191 results about "Dummy data" patented technology

A system may receive request electronic data messages and counter-request electronic data messages from various network participant nodes within a defined time interval. The system may extract data from the electronic data messages to generate input codes including indicators that characterize execution values and imputed variability levels for the electronic data messages and / or characterize the message types of the electronic data messages. The input codes are used to generate a time-interval-specific support vector machine for the defined time interval. The system may then generate dummy data including execution value and imputed variability level tuples. The dummy data is used to map boundary levels from the time-interval-specific support vector machine versus execution values.

The invention discloses a network intrusion detection method based on a self-attention residual generative adversarial network, which belongs to the technical field of network intrusion detection and comprises the following steps: collecting network flow data, and preprocessing the network flow data to obtain real training data; a FARD-WGAN-GP model is constructed and trained, and hybrid pseudo data is generated based on the trained FARD-WGAN-GP model; constructing a time sequence attention detection model; training a time sequence attention detection model based on the real training data and the pseudo data; network intrusion real-time detection is carried out based on the trained time sequence attention detection model; on the basis of the real-time detection result, optimizing the FARD-WGAN-GP model, and on the basis of the optimized FARD-WGAN-GP model, optimizing the time sequence attention detection model; and carrying out network intrusion detection based on the optimized time sequence attention detection model. According to the method, the quality of the generated data is improved, and the detection accuracy is improved.

The invention discloses a logistics freight vehicle transportation supervision system and method based on the Internet of Things, and relates to the technical field of vehicle transportation supervision, the supervision system is constructed based on multi-source data fusion and hierarchical anomaly detection, and the problems of false data and signal interference which are difficult to identify in traditional supervision can be effectively solved; through multi-level comparison and block chain evidence storage, supervision blind areas caused by manual tampering or equipment faults are greatly reduced; meanwhile, the self-learning analysis module dynamically recognizes repeated or regular pseudo data, and by iteratively updating a threshold value and improving an early warning level, false alarms can be remarkably reduced, and accurate positioning of potential anomalies can be enhanced; the real-time performance and traceability of freight vehicle transportation supervision are improved, and the method has significant improvement value for the traditional mode which depends on a single data source and lacks a rapid verification mechanism.

The invention discloses a visual question-answering system based on multi-modal knowledge autonomous learning and a construction method, and belongs to the technical field of artificial intelligence. The invention provides a visual question and answer model construction method fusing image information, natural language questions and external knowledge. A knowledge dynamic screening and replacement strategy is constructed by introducing an autonomous evolution mechanism, and whether knowledge is inserted or updated can be judged according to a current training state and knowledge similarity, so that the problem that a traditional static knowledge base cannot adapt to task changes is solved. According to the method, through pseudo data construction and multi-modal triple extraction, structured and high-association-degree'image-question-answer 'knowledge representation is established, so that knowledge has better semantic expression ability, and redundancy and noise are effectively reduced. According to the method, rapid retrieval and active calling of knowledge are achieved by means of the vector indexing technology, key support information can still be obtained in a complex scene, and the expression of the model in a visual question-answer task based on knowledge is remarkably improved.

A first ECU and a plurality of second ECUs are connected to a communication bus. The first ECU repeatedly transmits a beacon signal through the communication bus. Each of the first ECU and the plurality of second ECUs transmits data according to an order set in advance when the beacon signal is transmitted. The first ECU or the second ECU transmits dummy data whose transmission destination is different from the remaining ECUs when there is no transmission data to be transmitted to one of the remaining ECUs other than itself.

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.

A method can include performing a single-read operation at a read reference voltage to detect bits from memory cells. The method can also include determining second bits corresponding to the set of memory cells, the second bits corresponding to data being initially programmed into the set of memory cell. The method can further include determining a read voltage based on the first bits and the second bits.

The present invention provides a method for controlling a flash memory module. The flash memory module includes a plurality of dies, each die includes a plurality of blocks, each block includes a plurality of pages, and the method includes the steps of: selecting a super block, wherein the super block includes a plurality of first blocks respectively located in the plurality of dies; for each first block in the super block, determining whether the first block is a full block or a blank block; and if the first block is not the full block or the blank block, writing dummy data to the first block so that the first block becomes the full block; and erasing the plurality of first blocks in the super block, so that the plurality of first blocks become a plurality of blank blocks.

