• 전기학회논문지P
• /
• 제59권3호
• /
• pp.275-288
• /
• 2010

In order to improve the quality of electricity in large scale power systems, stability of power system has to be achieved. This can be done by the means of preventative diagnosis of power equipments and protection, monitoring and control of the power system. Since the recent adoption of digital controllers, an improvement in stability was observed; in particular, IED, which contained self-diagnostic abilities such as fault tolerance, allowed for automatic recovery via redundancy or switching-over functions should there be faults with the equipments. Furthermore, communication lines have been hugely simplified, thus adding to the improvement in stability significantly. Taking these error reports and forecasting emergency reports and by effectively responding to them in the overiding controlling systems, high levels of system stability can be obtained. Power Management System that is being applied to automated power sub-stations, takes the IEC61850 international standard as its specification. In this paper, additional research into achieving stability of already developed PMS system and also the stability of the overall system was carried out, and the results of development of communication servers, which play a pivotal role in connecting systems, are stated.

• 한국정밀공학회:학술대회논문집
• /
• 한국정밀공학회 2003년도 춘계학술대회 논문집
• /
• pp.1067-1070
• /
• 2003

In this paper, we present a remote measurement system for the wireless monitoring of ECU Sensor Signals of vehicle. In order to measure the ECU sensor signals, the interface circuit is designed to communicate ECU and designed terminal wirelessly according to the ISO, SAE regulation of communication protocol standard. A micro-controller 80C196KC is used for communicating ECU sensor signals. ECU sensor signals are transmitted to the RF-wireless terminal that was developed using the micro controller 80386EX. LCD, and RF-module. 80386EX software is programmed to monitor the ECU sensor signals using the Borland C++ compiler in which the half duplex method was used for the RS232 communication. The algorithms for measuring the ECU sensor signals are verified to monitor ECU state. At the same time, the information to fix the vehicle's problem can be shown on the developed monitoring software. The possibility for remote measurement of ECU sensor signals using 80386EX is also verified through the developed systems and algorithms.

• 한국진공학회지
• /
• 제20권2호
• /
• pp.86-92
• /
• 2011

본 연구에서는 새롭게 개발된 센서인 in-situ particle monitor (ISPM)와 기존센서의 기능을 업그레이드 한 센서인 self-plasma optical emission spectroscopy (SPOES)를 이용해 화학기상증착 진공공정을 진단하였다. 본 연구에서 사용된 증착공정 장비는 silane 가스를 이용한 silicon plasma enhanced chemical vapor deposition과 borophosphosilicate glass 증착장비이다. 두 장비의 증착 또는 클리닝 조건에서의 배출되는 오염입자와 배기가스를 개발된 센서를 이용해 공정상태를 실시간으로 진단하는 것과 개발된 센서의 센싱 능력을 검증하고자 하는 목적으로 연구가 진행되었다. 개발된 센서는 장비 배기구 설치되었으며, 공정압력, 유량, 플라즈마 파워 등의 공정변수 변화에 따른 오염입자 크기 및 분포와 배기 부산물의 변화를 측정하고, 측정 결과의 상호 연관성을 분석하였다.

• International journal of advanced smart convergence
• /
• 제10권2호
• /
• pp.21-30
• /
• 2021

With the rapid development of artificial intelligence and big data, a lot of medical data is effectively used, and the diagnosis and analysis of diseases has entered the era of intelligence. With the increasing public health awareness, ordinary citizens have also put forward new demands for panic disorder health services. Specifically, people hope to predict the risk of panic disorder as soon as possible and grasp their own condition without leaving home. Against this backdrop, the smart health industry comes into being. In the Internet age, a lot of panic disorder health data has been accumulated, such as diagnostic records, medical record information and electronic files. At the same time, various health monitoring devices emerge one after another, enabling the collection and storage of personal daily health information at any time. How to use the above data to provide people with convenient panic disorder self-assessment services and reduce the incidence of panic disorder in China has become an urgent problem to be solved. In order to solve this problem, this research applies the context awareness to the automatic diagnosis of human diseases. While helping patients find diseases early and get treatment timely, it can effectively assist doctors in making correct diagnosis of diseases and reduce the probability of misdiagnosis and missed diagnosis.

• International Journal of Computer Science & Network Security
• /
• 제24권9호
• /
• pp.127-134
• /
• 2024

The acute respiratory infection known as a coronavirus (COVID-19) may present with a wide range of clinical manifestations, ranging from no symptoms at all to severe pneumonia and even death. Expert medical systems, particularly those used in the diagnostic and monitoring phases of treatment, have the potential to provide beneficial results in the fight against COVID-19. The significance of healthcare mobile technologies, as well as the advantages they provide, are quickly growing, particularly when such applications are linked to the internet of things. This research work presents a knowledge-based smart system for the primary diagnosis of COVID-19. The system uses symptoms that manifest in the patient to make an educated guess about the severity of the COVID-19 infection. The proposed inference system can assist individuals in self-diagnosing their conditions and can also assist medical professionals in identifying the ailment. The system is designed to be user-friendly and easy to use, with the goal of increasing the speed and accuracy of COVID-19 diagnosis. With the current global pandemic, early identification of COVID-19 is essential to regulate and break the cycle of transmission of the disease. The results of this research demonstrate the feasibility and effectiveness of using a knowledge-based smart system for COVID-19 diagnosis, and the system has the potential to improve the overall response to the COVID-19 pandemic. In conclusion, these sorts of knowledge-based smart technologies have the potential to be useful in preventing the deaths caused by the COVID-19 pandemic.