• Journal of Intelligence and Information Systems
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• v.13 no.4
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• pp.91-104
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• 2007

Ontology matching technique is one of the most important techniques in the Semantic Web as well as in other areas. Ontology matching algorithm takes two ontologies as input, and finds out the matching relations between the two ontologies by using some parameters in the matching process. Ontology matching is very useful in various areas such as the integration of large-scale ontologies, the implementation of intelligent unified search, and the share of domain knowledge for various applications. In general cases, the performance of ontology matching is estimated by measuring the matching results such as precision and recall regardless of the requirements that came from the matching environment. Therefore, most research focuses on controlling parameters for the optimization of precision and recall separately. In this paper, we focused on the harmony of precision and recall rather than independent performance of each. The purpose of this paper is to propose a methodology that determines parameters for the desired ratio of precision and recall that is appropriate for the requirements of the matching environment.

• Journal of Navigation and Port Research
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• v.32 no.10
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• pp.851-856
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• 2008

Construction of large-scale container ports with the productivity improvements in container cranes shortened time of staying port to increase the level of service it harbors efforts accelerated. About container crane system exerted on the input, which is designed to look good performance considering the states feedback control system. The states observer designed of container cranes state variables that are expected to measurement noise or particular measurement signal. In the status of existing research, the feedback gain matrix and the state observer gain matrix are searched by being separated solving. But the feedback gain matrix and the state observer gain matrix are searched by RCGAs at once that be used robust search method in this paper.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.12 no.6
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• pp.1010-1015
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• 2008

The choice of RFID system is recently progressing(being) rapidly at various field. For the sake of RFID system popularization, However, We should solve privacy invasion to gain the pirated information of RFID tag. There is the safest M Ohkubos's skill among preexistent studying to solve these problems. But, this skill has a problem that demands a immense calculation capability caused an increase in tag number when we discriminate tags. So, This paper proposes the way of transplant to Grid environment for keeping Privacy Protection up and reducing the Tag Identification Time. And, We propose the Tag Identification Process Model to apply Even Division Algorithm to separate SP with same site in each node. If the proposed model works in Grid environment at once, it would reduce the time to identify tags to 1/k.

• Korean Journal of Remote Sensing
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• v.17 no.4
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• pp.285-295
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• 2001

We developed a CASE-II water model that will enable the simulation of remote sensing reflectance($R_{rs}$) at the coastal waters for the retrieval of suspended sediments (SS) concentrations from satellite imagery. The model has six components which are: water, chlorophyll, dissolved organic matter (DOM), non-chlorophyllous particles (NC), heterotrophic microorganisms and an unknown component, possibly represented by bubbles or other particulates unrelated to the five first components. We measured $R_{rs}$, concentration of SS and chlorophyll, and absorption of DOM during our field campaigns in Korea. In addition, we generated $R_{rs}$ from different concentrations of SS and chlorophyll, and various absorptions of DOM by random number functions to create a large database to test the model. We assimilated both the computer generated parameters as well as the in-situ measurements in order to reconstruct the reflectance spectra. We validated the model by comparing model-reconstructed spectra with observed spectra. The estimated $R_{rs}$ spectra were used to (1) evaluate the performance of four wavelengths and wavelengths ratios for accurate retrieval of SS. 2) identify the optimum band for SS retrieval, and 3) assess the influence of the SS on the chlorophyll algorithm. The results indicate that single bands at longer wavelengths in visible better results than commonly used channel ratios. The wavelength of 625nm is suggested as a new and optimal wavelength for SS retrieval. Because this wavelength is not available from SeaWiFS, 555nm is offered as an alternative. The presence of SS in coastal areas can lead to overestimation chlorophyll concentrations greater than 20-500%.

• Korean Journal of Remote Sensing
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• v.21 no.1
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• pp.91-98
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• 2005

On board satellite magnetometer measures all possible magnetic components, such as the core and crustal components from the inner Earth, and magnetospheric, ionospheric and' its coupled components from the outer Earth. Due to its dipole and non-dipole features, separation of the respective component from the measurements is most difficult unless the comprehensive knowledge of each field characteristics and the consequent modeling methods are solidly constructed. Especially, regional long wavelength magnetic signals of the crust are strongly masked by the main field and dynamic external field and hence difficult to isolate in the satellite measurements. In particular, the un-modeled effects of the strong auroral external fields and the complicated behavior of the core field near the geomagnetic poles conspire to greatly reduce the crustal magnetic signal-to-noise ratio in the polar region relative to the rest of the Earth. We can, however, use spectral correlation theory to filter the static lithospheric and core field components from the dynamic external field effects that are closely related to the geomagnetic storms affecting ionospheric current disturbances. To help isolate regional lithospheric anomalies from core field components, the correlations between CHAMP magnetic anomalies and the pseudo-magnetic effects inferred from satellite gravity-derived crustal thickness variations can also be exploited, Isolation of long wavelengths resulted from the respective source is the key to understand and improve the models of the external magnetic components as well as of the lower crustal structures. We expect to model the external field variations that might also be affected by a sudden upheaval like tsunami by using our algorithm after isolating any internal field components.

