60 results about "Nonlinear feature extraction" patented technology

The invention provides an underground water environment automatic monitoring super station state supervision method and system. The method comprises the following steps: collecting a multi-source dynamic time sequence data set and carrying out time-space alignment; generating a dynamic characteristic index set by using a nonlinear dynamic characteristic extraction method; based on the index set, constructing a self-adaptive cooperative measurement network to perform anomaly monitoring, and generating an anomaly detection result and a cooperative control instruction; generating real-time monitoring and early warning information by using a dynamic threshold adjustment algorithm; and generating an adaptive control instruction and a dynamic resource allocation scheme by using a neural network adaptive control strategy and a resource scheduling optimization algorithm. According to the method, a dynamic characteristic index set is generated, a C-C method is adopted to reconstruct a high-dimensional phase space, a Wolf algorithm and a G-P algorithm are combined to calculate related indexes and dimensions, and a multi-scale fractal mode is analyzed through an R / S analysis method and wavelet transform. The methods capture complex dynamic behaviors of the underground water system, and solve the problem that the traditional method is insufficient in non-linear feature extraction capability.

The invention relates to a non-linear feature extraction-based roadside end radar and camera fused three-dimensional target detection method and device, and a medium. The three-dimensional target detection method comprises the steps of synchronously completing data enhancement and downsampling of an image and a point cloud; extracting high-dimensional nonlinear features by using an encoder improved by a Kolmogov-Arnod network, and projecting the high-dimensional nonlinear features to a unified aerial view space; establishing cross-modal dependence by using multi-head cross attention; carrying out weight fusion by using nonlinear convolution to generate an integrated aerial view feature map; and the decoder and the detection head output the three-dimensional coordinate, the size and the category of the target. Compared with the prior art, the Kolmogorov-Arnod network is introduced to perform nonlinear enhancement on the image and the point cloud encoder, the cross-modal weight is dynamically calculated through multi-head cross attention in the aerial view space, and finally, the weighted fusion is completed by using the KANs convolution. Therefore, the three-dimensional detection precision in a complex traffic scene is remarkably improved while the consistency of the receptive field is ensured.

The invention discloses a large model dynamic batch processing method based on sequence splicing. The method comprises the following steps: receiving input sequences of a plurality of users in a batch; converting the input sequence into a corresponding token sequence; carrying out heterogeneous splicing on all token sequences along a sequence length dimension to form a unified joint token; the spliced joint tokens pass through a normalization layer, standardization operation is executed on the joint tokens, and data distribution is unified; carrying out linear projection on the joint token through a shared linear transformation layer to generate a joint query vector, a joint key vector and a joint value vector, and splitting the joint query vector, the joint key vector and the joint value vector into sub-vector groups corresponding to each user; executing multi-head attention calculation to obtain attention output of the user; and carrying out linear transformation on the attention output, and inputting a transformed result into a shared MLP to carry out nonlinear feature extraction and enhancement so as to obtain a final output corresponding to each user. According to the method, the problems of efficiency bottleneck and resource consumption when a large model processes mass data are effectively solved.

The invention discloses an anomaly detection method and device based on the receiving power of a low-orbit satellite-borne GNSS receiver, and the method comprises the steps: obtaining a source receiving power sequence of the low-orbit satellite-borne GNSS receiver, and carrying out the enhancement processing based on Doppler frequency shift compensation and ionospheric noise filtering, and then generating a to-be-detected receiving power sequence; performing feature extraction on the to-be-detected receiving power sequence and then generating a receiving power feature vector based on a multi-scale entropy feature and a chaotic feature; and comparing the received power feature vector with a pre-constructed dynamic anomaly threshold to generate an anomaly detection result. According to the method, time sequence mismatching caused by frequency shift is overcome, energy distortion caused by ionosphere disturbance is also remarkably weakened, so that the accuracy and sensitivity of a subsequent nonlinear feature extraction process are guaranteed, and particularly, the robustness and effectiveness of GNSS receiving power anomaly detection can be remarkably improved in a complex dynamic space environment.

