• The Journal of the Acoustical Society of Korea
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• v.27 no.2E
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• pp.64-68
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• 2008

This paper investigates noise reduction performance and performs convergence analysis of a Variable Error Data Normalized Step-Size Least Mean Square(VEDNSS LMS) algorithm. Adopting VEDNSS LMS results in higher system complexity, but noise is reduced providing fast convergence speed Mathematical analysis demonstrates that tap coefficient misadjustment converges. This is confirmed by computer simulation with the proposed algorithm.

• The Journal of the Korea Contents Association
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• v.19 no.2
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• pp.135-146
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• 2019

This study was designed to analyze trends in the entire convergence research beyond academic research through social media big data analysis at a time when interdisciplinary convergence research is emphasized along with the fourth industrial revolution. For this purpose, about 150,000 cases of texts and titles were acquired for about 10 years from January 2009 to September 2018 in connection with the convergence research in social media, and word cloud and network analysis were conducted. As a results, the research fields that were actively conducted for each period were eco-tech in 2009 and 2010, smart technology in 2011 and 2012, information and communication in 2013 and 2014, robots in 2015 and 2016, and artificial intelligence in 2017 and 2018. Also, the research areas that have been consistently conducted for about 10 years are culture, design, chemistry, nanotechnology, biotechnology, robot, IT, and information and communication. Since this study identifies trends in convergence research over time, it can be helpful to researchers who are planning convergence research direction by understanding the trends of convergence research.

• The Journal of Asian Finance, Economics and Business
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• v.7 no.12
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• pp.593-604
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• 2020

This study analyzes human development convergence and the impact of funds transfer to the regions using σ and β-convergence analysis method. Observations were made in all Indonesia's provinces in the period 2010-2019. The coefficient of variation calculation shows a dispersion in the inequality of human development, which means that convergence occurred. This is also documented by the clustering analysis results developed in the study. The results are in line with the hypothesis of neoclassical theory, which shows the tendency for provinces with lower human development levels to grow relatively faster. The dynamic panel data approach with the GMM model shows that a model built with explanatory variables for transfer of funds to regions may lead to the process of convergence of human development - 2.21% per year or 31 years to cover the half-life of convergence. This is a consequence of the Special Allocation Fund and the Village Fund, which positively impact the convergence process, and the General Allocation Fund and the Revenue Sharing Fund with negative signs slowing the convergence process. This evidence opens opportunities to review the justification of the weighting component in determining the amount of funds transferred to the region to accelerate the convergence process of human development.

• International Journal of Internet, Broadcasting and Communication
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• v.15 no.2
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• pp.112-117
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• 2023

This study aims to find out what tourists' interests and perceptions are like through online big data. Big data for a total of five years from 2018 to 2022 were collected using the Textom program. Sentiment analysis was performed with the collected data. Sentiment analysis expresses the necessity and emotions of city tours in online reviews written by tourists using city tours. The purpose of this study is to extract and analyze keywords representing satisfaction. The sentiment analysis program provided by the big data analysis platform "TEXTOM" was used to study positives and negatives based on sentiment analysis of tourists' online reviews. Sentiment analysis was conducted by collecting reviews related to the city tour. The degree of positive and negative emotions for the city tour was investigated and what emotional words were analyzed for each item. As a result of big data sentiment analysis to examine the emotions and sentiments of tourists about the city tour, 93.8% positive and 6.2% negative, indicating that more than half of the tourists are positively aware. This paper collects tourists' opinions based on the analyzed sentiment analysis, understands the quality characteristics of city tours based on the analysis using the collected data, and sentiment analysis provides important information to the city tour platform for each region.

• Proceedings of the Korea Contents Association Conference
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• 2014.06a
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• pp.389-390
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• 2014

• Journal of Internet of Things and Convergence
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• v.4 no.1
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• pp.23-29
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• 2018

The purpose of study was to analyze the trends of 2005 to 2018 revised 'convergence technology research' through text network analysis using NetMiner4.0 program. Data analysis was conducted by using keyword analysis, centrality analysis of 653 authors' keyword from 177 journals. The results of the study are as follows. First, Research on Converging Technology has been studied steadily over the past 13 years in Department of Industry Convergence. Second, the results of the search term frequency analysis show that the 'convergence technology', 'technology convergence', 'convergence', 'design', 'convergence education', 'STEAM', 'convergence research' were used as the main keywords of convergence technology research. Third, Community analysis results show that five communities have been classified five categories according to the characteristics of the search terms 'only IT', 'Cultural industry utilizing Convergence contents', 'Technology innovation and research analysis' And patent development'. Based on these results, we proposed the future directions of convergence technology research.

