• KIPS Transactions on Software and Data Engineering
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• v.4 no.1
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• pp.57-64
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• 2015

Activity recognition technology is gaining attention because it can provide useful information follow user's situation. In research of activity recognition before smartphone's dissemination, we had to infer user's activity by using independent sensor. But now, with development of IT industry, we can infer user's activity by using inner sensor of smartphone. So, more animated research of activity recognition is being implemented now. By applying activity recognition system, we can develop service like recommending application according to user's preference or providing information of route. Some previous activity recognition systems have a defect using up too much energy, because they use GPS sensor. On the other hand, activity recognition system which Google released recently (Google Activity Recognition) needs only a few power because it use 'Network Provider' instead of GPS. Thus it is suitable to smartphone application system. But through a result from testing performance of Google Activity Recognition, we found that is difficult to getting user's exact activity because of unnecessary activity element and some wrong recognition. So, in this paper, we describe problems of Google Activity Recognition and propose AGAR(Advanced Google Activity Recognition) applied method to improve accuracy level because we need more exact activity recognition for new service based on activity recognition. Also to appraise value of AGAR, we compare performance of other activity recognition systems and ours and explain an applied possibility of AGAR by developing exemplary program.

• Journal of Korea Technology Innovation Society
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• v.10 no.2
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• pp.230-254
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• 2007

Scientometrics means the metrics of science, and it plays a role in the measurement of scientists' activities by using research results like papers. Objective numerical values or indicators obtained by scientometric analysis may make a significant contribution to correct evaluation by excluding political or relationship bias or revealing significant researchers or their research fields that have been assessed relatively lower for their performance. In the period when the scale of technology was relatively large, such as for the development of heavy chemistry, it was possible to make a not-bad selection by an assessment made only with the intuition of the assessor. However, it is nearly impossible for experts with a narrow knowledge in a field to judge the direction of change correctly as the current science technology changes rapidly and tremendously. Even in an assessment of research performance of researchers or research institutions, only some notable researchers are able to attract attention owing to the Metthew effect, and the majority who have accumulated research results in depth are not able to receive reasonable research funding. Therefore, it might be natural that there is a growing need for assessment using scientometrics. The purpose of this research is to suggest some applicable scientometric techniques for an assessment of research performance to science and technology researchers and policy planners. In order to accomplish this, we used a frequency analysis of papers and its citations to assess the research performance of researchers, institutions, and countries for OLED technology as a case study. Furthermore, we position detailed technologies for OLED technology as arriving, growing, maturing, and declining technology by utilizing several network analysis techniques, make groups with researchers and institutions of same research interest. And we review the current international joint research of Korea, China, and Japan, who are in a competitive relationship. Also, at the end of each analysis result, we describe what kind of assessment should be available to Korean policy planners and research managers for such scientometric analysis results.

• Informatization Policy
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• v.22 no.4
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• pp.3-21
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• 2015

Since the 1990s, the notion of governance has been introduced as an alternative way of understanding the new trend of public administration. Then, it was followed by the emergence of the concept, e-governance (electronic governance) with the development of information and communication technologies. E-governance means an approach to understanding how to manage state affairs. It emphasizes the notion of governance where various actors (government-market-civil society, etc.) adjust and cooperate through mutual collaborative network, not a unilateral decision-making form of government, in order to solve common problems. It also carries the notion of 'electronic' in the sense that it operates on the basis of utilizing information and communication technology (ICT). This study systematically attempts to analyze research trends and methodologies of e-governance in major Korean journals and proceedings in the field of public administration and policy studies over the last 15 years. Some of the findings are worth noting: first, e-governance studies in Korea are in large measure concentrated on the government-centered perspective. Also, past studies tend to focus on problem-solving by suggesting policy alternatives. Past studies, thus, seem to pay relatively less attention to explanation of the phenomena through rigorous analysis. Under the circumstances, this review is expected to shed light on some of conceptual or methodological issues related e-governance studies, thus, to provide significant implications for future research of e-governance.

