• 대한전기학회:학술대회논문집
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• 대한전기학회 2007년도 심포지엄 논문집 정보 및 제어부문
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• pp.215-216
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• 2007

This paper presents a real-time scheduler for nuclear safety system. According to constraints and requirements of nuclear safety system, scheduler design analysis is done and algorithms are developed for implementation. Using DSP based hardware, a real-time scheduler is realized. Consequently, this paper shows the performance of periodical software through the monitoring program.

• 국제학술발표논문집
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• The 3th International Conference on Construction Engineering and Project Management
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• pp.27-29
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• 2009

Real-time control of on-site construction, based on high quality data, is essential to identify discrepancies between actual and planned performances. Additionally, real-time control enables timely corrective measures to be taken when needed to reduce the damages caused by the discrepancies. The focus of the presentation will be on our work, which uses automated data technologies to collect data needed for real time control.

• Spatial Information Research
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• 제17권3호
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• pp.319-328
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• 2009

센서 및 무선통신 기술의 발달로 인해 사물의 상태에 대한 실시간 모니터링이 가능해지고 있다. 이에 다양한 분야에서 실시간 모니터링을 활용하고자 하는 시도가 진행되고 있으며, 특히 시설물 관리 분야에서도 이를 활용하여 시설물 관리의 지능화를 추진하고 있다. 그러나 센서 기술, 통신 기술, 공간정보 등 핵심 요소기술을 바로 현장에 적용하기에는 다소 무리가 있다. 현장에서는 새로운 기술이 적용되기 이전의 업무프로세스를 통해 시설물 관리가 이루어지고 있기 때문이다. 즉, 새로운 기술을 현장에 적용하기 위해서는 필연적으로 업무프로세스의 개선을 병행하여 기술과 현장의 간극을 최소화함으로써 기술의 현장 적응력을 높일 필요성이 존재한다. 이에 본 연구에서는 기존의 지하시설물 유지관리 프로세스를 살펴보고, 실시간 모니터링이 가능하다는 가정 하에 지하시설물 유지관리 업무프로세스의 개선 방향을 제시하고자 한다. 본 연구에서는 지역난방 업무프로세스를 대상으로 하였다.

• 정보처리학회논문지A
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• 제10A권6호
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• pp.715-722
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• 2003

실시간 시스템은 시스템이 적시성을 보장하는지 파악하기 위해 실시간 감시 기법을 이용한다. 실시간 감시의 대상은 내부 시스템만이 아니라 네트워크 상에 존재하는 인격 시스템만이 포함된다. 각 시스템에서 발생하는 데이터를 감시하기 위해서는 데이터를 일시적 혹은 장기적으로 저장할 데이터 저장소가 필요하며, 이러한 데이터 저장소는 실시간 감시를 지원할 수 있도록 시간 제약과 데이터 저장소에 대한 접근성을 고려해 설계해야 한다. 이에 따라 본 논문에서는 시간 제약과 접근성을 고려한 XML 기반의 데이터 저장소 및 전송 구조를 제시한다. XML기반의 데이터 저장소는 표준화된 데이터 포맷인 XML을 기반으로 설계하여 TCP/IP 및 HTTP를 지원하는 모든 플랫폼에서 원격으로 데이터 저장소 접근이 가능하며, 별도의 변환과정 없이 데이터를 사용할 수 있다. XML 기반의 전송 구조는 DOM, XML-RPC 및 저장 후 전송 기법을 이용하여 데이터 접근 및 전송 시간을 최소화하도록 설계하였다. 더 나아가 본 논문에서는 XML 기반의 데이터 저장소 및 전송구조를 이용하여 실시간 감시를 수행할 때, 기준이 되는 시간적 한계치를 제시하기 위해 측정 실험을 수행하였다. 본 논문에서 설계한 XML 기반의 데이터 저장소 및 전송 구조 그리고 실험 결과는 기본적으로 실시간 감시 및 제어를 필요로 하는 분야 및 응용 분야에서 이용할 수 있다.

• 대한임베디드공학회논문지
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• 제19권3호
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• pp.151-158
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• 2024

Although on-device artificial intelligence (AI) has gained attention to diagnosing machine faults in real time, most previous studies did not consider the model retraining and redeployment processes that must be performed in real-world industrial environments. Our study addresses this challenge by proposing an on-device AI-based real-time machine fault diagnosis system that utilizes continual learning. Our proposed system includes a lightweight convolutional neural network (CNN) model, a continual learning algorithm, and a real-time monitoring service. First, we developed a lightweight 1D CNN model to reduce the cost of model deployment and enable real-time inference on the target edge device with limited computing resources. We then compared the performance of five continual learning algorithms with three public bearing fault datasets and selected the most effective algorithm for our system. Finally, we implemented a real-time monitoring service using an open-source data visualization framework. In the performance comparison results between continual learning algorithms, we found that the replay-based algorithms outperformed the regularization-based algorithms, and the experience replay (ER) algorithm had the best diagnostic accuracy. We further tuned the number and length of data samples used for a memory buffer of the ER algorithm to maximize its performance. We confirmed that the performance of the ER algorithm becomes higher when a longer data length is used. Consequently, the proposed system showed an accuracy of 98.7%, while only 16.5% of the previous data was stored in memory buffer. Our lightweight CNN model was also able to diagnose a fault type of one data sample within 3.76 ms on the Raspberry Pi 4B device.

