5 results about "Channel inversion" patented technology

System and method for an over-the-air computation (AirComp) scheme for federated edge learning (FEEL) doesn't require channel state information (CSI) at the edge devices (EDs) or edge server(ES). The disclosure adopts the majority vote (MV) principle and defines multiple subcarriers and orthogonal frequency division multiplexing (OFDM) symbols for voting options, which reduces to frequency-shift keying (FSK) over OFDM subcarriers as a special case. Thus, FSK-based over-the-air computation is provided for federated edge learning without channel state information. Since the votes from EDs are separated on orthogonal resources, the scheme eliminates the need for truncated-channel inversion (TCI) at the EDs and allows the ES to detect MV with a non-coherent detector.

The application discloses a digital air computing method based on binary complement coding, comprising the following steps: linear scaling and down rounding quantization are performed on a continuous analog signal to be calculated at a distributed device end, and the quantized value is converted into a binary complement sequence with a fixed length; a decoder is designed at a base station end based on a superposition result of each binary bit; each bit of the binary complement sequence is subjected to binary phase shift keying (BPSK) modulation, a frequency band is divided into a plurality of subcarriers by using orthogonal frequency division multiplexing (OFDM) technology, and a plurality of devices superimpose transmission on the same subcarrier for the same binary bit; equal power allocation is performed according to the weight priority of the binary bit, a transmission signal is preprocessed based on channel state information by using a truncated channel inversion strategy; a linear minimum mean square error (LMMSE) detector is applied to each subcarrier received signal at the base station end to estimate a superposition result; a multi-subcarrier mean square error minimization problem is constructed, and a greedy algorithm is used to optimize an active device set and a transmission power factor; and the application significantly improves the real-time performance and reliability of multi-device cooperative computing in actual application.

The application discloses a dam seepage field reconstruction and leakage channel inversion method and equipment, and the method comprises the steps of: constructing a structured topological graph reflecting the adjacency relationship between measuring points according to the spatial distribution characteristics of the dam seepage pressure monitoring section, and converting the monitoring data into a multi-dimensional feature tensor containing seepage pressure values, change rates and environmental factors; constructing a topological constraint-based space-time graph attention network model, introducing a topological adjacency mask in the attention score calculation process of the model graph attention layer to force the model to only aggregate features between nodes that exist in the topological structure, and embedding the spatial geometric constraint into the deep learning weight updating process; analyzing the attention weight matrix obtained by model training to invert the dominant leakage path inside the dam body, and outputting the reconstructed three-dimensional seepage field distribution based on sparse measuring points. The application eliminates the pseudo-correlation interference in the data-driven model through the topological hard constraint mechanism, and can realize the diagnosis of the dam seepage state.

Erase operations can be performed selectively on one of erase blocks or a memory array coupled to the same string by creating a pseudo PN junction that is located adjacent to the selected erase block. The pseudo PN junction is created by including channel inversion at least on those portions of the string coupled to unselected erase blocks, which further creates a flow of electrons. As a result of the channel inversion (along with channel accumulation created adjacent to the channel inversion), the flow of gate induced drain leakage (GIDL) holes are further generated from the pseudo PN junction and GIDL holes are induced to tunnel into memory cells of the selected erase block.

This invention relates to an elastic parameter identification method based on a multi-objective mayfly algorithm and Taylor expansion, comprising: S1: collecting well logging data and establishing an initial model based on the well logging data; S2: performing inversion based on the initial model to obtain the Pareto set under t iterations; S3: extracting the mayfly position at the center of the final Pareto set based on the Pareto set under t iterations; S4: completing a single-channel inversion based on the mayfly position at the center of the final Pareto set, and completing the inversion of the entire profile based on the single-channel inversion. This invention improves both the accuracy and stability of the inversion results in pre-stack AVA inversion, and derives a more reliable combined inversion method, improving the accuracy of the inversion and providing strong support for the accurate identification of high-quality reservoirs.