• Journal of the Korea Society of Computer and Information
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• v.14 no.3
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• pp.161-171
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• 2009

In this paper, we propose an automatic method for setting data constraints of a data cleansing service, which is for managing the quality of data exchanged between composite services based on SOA(Service-Oriented Architecture) and enables to minimize human intervention during the process. Because it is impossible to deal with all kinds of real-world data, we focus on business data (i.e. costumer order, order processing) which are frequently used in services such as CRM(Customer Relationship Management) and ERP(Enterprise Resource Planning). We first generate an extended-element vector by extending semantics of data exchanged between composite services and then build a rule-based system for setting data constraints automatically using the decision tree learning algorithm. We applied this rule-based system into the data cleansing service and showed the automation rate over 41% by learning data from multiple registered services in the field of business.

• Journal of Digital Convergence
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• v.15 no.3
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• pp.55-64
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• 2017

This study identified the important categories and items about evaluation criteria of e-learning quality assurance by comparing evaluation criteria between Korea and France case. For deriving the conclusion, this research analyzed the Korea quality assurance case which is consist of success or failure for evaluation of quality assurance, and built the generic quality model of e-learning evaluation criteria. A generic model about evaluation criteria, categories, and item of e-learning quality assurance, which should be reflected on French quality criteria, were developed based on statistical approach. This research suggests a evaluation criteria which can be applied to African and Asian countries, that are related to AUF, as well as Korea. The result of this study can be applied to all organizations around the world which prepare for e-learning quality assurance, and at the same time it will be a valuable resource for companies or institutions which want to be evaluated e-learning quality assurance.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.13 no.6
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• pp.229-235
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• 2013

This paper proposed the method to reduce and manage the amount of using power by using the Self-Learning of inference engine that evolves through learning increasingly smart ways for each spaces with in the Space-Based Energy Management System (SEMS, Space-based Energy Management System) that is defined as smallest unit space with constant size and similar characteristics by using the collectible Big Data from the various information networks and the informations of various sensors from the existing Energy Management System(EMS), mostly including such as the Energy Management Systems for the Factory (FEMS, Factory Energy Management System), the Energy Management Systems for Buildings (BEMS, Building Energy Management System), and Energy Management Systems for Residential (HEMS, Home Energy Management System), that is monitoring and controlling the power of systems through various sensors and administrators by measuring the temperature and illumination.

• Journal of Family Resource Management and Policy Review
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• v.8 no.1
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• pp.47-60
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• 2004

Recognizing a tremendous increase in the Internet users and popularity of E-learning through the Internet, this study attempted to analyze interactive financial education web sites for children. Using meta search engines and major search engines, interactive financial education web sites identified based on the three criteria and analyzed in terms of the appropriateness for specific age groups, the coverage of contents related to the basic knowledge for financial literacy, and the interactive activities. The results showed that financial education web sites for children were needed to be improved in terms of both quantity and quality. The study also provides a guideline how to search for an appropriate financial education web sites for children when parents want teach about money to their children.

• Industrial Engineering and Management Systems
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• v.2 no.1
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• pp.45-54
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• 2003

Enterprise Resource Planning(ERP) systems offer pervasive business functionality the applications encompass virtually all aspects of the business. Understanding and managing this pervasiveness will result in a successful and productive business application platform. Because of this pervasiveness, implementations have ranged from great successes to complete failures. This article has two distinctive parts. The first proposes and discusses a systematic process based on consulting experiences of LG CNS (leading information system company in Korea) for ERP selection. Also, the second provides the key factors that are critical to the successful implementation of ERP. The second part reports the results of a study carried out to assess a number of different ERP implementations in different organizations. A case study method of investigation was used, and the experiences of five Korean manufacturing companies were documented. The critical factors in the adoption of ERP are identified as: learning from the experiences of others, appointment of a process innovator, establishment of committees and project teams, training and technical support for the users, and appropriate changes to the organizational structure and managerial responsibilities.

• International journal of advanced smart convergence
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• v.8 no.3
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• pp.145-150
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• 2019

Due to the development of science and technology and the emergence of new industries, the environmental change of the digital contents industry is rapidly progressing. The scope of technological development in the digital contents industry is affecting not only the entertainment industry but also various industries. Recently, with the development of digital convergence using realistic content, games, video, and VR have provided new opportunities for the growth of the content industry. The researcher determined that a new education system would need to be changed as the digital contents industry developed. For this purpose, an AHP questionnaire was conducted for experts with a high basic understanding of the education platform based on previous studies. We proposed a platform model for human resource development as an education system that meets the demand of digital contents industry. The education system for nurturing talents needed by future society should include elements that can interest the learning of users. The platform should not be approached from a system point of view, but should be developed from the content and user's point of view, considering the platform's original purpose.

