515 results about "Phase space" patented technology

A wireless communication system transmits data on multiple carriers simultaneously to provide frequency diversity. Carrier interference causes a narrow pulse in the time domain when the relative phases of the multiple carriers are zero. Selection of the frequency separation and phases of the carriers controls the timing of the pulses. Both time division of the pulses and frequency division of the carriers achieves multiple access. Carrier interferometry is a basis from which other communication protocols can be derived. Frequency hopping and frequency shifting of the carriers does not change the pulse envelope if the relative frequency separation and phases between the carriers are preserved. Direct sequence CDMA signals are generated in the time domain by a predetermined selection of carrier amplitudes. Each pulse can be sampled in different phase spaces at different times. This enables communication in phase spaces that are not detectable by conventional receivers. The time-dependent phase relationship of the carriers provides automatic scanning of a beam pattern transmitted by an antenna array. In waveguide communications, the carrier frequencies and phase space may be matched to the chromatic dispersion of an optical fiber to increase the capacity of the fiber.

There is presented a reliable technique for measuring condition change in nonlinear data such as brain waves. The nonlinear data is filtered and discretized into windowed data sets. The system dynamics within each data set is represented by a sequence of connected phase-space points, and for each data set a distribution function is derived. New metrics are introduced that evaluate the distance between distribution functions. The metrics are properly renormalized to provide robust and sensitive relative measures of condition change. As an example, these measures can be used on EEG data, to provide timely discrimination between normal, preseizure, seizure, and post-seizure states in epileptic patients. Apparatus utilizing hardware or software to perform the method and provide an indicative output is also disclosed.

A machine-learning engine is disclosed that is configured to recognize and learn behaviors, as well as to identify and distinguish between normal and abnormal behavior within a scene, by analyzing movements and / or activities (or absence of such) over time. The machine-learning engine may be configured to evaluate a sequence of primitive events and associated kinematic data generated for an object depicted in a sequence of video frames and a related vector representation. The vector representation is generated from a primitive event symbol stream and a phase space symbol stream, and the streams describe actions of the objects depicted in the sequence of video frames.

EKG sensors ((150) are placed on a patient (140) to receive electrocardiogram (EKG) recording signals, which are typically combinations of original signals from different sources, such as pacemaker signals, QRS complex signals, and irregular oscillatory signals that suggest an arrhythmia condition. A computing module (120) uses independent component analysis to separate the recorded EKG signals. The separated signals are displayed to help physicians to analyze heart conditions and to identify probably locations of abnormal heart conditions. At least a portion of the separated signals can be further displayed in a chaos phase space portrait to help detect abnormality in heart conditions.

The use of microfluidic structures enables high throughput screening of protein crystallization. In one embodiment, an integrated combinatoric mixing chip allows for precise metering of reagents to rapidly create a large number of potential crystallization conditions, with possible crystal formations observed on chip. In an alternative embodiment, the microfluidic structures may be utilized to explore phase space conditions of a particular protein crystallizing agent combination, thereby identifying promising conditions and allowing for subsequent focused attempts to obtain crystal growth.

A multiple access telecommunication system is provided, which makes use of chaotic signals. Each transmitter has a chaotic system, which has at least one strange attractor in its phase space, and each receiver has chaotic systems, which corresponds to those in the transmitters from which it may receive signals. Each pair of users is assigned a set of periodic unstable orbits from a strange attractor, which that pair of users may use for signalling. At the transmitter side, a multiplexer receives data from plurality of users. These data are multiplexed in time, to produce an asynchronous data stream. The data stream is then mapped to a sequence of chaotic signals, in accordance with the set of trajectories for each transmitter and receiver pair.

The invention discloses a dynamic confirming method for performance index value normal fluctuating range, comprising following steps: obtaining a historical value of the network performance index, and dynamically obtaining the latest historical sample data; processing pre-treatment to obtain a normal sample data; processing phase space re-construction to obtain a training sample data; processing training modeling to take residual error white noise as condition of selecting the optimal supporting vector machine model; using the optimal supporting vector machine model to predict the data on the to-be predicted time point, and calculating the confidence interval of the predicted value to obtain the performance index value normal fluctuating range; detecting whether the optimal supporting vector machine model is suitable for the next prediction of the to-be predicted time point; if the model is not suitable, processing re-training. The invention also discloses a device for the dynamic confirmation performance index value normal fluctuating range. The invention significantly enhances the precision of the performance dynamic early warning, and reduces wrong report and missing report of the performance warning.

