105 results about "Adaptive randomization" patented technology

The invention discloses a self-adapting random resonance weak signal detection method, which achieves the self-adapting detection of weak periodic signal through combining a matching random resonance and an imposed signal induced random resonance. According to the theory of random resonance, firstly, numerical simulation is processed to receive correspondence between the noise intensity and the frequency of the best matching random resonance of a nonlinear bistable system in a range of extremely low frequency and under the condition of small-signal; and a high-frequency weak signal encountered in a project can be processed by using the change of normalized scale. For the detection of a weak signal with extremely low message-to-noise ratio and large noise intensity, the heights of voltage barriers of the bistable system can be changed by using the induced random resonance, thereby helping a weak eigenfrequency signal to fulfill the transition between voltage barriers, so that the bistable system reaches the random resonance state, thereby achieving the self-adapting detection of the weak periodic signal.

The invention discloses a method for extracting weak fault characteristic information of a planetary gear based on improved ensemble average empirical mode decomposition and adaptive stochastic resonance. The method comprises vibration signal acquisition, fault characteristic information effective IMF extraction and weak fault characteristic extraction based on adaptive stochastic resonance. The method can extract the weak fault characteristic information generated due to faults of the planetary gear, and can realize planetary gear fault diagnosis according to the extracted weak fault characteristic information; and the method is an effective planetary gear weak fault characteristic information extraction method.

The invention relates to a self-adaption stochastic resonance weak signal detecting method based on a particle swarm optimization algorithm. The method comprises the following steps of 1) particle swarm initialization; 2) step-changed stochastic resonance; 3) individual fitness evaluation; 4) particle speed and position updating; 5) termination condition judgment and 6) detection result output. The self-adaption stochastic resonance weak signal detecting method has the advantages that the simplicity is realized, the implementation is easy, the application range is wide, the convergence speed is high, high-frequency weak signals at high-noise background can be effectively detected, and a novel method is provided for stochastic resonance parameter self-adaption selection and practical application in engineering.

The invention discloses a weak signal extracting method based on self-adaptive stochastic resonance. Particularly, a frequency of a weak signal can be well adjusted to a frequency range easily generating the self-adaptive stochastic resonance through adjusting a scale changing factor which is secondarily sampled, so that the good performance of the self-adaptive stochastic resonance can be sufficiently utilized, the weak signal can be well extracted at a super-low signal to noise ratio, and the problem that the conventional weak signal processing method represents to be poor, even be invalid, at the super-low signal to noise ratio can be effectively solved; and meanwhile, a signal frequency of the weak signal which is secondarily sampled can be matched with a self-adaptive stochastic resonance system and can generate stochastic resonance through a fed-back automatic adjusting scale conversion factor under the condition of not knowing the frequency of the weak signal, so that the characteristic of the weak signal can be better extracted.

The invention discloses a self-adaptive random access method and system of a machine type communication terminal and belongs to the field of mobile communication. According to the method and system, a lead code is mainly sent through the machine type communication terminal, a base station sends a random access response message and makes judgment according to the connected terminal, a BO value is set adaptively based on the number of collision terminals, the collision rate of random re-access is reduced, and the access success rate is increased.

An adaptive stochastic transaction system in which a vendor or first transactor offers a trade object to a vendee or second transactor at a variable, stochastic offer value formed from a pseudorandom value. The vendor communicates the stochastic offer value to the vendee in the form of a stochastic decision tuple, including an offer interval signifying the validity period of the offer value. The tuple is formed and communicated to incline the vendee to accept the offer for the trade object. The system is adaptive in that vendee and communication system responses are monitored to maximize transaction system enterprise parameters.

Systems and methods for random fill caching and prefetching for secure cache memories are provided. The system dynamically de-correlates fetching a cache line to the processor from filling the cache with this cache line, due to a demand memory access, in order to provide greater security from information leakage due to cache side-channel attacks on cache memories. The system includes a random fill engine which includes a random number generator and an adjustable random fill window. Also provided is an adaptive random fill caching system which dynamically adapts the random fill window to a wide variety of computational workloads. Systems and methods for cache prefetching to improve system performance using adaptive random fill prefetching and random fill prefetching are also provided.

Methods for segmentation of an object from a background in an image are disclosed. Segmentation is achieved by an adapted Random Walker segmentation method using directed edges in a graph. The segmentation applies the minimization of an approximation of an energy function. A minimizer of the approximated energy function can be found by using iterative steps. Weights are assigned to an edge between two nodes. The weights are dependent on the direction of an edge. A system for segmentation of an object from a background is also disclosed.

