• Journal of the Architectural Institute of Korea Planning & Design
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• v.36 no.4
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• pp.41-49
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• 2020

The purpose of this study was to investigate the use of the Deep Neural Networks(DNN) model to classify user's emotions, in particular Electroencephalography(EEG) toward Virtual-Reality(VR) based 3D design alternatives. Four different types of VR Space were constructed to measure a user's emotion and EEG was measured for each stimulus. In addition to the quantitative evaluation based on EEG data, a questionnaire was conducted to qualitatively check whether there is a difference between VR stimuli. As a result, there is a significant difference between plan types according to the normalized ranking method. Therefore, the value of the subjective questionnaire was used as labeling data and collected EEG data was used for a feature value in the DNN model. Google TensorFlow was used to build and train the model. The accuracy of the developed model was 98.9%, which is higher than in previous studies. This indicates that there is a possibility of VR and Fast Fourier Transform(FFT) processing would affect the accuracy of the model, which means that it is possible to classify a user's emotions toward VR based 3D design alternatives by measuring the EEG with this model.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.8
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• pp.2787-2800
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• 2022

The aging society increases emergency situations of the elderly living alone and a variety of social crimes. In order to prevent them, techniques to detect emergency situations through voice are actively researched. This study proposes CutPaste-based anomaly detection model using multi-scale feature extraction in time series streaming data. In the proposed method, an audio file is converted into a spectrogram. In this way, it is possible to use an algorithm for image data, such as CNN. After that, mutli-scale feature extraction is applied. Three images drawn from Adaptive Pooling layer that has different-sized kernels are merged. In consideration of various types of anomaly, including point anomaly, contextual anomaly, and collective anomaly, the limitations of a conventional anomaly model are improved. Finally, CutPaste-based anomaly detection is conducted. Since the model is trained through self-supervised learning, it is possible to detect a diversity of emergency situations as anomaly without labeling. Therefore, the proposed model overcomes the limitations of a conventional model that classifies only labelled emergency situations. Also, the proposed model is evaluated to have better performance than a conventional anomaly detection model.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.22 no.6
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• pp.185-190
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• 2022

Open data has a lot of economic value. Not only Korea, but many other countries are doing their best to make various policies and efforts to expand and utilize open data. However, although Korea has a large amount of data, the data is not utilized effectively. Thus, attempts to utilize those data should be made in various industries. In particular, in the fashion industry, exchange and refund problems are the most common due to unpredictable consumers. Better feedback is necessary for service providers to solve this problem. We want to solve it by showing improved images of dissatisfactions along with user reviews including consumer needs. In this paper, user reviews are analyzed on online shopping mall websites to identify consumer needs, and product attributes are defined by utilizing the attributes of K-fashion data. The users' request is defined as a dissatisfaction attribute, and labeling data with the corresponding attribute is searched. The users' request is provided to the service provider in forms of text data or attributes, as well as an image to help improve the product.

• Korean Journal of Clinical Pharmacy
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• v.27 no.4
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• pp.221-227
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• 2017

Background: As personalized healthcare industry has attracted much attention, big data analysis of healthcare data is essential. Lots of healthcare data such as product labeling, biomedical literature and social media data are unstructured, extracting meaningful information from the unstructured text data are becoming important. In particular, text mining for adverse drug reactions (ADRs) reports is able to provide signal information to predict and detect adverse drug reactions. There has been no study on text analysis of expert opinion on Korea Adverse Event Reporting System (KAERS) databases in Korea. Methods: Expert opinion text of KAERS database provided by Korea Institute of Drug Safety & Risk Management (KIDS-KD) are analyzed. To understand the whole text, word frequency analysis are performed, and to look for important keywords from the text TF-IDF weight analysis are performed. Also, related keywords with the important keywords are presented by calculating correlation coefficient. Results: Among total 90,522 reports, 120 insulin ADR report and 858 tramadol ADR report were analyzed. The ADRs such as dizziness, headache, vomiting, dyspepsia, and shock were ranked in order in the insulin data, while the ADR symptoms such as vomiting, 어지러움, dizziness, dyspepsia and constipation were ranked in order in the tramadol data as the most frequently used keywords. Conclusion: Using text mining of the expert opinion in KIDS-KD, frequently mentioned ADRs and medications are easily recovered. Text mining in ADRs research is able to play an important role in detecting signal information and prediction of ADRs.

