• The Journal of the Korea Contents Association
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• v.15 no.10
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• pp.447-456
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• 2015

In this paper a new model of storytelling related to patent in the field of business as a sort of Intellectual Property(IP) was proposed. The patent related storytelling is investigated in the view points of variety of customers, purposes and applications which is different from the conventional OSMU, transmedia or crossmedia storytelling. In business there are several stages related to patent such as the initial conceptualization and development of technology, apply for and registration of patent, legal conflict like patent invalidation trial and action for infringement of patent and damages, and the commercialization stage like development of product based on patent, advertisement and marketing. Multiple sources optimized to the purpose in each stage of patent related business as well as to multiple convergence application of a patent. Similarly, multi-use refers to the fact that storytelling can be applied in each stage of patent oriented business. The effectiveness and usefulness of proposed MSMU model is also investigated.

• Journal of the Korean Geographical Society
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• v.49 no.2
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• pp.178-199
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• 2014

Changwon Industrial Complex is commonly framed as the best example of strong initiative of the Korean developmental state. And this explanation has been given in the theoretical frame of 'neo-Weberian accounts' i.e., strongly 'national-territorial' and state-centric terms of the predominant. I argue that a geopolitical economy approach focusing on the historical background of the development of Changwon Industrial Complex will shed light on crucial sociospatial dimensions of the Korean developmental state's industrial complex success. I examine, in particular, the multi-scalar processes through which the changes of the industrial complex building plans for the promotion of machine industry in 1960's have been influenced by the complex and dynamic interactions among social actors acting at diverse geographical scales. I show that the formation of the industrial complex in Korea was more heavily influenced by the interactions, contestations, and collaborations among social actors, acting in and through the state, rather than by the state initiative.

• Journal of the Korean housing association
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• v.25 no.1
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• pp.41-50
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• 2014

Architectural & Interior design communication is a process of exchanging information between architects and other professionals, clients, and prospectus users, and a design medium is a means of communication. Using non-immersive, conventional media, it is challenging for architects communicate physical details and users' activities in not yet built three-dimensional buildings to others. Recent advances online, Multi-User Virtual Environments (MUVEs) allow architects and other professionals to experience a virtually constructed building together using anthropomorphic avatars. In addition, MUVEs also enable them to be aware of the presence and activity of each other. Previous studies suggest that the aforementioned characteristics of MUVEs may facilitate communication between architects and others. But these are focused on communication in controlled experimental conditions. This paper discusses the ways in which MUVEs are applied for authentic and long-term collaboration, design studio, and cultural heritage reconstruction projects, produced by digital design group at the UC Berkeley and the Technion-Israel Institute of Technology, and analyzes the influences of MUVEs on those projects. MUVEs helped more precise communication between architects, electronic engineers, and medical staffs, who are collaborating for developing pioneering technology for hospitals. In design studios, MUVEs allowed students to experience other students' design outputs, and thus helped them share ideas mutually. In addition, in cultural heritage reconstruction projects, MUVEs were used for communicating with historians and residents in order to collect evidence. Based on this study, we propose that MUVEs have strong potential for enhancing the communication between architects and other professionals.

• Tunnel and Underground Space
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• v.23 no.2
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• pp.87-98
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• 2013

Around 70% of Korea is mountainous, an increase in tunnel construction. It's due to the growing interest of the public for the environment and land required for the road construction is very scarce. During construction of 'Daedong 1 tunnel' in the expressway expansion project between Naengjeong and Busan, there are shallow shaft due to this tunnel located in the valley and the shafts are separated, and penetrating location change was inevitable for construction was delayed because of complaint. So, we change the position of the penetrating by applying multi-channel TSP, and conducted a stability analysis. The analysis results showed that there is no problem on the stability of the tunnel. To secure the construction of additional stability, We installed instrument, performed mechanical excavation, added reinforcement at shallow shaft and conducted bench cut.

• The Transactions of the Korea Information Processing Society
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• v.6 no.9
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• pp.2384-2392
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• 1999

Software engineers are used to analyse the error behavior of computer programs using test cases which are collected for the testing phase when software errors are detected. In actual software testing and debugging, it is important to adopt dynamic slicing technique which is concerned on all the statements to be affected by the variables of current inputs and to use technique of its implementations. The traditional dynamic slicing has focused on the single slicing criterion algorithm. It has been thought that it is needed to develope and implement algorithm for used multiple criteria variables program slicing, which finds every slicing criterion variable where it is used multiple criteria variables. In this paper, we propose an efficient algorithm to make dynamic program slices when it has used multiple criteria variables. The results of the implementation are presented by the making table on execution history and the dynamic dependence graph. Also we can find that the proposed dynamic program slicing approach using multiple criteria variables is more efficient than the traditional single case algorithm on the practical testing environment.

• Korean Journal of Remote Sensing
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• v.33 no.5_2
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• pp.689-698
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• 2017

In this paper, a new classification method based on the combination of semi-supervised learning with spatial similarity of adjacent pixels is presented for crop classification in inaccessible areas. Iterative classification based on semi-supervised learning is applied to extract reliable training data from both the initial classification result with a small number of training data, and classification results of adjacent pixels are also considered to extract new training pixels with less uncertainty. To evaluate the applicability of the proposed method, a case study of the classification of field crops was carried out using multi-temporal Landsat-8 OLI acquired in the Daehongdan region, North Korea. From a case study, the misclassification of crops and forests, and isolated pixels in the initial classification result were greatly reduced by applying the proposed semi-supervised learning method. In addition, the combination of classification results of adjacent pixels for the extraction of new training data led to the great reduction of both misclassification results and isolated pixels, compared to the initial classification and traditional semi-supervised learning results. Therefore, it is expected that the proposed method would be effectively applied to classify areas in which it is difficult to collect sufficient training data.

