112 results about "Quantum particle" patented technology

According to some embodiments, system and methods for image improvement comprise: receiving a plurality of frames of a given region of interest, the frames comprised of a plurality of pixels; determining, based on a quantum property of the frames, a normalized pixel intensity value for each pixel of each of the plurality of frames; and generating an improved image of the given region of interest based on the plurality of frames and the corresponding normalized pixel intensity values for the frames, the order of the image being two. Also embodiments for generating an image of a target illuminated by quantum entangled particles, such as, photons, are disclosed.

According to some embodiments, system and methods for image improvement comprise: receiving a plurality of frames of a given region of interest, the frames comprised of a plurality of pixels; determining, based on a quantum property of the frames, a normalized pixel intensity value for each pixel of each of the plurality of frames; and generating an improved image of the given region of interest based on the plurality of frames and the corresponding normalized pixel intensity values for the frames, the order of the image being two. Also embodiments for generating an image of a target illuminated by quantum entangled particles, such as, photons, are disclosed.

The present system and method for simulating particles and waves is useful for calculations involving nuclear and full spectrum radiation transport, quantum particle transport, plasma transport and charged particle transport. The invention provides a mechanism for creating accurate invariants for embedding in general three-dimensional problems and describes means by which a series of simple single collision interaction finite elements can be extended to formulate a complex multi-collision finite element.

The invention relates to a chemical enterprise intelligent production plan control system based on quantum particle swarm algorithm. The control system comprises a production plan optimization module, a workshop intelligent dispatching module, a basic information management module, a system management module and a database. According to the characteristics of the production plan problems of a multistage multi-product multi-restriction mixed production line in a chemical enterprise, a model adopts the following hypotheses: (1) no permission of out of stock; (2) infinite product stock capacity; (3) raw material arrival date occurring at the beginning of each period of time, and product delivery date occurring at the end of each period of time; (4) stable production state, namely outputting products as long as raw materials are input during each working procedure; (5) selling surplus products at the current period of time when products meeting the demands; moreover, setting an objective function and calculating product output; and finally, and adopting quantum particle swarm algorithm to carry out model solution. The chemical enterprise intelligent production plan control system effectively combines various constraint conditions in a chemical enterprise and has high efficiency; moreover, the control system realizes effective sharing of the prior enterprise coordination production workshop to increase resource and equipment utilization rate.

The invention discloses a hyperspectral image wave band selection method based on the quantum-behaved particle swarm optimization algorithm to mainly solve the problems that in the prior art, searching capacity is low and classification accuracy is not high. The hyperspectral image wave band selection method includes the steps of firstly, inputting hyperspectral gray level images, and setting up a training set through samples with labels; secondly, initiating position vectors, code vectors, fitness values and local optimal information of particles and global optimal information of population; thirdly, renewing the position vectors and the code vectors of the particles; fourthly, calculating the fitness values of the particles according to the renewed code vectors; fifthly, renewing the local optimal information of the particles and the global optimal information of the population; sixthly, judging whether iteration is stopped or not, outputting the optimal wave bands corresponding to the global optimal information if the stopping conditions are satisfied, and executing the third step if the stopping conditions are not satisfied. By means of the hyperspectral image wave band selection method, effectiveness of wave band selection is improved, the optimal wave bands can be selected out as less as possible in a self-adaption mode on the premise that classification accuracy is ensured, and the hyperspectral image wave band selection method can be used for preprocessing the hyperspectral images before classification.

The invention discloses a method for optimizing a brushless DC motor fuzzy controller based on an improved particle swarm algorithm. The steps include that the whole solution space is divided into seven areas; adaptation degree of each particle is calculated according to a target function; the individual extremum of the particles and the global extremum are updated according to adaptation degree; the updated individual extremum and the global extremum are assigned to quantifying factors Ka and Kb and scaling factors Kp, Ki and Kd; and input and output performance indicators are assessed, if the input and output performance indicators meet the target function, the process ends, and if the input and output performance indicators do not meet the target function, Pi and Pg are substituted in a quantum particle swarm formula by applying the improved particle swarm algorithm, continuous optimization of the particles is performed in the space areas until the particles meet the target function and new particle swarms are generated. The globally optimal solution can be found out at the highest speed based on the improved particle swarm algorithm, and a motor stably operates under the rated rotating speed and is rapid in response without overshoot basically so that the method has great follow-up performance and dynamic and static characteristics.

