2047 results about "Low frequency" patented technology

The invention discloses a photovoltaic power generation energy storage device control method based on artificial intelligence, and the method comprises the steps: S1, distributing synchronous sampling nodes at a PV side, a bus side and an energy storage side, collecting six paths of quantities, and obtaining a time sequence matrix through incremental buffering, time correction and abnormity elimination; s2, detecting a locking event through a load power step, performing window interception and differential normalization, and combining reactance and correlation to generate an event space-time diagram; s3, the irradiance and the impact amplitude are superposed, and event features are extracted through multi-scale space-time convolution and high and low frequency coupling; s4, calculating a risk score, estimating tail distribution by using mixed Pareto when the risk score exceeds a threshold, and giving a confidence upper bound; s5, constructing an objective function based on the tail distribution and the event coupling probability; s6, the EMS executes reference power according to SOC amplitude limiting, slope detection triggers compensation or calls a plan, and derating feedback is carried out during border crossing; s7, collecting pressure, residual error and loss, and adaptively updating convolution and scheduling parameters; according to the invention, real-time high-precision prediction of the power peak generated by superposition of minute-level photovoltaic fluctuation and millisecond-level load step is realized.

The invention discloses a distribution line broadband waveform sampling method, and belongs to the technical field of power system monitoring. By collecting quantum vacuum fluctuation noise, temperature, humidity and electromagnetic parameters, a quantum environment coupling entropy model is constructed, an optimal sparse base dictionary is dynamically generated, and a noise entropy break variable is calculated. And when the entropy break variable exceeds a threshold value, triggering sub-Nyquist compressed sampling, otherwise, adopting Nyquist rate sampling to realize dynamic matching of sampling resources and signal complexity. A high-frequency component of a compressed signal is reconstructed by combining an adversarial compressed sensing network with a residual network, a low-frequency component is generated based on an original signal, the distribution consistency of high and low frequency entropies is optimized by using a Wasserstein distance, and finally a three-dimensional waveform matrix containing a timestamp, a frequency bin and quantum entropy confidence is output. The problems of low sampling efficiency, poor sparse representation adaptability, slow noise response, inconsistent reconstruction and the like in a non-stationary signal and complex noise environment are solved, and the precision and energy efficiency of broadband waveform sampling are remarkably improved.

The invention relates to the technical field of treatment scheme optimization, and provides an emergency treatment critical patient first-aid path optimization method and system based on big data, and the method comprises the steps: building a standardized event sequence through extracting historical diagnosis and treatment data of a hospital information system, and analyzing the time sequence correlation between events through a graph structure mining technology, key diagnosis and treatment nodes and high and low frequency paths are identified, and a layered first-aid path state transition network is constructed based on the key diagnosis and treatment nodes and high and low frequency paths. When a real-time patient is processed, the system matches an electronic health record of the patient with a network core state, dynamically plans a subsequent standard transfer path, and avoids a low-frequency risk path marked as a taboo, so that an emergency path optimization scheme of risk avoidance and path dynamic optimization is realized, and the treatment efficiency of emergency critical patients is improved. The treatment effect is improved, and the bad prognosis risk is reduced.

The invention provides a leather defect identification method based on double-branch frequency domain feature decoupling and a dynamic channel enhancement network. For the problem that the characteristics of a leather image are difficult to correctly extract due to the characteristics of complexity and non-uniformity of the leather image, a high and low frequency domain decoupling thought is introduced in the invention to extract main body and detail information respectively; a DCT (Discrete Cosine Transformation) guided high / low-frequency double-branch network is designed to respectively capture a local structure (high-frequency branch) of a microdefect and a continuity feature (low-frequency branch) of a macroscopic texture, a structure in which an SE (Square-and-Excitation) module is embedded in a feature fusion stage is designed, and channel weight and spatial position response are jointly optimized, so that the feature fusion is realized. And the significance expression of the key defect area is enhanced. In some public data sets, the method has better classification performance and higher diagnosis speed.

