12 results about "Transfer function" patented technology

A multi-user digital laser imaging system for imaging film from bar coded cartridges as a function of image values representative of the images to be printed. The imaging system includes memory for storing transfer functions and film models for each of several users. The transfer functions characterize the relationships between image values and expected imaged film transmittance quantities. The film models characterize the relationship between the transmittance quantities and laser drive values that will provide the appropriate film exposure. An image management subsystem generates lookup tables characterizing the relationship between image values and laser drive values on the basis of user-selected transfer functions and film models selected by the system through the use of film identification information read from the bar code. During imaging operations the image management subsystem accesses the lookup tables as a function of the image values to obtain the appropriate laser drive values. The transfer functions can be modified to accommodate user preferences. Test wedge calibration procedures are periodically implemented to correlate the film models to measured sensitometric characteristics. Density patch calibration procedures are performed during each imaging operation to correct for drifts caused by the film developing subsystem.

The invention discloses a spectral noise reduction method based on digital filtering. The spectral noise reduction method comprises the following steps of: 1, selecting a spectral signal S to be denoised, 2, designing a frequency-amplitude response function PF of the spectral signal S to be denoised, 3, converting the frequency-amplitude response function PF to obtain a corresponding filtering convolution transfer function H, 4, calculating the convolution Y of the spectral signal S to be denoised and the convolution transfer function H, 5, intercepting data with the same length as the S fromthe beginning of the Y, i.e., a result after noise reduction of the S, and 6, selecting parameters Wr and Wp with optimal filtering effects to obtain a corresponding optimal filtering convolution transfer function H, and then obtaining an optimal noise reduction result. Data points obtained through the method are more compact and uniform, an end point is not cut off, and the length of a data sequence can be selected according to the precision requirement; and polynomial order limitation does not exist, the Runge phenomenon occurring under a high-order condition does not exist, and the result is more precise.

The invention discloses a fault detection system and method for eliminating periodic disturbance based on complex coefficient gain, and relates to the technical field of fault detection. By optimizing the disturbance transfer function matrix and configuring the corresponding complex coefficient gain vector at the disturbance frequency point, the output signal of the detected system is filtered twice, so that the output response of the disturbance signal of the specific frequency spectrum in the residual signal is remarkably reduced, and the detection effect on small faults is greatly improved; compared with a traditional fault detection method based on an observer, tiny faults can be found and detected earlier, equipment can be stopped in time, and corresponding monitored equipment can be protected.

In a method and a media system of/for generation of at least one output signal (H_PL,H_PR) from at least one input signal belonging to a second set of sound signals (M) having a related second set of Head Related Transfer Functions, in which the media system can be a TV, a CD player, a DVD player, a Radio, a display, an amplifier, a headphone or a VCR, the method includes the steps of determining, for each signal in the second set of sound signals, a weighted relation (14) including at least one signal belonging to a third set of intermediate sound signals (CHI1, CHI2) and at least one weight value (Weights); determining a first set of Head Related Transfer Functions (HRTFs) based on the second set of sound signals, the second set of Head Related Transfer Functions and the weighted relation; and transferring at least one signal belonging to the third set of intermediate sound signals by means of at least one HRTF belonging to said first set of Head Related Transfer Functions in order to generate at least one output signal belonging to said first set of sound signals. Hereby, in the end, fewer HRTFs are determined for a subsequent transfer of input signal(s) to output signal(s). Accordingly, few convolutions are required.

The invention belongs to the field of model prediction control monitoring, and particularly discloses a process model mismatch detection method of a closed-loop model predictive control system. The method comprises the following steps: calculating a system residual value and an interference updating sequence of the control system based on input data and output data of the closed-loop model predictive control system and each transfer function in the control system; calculating a variance ratio of the system residual value to the interference updating sequence, taking the variance ratio as a quality index value of an actual process model in the control system, judging whether model mismatch exists or not based on the index value, and if the model mismatch exists, identifying all subspace matrixes of the control system by adopting a subspace identification method; and performing singular value decomposition on the subspace matrix corresponding to the state variable, calculating the ratioof the maximum singular value to the minimum singular value obtained by decomposition, and judging whether the actual process model is mismatched or not based on the ratio. According to the method, the actual mismatch model can be effectively detected and distinguished from various factors influencing the control performance only by utilizing the input and output data, and the method is efficientand reliable.

The invention discloses an iterative filtering identification method of a linear motor transfer function, belonging to the technical field of linear motor control. The identification method in the prior art is not flexible enough and has poor adaptability, which affects the accuracy of identification parameters. An iterative learning filter is integrated into a batch-processing least squares identification algorithm in the method. A corresponding filter can be generated according to the characteristics of a system to be identified, and there is no need to design a special filter. As the filtering algorithm is added, identified data is more accurate, and the design of a high-precision controller is facilitated. The method is undoubtedly of practical significance and practical value to the design of a parameter identification and control system. The method has strong anti-jamming ability and high parameter identification precision, can help to realize high-speed and high-precision control on a system, and avoids the problem concerning filter selection and the poor anti-jamming ability of the least squares algorithm.