The invention discloses an intelligent optimization-based camera calibration method, which comprises the following steps of: obtaining pixel coordinates of feature points of an image by adopting a harris sub-pixel angular point extraction method; calculating the initial values of internal and external parameters according to the pixel coordinates of the image and corresponding coordinates in the world coordinate system; initializing a particle swarm, randomly generating d particles evenly distributed around the initial values, and forming an i*d dimensional particle swarm matrix; taking an initial particle swarm as a current-generation optimal local matrix, calculating the fitness functions of particles of the initial particle swarm, and selecting and taking a particle with the minimal fitness value as the optimal particle; and renewing the particle swarm, calculating the fitness values of the new particle swarm, comparing and replacing with the previous-generation optimal local matrix, obtaining a new-generation optimal local matrix, then, obtaining a new optimal particle, and meanwhile, calculating and renewing the values of external parameters (R and T). The intelligent optimization-based camera calibration method has the advantages that: a quantum particle swarm intelligent optimization algorithm is introduced into camera calibration; and the accuracy is higher, the calculation of the initial values is simple, and the convergence rate is fast.

The invention discloses a radar signal recognition method based on a quantum particle swarm convolutional neural network. The method comprises the following steps: 1) training a convolutional neural network, namely acquiring a radar signal containing different modulation ways, converting time domain data into frequency domain data, obtaining a frequency domain characteristic data sequence, and taking the frequency domain characteristic data sequence as a training sample; sending the training sample into the convolutional neural network, performing forward calculation, and adjusting a weight and a threshold of the convolutional neural network by using a quantum particle swarm algorithm, so that a well trained convolutional neural network is obtained; and 2) based on the well trained convolutional neural network, performing radar signal recognition, namely performing time-frequency conversion on an acquired to-be-recognized radar signal; and sending the obtained frequency domain data into the well trained convolutional neural network in the step 1), and outputting a modulation way of the radar signal. Simulation experiments prove that the method disclosed by the invention improves accuracy and efficiency of recognition of a radar emitter signal, and good solution is provided for a radar signal emitter recognition problem in an increasingly complex electromagnetic environment.

The invention relates to a power transmission line icing prediction method based on a quantum particle swarm and a wavelet nerve network. The power transmission line icing prediction method based on the quantum particle swarm and a wavelet nerve network comprises the following steps of acquiring historical icing meteorological data, namely an ambient temperature, a humidity, a wind speed, a wind direction, an air pressure, a lead temperature and an icing thickness; establishing an icing thickness prediction model by means of the wavelet nerve network; performing initial parameter optimization on the model through adding a quantum particle swarm algorithm of interference factors; and inputting the historical icing data for obtaining a predicated power transmission line icing thickness. The power transmission line icing prediction method has advantages of high prediction precision, high convergence speed, etc. The power transmission line icing prediction method can effectively predicate a line icing change rule and can be applied for power transmission line icing disaster early warning and treatment.

The invention discloses an array type air pressure measurement compensation device and method based on quantum particle swarm wavelet neural network. A micro-processing module is started to send instruction to a pressure sensor array and a temperature sensor, so that the air pressure and temperature are measured; measurement data is transmitted to the micro-processing module and a host computer respectively; the host computer establishes a wavelet neural network according to the received air pressure and temperature, and the wavelet neural network is optimized by utilizing the quantum particle swarm algorithm, the quantum particle swarm wavelet neural network is trained at the same time, and an obtained air pressure correction compensation formula is transmitted to the micro-processing module; the micro-processing module calculates the accurate air pressure value whose error is compensated; and the accurate air pressure value is transmitted to a display module and displayed. Retardation error of air pressure measured by arrays is compensated on the basis of the quantum particle swarm wavelet neural network, temperature drift and non-linearity are compensated, errors are reduced, effective signals are enhanced, air pressure measurement is more accurate, and requirements for meteorology measurement are met.

The invention discloses a graph-theory-based intelligent optimization method for failure recovery of a smart distribution grid. The method comprises the following steps of 1) inputting network parameters including an original structure of a distribution network, line parameters of each branch, load of each node, DG (Distributed Generation) data and other parameters; 2) inputting a number of the current faulty line and zeroing the corresponding switching state thereof; 3) setting dimensionality, iterations and corresponding parameter values of a quantum particle swarm optimization algorithm; 4) initializing a position value xk, a quantum bit, a rotation angle, a local optimal vector xp and a global optimal vector xg of each particle; 5) correcting the position value of each particle based on a graph theory; 6) updating a rotation angle guiding value, a quantum rotation angle and a bit of each quantum particle in sequence; 7) updating the position value xk of each quantum particle; 8) updating the local optimal vector and the global optimal vector of each particle; 9) carrying out convergence test; and 10) outputting an optimal particle position value x to obtain a corresponding failure recovery strategy.

