• Journal of Korea Entertainment Industry Association
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• v.13 no.3
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• pp.13-25
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• 2019

Since reaching the top in the Billboard Main Album Chart 'Billboard 200' with Love Yourself: Tear in May of 2018, BTS once again took first place after just three months in the 'Billboard 200'(September 3, 2018) with the repackaged album Love Yourself: Answer. It opened the doors to the 'Hallyu 4.0' by conquering the main Billboard Chart with a song sung in Korean. BTS rose to the top on the 'Billboard 200' twice, thus being recognized globally for their musical talent(song, dance, promotion, etc.), and took their place in the mainstream music market of the world. BTS moved away from intuitive interaction such as mysticism, abnormality, irregularity, etc. but instead created their own world(BTS Universe) with fans around the world through two-directional communication such as consensus, sharing and co-existence. They are recognized as artists that went beyond being an idol group that simply released a few hit songs that had now elevated popular music to a new form of art. In result, they retained a highly loyal global fan base(A.R.M.Y.) and they are continuously creating good influence with them. This study analyzed the success factors of BTS using the S-M-C-R-E model as follows. ① Sender: BTS'7-person 7-colors fantasy and 'All-in-one storytelling' strategy of producer Bang Shi-hyuk ② Message: Create global consensus of 'you' rather than 'me' ③ Channel: Created real-time common grounds with global fans through social network platforms such as Youtube, Facebook and Instagram ④ Receiver: Formed highly loyal global fandom(A.R.M.Y.) that extends outside of Korea and Asia ⑤ Effect: Created additional economic value and spread good influence

• Journal of Intelligence and Information Systems
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• v.5 no.1
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• pp.103-123
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• 1999

Input filtering as a preprocessing method is so much crucial to get good performance in time series forecasting. There are a few preprocessing methods (i.e. ARMA outputs as time domain filters, and Fourier transform or wavelet transform as time-frequency domain filters) for handling time series. Specially, the time-frequency domain filters describe the fractal structure of financial markets better than the time domain filters due to theoretically additional frequency information. Therefore, we, first of all, try to describe and analyze specially some issues on the effectiveness of different filtering methods from viewpoint of the performance of a neural network based forecasting. And then we discuss about neural network model architecture issues, for example, what type of neural network learning architecture is selected for our time series forecasting, and what input size should be applied to a model. In this study an input selection problem is limited to a size selection of the lagged input variables. To solve this problem, we simulate on analyzing and comparing a few neural networks having different model architecture and also use an embedding dimension measure as chaotic time series analysis or nonlinear dynamic analysis to reduce the dimensionality (i.e. the size of time delayed input variables) of the models. Throughout our study, experiments for integration methods of joint time-frequency analysis and neural network techniques are applied to a case study of daily Korean won / U. S dollar exchange returns and finally we suggest an integration framework for future research from our experimental results.

• Journal of rehabilitation welfare engineering & assistive technology
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• v.6 no.1
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• pp.67-73
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• 2012

In this paper, Artificial Neural Network(ANN) based motion classification algorithm is proposed to classify wrist motions using surface electromyograms(sEMG). surface EMGs are obtained from two electrodes placed on the flexor carpi ulnaris muscle and extensor carpi ulnaris muscle of 26 subjects under no strain condition during wrist motions and used to recognize wrist motions such as up, down, left, right, and rest. Feature is extracted from obtained EMG signals in time domain for fast processing and used to classify wrist motions using ANN. DAMV, DASDV, MAV, and RMS were used as features and accuracies of motion classification based on ANN were 98.03% for DAMV, 97.97% for DASDV, 96.95% for MAV, 96.82% for RMS.

• Journal of Communications and Networks
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• v.13 no.5
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• pp.525-535
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• 2011

In this paper, we study the contribution of network coding (NC) in improving the multicast capacity of random wireless ad hoc networks when nodes are endowed with multi-packet transmission (MPT) and multi-packet reception (MPR) capabilities. We show that a per session throughput capacity of ${\Theta}$(nT$^3$(n)) can be achieved as a tight bound when each session contains a constant number of sinks where n is the total number of nodes and T(n) is the transmission range. Surprisingly, an identical order capacity can be achieved when nodes have only MPR and MPT capabilities. This result proves that NC does not contribute to the order capacity of multicast traffic in wireless ad hoc networks when MPR and MPT are used in the network. The result is in sharp contrast to the general belief (conjecture) that NC improves the order capacity of multicast. Furthermore, if the communication range is selected to guarantee the connectivity in the network, i.e., ${\Omega}$($\sqrt{log\;n/n}$)=T(n) = O(log log n / log n), then the combination of MPR and MPT achieves a throughput capacity of ${\Theta}$(log$^{\frac{3}{2}}$ n/$\sqrt{n}$) which provides an order capacity gain of ${\Theta}$(log$^2$ n) compared to the point-to-point multicast capacity with the same number of destinations.

• Proceedings of the KIEE Conference
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• 1992.07a
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• pp.225-230
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• 1992

In this paper, a new learning fuzzy logic controller(LFLC) is presented. The proposed controller is composed of the main control part and the learning part. The main control part is a fuzzy logic controller(FLC) based on linguistic rules and fuzzy inference. For the learning part, artificial neural network(ANN) is added to FLC so that the controller may adapt to unknown plant and environment. According to the output values of the ANN part, which is learned using error back-propagation algorithm, scale factors of the FLC part are determined. These scale factors transfer the range of values of input variables into corresponding universe of discourse in the FLC part in order to achieve good performance. The effectiveness of the proposed control strategy has been demonstrated through simulations involving the control of an unknown robot manipulator with load disturbance.

