• KIPS Transactions on Software and Data Engineering
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• v.6 no.5
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• pp.229-234
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• 2017

Because of the invisible nature of software and the bad coding habits (bad smell) of the existing developers, there are many redundant codes and unnecessary codes, which increases the complexity and makes it difficult to upgrade software. Therefore, it is required a code visualization so that developers can easily and automatically identify the complexity of the source code. To do this, it is necessary to construct SW visualization tool based on open source software and redefine the coupling and cohesion according to the object oriented viewpoint. Specially to identify a bad smell code pattern, we suggest how to plug-in diverse parsers within our tool. In this paper, through redefining coupling and cohesion from an object oriented perspective, we will extract bad smell code patterns within source code from inputting any pattern into the tool.

• Journal of the Korea Convergence Society
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• v.12 no.3
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• pp.115-124
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• 2021

The outbreak of covid-19 has brought the characteristics and advantages of non-contact services to increased prominence, and the development of smart hotels"has accelerated. This study aims to identify, categorize and define the smart service experience at different touch points of the customer experience. The concept and characteristics of the smart hotel were examined based on existing research and literature. An analytical framework was designed using smart experience factors and customer touch points of smart hotels. Selected Chinese smart hotels were then examined under this framework. The case analysis results show that the customer experience design of smart hotels has developed to different degrees, in terms of interactivity, personalization, accessibility, information and privacy security. Based on the above findings, this article suggests that the design of smart hotels should use integrated data to further enhance personalized service experience.

• Journal of the Korean Regional Science Association
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• v.36 no.1
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• pp.51-67
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• 2020

The purpose of this study is to estimate the housing area per capita for verifying if the public Big Data, of the building ledger and resident registration ledger, can be used as well as the National Census and Housing Survey. The Mankiw and Weil (MW) model was constructed by extracting samples of general detached houses and flat houses from the public big data, and compared with the result from traditional survey method. Then, the MW models of 25 municipalities in Seoul was established. As a result, it can be confirmed that it is possible to establish MW models comparable to regular surveys using public big data, and to establish a model for each basic localities which was difficult to use as a regular survey method. Public Big Data has the advantage of expanding the knowledge frontier, but there are some limitations because it uses data generated for other original purposes. Also, the difficult process of accessing personal information is a burden to carry out analysis. It is expected that continuing research should be needed on how public Big Data would be processed to complement or replace traditional statistical surveys.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2017.05a
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• pp.160-161
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• 2017

Sentiment analysis and opinion mining is the field of study that analyzes people's opinions, sentiments, evaluations, attitudes, and emotions from written language Opinion mining and sentiment analysis(OMSA) as a research discipline has emerged during last 15 years and provides a methodology to computationally process the unstructured data mainly to extract opinions and identify their sentiments. The relatively new but fast growing research discipline has changed a lot during these years. This paper presents a scientometric analysis of research work done on OMSA during 2007-2016. For the literature analysis, research publications indexed in Web of Science (WoS) database are used as input data. The publication data is analyzed computationally to identify year-wise publication pattern, rate of growth of publications, research areas. More detailed manual analysis of the data is also performed to identify popular approaches (machine learning and lexcon-based) used in these publications, levels (documents, sentences or aspect-level) of sentiment analysis work done and major application areass of OMSA.

• Journal of Bio-Environment Control
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• v.8 no.2
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• pp.136-146
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• 1999

A series of experiments were carried out in winter to investigate the indoor thermal environment in greenhouses with different kinds of heating systems, and characterize the energy consumption, heat transport and thermal energy efficiency of each system. By the Quantitative calculation of heat losses which transmit through the covers of greenhouse, the fundamental data of energy-saving of the particular heating system were obtained. And from the analysis of air temperature differences between indoor and outside, it was possible to select more effective energy-saving and comfortable heating system in greenhouses. The electric heater was more stable in thermal environment and cheaper in cost, since it could be used during the surplus time of electric power from 10：00 p.M. to 8：00 A.M. But the low air temperature in greenhouses besides these times resulted in a chilling problem of the crops. The heating system by hot air had the advantage to show nearly uniform temperature difference by the height above the ground. But the system had the disadvantage to require more energy consumption than the electric heating system.

• Journal of Science and Technology Studies
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• v.17 no.2
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• pp.41-79
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• 2017

The fourth industrial revolution, which is all the rage in recent years in South Korea, comes from Klaus Schwab's book. Schwab claims that recent rapid technological innovation has inevitably determined the future of our society, and regulations on related policies need to be relaxed. The debate on the Fourth Industrial Revolution in the Korean society is also centered on deregulation policies. In particular, it is strongly argued that personal data protection regulation should be relaxed in a big data environments. The Science and technology studies has long criticized technological determinism. The future of technology can be changed by the will of regulatory authorities and the intervention of civil society. In this article, the author examines various discussions at home and abroad around the deregulation of data protection, including de-identification of personal data. Through this, the author criticizes the way of accepting the fourth industrial revolution theory, and draw its implications for the Korean society.