• 대한의용생체공학회:의공학회지
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• 제16권2호
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• pp.175-182
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• 1995

태아 심음을 측정하기 위한 기존의 자기상관 함수 법은 처리과정이 간편한 반면에 많은 문제점을 가지고 있다. 초음파 도플러 신호가 열악할 경우 고전적인 자기상관 함수 법은 문턱 값의 선정과 창 함수 크기에 매우 민감하다. 특히 데이터 손실이 생길 때 정확한 태아 심박동 수를 찾기가 어렵다. 이들 문제점들을 보완하기 위하여 초음파 도플러 신호로부터 정확한 태아 심박동 수를 찾는 고해상 피치검출 알고리듬이 제안되었다. 이 알고리듬은 자기상관 함수법 보다 정확하고, 잡음에 강하며, 높은 신뢰성을 갖으나 계산량이 많아 실시간 처리가 어렵다. 본 논문에서는 실시간 처리에 적합한 새로운 태아 심음 추출 알고리듬을 제안하고, 제안된 알고리듬을 적용한 실시간 태아 심음 감시시스템에 관하여 연구하였다.

• Journal of Welding and Joining
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• 제16권5호
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• pp.76-85
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• 1998

The resistance spot welding process has been used for joining the sheet metal in automotive engineering. In the resistance spot welding, the weld quality is very important, because the quality of weld is one of the most important factors to the automobile quality. The size of he molten nugget has been utilized to estimate the weld quality. However, it is not easy to find the weld defects. For weldability estimation, we have to use the nondestructive method such as X-ray or ultrasonic inspection. But these kinds of approaches are not suitable for detecting the defects in real time. The purpose of this study is to develop the real time monitoring of the weld quality in the resistance spot welding. Obtained data were used to estimate weldability using fuzzy algorithm. It is sound that this monitoring and estimation system can be useful to improve the weld quality in the resistance spot welding process and it is possible to estimate the weldability in real time.

• Food Science and Biotechnology
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• 제15권4호
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• pp.595-598
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• 2006

Semi-quantitative monitoring of Lactobacillus sake and Lactobacillus plantarum, major and minor microorganisms in kimchi, respectively, and Lactobacillus paraplantarum, recently shown to be present in kimchi, was carried out by real-time polymerase chain reaction (PCR). Changes in the 3 species during kimchi fermentation were monitored by the threshold cycle ($C_T$) of real-time PCR. As fermentation proceeded at $15^{\circ}C$, the number of L. sake increased dramatically compared to those of L. plantarum and L. paraplantarum. During fermentation at $4^{\circ}C$, the growth rates of the 3 species decreased, but the proportions of L. plantarum and L. paraplantarum in the microbial ecosystem were almost constant. Considering the $C_T$ values of the first samples and the change in the $C_T$ value, the number of L. sake is no doubt greater than those of L. plantarum and L. paraplantarum in the kimchi ecosystem. L. sake seems to be one of the major microorganisms involved in kimchi fermentation, but there is insufficient evidence to suggest that L. plantarum is the primary acidifying bacterium.

• Journal of Positioning, Navigation, and Timing
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• 제11권4호
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• pp.269-277
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• 2022

In this paper, we overview the system development status of the national maritime precise point positioning-real-time kinematic (PPP-RTK) service in Korea, also known as the Precise POsitioning and INTegrity monitoring (POINT) system. The development of the POINT service began in 2020, and the open service is scheduled to start in 2025. The architecture of the POINT system is composed of three provider-side facilities-a reference station, monitoring station, and central control station-and one user-side receiver platform. Here, we propose the detailed functionality of each component considering unidirectional broadcasting of augmentation data. To meet the centimeter-level user positioning accuracy in maritime coverage, new reference stations were installed. Each reference station operates with a dual receiver and dual antenna to reduce the risk of malfunctioning, which can deteriorate the availability of the POINT service. The initial experimental results of a testbed from corrections generated from the testbed network, including newly installed reference stations, are presented. The results show that the horizontal and vertical accuracies satisfy 2.63 cm and 5.77 cm, respectively. For the purpose of (near) real-time broadcasting of POINT correction data, we designed a correction message format including satellite orbit, satellite clock, satellite signal bias, ionospheric delay, tropospheric delay, and coordinate transformation parameters. The (near) real-time experimental setup utilizing (near) real-time processing of testbed network data and the designed message format are proposed for future testing and verification of the system.

• 한국BIM학회 논문집
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• 제11권2호
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• pp.24-32
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• 2021

Recently, starting with smart construction research, interest in technology that automates construction site management using artificial intelligence technology is increasing. In order to automate construction site management, it is necessary to recognize objects such as construction equipment or workers, and automatically analyze the relationship between them. For example, if the relationship between workers and construction equipment at a construction site can be known, various use cases of site management such as work productivity, equipment operation status monitoring, and safety management can be implemented. This study derives a real-time object detection platform architecture that is required when performing construction site management using deep learning technology, which has recently been increasingly used. To this end, deep learning models that support real-time object detection are investigated and analyzed. Based on this, a deep learning model development process required for real-time construction site object detection is defined. Based on the defined process, a prototype that learns and detects construction site objects is developed, and then platform development considerations and architecture are derived from the results.