Sr2Al6O11:Eu2+,Dy3+ is known as a high efficient material for generating persistent luminescence. Due to its low structural stability, it is a challenge to prepare such orthorhombic material in large scale. In this wo...
Sr2Al6O11:Eu2+,Dy3+ is known as a high efficient material for generating persistent luminescence. Due to its low structural stability, it is a challenge to prepare such orthorhombic material in large scale. In this work, a facile and effective strategy was designed for the preparation of Sr2Al6O11:Eu2+,Dy3+ with high purity by combining the advantages of solid state reaction and chemical vapor deposition method. The prepared Sr2Al6O11:Eu2+,Dy3+ could effectively store the UV light energy and emit blue-green luminescence for 240 min by slow liberation of photo-excited electrons. Its blue-green afterglow was composed of two luminescent emissions which released from the Eu centers located in different crystal fields.
Cutting force coefficients are the key factors for efficient and accurate prediction of milling force. This paper presents a new method to calibrate the cutting force coefficients using the surface errors related to m...
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Cutting force coefficients are the key factors for efficient and accurate prediction of milling force. This paper presents a new method to calibrate the cutting force coefficients using the surface errors related to milling of thin-walled workpiece including effect of cutter runout. The surface error is separated into nominal surface error and perturbation component due to runout. By analyzing forming of surface error, cutter deformation and deflection of thin-walled workpiece, the result that cutter runout has no effect on the average surface error is achieved. Relationship between nominal surface error and cutting force coefficients is constructed, and also an approach for extraction of nominal surface error from measured surface error is proposed. Then, the cutting force coefficients are estimated conveniently. Milling tests are carried out to verify the proposed method. A good agreement between predicted results and experimental results is achieved, which shows that the method is efficient.
This paper is concerned with the identification problems of linear parameter varying (LPV) systems with randomly missing output data. Since one local linearized model cannot capture the global dynamics of the nonlinea...
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This paper is concerned with the identification problems of linear parameter varying (LPV) systems with randomly missing output data. Since one local linearized model cannot capture the global dynamics of the nonlinear industrial process, the multiple-model LPV model in which the global model is constructed by smoothly weighted combination of multiple local models is considered here. The problem of missing output variables data is commonly encountered in practice. In order to handle the multiple-model identification problems of LPV systems with incomplete data, the local model is taken to have a finite impulse response (FIR) model structure and the generalized expectation-maximization (EM) algorithm is adopted to estimate the unknown parameters of the global LPV model. To avoid the problems of ill-conditioned matrices and high sensitivity of parameters to noise, the prior information on the coefficients of each local FIR model is employed to construct the prior probability of unknown parameters. Then the maximum a posteriori (MAP) estimates of the global model parameters are derived via the generalized EM algorithm. The numerical example is presented to demonstrate the effectiveness of the proposed method.
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