The invention provides a wind power prediction system, which comprises a data collection server (1), a database server (2), an application workstation (3), a wind power prediction server (4), a data interface server (5) and reverse physical isolation equipment (6), wherein the data collection server (1) is used for operating data collection software, is communicated with the integrated communication management terminal of a wind power plant, and collects data; the database server (2) processes, carries out statistical analysis and stores the data; the wind power prediction server (4) operatesa wind power prediction module, uses a neural network integrated algorithm based on weighted least squares support vector machine and quantum particle swarm prediction on the basis of a numerical value weather forecast collected or provided by a SCADA (Supervisory Control And Data Acquisition) system, and is combined with the real-time operation working condition of a wind power plant fan to carryout short-term and ultra-short term prediction on the output situation of a single fan and the whole wind power plant; the data interface server (5) is used for obtaining the numerical value weatherforecast; and the reverse physical isolation equipment (6) is used for guaranteeing network safety. The invention also discloses a wind power prediction method, which can guarantee that prediction data which is used in field can embody recent power generation power features.

The invention discloses an electronic nose parameter synchronous optimization algorithm based on an improved quantum particle swarm optimization algorithm. The method comprises performing wavelet transformation on obtained original electronic nose data; then performing weighting treatment of wavelet coefficients; through the improved quantum particle swarm optimization algorithm based on a novel local attractor computing manner, finding out a weighting coefficient corresponding to the highest electronic nose identifying rate, and classifier parameters to obtain a characteristic matrix of electronic nose signals; inputting the characteristic matrix into a classifier for mode identification. The electronic nose parameter synchronous optimization algorithm based on the improved quantum particle swarm optimization algorithm has the advantages of enhancing early-stage ergodicity and later-stage local optimizing capacity of particles, improving the capacity of quantum particle swarms in searching for global optimal values, and especially for wound infection detection, improving the identification rate of an electronic nose, thereby selecting appropriate treatment methods for doctors and providing beneficial guidance for promoting quick recovery of wounds.

The invention relates to a micro-grid optimization method based on a good point set quantum particle swarm algorithm. The micro-grid optimization method based on a good point set quantum particle swarm algorithm comprises the steps: establishing a micro-grid multi-target optimization model including a micro-grid multi-target optimization objective function formula and a micro-grid multi-target optimization constrained condition formula; using a good point set to improve a quantum particle swarm algorithm; and using the good point set quantum particle swarm algorithm to solve the micro-grid multi-target optimization model. The micro-grid optimization method determines the capacity of a micro gas turbine according to the maximum load, and re-optimizes the capacity of an intermittent distributed power supply and an energy storage system. During the solution process, the micro-grid optimization method utilizes the good point set quantum particle swarm algorithm, thus guaranteeing the optimization result and guaranteeing the good selectivity and instructiveness at the same time. As a built-in filtering-based on scheduling strategy can give full play to the characteristics of the micro gas turbine and the energy storage system, the micro-grid optimization method has the advantage of a spectrum analysis method and also has better timeliness, and can obtain the position and capacity of the distributed power supply, the energy storage system and the micro gas turbine in the micro-grid through calculation so as to realize economical efficiency in the isolated micro-grid and the integral optimization of discharge of pollutants.

A novel and useful quantum computing machine architecture that includes a classic computing core as well as a quantum computing core. A programmable pattern generator executes sequences of instructions that control the quantum core. In accordance with the sequences, a pulse generator functions to generate the control signals that are input to the quantum core to perform quantum operations. A partial readout of the quantum state in the quantum core is generated that is subsequently re-injected back into the quantum core to extend decoherence time. Access gates control movement of quantum particles in the quantum core. Errors are corrected from the partial readout before being re-injected back into the quantum core. Internal and external calibration loops calculate error syndromes and calibrate the control pulses input to the quantum core. Control of the quantum core is provided from an external support unit via the pattern generator or can be retrieved from classic memory where sequences of commands for the quantum core are stored a priori in the memory. A cryostat unit functions to provide several temperatures to the quantum machine including a temperature to cool the quantum computing core to approximately 4 Kelvin.

A power plant boiler NOx prediction model device based on improved quantum particle swarm optimization disclosed by the present inventon is composed of an on-site data acquisition device, a computer device and a communication device. The on-site data acquisition device prepares for establishing the prediction model of NOx emission concentration of boiler in the next step. The computer device realizes the model algorithm operation, utilizes the improved quantum particle swarm optimization algorithm to optimize the initial parameters of the limit learning machine, takes the error minimization ofthe training data prediction value and the actual value as the goal, and then obtains the accurate NOx emission model. The invention has the advantages that the improved quantum particle swarm algorithm can efficiently and quickly calculate the optimal initial parameters of the limit learning machine, so that the accurate NOx emission model of a boiler of a thermal power plant is obtained, whichis of great significance for reducing the pollutant emission of a coal-fired unit.

