• Transactions of the Korean Society of Mechanical Engineers B
• /
• v.39 no.5
• /
• pp.449-453
• /
• 2015

Recently, surfaces with micro and nano structures are the focus of various research and engineering fields to enhance wetting characteristics of the surfaces. Hydrophilic surfaces with hierarchical structures are generally characterized by the interfacial behavior of water droplets. In this study, the interfacial behavior of water droplets is experimentally investigated considering the scale of structures. Using the dry etching and conventional lithography method, quantitative hierarchical structured surfaces are developed. The behavior of the liquid-vapor interface on the test sections is visualized using an automatic goniometer and a high-speed camera. On the basis of the visualized data, the interfacial behavior of water droplets is intensively investigated according to surface geometrical characteristics.

• Journal of KIBIM
• /
• v.11 no.3
• /
• pp.45-54
• /
• 2021

The augmented reality (AR) has recently became an attractive technology in construction industry, which can play a critical role in realizing smart construction concepts. The AR has a great potential to help construction workers access digitalized information about design and construction more flexibly and efficiently. Though several AR applications have been introduced for on-site made to enhance on-site and off-site tasks, few are utilized in actual construction fields. This paper proposes a new AR framework that provides on-site managers with an opportunity to easily access the information about construction resources such as workers and equipment. The framework records videos with the camera installed on a wearable AR device and streams the video in a server equipped with high-performance processors, which runs an object detection algorithm on the streamed video in real time. The detection results are sent back to the AR device so that menu buttons are visualized on the detected objects in the user's view. A user is allowed to access the information about a worker or equipment appeared in one's view, by touching the menu button visualized on the resource. This paper details implementing parts of the framework, which requires the data transmission between the AR device and the server. It also discusses thoroughly about accompanied issues and the feasibility of the proposed framework.

• Korean Journal of Construction Engineering and Management
• /
• v.23 no.4
• /
• pp.65-77
• /
• 2022

Schedule management is one of pivotal project success factors during construction projects. However, there are many difficulties in rapid scheduling and controlling since the existing planning techniques require considerable amount of manual work and manager's judgment in a construction project. This research aims to propose a new scheduling method and algorithm for automating and visualizing the planning process, which is called Countable-grid scheduling method. In this method, if the scope of work is defined via a visualized tool, the schedule plan is created automatically according to the productivity and workable conditions of each activity. The location of the work for each date can be visualized in grid-based approach. Moreover, the work schedule can be updated automatically according to the progress. The industrial applicability of the proposed method was verified in construction projects via case study with sample data. This research can contribute to the construction industry by automating the construction schedule management process.

• Mass Spectrometry Letters
• /
• v.14 no.2
• /
• pp.42-47
• /
• 2023

Carthamus tinctorius L. (known as safflower) is a valuable oil plant whose importance is increasing rapidly in the world due to its high adaptation to arid regions. The seeds of this unique plant are especially used in edible oil, soap, paint, varnish and lacquer production. Its flowers are used in vegetable dye production and medicinal purposes beside its features as a coloring and flavoring in food. After the oil is removed, the remaining pulp and plant parts are used as animal feed, and dry straw residues are used as fuel. Beside all these features, its usage as a herbal medicinal plants for various diseases has gained importance on recent years. In this study, it was designed a plant metabolomic approach which transfers all the recent data processing strategies of untargeted metabolomics in clinical applications to the present study. Q-TOF LC/MS-based analysis of the extracts (70% ethanol, hexane, and chloroform) for both seed and flowers was performed using a C18 column (Agilent Zorbax 1.8 µM, 100 × 2.1 mm). Differences were observed in seed and fruit extracts and these differences were visualized using principal component analysis (PCA) plots. The total number and intersections of the peaks in the extracts were visualized using peak count comparison graph. Based on the experimental results, the number of the detected peaks for seeds was higher than the ones for the flowers for all solvent systems to extract the samples.

