• 대한전기학회논문지:시스템및제어부문D
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• 제55권2호
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• pp.52-61
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• 2006

For the detection of moving objects, background subtraction methods are widely used. An adaptive Gaussian mixture model combined with probabilistic learning is one of the most popular methods for the real-time update of the complex and dynamic background. However, probabilistic learning approach does not work well in high traffic regions. In this paper, we Propose a reliable learning method of complex and dynamic backgrounds in high traffic regions.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제14권11호
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• pp.4268-4289
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• 2020

Advanced traffic signal timing method plays very important role in reducing road congestion and air pollution. Reinforcement learning is considered as superior approach to build traffic light timing scheme by many recent studies. It fulfills real adaptive control by the means of taking real-time traffic information as state, and adjusting traffic light scheme as action. However, existing works behave inefficient in complex intersections and they are lack of feasibility because most of them adopt traffic light scheme whose phase sequence is flexible. To address these issues, a novel adaptive traffic signal timing scheme is proposed. It's based on actor-critic reinforcement learning algorithm, and advanced techniques proximal policy optimization and generalized advantage estimation are integrated. In particular, a new kind of reward function and a simplified form of state representation are carefully defined, and they facilitate to improve the learning efficiency and reduce the computational complexity, respectively. Meanwhile, a fixed phase sequence signal scheme is derived, and constraint on the variations of successive phase durations is introduced, which enhances its feasibility and robustness in field applications. The proposed scheme is verified through field-data-based experiments in both medium and high traffic density scenarios. Simulation results exhibit remarkable improvement in traffic performance as well as the learning efficiency comparing with the existing reinforcement learning-based methods such as 3DQN and DDQN.

• Journal of Communications and Networks
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• 제8권4호
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• pp.432-441
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• 2006

As the community antenna television (CATV) networks becomes ubiquitous, instead of constructing an entirely new broadband network infrastructure, it has emerged as one of the rapid and economic technologies to interconnecting heterogeneous network to provide broadband access to subscribers. How to support ubiquitous real-time multimedia applications, especially in a heavy traffic environment, becomes a critical issue in modern CATV networks. In this paper, we propose a time guaranteed and efficient upstream minislots allocation algorithm for supporting quality-of-service (QoS) traffic over data over cable service interface specification (DOCSIS) CATV networks to fulfill the needs of realtime interactive services, such as video telephony, video on demand (VOD), distance learning, and so on. The proposed adaptive fast expansion algorithm and the loading statistics with dynamic swapping algorithm have been shown to perform better than that of the multimedia cable network system (MCNS) DOCSIS.

• 대한임베디드공학회논문지
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• 제19권2호
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• pp.73-81
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• 2024

Recently, mobile manipulators have been utilized in agriculture industry for weed removal and harvest automation. This paper proposes a domain adaptive fruit detection method for harvest automation, by utilizing OWL-ViT model which is an open-vocabulary object detection model. The vision-language model can detect objects based on text prompt, and therefore, it can be extended to detect objects of undefined categories. In the development of deep learning models for real-world problems, constructing a large-scale labeled dataset is a time-consuming task and heavily relies on human effort. To reduce the labor-intensive workload, we utilized a large-scale public dataset as a source domain data and employed a domain adaptation method. Adversarial learning was conducted between a domain discriminator and feature extractor to reduce the gap between the distribution of feature vectors from the source domain and our target domain data. We collected a target domain dataset in a real-like environment and conducted experiments to demonstrate the effectiveness of the proposed method. In experiments, the domain adaptation method improved the AP50 metric from 38.88% to 78.59% for detecting objects within the range of 2m, and we achieved 81.7% of manipulation success rate.

• 한국조명전기설비학회:학술대회논문집
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• 한국조명전기설비학회 2006년도 춘계학술대회 논문집
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• pp.309-314
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• 2006

Interior permanent magnet synchronous motor(IPMSM) has become a popular choice in electric vehicle applications, due to their excellent power to weight ratio. This paper proposes maximum torque control of IPMSM drive using adaptive learning fuzzy neural network and artificial neural network. This control method is applicable over the entire speed range which considered the limits of the inverter's current md voltage rated value. For each control mode, a condition that determines the optimal d-axis current $i_d$ for maximum torque operation is derived. This paper considers the design and implementation of novel technique of high performance speed control for IPMSM using adaptive teaming fuzzy neural network and artificial neural network. The hybrid combination of neural network and fuzzy control will produce a powerful representation flexibility and numerical processing capability. Also, this paper proposes speed control of IPMSM using adaptive teaming fuzzy neural network and estimation of speed using artificial neural network. The back propagation neural network technique is used to provide a real time adaptive estimation of the motor speed. The proposed control algorithm is applied to IPMSM drive system controlled adaptive teaming fuzzy neural network and artificial neural network, the operating characteristics controlled by maximum torque control are examined in detail. Also, this paper proposes the analysis results to verify the effectiveness of the adaptive teaming fuzzy neural network and artificial neural network.

