This paper presents a novel method for detection and recognition of glass defects in low resolution images. First, the defect region is located by the method of Canny edge detection, and thus the smallest connected re...
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Detecting motion pattern in dynamic crowd scenes is a challenging problem in computer vision field. In this paper, we propose a novel approach to detect the motion patterns from global perspective. To extract the disc...
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Background reconstruction plays an important role in many applications like video surveillance, motion analysis. Traditional Adaptive Gaussian Mixture Model will lose target when deal with arbitrary-long stationary ob...
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In order to improve the function of soft sensor to conduct variable selection, fault detection and model structure identification in the case of faulty state, a design method of new soft sensor is studied though the v...
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In order to improve the function of soft sensor to conduct variable selection, fault detection and model structure identification in the case of faulty state, a design method of new soft sensor is studied though the variable selection algorithm. A non-stationary time serial is introduced to describe the process output not being reflected by sensor variables and to detect whether the process enters the faulty state. A non-negative garrote method is adopted to identify the model structure and a modeling method for new soft sensors is presented. The obtained model can be used for both prediction, and detection of structural model change and the emergence of disturbance. Compared with the ordinary soft sensor based on partial least square algorithm, the advantages of the proposed method are demonstrated by a simulation example and an industrial application to temperature prediction of a blast furnace hearth.
During the last two decades, performance assessment of controlsystems has been receiving wide attention. However, estimation of the benchmark performance of nonlinear controlsystems still remains open. In this work,...
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In MAS (Multi-Agent system), communicating among agents is an important characteristic as it is important in transmitting information among agents, recognizing the status changing, and scheduling and accomplishing coo...
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The problem of real-time rectangle detection on high-resolution image arises in actual panel production. This paper proposes a robust real-time method for panel rectangle detection. In line extraction part, a new tech...
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This paper we propose a multi-objective optimization model to deal with the capacity planning in semiconductor manufacturing system, which is a typical multi-objective problem. Unlike traditional optimization methods,...
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In order to operate unknown constrained mechanisms with assistive robot manipulators, a dynamic hybrid compliance control algorithm was proposed in the paper. The controller using the proposed algorithm was designed t...
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Abstract In this paper, the model predictive control strategy based on input and output data sets for partial differential equation (PDE) unknown spatially-distributed system (SDS) is proposed. The control aim is that...
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Abstract In this paper, the model predictive control strategy based on input and output data sets for partial differential equation (PDE) unknown spatially-distributed system (SDS) is proposed. The control aim is that the outputs of low-dimensional temporal model reach the set points. Thus, it makes the control design easily and reduces the computational burden. The low-dimensional model is obtained by principal component analysis (PCA) method, and the state of the low-dimensional model is estimated based on spatially-distributed output. The terminal constraints are used to transform the cost function along an infinite prediction horizon into finite prediction horizon. The simulations demonstrated show the accuracy and efficiency of the proposed method.
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