• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.18 no.5
• /
• pp.86-92
• /
• 2017

As the elderly population increases, real-time personal health monitoring is gaining new ground with advances in wireless communications. Such an approach is particularly beneficial to elderly and physically challenged people, as well as those who live alone and are not be able to seek help in case of medical emergencies. The aim of this study is to implement a wearable band which monitors personal vital signs, such as the body temperature and heart rate, and assists with healthcare decisions using a fuzzy logic based decision support system. Since the vital sign data measured from sensors are imprecise and their normal variation with age is nonlinear and not crisp, a fuzzy system is employed to deal with this imprecise or uncertain information. The proposed wearable band is designed to continuously capture and transmit vital signs and healthcare decisions to suitable apps developed for smartphones. In this way, health alerts can be sent to the guardian or caregiver who is registered in the apps.

• Proceedings of the Korean Information Science Society Conference
• /
• 2008.06d
• /
• pp.455-460
• /
• 2008

건설교통부는 국가미래에 대비해 실질적으로 기여할 수 있는 전략적이고 중장기적인 실효성 있는 R&D 혁신 로드맵인 VC-10 사업을 진행하고 있다. 이 과제의 핵심 기술 중에 하나인 유비쿼터스 센서네트워크는 초소형 센서들로 구성된 망으로써 환경 및 재난 재해, u-City 건설, 스마트 하이웨이, 방범 및 국방, 시설 관리, 유통, 등과 같은 다양한 분야에 사용할 수 있는 유비쿼터스 사회의 핵심 요소이다. 이것은 실시간 네트워크를 통해 전송 또는 관리되고 언제, 어디서나, 어떠한 대상과 의사소통이 가능한 지능형 컴퓨팅 환경을 구현하는 것이다. 그러나 이러한 기술을 바탕으로 하는 기존의 시스템들은 특정분야를 위한 독자적인 프로토콜이나 저전력 통신을 제공하는 ZigBee 기반으로 설계되었기 때문에 소규모 범위에서만 서비스 제공이 가능하며 시스템간의 연동이 어렵다. 따라서 본 논문에서는 유비쿼터스 센서네트워크간의 연동성을 고려한 대규모 실시간 모니터링 시스템을 설계하여 다양한 분야로 서비스할 수 있는 테스트베드 구축에 대한 방안을 제시하고자 한다. 여기서 모니터링 대상은 국토의 지반, 환경, 그리고 도심지역 등이다. 이렇게 제시된 방안은 국내실정에 적합한 모니터링 시나리오를 작성하여 테스트베드를 구축 시에 요구된 사항들에 대한 정당성을 입증한다.

• Korean Journal of Remote Sensing
• /
• v.33 no.1
• /
• pp.69-77
• /
• 2017

S The need for an automated geo-positioning approach for near real-time results and to boost cost-effectiveness has become increasingly urgent. Following this trend, a new approach to automatically compensate for the bias of the rational function model (RFM) was proposed. The core idea of this approach is to remove the bias of RFM only using tie points, which are corrected by matching with the digital elevation model (DEM) without any additional ground control points (GCPs). However, there has to be a additional evaluation according to the quality of DEM because DEM is used as a core element in this approach. To address this issue, this paper compared the quality effects of DEM in the conduct of the this approach using the Shuttle Radar Topographic Mission (SRTM) DEM with the spatial resolution of 90m. and the National Geographic Information Institute (NGII) DEM with the spatial resolution of 5m. One KOMPSAT-2 stereo-pair image acquired at Busan, Korea was used as experimental data. The accuracy was compared to 29 check points acquired by GPS surveying. After bias-compensation using the two DEMs, the Root Mean Square (RMS) errors were less than 6 m in all coordinate components. When SRTM DEM was used, the RMSE vector was about 11.2m. On the other hand, when NGII DEM was used, the RMSE vector was about 7.8 m. The experimental results showed that automated geo-positioning approach can be accomplished more effectively by using NGII DEM with higher resolution than SRTM DEM.

• 한국태양에너지학회:학술대회논문집
• /
• 2009.04a
• /
• pp.102-108
• /
• 2009

Fossil power plants firing lower grade coals or equipped with modified system for NOx controls are challenged with maintaining good combustion conditions while maximizing generation and minimizing emissions. In many cases significant derate, availability losses and increase in unburned carbon levels can be attributed to poor combustion conditions as a result of poorly controlled local fuel and air distribution within the boiler furnace. In order to develop a on-line combustion tuning system, field test was conducted at operating power boiler. During the field test the exhaust gases' $O_2$, NOx and CO was monitored by using a spatially distributed monitoring grid located in the boiler's high temperature vestibule and upper convective back-pass region. At these locations, the flue gas flow is still significantly stratified, and air in-leakage is minimal which enables tracing of poor combustion zones to specific burners and over-fire air ports. using these monitored information we can improving combustion at every point within the furnace, therefore the boiler can operate at reduced excess $O_2$ and gas temperature deviation, reduced furnace exit gas temperature levels while also reducing localized hot spots, corrosive gas conditions, slag or clinker formation and UBC. Benefits include improving efficiency, reducing NOx emissions, increasing output and maximizing availability. Discussion concerning the reduction of greenhouse gases is prevalent in the world. When taking a practical approach to addressing this problem, the best way and short-term solution to reduce greenhouse gases on coal-fired power plants is to improve efficiency. From this point of view the real time optimized combustion tuning approach is the most effective and implemented with minimal cost.