An adaptive RACH operation is proposed for machine-type communications (MTC) in a 3GPP wireless network. The adaptive RACH operation is based on context information to reduce RACH collision probability, to control network overload, and to enhance system performance. The context information includes device related information and network related information. Device related information includes device type and service or application type. Network related information includes network load information and historical statistics information. Based on the context information, an MTC device adjusts various network access and RACH parameters by applying adaptive RACH operation in different levels. For example, in the application level and the network level, the MTC device adjusts its access probability or RACH backoff time for RACH access. In the radio access network (RAN) level, the MTC device adjusts its access probability or RACH backoff time, or transmits RACH preambles using adjusted RACH radio resources and preambles.

A mobile wireless device adapts transmit power levels and number of retransmissions of a preamble sent to a wireless network. The mobile wireless device measures characteristics of a downlink signal received from the wireless network. The mobile wireless device transmits a series of preambles to the wireless network, each successive preamble having an increased power level, starting at a power level based on the measured received signal characteristics and on parameters received from the wireless network, up to a maximum transmit power level. When the transmit power level of the preamble exceeds the maximum transmit power level and when the measured downlink signal quality falls below a threshold, the mobile wireless device limits the number of preamble retransmission to less than an allowed maximum number of retransmissions. A minimum number of retransmissions is determined and adapted to higher values for larger measured values of downlink signal quality.

The invention discloses a self-adapted random verification method used for simulating an ultra-large-scale chip. The specific realization process comprises the following steps: defining an attribute vector for representing each group of stimulation; randomly generating the plurality of attribute vectors, and taking the attribute vectors as parameters generated by random stimulation so as to generate a plurality of groups of random stimulation sequences with corresponding characteristics; carrying out simulation verification on the plurality of groups of random stimulation sequences and counting covering rates of a verification target in the simulation verification; comparing the covering rates of the random stimulation sequences; combining frequency results of the plurality of groups of random stimulation sequences and sorting all the attribute vectors; carrying out random weighting on the values of the attribute vectors according to the sorted results to generate a new attribute vector; and repeatedly iterating until the verification is finished. Compared with the prior art, the self-adapted random verification method used for simulating the ultra-large-scale chip has the advantages that the whole efficiency of the random verification is improved, the work needing to be carried out by a verification worker is reduced and the resources are more effectively utilized.

The invention relates to the field of early fault diagnosis of rotating machineries, and discloses a self-adaptive stochastic resonance early fault diagnosis method based on a grey wolf optimization algorithm. The self-adaptive stochastic resonance early fault diagnosis method improves the weak signal detection ability of a stochastic resonance method, and realizes precise diagnosis of machinery early faults. The self-adaptive stochastic resonance early fault diagnosis method introduces the grey wolf optimization algorithm into a bistable state stochastic resonance method, optimizes stochastic resonance structure parameters, selects the optimal structure parameter adaptively according to input signal features, realizes the optimal stochastic resonance output, and further achieves precise extraction of weak faults and accurate fault recognition. The self-adaptive stochastic resonance early fault diagnosis method is suitable for early fault diagnosis of rotating machinery.

The invention discloses a method for selecting a self-adaptive stochastic wave beam mode in a multi-antenna wireless cognitive network. A base station of the cognitive network constructs stochastic waves in a null space of a main user channel according to a certain signal-to-noise ratio (SNR) threshold, and transmits the stochastic waves by using opportunistic beam forming or opportunistic space division multiple access (SDMA). When the launched SNR is more than the threshold, the cognitive network is an inter-user interference limited system, the base station selects the stochastic wave forming mode of a single beam and selects the user which has the maximum received SNR from the sub-scribers as a current communication object; when the launched SNR is less than the threshold, the sub-scriber network is a noise interference limited system. The base station selects the opportunistic SDMA of multi-beams and then selects a plurality of scribers with the maximum received SNR from the sub-scribers as the communication objects, thus realizing the maximization of system capacity. The method of the invention provides a transmitting proposal supporting a plurality of stochastic wave beam models for the multi-antenna wireless cognitive network.

The invention discloses a method for generating a metamorphic test case based on an adaptive random strategy, and aims at randomly automatically generating a test case based on an adaptive strategy. The method comprises the steps of randomly generating a first group of original test cases; generating additional test cases according to metamorphic relation; forming a selected test case through the two cases; dividing an input field to obtain a subfield without being covered with the selected test case; generating a plurality of test cases randomly in the subfield; selecting one group of test cases as the candidate original test cases; generating the additional test cases according to the metamorphic relation; then selecting the test cases with the maximum distance as the next group of text cases according to the distance from the candidate test cases to the selected test cases and the distance from the candidate original test cases to the additional test cases. The test case generation method is simple and clear; the dispersion, effectiveness and coverage rate of the test cases in the input field are greatly improved.

