• Journal of Digital Contents Society
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• v.11 no.4
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• pp.545-552
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• 2010

To a developing country, the advancement of economic status through informatization is an important element contributing to the local society, nation, and to the global world. This research paper is a study on application of Korea's local informatization successful model to Indonesia. Through activated interaction between enterprises and policy makers from Korea and Indonesia, the research paper seeks to create research based network and provide opportunities of information access and business matching to local informatized and e-business enterprises. In research adopted regions, Indonesia's pekalongan region where infra is settled to certain extent by city development project is selected. Therefore, investigating on pekalongan's current geographical, humanistic status quo, and infra, the paper suggest a road map to customized pekalongan's local informatization based on Korean local informatizing successful model.

• The Transactions of the Korea Information Processing Society
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• v.6 no.5
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• pp.1312-1322
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• 1999

The world of linearity, which is regular, predictable and irrelevant to time sequence in most natural phenomenon, is a very small part. In fact, signals generated from natural phenomenon with which we're in contact are showed only slight linearity. Therefore it is very difficult to understand and analyze natural phenomenon with only predictable and regular linear systems. Due to these reasons researches concerning non-linear signals that of analysis were excluded being regarded as noise are being actively carried out. Countless signals generated from nonlinear system have the information about itself, and analyzing those signals and get information from it, that will be able to be used effectively in so may fields. Hence, in this paper we used a higher order spectrum, especially the bispectrum. After we prove the validity applying bispectrum to logistic map, which is typical chaotic signal. Subsequently by showing the result applying for actual signal analysis of EEG according to auditory stimuli, we show that higher order spectra is a very useful parameter in analysis of non-linear signals and the result of EEG analysis according to auditory stimuli.

• Industrial Engineering and Management Systems
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• v.13 no.2
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• pp.163-171
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• 2014

Under amazing increase in markets and certain demand on qualified service in the delivery system, global logistic optimization is becoming a keen interest to provide an essential infrastructure coping with modern competitive prospects. As a key technology for such deployment, we have been engaged in the practical studies on vehicle routing problem (VRP) in terms of Weber model, and developed a hybrid approach of meta-heuristic methods and the graph algorithm of minimum cost flow problem. This paper extends such idea to multi-depot VRP so that we can give a more general framework available for various real world applications including those in green or low carbon logistics. We show the developed procedure can handle various types of problem, i.e., delivery, direct pickup, and drop by pickup problems in a common framework. Numerical experiments have been carried out to validate the effectiveness of the proposed method. Moreover, to enhance usability of the method, Google Maps API is applied to retrieve real distance data and visualize the numerical result on the map.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.23 no.2
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• pp.109-116
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• 2005

3D Spatial Data, namely 3D Urban CG model express the building, road, river in virtual world and accumulate, manage the data in the GIS system. It is important infrastructure which expected in many usages. Recently 3D CG urban model needs much manual effort, time and costs to build them. In this paper, we introduce the integration of GIS, CG and automatic production of the $\lceil$3D Spatial Data Infrastructure$\rfloor$. This system make filtering, divide the polygon, generate the outlines of the GIS building map, design the graphic and property information and finally make automatic 3D CG models.

• Journal of Korea Game Society
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• v.21 no.5
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• pp.87-104
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• 2021

Ludo-narrative dissonance is a prevalent problem among open-world genre video games. However, Ghost of Tsushima (2020) alleviates this issue by designing its characters and narrative structure influenced by Akira Kurosawa's samurai films. The game's protagonist represents "Bushido," a samurai code, and the structure exudes similarity to Joseph Campbell's "Hero's Journey," which heavily influenced Kurosawa's films. The developers also designed the gameplay mechanics such as level-up system, map design, and side quests based on these narrative traits, ultimately making the goal of the narrative and the gameplay mechanics cohesive.

• International Journal of Internet, Broadcasting and Communication
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• v.13 no.1
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• pp.54-60
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• 2021

AR (Augmented Reality) is a technology that provides virtual content to the real world and provides additional information to objects in real-time through 3D content. In the past, a high-performance device was required to experience AR, but it was possible to implement AR more easily by improving mobile performance and mounting various sensors such as ToF (Time-of-Flight). Also, the importance of mobile augmented reality is growing with the commercialization of high-speed wireless Internet such as 5G. Thus, this paper proposes a system that can provide AR services via GNN (Graph Neural Network) using cameras and sensors on mobile devices. ToF of mobile devices is used to capture depth maps. A 3D point cloud was created using RGB images to distinguish specific colors of objects. Point clouds created with RGB images and Depth Map perform downsampling for smooth communication between mobile and server. Point clouds sent to the server are used for 3D object detection. The detection process determines the class of objects and uses one point in the 3D bounding box as an anchor point. AR contents are provided through app and web through class and anchor of the detected object.

