• Journal of Welding and Joining
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• v.20 no.5
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• pp.113-119
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• 2002

To prevent problems caused by welding deformation, preparation in the design stage is necessary. Countermeasures in the design stage is also the most cost-effective method. In this study, to give designers information on the welding deformation, a system to visualize the welding deformation is developed. The model to visualize the deformation is the stiffened plate common in steel structures. To increase computational efficiency, theoretical solutions to calculate the deformation of plate and stiffener are used instead of numerical analysis. Also, to secure accuracy, experiments to estimate bending moment causing welding deformations are performed. A computer program written with Visual C++ is developed for interactive data input, calculation of welding deformation and display of deformed shape. Designers can change the design in the early stage after checking the deformed shape by this system.

• The Journal of Information Systems
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• v.29 no.1
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• pp.159-179
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• 2020

Purpose: We present a scientometric review of the literature on platform government to serve three primary purposes: First, to cluster researches on platform government based on the research issues; second, to identify the major papers, authors, and keywords in the domain; and third, to explore the promising research areas of platform government. Design/methodology/approach: We collected the platform government research from Web of Science, and analyzed 1,536 articles that was published during time span of 1998-2019. Next, co-citation networks are constructed and analyzed by using CiteSpace to visualize the domain clusters and dynamic research trends in the platform government domain. Findings: We identified 13 sub areas of the platform government research: global investigation, consumer product quality, digital agora, civic crowd funding, and open data use etc. We also visualize the top 20 references with the strongest citation bursts, co-authors network, co-occurring keyword network, and timeline of co-citation clusters.

• Spatial Information Research
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• v.3 no.2
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• pp.135-146
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• 1995

The purpose of this study is to develop a GIS application and interface model (GISCELWAB) for the temporal and spatial simulation of surface runoff from a small watershed. The model was constituted by three sub - models : The input data extraction model (GISINDATA) which prepares cell-based input data automatically for a given watershed, the cell water balance model(CELWAB) which calculates the water balance for a cell and simulates surface runoff of watershed simultaneously by the interaction of cells, and the output data management model(GISOUTDISP) which visualize the results of temporal and spatial variation of surface runoff. The input data extraction model was developed to solve the time-consuming problems for the input-data preparation of distributed hydrologic model. The input data for CELWAB can be obtained by extracting ASCII data from a vector map. The output data management model was developed to convert the storage depth and discharge of cell into grid map. This model ean-bles to visualize the temporal and spatial formulation process of watershed storage depth and surface runoff wholly with time increment.

• Journal of KIBIM
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• v.11 no.2
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• pp.54-59
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• 2021

In the process of designing a building, the process of expressing the designer's ideas through images is essential. However, it is expensive and time consuming for a designer to analyze every individual case image to generate a hypothetical design. This study aims to visualize the basic design draft sketch made by the designer as a real image using the Generative Adversarial Network (GAN) based on the continuously accumulated architectural case images. Through this, we proposed a method to build an automated visualization environment using artificial intelligence and to visualize the architectural idea conceived by the designer in the architectural planning stage faster and cheaper than in the past. This study was conducted using approximately 20,000 images. In our study, the GAN algorithm allowed us to represent primary materials and shades within 2 seconds, but lacked accuracy in material and shading representation. We plan to add image data in the future to address this in a follow-up study.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.31 no.6 s.261
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• pp.635-643
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• 2007

As many engineers and technicians are involved in the design process of large scale and/or complex products, there are a lot of miss matches and interferences due to designers' faults and several kinds of CAD systems. Recently, CAD systems are applied to verify and check the assembly process. Digital Mock-Up(DMU) system, a tool to build a virtual mock-up in the design stage, has been used to prevent the interferences and miss matches during precision design processes. Using the virtual assembly tool, engineers are able to design precision and interference free parts without physical mock-ups. Instead of a single CAD source, several CAD systems are used to design a complex product. Several organizations are involved in the distributed design environment for heterogeneous multi-CAD assembly. XML and the lightweight CAD file are proposed for the multi-CAD assembly. XML data contains hierarchy of the heterogenenous multi-CAD assembly. STEP PDM schema and STEP ISO 10303-28 formations are applied to construct the XML data. The lightweight CAD file produced from various CAD files through ACIS kernel and InterOp not only contains mesn, B-Rep and topological data, but also is used to visualize CAD data and to verify dimensions. Developed system is executed on the desktop computers. It does not require commercial CAD systems to visualize 3D assembly data. Real-time interference and fitness checks, dimensional verification, and design and assembly verification are performed on the developed system. Assembly of heterogeneous models for a car is conducted to verify the effectiveness of the developed DMU system on the Internet.

• Journal of Intelligence and Information Systems
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• v.28 no.1
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• pp.131-154
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• 2022

