• Journal of the Korean earth science society
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• v.30 no.7
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• pp.834-844
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• 2009

Two-dimensional magnetotelluric (MT) inversion algorithm using minimum support (MS) stabilizer functional was implemented in this study to enhance the contrast of inverted images. For this implementation, this study derived a formula in discrete form for creeping model updates in the least-squares linearized inversion. A spatially varying regularization parameter determination algorithm, which is known as ACB (Active Constraint Balancing), was also adopted to stabilize the inversion process when using MS stabilizer as a model constraint. Inversion experiments for a simple isolated body model show well the feature of MS stabilizer in concentrating the anomalous body compared with the second-order derivative model constraint. This study also compared MS stabilizer and the second-order derivative model constraints for a model having multiple anomalous bodies to show the applicability of the algorithm into field data.

• Journal of the Korean Society for information Management
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• v.35 no.3
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• pp.123-145
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• 2018

In this study, we investigated the problems of existing e-journal subscriptions caused by open access of e-journals faced by university libraries. To solve these problems, we proposed an operational model for open access based subscription. The proposed operational model, which adapts the four concepts of subscription model, system, policy, and operation interpreted according to reality, applies open access on the premise of rational subscription of electronic journals. The proposed operational model was constructed based on national support, comprehensive operation, open access based model, and cooperation system. In particular, it emphasized the need for stable and continuous access to scholarly information through national support policy. The proposed operational model can be used as a basic data for the realization and research of open access in the domestic environment.

• The KIPS Transactions:PartD
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• v.13D no.7 s.110
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• pp.931-938
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• 2006

It is important to operate an education resources which could be integrated to an system. But most of existing education information system was not developed with standardization. It is need the core education asset and reusable education service to make a good education system. Consequently, it is needed to use Sharable Content Object Reference Model(SCORM) based contents managing in order to reuse the contents of education. And it needs assembling and producing method with reusable core asset of education system to develop the application program for education. In this thesis, we study the Teaching-Learning supporting system to support systematic education resources. Teaching-Learning support system is developed of educational domain assess through development process based on Model Driven Architecture(MDA) and core asset on each stage. Application program of education is developed using MDA automatic tool through analyzing and designing for contents storage which is based on contents meta model. We finally can develop the application software of education with low cost and high productivity by raising the reusability of education contents and by using the core asset to the whole development process.

• Journal of Korean Society of Rural Planning
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• v.28 no.1
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• pp.57-69
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• 2022

Crop classification is very important for estimating crop yield and figuring out accurate cultivation area. The purpose of this study is to classify crops harvested in fall in Idam-ri, Goesan-gun, Chungcheongbuk-do by using unmanned aerial vehicle (UAV) images and support vector machine (SVM) model. The study proceeded in the order of image acquisition, variable extraction, model building, and evaluation. First, RGB and multispectral image were acquired on September 13, 2021. Independent variables which were applied to Farm-Map, consisted gray level co-occurrence matrix (GLCM)-based texture characteristics by using RGB images, and multispectral reflectance data. The crop classification model was built using texture characteristics and reflectance data, and finally, accuracy evaluation was performed using the error matrix. As a result of the study, the classification model consisted of four types to compare the classification accuracy according to the combination of independent variables. The result of four types of model analysis, recursive feature elimination (RFE) model showed the highest accuracy with an overall accuracy (OA) of 88.64%, Kappa coefficient of 0.84. UAV-based RGB and multispectral images effectively classified cabbage, rice and soybean when the SVM model was applied. The results of this study provided capacity usefully in classifying crops using single-period images. These technologies are expected to improve the accuracy and efficiency of crop cultivation area surveys by supplementing additional data learning, and to provide basic data for estimating crop yields.