The invention provides an autonomous underwater robot signal processing method based on particle swarm optimization monostable adaptive stochastic resonance. The structure parameters of a monostable stochastic resonance system are optimized through a particle swarm optimization algorithm so as to realize the adaptive stochastic resonance of AUV (Autonomous Underwater Vehicle) control signals and status signals and improve the stochastic resonance effect of the AUV control signals and status signals, and finally the purposes of inhibiting external stochastic disturbance contained in the AUV control signals and status signals and enhancing the characteristics of fault signals are achieved through the phenomenon of stochastic resonance. The method solves the problems that the selection of the structure parameters lacks reasonable theoretical foundation and an optimal stochastic resonance effect is difficult to realize caused by the fact that a traditional single-parameter fixed-step adaptive stochastic resonance method ignores the interaction effect among the parameters, the external disturbance is inhibited and the characteristics of the fault signals are enhanced through the AUV control signals and status signals processed by an adaptive stochastic resonance system, and the method can be used in the fields such as fault diagnosis and fault-tolerant control of AUV thrusters.

The invention discloses a low concentration gas detection method based on adaptive stochastic resonance. Collected resistance signals responsive by the sensor are pretreated to obtain periodic signals with small parameters and suitable for input by a nonlinear bistable system; initial values of the system parameters and the optimizing scope are set; weighted signal-to-noise ratio is used as an effect evaluation index; the adaptive algorithm is used for searching the optimally matched system parameters; and the parameters corresponding to the maximum weighted signal-to-noise ratio are the optimal parameters of the system, and the stochastic resonance effect is at the maximum level at this moment. The invention uses the weighted signal-to-noise ratio characterized adaptive algorithm to search the optimal parameters of the bistable system, overcomes the defects of using signal-to-noise ratio and correlation coefficient as evaluation indexes of engineering signals, and restriction of difficult choosing or inaccurate choosing of system parameters of stochastic resonance, and effectively detects weak signals. The method is applied to the detection of low concentration gas, and the deterministic mixed gases with different concentration can be distinguished by comparing the maximum values of weighted signal-to-noise.

The invention discloses a novel SNR index based self-adaptive stochastic resonance rolling bearing fault diagnosis method. The method can realize fault feature extraction and fault type discriminationwithout knowing the fault characteristic frequency in advance, and can suppress the side frequency, and is not affected by external load fluctuations, the bearing rotating speed and the bearing type.At the same time, the invention also provides an effective method for extracting fault feature information and discriminating the fault type in the case of a small-scale fault feature frequency oscillation caused by a load variation or the like.

The invention belongs to the communication signal detection field and the non-linear field and particularly relates to an adaptive stochastic resonance system and method based on an artificial fish swarm algorithm. The system comprises a normalized stochastic resonance module, an artificial fish swarm adaptive module, a step adjustment module, a noise adjustment module and an iteration control module. Through the system, not only can a problem of difficulty in setting an optimization searching initial value of an adaptive system be solved through normalized stochastic resonance, adaptability is improved, no stochastic resonance, excessive stochastic resonance and insufficient resonance caused by parameter selection are considered, relatively strong search capability is kept through a step function adjusted according to resonance convergence conditions, but also local optimum can be properly discovered and jumped out from, and optimal value convergence efficiency is accelerated. An input signal to noise ratio is optimized through the noise adjustment module, and system signal to noise ratio gain is substantially improved.

The invention provides an adaptive stochastic resonance weak signal matching detection method and relates to the technical field of signal detection. The method, by analyzing signal-to-noise ratio gain of a stochastic resonance system, obtains the trilateral relationship of noise intensity of a received signal, system barrier height, and pre-processing gain of a received signal under the conditions of optimal resonance so as to maximize the output signal-to-noise ratio of the stochastic resonance system which has fixed parameters by pre-processing the received signal. The noise intensity of the received signal can be estimated through traditional methods. According to the invention, the system has a simple designing method, and is easy to realize, obviates the need for adjusting noise intensity and system parameters, does not need tedious optimization of actual environment. The method is simple and easy. The system parameters have a wide selection range. The method matches all types of frequency band signals by simply and directly adjusting the received signal, and can directly process the received signal. Various detectors can be applied to the obtained output sequence. The method is simple and easy to implement and can be widely applied to related fields of weak signal detection.

