90 results about "Dummy data" patented technology

The invention discloses a dam safety detection method and system based on multi-source detection data, and relates to the technical field of dam safety detection, and the method comprises the steps: cooperatively collecting dam multi-source data through multiple platforms and multiple sensors; the collected multi-source data are preprocessed; constructing a multi-scale teacher network, and performing high-precision feature learning and risk quantification by using labeled multi-source data; teacher network knowledge migration is carried out through a knowledge distillation technology, and a lightweight student network is trained in combination with cross-domain pseudo data; and deploying the trained lightweight student network to an unmanned aerial vehicle or a robot dog, and carrying out dam safety real-time detection. Through integration of multi-source data acquisition, cross-domain mutual training of teachers and students, lightweight model deployment and automatic early warning decision, pain points of single data, difficulty in cross-domain adaptation, insufficient precision, deployment limitation and low efficiency are solved, the system can be directly deployed on an unmanned aerial vehicle or a robot dog, dependence on cloud computing power is not needed, data acquisition and analysis time delay is reduced, and the system can be widely applied to unmanned aerial vehicles or robot dogs. And emergency scenes are quickly responded.

The invention relates to the technical field of deep learning, and particularly provides a joint classification method for hyperspectral images and laser radar data. The method comprises the following steps: firstly, extracting multi-scale features of multi-modal data through a decoupling three-branch encoder, and then carrying out attention guidance cross-modal fusion on three initial features of the same scale through an autonomously designed feature fusion module to obtain three multi-modal fusion features of different scales; inputting the deepest fusion feature into a weight sharing decoder, and obtaining pseudo data through a reconstruction task; and the real data and the pseudo data are identified by the structure consistency perception discriminator, and the discrimination loss is returned, so that the encoder parameters are updated, and iteration is performed to the optimal. And finally, inputting the three-scale fusion features obtained based on the optimal parameters into a multi-level feature fusion classifier to complete classification, thereby effectively improving the classification precision, and realizing the adaptive fusion and enhancement of the multi-modal features.

This provides a machine learning technique that can suppress overfitting of new knowledge when applying IFSL to SaB. [Solution] This disclosure provides a machine learning device 100 that performs continuous learning based on a small number of new class data compared to basic class data, comprising: a pre-training module 30 that learns the weights of a neural network (NN) using basic class data; a pseudo-continuous learning module 40 that learns the weights of the NN learned by the pre-training module 30 using pseudo-data generated based on the basic class data; and a new class learning module 50 that learns the weights of the NN learned by the pseudo-continuous learning module 40 using basic class and new class data.

According to some example embodiments an operating method of a memory controller includes buffering first user data received from a host in a first region of a buffer memory corresponding to a first memory region of a memory device, receiving a flush request from the host, duplicating the first user data in a second region of the buffer memory corresponding to a second memory region of the memory device, padding, as padded data, the first user data duplicated to the second region with dummy data, programming the padded data in the second memory region of the memory device, buffering second user data received from the host in the first region, and programming, in the first memory region of the memory device, the first user data buffered in the first region and the second user data buffered in the first region.

The invention discloses an artificial intelligence digital science and technology system based on big data analysis, and relates to the technical field of artificial intelligence, and the system comprises a backup early warning module which is used for collecting data in real time through a collector, and comparing whether the task attribute and task configuration of data backup are abnormal or not in real time; the pseudo data monitoring module is used for monitoring whether data artifacts exist in the data backup process or not in real time and extracting the monitored data artifacts in real time to mark pseudo data positions; and the hierarchical search module is used for receiving alarm signals of existence of the pseudo data in real time through the data receiver, searching the pseudo data in real time and tracking a crushing execution result. According to the artificial intelligence digital science and technology system based on big data analysis, it is ensured that whether backup data is abnormal or not can be judged in time and early warned during data backup, the situation of false data monitoring omission is avoided, and the accuracy of data backup of artificial intelligence digital science and technology is improved.

This invention relates to the field of deep learning technology, and in particular provides a joint classification method for hyperspectral images and LiDAR data. The method first extracts multi-scale features from multimodal data by decoupling a three-branch encoder. Then, a self-designed feature fusion module performs attention-guided cross-modal fusion on three initial features at the same scale, resulting in three multimodal fused features at different scales. The deepest fused feature is input to a weight-shared decoder, where pseudo-data is obtained through a reconstruction task. A structure consistency-aware discriminator identifies real data from pseudo-data and returns a discrimination loss, thereby updating the encoder parameters and iterating until optimal. Finally, the fused features at the three scales obtained based on the optimal parameters are input into a multi-level feature fusion classifier to complete the classification. This method effectively improves classification accuracy and achieves adaptive fusion and enhancement of multimodal features.