• International Journal of Computer Science & Network Security
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• v.21 no.7
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• pp.182-190
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• 2021

Deep learning is an advanced technology for large-scale data analysis, with numerous promising cases like image processing, object detection and significantly more. It becomes customarily to use transfer learning and fine-tune a pre-trained CNN model for most image recognition tasks. Having people taking photos and tag themselves provides a valuable resource of in-data. However, these tags and labels might be noisy as people who annotate these images might not be experts. This paper aims to explore the impact of noisy labels on fine-tuning pre-trained CNN models. Such effect is measured on a food recognition task using Food101 as a benchmark. Four pre-trained CNN models are included in this study: InceptionV3, VGG19, MobileNetV2 and DenseNet121. Symmetric label noise will be added with different ratios. In all cases, models based on DenseNet121 outperformed the other models. When noisy labels were introduced to the data, the performance of all models degraded almost linearly with the amount of added noise.

• ETRI Journal
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• v.45 no.6
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• pp.963-973
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• 2023

Non-orthogonal multiple access (NOMA) is considered a key candidate technology for next-generation wireless communication systems due to its high spectral efficiency and massive connectivity. Incorporating the concepts of multiple-input-multiple-output (MIMO) into NOMA can further improve the system efficiency, but the hardware complexity increases. This study develops an energy-efficient (EE) subchannel assignment framework for MIMO-NOMA systems under the quality-of-service and interference constraints. This framework handles an energy-efficient co-training-based semi-supervised learning (EE-CSL) algorithm, which utilizes a small portion of existing labeled data generated by numerical iterative algorithms for training. To improve the learning performance of the proposed EE-CSL, initial assignment is performed by a many-to-one matching (MOM) algorithm. The MOM algorithm helps achieve a low complex solution. Simulation results illustrate that a lower computational complexity of the EE-CSL algorithm helps significantly minimize the energy consumption in a network. Furthermore, the sum rate of NOMA outperforms conventional orthogonal multiple access.

• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.134-134
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• 2022

Streamflow forecasting plays a crucial role in water resource control, especially in highly urbanized areas that are very vulnerable to flooding during heavy rainfall event. In addition to providing the accurate prediction, the evaluation of effects and importance of the input predictors can contribute to water manager. Recently, machine learning techniques have applied their advantages for modeling complex and nonlinear hydrological processes. However, the techniques have not considered properly the importance and uncertainty of the predictor variables. To address these concerns, we applied the GA-BART, that integrates a genetic algorithm (GA) with the Bayesian additive regression tree (BART) model for hourly streamflow forecasting and analyzing input predictors. The Jungrang urban basin was selected as a case study and a database was established based on 39 heavy rainfall events during 2003 and 2020 from the rain gauges and monitoring stations. For the goal of this study, we used a combination of inputs that included the areal rainfall of the subbasins at current time step and previous time steps and water level and streamflow of the stations at time step for multistep-ahead streamflow predictions. An analysis of multiple datasets including different input predictors was performed to define the optimal set for streamflow forecasting. In addition, the GA-BART model could reasonably determine the relative importance of the input variables. The assessment might help water resource managers improve the accuracy of forecasts and early flood warnings in the basin.

• English Language & Literature Teaching
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• v.15 no.3
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• pp.151-170
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• 2009

Recently speech texts have been increasingly used for English education because of their various advantages as language teaching and learning materials. The purpose of this paper is to analyze speech texts in a corpus-based lexical approach, and suggest some productive methods which utilize English speaking or writing as the main resource for the course, along with introducing the actual classroom adaptations. First, this study shows that a speech corpus has some unique features such as different selections of pronouns, nouns, and lexical chunks in comparison to a general corpus. Next, from a collocational perspective, the study demonstrates that the speech corpus consists of a wide variety of collocations and lexical chunks which a number of linguists describe (Lewis, 1997; McCarthy, 1990; Willis, 1990). In other words, the speech corpus suggests that speech texts not only have considerable lexical potential that could be exploited to facilitate chunk-learning, but also that learners are not very likely to unlock this potential autonomously. Based on this result, teachers can develop a learners' corpus and use it by chunking the speech text. This new approach of adapting speech samples as important materials for college students' speaking or writing ability should be implemented as shown in samplers. Finally, to foster learner's productive skills more communicatively, a few practical suggestions are made such as chunking and windowing chunks of speech and presentation, and the pedagogical implications are discussed.