• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2014.10a
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• pp.854-856
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• 2014

Has been released of make it possible to control the using for smart devices of a wide variety home appliances and electronics in smart appliances in accordance with the one person multi devices. In addition, is increasing rapidly for the number of the product on cleaning robot and refrigerator, air conditioning, TV, etc. these devices are using the implement up DLNA system. And at home and abroad for development and has provided with Iot and Alljoyn such systems. But currently using home appliances or electronic devices of there are a lot of the operating system non installed than the installed products. In addition, smart appliances do not use for user than buying existing electronic products a lot more. In addition, more occur for smart appliances of that do not use for the user on smart appliances rather than buying existing electronics. In this paper, Suggested and implemented for system of control such as smart devices to existed home appliance on not have an operating system, Using mobile device for want users to quantify the data to transfer from arduino board.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.16 no.8
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• pp.1701-1710
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• 2012

Recently, it has been found that it is important to use a smart grid to reduce greenhouse-gas emissions worldwide. A smart grid is a digitally enabled electrical grid that gathers, distributes, and acts on information regarding the behavior of all participants (suppliers and consumers) to improve the efficiency, importance, reliability, economics, and sustainability of electricity services. The smart grid technology uses two-way communication, where users can monitor and limit the electricity consumption of their home appliances in real time. Likewise, power companies can monitor and limit the electricity consumption of home appliances for stabilization of the electricity supply. However, if information regarding the measured electricity consumption of a user is leaked, serious privacy issues may arise, as such information may be used as a source of data mining of the electricity consumption patterns or life cycles of home residents. In this paper, we propose a data transaction protocol for privacy protection in a smart grid. In addition, a power company cannot decrypt an encrypted home appliance ID without the user's password.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.35 no.9B
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• pp.1399-1406
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• 2010

Customers can monitor electricity use in real time in smart grid(ICT is grafted onto current grid), so various beneficial services can be provided to customer. We did a qualitative survey, HV(Home Visit) and FGD(Focus Group Discussion), in order to derive customer's cognition of using electricity in home and values that customers consider significantly and a quantitative survey in order to know willingness to use. Customers consider electricity indispensible for using home appliances, want to use safely far from electromagnetic waves, short circuit and electronic shock. Also, customers want to save energy conveniently with no stress. Customers want 'a function', 'information', 'motivation' for energy saving, and 'electromagnetic waves cutting', 'to prevent electronic shock', 'to prevent short circuit' for safe electricity use. In this study, we derived 4 services - energy monitoring, standby power cutting, remote control, energy consulting - based on customer values and unmet needs, which is connected to home network that customers can monitor total and each appliance's electricity usage in real time and control home appliances. The willingness to use of services is over 60% and especially energy monitoring and standby power cutting service have high willingness to use rate, about 80%.

• Journal of Advanced Navigation Technology
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• v.23 no.5
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• pp.473-480
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• 2019

The smart home is a technology that can monitor and control by connecting everything to a communication network in various fields such as home appliances, energy consumers, and security devices. The Smart home is developing not only automatic control but also learning situation and user's taste and providing the result accordingly. This paper proposes a model that can provide a comfortable indoor environment control service for the user's characteristics by detecting the user's behavior as well as the automatic remote control service. The whole system consists of ESP 8266 with sensor and Wi-Fi, Firebase as a real-time database, and a smartphone application. This model is divided into functions such as learning mode when the home appliance is operated, learning control through learning results, and automatic ventilation using indoor and outdoor sensor values. The study used moving averages for temperature and humidity in the control of home appliances such as air conditioners, humidifiers and air purifiers. This system can provide higher quality service by analyzing and predicting user's characteristics through various machine learning and deep learning.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.20 no.6
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• pp.51-58
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• 2020

The blind people have difficulty living an independent clothing life. The furniture and home appliance are adding AI or IoT with the recent growth of the smart appliance market. To support the independent clothing life of the blind, this paper suggests a smart wardrobe with closet control function, voice recognition function and clothes information recognition using CNN algorithm. The number of layers of the model was changed and Maxpooling was adjusted to create the model to increase accuracy in the process of recognizing clothes. Early Stopping Callback option is applied to ensure learning accuracy when creating a model. We added Dropout to prevent overfitting. The final model created by this process can be found to have 80 percent accuracy in clothing recognition.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2011.10a
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• pp.835-838
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• 2011

This paper presented an occupancy-based smart plug system for appliance energy saving in home. We measured current consumption of appliances in real time using smart plugs, and check the occupation of residents using occupancy sensors installed on the door. The proposed system saves energy to switch off the supply power of unnecessary usages in the unoccupied spaces. Experiments conducted have shown that current usage of appliances can be measured by using smart plugs and presence can be checked by using occupancy sensors.

• Journal of the Institute of Electronics and Information Engineers
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• v.52 no.10
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• pp.161-167
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• 2015

This paper presented an occupancy-based energy saving system for appliance energy saving in smart house. The developed system is composed of a sensing system and a home gateway system. The sensing system is set of wireless sensor nodes which have pyroelectric infrared (PIR) sensor to detect a motion of human and set of smart plugs which measure the current using CT (current transformer) sensor and send the current to home gateway wirelessly. We measured current consumption of appliances in real time using smart plugs, and checked the occupation of residents using occupancy sensors installed on the door and room. The proposed system saves electric energy to switch off the supply power of unnecessary usages in the unoccupied spaces. Experiments conducted have shown that electric energy usage of appliances can be saved about 34% checked by using occupation.

• The Journal of the Korea Contents Association
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• v.19 no.12
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• pp.9-17
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• 2019

The Internet of Things (IoT) is a technology that connects to the Internet with built-in sensors and communication functions on various objects and has been used in various fields such as smart home and smart factory. Recently, as interest in air pollution information such as fine dust is increasing, the apps for providing weather information and air pollution information based on IoT technology are being developed. In this study, we aims to provide visual air pollution and weather information and develop useful services such as appliance automation using this information. for this study, we designed and implemented Smart Services that provide home automation, messaging and alarm, visual air pollution and weather information using air pollution and weather information based on IoT.

• ETRI Journal
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• v.35 no.4
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• pp.677-686
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• 2013

In this paper, we first introduce a general architecture of an energy management system in a home area network based on a smart grid. Then, we propose an efficient scheduling method for home power usage. The home gateway (HG) receives the demand response (DR) information indicating the real-time electricity price, which is transferred to an energy management controller (EMC). Referring to the DR, the EMC achieves an optimal power scheduling scheme, which is delivered to each electric appliance by the HG. Accordingly, all appliances in the home operate automatically in the most cost-effective way possible. In our research, to avoid the high peak-to-average ratio (PAR) of power, we combine the real-time pricing model with the inclining block rate model. By adopting this combined pricing model, our proposed power scheduling method effectively reduces both the electricity cost and the PAR, ultimately strengthening the stability of the entire electricity system.

• 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%.