• Environmental Analysis Health and Toxicology
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• v.22 no.1 s.56
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• pp.91-101
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• 2007

The new chemicals are developed and circulated without the verified toxicity data. So, the accidents and occupational diseases, such as explosion, fire, suffocation about deadly poisons etc. are frequently to workers. Classifications of chemicals suited with guideline and an offer of correct chemical information data are the molt important thing for the establishment of suitable chemical management system. The GHS (Globally Harmonized System of classification and labeling of chemicals) is based with the chemical classifications and unification plan. The warning symbol and phrases are established for improvements of chemical information data system. According to these unified and improved systematic form of data, and the chemical information data, the workplaces will be presented many chemical safety and risk data correctly. In this paper, we will present constructions and accomplishment contents-based chemical management of workplace through development of chemical information data and the nice using for new chemical investigation and risk assessment of chemicals in workplaces.

• Korean Journal of Human Ecology
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• v.16 no.4
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• pp.761-773
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• 2007

The study undertakes an examination of nutrition labelling system and offers a strategic framework for improvement of the system in Korean context. Thus this study includes a review of Korean current nutrition labelling system (NLS), development of a strategy or a further study for its revision of NLS, and a suggestion of revised nutrition labelling guidelines. Participants were 600 university students in Busan and were asked to fill in a questionnaire. The data collected were processed with the SPSS statistical program to produce its frequency, percentage, average, and standard deviation with One-Way Anova and Duncan Test. The findings are as follows: the levels of consumer's practical use and awareness of NLS are low, the levels of their trust and satisfaction and their necessity for NLS are low too. Consequently, the consumer's attitudes to NLS are not related to nutrition labelling method(? system).

• Journal of the East Asian Society of Dietary Life
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• v.15 no.1
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• pp.126-135
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• 2005

The purpose of this study was to examine the consumer awareness and information seeking behaviors towards food additives. The data were collected from 506 adults living in Seoul, Daegu and Rusan by self-administered questionnaires. Frequencies and chi-square tests were conducted with SPSS. The results of the survey were as follows: (1) The consumers' concerns towards food additives were high, (2) Especially, many consumers were highly concerned about preservatives among food additives, (3) Two-fifths of the respondents thought the use of food additives had nothing to do with the quality of food, (4) Many respondents tried to consume the food containing less food additives, (5) Most respondents were not satisfied with the labeling of food additives, and (6) Many consumers needed the information about food additives, especially safety of food additives.

• Journal of the Korean Institute of Landscape Architecture
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• v.23 no.4
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• pp.20-27
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• 1996

The main purpose of this research is to develop a program supporting landscape planting design on AutoCAD basis using AutoLISP and DCL language. Current CAD use in landscape architecture field is mainly focused on customizing plant symbols for supporting two dimensional drafting rather than three dimensional consideration. This program is composed of eight module a such as PLANT module for inserting plant symbols, LABEL module for labeling task, SIMULATION module for simulating plant growth and seasonal color variation, TABLE module for generating plant table automatically, BUILDING module, BLOCK module, UTILITY module for deleting, transforming, shading symbols and DB MANAGER module for manipulating data. Design automation ability using automatic object recognition technique in this program allows AutoCAD to be used as a design tool in addition to its main role as a drafting tool through supporting landscape designers to generate many alternatives in the early phase of design.

• 한국경영정보학회:학술대회논문집
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• 2007.06a
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• pp.951-958
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• 2007

Radio Frequency Identification (RFID) is a technology for automated identification of objects and people. RFID may be viewed as a means of explicitly labeling objects to facilitate their "perception" by computing devices. RFlD systems have been gaining more popularity in areas especially in supply chain management and automated identification systems. However, there are many existing and potential problems in the RFlD systems which could threat the technology s future. To successfully adopt RFID technology in various applications. we need to develop the solutions to protect the RFID system s data information. This study investigates important issues related to privacy and security of RF1D based on the recent literature and suggests solutions to cope with the problem.

• Proceedings of the KSRS Conference
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• 2005.10a
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• pp.290-293
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• 2005

This paper proposes and evaluates a new approach to detect ships as targets from Radarslit-l SAR (Synthetic Aperture Radar) imagery in the vicinity of Korean peninsula. To be more specific, a labeling technique and morphological filtering in conjunction with some other methods are employed to automatically detect the ships. From the test, the ships are revealed to be detected. For ground truth data, information from a radar system is used, which allows assessing accuracy of the approach. The results showed that the proposed approach has the high potential in automatically detecting the ships