• Journal of the Korean Society for Library and Information Science
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• v.56 no.3
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• pp.47-72
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• 2022

This study aims to understand the trends of subjects related to real estate policies and public's emotional opinion on the policies. Two keywords related to real estate policies such as "real estate policy" and "real estate measure" were used to collect tweets created from February 25, 2008 to August 31, 2021. A total of 91,740 tweets were collected and we applied sentiment analysis and dynamic topic modeling to the final preprocessed and categorized data of 18,925 tweets. Sentiment analysis and dynamic topic model analysis were conducted for a total of 18,925 posts after preprocessing data and categorizing them into supply, real estate tax, interest rate, and population variance. Keywords of each category are as follows: the supply categories (rental housing, greenbelt, newlyweds, homeless, supply, reconstruction, sale), real estate tax categories (comprehensive real estate tax, acquisition tax, holding tax, multiple homeowners, speculation), interest rate categories (interest rate), and population variance categories (Sejong, new city). The results of the sentiment analysis showed that one person posted on average one or two positive tweets whereas in the case of negative and neutral tweets, one person posted two or three. In addition, we found that part of people have both positive as well as negative and neutral opinions towards real estate policies. As the results of dynamic topic modeling analysis, negative reactions to real estate speculative forces and unearned income were identified as major negative topics and as for positive topics, expectation on increasing supply of housing and benefits for homeless people who purchase houses were identified. Unlike previous studies, which focused on changes and evaluations of specific real estate policies, this study has academic significance in that it collected posts from Twitter, one of the social media platforms, used emotional analysis, dynamic topic modeling analysis, and identified potential topics and trends of real estate policy over time. The results of the study can help create new policies that take public opinion on real estate policies into consideration.

• Journal of Intelligence and Information Systems
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• v.22 no.4
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• pp.109-122
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• 2016

Recently, big data analysis has developed into a field of interest to individuals and non-experts as well as companies and professionals. Accordingly, it is utilized for marketing and social problem solving by analyzing the data currently opened or collected directly. In Korea, various companies and individuals are challenging big data analysis, but it is difficult from the initial stage of analysis due to limitation of big data disclosure and collection difficulties. Nowadays, the system improvement for big data activation and big data disclosure services are variously carried out in Korea and abroad, and services for opening public data such as domestic government 3.0 (data.go.kr) are mainly implemented. In addition to the efforts made by the government, services that share data held by corporations or individuals are running, but it is difficult to find useful data because of the lack of shared data. In addition, big data traffic problems can occur because it is necessary to download and examine the entire data in order to grasp the attributes and simple information about the shared data. Therefore, We need for a new system for big data processing and utilization. First, big data pre-analysis technology is needed as a way to solve big data sharing problem. Pre-analysis is a concept proposed in this paper in order to solve the problem of sharing big data, and it means to provide users with the results generated by pre-analyzing the data in advance. Through preliminary analysis, it is possible to improve the usability of big data by providing information that can grasp the properties and characteristics of big data when the data user searches for big data. In addition, by sharing the summary data or sample data generated through the pre-analysis, it is possible to solve the security problem that may occur when the original data is disclosed, thereby enabling the big data sharing between the data provider and the data user. Second, it is necessary to quickly generate appropriate preprocessing results according to the level of disclosure or network status of raw data and to provide the results to users through big data distribution processing using spark. Third, in order to solve the problem of big traffic, the system monitors the traffic of the network in real time. When preprocessing the data requested by the user, preprocessing to a size available in the current network and transmitting it to the user is required so that no big traffic occurs. In this paper, we present various data sizes according to the level of disclosure through pre - analysis. This method is expected to show a low traffic volume when compared with the conventional method of sharing only raw data in a large number of systems. In this paper, we describe how to solve problems that occur when big data is released and used, and to help facilitate sharing and analysis. The client-server model uses SPARK for fast analysis and processing of user requests. Server Agent and a Client Agent, each of which is deployed on the Server and Client side. The Server Agent is a necessary agent for the data provider and performs preliminary analysis of big data to generate Data Descriptor with information of Sample Data, Summary Data, and Raw Data. In addition, it performs fast and efficient big data preprocessing through big data distribution processing and continuously monitors network traffic. The Client Agent is an agent placed on the data user side. It can search the big data through the Data Descriptor which is the result of the pre-analysis and can quickly search the data. The desired data can be requested from the server to download the big data according to the level of disclosure. It separates the Server Agent and the client agent when the data provider publishes the data for data to be used by the user. In particular, we focus on the Big Data Sharing, Distributed Big Data Processing, Big Traffic problem, and construct the detailed module of the client - server model and present the design method of each module. The system designed on the basis of the proposed model, the user who acquires the data analyzes the data in the desired direction or preprocesses the new data. By analyzing the newly processed data through the server agent, the data user changes its role as the data provider. The data provider can also obtain useful statistical information from the Data Descriptor of the data it discloses and become a data user to perform new analysis using the sample data. In this way, raw data is processed and processed big data is utilized by the user, thereby forming a natural shared environment. The role of data provider and data user is not distinguished, and provides an ideal shared service that enables everyone to be a provider and a user. The client-server model solves the problem of sharing big data and provides a free sharing environment to securely big data disclosure and provides an ideal shared service to easily find big data.