• The Journal of the Institute of Internet, Broadcasting and Communication
• /
• v.18 no.1
• /
• pp.119-127
• /
• 2018

The formal data used in the business is managed in a table form without normalization due to lack of understanding and application of data modeling. If the balance of the database design is destroyed, it affects the speed of response to the data query, and the data obesity becomes high. In this paper, it is investigated how data obesity improved through database design through optimized data modeling. The data query path was clearly visualized by square design through data modeling based on the relationship between object (data) and object, from the radial and task - oriented isolation design where data obesity is excessive. In terms of data obesity, the obesity degree of the current research support database was 57.2%, but it was 16.2% in the new research support database, and the data obesity degree was reducd by 40.5%. In addition, by minimizing redundancy of data, the database has been improved to ensure the accuracy and integrity of the data.

• Journal of Intelligence and Information Systems
• /
• v.27 no.1
• /
• pp.47-64
• /
• 2021

The global spread of COVID-19 around the world has not only affected many parts of our daily life but also has a huge impact on many areas, including the economy and society. As the number of confirmed cases and deaths increases, medical staff and the public are said to be experiencing psychological problems such as anxiety, depression, and stress. The collective tragedy that accompanies the epidemic raises fear and anxiety, which is known to cause enormous disruptions to the behavior and psychological well-being of many. Long-term negative emotions can reduce people's immunity and destroy their physical balance, so it is essential to understand the psychological state of COVID-19. This study suggests a method of monitoring medial news reflecting current days which requires striving not only for physical but also for psychological quarantine in the prolonged COVID-19 situation. Moreover, it is presented how an easier method of analyzing social media networks applies to those cases. The aim of this study is to assist health policymakers in fast and complex decision-making processes. News plays a major role in setting the policy agenda. Among various major media, news headlines are considered important in the field of communication science as a summary of the core content that the media wants to convey to the audiences who read it. News data used in this study was easily collected using "Bigkinds" that is created by integrating big data technology. With the collected news data, keywords were classified through text mining, and the relationship between words was visualized through semantic network analysis between keywords. Using the KrKwic program, a Korean semantic network analysis tool, text mining was performed and the frequency of words was calculated to easily identify keywords. The frequency of words appearing in keywords of articles related to COVID-19 emotions was checked and visualized in word cloud 'China', 'anxiety', 'situation', 'mind', 'social', and 'health' appeared high in relation to the emotions of COVID-19. In addition, UCINET, a specialized social network analysis program, was used to analyze connection centrality and cluster analysis, and a method of visualizing a graph using Net Draw was performed. As a result of analyzing the connection centrality between each data, it was found that the most central keywords in the keyword-centric network were 'psychology', 'COVID-19', 'blue', and 'anxiety'. The network of frequency of co-occurrence among the keywords appearing in the headlines of the news was visualized as a graph. The thickness of the line on the graph is proportional to the frequency of co-occurrence, and if the frequency of two words appearing at the same time is high, it is indicated by a thick line. It can be seen that the 'COVID-blue' pair is displayed in the boldest, and the 'COVID-emotion' and 'COVID-anxiety' pairs are displayed with a relatively thick line. 'Blue' related to COVID-19 is a word that means depression, and it was confirmed that COVID-19 and depression are keywords that should be of interest now. The research methodology used in this study has the convenience of being able to quickly measure social phenomena and changes while reducing costs. In this study, by analyzing news headlines, we were able to identify people's feelings and perceptions on issues related to COVID-19 depression, and identify the main agendas to be analyzed by deriving important keywords. By presenting and visualizing the subject and important keywords related to the COVID-19 emotion at a time, medical policy managers will be able to be provided a variety of perspectives when identifying and researching the regarding phenomenon. It is expected that it can help to use it as basic data for support, treatment and service development for psychological quarantine issues related to COVID-19.