• Journal of Information Processing Systems
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• v.16 no.6
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• pp.1238-1249
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• 2020

Throughout the world, aging populations and doctor shortages have helped drive the increasing demand for smart healthcare systems. Recently, these systems have benefited from the evolution of the Internet of Things (IoT), big data, and machine learning. However, these advances result in the generation of large amounts of data, making healthcare data analysis a major issue. These data have a number of complex properties such as high-dimensionality, irregularity, and sparsity, which makes efficient processing difficult to implement. These challenges are met by big data analytics. In this paper, we propose an innovative analytic framework for big healthcare data that are collected either from IoT wearable devices or from archived patient medical images. The proposed method would efficiently address the data heterogeneity problem using middleware between heterogeneous data sources and MapReduce Hadoop clusters. Furthermore, the proposed framework enables the use of both fog computing and cloud platforms to handle the problems faced through online and offline data processing, data storage, and data classification. Additionally, it guarantees robust and secure knowledge of patient medical data.

• Journal of Contemporary Eastern Asia
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• v.20 no.1
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• pp.44-72
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• 2021

This study analyses North and South Koreas' coverage as framed by the main Polish press titles from 1989 to 2019. The main method applied is a computational textual analysis of press articles based on frequency, correlations and co-occurrences. The purpose is to map the topics of the examined articles in the context of relations between Poland and the two Koreas in various areas, predominantly political and economic relations. Emphasis is placed on the impact the carmaker Daewoo's investment in Poland in the mid-1990s had on bilateral Polish-South Korean relations. First, the authors argue that Korean issues in the Polish press, mainly in the second half of the 1990s, particularly concerned economic affairs. Secondly, they argue that after Poland's accession to the European Union in 2004, the country's interest in the two Koreas decreased, and since that time has remained at a more or less constant level. Finally, the authors discuss the outcome of the research in the context of the main developments in Polish-Korean relations, taking into consideration the results of a Polish public opinion survey presenting the international linkages between national public opinion and foreign policy.

• Korean Journal of Plant Resources
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• v.35 no.6
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• pp.820-824
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• 2022

Ecologically sustainable means of development is the point to support environmental homeostasis. One of our roles is to find bio-degradable resources that can be substituted for petroleum-based products to effectively abide by the natural viability. To counter the issues of deforestation and preserve biodiversity, it is necessary to produce a non-wood crop that can fulfill the requirement for raw material from which several products can be produced. Kenaf (Hibiscus cannabinus), a member of the family Malvaceae, is showing sufficient potentiality along this road-map. Due to its rich fiber content, it has been used extensively in various fields for long, probably as early as 4,000 BC. At present, kenaf has been used as provider of paper, plastics, fiber glass, biofuel, activated carbon and epoxy composite. This obviously catch one's attention towards its capability to replace petroleum-based products as a whole. Moreover, the plant shows considerable relevance in decreasing pollutants by virtue of its enormous absorption capacity. These multiple applications of kenaf justify its credibility to be the best resource for the better world. The paper presents an overview on its numerous uses reported in the literature that we have investigated and its great potential as a valuable multipurpose crop.

• Journal of information and communication convergence engineering
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• v.21 no.3
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• pp.208-215
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• 2023

Deep learning techniques provide powerful solutions to several pattern-recognition problems, including Raman spectral classification. However, these networks require large amounts of labeled data to perform well. Labeled data, which are typically obtained in a laboratory, can potentially be alleviated by data augmentation. This study investigated various data augmentation techniques and applied multiple deep learning methods to Raman spectral classification. Raman spectra yield fingerprint-like information about chemical compositions, but are prone to noise when the particles of the material are small. Five augmentation models were investigated to build robust deep learning classifiers: weighted sums of spectral signals, imitated chemical backgrounds, extended multiplicative signal augmentation, and generated Gaussian and Poisson-distributed noise. We compared the performance of nine state-of-the-art convolutional neural networks with all the augmentation techniques. The LeNet5 models with background noise augmentation yielded the highest accuracy when tested on real-world Raman spectral classification at 88.33% accuracy. A class activation map of the model was generated to provide a qualitative observation of the results.