• Proceedings of the Korean Institute of Communication Sciences Conference
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• 2004.07a
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• pp.266-266
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• 2004

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2007.04a
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• pp.397-400
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• 2007

In this paper, we discuss the user-customized interaction for intelligent home environments. The interactive system is based upon the integrated techniques using both speech and face recognition. For essential modules, the speech recognition and synthesis were basically used for a virtual interaction between user and proposed system. In experiments, particularly, the real-time speech recognizer based on the HM-Net(Hidden Markov Network) was incorporated into the integrated system. Besides, the face identification was adopted to customize home environments for a specific user. In evaluation, the results showed that the proposed system was easy to use for intelligent home environments, even though the performance of the speech recognizer did not show a satisfactory results owing to the noisy environments.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2004.10a
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• pp.203-206
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• 2004

Recently home network has been developed in the field of Information Technology. And there are many protocols for smart home, such as Lonworks, Echonet, KNX, LnCP etc. However, the performance evaluation has not been nearly known between the protocols. Hence, this paper evaluates the performance of KNX by Konnex Association and LnCP(Living network Control Protocol) by LG Electronics. We developed simulation model using flowchart of KNX and LnCP and simulation scenario through analysis of message to be generated in the home network. Furthermore, we evaluate simulation performance, such as mean transmission delay, maximum transmission delay, and collision rate of both protocols.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.20 no.9
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• pp.90-102
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• 2006

In this study, it has been attempted to build up a smart, stable, and integrated Next-generation Home Network (NHN) system capable of supporting more comfortable and convenient life in the future human housing domains, and to find out which institutional tasks are required to activate the related industries. Additionally, this study aims to review a variety of technologies suggested so far in several fields including information and communications, appliance, and building maintenance. This paper analyzes the advantages and disadvantages, acceptance structure and problems of standard technologies and it suggests the political methods and policies to embody them most effectively and integrally in the future. The said certification system is called 'Standardized Certification System for Korean-style Next-generation Home Network Buildings'(abbreviated to K-NHN) as a new system created for the purpose of developing, more effectively, NHN to be introduced in the future.

• Smart Media Journal
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• v.11 no.11
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• pp.17-24
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• 2022

With the spread of smart speakers based on voice recognition technology and deep learning technology, not only non-disabled people, but also the blind or physically handicapped can easily control home appliances such as lights and TVs through voice by linking home network services. This has greatly improved the quality of life. However, in the case of speech-impaired people, it is impossible to use the useful services of the smart speaker because they have inaccurate pronunciation due to articulation or speech disorders. In this paper, we propose a personalized voice classification technique for the speech-impaired to use for some of the functions provided by the smart speaker. The goal of this paper is to increase the recognition rate and accuracy of sentences spoken by speech-impaired people even with a small amount of data and a short learning time so that the service provided by the smart speaker can be actually used. In this paper, data augmentation and one cycle learning rate optimization technique were applied while fine-tuning ResNet18 model. Through an experiment, after recording 10 times for each 30 smart speaker commands, and learning within 3 minutes, the speech classification recognition rate was about 95.2%.

• Journal of Korean Home Economics Education Association
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• v.35 no.3
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• pp.47-66
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• 2023

The purpose of this study was to develop an educational smart app for classes by reconstructing some of the teaching-learning contents of the clothing preparation planning within the 'clothing preparation planning and selection' curriculum unit. To this end, a teaching-learning process plan was planned for the classes, a smart app was developed for classes, and feedback from home economics teachers and app development experts was received for the developed app. The main composition of the developed app consists of five steps. The first step is to set up a profile using a real photo, ZEPETO or Galaxy emoji, or iPhone Memoji. In the second step, students make a list of clothes by figuring out the types, quantities and conditions of their exisitng wardrobe items. Each piece of clothing is assigned an individual registration number, and stduents can take pictures of the front and back, along with describing key attributes such as type, color, season-appropriateness, purchase date, and current status. Step three guides students in deciding which garments to retain and which to discard. Building on the clothing inventory from the previous step, students classify items to keep and items to dispose of. In Step 4, Deciding How to Arrange Clothing, students decide how to arrange clothing by filling out an alternative scorecard. Through this process, students can learn in advance the subsection of resource management and self-reliance, laying the foundationa for future learning in 'Practice of Rational Consumption Life'. Lastly, in the fifth stage of determining the disposal method, this stage is to develop practical problem-oriented classes on how to dispose of the clothes to be discarded in the thirrd stage by exploring various disposal methods, engaging in group discussions, and sharing opinions. This study is meaningful as a case study as an attempt to develop a smart app for education by an instructor to align teaching plans and educational content with achievement standards for the class. In the future, upgrades will have to be made through user application.

