• The Transactions of The Korean Institute of Electrical Engineers
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• v.65 no.5
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• pp.730-737
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• 2016

Electric utilities may face new threats with increase in electric vehicles (EVs) in the personal automobile market. The peak demand will increase which may stress the distribution network equipment. The focus of this paper is on an adaptive control of smart household appliances by using an intelligent load management system (ILMS). The main objectives are to accomplish consumer needs and prevent overloading of power grid. The stress from the network is released by limiting the peak demand of a house when it exceeds a certain point. In the proposed strategy, for each smart appliance, the customers will set its order/rank according to their own preferences and then system will control the household loads intelligently for consumer reliability. The load order can be changed at any time by the customer. The difference between the set and actual value for each load's specific parameter will help the utility to estimate the acceptance of this intelligent load management system by the customers.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.6
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• pp.1957-1980
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• 2021

The significant aim of this research has always been to evaluate the mechanism for efficient and inherently aware usage of vitality in-home devices, thus improving the information of smart metering systems with regard to the usage of selected homes and the time of use. Advances in information processing are commonly used to quantify gigantic building activity data steps to boost the activity efficiency of the building energy systems. Here, some smart data mining models are offered to measure, and predict the time series for energy in order to expose different ephemeral principles for using energy. Such considerations illustrate the use of machines in relation to time, such as day hour, time of day, week, month and year relationships within a family unit, which are key components in gathering and separating the effect of consumers behaviors in the use of energy and their pattern of energy prediction. It is necessary to determine the multiple relations through the usage of different appliances from simultaneous information flows. In comparison, specific relations among interval-based instances where multiple appliances use continue for certain duration are difficult to determine. In order to resolve these difficulties, an unsupervised energy time-series data clustering and a frequent pattern mining study as well as a deep learning technique for estimating energy use were presented. A broad test using true data sets that are rich in smart meter data were conducted. The exact results of the appliance designs that were recognized by the proposed model were filled out by Deep Convolutional Neural Networks (CNN) and Recurrent Neural Networks (LSTM and GRU) at each stage, with consolidated accuracy of 94.79%, 97.99%, 99.61%, for 25%, 50%, and 75%, respectively.

• The KIPS Transactions:PartA
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• v.16A no.3
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• pp.209-216
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• 2009

The ubiquitous smart home is the home of the future that takes advantage of context information from the human and the home environment and provides an automatic home service for the human. Human location and motion are the most important contexts in the ubiquitous smart home. We present a real-time human tracker that predicts human location and motion for the ubiquitous smart home. We used four network cameras for real-time human tracking. This paper explains the real-time human tracker's architecture, and presents an algorithm with the details of two functions (prediction of human location and motion) in the real-time human tracker. The human location uses three kinds of background images (IMAGE1: empty room image, IMAGE2: image with furniture and home appliances in the home, IMAGE3: image with IMAGE2 and the human). The real-time human tracker decides whether the human is included with which furniture (or home appliance) through an analysis of three images, and predicts human motion using a support vector machine. A performance experiment of the human's location, which uses three images, took an average of 0.037 seconds. The SVM's feature of human's motion recognition is decided from pixel number by array line of the moving object. We evaluated each motion 1000 times. The average accuracy of all the motions was found to be 86.5%.

• Journal of Korea Multimedia Society
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• v.11 no.8
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• pp.1059-1071
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• 2008

Users in ubiquitous computing environment can easily access and use a multitude of devices and services anywhere and anytime. The key technology to realize this scenario is the method to intuitively provide proper user interfaces for each device and service. Previous attempts simply provided a designated user interface for each device and service or provided an abstract user interface to control common functions of different services. To select a target appliance, either user directly specified the target device or depended on sensors such as RFID tags and readers limiting the applicable scenarios. In this paper, we present UbiController which uniquely uses camera on the mobile device to recognize markers of appliances to acquire user interface for controlling task. UbiController aims to provide automatic discovery of multiple services in the smart home environment, support traditional GUI and novel camera-based recognition method as well as intuitive interaction methods for users. In this paper, we show experiments on the performance of UbiController's discovery and recognition methods and user feedback on interaction methods from a user study.

• Journal of Internet Computing and Services
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• v.16 no.3
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• pp.117-126
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• 2015

The concept of Creating Shared Value (CSV) has emerged as a solution for companies' sustainable growth and social problems through collaboration of business activities and social demand. Therefore, studies on shared value creativity and measurement of companies' performances are required. However, most recent studies have applied the CSV concept to existing business models or measure shared value considering only economic value. Few studies have considered the appropriateness of applying the CSV or discussed performance measurement with respect to industry characteristics. This study selects the industry expected to have the greatest created value with applying the CSV, and then suggests measurement of performance in this industry. First, through the expert interview, the study selects one from among South Korea's 10 main industries, which is expected to solve social demand through convergence with the IT industry, and to create new value for a traditional industry. The Home Appliance industry was selected as appropriate, because it has industrial growth stagnation under the smart environment. Moreover, it is facing problems such as energy savings and environmental issues. The study goes on to suggest, based on Michael Porter's three strategic levels, measurement variables of the value creation process and performance. This study has limitations from an empirical perspective. However, as the interest of applying CSV to business is growing, it is meaningful to explore the CSV strategies activities and measurement performance based on industry-specific characteristics.

