• Journal of the Korea Computer Graphics Society
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• v.28 no.1
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• pp.1-9
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• 2022

Non-small cell lung cancer (NSCLC) accounts for a high proportion of 85% among all lung cancer and has a significantly higher mortality rate (22.7%) compared to other cancers. Therefore, it is very important to predict the prognosis after surgery in patients with non-small cell lung cancer. In this study, the types of preoperative chest CT image patches for non-small cell lung cancer patients with tumor as a region of interest are diversified into five types according to tumor-related information, and performance of single classifier model, ensemble classifier model with soft-voting method, and ensemble classifier model using 3 input channels for combination of three different patches using pre-trained ResNet and EfficientNet CNN networks are analyzed through misclassification cases and Grad-CAM visualization. As a result of the experiment, the ResNet152 single model and the EfficientNet-b7 single model trained on the peritumoral patch showed accuracy of 87.93% and 81.03%, respectively. In addition, ResNet152 ensemble model using the image, peritumoral, and shape-focused intratumoral patches which were placed in each input channels showed stable performance with an accuracy of 87.93%. Also, EfficientNet-b7 ensemble classifier model with soft-voting method using the image and peritumoral patches showed accuracy of 84.48%.

• 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.

• Journal of Practical Agriculture & Fisheries Research
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• v.21 no.1
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• pp.99-114
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• 2019

In this study we examined the preferences of eight college living factors for students at Korea National College of Agriculture and Fisheries(KNCAF). Analytical techniques of unstructured data used opinion mining and text mining techniques, and the analysis results of text mining were visualized as word cloud. The college life factors included eight topics that were closely related to students: 'my present', 'my 10 years later', 'friendship', 'college festival', 'student restaurant', 'college dormitory', 'KNCAF', and 'long-term field practice'. In the text submitted by the students, we have established a dictionary of positive words and negative words to evaluate the preference by classifying the emotions of positive and negative. As a result, KNCAF students showed more than 85% positive emotions about the theme of 'student restaurant' and 'friendship'. But students' positive feelings about 'long-term field practice' and 'college dormitory' showed the lowest satisfaction rate of not exceeding 60%. The rest of the topics showed satisfaction of 69.3~74.2%. The gender differences showed that the positive emotions of male students were high in the topics of 'my present', 'my 10 years later', 'friendship', 'college dormitory' and 'long-term field practice'. And those of female were high in 'college festival', 'student restaurant' and 'KNCAF'. In addition, using text mining technique, the main words of positive and negative words were extracted, and word cloud was created to visualize the results.

• The Journal of the Korea Contents Association
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• v.20 no.4
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• pp.49-61
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• 2020

Recently, the convergence of genre-mixed collaboration and technology-mixed collaboration in performance art strategically fuses different goals and different properties, satisfying anticipated demand, and developing into experimental forms that bring out convergence contents of new value. To supplement problems of convergence attempts and heighten levels of completion, the effective collaborative processes of media experts must be studied and improved. This thesis attempts to study effective collaborative production plans of convergence performance that correspond to the demands of the times through multimedia convergence performance prototypes(Live performance play + Pre-made digital animation). It categorizes performance production processes into pre-production and production and researches the effective collaborative production processes of convergence performances that utilize these two forms of media through work selection, production direction establishment, human resource constituents, production schedule plan establishment, visualization processes, and performance practices and rehearsals. Continuous research must be conducted based on convergence performance contents planning, changes in production methods, and an understanding of distinct characteristics among convergence contents for the industrial development systemization and vitalization of convergence contents to be made possible.

• Journal of Broadcast Engineering
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• v.7 no.2
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• pp.96-106
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• 2002

We introduce a novel scheme to characterize and index events in TV golf programs using MPEG-7 descriptors. Our goal is to identify and localize the golf events of interest to facilitate highlight-based video indexing and summarization. In particular, we analyze multiple (low-level) visual features using domain-specific model to create a perceptual relation for semantically meaningful(high-level) event identification. Furthermore, we summarize a TV golf program with TV-Anytime segmentation metadata, a standard form of an XML-based metadata description, in which the golf events are represented by temporally localized segments and segment groups of highlights. Experimental results show that our proposed technique provides reasonable performance for identifying a variety of golf events.