The invention provides a multi-scale context aggregation and dynamic supervision medical image segmentation method and application thereof, and belongs to the technical field of medical image processing. In order to solve the problems of weak non-linear feature fitting ability, global context missing and unstable training convergence in the prior art, the ResUKAN + network is constructed. According to the method, a residual KAN convolution module is embedded in a full level of an encoder, and nonlinear feature extraction is enhanced by using a B-spline function; a multi-scale context aggregation module is arranged on a bottleneck layer, and dynamic pyramid pooling and a double attention mechanism are fused to capture global dependency; a dynamic auxiliary supervision head is introduced at the tail end of a decoder, complementary features are extracted through a heterogeneous receptive field, and loss calculation is optimized in combination with a dynamic weight mechanism which is exponentially attenuated along with a training period. According to the method, the segmentation precision and robustness of the fuzzy boundary and the multi-scale focus are remarkably improved, and the method is suitable for medical image intelligent diagnosis.

The invention belongs to the technical field of data science and physical field simulation, and discloses an AUV structure stress real-time prediction method based on a convolution auto-encoder. The method comprises the following steps: carrying out nonlinear feature extraction on high-dimensional structure stress field data by adopting a trained convolution auto-encoder to obtain low-dimensional representation; establishing a mapping relation between an input working condition and the low-dimensional representation by adopting a deep learning neural network, and predicting a low-dimensional feature under a new working condition; and reconstructing the predicted low-dimensional features by using a decoder part of the convolutional auto-encoder to obtain high-dimensional structure stress field data. According to the method, the nonlinear characteristics of the flow field can be adaptively extracted, and the accurate mapping relation between the input working condition and the full-field stress response is established, so that high-precision and high-efficiency real-time prediction of the stress field of the AUV structure is realized, and the calculation efficiency and the model generalization ability are remarkably improved.

The application relates to a code detection method and device, computer equipment and a storage medium. The method comprises the following steps: obtaining a target code to be detected; performing string division on the target code to obtain a target substring sequence; obtaining a substring vector corresponding to each target substring in the target substring sequence to form a vector sequence; performing linear feature extraction on the vector sequence to obtain first extracted features, and performing nonlinear feature extraction on the vector sequence to obtain second extracted features; performing fusion processing on the first extracted features and the second extracted features to obtain fusion features; and performing code detection based on the fusion features to obtain a code detection result corresponding to the target code. A cloud server can use an artificial intelligence-based malicious code detection model to implement the code detection method of the application, thereby achieving the purpose of reducing network attacks. The method can improve the accuracy of malicious code detection.

The invention relates to a data detection method for a distributed photovoltaic system and an energy storage system thereof, and relates to the technical field of photovoltaic energy storage data detection. According to the method, a high-precision distributed photovoltaic and energy storage system data detection model is constructed by combining the powerful nonlinear feature extraction and state reconstruction capability of the deep belief network with the precise setting of key parameters by a second-order oscillation firefly optimization algorithm. Therefore, the system can predict the key operation state of equipment or positions where sensor nodes are not directly installed according to the actually measured data of a limited number of intelligent fusion terminals in the area, so that the deployment number of physical sensors and communication modules is greatly reduced on the hardware level, and the equipment investment and the later maintenance cost are directly reduced.

The invention provides an electroencephalogram signal-based dysmnesia assessment system, which comprises a signal acquisition module for acquiring an electroencephalogram signal of a target object; the preprocessing module is used for performing 0.5-40Hz band-pass filtering on the electroencephalogram signal of the target object; the signal segmentation module is used for segmenting each channel and determining a plurality of non-overlapping Epoches corresponding to each channel; the feature extraction module is used for performing linear feature extraction and nonlinear feature extraction on each Epoch in each channel, and determining electroencephalogram signal fusion features of the target object based on the linear feature and the nonlinear feature of each Epoch; and the dysmnesia assessment module is used for inputting the electroencephalogram signal fusion features into a trained dysmnesia assessment model, and performing dysmnesia assessment on the target object by using the dysmnesia assessment model to obtain a dysmnesia assessment result of the target object. According to the scheme, dysmnesia evaluation of the target object can be efficiently realized.

The present application relates to a kind of electroencephalogram classification method based on physical information residual polynomial network, design to be trained network, first with polynomial feature extraction layer carries out nonlinear feature extraction, then through LSTM network capture long-term time series dependence of electroencephalogram, then realize the neural network architecture of E / I path separation, respectively to interface excitatory pathway and inhibitory pathway, then by the feature correlation analysis layer of embedding Wilson-Cowan neural population dynamics equation carries out physical constraint, finally in succession fusion layer, classification layer, classification layer completes the construction of to-be-trained network, then based on each sample formed by each multi-channel electroencephalogram, for to-be-trained network training, obtains electroencephalogram classification model, while guaranteeing electroencephalogram classification prediction accuracy, significantly enhance biological explainability.