• The Journal of the Korea Contents Association
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• v.20 no.9
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• pp.99-106
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• 2020

Though it has been known that self-efficacy is a predictor to the successful L2 learning, the majority of studies on self-efficacy cases were targeted at secondary school students. This paper aims to explore the effects of the intermediate college students' essay writing experiences on their English self-efficiency. For this purpose, pre and post course surveys were conducted on a hundred or so freshmen who took intermediate college English classes which focused on improving English reading and writing skills. Interviews with teachers were also conducted in order to find out whether the differences of their teaching styles had any meaningful impact on their students' self-efficacy. Paired t-test was run on the responses of the post-questionnaire to identify any differences in the self-efficacies of the students before and after taking the classes, and the one-way ANOVA was conducted to find out whether the different instruction types had any significant impact on the differences. The results of the both analyses confirmed the differences of self-efficacies by the two predictors at a statistically significant level. Based on the findings of this paper, various types of writing assignments and efficient procedures of teachers' feedback need to be developed further in order to design and run an effective college English course which can contribute to enhancing self-efficacy of students.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.49 no.1
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• pp.39-45
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• 2012

In this paper, a distributed 2-hop routing algorithm is proposed. The main purpose of the proposed algorithm is to reduce the overall power consumption of each sensor node so that the lifetime of WSN(wireless sensor network) is prolonged. At the beginning of each round, the base station transmits a synchronization signal that contains information on the priority table that is used to decide whether each sensor node is elected as a cluster head or not. The priority table is constructed so that sensor nodes closer to half energy distance from the base station get the higher priority. 2-hop routing is done as follows. Cluster heads inside half energy distance from the base station communicate with the base station directly. Those outside half energy distance have to decide whether they choose 2-hop routing or 1-hop routing. To do this, each cluster head outside half energy distance calculates the energy consumption needed to communicate with the base station via 1-level cluster head or directly. If less energy is needed when passing through the 1-level cluster head, 2-hop routing is chosen and if not, 1-hop routing is chosen. After routing is done each sensor nodes start sensing data.

• The KIPS Transactions:PartD
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• v.13D no.7 s.110
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• pp.889-900
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• 2006

Even though sensor networks are different in user requests and data characteristics depending on each application area, the existing researches on stream data transmission problem focus on the performance improvement of their methods rather than considering the original characteristic of stream data. In this paper, we introduce a hierarchical or distributed sensor network architecture and data model, and then evaluate the multivariate data reduction methods suitable for user requirements and data features so as to apply reduction methods alternatively. To assess the relative performance of the proposed multivariate data reduction methods, we used the conventional techniques, such as Wavelet, HCL(Hierarchical Clustering), Sampling and SVD (Singular Value Decomposition) as well as the experimental data sets, such as multivariate time series, synthetic data and robot execution failure data. The experimental results shows that SVD and Sampling method are superior to Wavelet and HCL ia respect to the relative error ratio and execution time. Especially, since relative error ratio of each data reduction method is different according to data characteristic, it shows a good performance using the selective data reduction method for the experimental data set. The findings reported in this paper can serve as a useful guideline for sensor network application design and construction including multivariate stream data.