The invention relates to the technical field of data center dynamic modeling, and discloses a twin model dynamic modeling method for a data center DCIM platform, which comprises the following steps: constructing a discrete state space model containing a thermal coupling matrix and a system matrix, collecting real-time power and temperature time sequence data, and calculating a cross-correlation function to lock hot air dynamic transmission lag time; calculating cut-off frequency based on physical attributes of the cabinet and decomposing data into high and low frequency components by using a complementary filter; according to the method, the model parameters are made to return to a physical source through a frequency domain decoupling mechanism, the problem of aliasing of airflow coupling and structural thermal inertia parameters in a traditional single-scale identification method is solved, and the method is suitable for large-scale identification. And the physical authenticity and prediction robustness of the twin model under a complex working condition are improved.

The invention discloses an optical remote sensing satellite high and low frequency relative radiation error correction method and system, and the method comprises the steps: S1, carrying out the systematic high frequency radiation error calibration of a remote sensing image, so as to eliminate stripe noise; s2, performing frequency domain decomposition on the image after high-frequency correction, and separating high and low frequency components; s3, calculating a global statistical moment of the low-frequency component as a correction reference; s4, calculating a local statistical moment of the low-frequency component column by column; s5, calculating an independent correction coefficient for each column according to the global and local statistical moments; s6, correcting the low-frequency component by using the correction coefficient; and S7, recombining the corrected low-frequency component with the original high-frequency component to generate a final image. The method is suitable for improving the quality of the remote sensing image with multiple composite radiation errors, high-frequency stripes and low-frequency brightness gradient can be effectively eliminated, original texture details of ground features are reserved to the maximum extent in the correction process, and the radiation fidelity is guaranteed.

The invention discloses an OCT (Optical Coherence Tomography) retina image denoising method based on a high-frequency enhanced diffusion model, which is characterized in that a double-branch diffusion model network constructed based on frequency perception Fourier transform attention (FFTA) is used for separating different frequency domains, enhancing specific frequency domain features and fusing the specific frequency domain features into a spatial domain feature map to realize stronger frequency domain perception and processing capability; the invention relates to a time step T coding method for a module of frequency domain attention. The time step T of the diffusion model is used as a parameter to be coded into a frequency domain attention module to participate in self-attention matrix calculation, so that frequency domain feature processing is aligned with the iteration step number of the diffusion model, and different frequency domain information is pointedly processed at different time steps T; the frequency selective hopping mechanism uses pooling operation to obtain a high / low frequency characteristic pattern, so that the network has adaptive frequency domain retention capability for different local parts.

The invention belongs to the technical field of metal crack detection, and discloses a metal microcrack ultrasonic detection method, which comprises the following modules: an ultrahigh frequency composite array probe with a center frequency dynamic adjustable range of 5-50 MHz; the dual-mode excitation and receiving module is used for independently generating a pulse reflection mode excitation signal and a diffraction mode chirp frequency modulation signal; the phase-controlled synthetic aperture dynamic focusing module is used for realizing + / -60-degree deflection and sub-wavelength level focusing of acoustic beams; the nonlinear harmonic enhancement module is used for exciting a crack nonlinear effect by superposing 1-2MHz fundamental waves; and a time-space-frequency three-domain data fusion module. According to the method, signal-to-noise ratio collapse caused by the diffraction effect in a traditional method is overcome, more importantly, the system has low-frequency penetrability and high-frequency resolution at the same time due to 80%-120% of broadband coverage of chirp frequency modulation signals, the diffraction signal gain is increased by 20 dB or above, a near-surface blind area is compressed to be within 0.2 mm, and the system can be applied to the field of radar imaging. And the core pain point of missing detection of the near-surface microcracks of the thin-wall component is solved.

