• 한국기계가공학회지
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• 제8권1호
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• pp.57-63
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• 2009

Magneto-Rheological(MR) fluid composes of a base fluid and ferromagnetic particles less than tens of micrometer size dispersed in the fluid. It is called as a smart material because its rheological properties are changable by a magnetic field. Its important applications are active devices such as controllable dampers and controllable clutches. The merit of those products is that their functional characteristics are controllable such that they enable active control strategies. This paper proposes an idea for machine tool applications of the MR fluid clutch as a safety device for power transmission. FEM has been used for magnetic field analyses and the results are compared with some former experiments. Some design syntheses of the MR clutches are suggested and hopefully considered that it may be an effective safety device for power transmission of machine tools.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제10권5호
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• pp.1998-2014
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• 2016

In recent years, cloud computing services based on smart phones and other mobile terminals have been a rapid development. Cloud computing has the advantages of mass storage capacity and high-speed computing power, and it can meet the needs of different types of users, and under the background, mobile cloud computing (MCC) is now booming. In this paper, we have put forward a new classification-based virtual machine placement (CBVMP) algorithm for MCC, and it aims at improving the efficiency of virtual machine (VM) allocation and the disequilibrium utilization of underlying physical resources in large cloud data center. By simulation experiments based on CloudSim cloud platform, the experimental results show that the new algorithm can improve the efficiency of the VM placement and the utilization rate of underlying physical resources.

• 한국정보통신학회:학술대회논문집
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• 한국해양정보통신학회 2011년도 추계학술대회
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• pp.769-772
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• 2011

본 연구는 연마와 분진을 한꺼번에 해결하고자 로봇형 진공식 표면연마 머신(Robotic Vacuum Sweeping Machine)을 개발을 통하여 금속표면의 연마작업을 수행할 때 금속표면으로부터 탈피되는 각 종이물질과 연마휠의 회전으로 발생되는 분진을 진공방식으로 집진하게 되고, 항상 작업자가 머신의 이동방향을 주시해야하는 불편함을 센서(카메라, 레이저스캐너 등)를 부착하여 작업환경인식과 작업진행시 발생할 수 있는 돌발적 위험상황에 대처하기 위한 장애물 판단이 가능한 구조를 가진 진공식 표면 연마기를 소개하도록 한다.

• 한국멀티미디어학회논문지
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• 제24권1호
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• pp.67-74
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• 2021

As IoT(Internet Of Things) devices like a smart sensor have constrained power sources, a power strategy is critical in WSN(Wireless Sensor Networks). Therefore, it is necessary to figure out the residual power of each sensor node for managing power strategies in WSN, which, however, requires additional data transmission, leading to more power consumption. In this paper, a residual power estimation method was proposed, which uses ignorantly small amount of power consumption in the resource-constrained wireless networks including WSN. A residual power prediction is possible with the least data transmission by using Machine Learning method with some training data in this proposal. The performance of the proposed scheme was evaluated by machine learning method, simulation, and analysis.

• International Journal of Computer Science & Network Security
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• 제23권9호
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• pp.186-191
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• 2023

Neural Networks are widely used for huge variety of tasks solution. Machine Learning methods are used also for signal and time series analysis, including electrocardiograms. Contemporary wearable devices, both medical and non-medical type like smart watch, allow to gather the data in real time uninterruptedly. This allows us to transfer these data for analysis or make an analysis on the device, and thus provide preliminary diagnosis, or at least fix some serious deviations. Different methods are being used for this kind of analysis, ranging from medical-oriented using distinctive features of the signal to machine learning and deep learning approaches. Here we will demonstrate a neural network-based approach to this task by building an ensemble of 1D CNN classifiers and a final classifier of selection using logistic regression, random forest or support vector machine, and make the conclusions of the comparison with other approaches.

• International Journal of Computer Science & Network Security
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• 제23권3호
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• pp.144-150
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• 2023

With progressive advancements in Man-made consciousness (computer based intelligence) and Profound Learning (DL), contributing altogether to Normal Language Handling (NLP), the precision and nature of Machine Interpretation (MT) has worked on complex. There is a discussion, but that its no time like the present the human interpretation became immaterial or excess. All things considered, human flaws are consistently dealt with by its own creations. With the utilization of brain networks in machine interpretation, its been as of late guaranteed that keen frameworks can now decipher at standard with human interpreters. In any case, simulated intelligence is as yet not without any trace of issues related with handling of a language, let be the intricacies and complexities common of interpretation. Then, at that point, comes the innate predispositions while planning smart frameworks. How we plan these frameworks relies upon what our identity is, subsequently setting in a one-sided perspective and social encounters. Given the variety of language designs and societies they address, their taking care of by keen machines, even with profound learning abilities, with human proficiency looks exceptionally far-fetched, at any rate, for the time being.