Recently, researches on unstructured data analysis have been actively conducted with the development of information and communication technology. In particular, topic modeling is a representative technique for discovering core topics from massive text data. In the early stages of topic modeling, most studies focused only on topic discovery. As the topic modeling field matured, studies on the change of the topic according to the change of time began to be carried out. Accordingly, interest in dynamic topic modeling that handle changes in keywords constituting the topic is also increasing. Dynamic topic modeling identifies major topics from the data of the initial period and manages the change and flow of topics in a way that utilizes topic information of the previous period to derive further topics in subsequent periods. However, it is very difficult to understand and interpret the results of dynamic topic modeling. The results of traditional dynamic topic modeling simply reveal changes in keywords and their rankings. However, this information is insufficient to represent how the meaning of the topic has changed. Therefore, in this study, we propose a method to visualize topics by period by reflecting the meaning of keywords in each topic. In addition, we propose a method that can intuitively interpret changes in topics and relationships between or among topics. The detailed method of visualizing topics by period is as follows. In the first step, dynamic topic modeling is implemented to derive the top keywords of each period and their weight from text data. In the second step, we derive vectors of top keywords of each topic from the pre-trained word embedding model. Then, we perform dimension reduction for the extracted vectors. Then, we formulate a semantic vector of each topic by calculating weight sum of keywords in each vector using topic weight of each keyword. In the third step, we visualize the semantic vector of each topic using matplotlib, and analyze the relationship between or among the topics based on the visualized result. The change of topic can be interpreted in the following manners. From the result of dynamic topic modeling, we identify rising top 5 keywords and descending top 5 keywords for each period to show the change of the topic. Existing many topic visualization studies usually visualize keywords of each topic, but our approach proposed in this study differs from previous studies in that it attempts to visualize each topic itself. To evaluate the practical applicability of the proposed methodology, we performed an experiment on 1,847 abstracts of artificial intelligence-related papers. The experiment was performed by dividing abstracts of artificial intelligence-related papers into three periods (2016-2017, 2018-2019, 2020-2021). We selected seven topics based on the consistency score, and utilized the pre-trained word embedding model of Word2vec trained with 'Wikipedia', an Internet encyclopedia. Based on the proposed methodology, we generated a semantic vector for each topic. Through this, by reflecting the meaning of keywords, we visualized and interpreted the themes by period. Through these experiments, we confirmed that the rising and descending of the topic weight of a keyword can be usefully used to interpret the semantic change of the corresponding topic and to grasp the relationship among topics. In this study, to overcome the limitations of dynamic topic modeling results, we used word embedding and dimension reduction techniques to visualize topics by era. The results of this study are meaningful in that they broadened the scope of topic understanding through the visualization of dynamic topic modeling results. In addition, the academic contribution can be acknowledged in that it laid the foundation for follow-up studies using various word embeddings and dimensionality reduction techniques to improve the performance of the proposed methodology.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2018.10a
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• pp.582-585
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• 2018

As traffic volume increases and road networks become more complicated, identifying for accurate traffic flow and driving smooth traffic flow are a concern of many countries. There are various analytical techniques and studies which desire to study about effective traffic flow. However, the necessary activity is finding the traffic flow pattern through data visualization including location information. In this paper aim to study a real-world urban traffic trajectory and visualize a pattern of traffic flow with a simulation tool. Our experiment is installing the sensor module in 40 taxis and our dataset is generated along 24 hours and unscheduled routes. After pre-processing data, we improved an open source traffic visualize tools to suitable for our experiment. Then we simulate our vehicle trajectory data with a dots animation over a period of time, which allows clearly view a traffic flow simulation and a understand the direction of movement of the vehicle or route pattern. In addition we further propose some novel timelines to show spatial-temporal features to improve an urban environment due to the traffic flow.

• KIPS Transactions on Software and Data Engineering
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• v.8 no.10
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• pp.421-426
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• 2019

In this paper, we propose a method to visualize the geometric features of the contact region between proteins in a protein complex. When proteins or ligands are represented as curved surfaces with irregularities, the property that the two surfaces contact each other without intersections is called shape compatibility. Protein-Protein or Protein-Ligand docking researches have shown that shape complementarity, chemical properties, and entropy play an important role in finding contact regions. Usually, after finding a region with high shape complementarity, we can predict the contact region by using residual polarity and hydrophobicity of amino acids belonging to this region. In the research for predicting the contact region, it is necessary to investigate the geometrical features of the contact region in known protein complexes. For this purpose, it is essential to visualize the geometric features of the molecular surface. In this paper, we propose a method to find the contact region, and visualize the geometric features of it as normal vectors and mean curvatures of the protein complex.

• Journal of the Korean Society of Visualization
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• v.1 no.2
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• pp.58-70
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• 2003

Meteorological data contains observation and numerical weather prediction model output data. The computerized analysis and visualization of meteorological data often requires very high computing capability due to the large size and complex structure of the data. Because the meteorological data is frequently formed in multi-variables, 3-dimensional and time-series form, it is very important to visualize and analyze the data in 3D spatial domain in order to get more understanding about the meteorological phenomena. In this research, we developed interactive 3-dimensional visualization techniques for visualizing meteorological data on a PC environment such as volume rendering, iso-surface rendering or stream line. The visualization techniques developed in this research are expected to be effectively used as basic technologies not only for deeper understanding and more exact prediction about meteorological environments but also for scientific and spatial data visualization research in any field from which three dimensional data comes out such as oceanography, earth science, and aeronautical engineering.

• Proceedings of the Korean Society of Marine Engineers Conference
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• 2006.06a
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• pp.195-196
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• 2006

Recently, there are increasing need to make a synthetic assessment about oceanic data which is collected over the various scientific field, in addition to just gathering oceanic data. In this study, we made a basic map using satellite image, aerial photo, multi-beam data, geological stratum data etc. And as well we are producing comprehensive SVT(Scientific Visualization Toolkit) which can visualize various kinds of oceanic data. These oceanic data include both survey data such as tidal height, tide, current, wave, water temperature, salinity, oceanic weather data and numeric modelling results such as ocean hydrodynamic model, wave model, erosion/sediment model, thermal discharged coastal water model, ocean water quality model. In this process, we introduce GIS(Geographic Information System) concepts to reflect time and spatial characteristics of oceanic data.