• Korean Journal of Agricultural Science
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• v.46 no.4
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• pp.955-969
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• 2019

Analyzing on-line consumer responses is directly related to the management performance of food companies. Therefore, this study collected and analyzed data from an on-line portal site created by consumers about food companies with issues and examined the relationships between the data and the management performance. Through this process, we identified consumers' awareness of these companies obtained from big data analysis and analyzed the relationship between the results and the sales and stock prices of the companies through a time-series graph and correlation analysis. The results of this study were as follows. First, the result of the text mining analysis suggests that consumers respond more sensitively to negative issues than to positive issues. Second, the emotional analysis showed that companies' ethics issues (Enterprise 3 and 4) have a higher level of emotional continuity than that of food safety issues. It can be interpreted that the problem of ethical management has great influence on consumers' purchasing behavior. Finally, In the case of all negative food issues, the number of word frequency and emotional scores showed opposite trends. As a result of the correlation analysis, there was a correlation between word frequency and stock price in the case of all negative food issues and also between emotional scores and stock price. Recently, studies using big data analytics have been conducted in various fields. Therefore, based on this research, it is expected that studies using big data analytics will be done in the agricultural field.

• Journal of the Korean earth science society
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• v.26 no.7
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• pp.624-629
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• 2005

Numerical data of the geological time scale in Earth Science I, II textbooks and those of University textbooks of Korea and other countries are briefly reviewed. Numerical data of the geologic time scale shown in Earth Science I, II textbooks are mostly out of date and many of them follow those in the University textbooks of Korea. The same situation is apparent for introductory Earth Science or Geology textbooks of other countries as old data exist in their text books as well. There are many new data in the International Stratigraphic Chart (ISC 2000) and International Geologic Time Scale (IGTS 2003) recently updated by International Commission on Stratigraphy (ICS) and A Geologic Time Scale (GTS 2004). Among the new data, some important things are Paleogene and Neogene Periods of Cenozoic Era, Mississippian and Pensilvanian Epochs of Carborniferous Period, Paleoproterozoic, Mesoproterozoic, and Neoproterozoic Eras of Proterozoic Eon, and Eoarchean, Paleoarchean, Mesoarchean, and Neoarchean Eras of Archean Eon. These new data should be used in the new Earth Science textbooks.

• Journal of the Korean Institute of Intelligent Systems
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• v.27 no.2
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• pp.179-186
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• 2017

Technology forecasting is predict future trend and state of technology by analyzing the results so far of developing technology. In general, a patent has novel information about the result of developed technology, because the exclusive right of technology included in patent is protected for a time period by patent law. So many studies on the technology forecasting using patent data analysis has been performed. The patent keyword data widely used in patent analysis consist of occurred frequency of the keyword. In most previous researches, the continuous data analyses such as regression or Box-Jenkins Models were applied to the patent keyword data. But, we have to apply the analytical methods of discrete data for patent keyword analysis because the keyword data is discrete. To solve this problem, we propose a patent analysis methodology using Bayesian Poisson discrete model. To verify the performance of our research, we carry out a case study by analyzing the patent documents applied by Apple until now.

• Journal of IKEEE
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• v.9 no.1 s.16
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• pp.31-39
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• 2005

This paper presented new biometrics data transfer model, and use MD5 (Message Digest5) and RSA (Ron Rivest, Adi Shamir, Len Adleman) algorithm to improve biometrics data's security. So, did so that can run user authentication more safely. That is, do so that may input fingerprint among biometrics through client, and transmit processed fingerprint to server. When fingerprint information is transmitted, it uses MD5 algorithm to solve problem that get seized unlawful living body information from outside and information does Digest. And did to pass through process that transmit again this by RSA method. Also, experimented general text data and living body data that is not encoded, transmission speed and security of living body data that encoding and transmit each comparison. By running user authentication through such improved method, is expected to be applied in several. fields by method to simplify certification procedure and is little more correct and stable.

• Journal of the Semiconductor & Display Technology
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• v.18 no.3
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• pp.122-126
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• 2019

These-days technology related to IoT (Internet of Thing) is widely used and there are many types of smart system based IoT like smart health, smart building and so on. In smart health system, it is possible to check someone's health by analyzing data from wearable IoT device like smart watch. Smart building system aims to collect data from sensor such as humidity, temperature, human counter like that and control the building for energy efficiency, security, safety and so forth. Furthermore, smart city system can comprise several smart systems like smart building, smart health, smart mobility, smart energy and etc. In this paper, we propose multimedia IoT based intelligent emergency alarm system for smart city. In existing IoT based smart system, it communicates lightweight data like text data. In the past, due to network's limitations lightweight IoT protocol was proposed for communicating data between things but now network technology develops, problem which is to communicate heavy data is solving. The proposed system obtains video from IP cameras/CCTVs, analyses the video by exploiting AI algorithm for detecting emergencies and prevents them which cause damage or death. If emergency is detected, the proposed system sends warning message that emergency may occur to people or agencies. We built prototype of the intelligent emergency alarm system based on MQTT and assured that the system detected dangerous situation and sent alarm messages. From the test results, it is expected that the system can prevent damages of people, nature and save human life from emergency.