The invention discloses a video crowd counting method based on a cascaded cross-domain feature interaction network. The method comprises the following steps: carrying out data enhancement processing of random cutting and horizontal flipping on a current frame and front and back frames of the current frame; and constructing a cross-domain feature interaction network composed of a spatial domain branch and a frequency domain branch. The frequency domain branch extracts frequency domain feature output of different stages through a high and low frequency signal aggregation module and a feature encoder based on adjacent frames; the spatial domain branch is based on a single-frame image, and static spatial semantic features are extracted through a feature encoder. Cascade fusion is carried out on the double-branch features on multiple scales, two-way channel cross attention is utilized to reconstruct time sequence correlation frequency domain features of a current frame, and fusion and reconstruction of the two domain features are achieved through a cross-domain feature mutual modulation module. And after the reconstructed double-branch features are processed by the fusion network, outputting a crowd density map of the current frame by a density regression head. And after training is completed, storing the optimal model for video crowd counting. According to the invention, through cross-domain feature cascade and bidirectional time sequence modeling, the accuracy and robustness of crowd counting in a video scene are effectively improved.

The invention relates to the technical field of data compression, in particular to a ZSTD compression algorithm optimization method based on joint coding and ternary decomposition, and the method comprises the following steps: S1, building a joint frequency table based on the joint distribution of the literal length and the matching length of a previous section, setting a frequency deviation criterion, and dynamically adjusting a low-frequency combination proportion; s2, decomposing the offset value structure into a leading segment, a trailing segment and a middle segment, performing bit width-entropy ratio analysis on the middle segment, and selecting a symbol index or bit stream coding; and S3, entropy discriminant coding is performed on the joint symbol and the offset symbol respectively, and a structure position is mapped synchronously to generate a structured compressed output stream. According to the method, collaborative optimization of frequency self-adaption, redundancy minimization and structured mapping is realized through joint symbol dynamic modeling and offset ternary structure entropy discriminant coding, so that the ZSTD compression efficiency and the coding stability are remarkably improved.

The invention discloses a current source type LCCL double-frequency resonance system and a parameter matching method thereof, which are suitable for synchronous double-frequency induction quenching of complex workpieces such as gears. The system is composed of two current type resonant inverters working at a low frequency and a high frequency respectively and a dual-frequency LCCL resonant cavity, and dual-frequency current superposition is realized through parallel output. The double-frequency LCCL resonant cavity has two parallel resonance points, a mode based on simplified topology is adopted to simplify parameter design, and meanwhile design errors are maintained at a low level. A power matching coefficient is introduced, the ratio of high-frequency output power to low-frequency output power is adjusted by adjusting the value of the power matching coefficient, and the problem of double-frequency power unbalance caused by frequency variation characteristics of load equivalent resistance and the like is solved. The superposed double-frequency current forms a low-frequency envelope embedded high-frequency oscillation composite waveform on the induction heating coil, cooperative control of tooth root deep quenching (low frequency) and tooth surface hard quenching (high frequency) is achieved, and the method has the advantages of being simple in parameter design, high in power decoupling performance, high in operation stability and the like.

The invention provides a signal acquisition system of a photoelectric detector. In the signal acquisition system, a transimpedance pre-amplification circuit is arranged at the output end of the photoelectric detector, and a passive high-pass filter circuit is connected between the transimpedance pre-amplification circuit and a post-voltage amplification circuit. And a feedback network is formed in the transimpedance pre-amplification circuit and is used for converting a current signal output by the photoelectric detector into a proportional first voltage signal and outputting the proportional first voltage signal. The filter circuit is a third-order LC high-pass filter circuit and is used for suppressing direct current, low frequency and self-beat frequency components and keeping a 10-90MHz pass band flat. The rear voltage amplification circuit adopts a voltage amplification structure of a voltage operational amplifier, the voltage gain Av is 6-10, and the super-band gain is limited through a shunt capacitor. According to the embodiment of the invention, through the structure and parameter setting, the noise gain peak value is suppressed, and the system stability is improved.

The invention relates to the technical field of power system operation and control, and discloses an improved model-free adaptive flexible low-frequency power transmission oscillation suppression method, which performs zero-phase lag filtering on noisy signals through a nonlinear tracking differentiator, effectively solves the problem of control divergence caused by signal noise, and improves the suppression efficiency. A tight-format dynamic linearization and linear expansion state observer technology is adopted, accurate capture of dynamic characteristics of a strong nonlinear system and real-time observation and compensation of internal and external full-dimensional disturbance are achieved without dependence of a physical model, and particularly, an adaptive estimation algorithm of an output variable quantity penalty factor is introduced. The problem that model parameter identification jumps severely under the broadband oscillation working condition is solved, the robustness of a control system is enhanced, and the self-adaptive optimality of a control strategy is guaranteed and interference of long-term online operation on steady-state performance is avoided by combining parameter optimization of a cuckoo search algorithm and an event triggering mechanism of time-frequency double criteria.

