• Proceedings of the Korea Information Processing Society Conference
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• 2013.05a
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• pp.478-479
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• 2013

Smart home is one of the emerging domains to come up after advances in home appliances and automation technologies. There are many commercial solutions for smart homes yet many of them have yet to truly exploit the potential of private cloud for low level contextual services and ability to migrate to public cloud for more processing and storage. We propose a private cloud gateway for smart home which will have the ability to sense the new devices, ability to detect context of the situation and act in an appropriate way. It will also record the user logs which will be audited for improvement of the overall system.

• The Journal of the Korea Contents Association
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• v.15 no.5
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• pp.414-425
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• 2015

Purpose: This study was conducted to explore the experience of smart-home healthcare among older adults who had participated in the smart-home technology development project. Methods: Data were collected by focus group interviews with 7 hypertension or diabetes participants(mean age 75 years). Qualitative contents analysis was used to analyze the verbal data. Results: The results were composed of 27 meanings units, 10 categories and 6 themes. The 6 themes are: 'improvement of health conditions through anytime anywhere self-monitoring', 'attractive experience to be counselling at home', 'frequent system and telecommunication problems', 'unfamiliar devices for elderly users', 'complementary strategies for activation', 'advancement of elderly-friendly devices and programs'. Conclusion: The smart-home healthcare system should be developed based on older adults' preferences. It is essential to make progress with professional resources and organizational supports in the future.

• The Journal of the Korea Contents Association
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• v.9 no.11
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• pp.80-89
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• 2009

The importance of personalized services has been attracted in smart home environments according to the development of ubiquitous computering. In this paper, we proposed the smart home personalized service system based on context information using the speech recognition. The proposed service consists of an OSGi framework based service mobile manager, service manager, voice recognition manager, and location manager. Also, this study defines the smart home space and configures the commands of units, sensor information, and user information that are largely used in the defined space as context information. In particular, this service identifies users who exist in the same space that shows a difficulty in the identification using RFID through the training model and pattern matching in voice recognition and supports the personalized service of smart home applications. In the results of the experiment, it was verified that the OSGi based automated and personalized service can be achieved through verifying users in the same space.

• Journal of Intelligence and Information Systems
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• v.13 no.4
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• pp.105-120
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• 2007

The primary purpose of this study lies in developing an intelligence level measurement model which can be applied to information home appliances. To accomplish the study purpose, the literature on computer engineering and intelligence is comprehensively researched and critical elements necessary for measuring the intelligence of smart home appliances are extracted. Then an intelligence level measurement model is derived, and the model is validated by several academic and practical experts using Delphi technique. The measurement model developed in the study, on the one hand, can provide users with some objective standards to evaluate the intelligence level of smart home appliances. On the other hand, it can help home appliance product developers or related service providers decide the target intelligence level of the products or services more specifically. Consequently, the model can contribute to the revitalization of the smart home appliance industry as a whole.

• 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.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2010.11a
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• pp.699-700
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• 2010

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2011.06a
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• pp.279-280
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• 2011

• Journal of Information Processing Systems
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• v.18 no.1
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• pp.115-129
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• 2022

Adopting Internet of Things (IoT)-based technologies in smart homes helps users analyze home appliances electricity consumption for better overall cost monitoring. The IoT application like smart home system (SHS) could suffer from large missing values gaps due to several factors such as security attacks, sensor faults, or connection errors. In this paper, a novel framework has been proposed to predict large gaps of missing values from the SHS home appliances electricity consumption time-series datasets. The framework follows a series of steps to detect, predict and reconstruct the input time-series datasets of missing values. A hybrid convolutional neural network-long short term memory (CNN-LSTM) neural network used to forecast large missing values gaps. A comparative experiment has been conducted to evaluate the performance of hybrid CNN-LSTM with its single variant CNN and LSTM in forecasting missing values. The experimental results indicate a performance superiority of the CNN-LSTM model over the single CNN and LSTM neural networks.

• Communications of the Korean Institute of Information Scientists and Engineers
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• v.31 no.3
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• pp.73-80
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• 2013

• The Journal of the Korea institute of electronic communication sciences
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• v.14 no.3
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• pp.537-546
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• 2019

In this paper, a system that integrates a home concentrate unit(HCU) in a smart electricity meter to collects water, gas, hot water, and heating usage required for AMI has been proposed. The collected data could be transmitted to the in house display(IHD) and server to provide a more economical AMI system. The developed system is less expensive in the network configuration than the existing system, which could reduce the operating cost, and be easy to install. By applying the developed system, the usage of electricity, water, gas, hot water and heating could be measured and these make it easier to apply AMI system. The main contents of the development are the smart electricity meter and embedding of HCU into the smart electricity meter, and transferring these data to IHD and server to structure the AMI system. The each developed unit has been networked to structure the AMI system to perform the actual meter reading operation and show the result.