The invention discloses a rapid yield analysis method based on xgboost proxy model truncation sampling, and the method comprises the following steps: generating a first random sample point set, and obtaining a training set through simulation; constructing a proxy simulator, and training the proxy simulator by using the training set; generating a second random sample point set, and inputting the second random sample point set into the proxy simulator to obtain a pseudo data set; establishing a filter, determining two hyperspherical surfaces based on the pseudo data set, dividing a process parameter space, evaluating a safety coefficient and the probability that a sample point enters a real simulator, and simulating the sample point of the process parameter space according to the probability to obtain a real sample set; iteratively optimizing the hyper-spherical surface and the proxy simulator by using the real sample set until convergence to obtain a final safety coefficient; and generating a third random sample point set, carrying out truncation sampling and failure discrimination by combining a real simulator and an agent simulator, and carrying out statistics on the final yield. According to the method, the xgboost network is adopted as an agent model to reduce the simulation cost, the sample size is reduced through truncation sampling, and rapid and accurate prediction of the yield of the integrated circuit is realized.

The invention relates to a bus hang-up prevention method and device and a chip. The method comprises the following steps: receiving and analyzing an access request sent by a master device, obtaining a request identifier and a response message parameter of the access request, and storing the response message parameter in a timeout detection information storage table; registering a corresponding relation between the access request and an overtime response timer according to the request identifier, and starting the overtime response timer; and when the overtime response timer is overtime, sending pseudo data corresponding to the access request to the master device according to the response message parameter corresponding to the access request in the overtime detection information storage table. By adopting the method, independent interception of partial access requests and corresponding response messages can be realized.

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.

To effectively remove only a characteristic streak-like noise in an X-ray transmission image appearing near a detection limit, and create a good X-ray transmission image that does not largely affect image information other than the streak-like noise.SOLUTION: An image correction method for an X-ray internal inspection system includes: picking up X-ray transmission images of a plurality of dummy analytes that are formed of the same component as a main component of an analyte and different in a thickness from each other to acquire dummy X-ray transmission images DD1-DD4; generating pieces of dummy data DC1-DC4 that are one-dimensional averaged data for the dummy X-ray transmission images DD1-DD4; calculating correction data DCA that is interpolation one-dimensional averaged data corresponding to an analyte with an arbitrary thickness d to be inspected through interpolation processing using the dummy data DC1-DC4; irradiating the analyte with an X-ray to acquire a pre-correction image that is a pre-correction X-ray transmission image; and executing correction using the correction data DCA on the respective pixels in the scanning direction of the pre-correction image to remove a streak-like noise DL from the pre-correction image and thereby create a post-correction image.SELECTED DRAWING: Figure 10

A server determines an array [[addr]] indicating a storage destination of each piece of data, generates an array of concealed values, and connects the generated array to the array [[addr]] to determine an array [[addr′]]. The server generates a sort permutation [[σ1]] for the array, applies the sort permutation [[σ1]] to the array [[addr′]], and converts the array [[addr′]] into an array with a sequence composed of first Z elements set to [[i]] followed by αi elements set to [[B]]. The server generates a sort permutation [[σ2]] for the converted array [[addr′]], generates dummy data, imparts the generated dummy data to the concealed data sequence, applies the sort permutations [[σ1]] and [[σ2]] to the data array imparted with the dummy data, and generates, as a secret hash table, a data sequence obtained by deleting the last N pieces of data from the sorted data array.

Systems and methods are disclosed to provide a security framework for the authentication of digital content and the prevention of unauthorized modifications, especially targeting the vulnerabilities introduced by deepfake technologies. It innovates by merging reverse engineering with expression manipulation detection to discern genuine from altered digital media. The process involves comparing historical data against synthetic or real-time content, generating a “video mesh” that enables precise manipulation identification. Enhanced by Smart Contracts for each content piece to record and verify changes, this system advances digital identity and transaction security significantly beyond current methodologies. Furthermore, it employs Generative AI within the Identity Intelligent Clip Reviewer to scrutinize blockchain-secured data for manipulation signs, issuing a Proof of Digital Manipulation (PODM) for verified authenticity. This comprehensive method ensures the integrity and trustworthiness of digital interactions across various platforms, marking a significant step forward in the protection against sophisticated cyber threats and unauthorized data alterations.

A memory system according to an embodiment outputs read enable signals RE and / RE during a standby time tWHR2 required for processing for outputting to a controller, and outputs dummy data preset in signals DQS and / or DQS and DQ from an output circuit.