The invention discloses a high-time-resolution flow field testing method and system based on sparse moment measured values and an intelligent power system, and belongs to the field of flow field testing and data reconstruction in bridge wind engineering. According to the method, overall low-frequency and local high-frequency sparse flow field data are obtained through a four-pulse fixed-frequency variable-frequency laser system and a four-exposure high-speed imaging PIV sampling system; a 32-dimensional nonlinear modal coefficient is extracted through a multi-scale convolution flow field sparse feature extraction model, then an unsampled time step coefficient is predicted through an LSTM intelligent power system model, and finally the unsampled time step coefficient is input into a multi-scale convolution auto-encoder to reconstruct a high-time-resolution flow field. The method does not need to depend on a pressure sequence, reduces the hardware and data processing cost through sparse sampling, accurately captures the flow field dynamics characteristics through intelligent modeling, solves the problems of expensive high-frequency hardware, loss of low-frequency reconstruction data and difficulty in model training in a traditional PIV test, and is suitable for flow field dynamics research and engineering optimization.

The invention discloses a remote sensing image semantic segmentation method and equipment based on an enhanced MambaVision architecture. The method comprises the following steps: performing initial feature extraction on a to-be-segmented high-resolution remote sensing image through a patch embedding module; the initial features are input into a MambaVision backbone network to extract multi-scale features, the backbone network comprises four layering stages, the stage 1 and the stage 2 adopt adaptive expansion convolution blocks to balance high and low frequency features, and the stage 3 and the stage 4 adopt two-dimensional selective scanning and a multi-head attention mechanism to capture global dependence; performing fusion and up-sampling on the multi-scale features through an MFMS decoder, and optimizing texture and boundary detection; the decoder comprises an MFMS attention module and a channel mixer; and performing multi-scale convolution fusion on the output of the MFMS attention module based on the channel mixer to generate a segmentation result. According to the method, through two-dimensional selective scanning, adaptive expansion convolution and a multi-frequency multi-scale MFMS decoder, the segmentation precision and efficiency are improved, and the method is suitable for high-resolution and large-size remote sensing images.

The present application provides a high and low frequency mixed circular connector, comprising: a low frequency contact (10) formed in a cylindrical shape extending in an axial direction; a high-frequency contact (20) formed in a cylindrical shape extending in the axial direction and provided in parallel with the low-frequency contact (10); the positioning piece (30) is arranged on the outer side of the high-frequency contact piece (20) in a manner of at least partially contacting with the high-frequency contact piece (20); the grounding piece (40) is arranged on the outer side of the positioning piece (30) in a mode of being in contact with the positioning piece (30); and a connector housing (50) which is formed in a cylindrical shape and accommodates the high-frequency contact member (20), the positioning member (30), and the ground member (40) therein so as to be at least partially in contact with the ground member (40). Consequently, conduction between the four-coaxial high-frequency contact and the connector housing can be realized with a simple and small structure.

The invention relates to the technical field of industrial vision, in particular to an automobile part surface defect detection method and system based on machine vision, and the method comprises the steps: firstly loading a CAD three-dimensional model, and rendering an ideal fringe reflection map in a virtual environment; calculating an optical distortion correction matrix by comparing the phase distortion with the phase distortion of an actual initial reflection image, driving a programmable light source to project a compensation pattern, and generating a normalized reflection intensity image; signal object decoupling is achieved through multi-scale wavelet transform, and high-frequency components and low-frequency components are separated; homomorphic filtering is applied to the low-frequency component to construct a homogenized background model, the high-frequency component is reversely corrected, and a high-signal-to-noise-ratio defect signal image is output; generating a coarse segmentation mask by the high-frequency signal through an adaptive local threshold, generating a morphology anomaly mask by the low-frequency signal through Hessian matrix morphological analysis, and fusing to form a collaborative segmentation mask; and extracting multi-dimensional geometric attributes of connected domains in the masks, and inputting the multi-dimensional geometric attributes into a decision tree to realize accurate classification and confidence output of defects.