A storage system is provided. The storage system includes a storage device having a plurality of memory cells, and a storage controller configured to control the storage device to store write data in a first memory cell among the plurality of memory cells, identify a current charge amount of a first cell string including at least one of the first memory cell and a current charge amount of a second cell string adjacent to the first cell string, and store dummy data in at least one memory cell connected to the first cell string or the second cell string based on the current charge amount of the first cell string and the current charge amount of the second cell string.

An arithmetic processing apparatus according to the present disclosure includes a digital filter. The arithmetic processing apparatus further includes a dummy data input unit configured to input dummy data to the digital filter when there is no input data input to the digital filter. The arithmetic processing apparatus further includes a cancellation processing unit configured to perform, on output data output from the digital filter, arithmetic processing for canceling an output component caused by the dummy data.

The application provides a vehicle trajectory positioning method, device and electronic equipment in a no-positioning-signal scenario. The method comprises: acquiring a multi-modal data stream of a vehicle in a target time period, the target time period being a time period from the vehicle driving out of the no-positioning-signal scenario to a positioning-signal scenario; performing a mirror flipping operation on the multi-modal data stream with the end timestamp of the multi-modal data stream as a center point to obtain an initial pseudo data stream of a preset time period of the vehicle; performing an inverse inversion processing on data values in the initial pseudo data stream to obtain a pseudo data stream, and inputting the pseudo data stream into a Kalman filter to perform fusion positioning processing on the pseudo data stream by the Kalman filter to obtain a driving trajectory of the vehicle in the target time period. The problem that the prior art needs to increase a reverse filter to realize vehicle positioning in a no-signal scenario by a forward and reverse filter and has a high no-signal positioning cost is solved.

An example electronic device may include communication circuitry, memory including storing instructions, and at least one processor including processing circuitry. The instructions, when executed by the at least one processor individually and / or collectively, may cause the electronic device to detect an event for an emergency situation, transmit a registration request message including on-demand S-NSSAI to a mobility management entity, receive a registration accept message including the on-demand S-NSSAI, transmit an establishment request message for a PDU session corresponding to the on-demand S-NSSAI to the mobility management entity, receive an establishment accept message for the PDU session from the mobility management entity, and in response to receiving the establishment accept message, transmit dummy data to a user data entity via the PDU session until an end of the event to maintain the PDU session.

The present disclosure relates to a communication apparatus and method for efficiently carrying out digital pre-distortion (DPD) on a transmission signal of a wireless communication system according to various operating conditions. The communication apparatus that carries out DPD on a transmission signal of a wireless communication system, according to an embodiment of the present disclosure, may comprise a power amplifier that amplifies the transmission signal. The communication apparatus may comprise a DPD apparatus that is connected to an input terminal of the power amplifier and carries out, on the basis of operating condition data of the communication apparatus, a DPD operation by using fake data generated for the transmission signal. The DPD apparatus may include a generator based on a conditional GAN. The generator may be configured to receive, as inputs, the operating condition data and input / output signals collected from the power amplifier and generate, on the basis of data acquired through repeated training of the conditional GAN using the collected input / output signals and the operating condition data, the fake data learned to compensate for nonlinear distortion at the output of the power amplifier.

The application discloses a tunnel scene LoRa-Mesh self-organizing network Beidou positioning system, comprising: a tunnel scene modeling module, which is used for generating a tunnel three-dimensional structure model and outputting structure deviation correction parameters; a deployment parameter generation module, which is used for generating a LoRa-Mesh backbone node deployment coordinate set, a Beidou positioning signal transmitting unit deployment coordinate set and a coverage parameter set; a LoRa-Mesh backbone node self-organizing network module, which is used for completing deployment and constructing a LoRa-Mesh network; a Beidou positioning signal transmitting module, which is used for completing deployment and reporting, via LoRa-Mesh, the deployment coordinates and working state data of a transmitting unit; a Beidou positioning signal simulation generation module, which is used for generating adaptive pseudo data one by one in combination with Beidou satellite original data; and a pseudo data issuing and transmitting control module, which is used for issuing pseudo data and controlling a transmitting unit to transmit Beidou radio frequency positioning signals. The application realizes stable coverage and continuous transmission of Beidou positioning signals in a tunnel, and improves positioning precision, system reliability and tunnel scene adaptation capability.