• Journal of the Society of Disaster Information
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• v.19 no.4
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• pp.924-935
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• 2023

Purpose: The central aim of this study is to leverage machine learning techniques for the classification of Intrusion Detection System (IDS) data, with a specific focus on identifying the variables responsible for enhancing overall performance. Method: First, we classified 'R2L(Remote to Local)' and 'U2R (User to Root)' attacks in the NSL-KDD dataset, which are difficult to detect due to class imbalance, using seven machine learning models, including Logistic Regression (LR) and K-Nearest Neighbor (KNN). Next, we use the SHapley Additive exPlanation (SHAP) for two classification models that showed high performance, Random Forest (RF) and Light Gradient-Boosting Machine (LGBM), to check the importance of variables that affect classification for each model. Result: In the case of RF, the 'service' variable and in the case of LGBM, the 'dst_host_srv_count' variable were confirmed to be the most important variables. These pivotal variables serve as key factors capable of enhancing performance in the context of classification for each respective model. Conclusion: In conclusion, this paper successfully identifies the optimal models, RF and LGBM, for classifying 'R2L' and 'U2R' attacks, while elucidating the crucial variables associated with each selected model.

• Electronics and Telecommunications Trends
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• v.19 no.5 s.89
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• pp.53-58
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• 2004

홈네트워크 서비스는 가정에서의 편안함과 즐거움을 주기 위한 기술이다. 홈네트워크 시장은 엔터테인먼트 서비스를 중심으로 본격적인 시장의 생성기로 접어들고 있으며, DLNA라고 하는 국제 산업 표준 단체에 의해 상호운용성에 대한 표준화가 진행되어 가고 있다. 본 논고에서는 홈네트워크 서비스를 지원하는 국내 및 국제 표준 미들웨어들을 소개하고 이들의 최근 동향, 그리고 홈네트워크와 관련된 국내 및 국외의 표준화 동향을 소개한다.

• Computers and Concrete
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• v.10 no.6
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• pp.649-662
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• 2012

This paper presents the results of an investigation on the compressive strength and weight loss of mortars containing three types of fillers as cement replacements; Limestone Filler (LF), Silica Fume (SF) and Trass (TR), subjected to elevated temperatures including $400^{\circ}C$, $600^{\circ}C$, $800^{\circ}C$ and $1000^{\circ}C$. Results indicate that addition of TR to blended cements, compared to SF addition, leads to higher compressive strength and lower weight loss at elevated temperatures. In order to model the influence of the different parameters on the compressive strength and the weight loss of specimens, artificial neural networks (ANNs) were adopted. Different diagrams were plotted based on the predictions of the most accurate networks to study the effects of temperature, different fillers and cement content on the target properties. In addition to the impressive RMSE and $R^2$ values of the best networks, the data used as the input for the prediction plots were chosen within the range of the data introduced to the networks in the training phase. Therefore, the prediction plots could be considered reliable to perform the parametric study.

• Journal of Distribution Science
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• v.18 no.9
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• pp.31-43
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• 2020

Purpose: This study examined a comprehensive model for assessing the success probability of electric vehicle (EV) commercialization in the Korean market. The study identified three risks associated with successful commercialization which were technology, social, policy, environmental, and consumer risk. Research design, methodology: The assessment of the riskiness was represented by a Bayes belief network, where the probability of success at each stage is conditioned on the outcome of the preceding stage. Probability of success in each stage is either dependent on input (i.e., investment) or external factors (i.e., air quality). Initial input stages were defined as the levels of investment in product R&D, battery technology, production facilities and battery charging facilities. Results: Reasonable levels of investment were obtained by expert opinion from industry experts. Also, a survey was carried out with 78 experts consisting of automaker engineers, managers working at EV parts manufacturers, and automobile industry researchers in government think tanks to obtain the conditional probability distributions. Conclusion: The output of the model was the likelihood of success - expressed as the probability of market acceptance - that depended on the various input values. A model is a useful tool for understanding the EV industry as a whole and explaining the likely ramifications of different investment levels.

• Asia-Pacific Journal of Business
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• v.15 no.1
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• pp.305-318
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• 2024

Purpose - The purpose of this study was to conduct a preliminary study to identify key trends on research articles indexed in KCI in relation to tourism in Jeju and sports tourism. Design/methodology/approach - Information regarding research articles focused on Jeju tourism and sports tourism indexed in KCI (145 and 120 articles respectively) were collected and finally abstract written in Korean of 100 and 91 articles on sports tourism and Jeju tourism respectively were chosen for the further analysis after removing redundant articles. R program was used to analyze keyword frequencies, co-occurring terms, and degree/betweeness centrality measures and visualize the keyword network results. Findings - Event, marketing, content, program, implication, service, stadium, and tourism destination have been identified as keywords with highest frequencies among research on sport tourism, whereas tourism destination, image, brand, content, data, Chinese, satisfaction, eco-tourism service, place of arrival were highly appearing terms among research on Jeju tourism. Research implications or Originality - This study highlighted that Jeju has been interlinked with a range of terms such as programs influencing Jeju tourism, natural environment, tourism-related resources (e.g., museums, dramas, etc.), whereas sports has been closely related to sports event and vaiours types of sports (e.g., bicycle, staking, and scuber), but not to Jeju-do.