The invention discloses a sewage discharge outlet flow real-time monitoring system based on water quality parameter linkage, and relates to the technical field of environmental monitoring. Through adaptive sampling period switching driven by abnormal fractions, the system is enabled to maintain low frequency in a normal state, and enters encrypted sampling when the risk rises; therefore, the capability of capturing short-time events is improved on the premise that the full-time data volume is not remarkably increased, key process fragments before triggering are reserved through the annular cache, and the integrity of event restoration and tracing is enhanced; an abnormal source is split into two types of information, namely deviation of flow relative to a self reference state and residual deviation of a flow-water quality coupling relationship, and the two types of information are normalized to a unified scoring scale and then are fused and output, so that abnormal judgment is expanded from single-point amplitude judgment to comprehensive identification of trend, fluctuation scale change and cross-parameter consistency damage. Therefore, the sensitivity to hidden modes such as non-significant change of flow and continuous deterioration of water quality is improved.

The invention relates to the technical field of power systems, and particularly provides a network construction type energy storage wide and low frequency disturbance suppression method suitable for a weak power grid environment. The method comprises the following steps: constructing an equivalent impedance model, and analyzing RLC resonance impedance matching conditions; constructing a quantitative model of impedance dynamic correlation; designing a multi-parameter adaptive virtual impedance module; constructing a cooperative control architecture of the adaptive virtual impedance and the network construction type energy storage converter; through verification of a continuous voltage drop simulation scene, adaptive adjustment parameters are optimized by adopting indexes of oscillation amplitude attenuation rate and stabilization time so as to ensure a wide and low frequency disturbance suppression effect under a weak power grid, and according to the method, broadband oscillation suppression under the continuous voltage drop scene is realized through adaptive virtual impedance, and dynamic accurate compensation is realized.

The invention discloses a low-frequency sound propagation loss forecasting method suitable for a polar region sea area, and the method comprises the steps: building a two-dimensional ice layer model of a forecast sea area according to remote sensing data based on the setting that sea ice of the polar region sea area is a uniform elastic medium; calculating hydrological conditions of the ocean sound channel of the polar sea area; establishing a forecast sea area two-dimensional seabed model according to the seabed topography sediment database; an under-ice sound field model is constructed based on a three-dimensional cylindrical coordinate system, sound pressure from unit intensity sound sources with different sizes and depths to a receiving point in a forecast sea area is calculated through a frequency domain finite element method in combination with a PML technology, and sound propagation loss of a needed position is obtained through calculation. According to the method, the application limitation of traditional ray and normal wave theories under complex boundary conditions is broken through through a finite element method, a synergistic effect mechanism of actual ice layer parameter space change, submarine topography fluctuation and sound velocity profile gradient characteristics is systematically analyzed, and sound field modeling is more accurately carried out on different ice layers and submarine topographies in the Chukong sea region all year round; the influence of sound wave propagation is simulated.

The invention discloses a track noise control method and system based on active acoustic control, and the method comprises the steps: collecting a track noise signal through a sensor array, and processing the signal through Fourier transform to obtain a spectrum distribution characteristic; judging the type of a noise source according to the spectral distribution characteristics, if the spectral distribution shows high-frequency dominance, determining the noise as wheel-rail contact noise, and if the spectral distribution shows low-frequency dominance, determining the noise as rail vibration noise, and obtaining a noise source contribution proportion; according to the noise source contribution proportion, a dynamically adjusted control signal is obtained; acquiring noise source coupling information which changes in real time from the dynamically adjusted control signal, and determining a fused noise source model; according to the fused noise source model, a least square method is adopted to optimize a control strategy, and an optimized control instruction sequence is obtained; and according to the optimized control instruction sequence, acquiring matching data of the current operation density and speed, and if the effect does not reach the standard, iteratively updating the model to obtain final control output.