Methods, devices, and systems for managing memory devices are provided. In one aspect, a memory system can include a memory device including memory pages, a memory controller coupled to the memory device. The memory controller is configured to send, to the memory device, a first command indicating to identify dummy data, and in response to receiving, from the memory device, a response indicating that a first memory page is a zero page and without receiving data of the first memory page, determine data comprised in the first memory page as dummy data.

This invention provides a continuous learning image classification method based on generative playback, relating to the fields of continuous learning and computer vision. This method uses an image classification network to continuously learn the image classification task; it uses an image generation network to learn the distribution of images for the current task, and when a new task arrives, it generates images of the learned task as playback data, mitigating the catastrophic forgetting problem of the image classification network during continuous learning. The method employs a Teacher-Student architecture, using the image classification network for the (t-1)th task as the Teacher model, and initializing task T with its parameters. t An image classification network is used as the Student model; the Teacher model remains fixed during subsequent training to assist the Student model in remembering old knowledge. When a new task arrives, an image generation network generates pseudo-data from past tasks, enabling the model to distinguish between old and new task knowledge when learning a new task.

The invention relates to a multi-modal artificial intelligence generation content unified identification method and system. For multi-modal to-be-identified data, directly identifying the multi-modal to-be-identified data by adopting a fast thinking mode or performing interpretability identification by adopting a slow thinking mode based on the scene demand identification large model; the large identification model is trained by adopting a fast and slow thinking dual-mode two-stage training framework, an interpretable authentic identification data set is constructed, and standardized labeling from evidence extraction, space-time positioning to thinking chain construction is carried out. In a dual-mode supervision fine tuning stage, for a fast thinking mode, supervision fine tuning is utilized to realize fast discrimination and multi-classification traceability in a high-concurrency and low-delay scene; for a slow thinking mode, learning a complete interpretable reasoning chain from evidence extraction to criterion induction to conclusion generation; then, the model output is optimized through a preference alignment stage. Compared with the prior art, the method can provide a unified and reliable technical scheme for authenticity verification and interpretable review in a complex content scene.

A driving device is provided. The driving device includes a driver and a timing controller (TCON). The driver is coupled to a pixel circuit and a backlight circuit of a display panel. The TCON is coupled to the driver. In a frame cycle, after a delayed interval, the TCON outputs output image data to the driver, such that the driver drives the pixel circuit and the backlight circuit according to the output image data and backlight image data at the same time. Alternatively, when the driver drives the pixel circuit, after an interval based on dummy data of the backlight image data, the driver drives the backlight circuit according to available data of the backlight image data.

The embodiment of the invention provides a normalized structure reconstruction and feature sharpness perception online adaptation method and related equipment, and belongs to the technical field of transfer learning. The method comprises the following steps: a normalization layer reconstruction stage: obtaining a batch normalization model; generating pseudo data matched with the source domain data distribution by using a data-free distillation technology; replacing a batch normalization layer in the batch normalization model with a grouping normalization layer or a layer normalization layer to form an initial student model; performing knowledge distillation on the initial student model by using the pseudo data to migrate knowledge of the batch normalization model to the reconstructed student model to obtain a normalization layer reconstruction model; an online adaptation stage: obtaining a test sample; screening the prediction confidence of the test sample based on the normalization layer reconstruction model to obtain a reliable sample subset; calculating feature sharpness loss based on the reliable sample subset; and with the purpose of minimizing the feature sharpness loss, updating parameters of the normalization layer reconstruction model to complete online adaptation.

Apparatus and method for generating pseudo-EGM data from ECG data are disclosed. The apparatus includes at least a processor and a memory communicatively connected to the at least a processor, wherein the memory contains instructions configuring the at least a processor to generate EGM model training data, wherein generating the EGM model training data includes receiving the EGM model training data, wherein the EGM model training data includes exemplary ECG data correlated to exemplary EGM data and time synchronizing the exemplary ECG data and the exemplary EGM data, train an EGM machine-learning model using the EGM model training data, receive subject data, wherein the subject data includes subject ECG data and generate subject EGM data as a function of the subject ECG data using the trained EGM machine-learning model.