The invention relates to a projection objective lens wave aberration detection device and method. Light emitted by a light source is uniformly radiated on a projection objective lens object plane, a scatter plate and a needle hole are installed on a masking stage, the needle hole is arranged on an object plane of a projection objective lens, the field of view is selected through the movement of the masking stage, a shearing grating is arranged on an image plane of the projection objective lens, the shearing grating is installed on a wafer stage together with a detector, the phase shifting function is realized through the transverse precise movement of the grating by the wafer stage, and simultaneously each frame of a shearing interference figure in the phase shifting process is recorded by utilizing the detector. Two-dimensional shearing is realized by utilizing the two-dimensional grating, the phase shifting function is integrated into the shearing interference instrument, and the high-precise measurement of the projection objective lens wave aberration is realized by adopting the characteristics of the phase space vector.

A machine-learning engine is disclosed that is configured to recognize and learn behaviors, as well as to identify and distinguish between normal and abnormal behavior within a scene, by analyzing movements and / or activities (or absence of such) over time. The machine-learning engine may be configured to evaluate a sequence of primitive events and associated kinematic data generated for an object depicted in a sequence of video frames and a related vector representation. The vector representation is generated from a primitive event symbol stream and a phase space symbol stream, and the streams describe actions of the objects depicted in the sequence of video frames.

The invention provides an engineering nonlinear vibration inspection method. The method adopts a wavelet nonlinear threshold value filtering wave to filter wide-frequency background noise of a three-direction acceleration signal and to remove DC bias so as to get the treated acceleration signals after frequency domain filtering and removal of trend term. Then speed signals and displacement signals are obtained after the acceleration signals is subject to a first time and second time integrals and removal of trend term. The characteristics and types of the nonlinear vibration are determined through a three-dimensional phase space diagram and other calculations of a reconstructed phase space tested by the three-dimensional phase space diagram. The invention adopts three-direction low resistance piezoelectricity acceleration meter to measure the vibration accelerations of three orthogonal directions. The invention has convenient installation, easy usage and reliable analysis, overcomes the problems of drifting, delay and aberration of the actual acceleration signals after passing through analog integration or digital integration. The invention plays an important role in the engineering nonlinear vibration inspection for mechanical mechanisms, bridges, dams and buildings, etc.

A technique for reconstructing phase currents based on measurements of a DC bus current. Non-observable regions of DC bus current samples to reconstruct phase currents are reduced in size by using two phase space vector modulation. The non-observable regions can be further reduced by omitting deadtime insertions for switch actuations when output current is higher than a given threshold. Voltage command vectors in non-observable areas can be constructed by two time averaged vectors one of which is constructed to obtain an observable DC bus current reflecting motor phase current. The additive vectors have the same combined time average value as that of the voltage command vector in order to preserve volt-seconds. Current samples may also be averaged to provide a more accurate reconstruction of phase current. Current samples are time delayed to avoid transients for more accurate readings. When three phase PWM inverter modulation results in inability to determine the motor phase current from the DC bus measurement, the technique includes dynamically switching to two phase PWM modulation to reduce the time interval when the motor phase current is not determinable through the DC bus current measurement.

The invention discloses a climate time sequence forecasting method based on an empirical mode decomposition and support vector machine, belonging to the field of short-term climate forecasting. The climate time sequence forecasting method comprises the following steps of: firstly, pretreating a time sequence through an empirical mode decomposition method; decomposing the time sequence to a plurality of intrinsic mode function components and a trend component, wherein the components can more accurately reflect changes in the original sequence and keep characters of the time sequence per se; next, carrying out phase space reconstruction on each component through a time sequence forecasting method; respectively establishing different support vector machine regression models for forecasting; and combining the forecasted result of each component to the forecasted result of the original sequence. The invention has the advantages of getting help from the empirical mode decomposition method for smooth processing of the time sequence, reducing interference or coupling information among the sequences on the basis of keeping the characters of the time sequence per se, enabling the accuracy of forecasting to be higher, and especially fitting for treating non-stationary climate time sequences with yearly precipitation or changed temperature.

The invention discloses an online lithium ion battery residual life predicting method based on relevance vector regression, belongs to the technical field of lithium ion battery life prediction, and solves the problem that the residual life of the existing lithium ion battery is predicted by an offline method with low precision. The method comprises the following steps: firstly selecting original samples, performing phase-space reconstruction to construct a training sample set; initializing the model parameters of RVM (relevance vector machine); performing RVM training to obtain a RVM prediction model; comparing the obtained prediction value with ynew, if yes, the constructed novel training set WS equal to WSUINS, retraining RVM, and updating the RVM prediction model; otherwise, keeping the RVM prediction model stable; performing recurrence prediction until the prediction value is smaller than the invalid threshold value U, and finishing the online prediction of the residual life of the predicted lithium ion battery. The method is suitable for prediction of the lithium ion battery residual life.

