• KIPS Transactions on Software and Data Engineering
• /
• v.12 no.6
• /
• pp.251-258
• /
• 2023

It is necessary to predict peak load accurately in order to supply electric power and operate the power system stably. Especially, it is more important to predict peak load accurately in winter and summer because peak load is higher than other seasons. If peak load is predicted to be higher than actual peak load, the start-up costs of power plants would increase. It causes economic loss to the company. On the other hand, if the peak load is predicted to be lower than the actual peak load, blackout may occur due to a lack of power plants capable of generating electricity. Economic losses and blackouts can be prevented by minimizing the prediction error of the peak load. In this paper, the latest deep learning model such as TCN is used to minimize the prediction error of peak load. Even if the same deep learning model is used, there is a difference in performance depending on the hyper-parameters. So, I propose methods for optimizing hyper-parameters of TCN for predicting the peak load. Data from 2006 to 2021 were input into the model and trained, and prediction error was tested using data in 2022. It was confirmed that the performance of the deep learning model optimized by the methods proposed in this study is superior to other deep learning models.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
• /
• v.14 no.5
• /
• pp.61-78
• /
• 2019

This study is about the effect of the founder's self-efficacy on the sales of the founding company by focusing on the factors that are currently emphasized in the founding education. In particular, this paper starts from the consciousness of the problem that the education that is being implemented to achieve the purpose of successful start-up among various government-based start-up support projects is failing to produce many start-up failures. Entrepreneurs cannot be assessed by objective financial data, but there is a high degree of uncertainty that should be determined based on their personal and learning abilities. In addition, many previous studies, which are likely to be successful when there is a high self-efficacy in a specific field due to the influence of factors such as personal experience or learning, will answer the direction of support for start-up companies. This study focuses on the impact of the founder's self-efficacy on the sales of the founding firms, especially the sales that are the key to the survival of the founding firms. This study has six major studies. First, to analyze whether the self-efficacy of entrepreneurs with respect to entrepreneurship affects the sales of entrepreneurs. Second, to analyze whether the self-efficacy of entrepreneurs with respect to market orientation affects the sales of entrepreneurs. Analysis of whether the founder's self-efficacy affects the sales of the founding firms. Fourth, analysis of whether the founder's self-efficiency affects the sales of the founding firms' understanding of management environment changes. An analysis of whether efficacy affects the sales of a start-up company, and sixth, an analysis of whether the founder's self-efficacy of business model building ability affects the sales of a start-up company. As a result of the empirical analysis, this study found that the self-efficacy of entrepreneurs on product differentiation capability and business model building capacity had a positive influence on the sales of entrepreneurs. The self-efficacy had a positive effect on self-efficacy, and the customer orientation had a positive effect on self-efficacy on business model building capacity. Also, it was confirmed that a path exists between the components of self-efficacy and that self-efficacy through the path has a positive effect on the sales of the start-up company. Therefore, the results of this study suggest the implications of establishing such a path and strengthening self-efficacy to create the survival and start-up performance of a start-up company if the goal of the start-up company is to survive when implementing various support projects for the start-up company.

• Journal of Information Technology Services
• /
• v.15 no.3
• /
• pp.147-155
• /
• 2016

According to rapid development of technology, web text is growing explosively and attracting many fields as substitution for survey. The user of Facebook is reaching up to 113 million people per month, Twitter is used in various institution or company as a behavioral analysis tool. However, many research has focused on meaning of the text itself. And there is a lack of study for text's creation subject. Therefore, this research consists of sex/age text classification with by using 20,187 Facebook users' posts that reveal the sex and age of the writer. This research utilized Convolution Neural Networks, a type of deep learning algorithms which came into the spotlight as a recent image classifier in web text analyzing. The following result assured with 92% of accuracy for possibility as a text classifier. Also, this research was minimizing the Korean morpheme analysis and it was conducted using a Korean web text to Authorship Attribution. Based on these feature, this study can develop users' multiple capacity such as web text management information resource for worker, non-grammatical analyzing system for researchers. Thus, this study proposes a new method for web text analysis.

• Journal of Intelligence and Information Systems
• /
• v.7 no.1
• /
• pp.63-79
• /
• 2001

Data Envelopment Analysis(DEA), a non-parametric productivity analysis tool, has become an accepted approach for assessing efficiency in a wide range of fields. Despite of its extensive applications and merits, some features of DEA remain bothersome. DEA offers no guideline about to which direction relatively inefficient DMUs improve since a reference set of an inefficient DMU, several efficient DMUs, hardly provides a stepwise path for improving the efficiency of the inefficient DMU. In this paper, we aim to show that DEA can be used to evaluate the efficiency of life insurance companies while overcoming its limitation with the aids of machine learning methods.

