• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.12 no.1
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• pp.257-264
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• 2012

The development of mobile devices and the spread of wireless network help share and exchange information and resources more easily. The bond them to Cloud Computing technology help pay attention to "Mobile Cloud" service, so there have been being a lot of studies on "Mobile Cloud" service. Especially, the important of 'Recommendation Service' which is customized for each user's preference and context has been increasing. In order to provide appropriate recommendation services, it enables to recognize user's current state, analyze the user's profile like user's tendency and preference, and draw the service answering the user's request. Most existing frameworks, however, are not very suitable for mobile devices because they were proposed on the web-based. And other context information except location information among user's context information are not much considered. Therefore, this paper proposed the context-aware framework, which provides more suitable services by using user's context and profile.

• Journal of the Korean Society of Food Science and Nutrition
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• v.38 no.3
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• pp.333-337
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• 2009

This study was conducted to determine contents and estimated availabilities of trace minerals (Cu, Fe, Mn, Zn) in soybean sprouts depending on culture periods such as 1, 3 and 5 days. The proportional changes of Cu estimated availability in cotyledon were between 57.31 and 98.34%, between 63.69 and 82.56% for Fe, 99.19 and 99.82% for Mn and 48.60 and 94.56% for Zn, respectively, based on dry matter. The estimated availabilities of Cu, Fe, Mn, and Zn in hypocotyl were between 34.63 and 56.0%, 20.74 and 40.33%, 86.5 and 95.88%, and 96.11 and 96.61%, respectively, on dry matter basis.

• The KIPS Transactions:PartA
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• v.16A no.2
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• pp.135-142
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• 2009

With the development of ubiquitous computing technology, the number of HCI applications is increasing, where they utilize various contexts to provide adaptive services to users according to the change of contexts, and also, technologies for collecting various sensor data and generating integrated contexts get more important. However, the research on the collection and integration of multi-sensor data is not sufficient when we consider the various utilization areas of the integrated contexts. In particular, they have some problems to be solved such as duplication of the context data and the high system load. In this paper, we propose an integrated context generation scheme based on Ant Colony System. Proposed scheme generates the context data as a form of XML and avoids the generation of unnecessary context information by detecting the repeated sensor information based on the ant colony system. As a result of detections, we reduce wasted resources and repositories when the integrated context is created. We also reduce the overhead for reasoning.

• Journal of KIISE:Software and Applications
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• v.32 no.9
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• pp.888-900
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• 2005

Several researches have been proposed to formalize the knowledge preconditions problem - j.e., an action or a plan is epistemically feasible. However, since the feasibility is only checked at design-time and is assumed that it will also 1)e feasible at run-time, it is not suitable in the context of Semantic Web services composition, where many agents should share the limited resources required for the execution of Web services composition. Therefore, in this paper, we formalize a transactionally executable Web services composition which enables to guarantee its atomicity. Moreover, in order to formalize the transactional executability, we propose $TL-ALCFK_{NF}$ which extends TL-ALCF with the modal operators K and A. Based on $TL-ALCFK_{NF}$, we show how to carry out the epistemic reasoning with TL-ALCF as a language to represent Semantic Web services composition.

• Journal of Convergence for Information Technology
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• v.11 no.9
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• pp.28-34
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• 2021

In the case of object detection using deep learning, both accuracy and real-time are required. However, it is difficult to use a deep learning model that processes a large amount of data in a limited resource environment. To solve this problem, this paper proposes an object detection model for small embedded devices. Unlike the general detection model, the model size was minimized by using a structure in which the pre-trained feature extractor was removed. The structure of the model was designed by repeatedly stacking lightweight convolution blocks. In addition, the number of region proposals is greatly reduced to reduce detection overhead. The proposed model was trained and evaluated using the public dataset PASCAL VOC. For quantitative evaluation of the model, detection performance was measured with average precision used in the detection field. And the detection speed was measured in a Raspberry Pi similar to an actual embedded device. Through the experiment, we achieved improved accuracy and faster reasoning speed compared to the existing detection method.