The invention relates to the technical field of robot control. A humanoid robot gait generation system based on hierarchical control is characterized by comprising a multi-layer control system operating at different refresh frequencies, and the multi-layer control system comprises a high-layer gait and trajectory planning layer operating at a low frequency and used for generating a macroscopic motion instruction; the middle-layer whole-body motion optimization layer runs at medium frequency, serves as a bridge of high-layer planning and bottom-layer control and is used for converting expected plantar and trunk tracks into reference motion of all joints of the whole body; the bottom joint servo control layer runs at the highest frequency and is responsible for accurately tracking a joint-level instruction output by the whole-body motion optimization layer; the state sensing and feedback closed-loop layer is used for feeding back state information, obtained in real time, of the body state of the robot to all the layers, providing a basis for track re-planning of a high layer and providing a real-time state initial value for optimization of a middle layer; and a feedback signal is provided for servo control of a bottom layer.

An image blur correction device acquires a blur detection signal, and separates it into a high frequency band signal component and a low frequency band signal component using an HPF and an LPF. A high frequency side prediction processing unit acquires an output of the HPF, and a low frequency side prediction processing unit acquires an output of the LPF via a down-sampler. A prediction processing unit updates a filter coefficient of a prediction filter using an adaptive algorithm, and performs linear prediction on the blur detection signal. An adder adds an output of the high frequency side prediction processing unit to an output of the low frequency side prediction processing unit via a up-sampler, and outputs a superimposed output signal. Image blur correction control is performed on the basis of a superimposed output signal.

The invention relates to the technical field of hyperspectral image reconstruction, and discloses a frequency separation and physical perception attention hyperspectral image reconstruction method and system, and the method comprises the steps: obtaining a compressed snapshot observation image, carrying out the inverse dispersion operation, and restoring the image into an initial three-dimensional signal; inputting the initial three-dimensional signal into a physical perception mask attention U-Net reconstruction network, and outputting a reconstructed hyperspectral image: performing high and low frequency separation guidance on the reconstructed hyperspectral image, and respectively inputting extracted high frequency and low frequency features into a selective multi-head space attention module, inputting the obtained high-frequency attention feature map and the obtained low-frequency attention feature map into a mask attention module, and generating a dynamic weight map of a high-fidelity region according to the physical light transmittance of a modulation mask and a position code; mask enhancement is carried out on the high-frequency features, moderate modulation is carried out on the low-frequency features, and a fusion feature map is obtained through fusion; and performing residual connection and inverse dispersion alignment operation and then outputting.

The invention discloses a wavelet attention shadow removal method based on soft prior guidance, and belongs to the field of image processing. The method comprises the following steps: constructing a multi-scale wavelet attention codec architecture, decomposing an input image into a low-frequency component representing illumination characteristics and a high-frequency component containing texture details by using discrete wavelet transform, and establishing multi-scale frequency domain representation; proposing probabilistic soft prior modeling, adaptively generating a continuous differentiable probability distribution diagram by using a convolutional sub-network and a double-slope Sigmoid function, and guiding differentiated repair of high and low frequency features in a dynamic weight mode; and cross-domain illumination correction based on gray world hypothesis is introduced, spatial domain enhancement is performed on a frequency domain restoration result, illumination consistency of cross-domain processing is ensured, and finally a shadow-free image is output. According to the method, wavelet domain feature optimization is guided through the probabilistic soft mask, high-quality shadow removal is realized, and the robustness and visual quality of shadow removal are remarkably improved while the light weight of the model is kept.

