• The Journal of the Acoustical Society of Korea
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• v.28 no.8
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• pp.697-705
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• 2009

An unsupervised (blind) method is proposed aiming at extracting rhythmic sources from commercial polyphonic music whose number of channels is limited to one. Commercial music signals are not usually provided with more than two channels while they often contain multiple instruments including singing voice. Therefore, instead of using conventional modeling of mixing environments or statistical characteristics, we should introduce other source-specific characteristics for separating or extracting sources in the under determined environments. In this paper, we concentrate on extracting rhythmic sources from the mixture with the other harmonic sources. An extension of nonnegative matrix factorization (NMF), which is called nonnegative matrix partial co-factorization (NMPCF), is used to analyze multiple relationships between spectral and temporal properties in the given input matrices. Moreover, temporal repeatability of the rhythmic sound sources is implicated as a common rhythmic property among segments of an input mixture signal. The proposed method shows acceptable, but not superior separation quality to referred prior knowledge-based drum source separation systems, but it has better applicability due to its blind manner in separation, for example, when there is no prior information or the target rhythmic source is irregular.

• Information Systems Review
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• v.22 no.3
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• pp.119-142
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• 2020

This study aims to examine the role of IT features as heuristic cues in choosing a content on YouTube. According to the heuristic-systematic model, people tend to rely on heuristic cues when they have to choose and process useful information quickly so that they could save time and reduce demands for thinking. Based on this line of reasoning, this study posits that YouTube users rely on certain IT features as heuristic cues in choosing contents before they actually watch them. Based on the prior literature and interviews with YouTube users, we develop a research model in which social endorsement, self-presentation, and interactivity are identified as potential determinants of users' attitude toward contents, which in turn influence their intention to watch them. To empirically test the research model, we conduct a laboratory experiment and a follow-up survey. The results of data analysis show that social endorsement for the content, YouTube creator's self-presentation, and interactivity have significant and positive effects on their attitude toward the content, leading to their intention to watch it. This study suggests that IT features on YouTube could be wisely utilized to increase the chance that users choose a particular content out of many competing contents when they search certain information on YouTube.

• Information Systems Review
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• v.21 no.1
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• pp.39-59
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• 2019

E-learning has improved the educational effect by making it possible to learn anytime and anywhere by escaping the traditional infusion education. As the use of e-learning system increases with the increasing popularity of e-learning, it has become important to measure e-learning satisfaction. In this study, we used the mixed research method to identify satisfaction factors of e-learning. The mixed research method is to perform both qualitative research and quantitative research at the same time. As a quantitative research, we collected reviews in Udemy.com by text mining. Then we classified high and low rated lectures and applied topic modeling technique to derive factors from reviews. Also, this study conducted an in-depth 1:1 interview on e-learning learners as a qualitative research. By combining these results, we were able to derive factors of e-learning satisfaction and dissatisfaction. Based on these factors, we suggested ways to improve e-learning satisfaction. In contrast to the fact that survey-based research was mainly conducted in the past, this study collects actual data by text mining. The academic significance of this study is that the results of the topic modeling are combined with the factor based on the information system success model.

• Information Systems Review
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• v.21 no.4
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• pp.197-220
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• 2019

This study aims to identify consumer motivations of open innovation project participation from digital transformation perspective. By extending a traditional intrinsic/extrinsic motivation framework, we propose a three-dimensional perspective of the self-driven, firm-driven, and sociality-driven motivations. This reveals the significance of the social effects of open innovation projects as an example of digital transformation by categorizing the motivations based on the 'influencer' of the motivation building and by highlighting the importance of sociality as an influencer. As a result, self-efficacy is identified as a key motivation when the influencer exists internally. Economic incentive and firm reputation are identified when the influencer exists externally. Finally, competition, peer evaluation and social contributions are identified when the influencer exists socially. The role of knowledge type sought through innovation projects is further introduced to explain its moderating effects on motivations. The study is validated in two steps. First, we investigate four cases of open innovation projects and examine what motivations are highlighted in each context. Second, we collect survey data from 203 online game users and ask them on their motivations. The results confirm most of our hypotheses and highlight the significance of sociality in the knowledge-seeking process in open innovation projects. This study largely contributes to digital transformation literature by extending the view of motivation and examining the moderating role of knowledge involved in the projects.

• Smart Media Journal
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• v.13 no.1
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• pp.86-100
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• 2024

Recently, a number of virtual reality exhibition-type content services have been produced using archive resources of visual art records as a means of promoting cultural policy-based public companies. However, it is by no means easy to accumulate 3D works of art as data. Looking at the current state of metadata in public institutions, there was no digitalization of resources when developing digital twins because it was built based on old international standards. It was found that data modeling evolution is inevitable to connect multidimensional data at a capacity and speed that exceeds the functions of existing systems. Therefore, the elements and concepts of data modeling design were first considered among previous studies. When developing virtual reality content, when it is designed for the migration of 3D modeling data, the previously created metadata was analyzed to improve the upper elements that must be added to 3D modeling. Furthermore, this study demonstrated the possibility by directly implementing the process of using newly created metadata in virtual reality content in accordance with the data modeling process. If this study is gradually developed in the future, metadata-based data modeling can become more meaningful in the use of public data than it is today.