• Information Systems Review
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• v.17 no.1
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• pp.49-64
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• 2015

Customer reviews which include subjective opinions for the product or service in online store have been generated rapidly and their influence on customers has become immense due to the widespread usage of SNS. In addition, a number of studies have focused on opinion mining to analyze the positive and negative opinions and get a better solution for customer support and sales. It is very important to select the key terms which reflected the customers' sentiment on the reviews for opinion mining. We proposed a document-level terms-based sentiment classification model by select in the optimal terms with part of speech tag. SVMs (Support vector machines) are utilized to build a predictor for opinion mining and we used the combination of POS tag and four terms extraction methods for the feature selection of SVM. To validate the proposed opinion mining model, we applied it to the customer reviews on Amazon. We eliminated the unmeaning terms known as the stopwords and extracted the useful terms by using part of speech tagging approach after crawling 80,000 reviews. The extracted terms gained from document frequency, TF-IDF, information gain, chi-squared statistic were ranked and 20 ranked terms were used to the feature of SVM model. Our experimental results show that the performance of SVM model with four POS tags is superior to the benchmarked model, which are built by extracting only adjective terms. In addition, the SVM model based on Chi-squared statistic for opinion mining shows the most superior performance among SVM models with 4 different kinds of terms extraction method. Our proposed opinion mining model is expected to improve customer service and gain competitive advantage in online store.

• The Korean Journal of Rehabilitation Nursing
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• v.1 no.1
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• pp.111-123
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• 1998

The related factors of the quality of life (QOL) in stroke patients was identified empirically. The subjects were 254 stroke patients who were discharged and taken follow-up care at the outpatient department. In this model, the physical, psychological, and social status were assumed to affect the QOL. And the social support was assumed to moderate these effects. NIH stroke state, ADL, and IADL were used to measure the physical status. Using CES-D, the psychological status was measured. The social status was defined as the job change after stroke attack. The satisfaction with the care by primary caregivers, significant others, and health professionals was measured as the social support. To identify the effect of the physical, psychological, and social status on the QOL, multiple regression analysis was carried out. The psychological and social status were found to be the significant predictors of the QOL(R2=0.27, p=0.00). Next, to identify the moderating effect of the social support, the subjects were divided into two groups, that is, the low social support group and the high social support group. It is found that the predicting variance is different between these two groups. In the low social support group, the psychological, social, and physical status predicted as much as 42% of the QOL. On the contrary, the psychological status predicted only 8% of the QOL in the high social support group. So it is concluded that the social support moderates the effects of the physical, psychological, and social status on QOL. Finally, to identify the social support which moderates those effects, the social support was divided into three classes. Each social support class was divided into the low and high social support group again. In the every class of social support, the difference between two groups was also identified. So the model of the QOL is recommended for the framework of the care for the stroke patients. Also these results support the claim that the long-term facilities for stroke patients are necessary.

• Journal of the Society of Disaster Information
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• v.17 no.3
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• pp.465-486
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• 2021

Purpose: Since most disaster information systems are centered on non-disabled people, the reality is that there is a lack of disaster information delivery systems for the vulnerable, such as the disabled, the elderly, and children, who are relatively vulnerable to disasters. The purpose of the service is to improve the safety of the disabled and the elderly by eliminating blind spots of informatization and establishing customized disaster information services to respond to disasters through IoT-based integrated control technology. Method: The model at the core of this study is the disaster alert propagation model and evacuation support model, and it shall be developed by reflecting the behavioral characteristics of the disabled and the elderly in the event of a disaster. The disaster alert propagation model spreads disaster situations collected using IoT technology, and the evacuation support model uses geomagnetic field-based measuring technology to identify the user's indoor location and help the disabled and the elderly evacuate safely. Results: Demonstration model demonstration resulted in an efficient qualitative evaluation of indoor location accuracy, such as the suitability of evacuation route guidance and satisfaction of services from the user's perspective. Conclusion: Disaster information and evacuation support services were established for the safety vulnerable groups of mobile app for model verification. The disaster situation was demonstrated through experts in the related fields and the disabled by limiting it to the fire situation. It was evaluated as "satisfaction" in the adequacy of disaster information delivery and evacuation support, and its functional satisfaction and user UI were evaluated as "normal" due to the nature of the pilot model. Through this, the disaster information and evacuation support services presented in this study were evaluated to support the safety vulnerable groups to a faster disaster evacuation without missing the golden time of disaster evacuation.