A machine-learning engine is disclosed that is configured to recognize and learn behaviors, as well as to identify and distinguish between normal and abnormal behavior within a scene, by analyzing movements and / or activities (or absence of such) over time. The machine-learning engine may be configured to evaluate a sequence of primitive events and associated kinematic data generated for an object depicted in a sequence of video frames and a related vector representation. The vector representation is generated from a primitive event symbol stream and a phase space symbol stream, and the streams describe actions of the objects depicted in the sequence of video frames.

The invention discloses a new short-term wind speed forecasting method of a wind farm, which comprises the following steps: collecting wind speed data of the wind farm, forming the time sequence of the historical wind speed and carrying out normalization treatment; applying the chaos analysis method for analyzing the time sequence of the historical wind speed after the normalization treatment for obtaining phase space reconstruction parameters of a wind power system in the area located by the wind farm, wherein the parameters are delay time and embedding dimension of the time sequence; utilizing the parameters for carrying out treatment on the time sequence of the historical wind speed after the normalization treatment, and obtaining a training sample set required by a support vector regression model for wind speed forecasting; adopting the training sample set for training the support vector regression model; utilizing the support vector regression model after training for carrying out short-term wind speed forecasting on the wind farm, and obtaining the normalized result of the short-term wind speed forecasting of the wind farm; and carrying out anti-normalization treatment on the obtained normalized result of the short-term wind speed forecasting of the wind farm, and obtaining the short-term wind speed forecasting result of the wind farm.

The invention discloses a method for generating a variable parameter chaotic signal, and a chaotic secure communication system. The method comprises the following steps: firstly, chaotic mapping is used to generate the chaotic signal; then a parameter set of the chaotic mapping is determined in advance, so as to process any pseudorandom signal, and enable the state of the pseudorandom signal and the elements of the parameter set to be in one-to-one correspondence; and a corresponding parameter is chosen to generate the variable parameter chaotic signal by chaotic mapping according to the state of the pseudorandom signal. The system comprises a chaotic signal generating module, an analog-to-digital conversion module, a parameter transformation module, a coding module, a multi-path choice switch, an encryption / decryption module, an encrypted message data cache, a plaintext data cache and a control module. The invention can increase the complexity of the output of the chaotic signal or a chaotic sequence, and effectively resist the analysis of the nonlinear prediction technology based on phase-space reconstruction. The output digital chaotic sequence can be taken as a pseudorandom number sequence or key stream to encrypt data; the output continuous chaotic signals can be used for designing a secret communication system based on chaos synchronization .

The invention relates to a method for automatic identification and detection of defects in a composite material. The method comprises steps of: detecting the composite material to generate an infrared image by using infrared thermal wave nondestructive testing equipment; conducting phase space reconstruction on the infrared sequence image to determine defect position of the composite material and segment defect area of the image; conducting phase space reconstruction on the infrared sequence image with defect area and carrying out singular value decomposition to obtain a singular matrix, and left and right projection matrixes; carrying out matrix reconstruction again on the two projection matrixes; extracting algebraic characteristics of time information and space information of the defect through singular value decomposition; constructing mixing characteristic vector as characteristic symptom of the defect; and utilizing results from a nerve network classifier to complete the identification and classification determination. The method of the invention can realize automatic identification and detection on defect in the composite material, carry out rapid detection on damage type of the composite material and provide rapid detection means according to usage condition of the composite material, and has critical reality meaning and research value.

The invention relates to a power abnormal failure data analyzing device and a diagnosing method, and belongs to the field of equipment failure detection. The analyzing device comprises a signal acquisition unit, a rotary capacitor filter circuit, a signal processor, a power supply module, a communication module and a host computer. The method for diagnosing abnormal failure data by using the device comprises: 1, by using a method of phase space reconstruction, evaluating embedding delay t and optimal embedding dimension d; 2, modeling the acquired data to obtain a neural network predicting initial model; 3, predicting a next output x[i+1]<->; 4, updating the weight of the next predicted BP network; and 5, judging the error. The device and the method have the advantages that the BP neural network is skillfully combined with the phase space reconstruction method, and the device and the method are suitable for various acquisition signal types, have accurate and rapid processing, are applicable to various complex storage working stations, and can effectively analyze and process signal failure, in particular fine failure data. A rotary filter capacitor has strong noise inhibiting capability.