The invention relates to a microgrid energy optimization scheduling method based on adaptive control. The microgrid energy optimization scheduling method comprises the steps of S1, obtaining a randomlinear control system; S2, constructing an adaptive microgrid optimization scheduling model according to the random linear control system obtained in the step S1; S3, performing MPC optimal schedulingon the adaptive microgrid optimal scheduling model constructed in the step S2. The beneficial effects of the method are that: according to the microgrid energy optimization scheduling method based onadaptive control, under a combined optimization scheduling framework of a random energy and an energy storage system, an optimization strategy based on adaptive random model predictive control is provided; the method is compared with a robust model predictive control method based on scene construction, different self-adaptive updating strategies and comparison models of different adjusting parameters are designed, the scheduling performance of the system and the self-adaptability of the model can be effectively improved, and the conservative property of the system is reduced.

The invention discloses an energy detection method based on deviation correction. The method comprises the specific steps of: firstly, carrying out secondary sampling treatment on a large-frequency receiving signal r(t) for reducing the large-frequency receiving signal r(t) into a small-frequency signal r'(t); then, inputting small-frequency signal r'(t) into an SR (Stochastic Resonance) system and outputting x(t); and finally, carrying out treatment based on the deviation correction on the x(t) to obtain a checking counting amount T(x), comparing the T(x) with a threshold and judging. According to the energy detection method based on the deviation correction, a direct-current component of an SR system output signal is effectively eliminated so that the detection performance is improved, particularly a good performance can be represented under a low SNR (Signal Noise Ratio), the influence noise uncertainty on the spectrum sensing performance is effectively inhibited, and the requirements of a CR (Cognitive Radio) system are better met; when a scale conversion factor of secondary sampling is calculated, the method also can adjust the scale conversion factor of the secondary sampling through the feedback of a spectrum amplitude value, so that an input authorization user signal is converted into a frequency at which a self-adapted random resonance system can easily generate random resonance, and therefore a signal to noise ratio gain of an output signal is maximized.

The invention discloses a three-dimensional reconstruction method based on point cloud optimization sampling and belongs to the field of computer vision. The objective of the invention is to solve theproblem that in the three-dimensional reconstruction process of dense point cloud, under the condition that the main texture characteristics are ensured, the reconstruction speed is low. According tothe method, a mean shift algorithm is mainly utilized to carry out region segmentation on a plurality of corrected images; an improved bidirectional DP algorithm is adopted; sequentially carrying outstereo matching on the same regions after the adjacent images are segmented to obtain a disparity map; secondly, eliminating interference noise of the disparity map by using bilateral filtering to obtain a plurality of dense depth point cloud maps, then obtaining main texture features through self-adaptive random sampling, removing smooth areas, finally calculating depth value estimation of eachpoint cloud, and extracting the main point cloud according to the sequence of the estimation values. According to the method provided by the invention, more obvious texture features can be obtained ata higher calculation speed, and the method has a wide application prospect in natural scene three-dimensional reconstruction.

The invention provides a random resonant current weak information identification method based on a linear searching strategy. A signal-to-noise ratio is used as a condition for generating random resonance by a control system, and an advantage of maximizing the output signal-to-noise ratio of a noise strength control system is realized. Furthermore a maximal signal-to-noise ratio max(SNR) is used as a system optimization target function. Through estimating a weight function of a system structure parameter a and a noise strength D in each iteration process, and then correcting the weight function in an iteration process, the system finally outputs the maximal signal-to-noise ratio, thereby improving calculating capability in determining the system structure parameter through optimizing the signal-to-noise ratio (SNR), and realizing adaptive random resonance of the current signal. Because system searching difficulty and weight function complexity are reduced in an iteration process, calculation time is reduced and the method is particularly suitable for periodical signal detection of the current.

The invention discloses a self-adaptive random test method based on iteration area equipartition and positioning, which is used for reducing blind generation of test cases in a random test and improving the efficiency of the random test. The method mainly comprises the steps that 1, determining the range of an input domain; 2, randomly generating a first test case in an input domain; 3, equally dividing the input domain, randomly selecting a sub-domain at the same time, then equally dividing (hypothetical equally dividing) again, randomly generating test cases for the sub-domains which are equally divided again, and forming a candidate test case set; and 4, aiming at the candidate test case set, determining an adjacent region set, forming an executed test case set by the test cases in theadjacent region, and determining the next test case to be executed by adopting an FSCS _ ART (self-adaptive random algorithm of a candidate set with a fixed size). The process is continuously repeateduntil program errors are found. Experimental verification proves that compared with a random test method, the method has the advantage that the performance is improved by 30%.