• Journal of Digital Convergence
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• v.10 no.11
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• pp.427-432
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• 2012

The article herein suggested a compound repository and a descriptive method to develop a compound knowledge process. A data target saved in a compound knowledge repository suggested in this article includes all compound knowledge meta data and digital resources, which can be divided into the three following factors according to the purpose: user roles, functional elements, and service ranges. The three factors are basic components to describe abstract models of repository. In this article, meta data of compound knowledge are defined by being classified into the two factors. A component stands for the property about a main agent, activity unit or resource that use and create knowledge, and a context presents the context in which knowledge object are included. An agent of the compound knowledge process performs classification, registration, and pattern information management of composite knowledge, and serves as data flow and processing between compound knowledge repository and user. The agent of the compound knowledge process consists of the following functions: warning to inform data search and extraction, data collection and output for data exchange in an distributed environment, storage and registration for data, request and transmission to call for physical material wanted after search of meta data. In this article, the construction of a compound knowledge repository for recommendation system to be developed can serve a role to enhance learning productivity through real-time visualization of timely knowledge by presenting well-put various contents to users in the field of industry to occur work and learning at the same time.

• Journal of the Korean Society of Clothing and Textiles
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• v.25 no.10
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• pp.1695-1706
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• 2001

본 연구는 조선시대 남자 포제(포제에 나타난 복식이미지의 구성요인을 밝히고 남자포제 유형별(철릭, 답호, 직령, 도포, 창의, 주의), 시기별(전기, 중기, 후기) 복식이미지를 알아봄으로서 조선시대 남자포제가 가진 복식이미지를 밝혀 현대 전통복식 디자인에 응용될 수 있는 기초적인 자료를 제공하고자 한다. 의복 자극물은 남자 평상복을 중심으로 하여 조선초기(1477년∼1543년)의 남자 포제로는 철릭, 답호, 직령 3점과 조선중기(18세기)는 도포, 창의 2점, 조선후기(17세기 후기∼20세기 초)는 주의 1점으로 하였고, 당 시대의 정화한 복식이미지를 살펴보기 위해 유물을 복원 제작하여 사용하였다. 이것을 모델에게 착장시켜 슬라이드로 제작한 후 자극물로 제시하였다. 의미지분척도외 구성은 자유언어연상법으로 형용사를 수집하여 23쌍의 형용사쌍을 구성하였다. 패널단은 대학생 남·여 총 600명으로 하였고 자료분석은 SAS을 이용하여 요인분석 분산분석 등을 사용하였다. 1. 조선시대 남자 포제의 요인구조는 품위성 요인(25.2%), 활동성 요인(l4.2%), 관할성 요인(37.9%), 현시성 요인(6.7%), 경연성 요인(5.7%)으로 구성되었으며, 이들 5개 요인의 전체변량 62.7% 중에서 품위성 요인, 활동성 요인, 관할성 요인이 전체변량의 50%를 넘어서 이 세 요인이 남자 포제에서 기본적으로 느껴지는 중요한 요인임을 알 수 있다. 2. 조선시대 남자 포제 유형별 복식이미지의 차이를 알아본 결과, 철릭은 가장 부자연스러운, 주름있는, 곡선적인, 부드러운, 특이한 이미지의 포제로 나타났으며, 답호는 가장 절제된, 직선적인 딱딱한, 특이한 이미지로, 직령은 가장 비활동적인, 답답한, 전통적인 이미지로 도포는 가장 품위있는. 관할한 이미지로 창의는 다른 포제에 비해 평범한, 단순한, 이미지로 주의는 가장 품위 없는, 일상적인, 활동적인, 단순한, 순수한 이미지의 포제로 평가되었다. 모든 남자포제가 전통적, 순수한 이미지의 포제로, 철릭을 제외한 모든 포제가 단순한 이미지로 나타나 조선시대 남자 포제가 공통적으로 가지는 이미지는 단순하고 순수한 이미지를 가지고 있음을 알 수 있다. 3. 남자 포제의 시기별 복식이미지에서는 조선전기(철릭, 답호, 직령)의 포제는 관할성 요인이 높은 의례적인, 관할한, 특이한 이미지로 평가되었고 조선중기(도포, 창의)의 포제는 품위있는, 절제된, 풍성한 이미지로 평가되었으며, 조선후기(주의)의 포제는 활동적인, 단순한, 직선적인 이미지로 나타났다. 따라서 시대별 남자 포제의 이미지는 시대적 여건과 상황에 따라 변화되어 왔으며, 시대에 따라 추구하는 이미지가 달랐다는 것을 알 수 있다.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.19 no.3
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• pp.73-82
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• 2019