The invention relates to a pollution flashover index forecasting method for a regional power grid, belonging to the technical field of transmission and distribution monitoring. The method of the invention comprises the following steps: (1) forecasting the current value of the equivalent salt deposit density in real time by an insulator surface equivalent salt deposit density forecasting model; (2) forecasting pollution flashover critical voltage by an insulator pollution flashover critical voltage forecasting model; (3) forecasting the insulator pollution flashover index of the power grid by a pollution flashover graded forecasting and prewarning model; and (4) judging the state. The invention has the advantages of providing a multivariable equivalent salt deposit density time sequence forecasting model based on phase space reconstruction, solving by a supporting vector machine model, solving the forecasting problem under the conditions of small sample of equivalent salt deposit density data and noise, and improving the forecasting prediction.

The invention provides a posting predication system based on a neural network technique. The system adopts phase-space reconstruction for realizing non-linear time sequence analysis and adopts a Browser / Server structure based on a J2EE platform. The system structurally includes a data pre-processing module, a predication analysis management module and predication modeling and simulation interface software and has characteristics of high convergence rate and low training error. The predication precision of established models is good. However, selection of a range of neural network training samples should be paid attention. Sample quantity can be reduced appropriately for predication of posting concerning to emergencies and samples of too early time may not be applied. In application, damage of models due to abnormal values should be paid attention and the abnormal values should be checked and adjusted, so that the model predication precision can be guaranteed. The established models adopt quantitative analysis and acquire a certain precision. Besides, the perception performance is good.

An encryption process includes choosing a secret key and a set of permutable functions defined on a phase space for encrypting / decrypting messages, choosing a code for encoding messages to be sent as a number belonging to the phase space. The set of permutable functions includes chaotic maps generated by a composite function of first and second functions, and an inverse of the first function. The secret key is defined by the second function.

The invention relates to a city expressway short-time traffic information predicting system and a method, which belong to the field of traffic information prediction. The system consists of a traffic information fusion sub system, a traffic information mathematical model predicting sub system and a traffic information communication and delivery sub system, wherein the traffic information fusion sub system uses the historical statistical traffic information and the real-time detection traffic information, carries out pretreatment and data fusion, and finally stores results into a collection database to be used by the predicting sub system. The traffic information mathematical model predicting sub system is based on the theory of the phase space reconstruction, adopts a chaotic time sequence self-adapting predicting method based on the time sequence reconstruction, and carries out analysis and prediction on the short-time traffic information within 15 minutes on the city expressway. The predicted traffic information is delivered in real time through the traffic information communication and delivery sub system. The invention provides the reference for the traffic management department to optimize the traffic management schemes and prevent the traffic accidents, the traffic resources are optimized, and the social benefits and the economic benefits are improved.

The invention relates to a method for investigating the glucose metabolism of a human being for disease-relevant and / or disease-related particularities. The the glucose concentration g(t1) to g(tn) of a body fluid is measured at time points t1 to tn that are distributed over a period of at least six hours. The data points are then determined in phase space coordinates from the glucose concentration measuring values g(t1) to g(tn). The data points are then processed to highlight disease-relevant and / or disease-related particularities of the glucose metabolism of the investigated human being. The processing of the data points can be implemented by plotting them in a phase space representation or by determining from the data points a disturbance parameter that is correlated to the severity of a disturbance of glucose metabolism.

The invention relates to a quantitative estimation and prediction method for icing load of a power transmission line, and belongs to the technical field of online monitoring of overhead power transmission lines. The method includes establishing a chaos time sequence model of a power transmission line icing process by using icing process historical data of a monitoring point and based on the phase space reconstruction theory; and establishing a quantitative estimation and prediction model of the icing load of the power transmission line based on a machine learning method of a support vector machine (SVM). According to the models, icing online estimation results based on a mechanical model are amended, or online estimated values are replaced when a mechanical sensing device fails, and trend prediction is performed on the icing process of the power transmission line based on micrometeorological information known in advance. The method has the advantages of being capable of amending online estimation results of the icing load of the power transmission line, provided with the capacity of estimating the icing load of the power transmission line based on micrometeorological data when the mechanical sensing device fails, and capable of predicting the icing load trend of the power transmission line according to the micrometeorological information of monitoring points known in advance.

