81 results about "Dimensional reduction" patented technology

A method of identifying and localizing objects belonging to one of three or more classes, includes deriving vectors, each being mapped to one of the objects, where each of the vectors is an element of an N-dimensional space. The method includes training an ensemble of binary classifiers with a CISS technique, using an ECOC technique. For each object corresponding to a class, the method includes calculating a probability that the associated vector belongs to a particular class, using an ECOC probability estimation technique. In another embodiment, increased detection accuracy is achieved by using images obtained with different contrast methods. A nonlinear dimensional reduction technique, Kernel PCA, was employed to extract features from the multi-contrast composite image. The Kernel PCA preprocessing shows improvements over traditional linear PCA preprocessing possibly due to its ability to capture high-order, nonlinear correlations in the high dimensional image space.

A method of identifying and localizing objects belonging to one of three or more classes, includes deriving vectors, each being mapped to one of the objects, where each of the vectors is an element of an N-dimensional space. The method includes training an ensemble of binary classifiers with a CISS technique, using an ECOC technique. For each object corresponding to a class, the method includes calculating a probability that the associated vector belongs to a particular class, using an ECOC probability estimation technique. In another embodiment, increased detection accuracy is achieved by using images obtained with different contrast methods. A nonlinear dimensional reduction technique, Kernel PCA, was employed to extract features from the multi-contrast composite image. The Kernel PCA preprocessing shows improvements over traditional linear PCA preprocessing possibly due to its ability to capture high-order, nonlinear correlations in the high dimensional image space.

The present invention discloses an object image detection method, which uses a coarse-to-fine strategy to detect objects. The method of the present invention comprises steps: acquiring an image and pre-processing the image to achieve dimensional reduction and information fusion; using a trained filter to screen features; and sequentially using a coarse-level MLP verifier and a fine-level MLP verifier to perform a neural network image detection to determine whether the features of the image match the features of the image of a target object. The present invention simultaneously uses three mainstream image detection methods, including the statistic method, neural network method and adaboost method, to perform image detection. Therefore, the present invention has the advantages of the rapidity of the adaboost method and the accuracy of the neural network method at the same time.

A medical ventilator capable of early detecting and recognizing types of pneumonia, a gas recognition chip, and a method for recognizing gas thereof are disclosed. The gas recognition chip of the medical ventilator comprises a sensor array, a sensor interface circuit, a stochastic neural network chip, a memory and a microcontroller. The sensor array receives a plurality of multiple types of gases to produce odor signals corresponding to each type of gas. The sensor interface circuit analyzes the odor signals to produce gas pattern signals corresponding to each type of gas. The stochastic neural network chip amplifies the differences between the gas pattern signals and performs dimensional reduction on the gas pattern signals to aid the analysis. The memory stores training data. The microcontroller performs a mixed gas recognizing algorithm to early detect and recognize the type of the pneumonia according to the gas training data.

A method and system for simulating and analyzing the behavior of a structural component of a computerized model in response to a simulated event to determine an optimized shape for the component is disclosed. The shape is optimized using an implicit dimensional reduction rather than an explicit geometric replacement by discarding data of a 3D discretization that has little or no bearing on the performance of the component to a simulated event. The reduced dataset is then collapsed onto a lower dimension projection that is applied over a force vector that is representative of the simulated event to determine the behavior of the component to the simulated event. Optimization tools may then be used to modify the physical attributes of the component and performance of the component once again simulated until an optimized component is determined.

The invention discloses a nonnegative local coordinate factorization-based clustering method, which comprises the following steps that: (1) a sample characteristic matrix is built; (2) a low-dimensional sparse matrix is iteratively outputted; (3) and the low-dimensional sparse matrix is clustered and analyzed. A sparse coding concept is introduced into the nonnegative matrix factorization (NMF) process, nonnegative local coordinate factorization is undertaken on a high-dimensional sample characteristic matrix, so the factorized coefficient matrix is used as a low-dimensional expression of the high-dimensional sample characteristic matrix, and the low-dimensional matrix is clustered to analyze, so the clustering analysis is simple and valid; and at the same time, data after the dimensional reduction has good explanatory property. Compared with the dimensional reduction method of the prior art, the judgment capacity of the clustering analysis can be further improved.

The present invention relates to a device and a method for extracting information from remotely detected characteristic signals. A data stream (24) derivable from electromagnetic radiation (14) emitted or reflected by an object (12) is received. The data stream (24) comprises a sequence of frames (66, 70, 92, 94; 108, 110, 112, 122, 124; 164, 66, 168), at least some of the frames comprise a frame section (68, 72; 174, 180, 186) 5 representative of a region of interest (56) attributable to the object (12). The region of interest (56) exhibits a continuous or discrete characteristic signal (136; 192) including physiological information (200) indicative of at least one at least partially periodic vital signal (20; 208). The sequence of frames (66, 70, 92, 94; 108, 110, 112, 122, 124; 164, 166, 168) further comprises a disturbing signal portion at least partially indicative of undesired object motion. 10 The characteristic signal (136; 192) can be stabilized by deriving a derivative signal form (78, 88, 98, 100, 102; 172, 176) from at least some frames of said sequence of frames (66, 70, 92, 94; 108, 110, 112, 122, 124; 164, 166, 168) through a dimensional reduction. A positional shift (74, 178) of a present derivative signal form (88, 98, 100, 102; 176) relative to a previous derivative signal form (78, 172) can be estimated. A present frame section (72; 180, 186) can be determined under consideration of the estimated positional shift (74, 178). Hence, the region of interest (56) can be tracked for at least partially compensating undesired object motion. Consequently, the disturbing signal portion can be at least partially compensated. The characteristic signal (136; 192) can be extracted from the sequence of frames (66, 70, 92, 94; 108, 110, 112, 122, 124; 164, 166, 168) under consideration of a sequence of determined frame sections (68, 72; 174, 180, 186).

A radiopaque marker configured for being securing to a tubular member adapted for introduction into a patient and having relief sections to provide localized accommodation of diametrical-circumferential dimensional reduction.

In the present work, quantum clustering is extended to provide a dynamical approach for data clustering using a time-dependent Schrödinger equation. To expedite computations, we can approximate the time-dependent Hamiltonian formalism by a truncated calculation within a set of Gaussian wave-functions (coherent states) centered around the original points. This allows for analytic evaluation of the time evolution of all such states, opening up the possibility of exploration of relationships among data points through observation of varying dynamical-distances among points and convergence of points into clusters. This formalism may be further supplemented by preprocessing, such as dimensional reduction through singular value decomposition and / or feature filtering. Additionally, the parameters of the analysis can be modified in order to improve the efficiency of the dynamic quantum clustering processes.

The invention relates to a method for mining data of an electronic nose based on supervised explicit manifold learning algorithm. The method for mining the data of the electronic nose through the explicit manifold learning algorithm comprises the following steps of collection of gas samples, characteristic extraction of the gas samples, determination of near neighbor of each point in a characteristic value matrix, relation calculation of any two characteristic value points and data dimension reduction of the explicit manifold algorithm. The data mining method of the electronic nose with the supervised explicit manifold learning algorithm comprises all above steps and is additionally provided with one step after the characteristic extraction of the gas sample: considering the type information, and determining the near neighbor of each point in the characteristic value matrix. The method has beneficial effects that the explicit manifold learning algorithm is used for reducing the dimension of the electronic nose data, and an explicit dimensional-reduction expression is provided; and the supervised manifold learning algorithm is used for reducing the dimension of the electronic nose data, the relation difference of each point of difference sources in the characteristic value matrix is considered, and the reservation of the detail information guarantees high resolution of an electronic nose system.