• Journal of Intelligence and Information Systems
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• v.18 no.4
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• pp.43-57
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• 2012

To enhance the competitive advantage in a constantly changing business environment, an enterprise management must make the right decision in many business activities based on both internal and external information. Thus, providing accurate information plays a prominent role in management's decision making. Intuitively, historical data can provide a feasible estimate through the forecasting models. Therefore, if the service department can estimate the service quantity for the next period, the service department can then effectively control the inventory of service related resources such as human, parts, and other facilities. In addition, the production department can make load map for improving its product quality. Therefore, obtaining an accurate service forecast most likely appears to be critical to manufacturing companies. Numerous investigations addressing this problem have generally employed statistical methods, such as regression or autoregressive and moving average simulation. However, these methods are only efficient for data with are seasonal or cyclical. If the data are influenced by the special characteristics of product, they are not feasible. In our research, we propose a forecasting framework that predicts service demand of manufacturing organization by combining Case-based reasoning (CBR) and leveraging an unsupervised artificial neural network based clustering analysis (i.e., Self-Organizing Maps; SOM). We believe that this is one of the first attempts at applying unsupervised artificial neural network-based machine-learning techniques in the service forecasting domain. Our proposed approach has several appealing features : (1) We applied CBR and SOM in a new forecasting domain such as service demand forecasting. (2) We proposed our combined approach between CBR and SOM in order to overcome limitations of traditional statistical forecasting methods and We have developed a service forecasting tool based on the proposed approach using an unsupervised artificial neural network and Case-based reasoning. In this research, we conducted an empirical study on a real digital TV manufacturer (i.e., Company A). In addition, we have empirically evaluated the proposed approach and tool using real sales and service related data from digital TV manufacturer. In our empirical experiments, we intend to explore the performance of our proposed service forecasting framework when compared to the performances predicted by other two service forecasting methods; one is traditional CBR based forecasting model and the other is the existing service forecasting model used by Company A. We ran each service forecasting 144 times; each time, input data were randomly sampled for each service forecasting framework. To evaluate accuracy of forecasting results, we used Mean Absolute Percentage Error (MAPE) as primary performance measure in our experiments. We conducted one-way ANOVA test with the 144 measurements of MAPE for three different service forecasting approaches. For example, the F-ratio of MAPE for three different service forecasting approaches is 67.25 and the p-value is 0.000. This means that the difference between the MAPE of the three different service forecasting approaches is significant at the level of 0.000. Since there is a significant difference among the different service forecasting approaches, we conducted Tukey's HSD post hoc test to determine exactly which means of MAPE are significantly different from which other ones. In terms of MAPE, Tukey's HSD post hoc test grouped the three different service forecasting approaches into three different subsets in the following order: our proposed approach > traditional CBR-based service forecasting approach > the existing forecasting approach used by Company A. Consequently, our empirical experiments show that our proposed approach outperformed the traditional CBR based forecasting model and the existing service forecasting model used by Company A. The rest of this paper is organized as follows. Section 2 provides some research background information such as summary of CBR and SOM. Section 3 presents a hybrid service forecasting framework based on Case-based Reasoning and Self-Organizing Maps, while the empirical evaluation results are summarized in Section 4. Conclusion and future research directions are finally discussed in Section 5.