• Journal of Life Science
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• v.27 no.7
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• pp.746-753
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• 2017

In order to rapidly identify four drums species, Larimichthys polyactis, L. crocea, Atrobucca nibe, and Pseudotolithus elongates, a highly efficient and quick method has been developed using multiplex polymerase chain reaction (PCR) with species-specific primers. Around 1.4 kbp of the mitochondrial COI gene sequences from the four drums species were aligned, and species-specific forward primers were designed, based on the single nucleotide polymorphism (SNP). The optimal conditions for PCR amplification were selected through cross-reactivity, using a gradient PCR method. The PCR results demonstrated species-specific amplification for each species at annealing temperatures between 50 and $62^{\circ}C$. Multiplex species-specific PCR (MSS-PCR) amplification reactions with four pairs of primers were performed for sixteen specimens of each species. MSS-PCR lead to a species-specific amplification of a 1,540 bp fragment in L. polyactis, 1,013 bp in A. nibe, 474 bp in L. crocea, and 182 bp in P. elongates, respectively. The four different sizes of each PCR product can be quickly and easily detected by single gel electrophoresis. The sensitivity of the MSS-PCR of the DNA was up to $0.1ng/{\mu}l$ as a starting concentration for the four different species tested. These results suggest that MSS-PCR, with species-specific primers based on SNP, can be a powerful tool in the rapid identification of the four drums species, L. polyactis, L. crocea, A. nibe, and P. elongates.

• Journal of Korea Water Resources Association
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• v.53 no.6
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• pp.395-408
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• 2020

This paper aims to develop and apply three different machine learning approaches (i.e., artificial neural networks (ANN), adaptive neuro-fuzzy inference systems (ANFIS), and wavelet-based neural networks (WNN)) combined with an evolutionary optimization algorithm and the k-fold cross validation for multi-step (days) streamflow forecasting at the catchment located in Algeria, North Africa. The ANN and ANFIS models yielded similar performances, based on four different statistical indices (i.e., root mean squared error (RMSE), Nash-Sutcliffe efficiency (NSE), correlation coefficient (R), and peak flow criteria (PFC)) for training and testing phases. The values of RMSE and PFC for the WNN model (e.g., RMSE = 8.590 ㎥/sec, PFC = 0.252 for (t+1) day, testing phase) were lower than those of ANN (e.g., RMSE = 19.120 ㎥/sec, PFC = 0.446 for (t+1) day, testing phase) and ANFIS (e.g., RMSE = 18.520 ㎥/sec, PFC = 0.444 for (t+1) day, testing phase) models, while the values of NSE and R for WNN model were higher than those of ANNs and ANFIS models. Therefore, the new approach can be a robust tool for multi-step (days) streamflow forecasting in the Seybous River, Algeria.

• Information Systems Review
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• v.17 no.3
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• pp.95-111
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• 2015

The recommendation system automatically provides the predicted items which are expected to be purchased by analyzing the previous customer behaviors. This recommendation system has been applied to many e-commerce businesses, and it is generating positive effects on user convenience as well as the company's revenue. However, there are several limitations of the existing recommendation systems. They do not reflect specific criteria for evaluating products or the factors that affect customer buying decisions. Thus, our research proposes a collaborative recommendation model algorithm that utilizes each customer's online product reviews. This study deploys topic modeling method for customer opinion mining. Also, it adopts a kernel-based machine learning concept by selecting kernels explaining individual similarities in accordance with customers' purchase history and online reviews. Our study further applies a multiple kernel learning algorithm to integrate the kernelsinto a combined model for predicting the product ratings, and it verifies its validity with a data set (including purchased item, product rating, and online review) of BestBuy, an online consumer electronics store. This study theoretically implicates by suggesting a new method for the online recommendation system, i.e., a collaborative recommendation method using topic modeling and kernel-based learning.

• Korean Journal of Remote Sensing
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• v.32 no.2
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• pp.155-169
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• 2016

In this study, we developed a multi-sensor blending short-term rainfall forecasting technique using radar and satellite data during extreme rainfall occurrences in Busan and Gyeongnam region in August 2014. The Tropical Z-R relationship ($Z=32R^{1.65}$) has applied as a optimal radar Z-R relation, which is confirmed that the accuracy is improved during 20mm/h heavy rainfall. In addition, the multi-sensor blending technique has applied using radar and COMS (Communication, Ocean and Meteorological Satellite) data for quantitative precipitation estimation. The very-short-term rainfall forecasting performance was improved in 60 mm/h or more of the strong heavy rainfall events by multi-sensor blending. AWS (Automatic Weather System) and MAPLE data were used for verification of rainfall prediction accuracy. The results have ensured about 50% or more in accuracy of heavy rainfall prediction for 1-hour before rainfall prediction, which are correlations of 10-minute lead time have 0.80 to 0.53, and root mean square errors have 3.99 mm/h to 6.43 mm/h. Through this study, utilizing of multi-sensor blending techniques using radar and satellite data are possible to provide that would be more reliable very-short-term rainfall forecasting data. Further we need ongoing case studies and prediction and estimation of quantitative precipitation by multi-sensor blending is required as well as improving the satellite rainfall estimation algorithm.