• 스마트미디어저널
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• 제5권4호
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• pp.131-137
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• 2016

스마트폰을 휴대하기 어려운 공간에서 스마트폰의 착신정보를 알지 못해 중요한 통화를 놓치는 상황을 겪게 되는 현상이 빈번이 발생하기 때문에 이를 해결할 수 있는 기술 개발이 시급하고 취미/여가 생활 중의 스마트폰 도난/분실에 대한 사례가 갈수록 증가하고 있으며 특히 해수욕장, 수영장, 찜질방, 사우나, 스파 등 실내 장소에서 도난 행위가 많이 발생하고 있기 때문에 취미/여가 생활 중 핸드폰을 보호할 수 있는 방안이 필요하다. 기존의 웨어러블 디바이스 '스마트 워치'의 경우 고가의 기기일뿐더러 운동, 취미, 여가 활동 시 기기의 파손과 고장에 따른 A/S비용에 대한 부담감으로 인하여 사용에 대한 부담감이 높기 때문에 부담감을 줄일 수 있고 활용성을 강조할 수 있는 제품의 개발이 시급하다. 본 논문에서는 이러한 문제들을 해결하기 위해 저전력 기반의 스마트폰 착신정보 시스템을 제안하고자 한다.

• 디지털산업정보학회논문지
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• 제18권3호
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• pp.9-23
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• 2022

This study aims to examine research trends on the Internet of Things and smart city based on papers from the United States, Japan, and Korea. We collected 7113 papers related to the Internet of Things and smart city published from 2016 to 2021 in Elsevier's Scopus. Keyword frequency and centrality analysis were performed based on the abstracts of the collected papers. We found keywords with high frequency of appearance by calculating keyword frequency and identified central research keywords through the centrality analysis by country. As a result of the analysis, research on security, machine learning, and edge computing related to the Internet of Things and smart city were the most central and highly mediating research conducted in each country. As an implication, studies related to deep learning, cybersecurity, and edge computing in Korea have lower degree centrality and betweenness centrality compared to the United States and Japan. To solve the problem it is necessary to combine these studies with various fields. The future research direction is to analyze research trends on the Internet of Things and smart city in various regions such as Europe and China.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2020년도 춘계학술발표대회
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• pp.91-94
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• 2020

본 연구에서는 4차 산업혁명의 핵심 분야인 가상현실을 헬스 엔터테인먼트 서비스에 응용하는 시스템을 개발하였다. 스마트폰에 내장된 GPS와 GYRO센서를 활용하여 로잉머신의 동작 상태를 이중 데이터로 측정하고, 분석한 값을 활용해서 Unity를 사용하여 AR 어플리케이션을 설계, 구현하였다. 어플리케이션을 AR 글라스를 통해 실행한 결과, 생동감 넘치는 운동 환경을 사용자에게 제공한다. 그러나 사용자의 시각적 부담 과다로 인하여 로잉머신 운동효과 경험에 부분적 장애를 유발할 수 있어 2차적 개선으로 VR 콘텐츠로 전환을 적용하여 안전한 운동효과를 검증하였다. 본 연구의 VR 콘텐츠 개선기술을 적용하면 사용자 안전에 우선하는 헬스 엔터테인먼트 시장의 활성화가 기대된다.

• 한국공간구조학회논문집
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• 제23권3호
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• pp.95-103
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• 2023

With the increasing number of aging buildings across Korea, emerging maintenance technologies have surged. One such technology is the non-contact detection of concrete cracks via thermal images. This study aims to develop a technique that can accurately predict the depth of a crack by analyzing the temperature difference between the crack part and the normal part in the thermal image of the concrete. The research obtained temperature data through thermal imaging experiments and constructed a big data set including outdoor variables such as air temperature, illumination, and humidity that can influence temperature differences. Based on the collected data, the team designed an algorithm for learning and predicting the crack depth using machine learning. Initially, standardized crack specimens were used in experiments, and the big data was updated by specimens similar to actual cracks. Finally, a crack depth prediction technology was implemented using five regression analysis algorithms for approximately 24,000 data points. To confirm the practicality of the development technique, crack simulators with various shapes were added to the study.