• Journal of the Korean Institute of Gas
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• v.22 no.5
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• pp.1-17
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• 2018

Related incidents and accidents are frequent after 2000 years, such as the outbreak of the Taian peninsula crude oil spillage and Gumi hydrofluoric acid leakage accident. In the wake of such environmental pollution accidents, Consensus has been formed to enact legislation on liability for the compensation of environmental pollution in 2014 and the rescue, and has been in force since January 2016. Therefore, in the domestic insurance industry, the introduced environmental liability insurance system needs to be managed through the standardization formula of a new insurance model for managing the environmental risk. This study has been carried out by the emergence of a safe insurance model with a risky nature of the risk type, which is one of the services of the knowledge base. The verification of the six assurance media on the occurrence of environmental pollution such as chemical, waste, marine, soil, etc. is expressed through semantic interoperability through this possible ontology. The insurance model was designed and presented by deducing the relationship between the amount of money and the amount of money that was written in the area of existing expertise, In order to exclude the possible consequences, the concept of abstract is conceptualized in the form of a customer, and a plan for the future development of an ontology-based decision support system is proposed to reduce the cost and resources consumed every year. It is expected that standardization of the verification standard of the mass of mass will minimize errors and reduce the time and resources required for verification.

• Korean Journal of Plant Resources
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• v.20 no.1
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• pp.79-85
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• 2007

The species in genus Saxifiraga distributed in circumpolar arctic are taxonomically difficult to study. RAPD analyses were performed to compare the genetic diversity of the 16 Saxifrages originated from the Norwegian Arctic Svalbard and Korea. The 12 accessions of URP primers were tested and 4 of which showed polymorphism were selected. Total 79 (44.8%) DNA bands were scored and analyzed by UPGMA cluster analysis. The results indicated that all of the 9 Saxifraga species from Svalbard showed high genetic diversity than those from Korea. The Similarity matrix and cluster analyses indicated that the Saxifraga species from Svalbard and Korea can be divided into two different subgroups. RAPDs of the Saxifraga species of Korea showed higher homologous patterns than those of Arctic Saxifrage. Among the Saxifraga species, we found that the morphological similarity reflects the genetic similarity. The geographic distance, clonal reproduction, and environmental condition may contribute the high level of genetic diversity between Saxifraga species from the two isolated regions.

• Journal of Internet Computing and Services
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• v.18 no.2
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• pp.21-30
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• 2017

IoT(Internet of Things) can enable networking and computing using any devices is rapidly proliferated. In the existing IoT environment, bottlenecks and service delays can occur because it processes data and provides services to users using central processing based on Cloud. For this reason, Edge Computing processes data directly in IoT nodes and networks to provide the services to the users has attracted attention. Also, numerous researchers have been attracted to intelligent service efficiently based on Edge Computing. In this paper, expert system-based context awareness scheme for Edge Computing in IoT environment is proposed. The proposed scheme can provide customized services to the users using context awareness and process data in real-time using the expert system based on efficient cooperations of resource limited IoT nodes. The context awareness services can be modified by the users according to the usage purpose. The three service modes in the security system based on smart home are used to test the proposed scheme and the stability of the proposed scheme is proven by a comparison of the resource consumptions of the servers between the proposed scheme and the PC-based expert system.

• Economic and Environmental Geology
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• v.36 no.4
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• pp.295-304
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• 2003

The Dukpyung and the Chubu areas were selected to investigate the migration and enrichment of arsenic and other toxic elements in soils and crop plants in areas covered with black shales. Rock and soil samples digested in 4-acid solution (HCI+HNO$_3$+HF+HC1O$_4$) were analyzed fer arsenic and other heavy metals by ICP-AES and ICP-MS, and plant samples by INAA. Mean concentration of As in Okchon black shale is higher than those of both world average values of shale and black shale. Especially high concentration of 23.2 mg/kg As is found in black shale from the Dukpyung area. Mean concentration of As is highly elevated in agricultural soils from the Duk-pyung (28.2 mg/kg) and the Chubu areas (32.6 mg/kg). Arsenic is highly elevated in rice stalks and leaves from the Dukpyung (1.14 mg/kg) and the Chubu areas (1.35 mg/kg). The biological absorption coefficient (BAC) of As in plant species decreases in the order of rice leaves>com leaves>red pepper>soybean leaves=sesame leaves>corn stalks>corn grains.