• International journal of advanced smart convergence
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• v.12 no.3
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• pp.104-108
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• 2023

This paper deals with research on innovative systems using Python-based artificial intelligence technology in the field of plant growth monitoring. The importance of monitoring and analyzing the health status and growth environment of plants in real time contributes to improving the efficiency and quality of crop production. This paper proposes a method of processing and analyzing plant image data using computer vision and deep learning technologies. The system was implemented using Python language and the main deep learning framework, TensorFlow, PyTorch. A camera system that monitors plants in real time acquires image data and provides it as input to a deep neural network model. This model was used to determine the growth state of plants, the presence of pests, and nutritional status. The proposed system provides users with information on plant state changes in real time by providing monitoring results in the form of visual or notification. In addition, it is also used to predict future growth conditions or anomalies by building data analysis and prediction models based on the collected data. This paper is about the design and implementation of Python-based plant growth monitoring systems, data processing and analysis methods, and is expected to contribute to important research areas for improving plant production efficiency and reducing resource consumption.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2017.10a
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• pp.146-147
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• 2017

Big data analysis is a technique for effectively analyzing unstructured data such as the Internet, social network services, web documents generated in the mobile environment, e-mail, and social data, as well as well formed structured data in a database. The most big data analysis techniques are data mining, machine learning, natural language processing, and pattern recognition, which were used in existing statistics and computer science. Global research institutes have identified analysis of big data as the most noteworthy new technology since 2011. Therefore, companies in most industries are making efforts to create new value through the application of big data. In this study, we analyzed using the Social Matrics which a big data analysis tool of Daum communications. We analyzed public perceptions of "Internet of things" keyword, one month as of october 8, 2017. The results of the big data analysis are as follows. First, the 1st related search keyword of the keyword of the "Internet of things" has been found to be technology (995). This study suggests theoretical implications based on the results.

• Journal of Korean Institute of Industrial Engineers
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• v.39 no.3
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• pp.212-221
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• 2013

As the convergence among technologies is reorganizing industry sectors, it is quite important to evaluate the probability of technological convergence, and to analyze how the technologies in a certain industry sector affect the same or other industry sectors. As a result, the large number of studies have been focused on the industry convergence. However, most of them have dealt mainly with case studies or strategy and policies and few efforts have been made to study it using quantitative data. The investigation of industry convergence using quantitative data will help understand the characteristics of industry and forecast the future of the industry from an objective point of view. Therefore, this research proposes a methodology to evaluate the possibilities of industry convergence using patent data. In particular, we emphasize the possibilities of technology convergence and suggest a technology relationship matrix to evaluate the technology convergence, as an antecedent of industry convergence. The feasibility and utility of the suggested methodology was verified with a case study on the convergence of IT and BT. The research results are expected to provide a useful guideline for developing a measure of convergence.

• Knowledge Management Research
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• v.22 no.4
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• pp.237-260
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• 2021

This study aims to make suggestions for more effective budget utilization and R&D project investment through verification of the efficiency of research results for the government's R&D projects. Efficiency was analyzed using Data Envelope Analysis(DEA) and 'Malmquist Index analysis' for the research results of convergence research projects organized by the National Science and Technology Research Association. The analysis targets were convergence research projects organized by the National Science and Technology Research Association and general entrusted research projects by government-funded research institutes, and dynamic analysis was conducted using DEA and Malmquist Index analysis. As a result of the analysis, the convergence research project showed high efficiency from the static perspective of DEA. On the other hand, from a dynamic perspective through the Malmquist Index analysis method, the efficiency of the general consignment project gradually declined, while the efficiency of the convergence research project improved every year. This suggests that convergence studies have higher results than general studies. Through the results of this study, we intend to present objective standards for performance evaluation of government R&D investment and provide objective implications for rational investment policies and research project planning of research personnel and research funds to improve efficiency for government-funded research institutes.