• Journal of Broadcast Engineering
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• v.20 no.4
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• pp.580-597
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• 2015

Broadcast encryption system is a cryptographic primitive that enables a sender to broadcast a message to a set of receivers in a secure channel. Out of previous proposed broadcast encryption systems, the most effective is the one that uses the Subset Difference(SD) method in a binary tree structure. The SD method has been realized by two underlying approaches: Pseudo-Random Generator(PRG) and Secret Sharing(SS). 2-SD method is the generalized version of the SD method by which two subsets of revoked receivers can be dealt with by one subset (in an SD-based broadcast encryption system). The primary advantage of the 2-SD method is to further reduce the size of transmission overhead, compared to the SD method. Until now, however, there is no known broadcast encryption system that is based on such a 2-SD technique using either PRG or SS basis. In this paper, we suggest a new 2-SD broadcast encryption system using the SS-based technique that was suggested by Jae Hwan Lee et al. in 2014[9]. The new system can reduce the size of ciphertext by 25% of the one in the previous SS-based broadcast encryption system. Also, on a theoretical note, ours is the first 2-SD broadcast encryption system that is provably secure.

• Journal of the Institute of Convergence Signal Processing
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• v.12 no.2
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• pp.96-101
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• 2011

In recent days, the demand for the remote ECG monitoring system has been increasing and the automation of the monitoring system is becoming quite of a concern. Automatic detection of the abnormal ECG beats must be a necessity for the successful commercialization of these real time remote ECG monitoring system. From these viewpoints, in this paper, we proposed an automatic detection algorithm for the abnormal ECG beats using QRS width and RR interval patterns. In the previous research, many efforts have been done to classify the ECG beats into detailed categories. But, these approaches have disadvantages such that they produce lots of misclassification errors and variabilities in the classification performance. Also, they require large amount of training data for the accurate classification and heavy computation during the classification process. But, we think that the detection of abnormality from the ECG beats is more important that the detailed classification for the automatic ECG monitoring system. In this paper, we tried to detect the VEB which is most frequently occurring among the abnormal ECG beats and we could achieve satisfactory detection performance when applied the proposed algorithm to the MIT/BIH database.

• Bulletin of the Society of Naval Architects of Korea
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• v.27 no.3
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• pp.38-46
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• 1990

A numerical solution method of the boundary integral equation for axisymmetric potential flows is presented. Those are represented by ring source and ring vorticity distribution. Strengths of ring source and ring vorticity are approximated by linear functions of a parameter $\zeta$ on a segment. The geometry of the body is represented by a cubic B-spline. Limiting integral expressions as the field point tends to the surface having ring source and ring vorticity distribution are derived upto the order of ${\zeta}ln{\zeta}$. In numerical calculations, the principal value integrals over the adjacent segments cancel each other exactly. Thus the singular part proportional to $\(\frac{1}{\zeta}\)$ can be subtracted off in the calculation of the induced velocity by singularities. And the terms proportional to $ln{\zeta}$ and ${\zeta}ln{\zeta}$ can be integrated analytically. Thus those are subtracted off in the numerical calculations and the numerical value obtained from the analytic integrations for $ln{\zeta}$ and ${\zeta}ln{\zeta}$ are added to the induced velocity. The four point Gaussian Quadrature formula was used to evaluate the higher order terms than ${\zeta}ln{\zeta}$ in the integration over the adjacent segments to the field points and the integral over the segments off the field points. The root mean square errors, $E_2$, are examined as a function of the number of nodes to determine convergence rates. The convergence rate of this method approaches 2.

• Journal of Convergence for Information Technology
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• v.11 no.11
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• pp.57-65
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• 2021

We propose a novel method to detect abnormal data of specific symptoms using deep learning in air pollution measurement system. Existing methods generally detect abnomal data by classifying data showing unusual patterns different from the existing time series data. However, these approaches have limitations in detecting specific symptoms. In this paper, we use DeepLab V3+ model mainly used for foreground segmentation of images, whose structure has been changed to handle one-dimensional data. Instead of images, the model receives time-series data from multiple sensors and can detect data showing specific symptoms. In addition, we improve model's performance by reducing the complexity of noisy form time series data by using 'piecewise aggregation approximation'. Through the experimental results, it can be confirmed that anomaly data detection can be performed successfully.

• Journal of the Korea Convergence Society
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• v.10 no.3
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• pp.167-176
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• 2019

The conversational agent, which is at the forefront of the 4th industry, aims to personalize the user-centered focus in the future and holds an important position to have a hub that can be connected to various IoT devices. It is a challenge for interactive agents to recognize the user's emotions and provide the correct interaction to personalization. The study first I looked at emotional definitions and scientific and engineering approaches. Then I recognized through social perspectives what social function and what factors emotions have and how they can be used to understand emotions. Based on this, I explored how users can be discovered emotional social factors in communication. This research has shown that social factors can be found in the user's speech, which can be linked to the social meaning of emotions. Finally, I propose a model to discover social factors in user communication. I hope that this will help designer and researcher to study user-centered design and interaction in designing interactive agents.