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.

The CDR in a reception device (sink device) at a subsequent stage can be prevented from being unlocked even when jitter-fluctuation occurs due to transmission via an unstable transmission path such as a network. The clock data recovery unit extracts a clock and data on the basis of the baseband video data of a first format corresponding to a predetermined transmission path input from the predetermined transmission path. The extracted data is processed by the processing unit on the basis of the extracted clock. In this case, in a case where the occurrence of the jitter-fluctuation is detected, dummy data capable of clock extraction at the subsequent stage is output. For example, the processing unit outputs baseband video data in a second format, and the baseband video data in the second format is baseband video data corresponding to a predetermined wired interface.

A display device includes a substrate, a plurality of scan lines disposed on the substrate, a plurality of data lines and a plurality of dummy data lines intersecting the scan lines, a plurality of first pixel circuit units connected to the scan lines and the plurality of data lines, a first light-emitting element connected to the first pixel circuit unit, a plurality of second pixel circuit units connected to the scan lines and the plurality of dummy data lines, and a second light-emitting element connected to the second pixel circuit unit, where at least one of a plurality of scan lines intersects another adjacent scan line.

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.

Disclosed is a display device including a display panel, a driving controller that transmits a data control signal, and a data driving circuit. The data driving circuit receives the data control signal and provides a data signal to the display panel based on the data control signal. The data control signal includes frame protocol data for setting an operation of the data driving circuit. The frame protocol data includes a protocol data number signal, frame configuration data, and scrambled dummy data.

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 invention relates to the technical field of signal processing, and discloses a DOA estimation method for a vehicle-mounted millimeter wave radar based on spatio-temporal joint estimation, and the method comprises the steps: constructing a time domain data model received by an array; acquiring a time average vector based on the time domain data model; constructing a pseudo data matrix according to the time average vector, and processing the pseudo data matrix to obtain a processing result; constructing a covariance data matrix according to the processing result; performing eigenvalue decomposition on the covariance data matrix, and constructing an equation according to a decomposition result; and solving the equation to obtain an actual value generalized least square solution, and estimating the DOA based on the actual value generalized least square solution. The DOA estimation precision can be improved, and the calculation amount can be effectively reduced.

To provide a synchronous serial interface circuit which can reduce processing load without storing dummy data.SOLUTION: A synchronous serial interface circuit 10 includes: a sending buffer 12 for storing data to send; a receiving buffer 14 for storing data to receive; a control register 22 for setting the number of frames of commands adjusted to a device to communicate with and the directions of sending or receiving of each frame; and a frame number control circuit 24 for controlling sending or receiving operations and controlling the sending buffer and the receiving buffer according to the set content set by the control register 22.SELECTED DRAWING: Figure 1

The present application proposes a face authentication method based on image patch sequence loss, including: S1, obtaining a portrait authentication data set containing training images and their label information; S2, performing data processing on the portrait authentication data set, including: uniformly cutting the training images and disrupting the initial patch sequence set of the training images to obtain the first patch sequence set of the training images; enhancing the first patch sequence set to obtain the second patch sequence set; randomly exchanging the patch blocks in the first patch sequence set and the second patch sequence set of different training images, thereby obtaining a processed portrait authentication data set; S3, using the processed portrait authentication data set to train a neural network classification model; S4, inputting the image to be detected into the neural network classification model. The portrait authentication method of the present application greatly saves training costs and data collection costs, and has a high discrimination accuracy.

A method and apparatus for wireless communications between a Human Interface Device (HID) and a host. The host receives a plurality of virtual endpoints for a single HID, and transmits multiple polling tokens to the virtual endpoints in each period. The HID responds either with sensor data, or dummy data when there is no new sensor data. The dummy data keeps the polling proceeding, allowing time for more sensor data to appear in the same period.

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.

A display device includes: a data driver for supplying a data signal to each of a plurality of data lines; and a pixel unit including a plurality of sub-pixels. The pixel unit further includes one dummy data line disposed separate from a data line of a first column among the data lines. The data line of the first column is connected to sub-pixels disposed on odd-numbered pixel rows among sub-pixels disposed on a first pixel column, and is connected to sub-pixels disposed on even-numbered pixel rows among sub-pixels disposed on a third pixel column. The dummy data line is connected to sub-pixels disposed on the even-numbered pixel rows among the sub-pixels disposed on the first pixel column.