• Journal of Electrical Engineering and Technology
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• v.10 no.2
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• pp.586-594
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• 2015

This paper proposes a maximum efficiency operation strategy for three-level T-type inverter in entire operation areas. The three-level T-type inverter has higher and lower efficiency areas compared with two-level inverter. The proposed strategy aims to operate in the maximum efficiency point for the low-voltage and low-power home appliances. The three-level T-type inverter is analyzed in detail, and the two operation mode selection strategy is developed. The proposed algorithm is verified by theoretical analysis and experimental results.

• Proceedings of the KIPE Conference
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• 2008.06a
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• pp.547-549
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• 2008

This paper presents a implementation of the monitoring program for the analysis of electric consumption characteristics in home appliances, which uses a Labview program from National Instruments Corporation. It is can provide to be easy for small-scale researcher, development groups or individual researcher to analyze electric power consumption characteristics. Also, this paper apply to actual private home and then, analyze result of monitoring and a database result of power quality etc, like the power consumption form, power factor and harmonic which follow season, and time zone. For this measured the apartment where the family which is four lives in the object.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.12 no.3
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• pp.193-197
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• 2012

This paper focuses on identifying which appliance is currently operating by analyzing electrical load signature for home energy monitoring system. The identification framework is comprised of three steps. Firstly, specific appliance features, or signatures, were chosen, which are DC (Duty Cycle), SO (Slope of On-state), VO (Variance of On-state), and ZC (Zero Crossing) by reviewing observations of appliances from 13 houses for 3 days. Five appliances of electrical rice cooker, kimchi-refrigerator, PC, refrigerator, and TV were chosen for the identification with high penetration rate and total operation-time in Korea. Secondly, K-NN and Naive Bayesian classifiers, which are commonly used in many applications, are employed to estimate from which appliance the signatures are obtained. Lastly, one of candidates is selected as final identification result by majority voting. The proposed identification frame showed identification success rate of 94.23%.

• Journal of the Korean Society of Mechanical Technology
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• v.13 no.1
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• pp.57-64
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• 2011

Recently, all-electrified houses have constructed and some tasks are being carried out to monitor energy consumption patterns. The energy monitoring includes various elements such as temperature, pressure, water flow, $CO_2$, and electricity. In this monitoring, electricity consumption is one of the essential factors because the performance and energy saving levels are described by this physical quantity. This quantity depends on materials, structure, operation of the house, which operation means consumer's life style and usage patterns of home appliances furthermore. Firstly, this paper shows a control simulation method which were developed a few years ago. And some different methods are proposed considering flexible experimental condition.

• Journal of the Korea Society of Computer and Information
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• v.24 no.3
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• pp.151-157
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• 2019

In this paper, we present a prototype system for controlling IoT home appliances via voice-based commands. A voice command has been widely deployed as one of unobtrusive user interfaces for applications in a variety of IoT domains. However, interoperability between diverse IoT systems is limited by several dominant companies providing voice assistants like Amazon Alexa or Google Now due to their proprietary systems. A global IoT standard, oneM2M has been proposed to mitigate the lack of interoperability between IoT systems. In this paper, we deployed oneM2M-based platforms for a voice record device like a wrist band and LED control device like a home appliance. We developed all the components for recording voices and controlling IoT devices, and demonstrate the feasibility of our proposed method based on oneM2M platforms and Google STT (Speech-to-Text) API for controlling home appliances by showing a user scenario for turning the LED device on and off via voice commands.

• Journal of Advanced Navigation Technology
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• v.15 no.6
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• pp.1000-1007
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• 2011

Home Networking will be implementing the interactive network by home appliances over wireless/wireline network framwork. That is, Home appliances, which are being operated within home space configure the network through wireline/wireless network infrastructure for interworking and interacitive services by external internet access. Based on home networking, smarthome is home space where can use automatic telecommunication and interactive service by home appliances. we can call smarthome based on home networking infrastructure as the conceptual gateway for evolving future converged space like u-city. From simple home control service to home automation service over home networking infrastructure, smarthome service is evolving to up-to-date intelligent life environment in growth of IT technology. however, its service model development was based on supplier-centered based on advanced IT technology. because of this situation, smarthome service has not been acknowledged IT underprivileged people as well as IT early-adaptor. so, this research paper will consider and try to find out what will be the feasible factors to make the best service for IT underprivileged people.

• Journal of the Korean Institute of Intelligent Systems
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• v.24 no.4
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• pp.403-411
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• 2014

In this paper, an algorithm of extracting the features of home appliances and automatically building a database to identify home appliances is designed and presented. For the verification, a software library supporting this algorithm is implemented and added to an power management system server, which was already implemented to support real-time monitoring of home appliances' power consumption status and controlling their power. The implemented system consists of a system server and clients, each of which measures the power consumed by a home appliance plugged in it and transmits the information to the server in real-time over a wireless network. Through experiments, it is verified that it is possible to identify any home appliance connected to a specific client.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.4 no.4
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• pp.300-306
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• 2011

In this paper, we suggest a usage profiles for electric home appliances. In Korea, it is published the records for total consumption of electricity in a house but the electric home appliance consumption records in a households are not. To build the data, we must collect the usage of every appliances in a house and the information of the household which live in the house. Unfortunately, it is hard to get the data because of the worry about the breach of privacy. In this paper, we make a scenarios on the electricity consumption pattern of a few households type. Based on the conjecture, we make the power consumption profiles for some home appliances. Comparison to the total electric consumption records for a house, we found our scenarios are quite reasonable.

• International Journal of Computer Science & Network Security
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• v.22 no.3
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• pp.303-311
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• 2022

The tremendous growth of the Internet of things is unbelievable. Many IoT devices have emerged on the market over the last decade. This has made our everyday life easier inside our homes. The technology used at home has changed significantly over the past several decades, leading to what is known today as the smart home. However, this growth has also brought new challenges to our home security and privacy. With the smart home becoming more mainstream, cybersecurity issues have become a fundamental concern. The smart home is an environment where heterogeneous devices and appliances are interconnected through the Internet of Things (IoT) to provide smart services to residents. These services include home climate control, energy management, video on demand, music on-demand, remote healthcare, remote control, and other similar services in a ubiquitous manner. Smart home devices can be controlled via the Internet using smartphones. However, connecting smart home appliances to wireless networks and the Internet makes individuals vulnerable to malicious attacks. Remote access within the same environment or over the Internet requires an effective access control mechanism. This paper intends to shed light on how smart home devices are working as well as the type of security and privacy threats of the smart home. It also illustrated the types of authentication methods that can be used with smart home devices. In addition, a comparison of Smart home IoT-based security protocols was presented along with a security countermeasure that can be used in a smart home environment. Finally, a few open problems were mentioned as future research directions for researchers.

• Journal of the Korea Convergence Society
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• v.12 no.2
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• pp.21-28
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• 2021

In this study, we propose a deep learning-based NILM technique using actual measured power data for 5 kinds of home appliances and verify its effectiveness. For about 3 weeks, the active power of the central power measuring device and five kinds of home appliances (refrigerator, induction, TV, washing machine, air cleaner) was individually measured. The preprocessing method of the measured data was introduced, and characteristics of each household appliance were analyzed through spectogram analysis. The characteristics of each household appliance are organized into a learning data set. All the power data measured by the central power measuring device and 5 kinds of home appliances were time-series mapping, and training was performed using a LSTM neural network, which is excellent for time series data prediction. An algorithm that can disaggregate five types of energies using only the power data of the main central power measuring device is proposed.