• ETRI Journal
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• 제36권5호
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• pp.741-751
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• 2014

Smart TV is expected to bring cloud services based on virtualization technologies to the home environment with hardware and software support. Although most physical resources can be shared among virtual machines (VMs) using a time sharing approach, allocating the proper amount of memory to VMs is still challenging. In this paper, we propose a novel mechanism to dynamically balance the memory allocation among VMs in virtualized Smart TV systems. In contrast to previous studies, where a virtual machine monitor (VMM) is solely responsible for estimating the working set size, our mechanism is symbiotic. Each VM periodically reports its memory usage pattern to the VMM. The VMM then predicts the future memory demand of each VM and rebalances the memory allocation among the VMs when necessary. Experimental results show that our mechanism improves performance by up to 18.28 times and reduces expensive memory swapping by up to 99.73% with negligible overheads (0.05% on average).

• Journal of Electrical Engineering and Technology
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• 제12권1호
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• pp.64-71
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• 2017

Considering the volatility, intermittent and random of photovoltaic (PV) generation systems, accurate forecasting of PV power output is important for the grid scheduling and energy management. In order to improve the accuracy of short-term power forecasting of PV systems, this paper proposes a prediction model based on environmental factors and support vector machine optimized by genetic algorithm (GA-SVM). In order to improve the prediction accuracy of this model, weather conditions are divided into three types, and the gray correlation coefficient algorithm is used to find out a similar day of the predicted day. To avoid parameters optimization into local optima, this paper uses genetic algorithm to optimize SVM parameters. Example verification shows that the prediction accuracy in three types of weather will remain at between 10% -15% and the short-term PV power forecasting model proposed is effective and promising.

• 한국소음진동공학회논문집
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• 제18권8호
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• pp.872-878
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• 2008

Many industrial operations require continuous or nearly-continuous operation of machines, interruption of which can result in significant cost loss. The condition monitoring of these machines has received considerable attentions in recent years. Rapid developments in semiconductor, computing, and communication with a remote site have led to a new generation of sensor called "smart" sensors which are capable of wireless communication with a remote site. The purpose of this research is to develop a new type of smart sensor for on-line condition monitoring. This system is addressed to detect conditions that may lead to equipment failure when it is running. Moreover it will reduce condition monitoring expense using low cost MEMS accelerometer. This system is capable for signal preprocessing task and analog to digital converter which is controlled by CPU. This sensor communicates with a remote site PC using TCP/IP protocols. The developed sensor executes performance tests for data acquisition accuracy estimations.

• 한국전자통신학회논문지
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• 제11권11호
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• pp.1107-1112
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• 2016

본 연구는 한국전력 eIoT(: energy Internet of Thing) 플랫폼을 활용한 스마트오피스 기능이고, 망구성은 센싱 디바이스, 게이트웨이, 플랫폼, 그리고 서비스서버로 구성 한다. 핵심 기능들은 게이트웨이와 디바이스간 LoRa(: Long Range) 기술을 활용하여 프로토콜 데이터 전달하는 부분, 지능화된 애플리케이션 처리 부분 그리고 PS-LTE(: Public Safety-Long-Term Evolution) 시스템에 연동되는 공공 안전 데이터처리 부분이다. 그리고 스마트오피스에서 서비스될 수 있는 리소스트리가 제시되며, 이것은 애플리케이션 서버와 디바이스에서 공통적으로 사용된다.

• 한국정보통신학회:학술대회논문집
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• 한국정보통신학회 2014년도 춘계학술대회
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• pp.850-852
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• 2014

최근, 사물 지능 통신, 즉 M2M 의 개발이 스마트 홈을 포함한 여러 분야에서 활발하게 이루어지고 있다. 기존 사물 지능형 통신에서의 센서노드들의 역할은 정보를 수집하여 상위 어플리케이션으로 전달하는 역할에만 그쳤다. 본 연구에서는 기존 사물 지능형 통신에서의 센서노드들의 제한적인 역할을 개선하여 마이크로 컨트롤러 상에서의 추론이 가능하게 함으로써 센서노드 레벨에서 기본적인 상황인식 서비스를 가능하게 하며 이를 위한 마이크로 컨트롤러 기반의 스마트 홈 전용 규칙기반 추론 시스템의 개발을 제안한다.