The invention provides a feature extraction method for self-adapting stochastic resonance type seismic waves. The feature extraction method comprises the following steps: estimating noise intensity byvirtue of maximum likelihood estimation, establishing a second-order nonlinear bistable stochastic resonance system, and realizing noise matching and frequency matching by optimizing output signal tonoise ratio gain measure and synchronizing Cramers escape rate; and changing the signal frequency according to a preset step length, selecting a maximum signal to noise ratio value as an optimal matching value, and extracting the corresponding signal frequency, namely a target characteristic line spectrum frequency. According to the method, the signal energy and the local signal to noise ratio ata target characteristic line spectrum can be substantially increased, and characteristic signals completely drown in noise can be obviously enhanced and output.

The invention discloses a parameter search range determining method based on adaptive random resonance. The parameter search range determining method based on adaptive random resonance comprises the following steps of outputting an upper limit (amax) and a lower limit (amin) of a stable first parameter (a), which needs to be determined, of a random resonance system according to outputting of the random resonance system; determining a lower limit (bmin) of a second parameter (b) of the random resonance system according to the lower limit (amin) of the first parameter (a); transmitting a mixing signal to the random resonance system; calculating power spectra outputted by the random resonance system; continuously increasing the second parameter (b) of the random resonance system by using a fixed step length as a change interval until the power spectra are diverged; and using the second parameter (b), which corresponds to a previous operation of diverging of the power spectra, of the random resonance system as an upper limit (bmax) of the determined parameter (b) of the random resonance system. By the parameter search range determining method based on adaptive random resonance, the complexity of an algorithm is reduced effectively, and the success rate of induced random resonance is improved.

The invention discloses a communication signal detection method based on adaptive stochastic resonance. The problem that the system structure parameters and the calculation step size of the stochasticresonance in the prior art are difficult to be selected is solved. The method comprises the following steps of: inputting a communication signal, determining parameters of the particle swarm and initializing the parameters; sampling the received signal in time; establishing a model of adaptive stochastic resonance system; calculating fitness of stochastic resonance system by particle swarm optimization algorithm; determining whether the iteration is terminated; outputting detected signal. A particle swarm is established by taking a stochastic resonance system as a model, Particle Swarm Optimization (PSO) is used to obtain the optimal structural parameters of the adaptive stochastic resonance (ASR) system. The output signal-to-noise ratio (SNR) and bit error rate (BER) are used as fitnessvalues. The weak signal under noise background can be detected optimally. The invention greatly improves the output signal-to-noise ratio, reduces the bit error rate, and is used for digital signal detection.

The invention discloses an adaptive stochastic resonance underwater sound signal detection method based on multi-parameter optimization and solves the problem that stochastic resonance system structural parameters and a calculation step are difficult to select in the prior art. The method disclosed by the invention comprises the following implementation steps: determining search ranges and searchprecisions of multiple parameters of the stochastic resonance system; performing binary coding; initializing population; determining a number of times that individuals in the population are selected;performing a crossover operation; performing a mutation operation; performing binary decoding; receiving an underwater acoustic signal by a receiver; calculating a fitness value; and outputting judgment. The method disclosed by the invention takes output signal-to-noise ratio and bit error rate as fitness function and performs stochastic resonance structural parameters and the calculation step byusing a genetic algorithm, so that a weak underwater sound signal in an offshore underwater complex channel is optimally detected. The method disclosed by the invention greatly improves signal-noise-ratio gain of an output signal, reduces the bit error rate and can be used for underwater signal detection in offshore underwater communication.

The invention discloses a self-adaptive stochastic resonance denoising method for a silicon single crystal growth image under a low signal-to-noise ratio. For the silicon single crystal growth image under the low signal-to-noise ratio, the bistable stochastic resonance is combined with the PSO optimization algorithm, and the self-adaptive stochastic resonance image denoising algorithm based on PSOis designed. According to the method disclosed by the invention, the stochastic resonance is used for detecting weak signals in a lossless mode, so thunder the silicon single crystal growth image under the low signal-to-noise ratio is denoised and enhanced. The quality of the image is improved and the PSO optimization algorithm is utilized. The Donoho noise standard deviation is used as the fitness function of the optimization algorithm. The system parameters of the stochastic resonance are adjusted in real time so as to obtain the optimal resonance output effect, and the image denoising effect is realized. After the silicon single crystal growth image is processed by the method, the noise can be effectively removed. The quality of the image can be improved. Therefore, the meniscus of thesilicon single crystal image under the low signal-to-noise ratio can be accurately detected. The foundation is laid for the accurate detection of the diameter of crystals.