The present application relates to a kind of lithium battery SOH and RUL collaborative prediction method based on nonlinear enhancement LSTM, belong to battery life prediction technical field, solve the problem of large amount of electrochemical data demand, single and insufficient feature extraction, waste of computing resources and deep, weak nonlinear feature extraction capability and low SOH and RUL prediction accuracy in prior art.The present application is based on data-driven, through multi-channel convolutional neural network multi-dimensional feature extraction, and based on nonlinear enhancement LSTM model carries out battery sequence data feature extraction, in-depth mining nonlinear feature of battery parameter, SOH evaluation result is used as the input of RUL prediction, using neural network method carries out RUL prediction, the prediction accuracy of SOH and RUL of lithium battery is high;Data feature is fully extracted, and the amount of electrochemical data demand is reduced;Using the same pre-training nonlinear enhancement LSTM model shares input, and the computing resource is greatly saved.

The invention relates to an electroencephalogram signal classification method based on a physical information residual polynomial network, and the method comprises the steps: designing a to-be-trained network, carrying out the nonlinear feature extraction through a polynomial feature extraction layer, capturing the long-term time sequence dependence relation of electroencephalogram signals through an LSTM network, and achieving the neural network architecture of E / I path separation. The method comprises the following steps: respectively connecting an excitability pathway and an inhibitory pathway, then carrying out physical constraint by a feature correlation analysis layer embedded with a Wilson-Cowan neural population kinetic equation, finally, sequentially connecting a fusion layer and a classification layer in series, and completing the construction of a to-be-trained network by the classification layer, and further constructing a to-be-trained network based on each sample formed by each multi-channel electroencephalogram signal. According to the method, the to-be-trained network is trained, the electroencephalogram signal classification model is obtained, and the biological interpretability is remarkably enhanced while the electroencephalogram signal classification prediction accuracy is guaranteed.

The present application relates to the technical field of medical monitoring, in particular to a vascular hemodynamic abnormality monitoring system and a pulse wave analysis method thereof, the core of the present application lies in that a flexible piezoelectric sensor array is used to capture human vascular pulse wave signals, the signals are transmitted to a wearable monitoring device for preliminary preprocessing, and then transmitted to a data processing host through a wireless manner, a pulse wave analysis module in the host adopts a multi-level analysis method, including multi-scale signal decomposition and reconstruction, nonlinear feature extraction and pattern recognition, multi-parameter adaptive judgment standard, deep learning feature automatic extraction and multi-site information fusion decision, ensuring the accuracy and comprehensiveness of the analysis, finally, the processing result is transmitted to an application platform to generate a vascular health report, realizing non-invasive continuous monitoring, this innovation effectively fills the gap of the prior art in continuous monitoring, and has important significance for real-time monitoring of vascular health status and prevention of vascular diseases.

The invention discloses a text generation method and device, equipment, a storage medium and a product. The method comprises the following steps: inputting a text vector of a text to be inquired into a text generation model, and carrying out encoding processing on the text vector by utilizing an encoder in the text generation model to obtain a first encoding vector and a second encoding vector; performing linear feature extraction on the first coding vector and the second coding vector to obtain a first fusion vector corresponding to the first coding vector and a second fusion vector corresponding to both the first coding vector and the second coding vector; performing nonlinear feature extraction on the first fusion vector and the first coding vector to obtain a first feature vector; performing cross fusion on the first feature vector and the second fusion vector to obtain a second feature vector; and decoding the second feature vector to obtain a reply text matched with the to-be-inquired text. According to the embodiment of the invention, the text generation accuracy can be improved when the text is generated.

The invention discloses a ship classification and identification method and device based on underwater acoustic uniform linear array data features, and relates to the field of underwater acoustic signal processing. A uniform linear array is constructed by constructing the relation between array structure parameters and phase-space reconstruction parameters in the nonlinear feature calculation process; according to the method, nonlinear feature extraction can be directly carried out on array data on underwater sound time-domain signals which are simultaneously collected by a uniform linear array and come from a plurality of channels, an additional phase space reconstruction step is not needed, and the method can be used for ship classification and identification. The method not only improves the calculation efficiency, but also can make full use of multi-channel information, improves the stability and robustness of feature extraction, enables the method to more accurately represent the dynamic characteristics of signals in a complex underwater environment, and can accurately carry out the classification and recognition of ship targets.