• Proceedings of the Korean Information Science Society Conference
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• 2008.06d
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• pp.444-448
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• 2008

The ubiquitous smart home is the home of the future that takes advantage of context information from the human and the home environment and provides an automatic home service for the human. Human location and motion are the most important contexts in the ubiquitous smart home. We present a real-time human tracker that predicts human location and motion for the ubiquitous smart home. We used four network cameras for real-time human tracking. This paper explains the real-time human tracker's architecture, and presents an algorithm with the details of two functions (prediction of human location and motion) in the real-time human tracker. The human location uses three kinds of background images (IMAGE1: empty room image, IMAGE2:image with furniture and home appliances in the home, IMAGE3: image with IMAGE2 and the human). The real-time human tracker decides whether the human is included with which furniture (or home appliance) through an analysis of three images, and predicts human motion using a support vector machine. A performance experiment of the human's location, which uses three images, took an average of 0.037 seconds. The SVM's feature of human's motion recognition is decided from pixel number by array line of the moving object. We evaluated each motion 1000 times. The average accuracy of all the motions was found to be 86.5%.

• Journal of the Korea Furniture Society
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• v.28 no.4
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• pp.268-277
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• 2017

This paper analyzed the modern trends and the present state of kitchen furniture and electric appliances depending on the advancement in IOT technology, in order to contribute to seeking a method of approaching consumers as an effective way for kitchen furniture and electric appliance companies to develop IOT products. Prior to analysis, the chronological transitions of Korean kitchen furniture and examined the effects of Korea's own emotions and characteristics on modern kitchen. For this study, the changes in Korean kitchen space were explored, based on previous studies, recent trend magazines, articles and professional books and the trends of modern kitchen furniture were observed. Also, the concept of IOT technology was considered and then the tendencies of modern IOT kitchen furniture and electric appliances were analyzed. The case study targeted the recent three-year trends of two types of domestic kitchen furniture and small-sized IOT kitchen electric appliances, which are sold, or have been introduced on foreign online stores and have been unveiled at CES Electric Appliances Exhibition. As a result of the study, it was found that many foreign countries, including the US have been developing various IOT kitchen electric appliances. The enterprise concept is to change kitchen with their IOT products, not changing future kitchen with IOT technology. This implies that approaches to product development may change. Moreover, it was recognized that for kitchen design, characteristics of product users need to be considered, according to Korean ideology.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2014.10a
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• pp.845-847
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• 2014

In recent years, the mobile OS market, enlargement and, at the same time, Android has mounted various smart phones and feature air conditioning, smart TV, cleaning robot, camera, etc. that the number is being spread at a rapid pace. But a lot of devices to control the modules and applications at once, this is not a device for controlling the dissemination of applications all in one place, and in charge of the devices that allow you to manage applications are lacking. Accordingly, in this paper, use the Web Server registered in the appliance can be controlled by management, Web access, is proposing and designing the system. This is the current research is actively in progress in the field of IoT (Internet of Things) would be utilized.

• The Journal of the Korea institute of electronic communication sciences
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• v.16 no.6
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• pp.1223-1230
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• 2021

Recently, energy resource management is a major concern around the world. Energy management activities minimize environmental impacts of the energy production. This paper presents design and prototyping of a home electric energy monitoring system that provides residential consumers with real time information about their electricity use. The developed system is composed of an in-house sensing system and a server system. The in-home sensing system is a set of wireless smart plug which have an AC power socket, a relay to switch the socket ON/OFF, a CT sensor to sense current of load appliance and a Kmote. The Kmote is a wireless communication interface based on TinyOS. Each sensing node sends its detection signal to a home gateway via wireless link. The home gateway stores the received signals into a remote database. The server system is composed of a database server and a web server, which provides web-based monitoring system to residential consumers. We analyzed and presented energy consumption data from electrical appliances for 3 months in home. The experimental results show the promising possibilities to estimate the energy consumption patterns and the current status.

• Journal of KIISE:Computer Systems and Theory
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• v.33 no.9
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• pp.642-649
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• 2006

In order to recognize a situation from a environment which provides an intelligent service, we propose state-transition based situation modeling which is suitable for a low computing power and restricted resources like embedded systems, and we designed its application to a situation-action converter(SAC)which is consist of two parts; situation detector recognized wanted situations and action generator generated various control actions. Then, we implemented a situation manager for smart scheduler service by using a SAC which is installed to a ARM processor based embedded Linux evaluation board.