The invention discloses a method and a device for analyzing brain wave signals, which comprises steps of: (1) collecting original brain wave signals; (2) removing artifacts or noises in the original brain wave signals; (3) phase-space reconstructing of the preprocessed brain wave signals for obtaining a recurrence plot; (4) quantitative analyzing of the recurrence plot for calculating assured value DET of the recurrence plot; wherein, the artifacts in the original brain wave signals are removed by a mixed method of wavelet transformation and empirical mode decomposition. The phase-space reconstructing reconstructs phase-space vector by a method of delaying time so as to obtain the recurrence plot and calculate the ratio of recursion points on diagonal structure of the recurrence plot to all recursion points for obtaining the assured value DET. The method and the device provide good quantified indexes for doctors and accurate and real-time changing situation of depth of anesthesia during the process of the anesthesia, or are used for researching and analyzing change of brain functions, situation of sedation and analgesis, change of a cognizing function, etc.

A system and method for power conversion synchronizes multiple phases at a desired phase angle difference. The power conversion involves variable frequency switching, fixed on-time and provides power factor correction. A relative measure of a phase angle difference between two phases permits each phase to be controlled to obtain the desired phase angle difference. The power conversion involves transition mode switching to help reduce switching losses. A phase angle difference detector may be provided for each phase. The various phases may have different inherent frequencies that vary with switching frequency, and are synchronized to an average frequency.

The invention discloses a continuous intersection traffic control method and device. The continuous intersection traffic control method comprises: step 1, collecting signal lamp information and vehicle information on vehicle running route; step 2, calculating a phase space-time diagram of N signal lamps on the vehicle running route according to the information collected in the step 1) in combination with pre-loaded map information; step 3, setting a speed vk, wherein k is equal to 1, 2, ..., n, and n is a natural number; step 4, judging whether a passing time of passing by the ith signal lamp at the speed vk is within a green light phase in the phase space-time diagram, if so, the speed vk is an effective speed ui, and on the contrary, entering step 3; step 5, calculating multiple groups of effective speed combinations passing by N intersections, figuring out the total oil consumption of each group of effective speed combinations, using the effective speed combination with the lowest total oil consumption as an economic vehicle speed, and prompting the economic vehicle speed to drivers. The continuous intersection traffic control method disclosed by the invention can be used for achieving the aim of economic driving.

The invention relates to a method for determining the detection efficiency of an internal exposure HPGe detector based on CT data. Based on different energy gamma ray full-energy peak detection efficiency measuring data, Monte Carlo particle transport numerical values are adopted for computing, so that geometrical parameters of a detector sensitive area are adjusted, and the geometrical parameters of the detector sensitive area are obtained; the Monte Carlo particle transport numerical values are adopted for computing, so that the corresponding differential detection efficiency distribution of different energy gamma rays on the surfaces of the detector in different discrete areas and different discrete angle phase spaces is solved; based on CT medical image data of a human body to be detected, a voxel model of human anatomy structural features and a voxel model of an interesting organ or area are established, and the interesting organ or area serves as a source area; an exponential decay formula is used for solving the direct-through gamma ray share from a source area voxel to a surface element on the surface of the detector, and established differential detection efficiency distribution data are used for obtaining the detector full-energy peak detection efficiency specific to the source area through the multiple integral.

A method and system for biometric identity confirmation is based on the pulse wave of a subject. During an initial enrollment mode, pulse wave data for a known subject are used to generate subject characterization data for the known subject. During a subsequent operational mode, pulse wave data for a test subject are analyzed using the subject characterization data to confirm whether the identity of the test subject matches the known subject. The subject characterization data can be a probability density in a phase space in which at least two quasi-periodic variables based on the pulse wave (e.g., blood pressure and volume time-series data) are correlated.

The invention discloses an electroencephalogram signal analysis method and an electroencephalogram signal analysis device to identify the brain state. The method comprises the following steps of: segmenting the acquired electroencephalogram signal; analyzing the state of each segment of electroencephalogram signal and preprocessing to remove interference; and calculating an ordering entropy of each segment of electroencephalogram signal, and determining the activity state of the brain according to ordering entropy values. The invention can be used for analyzing electroencephalogram signals under the conditions of different cognitive function activities of the brain and physiological statues, and can be particularly applied to studying the changes of brain activities in sleep and the like. Starting from a phase space, ordering entropies are adopted for statistical analysis of the structural change modes of the electroencephalogram signals to disclose the uncertainty, stable degree and information content of the electroencephalogram signals.