The invention discloses an ultra-short-term wind power combination prediction method based on a support vector machine, and the method comprises the steps: firstly carrying out the linear interpolation replacement of to-be-processed wind power historical data according to the data of an adjacent time period, and carrying out the normalization of the preprocessed data; secondly, decomposing the processed wind power data into an eigenfunction sequence and a residual error sequence by using empirical mode decomposition; secondly, establishing a quantum particle swarm-support vector machine model for the eigenfunction sequence and the residual sequence obtained by decomposition, and performing training optimization to obtain a predicted value of each sequence; and finally, superposing the prediction values of the sequences to obtain a final wind power prediction value, and carrying out error evaluation analysis. Compared with a support vector machine direct prediction result or a result without data feature decomposition, the prediction result of the method is improved, and meanwhile the situation that local errors are too large does not occur. Compared with an existing wind power prediction scheme, the method is higher in robustness, higher in calculation speed, less in data requirement and better in prediction effectThe invention discloses an ultra-short-term wind power combination prediction method based on a support vector machine, and the method comprises the steps: carrying out the linear interpolation replacement of to-be-processed wind power historical data according to the data of an adjacent time period, and carrying out the normalization of the preprocessed data; secondly, decomposing the processed wind power data into a cost characteristic function sequence and a residual sequence by utilizing empirical mode decomposition; secondly, establishing a quantum particle swarm-residual sequence for the intrinsic function sequence and the residual sequence obtained by decomposition; carrying out training optimization on the support vector machine model to obtain a predicted value of each sequence; and finally, superposing the predicted values of the sequences to obtain a final wind power predicted value, and carrying out error evaluation analysis. Compared with a result of direct prediction of a support vector machine or no data feature decomposition, the prediction result of the method is improved, and meanwhile, the situation of overlarge local error does not occur. Compared with an existing wind power prediction scheme, the method is higher in robustness, higher in calculation speed, less in data demand and better in prediction effect.

The invention discloses an optimized dispatching method of a micro-energy network based on an improved quantum particle swarm algorithm, which comprises the following steps: establishing an objectivefunction of the daily cost of the micro-energy network; calculating the output constraint of equipment in the micro-energy network; establishing Quantum particle swarm optimization (QPSO) model, and the population size, setting the maximum number of iterations and the upper and lower limits of the domain search radius of QPSO model to calculate the optimal output of each equipment and the daily minimum cost of micro-energy network. A method for optimize dispatch of micro-energy grid based on improve quantum particle swarm algorithm of that invention can realize the consumption of renewable energy, minimizes the daily operation economy and the environmental treatment cost under the reliable operation of the micro-energy grid, and provide a new solution algorithm for the optimization dispatch problem of the micro-energy grid containing renewable energy and energy storage.

The invention discloses a handwritten picture classification method based on a quantum neural network. The implementation steps are as follows: (1) extracting handwritten picture features; (2) constructing a particle population of a binary quantum particle swarm algorithm; (3) constructing a convolutional neural network by using the particle population; (4) training the convolutional neural network; (5) selecting an optimal convolutional neural network; (6) judging whether the classification accuracy of the optimal convolutional neural network is smaller than 0.85 or not, and if yes, executingthe step (7); otherwise, executing the step (8); (7) updating the structure and parameters of the convolutional neural network corresponding to the position information of each particle by using a quantum updating strategy, and executing the step (3); and (8) outputting a classification result of the optimal convolutional neural network. The method has the advantages of being high in classification accuracy and capable of processing large-scale complex handwritten picture classification, and the problem that in the prior art, a large number of professional knowledge and design experiences ofthe convolutional neural network are needed is effectively solved.

Systems, apparatuses, methods, and computer program products are disclosed for quantum entanglement random number generation (QERNG). An example method for QERNG includes, among other operations, generating a quantum entanglement random number based on a subset of a first set of entangled quantum particles associated with a first computing device. Each entangled quantum particle in the first set of entangled quantum particles may be entangled with a respective entangled quantum particle in a second set of entangled quantum particles associated with a second computing device. In some instances, the example method may further include generating a cryptographic key based on the quantum entanglement random number, encrypting an electronic communication based on the cryptographic key, and transmitting the encrypted electronic communication to the second computing device.