• The Journal of the Convergence on Culture Technology
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• v.5 no.4
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• pp.413-420
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• 2019

The purpose of the present study is to develop a model for predicting hypercholesterolemia using an integrated set of body fat mass variables based on machine learning techniques, beyond the study of the association between body fat mass and hypercholesterolemia. For this study, a total of six models were created using two variable subset selection methods and machine learning algorithms based on the Korea National Health and Nutrition Examination Survey (KNHANES) data. Among the various body fat mass variables, we found that trunk fat mass was the best variable for predicting hypercholesterolemia. Furthermore, we obtained the area under the receiver operating characteristic curve value of 0.739 and the Matthews correlation coefficient value of 0.36 in the model using the correlation-based feature subset selection and naive Bayes algorithm. Our findings are expected to be used as important information in the field of disease prediction in large-scale screening and public health research.

• Korean Journal of Remote Sensing
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• v.37 no.5_1
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• pp.1061-1070
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• 2021

In this manuscript, we tried to improve the performance of the FC-DenseNet by applying an attention gate for the classification of cropping areas. The attention gate module could facilitate the learning of a deep learning model and improve the performance of the model by injecting of spatial/spectral weights to each feature map. Crop classification was performed in the onion and garlic regions using a proposed deep learning model in which an attention gate was added to the skip connection part of FC-DenseNet. Training data was produced using various PlanetScope satellite imagery, and preprocessing was applied to minimize the problem of imbalanced training dataset. As a result of the crop classification, it was verified that the proposed deep learning model can more effectively classify the onion and garlic regions than existing FC-DenseNet algorithm.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.21 no.5
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• pp.141-147
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• 2021

In this paper, in order to improve the utilization of hyperspectral large-capacity data feature information by reducing complex computations by dimension reduction of neural network inputs in embedded systems, the band selection algorithm is applied in each subset. Among feature extraction and feature selection techniques, the feature selection aim to improve the optimal number of bands suitable for datasets, regardless of wavelength range, and the time and performance, more than others algorithms. Through this experiment, although the time required was reduced by 1/3 to 1/9 times compared to the others band selection technique, meaningful results were improved by more than 4% in terms of performance through the K-neighbor classifier. Although it is difficult to utilize real-time hyperspectral data analysis now, it has confirmed the possibility of improvement.

• Journal of the Korea Convergence Society
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• v.12 no.7
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• pp.53-60
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• 2021

LOD-based recommender systems usually leverage the data available within LOD datasets, such as DBpedia, in order to recommend items(movies, books, music) to the end users. These systems use a semantic similarity algorithm that calculates the degree of matching between pairs of Linked Data resources. In this paper, we proposed a new approach to measuring semantic distance in an LOD-based recommender system by assigning weights converted from user ratings to links in the LOD graph. The semantic distance measurement model proposed in this paper is based on a processing step in which a graph is personalized to a user through weight calculation and a method of applying these weights to LDSD. The Experimental results showed that the proposed method showed higher accuracy compared to other similar methods, and it contributed to the improvement of similarity by expanding the range of semantic distance measurement of the recommender system. As future work, we aim to analyze the impact on the model using different methods of LOD-based similarity measurement.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.11 no.6
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• pp.697-702
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• 2018

Recently, the number of private cars has increased sharply due to the increase in national income. The sudden increase in the number of vehicles in limited territory has caused serious traffic congestion and the traffic congestion cost wasted on the road due to such traffic congestion is increasing every year. To solve this problem, we propose a traffic signal control system using image processing. In this paper, we use the camera installed at the intersection to measure the amount of traffic flowing in and out of each road simultaneously. We propose a traffic signal control system that can prevent traffic congestion before it happens. In the case of applying the traffic signal control system proposed in this paper to the daytime, the traffic volume could be measured accurately. However, the result of the experiment with the night-time general camera and the headlight with the infrared camera at the night-time of 72.8% was 86.6%.