The application discloses a wind turbine generator unit variable flow system performance degradation identification method and related device, comprising: collecting the temperature of IGBT module in the variable flow system, the DC bus voltage, the output power harmonic distortion rate, and constructing a data set based on the same; performing data processing on the data set to obtain a processed data set; performing nonlinear feature extraction on the data set to obtain a dimension-reduced principal component score vector; constructing a hidden Markov model; and calculating the probability of the wind turbine generator unit variable flow system being in each degradation state based on the hidden Markov model and the dimension-reduced principal component score vector. The method and related device can realize performance degradation identification of the variable flow system, and have lower requirements on data quality.

The invention provides a milling chatter prediction method and system based on time distribution and mixed attention, and belongs to the field of milling state monitoring. The method comprises the following steps: collecting a multi-channel original time sequence signal in a milling process, and pre-processing the multi-channel original time sequence signal; inputting the preprocessed signal into a time sequence distribution convolution feature extraction module, and carrying out nonlinear feature extraction and dimension reduction to obtain a multi-channel time sequence feature sequence; inputting the multi-channel time sequence feature sequence into a global time sequence attention modeling unit, and outputting global dynamic evolution features; inputting the global dynamic evolution characteristics into a time dimension self-attention module and a channel attention module, and performing key time slice screening and multi-sensor channel importance re-calibration to obtain fusion characteristics; and inputting the fusion features into a classifier, and outputting the chatter probability in a future time window. According to the method, accurate capture and early warning of the chatter initiation precursor are realized, and an effective technical means is provided for active control of the stability of the machining process.

The invention provides a multi-radar cooperative target recognition method based on a graph neural network. The method comprises the following implementation steps: acquiring a training sample set and a test sample set comprising HRRP data and graph adjacency matrixes of each target; constructing a multi-radar cooperative target recognition network model and carrying out iterative training on the multi-radar cooperative target recognition network model; and obtaining a multi-radar cooperative target identification result. According to the invention, a graph convolution module carries out multiple times of graph convolution and nonlinear transformation on the node characteristics according to the node characteristics of each target, the characteristics of neighbor nodes of the node and the weight of the edge where the node and the neighbor nodes of the node are located in a graph adjacency matrix; according to the method, the data received by the radars from different angles can be subjected to dynamic collaborative complementary enhancement according to the relevance of multi-radar physical space information, and deep nonlinear feature extraction can also be carried out to obtain features with higher discrimination capability, so that the accuracy of target recognition in a complex scene is effectively improved.

The application discloses a kind of coupling component and heat treatment process driven titanium alloy microstructure pixel space diffusion generation method, to solve the problem that microstructure image is difficult to accurately, efficiently generated under the condition of present specific composition and heat treatment process. By constructing image acquisition module, pre-processing module and classification module to establish high robustness dataset;Based on the conditional encoding module of triangular / linear fusion encoding and multi-label embedding, realize the high-precision nonlinear feature extraction of process parameters;Based on the neural network U-net module of multi-condition fusion, realize the accurate semantic controllable fusion of condition information;Based on the prediction module, solve the problem of unstable model convergence and weak generalization;Based on the DDIM module using implicit sampling, overcome the technical bottleneck of slow generation speed, realize high-fidelity image generation. Finally, it is verified that the method can quickly realize the credible conversion of pixel-level high-fidelity titanium alloy microstructure image according to specific conditions.

The application provides a full-link cooperative upper limb rehabilitation personalized training method, belongs to the technical field of upper limb rehabilitation training, and is used for solving the problems of training scheme solidification, insufficient individualization and precision, and lack of full-link cooperative closed loop in related technologies. The method integrates user basic attributes, historical training, clinical reference and real-time sensing data, generates an individualized basic scheme through nonlinear feature extraction and rehabilitation stage determination, dynamically adjusts training parameters through a lightweight prediction model, generates a doctor-patient dual version report combined with an individualized evaluation algorithm, and optimizes full-link parameters through multi-directional feedback closed loop. The method realizes full-link cooperative adaptation, improves the precision, safety and individualization of rehabilitation training, and adapts to the needs of patients at different stages.

