42 results about "Nonlinear feature extraction" patented technology

For an invasion detection model construction problem, the present invention provides a new network invasion abnormity detection method of supervising the non-linear feature extraction and a regularization random weight neural network (RRWNN). A kernel partial least square (KPLS) algorithm is used to process the colinearity of the input features and the complicated nonlinear mapping. The extracted potential features are inputted to an RRWNN algorithm to construct an invasion detection model possessing a higher learning speed and a better generalization performance. A global optimization strategy is adopted to select the modeling parameters of a KPLS-RRWNN-based invasion detection model, and the simulation based on the KDD99 data indicates the validity of the method.

The invention discloses a complexity based pressure center nonlinear feature extraction method. Conventional COP (center of pressure) feature parameters cannot effectively describe nonlinear features of shaking of human bodies. The method is used for extracting COP nonlinear dynamical features by utilizing an established model based on neighborhood coarse graining two-dimensional Lempel-Ziv complexity and specifically includes step1, acquiring COP signals; step 2, performing sequence reconstructing based on neighborhood coarse graining; and step 3, calculating normalized Lempel-Ziv complexity of the COP signals based on a reconstructed sequence. The complexity based pressure center nonlinear feature extraction method effectively solves the problem about how to apply the Lempel-Ziv complexity to process the two-dimensional COP signals to extract nonlinear features of the COP signals, so that quantitative description can be performed to irregular degree of posture shaking of the human bodies.

The invention relates to a code detection method and device, computer equipment and a storage medium. The method comprises the steps of obtaining a to-be-detected target code; performing character string division on the target code to obtain a target sub-character string sequence; obtaining sub-character string vectors respectively corresponding to each target sub-character string in the target sub-character string sequence to form a vector sequence; performing linear feature extraction on the vector sequence to obtain a first extraction feature, and performing nonlinear feature extraction on the vector sequence to obtain a second extraction feature; performing fusion processing on the first extraction feature and the second extraction feature to obtain a fusion feature; and performing code detection based on the fusion feature to obtain a code detection result corresponding to the target code. The cloud server can utilize a malicious code detection model based on artificial intelligence to realize the code detection method, and the purpose of reducing network attacks is realized. By adopting the method, the malicious code detection accuracy can be improved.

The invention discloses a tribological state online identification method based on friction signal recursive characteristics. The method comprises the following steps that: friction vibration signalsin a friction and wear process are collected through an acceleration sensor installed on the side edge of a clamp on a sliding friction and wear testing machine and are expressed as X=[x (1), x (2),..., x (t),..., x (n)]; nonlinear feature extraction is carried out ton the obtained friction vibration signals and a quantitative recursion parameters of the signals are extracted; feature extraction is carried out on the extracted quantitative recursive parameters, and a feature parameter set which has been subjected to dimension reduction and obtained by processing is adopted as a characterization quantity of the recursive characteristic of the friction signals; a system-dependent polynomial fitting equation of the characteristic parameter set relative to friction control parameters is established, and the polynomial fitting equation is made to predict the recursive characteristic parameters of the friction vibration signals under different control parameter conditions and by means of nonlinear mapping and mathematical modeling capability of self-organized data mining; and the frictional wear state of the actually measured friction signals is accurately identified. According to the method of the invention, the tribological state of a sliding friction pair can be effectively monitored and identified.

The present invention relates to a manifold learning network and a method for classifying computer vision image sets based on it. The manifold learning network includes an input layer, a fully connected layer, a correction layer, a Riemann pooling layer, a second logarithmic mapping layer and an output layer , which is mainly used to classify the collected video sequences, by building a simple learning network on the Riemannian manifold, and using bidirectional two-dimensional principal component analysis technology to optimize the weight parameters to complete the nonlinear feature extraction and Classification. Compared with the prior art, the present invention can effectively improve the object classification accuracy in complex scenes, and at the same time, the training time is shorter.

The invention discloses a method for diagnosing the minor faults of a CRH5 high-speed railway inverter. A multi-modal kernel principal component analysis method is provided. Data acquired by a sensor is preprocessed. A system model on a normal condition is obtained. Whether an electrical system has a fault is detected by processing online data according to the characteristic of the electrical system. Specific data is processed and analyzed. Acquired data has two dimensionalities. Two dimensionalities including period and modal are added to original data so that the original data is projected onto a four-dimensional space. Thus, the original nonlinear data is more accurately distributed in a high-dimensional space. The characteristic of each mode is extracted according to a nonlinear characteristic extraction method. Compared with a linear PCA algorithm and a nonlinear KPCA algorithm, the method is more precise in detection of the minor faults of a CRH5 high-speed railway inverter, more accurate in diagnosed results, lower in missing report rate, and more advantageous.

The invention discloses a method for extracting nonlinear features of physiological signals based on empty arrays, which is applied in the field of physiological signal processing. Aiming at the lack of parameter extraction and analysis of nonlinear features of physiological signals based on empty arrays in the prior art, the present invention uses The acquired physiological signal is symbolized by equivalent arrangement to obtain a first vector and its first arrangement type; then find a symmetrical arrangement type of the first arrangement type, and classify the first arrangement type and its symmetrical arrangement type into a first combination; Then find the symmetric vector of the first vector, and obtain the permutation type of the symmetric vector, record it as the second permutation type, classify the first permutation type and the second permutation type into the second combination; then according to the first combination and the second Combining to build a joint permutation group, and counting the feature quantities in the joint permutation group; finally extracting the nonlinear characteristic parameters of the physiological signal according to the feature quantity; the method of the present invention can correctly and effectively characterize the physiological signals under different physiological states based on the empty permutation.

The invention relates to a soft sensing method for load parameters of a ball mill. The method is that a hardware supporting platform is used to obtain vibration signals, vibration sound signals and current signals of a ball mill cylinder to soft sense ball mill internal parameters (ratio of material to ball, pulp density and filling ratio) characterizing ball mill load. The method comprises the following steps that: the vibration, the vibration sound, the current data and the time-domain filtering of the ball mill cylinder are acquired, time frequency conversion is conducted to the vibration and the vibration sound data, kernel principal component analysis based nonlinear features of the sub band of the vibration and the vibration sound data in frequency domain are extracted, nonlinear features of the time domain current data are extracted, feature selection is conducted to the fused nonlinear feature data and a soft sensing model based on a least squares support vector machine is established. The soft sensing method of the invention has the advantages that the sensitivity is high, the sensed results are accurate, the practical value and the popularization prospect are very good, and the realization of the stability control, the optimization control, the energy saving and the consumption reduction of the grinding production process is facilitated.

The invention provides a feature extraction method for signals of an electronic tongue based on manifold learning. The method comprises the following steps: detecting tea samples with the electronic tongue so as to obtain sensor-responsive sequence signals; analyzing and rejecting abnormal samples by using a principal component residual method and a Mahalanobis distance method; optimizing the parameters of a manifold learning algorithm and selecting the parameters of the manifold learning algorithm on the basis of the correct recognition rata of the quality grade of Longjing tea; carrying out non-linear feature extraction on sensor-responsive signals by using the manifold learning algorithm so as to obtain characteristics characterizing the taste information of the tea samples; and inputting the taste characteristics of the tea samples into a classifier and determining the quality grade of the Longjing tea. The feature extraction method can reject the abnormal values of the tea samples; and the parameter-optimized manifold learning algorithm can better characterize the non-linear characteristics of tea samples of different grades and improve signal difference of samples having undergone nonlinear mapping in high-dimensional feature space.