1.5. Further studies and field surveys on the other regions in Korea will lead to draw out a more applicable ANN model.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.47 no.1
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• pp.92-103
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• 2010

In this paper, a method for the recognition of dynamic hand gestures in various backgrounds using Dynamic Space Time Warping(DSTW) algorithm is proposed. The existing method using DSTW algorithm compares multiple candidate hand regions detected from every frame of the query sequence with the model sequences in terms of the time. However the existing method can not exactly recognize the models because a false path can be generated from the candidates including not-hand regions such as background, elbow, and so on. In order to solve this problem, in this paper, we use the invariant moments extracted from the candidate regions of hand and compare the similarity of invariant moments among candidate regions. The similarity is utilized as a weight and the corresponding value is applied to the matching cost between the model sequence and the query sequence. Experimental results have shown that the proposed method can recognize the dynamic hand gestures in the various backgrounds. Moreover, the recognition rate has been improved by 13%, compared with the existing method.

• Food Science of Animal Resources
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• v.35 no.1
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• pp.91-100
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• 2015

The present study was conducted to screen candidate probiotic strains for anti-inflammatory activity. Initially, a nitric oxide (NO) assay was used to test selected candidate probiotic strains for anti-inflammatory activity in cultures of the murine macrophage cell line, RAW 264.7. Then, the in vitro probiotic properties of the strains, including bile tolerance, acid resistance, and growth in skim milk media, were investigated. We also performed an in vitro hydrophobicity test and an intestinal adhesion assay using Caenorhabditis elegans as a surrogate in vivo model. From our screening, we obtained 4 probiotic candidate lactic acid bacteria (LAB) strains based on their anti-inflammatory activity in lipopolysaccharide (LPS)-stimulated RAW 264.7 cell cultures and the results of the in vitro and in vivo probiotic property assessments. Molecular characterization using 16S rDNA sequencing analysis identified the 4 LAB strains as Lactobacillus plantarum. The selected L. plantarum strains (CAU1054, CAU1055, CAU1064, and CAU1106) were found to possess desirable in vitro and in vivo probiotic properties, and these strains are good candidates for further investigations in animal models and human clinical studies to elucidate the mechanisms underlying their anti-inflammatory activities.

• Journal of the Korea Society of Computer and Information
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• v.26 no.1
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• pp.93-101
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• 2021

In this paper, a vehicle license plate detection scheme is proposed that uses the spatial attention areas to detect accurately the license plates in various real-road situations. First, the previous WPOD-NET was analyzed, and its detection accuracy is evaluated as lower due to the unnecessary noises in the wide detection candidate areas. To resolve this problem, a vehicle license plate detection model is proposed that uses the candidate area of the license plate as a spatial attention areas. And we compared its performance to that of the WPOD-NET, together with the case of using the optimal spatial attention areas using the ground truth data. The experimental results show that the proposed model has about 20% higher detection accuracy than the original WPOD-NET since the proposed scheme uses tight detection candidate areas.

• Korean Management Science Review
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• v.10 no.2
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• pp.79-91
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• 1993

Strategic decisions related to the design of a physical distribution system can be classified into three basic components : facility location, transportation, inventory decisions. In this research the interdependence of those decisions are expressed in a mathematical model such that the total relevant cost of the system is minimized. We suggested a heuristic technique for solving the model. In broad terms, our solution technique combines a heuristic method for determining which candidate DCs to open and an exact method for minimizing costs given a set of open DCs. And we also developed a decision supporting package for the design of a physical distribution system.

• Proceedings of the IEEK Conference
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• 1999.06a
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• pp.579-582
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• 1999

In this paper, we propose a human face detection algorithm using adaptive skin color model and neural networks. To attain robustness in the changes of illumination and variability of human skin color, we perform a color segmentation of input image by thresholding adaptively in modified hue-saturation color space (TSV). In order to distinguish faces from other segmented objects, we calculate invariant moments for each face candidate and use the multilayer perceptron neural network of backpropagation algorithm. The simulation results show superior performance for a variety of poses and relatively complex backgrounds, when compared to other existing algorithm.

• Proceedings of the KIEE Conference
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• 1998.07c
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• pp.979-981
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• 1998

This paper presents a stability proof for the nonlinear feedback linearization-observer/sliding mode model following controller (NFL-O/SMMFC). The separation principle is derived, and the closed-loop stability is proved by a Lyapunov function candidate using an addition form of the sliding surface vector and the estimation error.

• Proceedings of the KIEE Conference
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• 1998.07c
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• pp.991-993
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• 1998

This paper presents a stability proof for the nonlinear feedback linearization-observer-based sliding mode model following controller (NFL-O-based SMMFC). The closed-loop stability is proved by a Lyapunov function candidate using an addition form of the sliding surface vector and the estimation error.

• Journal of Internet of Things and Convergence
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• v.5 no.2
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• pp.67-72
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• 2019

As Internet voting can take place regardless of a voter's location, the participation rate of the voters would be increased and economic costs will be reduced. But the drawback of it is that all participants have to trust the election management server. If the server colludes with the specific candidate, the other candidates cannot prove rigged election. In addition, majority of researches on Internet voting are mainly focused on the voting restricted by the region and the country. Thus, it's not appropriate for the election in Internet community such as YouTube channels. As the Internet community is composed of members from all around the world, the new type of voting model is needed. In this study, we propose the smart contract based Internet voting model applicable on the blockchain network. The proposed smart contract model consists of candidate registration, voter registration, voting and counting stages. In the proposed model, anonymity of the voter is assured in the voter registration and voting stages, and all candidates can confirm the fairness of the election in the counting stage.

• Journal of KIISE:Software and Applications
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• v.32 no.5
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• pp.385-395
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• 2005

Long sentence analysis has been a critical problem in machine translation because of high complexity. The methods of intra-sentence segmentation have been proposed to reduce parsing complexity. This paper presents the intra-sentence segmentation method based on maximum entropy probability model to increase the coverage and accuracy of the segmentation. We construct the rules for choosing candidate segmentation positions by a teaming method using the lexical context of the words tagged as segmentation position. We also generate the model that gives probability value to each candidate segmentation positions. The lexical contexts are extracted from the corpus tagged with segmentation positions and are incorporated into the probability model. We construct training data using the sentences from Wall Street Journal and experiment the intra-sentence segmentation on the sentences from four different domains. The experiments show about $88\%$ accuracy and about $98\%$ coverage of the segmentation. Also, the proposed method results in parsing efficiency improvement by 4.8 times in speed and 3.6 times in space.