• Journal of Korea Water Resources Association
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• v.53 no.2
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• pp.97-105
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• 2020

Recently, Artificial Neural Network receives attention as a data prediction method. Among these, a Long Shot-term Memory (LSTM) model specialized for time-series data prediction was utilized as a prediction method of hydrological time series data. In this study, the LSTM model was constructed utilizing deep running open source library TensorFlow which provided by Google, to predict inflows of multipurpose dams. We predicted the inflow of the Yongdam Multipurpose Dam which is located in the upper stream of the Geumgang. The hourly flow data of Yongdam Dam from 2006 to 2018 provided by WAMIS was used as the analysis data. Predictive analysis was performed under various of variable condition in order to compare and analyze the prediction accuracy according to four learning parameters of the LSTM model. Root mean square error (RMSE), Mean absolute error (MAE) and Volume error (VE) were calculated and evaluated its accuracy through comparing the predicted and observed inflows. We found that all the models had lower accuracy at high inflow rate and hourly precipitation data (2006~2018) of Yongdam Dam utilized as additional input variables to solve this problem. When the data of rainfall and inflow were utilized together, it was found that the accuracy of the prediction for the high flow rate is improved.

• Journal of the Korean Academy of Child and Adolescent Psychiatry
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• v.20 no.3
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• pp.129-139
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• 2009

Objectives : This study evaluated the effectiveness of school-based mental health screening and treatment linkage programs, focusing on attention-deficit hyperactivity disorder (ADHD) and adolescent depression. Methods : All 11,158 children and adolescents aged 8-14 years in Seoul received a school-based mental health screening, consisting of the Korean versions of the ADHD rating scale and the Child Behavior Checklist (CBCL), for ADHD, and the Center for Epidemiological Studies Depression Scale (CES-DS) and the Suicidal Ideation Questionnaire-Junior (SIQ-JR), for depression. The high-risk children's and adolescents' diagnoses were confirmed using the Diagnostic Interview Schedule for Children-IV (DISC-IV). Treatment linkage programs were managed by the Seoul Metropolitan Community Mental Health Center (Program 1), the Seoul Metropolitan Office of Education (Program 2), or the Dongjak District Office of Education (Program 3). We estimated and compared the referral rates of the three programs. Results : Program 1 screened and referred 22.9% of ADHD youths to mental health services, Program 2, 68.8%, and Program 3, 40.0%. Program 1 screened and referred 22.8% of depressed youths to mental health services, Program 2, 53.8%, and Program 3, 88.9%. Key elements for successful screening and referral programs were an effective school/community mental health center/Office of Education network, the parents' financial support and perception of their child's mental health status as being problematic, and the teachers' active engagement. Conclusion : This is the first study investigating the effectiveness of school-based mental health screenings' linkage to treatment for primary and middle school students in Korea. An effective network for community mental health and improvements in parents' and teachers' perceptions regarding mental health are needed for more successful treatment linkage.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.18 no.1
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• pp.47-58
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• 2018

The heterogeneous cellular network (HCN) is most significant as a key technology for future fifth generation (5G) wireless networks. The heterogeneous network considered consists of randomly macrocell base stations (MBSs) overlaid with femtocell base stations (BSs). The stochastic geometry has been shown to be a very powerful tool to model, analyze, and design networks with random topologies such as wireless ad hoc, sensor networks, and multi- tier cellular networks. The HCNs can be energy-efficiently designed by deploying various BSs belonging to different networks, which has drawn significant attention to one of the technologies for future 5G wireless networks. In this paper, we propose switching off/on systems enabling the BSs in the cellular networks to efficiently consume the power by introducing active/sleep modes, which is able to reduce the interference and power consumption in the MBSs and FBSs on an individual basis as well as improve the energy efficiency of the cellular networks. We formulate the minimization of the power onsumption for the MBSs and FBSs as well as an optimization problem to maximize the energy efficiency subject to throughput outage constraints, which can be solved the Karush Kuhn Tucker (KKT) conditions according to the femto tier BS density. We also formulate and compare the coverage probability and the energy efficiency in HCNs scenarios with and without coordinated multi-point (CoMP) to avoid coverage holes.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.42 no.2
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• pp.471-492
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• 2017

The demand and interest in big data analytics are increasing rapidly. The concepts around big data include not only existing structured data, but also various kinds of unstructured data such as text, images, videos, and logs. Among the various types of unstructured data, text data have gained particular attention because it is the most representative method to describe and deliver information. Text analysis is generally performed in the following order: document collection, parsing and filtering, structuring, frequency analysis, and similarity analysis. The results of the analysis can be displayed through word cloud, word network, topic modeling, document classification, and semantic analysis. Notably, there is an increasing demand to identify trending topics from the rapidly increasing text data generated through various social media. Thus, research on and applications of topic modeling have been actively carried out in various fields since topic modeling is able to extract the core topics from a huge amount of unstructured text documents and provide the document groups for each different topic. In this paper, we review the major techniques and research trends of text analysis. Further, we also introduce some cases of applications that solve the problems in various fields by using topic modeling.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.46 no.4
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• pp.29-39
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• 2009