• Journal of KIISE:Databases
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• v.36 no.1
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• pp.50-62
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• 2009

In wireless and mobile environments, data broadcasting is recognized as an effective way for data dissemination due to its benefits to bandwidth efficiency, energy-efficiency, and scalability. In this paper, we address the problem of delayed query processing raised by tree-based index structures in wireless broadcast environments, which increases the access time of the mobile clients. We propose a novel distributed index structure and a clustering strategy for streaming XML data which enable energy and latency-efficient broadcast of XML data. We first define the DIX node structure to implement a fully distributed index structure which contains tag name, attributes, and text content of an element as well as its corresponding indices. By exploiting the index information in the DIX node stream, a mobile client can access the wireless stream in a shorter latency. We also suggest a method of clustering DIX nodes in the stream, which can further enhance the performance of query processing over the stream in the mobile clients. Through extensive performance experiments, we demonstrate that our approach is effective for wireless broadcasting of XML data and outperforms the previous methods.

• The Journal of the Korea Contents Association
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• v.19 no.1
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• pp.1-15
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• 2019

One of the biggest difficulties in the vocational training field is the dropout problem. A large number of students drop out during the training process, which hampers the waste of the state budget and the improvement of the youth employment rate. Previous studies have mainly analyzed the cause of dropouts. The purpose of this study is to propose a machine learning based model that predicts dropout in advance by using various information of learners. In particular, this study aimed to improve the accuracy of the prediction model by taking into consideration not only structured data but also unstructured data. Analysis of unstructured data was performed using Word2vec and Convolutional Neural Network(CNN), which are the most popular text analysis technologies. We could find that application of the proposed model to the actual data of a domestic vocational training institute improved the prediction accuracy by up to 20%. In addition, the support vector machine-based prediction model using both structured and unstructured data showed high prediction accuracy of the latter half of 90%.

• Journal of the Korea Society of Computer and Information
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• v.26 no.3
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• pp.29-34
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• 2021

With the advent of the 4th industrial revolution era, IT technology is creating new services that have not existed by converging with various existing industries and fields. In particular, in the field of artificial intelligence, chatbots and the latest technologies have developed dramatically with the development of natural language processing technology, and various business processes are processed through chatbots. This study is a study on a system that provides a close answer to the question the user wants to find by creating a structural form for legal inquiries through Slot Filling-based chatbot technology, and inputting a predetermined type of question. Using the proposal system, it is possible to construct question-and-answer data in a more structured form of legal information, which is unstructured data in text form. In addition, by managing the accumulated Q&A data through a big data storage system such as Apache Hive and recycling the data for learning, the reliability of the response can be expected to continuously improve.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.2
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• pp.311-320
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• 2020

This study conducted a comparative analysis on domestic and foreign computer science curriculum, in order to introduce the data structure education as a core foundation of computer science. The findings show that the scope and level of data structure contents included in elementary school software education are poorer than those in U.S.A and England. To resolve such a problem, it selected some data structure education factors and a Delphi survey about the importance of contents and the fitness of education periods were administered to experts. Although they responded that 'text information', 'array', 'stack' and 'queue' for linear structures', and 'tree' for non-linear structures are important, their opinions were different in education periods by its factors. The generalization of the findings may be limited, given that the analysis was based on the survey of some experts, but this study has an implication, in that it provides important information for improving elementary school software curriculum for the introduction of data structures.

• The Journal of the Korea Contents Association
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• v.22 no.1
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• pp.138-147
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• 2022

Recently, chatbots have been applied in various fields and have shown good results, and many attempts to use chatbots in shopping mall product recommendation services are being conducted on e-commerce platforms. In this paper, for a conversation system that recommends a fashion that a user wants based on conversation between the user and the system and fashion image information, a transformer model that is currently performing well in various AI fields such as natural language processing, voice recognition, and image recognition. We propose a multimodal-based improved transformer model that is improved to increase the accuracy of recommendation by using dialogue (text) and fashion (image) information together for data preprocessing and data representation. We also propose a method to improve accuracy through data improvement by analyzing the data. The proposed system has a recommendation accuracy score of 0.6563 WKT (Weighted Kendall's tau), which significantly improved the existing system's 0.3372 WKT by 0.3191 WKT or more.