• Journal of Broadcast Engineering
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• v.18 no.5
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• pp.749-757
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• 2013

This study aims to find out the influences of user behavior, content characteristics, functional factors and user's perception in cost on user satisfaction and continuous intention in N-Screen service. Web survey was conducted for 498 users who have used N screen service. The results show that the most influential factor contributing to user satisfaction is user interface. VOD diversity, payment system, cost, channel diversity are also meaningful factors. Identifying the critical factors which impact on user satisfaction, this study can provide the basic data for activating OTT service in smart media environment.

• Journal of the Korea institute for structural maintenance and inspection
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• v.18 no.4
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• pp.118-125
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• 2014

As the earth's current global warming has caused elevation of sea water temperature, size of storms is foreseen to increase and consequently large damages on port facilities are to be expected. In addition, due to the improved processing efficiency of port cargo volume and increasing necessity for construction of eco-friendly port, demands for various forms of port facilities are anticipated. In this study, two kinds of nondestructive evaluation (NDE) techniques (rebound hardness and ultrasonic pulse velocity methods) are investigated for the effective maintenance of smart green harbor system. A new methodology to estimate the underwater concrete strengths is proposed and its feasibility is verified throughout a series of experimental works.

• Smart Structures and Systems
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• v.15 no.5
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• pp.1373-1392
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• 2015

Placing sensors at appropriate locations is an important task in the design of an efficient structural health monitoring (SHM) system for a large-scale civil structure. In this paper, a hybrid optimization algorithm called virus monkey algorithm (VMA) based on the virus theory of evolution is proposed to seek the optimal placement of sensors. Firstly, the dual-structure coding method is adopted instead of binary coding method to code the solution. Then, the VMA is designed to incorporate two populations, a monkey population and a virus population, enabling the horizontal propagation between the monkey and virus individuals and the vertical inheritance of monkey's position information from the previous to following position. Correspondingly, the monkey population in this paper is divided into the superior and inferior monkey populations, and the virus population is divided into the serious and slight virus populations. The serious virus is used to infect the inferior monkey to make it escape from the local optima, while the slight virus is adopted to infect the superior monkey to let it find a better result in the nearby area. This kind of novel virus infection operator enables the coevolution of monkey and virus populations. Finally, the effectiveness of the proposed VMA is demonstrated by designing the sensor network of the Canton Tower, the tallest TV Tower in China. Results show that innovations in the VMA proposed in this paper can improve the convergence of algorithm compared with the original monkey algorithm (MA).

• Journal of Digital Contents Society
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• v.15 no.6
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• pp.711-719
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• 2014

Along with the advent of ubiquitous computing environment, it is becoming a part of our common life style that the demands for enjoying the wireless internet using intelligent portable device such as smart phone and iPad, are increasing anytime or anyplace without any restriction of time and place. The recommending service becomes a very important technology which can find exact information to present users, then is easy for customers to reduce their searching effort to find out the items with high purchasability in e-commerce. Traditional mining association rule ignores the difference among the transactions. In order to do that, it is considered the importance of type of merchandise or service and then, we suggest a new recommending service using mining sequential pattern based on weight to reflect frequently changing trends of purchase pattern as time goes by and as often as customers need different merchandises on e-commerce being extremely diverse. To verify improved better performance of proposing system than the previous systems, we carry out the experiments in the same dataset collected in a cosmetic internet shopping mall.

• KIPS Transactions on Software and Data Engineering
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• v.9 no.5
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• pp.153-160
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• 2020

Energy Consumption Predictions for Industries has a prominent role to play in the energy management and control system as dynamic and seasonal changes are occurring in energy demand and supply. This paper introduces and explores the steel industry's predictive models of energy consumption. The data used includes lagging and leading reactive power lagging and leading current variable, emission of carbon dioxide (tCO2) and load type. Four statistical models are trained and tested in the test set: (a) Linear Regression (LR), (b) Radial Kernel Support Vector Machine (SVM RBF), (c) Gradient Boosting Machine (GBM), and (d) Random Forest (RF). Root Mean Squared Error (RMSE), Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE) are used for calculating regression model predictive performance. When using all the predictors, the best model RF can provide RMSE value 7.33 in the test set.