The need for radio spectrum is recently considered as a huge hurdle towards the rapid development of wireless networks. Large parts of the spectrum are allocated to licensed radio services in proprietary way. However, enormous success of the wireless services and technologies in the unlicensed bands has brought new ideas and innovations. In recent years cognitive radio has gained much attention for solving the spectrum scarcity problem. It changes the way spectrum is regulated so that more efficient spectrum utilization is possible. Multi-hop relay technology on the other hand has intensively been studied in the area of ad hoc and peer-to-peer networks. But in cellular network, only recently the integration of multi-hop capability is considered to enhance the performance significantly. Multi-hop relaying can extend the coverage of the cell to provide high data rate service to a greater distance and in the shadowed regions. Very few papers still exist that combine these methods to maximize the spectrum utilization. Thus we propose a network architecture combining these two technologies in a way to maximize the system throughput. We present the throughput capacity equations for the proposed system model considering various system parameters like utilization factor by the primary users and primary users' transmission radius and through extensive numerical simulations we analyze the significance of work.

• KOREAN JOURNAL OF PACKAGING SCIENCE & TECHNOLOGY
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• v.23 no.3
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• pp.201-209
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• 2017

The Big Data, the element of the Fourth Industrial Revolution, is drawing attention as the 4th Industrial Revolution is mentioned in the 2016 World Economic Forum. Big Data is being used in various fields because it predicts the near future and can create new business. However, utilization and research in the field of packaging are lacking. Today packaging has been demanded marketing elements that effect on consumer choice. Big data is actively used in marketing. In the marketing field, big data can be used to analyze sales information and consumer reactions to produce meaningful results. Therefore, this study proposed a method of applying big data in the field of packaging focusing on marketing. In this study suggest that try to utilize the private data and community data to analyze interaction between consumers and products. Using social big data will enable to understand the preferred packaging and consumer perceptions and emotions in the same product line. It can also be used to analyze the effects of packaging among various components of the product. Packaging is one of the many components of the product. Therefore, it is not easy to understand the impact of a single packaging element. However, this study presents the possibility of using Big Data to analyze the perceptions and feelings of consumers about packaging.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.10
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• pp.4678-4702
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• 2018

Traffic Matrix estimation has always caught attention from researchers for better network management and future planning. With the advent of high traffic loads due to Cloud Computing platforms and Software Defined Networking based tunable routing and traffic management algorithms on the Internet, it is more necessary as ever to be able to predict current and future traffic volumes on the network. For large networks such origin-destination traffic prediction problem takes the form of a large under- constrained and under-determined system of equations with a dynamic measurement matrix. Previously, the researchers had relied on the assumption that the measurement (routing) matrix is stationary due to which the schemes are not suitable for modern software defined networks. In this work, we present our Compressed Sensing with Dynamic Model Estimation (CS-DME) architecture suitable for modern software defined networks. Our main contributions are: (1) we formulate an approach in which measurement matrix in the compressed sensing scheme can be accurately and dynamically estimated through a reformulation of the problem based on traffic demands. (2) We show that the problem formulation using a dynamic measurement matrix based on instantaneous traffic demands may be used instead of a stationary binary routing matrix which is more suitable to modern Software Defined Networks that are constantly evolving in terms of routing by inspection of its Eigen Spectrum using two real world datasets. (3) We also show that linking this compressed measurement matrix dynamically with the measured parameters can lead to acceptable estimation of Origin Destination (OD) Traffic flows with marginally poor results with other state-of-art schemes relying on fixed measurement matrices. (4) Furthermore, using this compressed reformulated problem, a new strategy for selection of vantage points for most efficient traffic matrix estimation is also presented through a secondary compression technique based on subset of link measurements. Experimental evaluation of proposed technique using real world datasets Abilene and GEANT shows that the technique is practical to be used in modern software defined networks. Further, the performance of the scheme is compared with recent state of the art techniques proposed in research literature.