The invention discloses a photovoltaic power ultra-short-term prediction method, system and device and a storage medium, and relates to the technical field of new energy prediction. The method comprises the following steps: constructing an improved ensemble empirical mode decomposition algorithm to decompose original photovoltaic power data, and distinguishing and reconstructing a plurality of decomposed intrinsic mode functions to obtain reconstruction sequences of high, medium and low frequency components; for high-frequency and intermediate-frequency components, carrying out dynamic denoising by adopting an extended Kalman filtering algorithm; and inputting the original data, the low-frequency component, the denoised high-frequency component and the denoised intermediate-frequency component into a WTConv1d module, extracting multi-scale features through a wavelet filter and convolution operation, and inputting a multi-dimensional input matrix constructed by the multi-scale features and meteorological data into a Transform model for prediction. According to the method, noise interference can be effectively suppressed, multi-time scale feature expression is enhanced, and a self-attention mechanism is fully utilized to capture a cross-scale dependency relationship, so that the prediction precision and stability are improved.

The invention discloses a double-frequency slot antenna structure with a back cavity and a design method, which are used for an omnidirectional communication system. The antenna can work in two frequency bands, the antenna comprises a disc metal cavity, eight arc-shaped gaps above the cavity, and some metal columns and metal sheets in the middle of the cavity, and the antenna feeds the gaps from the center of the bottom of the cavity in a coaxial probe feeding mode; a filtering mechanism is formed through a metal column and a metal sheet in the middle of the cavity, and high frequency is blocked through low frequency, so that high-band radiation waves are limited in the small cavity in the middle and then are radiated out from four first arc-shaped gaps above, and low-band radiation waves are radiated out from four second arc-shaped gaps above the whole disc cavity after passing through the filtering mechanism; high-frequency energy and low-frequency energy can be separated, different cavity sizes are formed at different frequencies, and working frequency radiation is controlled by combining the length of the gap.

The invention belongs to the technical field of new energy storage, and relates to a full-life-cycle capacity optimization configuration method for a photovoltaic-hybrid energy storage system. A traditional configuration method of photovoltaic energy storage does not accurately consider energy storage characteristics and whole life cycle cost, and is poor in economical efficiency and reliability. According to the method, a constraint model taking the minimum sum of modal estimation bandwidths as a target is constructed based on VMD decomposition, photovoltaic power is decomposed into different components, sorting is carried out based on modal center frequencies, and a differential power distribution strategy of high frequency-super capacitor, intermediate frequency-lithium battery and low frequency-vanadium battery is provided; the method achieves the precise matching of the energy storage technology and the power fluctuation characteristics, constructs a target function covering the whole life cycle investment, operation and maintenance, replacement and scrap cost, carries out the quantitative screening of an economical efficiency optimal scheme, and solves the limitation that a frequency demarcation point does not have a clear basis. Compared with two types of energy storage schemes which are divided into'high frequency and low frequency ', the'low-medium-high frequency' scheme is lower in comprehensive cost, and a technical scheme is provided for capacity configuration of the photovoltaic energy storage system.

The invention relates to the technical field of remote sensing image processing, in particular to an image super-resolution system and method based on high and low frequency separation perception Mama, and the method comprises the steps: firstly carrying out the shallow convolution feature extraction of a low-resolution image; then entering a plurality of frequency sensing Mama groups, performing frequency separation and enhancement on each group through a high and low frequency feature adaptive enhancement module, and performing depth feature transformation through a plurality of frequency sensing Mama blocks; the extracted depth features are refined through a global channel-space attention module, and finally a high-resolution image is reconstructed through up-sampling. Through organic combination of the modules, the defects of insufficient frequency perception, low global modeling efficiency, insufficient feature optimization and the like are effectively overcome, and high-quality collaborative reconstruction of remote sensing image structures and textures is realized.

Logic may generate or detect a communication as a punctured block of orthogonal frequency division multiplex (OFDM) symbols on a carrier with a channel bandwidth of 5 megahertz or less, the punctured block having a first set of highest frequency resource blocks (RBs) of the OFDM symbols punctured or having a second set of highest frequency RBs and lowest frequency RBs punctured. The punctured block may comprise a physical downlink control channel (PDCCH) block, a synchronization signal / physical broadcast channel (SS / PBCH) block, or a downlink bandwidth part (DL BWP) on a carrier with a channel bandwidth of 3 MHz, 5 MHz, or other channel bandwidth less than 5 MHz. And logic may cause transmission of or demodulate and decode the punctured block.