• Journal of Internet Computing and Services
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• v.14 no.6
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• pp.1-8
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• 2013

Data mining techniques are used to find important and meaningful information from huge databases, and pattern mining is one of the significant data mining techniques. Pattern mining is a method of discovering useful patterns from the huge databases. Frequent pattern mining which is one of the pattern mining extracts patterns having higher frequencies than a minimum support threshold from databases, and the patterns are called frequent patterns. Traditional frequent pattern mining is based on a single minimum support threshold for the whole database to perform mining frequent patterns. This single support model implicitly supposes that all of the items in the database have the same nature. In real world applications, however, each item in databases can have relative characteristics, and thus an appropriate pattern mining technique which reflects the characteristics is required. In the framework of frequent pattern mining, where the natures of items are not considered, it needs to set the single minimum support threshold to a too low value for mining patterns containing rare items. It leads to too many patterns including meaningless items though. In contrast, we cannot mine any pattern if a too high threshold is used. This dilemma is called the rare item problem. To solve this problem, the initial researches proposed approximate approaches which split data into several groups according to item frequencies or group related rare items. However, these methods cannot find all of the frequent patterns including rare frequent patterns due to being based on approximate techniques. Hence, pattern mining model with multiple minimum supports is proposed in order to solve the rare item problem. In the model, each item has a corresponding minimum support threshold, called MIS (Minimum Item Support), and it is calculated based on item frequencies in databases. The multiple minimum supports model finds all of the rare frequent patterns without generating meaningless patterns and losing significant patterns by applying the MIS. Meanwhile, candidate patterns are extracted during a process of mining frequent patterns, and the only single minimum support is compared with frequencies of the candidate patterns in the single minimum support model. Therefore, the characteristics of items consist of the candidate patterns are not reflected. In addition, the rare item problem occurs in the model. In order to address this issue in the multiple minimum supports model, the minimum MIS value among all of the values of items in a candidate pattern is used as a minimum support threshold with respect to the candidate pattern for considering its characteristics. For efficiently mining frequent patterns including rare frequent patterns by adopting the above concept, tree based algorithms of the multiple minimum supports model sort items in a tree according to MIS descending order in contrast to those of the single minimum support model, where the items are ordered in frequency descending order. In this paper, we study the characteristics of the frequent pattern mining based on multiple minimum supports and conduct performance evaluation with a general frequent pattern mining algorithm in terms of runtime, memory usage, and scalability. Experimental results show that the multiple minimum supports based algorithm outperforms the single minimum support based one and demands more memory usage for MIS information. Moreover, the compared algorithms have a good scalability in the results.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.09a
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• pp.435-438
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• 2003

A townscape has been a main factor in urban-development problems in Japan. In the townscape, keeping harmony with environment is a common goal. But useful and meaningful goals are expressing individuality and impression of the town in the townscape. In this paper, we propose the colony planning support system system to improve the townscape. The system finds propositional colour combinations based on three elements, town image, colour harmony, and cost. The targets of this model are mostly townscapes in residential areas that already exist, In this paper, we introduce the construction of a Kansei evaluation model to quantify the impression. First, we conducted computer-based evaluational experiments for 20 subjects using the SD method to clarify the relationship between town image and street colours. We chose 16 adjective words related to town image and prepared 100 colour picture samples for the evaluation. After the experiments, we constructed the model using a neural network for each word. We chose 62 experimental results for the training data of the neural network and 20 results for the testing data. Each colour in the data was selected to have unique hue, brightness or saturation attributes, After the construction, we tested the model for accuracy. We input the testing data into the constructed model and calculated errors between the output from the model and the experimental results. Testing of the model showed that the model worked well for more than 80％ of the samples. The model demonstrated influences of colours on the town image.

• International Journal of Naval Architecture and Ocean Engineering
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• v.8 no.2
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• pp.177-187
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• 2016

To evaluate the tractive performance of tracked trencher on seafloor surface, a new shear stress-displacement empirical model was proposed for saturated soft-plastic soil (SSP model). To validate the SSP model, a test platform, where track segment shear test can be performed in seafloor soil simulacrum (bentonite water mixture), was built. Series shear tests were carried out. Test results indicate that the SSP model can describe the mechanical behavior of track segment with good approximation in seafloor soil simulacrum. Through analyzing the main external forces applied to seafloor tracked trencher during the uniform linear trenching process, a drawbar pull prediction model was deduced with the SSP model. A tracked walking mechanism of the seafloor tracked trencher prototype was built, and verification tests were carried out. Test results indicate that this prediction model was feasible and effective; moreover, from another side, this conclusion also proved that the SSP model was effective.