• Journal of Korea Water Resources Association
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• v.57 no.2
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• pp.127-137
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• 2024

Physical models, which are often used for water resource management, are difficult to build and operate with input data and may involve the subjective views of users. In recent years, research using data-driven models such as machine learning has been actively conducted to compensate for these problems in the field of water resources, and in this study, an artificial neural network was used to simulate long-term rainfall runoff in the Osipcheon watershed in Samcheok-si, Gangwon-do. For this purpose, three input data groups (meteorological observations, daily precipitation and potential evapotranspiration, and daily precipitation - potential evapotranspiration) were constructed from meteorological data, and the results of training the LSTM (Long Short-term Memory) artificial neural network model were compared and analyzed. As a result, the performance of LSTM-Model 1 using only meteorological observations was the highest, and six LSTM-MLP ensemble models with MLP artificial neural networks were built to simulate long-term runoff in the Fifty Thousand Watershed. The comparison between the LSTM and LSTM-MLP models showed that both models had generally similar results, but the MAE, MSE, and RMSE of LSTM-MLP were reduced compared to LSTM, especially in the low-flow part. As the results of LSTM-MLP show an improvement in the low-flow part, it is judged that in the future, in addition to the LSTM-MLP model, various ensemble models such as CNN can be used to build physical models and create sulfur curves in large basins that take a long time to run and unmeasured basins that lack input data.

• Convergence Security Journal
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• v.23 no.4
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• pp.109-119
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• 2023

C4I system is an integrated battlefield information system that automates the five elements of command, control, communications, computers, and information to efficiently manage the battlefield. C4I systems play an important role in collecting and analyzing enemy positions, situations, and operational results to ensure that all services have the same picture in real time and optimize command decisions and mission orders. However, the current C4I has limitations whenever a new weapon system is introduced, as it only provides battlefield visualization in a single area focusing on the battlefield situation for each military service. In a future battlefield that expands not only to land, sea, and air domains but also to cyber and space domains, improved command and control decisions will be possible if organic data from various weapon systems is gathered to quickly visualize the battlefield situation desired by the user. In this study, the visualization technology applicable to the future C4I system is divided into map area, situation map area, and display area. The technological implementation of this future C4I system is based on various data and communication means such as 5G networks, and is expected to enable hyper-connected battlefield visualization that utilizes a variety of high-quality information to enable realistic and efficient battlefield situation awareness.

• Journal of the Korean Society for Library and Information Science
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• v.58 no.2
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• pp.225-249
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• 2024

The purpose of this study is to develop an evaluation tool for map-based data visualization platforms and to conduct heuristic usability evaluations on existing platforms representing inter-regional information. We compared and analyzed the usability evaluation criteria of map-based platforms from the previous studies along with Nielsen's (1994) 10 usability evaluation principles. We proposed nine evaluation criteria, including (1) visibility, (2) representation of the real world, (3) consistency and standards, (4) user control and friendliness, (5) flexibility, (6) design, (7) compatibility, (8) error prevention and handling, and (9) help provision and documentation. Additionally, to confirm the effectiveness of the proposed criteria, four experts was invited to evaluate five domestic and international map-based data visualization platforms. As a result, the experts were able to rank the usability of the five platforms using the proposed map-based data visualization usability evaluation criteria, which included quantified scores and subjective opinions. The results of this study are expected to serve as foundational material for the future development and evaluation of map-based visualization platforms.

• Journal of the Institute of Convergence Signal Processing
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• v.24 no.4
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• pp.178-185
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• 2023

Rapid urbanization and advancements in technology have led to a surge in the number of automobiles, resulting in frequent traffic accidents, and consequently, an increase in human casualties and economic losses. Therefore, there is a need for technology that can predict the risk of traffic accidents to prevent them and minimize the damage caused by them. Traffic accidents occur due to various factors including traffic congestion, the traffic environment, and road conditions. These factors give traffic accidents spatiotemporal characteristics. This paper analyzes traffic accident data to understand the main characteristics of traffic accidents and reconstructs the data in a time series format. Additionally, an LSTM-MLP based model that excellently captures spatiotemporal characteristics was developed and utilized for traffic accident prediction. Experiments have proven that the proposed model is more rational and accurate in predicting the risk of traffic accidents compared to existing models. The traffic accident risk prediction model suggested in this paper can be applied to systems capable of real-time monitoring of road conditions and environments, such as navigation systems. It is expected to enhance the safety of road users and minimize the social costs associated with traffic accidents.

• Journal of Industrial Convergence
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• v.22 no.7
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• pp.15-21
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• 2024

A hiking stick is generally one of the walking aids that allow hikers to walk while relying on their own bodies when walking. A rechargeable battery must be built into the hiking stick, which is an auxiliary device, in order to perform various functions. A separate power supply is required to charge the rechargeable battery. This study is about a self-generated power supply and develops a power generation device using a screw with higher power generation efficiency than the existing method. It is differentiated from the method suggested in this study by comparing and analyzing it with the existing power generation method, and identifying problems therewith. The screw-type power generation device generates power when the climbing stick comes into contact with the ground and when it is separated from the ground. The built-in power generation device does not require a separate power supply, and it can be used by attaching the role of a mobile phone auxiliary battery and a lighting lamp, and it has the effect of being able to find it through location tracking by embedding a GPS sensor, etc., and using lighting to keep the user safe in emergency situations such as distress. The existing generator with built-in mountain climbing stick is difficult to charge due to very weak current and low practicality, but the generator developed in this research could achieve high efficiency to obtain a sufficient current, so it is possible to charge a battery and practicality.