• The KIPS Transactions:PartA
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• v.14A no.7
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• pp.399-408
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• 2007

Currently various techniques for efficiently developing sensor network applications are developed. However, these techniques provide the method for developing only single sensor network application easily and rapidly. Since sensor network applications control various sensor nodes based on core components of operating system, the technique to develop applications by defining common functionalities of various applications and selecting variable functionalities according to the work flow of specific application is efficient. Accordingly, this paper presents an experimental study that identifies commonality of sensor network application domain and supports optional development according to the variability of application by applying product line approach to developing sensor network application. Through the experimental study, we describe the characteristics of sensor network application domain compared with general business domain for product line development. Also, we show the effectiveness of the proposed approach by presenting that core assets designed using the proposed variability feature model and VEADL are reused according to the functionalities of each sensor node.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.19 no.6
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• pp.219-225
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• 2019

There is a problem with the existing method of selecting the difficulty levels of Hanja characters. Some Hanja characters selected by the existing methods are different from Sino-Korean words used in real life and it is impossible to know how many times the Hanja characters are used. To solve this problem, we measure the difficulty of Hanja characters using the multiple regression analysis with the frequency as the features. Based on the elementary textbooks, FWS and FHU are counted. A questionnaire is written using the two frequencies and stroke together to answer the appropriate timing of learning the Hanja characters and use them as target variables for regression. Use stepwise regression to select the appropriate features and perform multiple linear regression. The R² score of the model was 0.1105 and the RMSE was 0.1105.

• Journal of the Korea Society of Computer and Information
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• v.13 no.7
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• pp.37-49
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• 2008

Services are reusable units in business level. Ubiquitous computing provides computing services anytime and anywhere. The combination of both is becoming an important paradigm of computing environment. Fundamentals of services require flexibility and interoperability, and key elements of ubiquitous modeling require interoperability and context-awareness. There are two kinds of methods to identify services. The top-down approach is based on business process, and the bottom-up approach is based on components. The first approach depends on experts' intuitions, while the second approach suffers the incapability of expressing non-functional expression through components. Although a feature-based approach is capable of expressing non-functional expression and identifying services in ubiquitous environment, the research on this issue is not adequately addressed by far. To promote this research, this paper proposes a feature-based method to identify services in ubiquitous computing. The method extracts initial-candidate-services from a feature model. Then, the ultimate services are identified through optimizing and analyzing the candidate-services. The proposed method is expected to enhance the service reusability by effectively analyzing ubiquitous domain based on feature, and varying reusable service units.

• The Journal of the Korea institute of electronic communication sciences
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• v.18 no.1
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• pp.39-44
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• 2023

Renewable energy such as solar and wind power is a resource that is sensitive to weather conditions and environmental changes. Since the amount of power generated by a facility can vary depending on the installation location and structure, it is important to accurately predict the amount of power generation. Using meteorological data, a data preprocessing process based on principal component analysis was conducted to monitor the relationship between features that affect energy production prediction. In addition, in this study, the prediction was tested by reconstructing the dataset according to the sensitivity and applying it to the machine learning model. Using the proposed model, the performance of energy production prediction using random forest regression was confirmed by predicting energy production according to the meteorological environment for new and renewable energy, and comparing it with the actual production value at that time.