• Journal of Welding and Joining
• /
• v.11 no.2
• /
• pp.27-41
• /
• 1993

The estimation of nugget sizes was attempted by utilizing the artificial neural networks method. Artificial neural networks is a highly simplified model of the biological nervous system. Artificial neural networks is composed of a large number of elemental processors connected like biological neurons. Although the elemental processors have only simple computation functions, because they are connected massively, they can describe any complex functional relationship between an input-output pair in an autonomous manner. The electrode head movement signal, which is a good indicator of corresponding nugget size was determined by measuring the each test specimen. The sampled electrode movement data and the corresponding nugget sizes were fed into the artificial neural networks as input-output pairs to train the networks. In the training phase for the networks, the artificial neural networks constructs a fuctional relationship between the input-output pairs autonomusly by adjusting the set of weights. In the production(estimation) phase when new inputs are sampled and presented, the artificial neural networks produces appropriate outputs(the estimates of the nugget size) based upon the transfer characteristics learned during the training mode. Experimental verification of the proposed estimation method using artificial neural networks was done by actual destructive testing of welds. The predicted result by the artifficial neural networks were found to be in a good agreement with the actual nugget size. The results are quite promising in that the real-time estimation of the invisible nugget size can be achieved by analyzing the process variable without any conventional destructive testing of welds.

• Journal of Welding and Joining
• /
• v.16 no.5
• /
• pp.119-133
• /
• 1998

To implement full automation in pipe welding, it si most important to develop special sensors and their related systems which act like human operator when detecting irregular groove conditions. In this study, an automatic pipe Gas Metal Arc Welding (GMAW) system was proposed to full control pipe welding procedure with intelligent sensor systems. A five-axes manipulator was proposed for welding torch to automatically access to exact welding position when pipe size and welding angle were given. Pool status and torch position were measured by using a weld-pool image monitoring and processing technique in root-pass welding for weld seam tracking and weld pool control. To overcome the intensive arc light, pool image was captured at the instance of short circuit of welding power loop. Captured image was processed to determine weld pool shape. For weld seam tracking, the relative distance of a torch position from the pool center was calculated in the extracted pool shape to move torch just onto the groove center. To control penetration of root pas, gap was calculated in the extracted pool image, and then weld conditions were controlled for obtaining appropriate penetration. welding speed was determined with a fuzzy logic, and welding current and voltage were determined from a data base to correspond to the gap. For automatic fill-pass welding, the function of human operator of real time weld seam control can be substituted by a sensor system. In this study, an arc sensor system was proposed based on a fuzzy control logic. Using the proposed automatic system, root-pass welding of pipe which had gap variation was assured to be appropriately controlled in welding conditions and in torch position by showing sound welding result and good seam tracking capability. Fill-pass welding by the proposed system also showed very successful result by tracking along the offset welding line without any control of human operator.

• Journal of the Korean Society for Aeronautical & Space Sciences
• /
• v.38 no.3
• /
• pp.280-287
• /
• 2010

The first Korean satellite launch vehicle, KSLV-I(Korea Space Launch Vehicle-I), was launched for its first flight test on Aug. 25, 2009 from Naro Space Center located in south Jolla province. Because launch vehicles usually fly long range with large amount of propellants aboard, preparation of countermeasures against potential malfunctions during flight is essential in launch operation. In this paper, the flight safety operation, prepared to guarantee flight safety during launch operation of KSLV-I, is presented. Prior to flight test, flight safety analysis is performed to estimate associated risk levels quantitatively, and during flight, flight safety systems are operated to cope with any risky situations. Real-time flight monitoring including computation of instantaneous impact point using tracking data is executed normally and the flight test is completed without activation of flight termination system.

• Journal of the Korea Society of Computer and Information
• /
• v.25 no.7
• /
• pp.143-150
• /
• 2020

Recently, many companies are interested in smart factory services. Because various smart factory services are provided by the combination of mobile devices, cloud computing, and IoT services. However, many workers turn away from these systems because most of them are not implemented from the worker's point of view. To solve this, we implemented a development tool that allows field workers to produce their own services so that workers can easily create smart factory services. Manufacturing data is collected in real time from sensors which are connected to manufacturing facilities and stored within smart factory platforms. Implemented development tools can produce services such as monitoring, processing, analysis, and control of manufacturing data in drag-and-drop. The implemented system is effective for small manufacturing companies because of their environment: making various services quickly according to the company's purpose. In addition, it is assumed that this also will help workers' improve operation skills on running smart factories and fostering smart factory capable personnel.

• Journal of the Institute of Electronics and Information Engineers
• /
• v.51 no.5
• /
• pp.20-28
• /
• 2014

This paper proposes a faulty node detection scheme that performs collective monitoring of a distributed networked control systems using interval weighting factor. The algorithm is designed to observe every node's behavior collectively based on the pseudo-random Bose-Chaudhuri-Hocquenghem (BCH) code. Each node sends a single BCH bit simultaneously as a replacement for the cyclic redundancy check (CRC) code. The fault judgement is performed by performing sequential check of observed detected error to guarantee detection accuracy. This scheme can be used for detecting and preventing serious damage caused by node failure. Simulation results show that the fault judgement based on decision pattern gives comprehensive summary of suspected faulty node.

• Journal of the Institute of Electronics and Information Engineers
• /
• v.50 no.1
• /
• pp.205-214
• /
• 2013

In this paper, we analyzed the various EV charging-infra information(charging status, charging pattern, charging rate, charging fee, etc.) through the charging infra simulator which would be of help to effectively construct the EV charging infrastructure. The proposed simulator virtually made the EV motoring pattern referred to TMS(Traffic Monitoring System) & Ministry of Land, Transport and Maritime Affairs, and analyzed the charging-infra information(amount of charging, accumulated charging fee, etc.) based on vehicle types, charging type, time and days using EV charging-fee list noticed by KEPCO. Through this simulator, we deducted some considerable contents to build the EV charging infrastructure similarly with real environment.