32results about How to "Avoid overestimation" patented technology

Compound solubility and permeability are coordinately determined. High throughput solubility assays are set up, and the filtrate from the solubility assay is used as an input for PAMPA. In this way, the compound concentration during PAMPA is known exactly, and can be used as input to calculate Pe.

The invention relates to a computer image processing and visual detecting genecology. In order to provide a method which can exactly estimate the noise function of different CCD (charge coupled device) images and further can not increase the computing complexity, the technical scheme adopted by the invention is as follows: a signal-correlated noise estimating method for an image sensor comprises the following steps of: searching smooth blocks of an image by adopting a high pass operator-based image structure analyzer; sampling the blocks according to the arrangement mode of CFA (colorful filter array), to obtain a set which corresponds to each gray value, and computing the mean value and variance of the set, to obtain a set of noise estimating sample points; according the smoothness degree of the set of the image block of each sample point, computing the reliability of the sample point; and finally in combination with the sample points, the reliability of the sample points and the base obtained by using PCA (principal component analysis) in advance, rebuilding a noise horizontal curve by a sparse representation technology. The method is mainly used for processing the image.

The invention discloses a deep reinforcement learning algorithm based on time sequence difference, which is used for solving a hybrid flow shop scheduling problem of a related parallel machine, combines a convolutional neural network with TD learning in reinforcement learning, and performs behavior selection according to input state features; and therefore, the scheduling decision process of the actual order response type production and manufacturing system is better met. A scheduling problem is converted into a multi-stage decision problem, a convolutional neural network model is used to fit a state value function, manufacturing system processing state characteristic data is input into a model, a time sequence difference method is used to train the model, a heuristic algorithm or an allocation rule is used as a scheduling decision candidate behavior, and a reinforcement learning reward and punishment mechanism is combined, thus selecting an optimal combined behavior strategy for each scheduling decision. Compared with the prior art, the algorithm provided by the invention has the advantages of strong real-time performance, high flexibility and the like.

The invention discloses an image noise evaluating system and an evaluating method thereof, which are used for evaluating the noise of current images, wherein, a storage device stores the former image of the current images; a multi-windowed noise evaluating device carries out noise evaluation on areas of the current images and on the corresponding area of the former image; a noise evaluating index and a noise adjustment evaluating index which are corresponding to the windows are output; a comparing device is used for comparing multiple noise adjustment evaluating indexes, choosing the minimum value thereof and outputting a window index at the same time; the window index works as the window of the minimum noise adjustment evaluating index. When the minimum noise adjustment evaluating index is smaller than a critical value, a detecting device which can move in whole domain outputs the noise evaluating index which is corresponding to the minimum noise adjustment evaluating index as a noise evaluating value of the current image.

Methods and devices for determining an available load in a cell of a cellular radio system are provided where the determination of the available load is made in response to an estimated inter cell interference load. An estimated inter cell interference is determined. The inter cell interference change events are monitored by determining when a User Equipment changes serving cell or when the User Equipment adds or removes a cell to the active set of the User Equipment. The determined inter cell interference is adjusted by an estimated load caused by the User Equipment causing an inter cell interference change event, and the available load is determined in response to the adjusted inter cell interference. The method can continuously update inter cell interference. Moreover, the update can be made fast and thereby allow an improved estimation of the available cell load.

Disclosed is a method and an apparatus for measuring the growth of leaf disks. The method comprises the following steps: a) Calibrating the capture system, b) capturing at least 2 images of a leaf disk, c) processing the image data, comprising i) segmenting the leaf disks by threshold segmentation, ii) multiple morphological erosion steps, iii) edge cleaning to remove the edge of the storage vessel, d) 3D reconstruction: generating a disparity map by means of a stereo algorithm, calculating a 3D surface model based on the previously determined calibration parameters from the disparity map, e) smoothing the previously obtained surface model, f) determining the growth rates from the time series of the areas. The apparatus comprises at least one camera, a lighting unit, a translation table in the X/Y plane for the camera and (infrared) lighting, storage vessels for leaf disks, and electronic evaluation and control units.

The invention discloses a power distribution network sag frequency estimation method based on an optimized K-nearest neighbor method and process immunity, which is characterized in that an optimal K-nearest neighbor parameter is searched based on a ten-fold cross validation method, and accurate distribution characteristics can be obtained for a line with relatively complex fault distribution; the defects that a traditional method is insufficient in approximation capability, too complex in model, poor in anti-interference capability and the like are overcome, and therefore a more objective and real voltage sag frequency estimation result is obtained. Based on the protection cooperation condition of a feeder automation system of a power distribution network, the voltage sag mode suffered by a user is analyzed and summarized, and for the defect that the influence of an existing method on voltage sag + short-time voltage interruption and continuous voltage sag is not considered sufficiently, a user process parameter change equation before and after aggregation is established and solved, so that the user sag mode is obtained. According to the method, the problems of under-estimation and over-estimation of the sag in the traditional method are avoided, and meanwhile, the defect that short-time interruption is not brought into statistics in the prior art is overcome.

The invention discloses a flow industry flexibility adjustable robust monitoring method considering production uncertainty, which comprises the following steps: constructing a multi-energy coupling model considering production constraints to monitor the state change in the operation process of an energy system of the flow industry, and further monitoring the system flexibility; the flexibility monitoring index corresponding to the frequency modulation service demand is extracted to monitor the flexibility regulation and control capability of the energy system, and a basis is provided for monitoring the flexibility capability of the energy system of the process industry and providing the potential cost of the flexibility service by an operator; on the basis of a data driving method, constructing an uncertain set of uncertain data of the process industry energy supply system so as to reduce the conservative degree of robust optimization; an adjustable robust optimization framework is proposed to solve the proposed flexibility monitoring index, robust dual transformation is carried out for a robust optimization formula containing an uncertain set, a conservative factor is introduced to adjust the conservative degree of robust optimization, and finally a deterministic optimization problem which can be solved by a commercial solver is transformed.