Examples of the present disclosure relate generally to implementing higher-layer processing on time-sensitive data blocks at a physical-layer-interface device. Some examples include logic to perform operations, the operations including providing data blocks to a physical-layer-interface device. The operations may also include adding dummy data into one or more time-sensitive data blocks of the data blocks being provided to the physical-layer-interface device. A size of the dummy data corresponding to a size of higher-layer-processing data. Other example operations may include removing higher-layer-processing data from a first ingressing data block. The other operations may also include removing a portion of the first ingressing data block and adding the portion to a subsequent ingressing data block. A size of the portion corresponding to the size of integrity-detection data. The other operations may also include removing the integrity-detection data from an ingressing data block. Related methods, systems, and devices are also disclosed.

A controller may detect an event occurring on a solid state device. The event may include at least one of: a program error on a physical block of a first virtual block, a read error on the physical block, or a power cycle of the solid state drive. The controller may program user data stored in the first virtual block onto a second virtual block based on detecting the event. The controller may include the first virtual block in a pool of virtual blocks scheduled for erase operations. When the first virtual block is in the open state, the controller may program dummy data on the first virtual block prior to performing an erase operation on the virtual block, or perform a flash write operation on the first virtual block prior to performing the erase operation on the virtual block.

The invention provides an array substrate, a display panel and a display device, and the array substrate comprises a substrate which is provided with a display area and a non-display area, and the non-display area comprises a gate drive circuit area and a virtual pixel area located between the gate drive circuit area and the display area; the first conducting layer is located on the substrate, the first conducting layer comprises a grid line and a grid electrode which are located in the display area, and the grid line extends to the virtual pixel area; the second conducting layer is located on the side, away from the substrate, of the first conducting layer, the second conducting layer comprises a data line, a source electrode, a drain electrode and a dummy data line, the data line, the source electrode and the drain electrode are located in the display area, the dummy data line is located in the dummy pixel area, and the data line and the dummy data line intersect with the grid line; the semiconductor layer is located between the second conductive layer and the first conductive layer, and the semiconductor layer comprises a first active layer located in the display area and a second active layer located in the virtual pixel area; at the intersection points of the adjacent data lines, the dummy data lines and the grid lines, the width of the first active layer in the extending direction of the grid lines is larger than that of the second active layer in the extending direction of the grid lines.

The invention relates to an LIMS-based report anti-counterfeiting verification method and system. The method comprises the steps of obtaining a core data field associated with a target report according to a report generation instruction; performing format processing on the core data field to generate a standardized data character string; calculating the standardized data character string to generate a first content hash value; packaging based on the first content hash value, and performing asymmetric encryption operation on the packaged data to obtain a digital signature; integrating the first content hash value, the digital signature, the unique report identifier of the target report and the network access address of the remote verification server into an anti-counterfeiting data load, and encoding the anti-counterfeiting data load into a two-dimensional encrypted verification code; and embedding the two-dimensional encrypted verification code into the target report to obtain an anti-counterfeiting report. According to the invention, the dynamic and encrypted binding of the original data of the laboratory information management system can be realized, and the convenient, instant and high-reliability verification of the authenticity and integrity of the report can be realized.

Provided in the present disclosure are an array substrate, a display panel and a display apparatus. The array substrate comprises: a base substrate, which has a display area and a non-display area, wherein the non-display area comprises a gate drive circuit area and a dummy pixel area located between the gate drive circuit area and the display area; a first conductive layer, which is located on the base substrate, and comprises gate lines and gate electrodes, which gate lines and gate electrodes are located in the display area, wherein the gate lines extend to the dummy pixel area; a second conductive layer, which is located on the side of the first conductive layer facing away from the base substrate, and comprises data lines, source electrodes and drain electrodes, which are located in the display area, and dummy data lines, which are located in the dummy pixel area, wherein the data lines and the dummy data lines intersect the gate lines; and a semiconductor layer, which is located between the second conductive layer and the first conductive layer, and comprises a first active layer located in the display area and a second active layer located in the dummy pixel area, wherein at points adjacent to the intersection points of the data lines and the dummy data lines with the gate lines, the width of the first active layer in the extension direction of the gate lines is greater than the width of the second active layer in the extension direction of the gate lines.

A system and method for providing a unique signature for proprietary data. The proprietary data may be transmitted to multiple trusted parties. Each of the trusted parties will have proprietary data with a unique signature to that trusted party. The signature allows a user to determine the source of the proprietary data breach via the signature.

