• 대한인간공학회지
• /
• 제28권4호
• /
• pp.155-159
• /
• 2009

In this paper ubiquitous health care system that can monitor health condition was implemented through measuring the sensor that is installed in vehicles. Implemented system consists of various wireless sensors and DB server, transmitting information that is sensed in real-time. In addition, through the sensed data based algorithm, the system which couples Web-based JSP program with Flash GUI, providing information as well as emergency service was established.

• 한국통신학회논문지
• /
• 제35권2B호
• /
• pp.225-231
• /
• 2010

본 논문에서는 유헬스(u-Health) 서비스를 제공하는 센서 네트워크 시스템을 제안한다. 본 시스템은 거주자의 생체 정보를 수집하고 응급 상황을 자동으로 판단한다. LEID(Lighting Embedded Information Device)는 센서 네트워크에 이용 되는 장치로서 생활공간에 필요 불가결한 조명 기기에 센서 네트워크 기술을 접목시킨 장치이다. 본 논문에서 제안한 유헬스 시스템의 실용성 검증을 위해 국민대학교에 위치한 지능형 홈 센터에 설치하였고 실생활을 가정하여 시험하였다. 본 논문에서 제안한 유헬스 시스템은 지속적인 관리가 필요한 환자가 있는 기관에 사용될 수 있다.

• Smart Structures and Systems
• /
• 제30권6호
• /
• pp.583-599
• /
• 2022

In this paper, a multi-objective wireless sensor network configuration optimization method is proposed. The proposed method aims to determine the optimal information and lifespan wireless sensor network for structural health monitoring of large-scale infrastructures. In particular, cluster-based wireless sensor networks with multi-type of sensors are considered. To optimize the lifetime of the wireless sensor network, a cluster-based network optimization algorithm that optimizes the arrangement of cluster heads and base station is developed. On the other hand, based on the Bayesian inference, the uncertainty of the estimated parameters can be quantified. The coefficient of variance of the estimated parameters can be obtained, which is utilized as a holistic measure to evaluate the estimation accuracy of sensor configurations with multi-type of sensors. The proposed method provides the optimal wireless sensor network configuration that satisfies the required estimation accuracy with the longest lifetime. The proposed method is illustrated by designing the optimal wireless sensor network configuration of a cable-stayed bridge and a space truss.

• 한국정보통신학회논문지
• /
• 제15권10호
• /
• pp.2265-2271
• /
• 2011

본 논문에서는 인간의 신체와 관련된 상황정보를 기반으로 한 u-헬쓰케어 시스템 설계 연구이다. 최근 운동 클럽 에서 전문 트레이너와 함께 체계적인 운동을 하는 것이 웰빙에서 이슈가 되었지만, 그중에서 기존의 헬쓰케어 시스템은 단지 고정된 시스템에서 현재 자신의 혈압과 맥박, 심전도, 근전도 등의 일반적인 정보만 제공하기 때문에 장시간이나 장기간의 운동을 하기에는 시간이 지날수록 흥미도가 떨어지고, 사용자 자신에 맞는 관리를 하기가 어렵다. 본 논문에서 개발된 u-헬쓰케어 시스템은 센서 네트워크와 DB를 접목한 상황 인식 시스템을 이용하여 자신의 현재 상태와 운동 진행 상태를 분석한 맞춤 운동을 할 수 있는 상황인식 기반의 서비스를 제공하는 u-헬쓰케어 시스템을 설계 개발 하였다.

• 대한안전경영과학회:학술대회논문집
• /
• 대한안전경영과학회 2009년도 춘계학술대회
• /
• pp.439-444
• /
• 2009

Recently ubiquitous sensor network(USN) technique has been applied into various industrial fields such as construction of u-city, civil and architectural engineering, medical areas. The main objective of this paper is to design a u-health system based on USN. Compared to the previous researches, the proposed system in this paper was designed to activate with collected information about health in outdoor environment.