• Journal of Digital Contents Society
• /
• v.18 no.8
• /
• pp.1671-1676
• /
• 2017

In theses days, data has been explosively generated in diverse industrial areas. Accordingly, many industries want to collect and analyze these data to improve their products or services. However, collecting user data can lead to significant personal information leakage. Local differential privacy (LDP) proposed by Google is the state-of-the-art approach that is used to protect individual privacy in the process of data collection. LDP guarantees that the privacy of the user is protected by perturbing the original data at the user's side, but a data collector is still able to obtain population statistics from collected user data. However, the prevention of leakage of personal information through such data perturbation mechanism may cause the significant reduction in the data utilization. Therefore, the degree of data perturbation in LDP should be set properly depending on the data collection and analysis purposes. Thus, in this paper, we develop the simulation tool which aims to help the data collector to properly chose the degree of data perturbation in LDP by providing her/him visualized simulated results with various parameter configurations.

• Journal of Biomedical Engineering Research
• /
• v.42 no.4
• /
• pp.175-185
• /
• 2021

Purpose: Vital sign are used to help assess the general physical health of a person, give clues to possible diseases, and show progress toward recovery. Researchers are using vital sign data and AI(artificial intelligence) to manage a variety of diseases and predict mortality. In order to analyze vital sign data using AI, it is important to select and extract vital sign data suitable for research purposes. Methods: We developed a method to visualize vital sign and early warning scores by processing retrospective vital sign data collected from EMR(electronic medical records) and patient monitoring devices. The vital sign data used for development were obtained using the open EMR big data MIMIC-III and the wearable patient monitoring device(CareTaker). Data processing and visualization were developed using Python. We used the development results with machine learning to process the prediction of mortality in ICU patients. Results: We calculated NEWS(National Early Warning Score) to understand the patient's condition. Vital sign data with different measurement times and frequencies were sampled at equal time intervals, and missing data were interpolated to reconstruct data. The normal and abnormal states of vital sign were visualized as color-coded graphs. Mortality prediction result with processed data and machine learning was AUC of 0.892. Conclusion: This visualization method will help researchers to easily understand a patient's vital sign status over time and extract the necessary data.

• 한국디지털정책학회:학술대회논문집
• /
• 2006.12a
• /
• pp.409-418
• /
• 2006

The purpose of this paper is to present a low cost u-City portal development idea and to propose an exclusive system architecture using 3-D interface layers. 3-D interface layers consist of reused ideas of data from existed public data produced from GIS in order to reduce Produce Processes. 3-D interface layers implement a u-City portal systems that tags from physical spaces 1 ink to mobiles from ubiquitous networks between electronic spaces and physical spaces. Primary produce of this study exhibits an exclusive architecture of a u-City portal for speedy and low cost web 3-D interface layers and GIS data, and for implementation interface of 3-D types on USN of physical spaces. Secondary produce of this study represents that a 3-D u-City portal system has visualized speedy implementation characteristics for implementation of the application systems to execute an ubiquitous concept by returning electronic space to physical space, and to present the low cost 3-D u-City portal than an existed 3-D u-city development strategy. Therefore continuous expansion and study of the 3-D interface physical space under a 상황인지(Context Awareness)ubiquitous will appear the innovated u-City portal systems.

• The Journal of the Korea Contents Association
• /
• v.20 no.3
• /
• pp.223-234
• /
• 2020

This research is the association rule analysis using Apriori algorithm of data mining focusing on 32 predictive key words extracted from Hong (2019) affecting successful aging in Korea. And, to examine rules and patterns of those key words or predictive variables, this research used support, confidence, and lift. The data was analyzed with the R version 3. 5. 1 program, and visualized using arulesViz package and visNetwork. It was found that the variables highly associated with successful aging in Korea were 'hobby', 'volunteer service', 'preparation', and 'exercise'. This research concludes that, the variable which needs to be considered first of all for successful aging in Korea is 'hobby', followed by 'volunteer service', 'preparation', and 'exercise'.