• International journal of advanced smart convergence
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• v.7 no.3
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• pp.23-36
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• 2018

In wireless communication, efficient spectrum usage is an issue that has been an attractive research area for many technologies. Recently new technologies innovations allow compact radios to transmit with power efficient communication over very long distances. For example, Low-Power Wide Area Networks (LPWANs) are an attractive emerging platform to connect the Internet-of-Things (IoT). Especially, LoRa is one of LPWAN technologies and considered as an infrastructure solution for IoT. End-devices use LoRa protocol across a single wireless hop to communicate to gateway(s) connected to the internet which acts as a bridge and relays message between these LoRa end-devices to a central network server. The use of the (ISM) spectrum sharing for such long-range networking motivates us to implement spectrum sensing testbed for cognitive radio network based on LoRa and GNU radio. In cognitive radio (CR), secondary users (SUs) are able to sense and use this information to opportunistically access the licensed spectrum band in absence of the primary users (PUs). In general, PUs have not been very receptive of the idea of opportunistic spectrum sharing. That is, CR will harmfully interfere with operations of PUs. Subsequently, there is a need for experimenting with different techniques in a real system. In this paper, we implemented spectrum sensing for cognitive radio networks based on LoRa and GNU Radio, and further analyzed corresponding performances of the implemented systems. The implementation is done using Microchip LoRa evolution kits, USRPs, and GNU radio.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.20 no.5
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• pp.207-214
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• 2020

Nowadays, the use of smart phones and various electronic devices is increasing, the Internet and SNS are activated, and we live in the flood of information. The amount of information has grown exponentially, making it difficult to look at a lot of information, and more and more people want to see only key keywords in a document, and the importance of research to extract topics that are the core of information is increasing. In addition, it is also an important issue to extract the topic and compare it with the past to infer the current trend. Topic modeling techniques can be used to extract topics from a large volume of documents, and these extracted topics can be used in various fields such as trend prediction and data analysis. In this paper, we inquire the topic of the three-year papers of 2016, 2017, and 2018 in the field of computing using the LDA algorithm, one of Probabilistic Topic Model Techniques, in order to analyze the rapidly changing trends and keep pace with the times. Then we analyze trends and flows of research.

• Journal of Digital Convergence
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• v.18 no.8
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• pp.231-242
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• 2020

The thesis presents a system that continuously collects the human body's physiological vital information at rest with sensors and ICT information technology and predicts diabetes using the collected information. it shows the artificial neural network machine learning method and essential basic variable values. The study method analyzed the correlation between heart rate measurements of BCG and ECG sensors in 20 DM- and 15 DM+ subjects. Artificial Neural Network (ANN) machine learning program was used to predictability of diabetes. The input variables are time domain information of HRV, heart rate, heart rate variability, respiration rate, stroke volume, minimum blood pressure, highest blood pressure, age, and sex. ANN machine learning prediction accuracy is 99.53%. Thesis needs continuous research such as diabetic prediction model by BMI information, predicting cardiac dysfunction, and sleep disorder analysis model using ANN machine learning.

• International Commerce and Information Review
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• v.17 no.1
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• pp.57-85
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• 2015

This paper is a study on export support system that a small company can make the export performance early in the overseas market and also can enhance international marketing capabilities. The model is solved to operational problems of the conventional export support program, and it contained a further support action to complement the weakness of the program. We want to create a Smart-Platform(Export Power Double Plus-Platform: EPDP)that we can improve the export capability of SMEs in the short term. The model will be very useful to export support agencies as an integrated support platform that supports the international marketing of the korean SMEs. And added some case studies of SMEs in Daejeon city.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2015.10a
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• pp.924-926
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• 2015

In this paper, we use the interface, which many countries concentrates research of Brain - Computer Interface with the device and MyndPlay based on the IoT intelligent Arduino. Finally we will make the Brain - Computer Connection environment, the purpose of Brain - Computer Interface. Recognizes the EEG of a person who wearing the equipment, analyze, classify, and we did a research to design an intelligent thing to suit user's condition. In addition, we use the XBee, and Bluetooth to communicate to other devices, such as smart phone. In conclusion, this paper check users current status via brain waves, and it allows to control the power and other objects by using the EEG(Electroencephalography).