• Journal of Convergence for Information Technology
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• v.9 no.9
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• pp.229-236
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• 2019

This study aims to examine the moderating effect of Extended Song Sharing for helping alcoholics to deal with stress. ESS is structure with non-verbal techniques approaches traditional Song Sharing technique. For this study, 64 alcoholics hospitalized were experimental (n=33) and control groups (n=31). The group was asked to participate in 12 ESS programs twice a week, every 45 minutes for six weeks. During this period, all those who belonged to the experimental and control groups were required to continue to participate in treatment programs prescribed by hospitals. Result: ESS interventional effects on subjects were evaluated using a stress response scale. As a result of the experiment, the WCC score of the experimental group increased 7.3% (p=0.000) but there was almost no change in the control group (1.5% increase, p=0.019). Effects of WCC types were higher in the experimental group, followed by 13.67% (p=0.006) in coping with wishful thinking, 9.66% (p=0.000) in the pursuit of social support, 6.35% (p=0.000) in dealing with problems, and 4% (p=0.000) in coping with emotions. ESS activities have been confirmed to improve the way alcoholics cope with stress.

• Convergence Security Journal
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• v.21 no.1
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• pp.177-189
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• 2021

Defense R&D is a key process for securing weapons systems determined by mid- and long-term needs to cope with changing future battlefield environments. In particular, the test and evaluation provides information necessary to determine whether or not to switch to mass production as the last gateway to research and development of weapons systems and plays an important role in ensuring performance linked to the life cycle of weapons systems. Meanwhile, if you look at the recent changes in the operational environment of the Korean Peninsula and the defense acquisition environment, you can see three main characteristics. First of all, continuous safety accidents occurred during the operation of the weapon system, which increased social interest in the safety of combatants, and the efficient execution of the limited defense budget is required as acquisition costs increase. In addition, strategic approaches are needed to respond to future battlefield environments such as robots, autonomous weapons systems (RAS), and cyber security test and evaluation. Therefore, in this study, we would like to present strategies for improving the testing and evaluation of weapons systems by considering the characteristics of the security environment that has changed recently. To this end, the improvement strategy was derived by analyzing the complementary elements of the current weapon system operational test and evaluation system in a multi-dimensional model and prioritized through the hierarchical analysis method (AHP).

• Journal of the Korea Convergence Society
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• v.12 no.1
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• pp.133-141
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• 2021

This research suggests a design process that effectively complements the human-centered design through an objective data-driven approach. The subjective human-centered design process can often lack objectivity and can be supplemented by the data-driven approaches to effectively discover hidden user needs. This research combines the data mining analysis with co-design process and verifies its applicability through the case study on the smart heating jacket. In the data mining process, the clustering can group the users which is the basis for selecting the target groups and the decision tree analysis primarily identifies the important user perception attributes and values. The broad point of view based on the data analysis is modified through the co-design process which is the deeper human-centered design process by using the developed workbook. In the co-design process, the journey maps, needs and pain points, ideas, values for the target user groups are identified and finalized. They can become the basis for starting new product development.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.16 no.3
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• pp.73-86
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• 2021

The primary purpose of this paper is to explore the advanced models that predict entrepreneurial innovativeness most accurately. For the first time in the field of entrepreneurship research, it presents a model that predicts entrepreneurial innovativeness based on machine learning corresponding to data scientific approaches. It uses 22,099 the Global Entrepreneurship Monitor (GEM) data from 62 countries to build predictive models. Based on the data set consisting of 27 explanatory variables, it builds predictive models that are traditional statistical methods such as multiple regression analysis and machine learning models such as regression tree, random forest, XG boost, and artificial neural networks. Then, it compares the performance of each model. It uses indicators such as root mean square error (RMSE), mean analysis error (MAE) and correlation to evaluate the performance of the model. The analysis of result is that all five machine learning models perform better than traditional methods, while the best predictive performance model was XG boost. In predicting it through XG boost, the variables with high contribution are entrepreneurial opportunities and cross-term variables of market expansion, which indicates that the type of entrepreneur who wants to acquire opportunities in new markets exhibits high innovativeness.

• Journal of Digital Convergence
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• v.19 no.3
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• pp.319-329
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• 2021

This study aims to investigate Korean fashion-related research trends and knowledge structures on digital transformation through information-based approaches. Accordingly, we first identified the current status of the relevant research in Korean academic literature by year and journal; subsequently, we derived key research topics through network analysis, and then analyzed major research trends and knowledge structures by time. From 2010 to 2020, we collected 159 studies published on Korean academic platforms, cleansed data through Python 3.7, and measured centrality and network implementation through NodeXL 1.0.1. The results are as follows: first, related research has been actively conducted since 2016, mainly concentrated in clothing and art areas. Second, the online platform, AR/VR, appeared as the most frequently mentioned topic, and consumer psychological analysis, marketing strategy suggestion, and case analysis were used as the main research methods. Through clustering, major research contents for each sub-major of clothing were derived. Third, major subject by period was considered, which has, over time, changed from consumer-centered research to strategy suggestion, and design development research of platforms or services. This study contributes to enhancing insight into the fashion field on digital transformation, and can be used as a basic research to design research on related topics.