• Smart Structures and Systems
• /
• 제14권2호
• /
• pp.145-158
• /
• 2014

The objective of this study is to improve the survivability and reliability of the FBG sensor network in the structural health monitoring (SHM) system. Therefore, a model reconstruction soft computing recognition algorithm based on support vector regression (SVR) is proposed to achieve the high reliability of the FBG sensor network, and the grid search algorithm is used to optimize the parameters of SVR model. Furthermore, in order to demonstrate the effectiveness of the proposed model reconstruction algorithm, a SHM system based on an eight-point fiber Bragg grating (FBG) sensor network is designed to monitor the foreign-object low velocity impact of a CFRP composite plate. Simultaneously, some sensors data are neglected to simulate different kinds of FBG sensor network failure modes, the predicting results are compared with non-reconstruction for the same failure mode. The comparative results indicate that the performance of the model reconstruction recognition algorithm based on SVR has more excellence than that of non-reconstruction, and the model reconstruction algorithm almost keeps the consistent predicting accuracy when no sensor, one sensor and two sensors are invalid in the FBG sensor network, thus the reliability is improved when there are FBG sensors are invalid in the structural health monitoring system.

• International journal of advanced smart convergence
• /
• 제5권1호
• /
• pp.1-7
• /
• 2016

Recently, introduced a cloud computing technology to the IT industry, smart phones, it has become possible connection between mobility terminal such as a tablet PC. For dissemination and popularization of movable wireless terminal, the same operation have focused on a viable mobile cloud in various terminal. Also, it evolved Wireless Sensor Network(WSN) technology, utilizing a Body Sensor Network(BSN), which research is underway to build large Ubiquitous Sensor Network(USN). BSN is based on large-scale sensor networks, it integrates the state information of the patient's body, it has been the need to build a managed system. Also, by transferring the acquired sensor information to HIS(Hospital Information System), there is a need to frequently monitor the condition of the patient. Therefore, In this paper, possible sensor information exchange between terminals in a mobile cloud environment, by integrating the data obtained by the body sensor HIS and interoperable data DBaaS (DataBase as a Service) it will provide a base of mBodyCloud System. Therefore, to provide an integrated protocol to include the sensor data to a standard HL7(Health Level7) medical information data.

• 한국정밀공학회지
• /
• 제29권12호
• /
• pp.1313-1320
• /
• 2012

Condition monitoring of the reciprocating compressor is important task. As a traditional method, health monitoring system of refrigerator depends on decision of a skilled person based on his experience. However, the skilled person cannot monitor all the compressors completely. If a sampled compressor is faulty, thousands of compressors manufactured at that place are regarded as faulty compressors. Therefore it is necessary to monitor all compressors in the production line. In the paper real time health monitoring system is developed based on high order time frequency method and artificial neural network. The system is installed in the mass production line. The result of the application has been very successful, and currently the system is working very well on the production line.

• 한국정보전자통신기술학회논문지
• /
• 제2권4호
• /
• pp.73-80
• /
• 2009

본 논문에서는 유비쿼터스 헬스케어와 지능형 홈 네트워크 시스템을 위한 전력선 통신과 Zigbee 통신 기반의 통합 게이트웨이를 구현하였다. 전체 시스템은 센서, 통합 게이트웨이, Zigbee 모듈, 전력선 모뎀으로 구성되어있다. 헬스케어 센서에 의해서 수집된 데이터는 통합 게이트웨이에서 저장 및 분석하여 헬스케어제어장치로 전송할 수 있으며, 의료시설 및 각 가정에 쾌적한 환경을 제공하기 위하여 가전 기기를 제어한다. 구현된 통합 게이트웨이는 다양한 유비쿼터스 헬스케어와 지능형 홈 네트워크를 지원할 수 있다.

• International Journal of Computer Science & Network Security
• /
• 제21권3호
• /
• pp.177-184
• /
• 2021

All over the world, people are affected by many chronic diseases and medical practitioners are working hard to find out the symptoms and remedies for the diseases. Many researchers focus on the feature detection of the disease and trying to get a better health recommendation system. It is necessary to detect the features automatically to provide the most relevant solution for the disease. This research gives the framework of Health Recommendation System (HRS) for identification of relevant and non-redundant features in the dataset for prediction and recommendation of diseases. This system consists of three phases such as Pre-processing, Feature Selection and Performance evaluation. It supports for handling of missing and noisy data using the proposed Imputation of missing data and noise detection based Pre-processing algorithm (IMDNDP). The selection of features from the pre-processed dataset is performed by proposed ensemble-based feature selection using an expert's knowledge (EFS-EK). It is very difficult to detect and monitor the diseases manually and also needs the expertise in the field so that process becomes time consuming. Finally, the prediction and recommendation can be done using Support Vector Machine (SVM) and rule-based approaches.