• Information Systems Review
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• v.4 no.1
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• pp.19-31
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• 2002

The JTEL's success case of implementing ERP through ASP is studied in this paper. This paper especially presents the detail processes of implementing ERP through ASP and the advantages of that. Critical success factors in this case study are that pre-prepared ERP template and repetitive prototyping methodology was successfully utilized and that end users involved positively and accepted the standard best-practice processes of the ERP template in this project. These factors reduced ERP training periods and also the whole implementation periods, which made the project time quite short and TCO less. Considering these success factors, ASP method provides the advantages of global e-business IT environment, continuous new IT technologies and flexible response to the business changes to the small and medium firms. Finally, the paper suggests the new direction and possibility for small and medium firms focusing on the core competency and utilizing new system infrastructure through ASP method compared with in-house implementation.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.19 no.3
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• pp.253-261
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• 2001

Nowadays, various water quality prediction models have been studied, then these models can support the method which evaluate the effect of various alternative water quality management by simulation without experimentation. But, It is necessary to create complex input data file for applying these water quality model and even though the appropriate result is extracted, it is impossible to use as decision making data effectively because of the limitation of expression of model itself. As this study is about the stream water quality modeling, for overcoming the model's above limitation, by developing an interface which can calculate the pollutant load of watershed, I could create a input data file and visualize the simulation result so that I was going to integrate water quality model and GSIS using Flexible coupling applied to GSIS in the pre-process and post-process on model. The QUAL2E model, used in this study, is verified by stream water quality model in previous various results of study and has many examples through previous study, because that is appropriate to water quality model, especially in Korea, and comparatively accurate and their usage is quite simple.

• Journal of Korean Society for Geospatial Information Science
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• v.10 no.2 s.20
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• pp.39-47
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• 2002

For the purpose of data construction for the systematic management and sight-seeing guidance of the natural park, the Kumoh Mt. Province Park was selected as a pilot area. Digital topographic maps, thematic maps and satellite imagery covering the object area were processed and then landform analysis for elevation, slope, aspect and so on was conducted through DEM generation, and the landcover map and NDVI maP were extracted from Landsat TM data. The database was then constructed with these spatial data for GSIS. The image map was generated from IKONOS satellite data, which cover the pilot area data, with one meter resolution and also 3D visualization which was overlaid with main paths up a mountain were conducted. And the moving image files were produced along main paths up including main natural spectacular sights, cultural assets and management facilities. It is expected that the research result can be utilized as the fundamental data for re-assessing suitable land use and constructing Web-based guidance system.

• The Journal of the Korea Contents Association
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• v.16 no.3
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• pp.116-124
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• 2016

In this paper, we propose digital exhibition contents for the aquarium, especially 'Gyeongpo Lagoon Ecological Museum'. We try to plan appropriate media contents that can be mixed with the aquarium storytelling. The aquarium must have special spaces that are not related with an exhibition directly but are necessary for survival of fishes such as a water circulation room. We design the special spaces using media contents and let visitors feel that the whole aquarium is in the ocean. First of all, we investigate the aquarium currently under construction. And then, we propose the appreciate media platforms and plan individual content. We suggest 3 types of media platforms, 3D water projection, interactive games utilizing Kinect, and a fog screen. Moreover, we produce 2D drawing concepts, 3D modeling images, and virtual exhibitions in virtual reality for the representation of the location in which those platforms are installed and the media contents we plan. The pre-visualization is helpful for the media platform construction and is used as a rough sketch to producing an animation shown in the exhibition.

• The Journal of the Korea Contents Association
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• v.14 no.9
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• pp.54-63
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• 2014

It is general that the field plant is too large to be monitored by human works. So it is crucial to prepare one automated monitoring system to prevent unexpected accidents in advance. However, most of previous monitoring systems were to be implemented by human programmer independently, so the total developing cost of a set of similar monitoring systems is so high. In order to overcome this disadvantage, we propose a new specification language for meta-description of monitoring system. Also we propose a generation tool for monitoring system with the input meta-description files. Using these meta-description files, we show it is so fast and effective to get a new monitoring system for a specific field plant. In experiment we have shown that our generation system work successfully in newly developing a monitoring system for the water-vessel plant.