• Journal of the Korean Institute of Intelligent Systems
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• v.20 no.5
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• pp.638-644
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• 2010

This paper presents ideas of service scenarios for home residents using electric power monitoring system per appliance, the implementation of the monitoring system, and analysis of acquired electric power usage pattern. By acquiring and analyzing electric power usage pattern, home residents can get information of power usage pattern of every legacy (non-Demand Response-ready) appliance. Further they can get pieces of recommendation how to reduce energy consumption, intelligent standby power blocking service, and alarming service to abnormality of appliances. In order to check the feasibility of the ideas, a system that can acquire electric power pattern per appliance is implemented, and electric power pattern of some appliances are stored to a database and it was analyzed to show if auto-identification of a type of a device is possible, which is a basic required function for the scenarios presented.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.25 no.5
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• pp.1001-1009
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• 2015

In Smart Grid, smart meter sends our electric signal data to the main server of power supply in real-time. However, the more efficient the management of power loads become, the more likely the user's pattern of usage leaks. This paper points out the threat of privacy and the need of security measures in smart device environment by showing that it's possible to identify the appliances and the specific usage patterns of users from the smart meter's data. Learning algorithm PCA is used to reduce the dimension of the feature space and k-NN Classifier to infer appliances and states of them. Accuracy is validated with 10-fold Cross Validation.

• Journal of Internet Computing and Services
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• v.24 no.2
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• pp.57-66
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• 2023

Currently, a new energy system is emerging that implements consumption reduction by improving energy efficiency. Accordingly, as smart grids spread, the rate system by timing is expanding. The rate system by timing is a rate system that applies different rates by season/hour to pay according to usage. In this study, external factors such as temperature/day/time/season are considered and the time series prediction model, LSTM, is used to predict energy power usage data. Based on this energy usage prediction model, energy usage charges are reduced by analyzing usage patterns for each device and transferring power energy from the maximum load time to the light load time. In order to analyze the usage pattern for each device, a clustering technique is used to learn and classify the usage pattern of the device by time. In summary, this study predicts usage and usage fees based on the user's power data usage, analyzes usage patterns by device, and provides customized demand transfer services based on analysis, resulting in cost reduction for users.

• The Journal of the Korea Contents Association
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• v.21 no.7
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• pp.119-126
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• 2021

Recently OTT video service has grown rapidly and has a negative impact on the growth of pay-TV subscribers. Consequently the substitution of OTT services for pay-TV services will begin. This study explores OTT usage patterns, dividing into three sectors (OTT service use, OTT usage volume, and paid-OTT service use), by considering users' demographice factors, smart device ownership, pay-TV subscription, and bundle service use, by employing the Mediapanel dataset. The results showed that age, income, smartTV usage, market size, cable/satellite subscribers, TV VOD expenses, movie VOD expenses, and mobile bundle use are associated with OTT service use. The results also showed that age, household sizes, tablePC usgage, cable/satellite subscribers, TV VOD expenses, other VOD expenses, and unlimited data plans are associated with OTT usage volume. Lastly the results showed that age, gender, income, tabletPC usage, cable subscribers, other VOD expenses, mobile bundle usage, and unlimited data plans are associated with paid-OTT service use.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.67 no.3
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• pp.460-466
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• 2018

For the efficient energy consumption in an urban railway station, it is necessary to know the patterns of electrical loads for each usage in detail. The electrical loads in an urban railway station have different characteristics from other normal electrical load, such as the peak load timing during a day. The lighting, HVAC, communication, and commercial loads make up large amount of electrical load for equipment in an urban railway station, and each of them has the unique specificity. These loads for each usage were estimated without measuring device by the polynomial regression method with big data such as total amount of electrical load and weather data. In the simulation with real data, the optimal polynomial regression model was third order polynomial regression model with 9 or 10 independent variables.

• Journal of the HCI Society of Korea
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• v.11 no.1
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• pp.29-38
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• 2016

Mobile tour guide applications help tourists to search for and visit their surrounding POIs(Points of Interest) of their locations and to obtain their guide information. With the development of wearable devices like smart watches and wearable glasses, people using multiple mobile devices are increasing; a tourist may use a hand-held device, a wearable device or both to obtain tour information. However, most mobile tour guides using mobile devices provide the tour information with little consideration of their hand-held and wearable characteristics. In particular, a tourist with multiple mobile devices who search for the tour information from multiple mobile devices must input their intention separately to each device. To alleviate these problems, we propose a mobile tour guide system with the following features: one is to reduce redundant user input by sharing the touring context between hand-held and wearable devices; the other is to present tour information according to capabilities and usage pattern of the devices. The proposed system guides tourists by complementing disadvantage of the devices and also minimizes user interaction between applications and tourists.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2013.10a
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• pp.324-327
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• 2013

Recently, due to evolving cloud computing technology, we can easily and transparently utilize both local computing resource and remote computing resource in real life. Especially, enhancing smart device technologies and network infrastructures promote an increase of needs to share files between local smart devices and cloud storages. However, since smart devices have a limited storage space, storing files on cloud storage causes a starvation problem of local storage. It means that users can face a storage-lack problem even a cloud storage service provide a huge file storing space. In this research, we propose a method to manage files between smart devices and cloud storages. Our approach calculate file usage pattern based on recently used date, and then this approach determines local files being migrated. As a result, our approach is sufficient for handling data synchronization between big data storage farm and local thin client which contains limited storage space.

• KIPS Transactions on Software and Data Engineering
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• v.6 no.1
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• pp.15-22
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• 2017

With the advance of Internet Technology, various social network services are created and used by users. Especially, the use of smart devices makes that multimedia contents can be used and distributed on social network services. However, since the crime rate also is increased by users with illegal purposes, there are needs to protect contents and block illegal usage of contents with multimedia forensics. In this paper, we propose a multimedia forensic technique which is identifying the video source. First, the scheme to acquire the sensor pattern noise (SPN) using morphology filtering is presented, which comes from the imperfection of photon detector. Using this scheme, the SPN of reference videos from the reference device is estimated and the SPN of an unknown video is estimated. Then, the similarity between two SPNs is measured to identify whether the unknown video is acquired using the reference device. For the performance analysis of the proposed technique, 30 devices including DSLR camera, compact camera, camcorder, action cam and smart phone are tested and quantitatively analyzed. Based on the results, the proposed technique can achieve the 96% accuracy in identification.

• Journal of Digital Convergence
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• v.14 no.11
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• pp.275-281
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• 2016

In modern society, which boomed it became easier to obtain the necessary information to the emergence of a variety of smart devices. Due to this, the frequency of using the content based on the Web is growing rapidly. In addition, companies are turning into a production and modify the content using the CMS under the web-based. It can be a very important part to provide users with the content. Currently web services are designing a UI to the device and provided. To improve the ease of use, they are enhancing services only by survey and analysis of the patterns of all users. Most are designed without considering the UX only in the technical aspects. In this paper, to break the limits that apply to all users of the Web service pattern analysis, we propose a visualization system via the animation based on the individual user's movement patterns and usage patterns. Through this convergence is expected to be able to transform the web from the central manager to the user UX and the planning aspects researchers.