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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Deblurring camera-based document image is an important task in digital document processing, since it can improve both the accuracy of optical character recognition systems and the visual quality of document images. Tr...
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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.
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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The long term geo-electric potential (LTGP) signals measured in a variety of experiment contexts have shown strong indications on their precursor nature to major earthquake events. Measurements of LTGP signals in West...
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ISBN:
(纸本)9780889868656
The long term geo-electric potential (LTGP) signals measured in a variety of experiment contexts have shown strong indications on their precursor nature to major earthquake events. Measurements of LTGP signals in Western Greece have been collected for more than twenty years. The measured signals are processed in several ways to extract features with a direct correlation to major earthquake events. In the field of spectral analysis, new methods that do not imply linearity and stationarity of the analysed signal have been introduced. Such a novel promising method is the Teager Huang Transform (THT) that combines Empirical Mode Decomposition (EMD) of a signal to a number of components on which the application of Teager Kaiser Operator (TKO) can lead to a sufficient estimation of the signal's spectral content. In this paper, we apply the TKO, EMD and THT on LTGP signals, aiming to have an accurate view of LTGP signals content precursor to major earthquake events.
Due to the dense and self-deployment of home eNodeBs (HeNBs) in femtocells, serious inter-cell interference may arise without good coordination. To deal with it, a distributed interference coordination scheme for femt...
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This paper introduces a model that agents use an information updating rule combining non-Bayesian learning and Bayesian learning in a social network. signals from some distinguishing individuals aggregate through the ...
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Accurate registration of 3-D point clouds is a common problem in computer vision. This paper presents a new two-stage algorithm for point clouds registration. A novel local invariant feature which is a k-dimensional v...
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ISBN:
(纸本)9781612847719
Accurate registration of 3-D point clouds is a common problem in computer vision. This paper presents a new two-stage algorithm for point clouds registration. A novel local invariant feature which is a k-dimensional vector is proposed and used in our coarse registration stage. Two new structural constraints combined with the Iterative Closest Point (ICP) algorithm are adopted in our fine registration stage. The accuracy and effectiveness of our algorithm is visually and quantitatively demonstrated by the comparative experiments on synthetic and real 3-D data.
This paper investigates an social learning model with time-varying weights, in which the individual updates her belief through observing private signal caused by social event and communicating with those regarded as n...
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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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