The invention belongs to the technical field of data storage, and provides a method for solving the read error problem of a block which is not fully written and a solid state disk. The method comprises the following steps: S1, initializing a timer; s2, if the current pca is in the power failure protection state, filling the open block with dummy data when the current pca is valid; s3, when the current firmware algorithm is in an idle state and the timer is overtime, acquiring pca; s4, judging whether the current pca is valid or not; if the current pca is valid, when the current pca is consistent with the last pca, filling the open block with dummy data; when the current pca is inconsistent with the last pca, returning to S3; if the spare block is invalid, when the timestamp of the spare block is overtime and the current pca is consistent with the last pca, filling the spare block with dummy data; and if the spare block timestamp is not overtime or the current pca is inconsistent with the last pca, returning to S3. Differentiation processing is carried out for different scenes, the stability of the written data is improved, it is guaranteed that the voltage of the NAND Flash is in a normal state before writing, and therefore the data retention capacity of the solid state disk is improved.

Implementations described herein relate to production state awareness dummy data management. In some implementations, a memory device may detect a flag associated with a production state awareness process of the memory device. A first value of the flag may indicate for the memory device to write a default amount of dummy data during a write operation and a second value of the flag may indicate for the memory device to write a reduced amount of dummy data during the write operation. The memory device may perform a write operation in accordance with the flag.

The present application relates to a packaging anti-counterfeiting system based on frequency domain transformation, comprising a mobile terminal APP, an anti-counterfeiting outer package, a verification server and an anti-counterfeiting database. The present application also relates to an anti-counterfeiting verification method, comprising the steps of: collecting an original image of the anti-counterfeiting outer package; performing preprocessing, geometric correction processing and frequency domain decoding on the original image; comparing with standard information to complete the verification operation; and generating a digital certificate. The present application realizes visual invisibility of anti-counterfeiting information; does not need to change existing process parameters or add special materials; avoids moire and dot clustering problems; significantly improves the stability of hidden code embedding and extraction; adapts to the collection requirements of ordinary smart phones in complex scenes; effectively resists mainstream counterfeiting methods; balances the integrity of traceability and the convenience of verification without relying on a continuous network environment; simplifies the mobile terminal operation complexity, adapts to smart phones with different hardware configurations, balances the robustness of the algorithm and the decoding efficiency; and has the characteristics of resisting local contamination.

The invention relates to the technical field of package labels, in particular to an anti-counterfeit package label generation and verification method and system, and the method comprises the following steps: S1, data collection: collecting the basic information of a commodity through a commodity information collection device; s2, anti-counterfeit label generation: generating an anti-counterfeit label based on image recognition and micro-feature extraction; s3, label printing: printing the generated two-dimensional code or RFID label on a commodity package; s4, data storage: the generated anti-counterfeit label codes and the corresponding commodity information are stored in a database; s5, label verification: extracting an anti-counterfeit label code, and comparing the anti-counterfeit label code with information in the anti-counterfeit database; and S6, exception handling: recording exceptions, starting an early warning mechanism, and notifying related departments to handle the exceptions. According to the invention, each anti-counterfeit label has different coding modes in different time and environments, and the safety and flexibility of an anti-counterfeit system are improved.

According to the out-of-distribution detection method based on foreground controllable substitution and hierarchical label mining, a controllable image editing technology is innovatively introduced, a foreground substitution means is used, on the premise that original sample background information is kept unchanged, targeted modification is carried out on a foreground area, and an out-of-distribution detection algorithm which is more consistent with an out-of-distribution algorithm is generated. OOD) features are provided. Meanwhile, a near OOD label mining method based on a hierarchical label relation is provided, a hierarchical structure in a label system is mined, a near OOD label which is closer to an original ID label in semantics is automatically searched, the foreground replacement process is guided, and therefore a pseudo OOD sample with more semantic rationality and detection effectiveness is generated. And through explicit comparison of in-distribution and pseudo OOD samples, the model is promoted to learn compact and effective in-distribution and out-of-distribution decision boundaries, so that a complete out-of-distribution detection improvement scheme is formed, and the method has relatively high practicability and popularization value.

Methods, devices, and systems for managing memory devices are provided. In one aspect, a memory system can include a memory device including memory pages, a memory controller coupled to the memory device. The memory controller is configured to send, to the memory device, a first command indicating to identify dummy data, and in response to receiving, from the memory device, a response indicating that a first memory page is a zero page, determine data comprised in the first memory page as dummy data.