• 대한임베디드공학회논문지
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• 제5권2호
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• pp.103-110
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• 2010

Due to the increasing amount of electronic control system in a vehicle, the automotive software is increasingly sophisticated and complicated. Therefore it may be faced a time critical problem caused by its complexity. In order to solve such problems, the automotive electronic system can use a real-time scheduling mechanism based on predictability. We first consider the standard specification of the AUTOSAR OS and uC/OS-II such as its scheduling theory with time determinism. In this paper, we propose the scheduling algorithm to be conformable to a conformance class of OSEK/VDX specification. Algorithm analysis shows that our scheduling algorithm outperforms an existing Trampoline OS by intuition.

• 한국소음진동공학회논문집
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• 제14권4호
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• pp.267-275
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• 2004

This research is concerned with the development of a real-time adaptive PPF controller for the active vibration suppression of smart structure. In general, the tuning of the PPF controller is carried out off-line. In this research, the real-time learning algorithm is developed to find the optimal filter frequency of the PPF controller in real time and the efficacy of the algorithm is proved by implementing it in real time. To this end, the adaptive algorithm is developed by applying the gradient descent method to the predefined performance index, which is similar to the method used popularly in the optimization and neural network controller design. The experiment was carried out to verify the validity of the adaptive PPF controller developed in this research. The experimental results showed that adaptive PPF controller is effective for active vibration control of the structure which is excited by either impact or harmonic disturbance. The filter frequency of the PPF controller is tuned in a very short period of time thus proving the efficiency of the adaptive PPF controller.

• 대한전기학회논문지:전력기술부문A
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• 제48권6호
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• pp.760-772
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• 1999

The CAN is a serial communication protocol for distributed real-time control and automation systems. Data generated from field devices in the distributed control of power plant are classified into three categories: real-time event data, real-time control data, non-real-time data. These data share a CAN medium. If the traffic of the CAN protocol is not efficiently controlled, performance requirements of the power plant system could not be satisfied. This paper proposes a bandwidth allocation algorithm that can be applicable to the CAN protocol. The bandwidth allocation algorithm not only satisfies the performance requirements of the real-time systems in the power plant but also fully utilizes the bandwidth of CAN. The bandwidth allocation algorithm introduced in this paper is validated using the integrated discrete-event/continuous-time simulation model which comprises the CAN network and distributed control system of power plant.

• 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.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 2001년도 춘계학술대회 논문집
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• pp.602-605
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• 2001

This paper focus on a method to use Ethernet Network for Industrial Communication Network. Ethernet use the CSMA/CD MAC(Medium Access Control) Protocol at the Data-Link Layer, Which isn't suit for Industrial Communication Network requiring Real-Time Communication, periodic data processing, critical data processing characteristics. In this paper we proposed the Virtual Polling Algorithm at the Application Layer will be solution of using the Ethernet Network for the Industrial Communication Network, Proposed Algorithm terminate the Collision in the network thus Delay Time is reduced and Real-Time Communication will be implemented.

• ETRI Journal
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• 제37권3호
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• pp.551-561
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• 2015

We propose a novel multiple-object tracking algorithm for real-time intelligent video surveillance. We adopt particle filtering as our tracking framework. Background modeling and subtraction are used to generate a region of interest. A two-step pedestrian detection is employed to reduce the computation time of the algorithm, and an iterative particle repropagation method is proposed to enhance its tracking accuracy. A matching score for greedy data association is proposed to assign the detection results of the two-step pedestrian detector to trackers. Various experimental results demonstrate that the proposed algorithm tracks multiple objects accurately and precisely in real time.

• 제어로봇시스템학회논문지
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• 제13권11호
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• pp.1074-1081
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• 2007

This paper presents a method for real-time face detection and tracking which combined Adaboost and Camshift algorithm. Adaboost algorithm is a method which selects an important feature called weak classifier among many possible image features by tuning weight of each feature from learning candidates. Even though excellent performance extracting the object, computing time of the algorithm is very high with window size of multi-scale to search image region. So direct application of the method is not easy for real-time tasks such as multi-task OS, robot, and mobile environment. But CAMshift method is an improvement of Mean-shift algorithm for the video streaming environment and track the interesting object at high speed based on hue value of the target region. The detection efficiency of the method is not good for environment of dynamic illumination. We propose a combined method of Adaboost and CAMshift to improve the computing speed with good face detection performance. The method was proved for real image sequences including single and more faces.

• 한국컴퓨터정보학회논문지
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• 제12권3호
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• pp.147-153
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• 2007

태스크 스케줄링은 여러 개의 수행 가능한 태스크 중에서 하나의 태스크를 선정하여 프로세서에 할당하는 중요한 역할을 한다. 실시간 시스템에서 경성 실시간 태스크를 적시에 스케줄링하지 못하면 시스템이 다운되는 최악의 상태가 발생하므로 실시간 시스템은 효율성, 자원 및 속도 등의 문제 외에도 시간 제약도 진지하게 고려해야 한다. 본 논문에서는 L-RE 좌표를 이용하여 실시간 다중프로세서 시스템에서의 새로운 우선순위 기준 스케줄링 알고리즘을 제안한다. L-RE 좌표 알고리즘은 태스크의 스케줄링 효율을 높이기 위해 고안 되었으며 우선순위를 할당하는데 데드라인과 함께 유휴시간을 고려하고 있다. 시뮬레이션 결과는 LR-E 알고리즘이 EDF보다 스케줄의 원활성을 높이고 또 LLF보다는 문맥교환 수를 줄일 수 있음을 보여준다.

• International Journal of Automotive Technology
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• 제7권2호
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• pp.227-235
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• 2006

In this paper a robust image processing method with modified Otsu algorithm to recognize the road lane for a real-time controlled autonomous vehicle is presented. The main objective of a proposed method is to drive an autonomous vehicle safely irrespective of road image qualities. For the steering of real-time controlled autonomous vehicle, a detection area is predefined by lane segment, with previously obtained frame data, and the edges are detected on the basis of a lane width. For stable as well as psudo-robust autonomous driving with "good", "shady" or even "bad" road profiles, the variable threshold with modified Otsu algorithm in the image histogram, is utilized to obtain a binary image from each frame. Also Hough transform is utilized to extract the lane segment. Whether the image is "good", "shady" or "bad", always robust and reliable edges are obtained from the algorithms applied in this paper in a real-time basis. For verifying the adaptability of the proposed algorithm, a miniature vehicle with a camera is constructed and tested with various road conditions. Also, various highway road images are analyzed with proposed algorithm to prove its usefulness.

• 한국게임학회 논문지
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• 제3권2호
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• pp.24-29
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• 2003

The market of digital avatar with internet and digital technology is increasing rapidly. The users want to express any free-formed motion of their avatars in the cyber space. The user s motion capturing method as the avatar's motion can express any free-formed motion of the avatar in real-time but the methods are expensive and inconvenient. In this paper, we proposed a new method of expressing any free-formed motion of the avatar in real-time. The proposed method is an algorithm for real-time control of a 3D avatar in symmetry-formed free motion. Specially, the algorithm aims at the motion control of a 3D avatar for online dancing games. The proposed algorithm uses the skeleton character model and controls any one of two hands of the character model by a joystick with two sticks. In the symmetry-formed motion, the position and orientation of one hand can determine the position and orientation of the other hand. And the position and orientation of a hand as an end-effector can determine the pose of the arm by Inverse Kinematics. So the algorithm can control the symmetry-formed free motions of two arms by one joystick with two sticks. In the dance game, the algorithm controls the arm motion by the joystick and the other motion by the motion captured DB.