The invention provides a laser array adaptive control method and system based on artificial intelligence, and relates to the technical field of laser control, and the method comprises the steps: obtaining real-time and target light field distribution, constructing a coupling interference topological graph through nonlinear feature extraction, and achieving the decoupling of a contribution component and an interference component; a control strategy containing global coupling constraint is generated in combination with a graph neural network and a dual decomposition mechanism, the control strategy is decomposed into a local control target and coupling consistency constraint, and iterative optimization is performed to generate a modulation control instruction, so that the array light field control precision is improved, and the adaptability of a system to environmental disturbance is enhanced.

The invention belongs to the technical field of power equipment, and provides a power equipment demand prediction method and system, and the method comprises the steps: constructing a multi-dimensional phase space based on a generalized embedding theorem according to the related data of a power industry, a total production value, a resident consumption price index, and the values of the industry historical purchase amount of equipment at different time points; based on a multi-dimensional phase space, establishing a topological conjugate relationship between the initial attractor and the delay attractor through delay coordinate mapping, and performing attractor reconstruction of a multivariable time sequence; extracting seasonal and long-term tendency information in the time sequence by using nonlinear features of neural network learning mapping; carrying out spatio-temporal information aggregation and embedded dimension adjustment, and determining a delay attractor so as to obtain equipment demand prediction quantity; a plurality of influence factors such as power industry related data, a total production value, a resident consumption price index and the industry historical purchase quantity of equipment are comprehensively considered, the dynamic change characteristics of a market demand system are more effectively revealed, and the prediction accuracy is remarkably improved.

The application discloses a kind of based on skin electric signal's flight student psychological stress data acquisition method, it is related to machine learning technical field.The technical points of the present application include: collecting the skin electric signal of multiple flight students when flight simulation training, as physiological signal data set;The motion artifact removal is carried out to the skin electric signal collected;The skin electric signal after removing artifact is carried out frequency domain, time domain and nonlinear feature extraction, forms skin electric signal feature set;According to skin electric signal feature set, train psychological stress data acquisition model based on parallel neural network;The skin electric signal of new flight student is input in the psychological stress data acquisition model trained when flight simulation training, and the psychological stress data of new flight student is acquired.The present application can accurately express the fluctuation of psychological state of flight student when carrying out simulation flight training, help flight student summarizes flight experience, and provides help for subsequent flight training of flight student.

The invention discloses an inspection scheduling method and system based on MEMS piezoelectric intelligent sensing and reinforcement learning, and the method comprises the following steps: carrying out the active ultrasonic monitoring through an MEMS piezoelectric intelligent sensing element, and carrying out the nonlinear feature extraction and preprocessing of a collected piezoelectric signal; risk weights of the road sections are calculated based on multi-source data fusion, and high-risk road sections are identified; finely dividing the high-risk road sections, and determining key monitoring areas; establishing a probability distribution model of a transverse wheel track band on the high-risk road section; the probability distribution model is used for guiding the piezoelectric sensors to be optimally arranged at candidate positions of the cross section two-dimensional space; a multi-level sensing and data processing system is constructed to realize data acquisition, transmission, processing and decision making; and based on a reinforcement learning algorithm, generating an inspection scheduling scheme in combination with risk grading and time window constraint. According to the invention, early warning of early damage of the road structure in the high-risk area and optimal configuration of resources are realized, the inspection efficiency is improved, and the operation and maintenance cost is reduced.

The invention provides a domestic passive meteorological satellite cloud base height high-precision inversion method and system, and belongs to the field of atmospheric cloud parameter remote sensing inversion and quantitative remote sensing mechanisms. According to the method, re-analysis data such as satellite multi-channel atmospheric top reflectivity observation, brightness temperature observation and atmospheric temperature profile are fused, a complex nonlinear feature extraction and modeling method is developed, and the nonlinear mapping capability among the atmospheric top reflectivity, the brightness temperature and the cloud base height is improved; the night and all-day inversion capability is enhanced by combining all-time infrared channel characteristics and layer junction information; through a multi-source sample expansion and stack learning strategy, the generalization adaptability of the model is improved; and a sample expansion and auxiliary training mechanism is designed, so that the problem of insufficient high-quality training data is effectively relieved, and finally, high-precision inversion of the all-day cloud base height of a domestic static or polar orbit meteorological satellite is realized.

