• The Journal of Bigdata
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• v.4 no.1
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• pp.63-72
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• 2019

For power energy, optimal generation and distribution plans based on accurate demand forecasts are necessary because it is not recoverable after they have been delivered to users through power generation and transmission processes. Failure to predict power demand can cause various social and economic problems, such as a massive power outage in September 2011. In previous studies on forecasting power demand, ARIMA, neural network models, and other methods were developed. However, limitations such as the use of the national average ambient air temperature and the application of uniform criteria to distinguish seasonality are causing distortion of data or performance degradation of the predictive model. In order to improve the performance of the power demand prediction model, we divided Korea into five major regions, and the power demand prediction model of the linear regression model and the neural network model were developed, reflecting seasonal characteristics through regional characteristics and migration period learning techniques. With the proposed approach, it seems possible to forecast the future demand in short term as well as in long term. Also, it is possible to consider various events and exceptional cases during a certain period.

• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
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• v.9 no.1
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• pp.845-858
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• 2019

The most serious problem company facing in today's business environment is the failure of new product development outcomes. Statistically, almost half of the new products released each year failed. Despite the innovative technological advances, consumers' expectation level become much higher and global competition is intensifying. In addition, the new product life cycle is becoming shorter and shorter. It is difficult for a company to survive without developing long-lived products. The most important issue in a company's success and failure is the successful development and introduction of new products. Previous research has presented many determinants to achieve a successful new product development. This study focuses on dynamic competence as an important determinant, and identifies the constituting elements. Enterprises need to acquire, absorb, integrate and reconfigure their resources to survive and develop continuously. It is necessary to hold a dynamic ability switching resource bases in order to adapt to changing environments. The results of this study are as follows: First, the effect of learning, reconfiguration, and alliance capabilities on the new product development of small and medium-sized manufacturing enterprises seems to be positive. Second, the integrative and reconfiguration capabilities positively affect a new product development under high environmental turbulence.

• Journal of KIISE:Software and Applications
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• v.36 no.10
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• pp.816-824
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• 2009

In the face space, face images due to illumination and pose variations have a nonlinear distribution. Active Appearance Models (AAM) based on the linear model have limits to the nonlinear distribution of face images. In this paper, we assume that a few clusters of face images are given; we build local AAMs according to the clusters of face images, and then select a proper AAM model during the fitting phase. To solve the problem of updating fitting parameters among the models due to the model changing, we propose to build in advance relationships among the clusters in the parameter space from the training images. In addition, we suggest a gradual model changing to reduce improper model selections due to serious fitting failures. In our experiment, we apply the proposed model to Yale Face Database B and compare it with the previous method. The proposed method demonstrated successful fitting results with strongly illuminated face images of deep shadows.

• Journal of the Korea Society of Computer and Information
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• v.15 no.10
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• pp.183-190
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• 2010

The present study attempted to identify the metacognitive strategies used by L2 writers at the university level as well as their particular aspects which might influence the use of these strategies. Twenty-seven participants, all of whom were enrolled in an English course, were asked to write an expository essay and then complete a questionnaire which includes their beliefs and attitudes toward L2 writing experience and the metacognitive strategies during the writing process. It was found that even though inexpert writers knew and employed as many strategies as the expert counterparts did, they were unsuccessful in the quality of their texts. Simply possessing a repertoire of metacognitive strategies was not enough for successful L2 writing. The failure of the inexpert writers to apply these metacognitive strategies in an effective manner was influenced by affective factors such as anxiety, self-confidence, self-concept, etc. As a result of this study, a pedagogical implication is suggested.

• Journal of the Institute of Electronics and Information Engineers
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• v.50 no.8
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• pp.232-237
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• 2013

A newly developed fall detection algorithm using the HMM (Hidden Markov Model) extracted from the video is introduced. To distinguish between the fall from personal difference fall pattern or the normal activities of daily living (ADL), HMM machine learning algorithm is used. For getting fall feature vector of video, the motion vector from the optical flow is applied to the PCA (Principal Component Analysis). The combination of the angle, ratio of long-short axis, velocity from results of PCA make the new fall feature parameters. These parameters were applied to the HMM and the results were compared and analyzed. Among the newly proposed various kinds of fall parameters, the angle of movement showed the best results. The results show that this parameter can distinguish various types of fall from ADLs with 91.5% sensitivity and 88.01% specificity.