• Journal of the Institute of Convergence Signal Processing
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• v.7 no.3
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• pp.87-94
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• 2006

For realizing an ubiquitous home network(uHome-net), various sensors should be able to be connected to an integrated wire/wireless sensor network. This paper describes an application case of applying LonWorks technology being widely used in control network to wire/wireless sensor network in uHome-net and the design and application of LonRF device that consists of a neuron chip including LonTalk protocol, a 433.92MHz RF transceiver, a sensor, and application programs. As an application example of the LonRF device, the LonRF smart badge that can measure the 3D location of objects in indoor environment and interwork with the uHome-net was developed. LonRF device based home network services were realized on the uHome-net testbed such as indoor positioning service, remote surveillance service and remote metering service were realized. This research shows that LonWorks technology based sensor network could be applicable to the control network in an ubiquitous home network and the LonRF device can be used as a wireless node in various sensor networks.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.7 no.11
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• pp.2853-2873
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• 2013

Over the last few years, one of the most common purposes of smart homes is to provide human centric services in the domain of u-healthcare by analyzing inhabitants' daily living. Currently, the major challenges in activity recognition include the reliability of prediction of each classifier as they differ according to smart homes characteristics. Smart homes indicate variation in terms of performed activities, deployed sensors, environment settings, and inhabitants' characteristics. It is not possible that one classifier always performs better than all the other classifiers for every possible situation. This observation has motivated towards combining multiple classifiers to take advantage of their complementary performance for high accuracy. Therefore, in this paper, a method for activity recognition is proposed by optimizing the output of multiple classifiers with Genetic Algorithm (GA). Our proposed method combines the measurement level output of different classifiers for each activity class to make up the ensemble. For the evaluation of the proposed method, experiments are performed on three real datasets from CASAS smart home. The results show that our method systematically outperforms single classifier and traditional multiclass models. The significant improvement is achieved from 0.82 to 0.90 in the F-measures of recognized activities as compare to existing methods.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.16 no.10
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• pp.2171-2176
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• 2012

PCs and its peripherals uses more power in standby mode even if they are not actually being used. In particular, PCs and its peripherals used in school and office, plugged in at all times during working hours, but not used much time, even if turned off consume a portion of the power. In this paper, we developed Networked Smart Plug for Power Management of PC & Peripherals which is consist of EMS, Smart Plug, PC Agent and Smart Phone App. for power saving of PC and a variety of peripheral devices in office or home, and evaluated the performance of the system.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.8
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• pp.4006-4024
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• 2017

Internet of Things (IoT) will transform our daily life by making different aspects of life smart like smart home, smart workplace, smart health and smart city etc. IoT is based on network of physical objects equipped with sensors and actuators that can gather and share data with other objects or humans. Secure communication is required for successful working of IoT. In this paper, a total of 13 lightweight cryptographic algorithms are evaluated based on their implementation results on 8-bit, 16-bit, and 32-bit microcontrollers and their appropriateness is examined for resource-constrained scenarios like IoT. These algorithms are analysed by dissecting them into their logical and structural elements. This paper tries to investigate the relationships between the structural elements of an algorithm and its performance. Association rule mining is used to find association patterns among the constituent elements of the selected ciphers and their performance. Interesting results are found on the type of element used to improve the cipher in terms of code size, RAM requirement and execution time. This paper will serve as a guideline for cryptographic designers to design improved ciphers for resource constrained environments like IoT.