• 스마트미디어저널
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• 제11권9호
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• pp.47-55
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• 2022

스마트팩토리 제조공정에서의 품질관리는 중요한 요소이다. 현재, 금형 공정으로 생산되는 생활가전 제조부품의 품질검사는 대부분 작업자의 육안으로 진행되고 있으며 이로 인한 검사의 오류율이 높은 실정이다. 이러한 품질공전 개선을 위하여 결함 자동검출 시스템을 설계하여 구현하였다. 제안 시스템은 특정 위치에서 고성능 스캔 카메라로 대상물을 촬영하여 영상을 획득하고, 비전검사 알고리즘에 따라 긁힘, 찍힘, 이물질에 의한 불량품을 판독한다. 본 연구에서는 긁힘에 대한 불량 인식율을 높이기 위하여 깊이 정보 기반 분기 판단 알고리즘(Depth-based branch decision algorithm, DBD)을 개발하여 정확도를 높였다.

• International Journal of Internet, Broadcasting and Communication
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• 제13권2호
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• pp.7-13
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• 2021

Machine vision is a technology that helps the computer as if a person recognizes and determines things. In recent years, as advanced technologies such as optical systems, artificial intelligence and big data advanced in conventional machine vision system became more accurate quality inspection and it increases the manufacturing efficiency. In machine vision systems using deep learning, the image quality of the input image is very important. However, most images obtained in the industrial field for quality inspection typically contain noise. This noise is a major factor in the performance of the machine vision system. Therefore, in order to improve the performance of the machine vision system, it is necessary to eliminate the noise of the image. There are lots of research being done to remove noise from the image. In this paper, we propose an autoencoder based machine vision system to eliminate noise in the image. Through experiment proposed model showed better performance compared to the basic autoencoder model in denoising and image reconstruction capability for MNIST and fashion MNIST data sets.

• 한국의류학회지
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• 제47권1호
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• pp.85-109
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• 2023

Laundry has traditionally been regarded as one of the most demanding household chores, but the introduction of smart washing machines is changing this perception. Although smart washing machines have been on the market for several years and consumers' perceptions of washing machines have changed, little is known about consumers' perceptions of smart washing machines. The purpose of this study is to determine what users expect from smart washing machines. We conducted two focus group interviews with sixteen participants who had used smart home appliances to acquire qualitative data. Stimuli created by the interviewees were applied in the focus group interviews to collect more insightful data. We analyzed the data using the three-step method and QSR NVivo. Analysis revealed ten categories of predictive expectations, including seven utilitarian attributes (i.e., smart functionality, smart user interface, reliability, controllability, interactivity, functional value, and economic value) and three hedonic attributes (i.e., fashionable value, psychological value, and social value). The results of this study have implications for the development of smart washing machines that would satisfy consumers by taking user expectations into account.

• 한국소음진동공학회논문집
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• 제19권5호
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• pp.523-529
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• 2009

Smart sensor is known as intelligent sensor, it is different with other conventional sensors in the case of intelligent system embedded on it. Smart sensor has many benefits e.g. low-cost in usage, self-decision and self-diagnosis abilities. This sensor consists of perception element(sensing element), signal processing and technology of communication. In this work, a bridge and structure of smart sensor has been investigated to be capable to condition monitoring routine. This investigation involves low power consumption, software programming, fast data acquisition ability, and authoritativeness warranty. Moreover, this work also develops smart sensor to be capable to perform high sampling rate, high resolution of ADC, high memory capacity, and good communication for data transfer. The result shows that the developed smart sensor is promising to be applied to various industrial fields.

• 한국공작기계학회논문집
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• 제13권1호
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• pp.22-27
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• 2004

A new method that estimates the sensing ability of HH smart sensor is proposed. The new signal processing method have been developed that can distinguish among different materials relatively. The HH smart sensor was developed far recognition of materials. The HH smart sensor was made for experiment. Then, it was estimated the ability to recognize objects according to acceleration value. The sensing ability of HH smart sensor has been estimated with the $R_{SAI}$ method. Experiments and analysis were executed to estimate the ability to recognize objects according to acceleration value changing. Dynamic characteristics of HH smart sensor were evaluated relatively through a new $R_{SAI}$ method that uses the power spectrum density. Applications of this method are for finding abnormal conditions of objects (auto-manufacturing), feeling of objects (medical product), robotics, safety diagnosis of structure, etc.