• Journal of Digital Convergence
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• v.18 no.2
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• pp.439-445
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• 2020

This is a user experience study of factors that should be considered in designing home appliances experience service. Untact services are emerging, but the development of home appliances sector is in early stage. Based on the six factors of Stephen P. Anderson's Creating Pleasurable Interface Model, this study conducted surveys, 1:1 in-depth interviews, and participation observations to measure and analyze user experience. In this study, I compared 4060s and 2030s's user experience in that the untact services raise the digital alienation among middle-senior-aged. As a result, there were significant differences between the two, including the opposite satisfaction in terms of reliable, usable and pleasurable factors. I hope that this study will be of strategic help in designing future home appliance experience services.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.23 no.2
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• pp.169-174
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• 2023

Currently, overall power usage is also increasing as power demand such as homes, offices, and factories increases. The increase in power use also raised interest in standby power as a change in awareness of energy saving appeared. Home and office devices are consuming power even in standby conditions. Accordingly, there is a growing need to reduce standby power, and it aims to have standby power of 1W or less. An intelligent outlet uses a near-field wireless network to connect to a home network and cut or reduce standby power of a lamp or appliance connected to an outlet. This research aims to develop a monitoring system and an intelligent outlet that can remotely monitor the amount of electricity used in a lighting lamp or a home appliance connected to an outlet using a short-range wireless network (Zigbee). Also, The intelligent outlet and monitoring system developed makes it possible for a user to easily cut off standby power by using a portable device. Intelligent outlets will not only reduce standby power but also be applicable to fire prevention systems. Devices that cut off standby power include intelligent outlets and standby power cutoff switches, so they will prevent short circuits and fires.

• Journal of IKEEE
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• v.24 no.2
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• pp.632-637
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• 2020

The non-intrusive load monitoring (NILM) is an important way to cost-effective real-time monitoring the energy consumption and time of use for each appliance in a home or business using aggregated energy from a single recording meter. In this paper, we collect from the smart farm's power consumption data acquisition system to the server via an LTE modem, converted the total power consumption, and the power of individual electric devices into HDF5 format and performed NILM analysis. We perform NILM analysis using open source denoising autoencoder (DAE), long short-term memory (LSTM), gated recurrent unit (GRU), and sequence-to-point (seq2point) learning methods.

• Journal of the Korean Magnetics Society
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• v.23 no.2
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• pp.68-76
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• 2013

Currently, wireless power transmission technology based on magnetic induction was employed in battery charger for smart phone application. The system consists of wireless power transmitter in base station and receiver in smart phone. Size and thickness of receiver was strictly limited in the newest smart phone. In order to achieve high efficiency of a tiny small wireless power receiver module, sub-millimeter thick electromagnetic wave shielding sheet having high permeability and Q was essential component. It was found that magnetic field from transmitter to receiver can be intensified by sufficient shielding cause to minimize leakage magnetic flux by those magnetic properties. This leads to high efficiency of wireless power transmission and protects crucial integrated circuit of main board from electromagnetic noise. The important soft magnetic materials were introduced and summarized for the current small-power wireless power charger and NFC application and mid-power home appliance and high-power automotive application in the near future.

• 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.

• 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.

• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
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• v.9 no.6
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• pp.711-718
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• 2019

Spectrum analyzer and electromagnetic field meter were used to investigate the EM generation behaviour in different types of home electrical appliances. During microwave oven operation, the EM power measured at a point 30cm apart was measured in the range of 8~11mW/㎡, the strength of the low frequency magnetic field was 60~80mG and the electric field strength was measured at 150~160V/m. For smart phone wireless charging pad, it was measured at an electromagnetic power of 0.4mW/㎡, an electric field of 160 V/m and a magnetic field of 1mG at a point 10cm away. For microwave oven and wireless charging pad, if used within 10cm, the size of the electric field has been measured at a large value that exceeds the human body protection standard and may be hazardous to humans. On the other hand, home appliances such as TVs, hairdryers and refrigerators all showed very low levels of electromagnetic waves, electric fields and magnetic fields, with no harmful effects seen. For electromagnetic shielding, the metal Cu fabric and metal foil had a high level of EM shielding, while polymer films had a low EM shielding characteristic.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.21 no.4
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• pp.109-116
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• 2021

In this paper, we propose a non-intrusive load monitoring(NILM) system which can find the power of each home appliance from the aggregated total power as the activation in the trading market of the distributed resource and the increasing importance of energy management. We transform the amount of appliances' power into a power on-off state by preprocessing. We use LSTM as a model for predicting states based on these data. Accuracy is measured by comparing predicted states with real ones after postprocessing. In this paper, the accuracy is measured with the different number of electronic products, data postprocessing method, and Time step size. When the number of electronic products is 6, the data postprocessing method using the Round function is used, and Time step size is set to 6, the maximum accuracy can be obtained.