• IEMEK Journal of Embedded Systems and Applications
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• v.19 no.1
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• pp.19-25
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• 2024

This paper proposes an artificial intelligence-based personal information area object detection optimization method in an embedded system to de-identify personal information in video data. As an object detection optimization method, first, in order to increase the detection rate for personal information areas when detecting objects, a gyro sensor is used to collect the shooting angle of the image data when acquiring the image, and the image data is converted into a horizontal image through the collected shooting angle. Based on this, each learning model was created according to changes in the size of the image resolution of the learning data and changes in the learning method of the learning engine, and the effectiveness of the optimal learning model was selected and evaluated through an experimental method. As a de-identification method, a shuffling-based masking method was used, and double-key-based encryption of the masking information was used to prevent restoration by others. In order to reuse the original image, the original image could be restored through a security key. Through this, we were able to secure security for high personal information areas and improve usability through original image restoration. The research results of this paper are expected to contribute to industrial use of data without personal information leakage and to reducing the cost of personal information protection in industrial fields using video through de-identification of personal information areas included in video data.

• Journal of Wetlands Research
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• v.25 no.4
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• pp.306-314
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• 2023

Stream sediments are an important component of water quality management because they are receptors of various pollutants such as heavy metals and organic matters emitted from upland sources and can be secondary pollution sources, adversely affecting water environment. To effectively manage the stream sediments, identification of primary sources of sediment contamination and source-associated control strategies will be required. We evaluated the performance of machine learning models in identifying primary sources of sediment contamination based on the physico-chemical properties of stream sediments. A total of 356 stream sediment data sets of 18 quality parameters including 10 heavy metal species(Cd, Cu, Pb, Ni, As, Zn, Cr, Hg, Li, and Al), 3 soil parameters(clay, silt, and sand fractions), and 5 water quality parameters(water content, loss on ignition, total organic carbon, total nitrogen, and total phosphorous) were collected near abandoned metal mines and industrial complexes across the four major river basins in Korea. Two machine learning algorithms, linear discriminant analysis (LDA) and support vector machine (SVM) classifiers were used to classify the sediments into four cases of different combinations of the sampling period and locations (i.e., mine in dry season, mine in wet season, industrial complex in dry season, and　industrial complex in wet season). Both models showed good performance in the classification, with SVM outperformed LDA; the accuracy values of LDA and SVM were 79.5% and 88.1%, respectively. An SVM ensemble model was used for multi-label classification of the multiple contamination sources inlcuding landuses in the upland areas within 1 km radius from the sampling sites. The results showed that the multi-label classifier was comparable performance with sinlgle-label SVM in classifying mines and industrial complexes, but was less accurate in classifying dominant land uses (50~60%). The poor performance of the multi-label SVM is likely due to the overfitting caused by small data sets compared to the complexity of the model. A larger data set might increase the performance of the machine learning models in identifying contamination sources.

• Convergence Security Journal
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• v.20 no.5
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• pp.53-63
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• 2020

In accordance with the revision of the Data 3 Act, such as the Personal Information Protection Act, it is possible to process pseudonym information without the consent of the information subject for statistical creation, scientific research, and preservation of public records, and unlike personal information, it is legal for personal information leakage notification and personal information destruction There are exceptions. It is necessary to revise the pseudonym information in that the standard for the pseudonym processing differs by country and the identification guidelines and anonymization are identified in the guidelines for non-identification of personal information in Korea. In this paper, we focus on the use of personal information in accordance with the 4th Industrial Revolution, examine the concept of pseudonym information for safe use of newly introduced pseudonym information, and generate / use / provide / destroy domestic and foreign non-identification measures standards and pseudonym information. At this stage, through the review of the main contents of the law or the enforcement ordinance (draft), I would like to make suggestions on future management / technical protection measures.

• Journal of Korean Library and Information Science Society
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• v.53 no.2
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• pp.355-375
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• 2022

This study is to grasp LRM's feature and applications plan to reflect LRM to cataloging related standards and individual system through comparing and analyzing LRM with the FR model in terms of entities, attributes, and relationships. The application plan is suggested as follows. First, the entity can be extended by defining sub-entities of each entity in the standards and the individual system in order to reflect LRM, even though entities such as families, groups, identifiers, authorized access points, concepts, objects, events, agency and rules have been deleted in LRM. Second, the attribute should be subdivided in the standards and the individual system in order to apply LRM, though many attributes have been changed to relationships for linked data and decreased in LRM. In particular, more specific and detailed property names in the standards and the individual system should be clearly presented, and the vocabulary encoding scheme corresponding to each property should be also developed, since properties with similar functions or repetition in various entities, and material specific properties are generalized and integrated into comprehensive property names. Third, the relationship should be extended through newly declaring the refinement or subtype of the relationship and considering a multi-level relationship, since the relationship itself is general and abstract under increasing the number of relationships in comparing to the property. This study will be practically utilized in cataloging related standards and individual system for applying LRM.