In order to build a smart factory, building a CPPS (Cyber Physical Product System) is an important system that must be accompanied. Through the CPPS, it is the reality of smart factories to move physical factories to a digital-based cyber world and to intelligently and autonomously monitor and control them. But The existing CPPS architectures present only an abstract modeling architecture, and the research that applied the OPC UA Framework (Open Platform Communication Unified Architecture), an international standard for data exchange in the smart factory, as the basic system of CPPS It was insufficient. Therefore, it is possible to implement CPPS that can include both cloud and IoT by collecting field data distributed by CPPS architecture applicable to actual factories and concentrating data processing in a centralized In this study, we implemented CPPS architecture through central OPC UA Server based on OPC UA conforming to central processing OPC UA Framework, and how CPPS logical process and data processing process are automatically generated through OPC UA modeling processing We have proposed the CPPS architecture including the model factory and implemented the model factory to study its performance and usability.

• Journal of Korea Entertainment Industry Association
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• v.15 no.2
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• pp.1-10
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• 2021

The purpose of this study is to explore a significant HPWS(High Performance Work System) model for the entertainment industry. HPWS is one of the most studied themes for managing human resources as well as a set of practices to elicit employees' commitment to an organization. Recently, the entertainment industry is growing rapidly, but it is difficult for entertainment firms to retain a stable profit unlike the manufacturing industry. This is because the performance of entertainment business tends to rely heavily on the capabilities and synergy of human resources. In order to suggest a systematic way to manage these, this research identified an effective HPWS model for entertainment business and provides a competitive advantage to entertainment firms, using ANP(Analytic Network Process). ANP is a multicriteria decision making technique that allows dependences and feedbacks among decision elements in the hierarchical or network structures in a holistic manner. The pairwise comparison data that prioritized the criteria of HPWS was collected from 28 team leaders in entertainment firms. According to our results, the most critical factor for HPWS in entertainment business is "employee involvement in decision-making." The sub-factors such as "open communication," "distributive decision-making," and "performance-driven reward" have a greater effect. These findings could provide implications for entertainment firms to determine which practices should be taken into account to accomplish HPWS.

• Journal of Industrial Convergence
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• v.21 no.6
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• pp.61-71
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• 2023

The objective of this research is to examine the mediating roles of nursing professional values and occupational stress in the relationship between self-leadership and patient-centered nursing among nurses employed at COVID-19 designated hospitals. This study were 160 nurses at a COVID-19 designated hospitals, and the data were collected from January 10 to February 30, 2022. The collected data were analyzed by independent t-test, one-way ANOVA, correlation analysis, multiple linear regression analysis, and SPSS PROCESS Macro model No 4 bootstrapping method. The average score for self-leadership was 61.3±8.55, nursing professional values was 95.5±11.66, occupational stress was 51.3±4.76, and patient-centered nursing was 59.3±7.63. The mediating effect of nursing professional values and occupational stress was confirmed in the influence relationship between self-leadership and patient-centered nursing of nurses at COVID-19 designated hospitals. This result suggests that the content related to improve nursing professional values and reduce occupational stress should be considered when applying the patient-centered nursing enhancement program.