• The Journal of Bigdata
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• v.3 no.1
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• pp.55-62
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• 2018

In this study, we have developed a model for predicting the number of patients (flu, pneumonia, and outbreak) using Big Data, which has been mainly performed overseas. Existing patient number system by government adopt procedures that collects the actual number and percentage of patients from several big hospital. However, prediction model in this study was developed combing a real-time collection of disease-related words and various other climate data provided in real time. Also, prediction number of patients were counted by machine learning algorithm method. The advantage of this model is that if the epidemic spreads rapidly, the propagation rate can be grasped in real time. Also, we used a variety types of data to complement the failures in Google Flu Trends.

• The Journal of Korean Institute of Information Technology
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• v.17 no.12
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• pp.21-28
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• 2019

Side effects of drugs mean harmful and unintended effects resulting from drugs used to prevent, diagnose, or treat diseases. These side effects can lead to patients' death and are the main causes of drug developmental failures. Thus, various methods have been tried to identify side effects. These can be divided into biological and systems biology approaches. In this study, we use systems biology approach and focus on using various phenotypic information in addition to the chemical structure and target proteins. First, we collect datasets that are used in this study, and calculate similarities individually. Second, we generate a set of features using the similarities for each drug-side effect pair. Finally, we confirm the results by AUC(Area Under the ROC Curve), and showed the significance of this study through a comparison experiment.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.22 no.4
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• pp.73-79
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• 2022

The Python language widely used in artificial intelligence (AI) projects is an interpreter language, and errors occur at runtime. In order to prevent project failure due to errors, it is necessary to handle exceptions in code that can cause exceptional situations in advance. In particular, in AI projects that require a lot of resources, exceptions that occur after long execution lead to a large waste of resources. However, since exception handling depends on the developer's experience, developers have difficulty determining the appropriate exception to catch. To solve this need, we propose an approach that recommends exceptions to catch to developers during development by learning the existing exception handling statements. The proposed method receives the source code of the try block as input and recommends exceptions to be handled in the except block. We evaluate our approach for a large project consisting of two frameworks. According to our evaluation results, the average AUPRC is 0.92 or higher when performing exception recommendation. The study results show that the proposed method can support the developer's exception handling with exception recommendation performance that outperforms the comparative models.

• Convergence Security Journal
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• v.22 no.5
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• pp.107-113
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• 2022

The AR/VR-based education and training system is expected to contribute greatly to accident prevention and budget reduction as well as practical training effects similar to the battlefield environment. Research to use AR/VR for learning is ongoing, and technology can be improved without experiencing failures that can occur in the real world. Major advanced countries in defense recognized the advantages of AR/VR technology early on, and developed and utilized systems using them in various fields, from mastery of individual weapon system operation to comprehensive combat training systems, war history education, and post-traumatic stress treatment. Therefore, the purpose of this study is to examine the cases of AR/VR application education and training in advanced defense countries and to draw implications for the South Korean military.

• Journal of The Korean Association For Science Education
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• v.34 no.5
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• pp.437-447
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• 2014

Learning progressions (LP), which describe how students may develop more sophisticated understanding over a defined period of time, can inform the design of instructional materials and assessment by providing a coherent, systematic measure of what can be regarded as "level appropriate." We developed LPs for the nature of matter for grades K-16. In order to empirically test Korean students, we revised one of the constructs and associated assessment items based on Korean National Science Standards. The assessment was administered to 124 Korean secondary students to measure their knowledge and submicroscopic representations, and to assign them to a level of learning progression for the particle nature of matter. We characterized the level of students' understanding and models of the particle nature of matter, and described how students interpret various representations of atoms and molecules to explain scientific phenomena. The results revealed that students have difficulties in understanding the relationship between the macroscopic and molecular levels of phenomena, even in high school science. Their difficulties may be attributed to a limited understanding of scientific modeling, a lack of understanding of the models used to represent the particle nature of matter, or limited understanding of the structure of matter. This work will inform assessment and curriculum materials development related to the fundamental relationship between macroscopic, observed phenomena and the behavior of atoms and molecules, and can be used to create individualized learning environments. In addition, the results contribute to scientific research literature on learning progressions on the nature of matter.