The application discloses a kind of nonlinear feature extraction methods of medical internet of things data, obtain medical internet of things data and carry out mean removal operation and obtain original data and are divided into training set, verification set and test set;Build suitable for medical internet of things data anomaly detection model, utilize sparse, binary random projection matrix original data is embedded from original feature space to high-dimensional transition space, the dimension of transition space is higher than the dimension of original feature space;Using PCA extracts principal component information and projects data from high-dimensional transition space to low-dimensional feature space to obtain nonlinear projection vector;Using training set and verification set, the best hyperparameter group is obtained by bayesian hyperparameter optimization to anomaly detection model;According to the best hyperparameter group and test set, the performance of the anomaly detection model is calculated;The medical internet of things data to be detected is input into the best final detection model to obtain the corresponding nonlinear feature extraction result.The application realizes very good nonlinear feature processing effect.

The present application relates to a kind of community old people health management system and method based on metabolic equivalent, through the comprehensive collection of multidimensional health data, and the processing of multi-source heterogeneous time series data, construct health database;Then adopt mixed effect model and gradient boosting decision tree model, with metabolic equivalent as core quantitative index, extract individual random effect as personalized correction factor by mixed effect model, output personalized metabolic equivalent prediction value combined with gradient boosting decision tree model, solve the problem of existing technology evaluation standard missing, group evaluation one-size-fits-all;Then through time domain, frequency domain, nonlinear feature extraction, the motion load characteristics are fully described, and the association between motion load and health risk is accurately captured by Transformer-LSTM hybrid network, which significantly improves the prediction accuracy of cardiovascular events and fall risk;Finally, through the collaborative optimization model of multi-agent reinforcement learning, dynamically generate and optimize multi-dimensional exercise programs according to the real-time health status of the user.

The application discloses a three-level early warning processing method for sewage plant data anomaly detection, comprising the following steps: a data acquisition step; obtaining sewage plant operation data X from a database of a sewage treatment plant; a data preprocessing step; performing spatio-temporal correlation feature analysis, correlation analysis of abnormal data scenes and high-order nonlinear feature extraction on the sewage plant operation data X; a data analysis step; three-level early warning decision and root cause positioning; hierarchical response linkage and closed-loop management. The three-level early warning processing method for sewage plant data anomaly detection and the control method thereof have the advantages of improving the accuracy, explainability and engineering practicability of anomaly detection, providing strong support for intelligent and fine operation of the sewage treatment process and the like.

The invention provides a full-link collaborative upper limb rehabilitation personalized training method, belongs to the technical field of upper limb rehabilitation training, and is used for solving the problems of training scheme solidification, insufficient personalization and accuracy and lack of a full-link collaborative closed loop in related technologies. According to the method, basic attributes, historical training, clinical reference and real-time perception data of a user are integrated, a personalized basic scheme is generated through nonlinear feature extraction and rehabilitation stage judgment, training parameters are driven to be dynamically adjusted through a lightweight prediction model, a doctor-patient double-version report is generated in combination with a personalized evaluation algorithm, and the doctor-patient double-version report is used for rehabilitation. And full-link parameters are optimized through a multi-directional feedback closed loop. Full-link collaborative adaptation is achieved, the accuracy, safety and individuation degree of rehabilitation training are improved, and requirements of patients in different stages are met.

This invention relates to the field of computer security authentication technology, and more particularly to a security authentication method and system based on a secure computer. The method includes the following steps: deploying a sensor array and acquiring data from the secure computer to obtain calibrated spatiotemporal synchronization data; performing temperature and current fusion processing on the calibrated spatiotemporal synchronization data to obtain a thermoelectric coupling feature spectrum; performing point-to-point temporal correlation analysis on the thermoelectric coupling feature spectrum to obtain a point-to-point cross-correlation matrix; extracting nonlinear features from the point-to-point cross-correlation matrix to obtain a nonlinear dynamic feature set; performing dynamic behavior analysis on the nonlinear dynamic feature set to obtain a dynamic phase spectrum of secure computing behavior; and calculating the phase difference of the dynamic phase spectrum of secure computing behavior to obtain a phase difference vector. This invention improves the early detection capability of difficult-to-reproduce and novel hardware-level threats through real-time monitoring and anomaly identification of the computer's microscopic physical characteristics.