• Journal of the Korea Society of Computer and Information
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• v.22 no.12
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• pp.1-7
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• 2017

More new devices and technologies are recently adapted on homes to aim at enhancing our lifestyle. But though they are not interconnected each other but controlled separately. To solve this problem, we have designed and implemented a common prototype based on openHAB and RaspberryPi they could speak to each other to create a really automated and smart environment at home. The proposed prototype can merge the existing variable devices, add and remove new features at runtime because of its modular design. The proposed prototype based on a low-cost platform showed its potential as a smart home and provide a new UX to users.

• Journal of Digital Convergence
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• v.12 no.7
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• pp.229-237
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• 2014

Today, the development and performance of a variety of smart phones is increasing with the number of smart phone users were. Advances in mobile networks and devices are spread smart phones. Smart home was increasing the interest. But until now, the study of user-centric services that the service was not implemented. Therefore, in this paper, the situation of the user, the user-centric service model that provides a smart home provisioning system OWL modeling is proposed.

• Journal of the Korea Society of Computer and Information
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• v.22 no.7
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• pp.17-29
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• 2017

IRecently, researches on the recognition of indoor user situations through various sensors in a smart home environment are under way. In this paper, the case study was conducted to determine the operation of the robot vacuum cleaner by inferring the user 's indoor situation through the operation of home appliances, because the indoor situation greatly affects the operation of home appliances. In order to collect learning data for indoor situation awareness model learning, we received feedbacks from user when there was a mistake about the cleaning situation. In this paper, we propose a semi-supervised learning method using user feedback data. When we receive a user feedback, we search for the labels of unlabeled data that most fit the feedbacks collected through genetic algorithm, and use this data to learn the model. In order to verify the performance of the proposed algorithm, we performed a comparison experiments with other learning algorithms in the same environment and confirmed that the performance of the proposed algorithm is better than the other algorithms.

• Journal of the Korea Society of Computer and Information
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• v.22 no.7
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• pp.63-74
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• 2017

Recently in the area of ICT, the M2M and IoT are in the spotlight as a cutting edge technology with the help of advancement of internet. Among those fields, the smart home is the closest area to our daily lives. Smart home has the purpose to lead a user more convenient living in the house with WLAN (Wireless Local Area Network) or other short-range communication environments using automated appliances. With an arrival of the age of IoT, this can be described as one axis of a variety of applications as for the M2H (Machine to Home) field in M2M. In this paper, we propose a novel technique for estimating the location of a terminal that freely move within a specified area using the RSSI (Received Signal Strength Indication) in the WLAN environment. In order to perform the location estimation, the Fingerprint and KNN methods are utilized and the LMS with the gradient descent method and the proposed algorithm are also used through the error correction functions for locating the real-time position of a moving user who is keeping a smart terminal. From the estimated location, the nearest fixed devices which are general electric appliances were supposed to work appropriately for self-operating of virtual smart home. Through the experiments, connection and operation success rate, and the performance results are analyzed, presenting the verification results.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2016.05a
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• pp.533-536
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• 2016

In this paper, we take advantage of the SEN Remote HMI App as a tool based on Raspberry Pi server for the smart home control. This is a smart home control system built in an open and a low-cost Raspberry Pi as a server, and take advantage of Android-based graphical programming software SEN Remote HMI. If you build it based system expansion and additional works interface via the Raspberry Pi and Arduino, it is expected to be extended to the Internet of Things (IoT) enabling environment for the implementation of smart home.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.41 no.6
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• pp.678-680
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• 2016

Due to changes in the energy environment, it's very popular to introduce the solar energy at home. More effective energy management is achieved together with an energy storage system(ESS). The electricity generated by solar can be used effectively to achieve the peak cut and price reduction. In this paper, we developed Smart Energy Profile(SEP) to make an ESS as a component of home energy management system(HEMS) cooperating with home network. First, we defined the functions equipped on the ESS and then developed a standard-based protocol to achieve compatibility between products. Our main contribution is to establish the foundation to introduce the HEMS at home.

• 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.

• Journal of Korea Society of Digital Industry and Information Management
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• v.17 no.3
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• pp.17-26
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• 2021

Internet of Things is an infrastructure of the interconnected devices. In Internet of Things environment, many smart devices are used in daily life. It requires a new retrieval method using multiple devices. We propose an information retrieval method using both device characteristics and device usage characteristics in multi-device environments. Firstly, information retrieval is performed using a general purpose device. And then, it is performed using dedicated devices. Our method uses both characteristics of the devices and usage characteristics of them. Moreover, it considers queries on the general purpose device. This paper proposes a new retrieval method and describes algorithms. Then, it presents smart home scenarios. Performance evaluation is performed using the scenarios. The evaluation results show higher precision and efficiency than previous researches. The proposed method gets information more accurately and quickly in IOT multiple device environments.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.40 no.2
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• pp.313-324
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• 2015

In this paper, in order to support the construction of a smart home environment of low power consumption, we propose a heterogeneous interface determination engine and architecture. Technology of "smart home" is in the spotlight according to the development of IT technology nowadays. Smart homes are configured with multiple sub-networks, and each sub-network is formed by the smart devices using various communication interfaces. Thus, in the smart home environment, interlocking technology between heterogeneous interfaces is essentially required for supporting communication between different networks. Further, each communication interface is a difference in power consumption, and home smart devices are often operated in 24 hours, especially smart phones and other wireless devices are sensitive to power consumption. Therefore, in order to build a energy efficient home network, It is important to select the appropriate interface to handle traffic depending on the situation. In this paper, we propose "The Heterogeneous Interface Decision Engine and Architecture for constructing of Low Power Home Network," and analyze the performance of the proposed method and verify the validity through experiments on the test bed.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.3 no.1
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• pp.84-95
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• 2009

The ubiquitous smart home is the home of the future, which exploits context information from both the human and the home environment, providing an automatic home service for the human. Human location and motion are the most important contexts in the ubiquitous smart home. In this paper, we present a real-time human tracker that predicts human location and motion for the ubiquitous smart home. The system uses four network cameras for real-time human tracking. This paper explains the architecture of the real-time human tracker, and proposes an algorithm for predicting human location and motion. To detect human location, three kinds of images are used: $IMAGE_1$ - empty room image, $IMAGE_2$ - image of furniture and home appliances, $IMAGE_3$ - image of $IMAGE_2$ and the human. The real-time human tracker decides which specific furniture or home appliance the human is associated with, via analysis of three images, and predicts human motion using a support vector machine (SVM). The performance experiment of the human's location, which uses three images, lasted an average of 0.037 seconds. The SVM feature of human motion recognition is decided from the pixel number by the array line of the moving object. We evaluated each motion 1,000 times. The average accuracy of all types of motion was 86.5%.