• 한국정보통신학회:학술대회논문집
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• 한국해양정보통신학회 2005년도 추계종합학술대회
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• pp.366-369
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• 2005

연속 영상을 이용하여 실시간으로 움직임 객체를 추출하고 추적하기 위해 배경분리(Background Subtraction) 기법을 주로 사용한다. 외부 환경에서는 조명의 변화, 나무의 흔들림과 같은 반복적인 움직임 그리고 급격히 움직이는 객체 등과 같이 고려해야할 많은 환경 변화 요인들이 존재한다. 이러한 외부 환경의 변화를 적응적으로 반영하여 배경을 분리할 수 있는 배경 모델로는 주로 가우시안 혼합 모델(GMM: Gaussian Mixture Model)이 적용되고 있으며, 실시간 성능 등을 개선시킨 적응적 가우시안 혼합 모델 등이 사용되고 있다. 본 논문은 개선된 적응적 가우시안 혼합 모델을 적용하고 고정된 학습률 ${\alpha}$(일반적으로 작은 값)을 사용함으로써 물체의 갑작스러운 움직임 등에 빠르게 적응하지 못하는 문제점을 해결하기 위해 가우시안 분포 수의 적응적 조절 기능과 픽셀 값을 분산을 이용하여 학습률 ${\alpha}$값을 동적으로 제어하는 방법을 제안하고 성능을 평가하였다.

• 정보처리학회논문지:컴퓨터 및 통신 시스템
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• 제11권4호
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• pp.105-112
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• 2022

최근 환경의 변화에 적응적이고 특정 목적에 부합하는 오토스케일링 정책을 만들기 위해 강화학습 기반 오토스케일링을 사용하는 연구가 많이 이루어지고 있다. 하지만 실제 환경에서 강화학습 기반 수평적 파드 오토스케일러(HPA, Horizontal Pod Autoscaler)의 정책을 학습하기 위해서는 많은 비용과 시간이 요구되며, 서비스를 배포할 때마다 실제 환경에서 강화학습 기반 HPA 정책을 처음부터 다시 학습하는 것은 실용적이지 않다. 본 논문에서는 쿠버네티스에서 강화학습 기반 HPA를 구현하고, 강화학습 기반 HPA 정책에 대한 학습을 가속화하기 위해 대기행렬 모델 기반 시뮬레이션을 활용한 전이 학습 기법을 제안한다. 시뮬레이션을 활용한 사전 학습을 수행함으로써 실제 환경에서 시간과 자원을 소모하며 학습을 수행하지 않아도 시뮬레이션 경험을 통해 정책 학습이 이루어질 수 있도록 하였고, 전이 학습 기법을 사용함으로써 전이 학습 기법을 사용하지 않았을 때보다 약 42.6%의 비용을 절감할 수 있었다.

• 한국멀티미디어학회논문지
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• 제21권10호
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• pp.1182-1194
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• 2018

The Dynamic Adaptive Streaming over HTTP(DASH) is envisioned to evolve to meet an increasing demand on providing seamless video streaming services in the near future. The DASH performance heavily depends on the client's adaptive quality selection algorithm that is not included in the standard. The existing conventional algorithms are basically based on a procedural algorithm that is not easy to capture and reflect all variations of dynamic network and traffic conditions in a variety of network environments. To solve this problem, this paper proposes a novel quality selection mechanism based on the Deep Q-Network(DQN) model, the DQN-based DASH Adaptive Bitrate(ABR) mechanism. The proposed mechanism adopts a new reward calculation method based on five major performance metrics to reflect the current conditions of networks and devices in real time. In addition, the size of the consecutive video segment to be downloaded is also considered as a major learning metric to reflect a variety of video encodings. Experimental results show that the proposed mechanism quickly selects a suitable video quality even in high error rate environments, significantly reducing frequency of quality changes compared to the existing algorithm and simultaneously improving average video quality during video playback.

• 한국컴퓨터정보학회논문지
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• 제10권6호
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• pp.187-194
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• 2005

연속 영상을 이용하여 실시간으로 움직임 객체를 추출하고 추적하기 위해 배경분리(Background Subtraction) 기법을 주로 사용한다. 외부 환경에서는 조명 조건의 변화, 나무의 흔들림과 같은 반복적인 움직임 그리고 급격히 움직이는 객체 등과 같이 고려해야할 많은 환경 변화 요인들이 존재한다. 이러한 외부 환경의 변화를 적응적으로 반영하여 배경을 분리할 수 있는 배경 모델로는 주로 가우시안 혼합 모델 (GMM: Gaussian Mixture Model)이 적용되고 있으며, 실시간 성능 등을 개선시킨 적응적 가우시안 혼합 모델 등이 제안되어 사용되고 있다. 본 논문은 개선된 적응적 가우시안 혼합 모델을 적용하고 고정된 학습률 a(일반적으로 작은 값)을 사용함으로써 물체의 갑작스러운 움직임 등에 빠르게 적응하지 못하는 문제점을 해결하기 위해 가우시안 분포 수의 적응적 조절 기능과 픽셀 값의 분산 등을 이용하여 학습률 a값을 동적으로 제어하는 방법을 제안하고 성능을 평가하였다.

• 한국정보처리학회논문지
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• 제5권10호
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• pp.2609-2614
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• 1998

본 연구에서는 신경망 모형을 이용한 시계열 예측에 있어서 분석할 시계열의 특성에 맞는 적응적 학습률을 구하고 초기 값의 동적인 적용을 통한 개선된 학습방법을 이용하여 신경망 예측을 하고 통계적인 Box-Jenkins예측 결과와 비교해 봄으로써 두 방법간의 시계열 예측 효율성을 비교한다. 신경망 모형에 맞는 적응적 학습률은 표준 직교 배열표에 의해 실험계획을 한 25가지의 모수 조합으로부터 구하고, 신경망 학습의 초기값은 Easton의 제어상자를 동적으로 적용하여 실시간으로 선택할 수 있도록한다. 실증분석에 적용된 시계열자료는 1700년부터 1988년까지의 태양 흑점 자료이다.