• Journal of Korean Society for Quality Management
• /
• v.46 no.1
• /
• pp.153-168
• /
• 2018

Purpose: The balanced scorecard (BSC) has been adopted to evaluate factors affecting performances in organizational dealer marketing in Chinese cosmetics market. Four performance measures in BSC: learning & growth, internal business processes, customer performance, and financial performance are employed in our empirical study. Methods: We conducted surveys of dealers in a Chinese cosmetics company and used total 463 samples for analysis. Confirmatory factor analysis and structural equation model analysis were employed using AMOS 20.0. Results: This study found that internal business process had a positive relation with customer performance and learning and growth. Also, customer performance and learning & growth positively affected financial performances. Conclusion: This study has some academic and practical contributions in that the revised BSC model reflects the special aspects of Chinese cosmetics market and it can be used as a guide for companies in the Chinese cosmetics market to understand which factors are affecting performances.

• Journal of the Korea Society of Computer and Information
• /
• v.26 no.10
• /
• pp.53-59
• /
• 2021

In this paper, we propose a system that can perform dumbbell exercise by recognizing the weight of dumbbells without wearing and device. With the development of virtual reality technnology, many studies are being conducted to simulate the pysical feedback of the real world in the virtual world. Accurate motion recognition is important to the elderly for rehabilitation exercises. They cannot lift heavy dumbbells. For rehabilitation exercise, correct body movement according to an appropriate weight must be performed. We use a machine learning algorithm for the accuracy of motion data input in real time. As an experiment, we was test three types of bicep, double, shoulder exercise and verified accuracy of exercise. In addition, we made a virtual gym game to actually apply these exercise in virtual reality.

• Journal of the Korea Institute of Information Security & Cryptology
• /
• v.29 no.2
• /
• pp.321-330
• /
• 2019

Prevention of corporate confidentiality leakage by insiders in enterprises is an essential task for the survival of enterprises. In order to prevent information leakage by insiders, companies have adopted security solutions, but there is a limit to effectively detect abnormal behavior of insiders with access privileges. In this study, we use the Unsupervised Learning algorithm of the machine learning technique to effectively and efficiently cluster the normal and abnormal access logs of the worker's work screen in the manufacturing information system, which includes the company's product manufacturing history and quality information. We propose an optimal feature selection model for anomaly detection by studying clustering methods.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.16 no.8
• /
• pp.2473-2489
• /
• 2022

Urban development has brought about the increasing saturation of urban traffic demand, and traffic congestion has become the primary problem in transportation. Roads are in a state of waiting in line or even congestion, which seriously affects people's enthusiasm and efficiency of travel. This paper mainly studies the discrete domain path planning method based on the flow data. Taking the traffic flow data based on the highway network structure as the research object, this paper uses the deep learning theory technology to complete the path weight determination process, optimizes the path planning algorithm, realizes the vehicle path planning application for the expressway, and carries on the deployment operation in the highway company. The path topology is constructed to transform the actual road information into abstract space that the machine can understand. An appropriate data structure is used for storage, and a path topology based on the modeling background of expressway is constructed to realize the mutual mapping between the two. Experiments show that the proposed method can further reduce the interpolation error, and the interpolation error in the case of random missing is smaller than that in the other two missing modes. In order to improve the real-time performance of vehicle path planning, the association features are selected, the path weights are calculated comprehensively, and the traditional path planning algorithm structure is optimized. It is of great significance for the sustainable development of cities.

• Smart Media Journal
• /
• v.12 no.1
• /
• pp.32-46
• /
• 2023

Natural Language Processing (NLP) has grown tremendously in recent years. Typically, bilingual, and multilingual translation models have been deployed widely in machine translation and gained vast attention from the research community. On the contrary, few studies have focused on translating between spoken and sign languages, especially non-English languages. Prior works on Sign Language Translation (SLT) have shown that a mid-level sign gloss representation enhances translation performance. Therefore, this study presents a new large-scale Korean sign language dataset, the Museum-Commentary Korean Sign Gloss (MCKSG) dataset, including 3828 pairs of Korean sentences and their corresponding sign glosses used in Museum-Commentary contexts. In addition, we propose a translation framework based on self-supervised learning, where the pretext task is a text-to-text from a Korean sentence to its back-translation versions, then the pre-trained network will be fine-tuned on the MCKSG dataset. Using self-supervised learning help to overcome the drawback of a shortage of sign language data. Through experimental results, our proposed model outperforms a baseline BERT model by 6.22%.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
• /
• v.8 no.1
• /
• pp.111-125
• /
• 2013

This study is to find out that how the personal, collective, and organizational learning method three learning organization affect the job satisfaction, organizational commitment, and innovative action through the questionnaire of company members, and to verify the moderating effects of shared values in this relation. The results show that three learning organization have a positive effect on the job satisfaction, and a partial effect on organizational commitment and innovative action, and shared values influence positively on the job satisfaction, organizational commitment, and innovative action. The moderating effects of shared values in the relation between three learning organization and Organizational Effectiveness are as follows : First, shared values can moderating the influence of collective and organizational learning organization on the job satisfaction, but can't moderating the relation between a personal learning method and the job satisfaction. Second, shared values can moderating the influence of collective and organizational learning organization on the organizational commitment, but can't moderating the relation between a personal learning method and the organization commitment. Third, shared values can moderating the influence of personal and organizational learning organization on the innovative action, but can't moderating the relation between the collective learning method and the innovative action.