44 results about "Positive data" patented technology

The invention relates to the technical field of code security analysis, and discloses a control flow analysis and retrieval enhancement-based large model code security review method, which comprises the following steps of: analyzing an incremental code to generate an abstract syntax tree and data flow analysis information; retrieving local knowledge base association business rules and historical vulnerabilities based on grammatical features, and generating a context enhancement prompt; calling a large language model to jointly analyze codes and contexts, identifying vulnerabilities and outputting a structured report; and combining with a historical false alarm data optimization result and then integrating to a development assembly line. Multi-source information is fused through an RAG technology to enhance semantic understanding, and control flow node tracking and cross-version semantic association are combined, so that the problems of a traditional tool business logic vulnerability detection blind area, incremental code analysis failure and high false alarm rate are solved, synchronous improvement of security examination accuracy and efficiency is realized, and the method is adaptive to an agile development scene.

A computing server retrieves a list of custom-defined categories of a database and accesses a plurality of training samples for training a machine-learned encoder model. The computing server trains the machine-learned encoder model that generates embeddings of data instances. The machine-learned encoder model is trained to separate a plurality of embeddings of positive data instances belong to the target category from a plurality of embeddings of negative data instances. The computing server receives a target data instance that is to be imported to the third-party data platform and generates features of the target data instance to prepare the target data instance for further processing. The computing server applies the machine-learned encoder model to the target data instance to determine an assignment of a category from the list of custom-defined categories. The computing server exports the target data instance including the determined assignment to the third-party data platform.

The invention relates to the technical field of data processing, in particular to a threat recognition system and method.The method comprises the steps that a self-adaptive collection module obtains network flow data and operation log output feature vectors; the multi-dimensional rule module generates rule confidence; the statistical modeling module outputs an abnormal probability score; the machine learning module outputs a classification probability in response to the abnormal probability score dynamic selection model combination; the simulation decision module is fused with the multi-source data to execute threat diffusion simulation and output hazard indexes; the grading execution module triggers grading response according to the hazard index; and the feedback optimization module adjusts model parameters in a cross-layer manner. According to the method, noise interference is eliminated through feature optimization, a multi-dimensional model is fused to calibrate an output result, a threat grading mechanism preferentially responds to key threats, and closed-loop feedback is continuously injected into a false alarm data training model, so that the problem of high false alarm rate caused by insufficient training data coverage of a machine learning model in a complex dynamic network environment is solved; security resource allocation is optimized and key threat response delay is shortened.

The invention provides a display panel and a display device, the display panel divides a second data line to a (2a * n + 1) th data line in (2a * n + 2) data lines into n data line groups, and the ith data line in each data line group is electrically connected with the (i + a) th data line in the data line group so as to share one data channel, the first type of sub-pixels in the yth column of sub-pixels is electrically connected with the yth data line, and the second type of sub-pixels in the yth column of sub-pixels is electrically connected with the (y + 1) th data line, so that each column of sub-pixels is controlled by two data lines, and in the display period of one frame of picture, the number of the sub-pixels in each column of sub-pixels is increased. Part of sub-pixels in each column of sub-pixels can receive data voltage of positive polarity through one data line, and the other part of sub-pixels can receive data voltage of negative polarity through the other data line, so that the polarity of each sub-pixel is opposite to that of the adjacent sub-pixels, and the problem of vertical stripes can be eliminated.

A data driver according to an embodiment and a display device including the same are disclosed. The data driver includes a voltage output circuit including a first amplifier configured to amplify input positive pixel data and output the amplified positive pixel data as a positive data voltage and a second amplifier configured to amplify negative pixel data and output the amplified pixel data as a negative data voltage; a selection circuit configured to alternately connect the first amplifier and the second amplifier to a first data line and a second data line, and connect the first and second data lines before polarities of data voltages that are applied to the first and second data lines are inverted; and a control circuit configured to vary a charge sharing time for connecting the first and second data lines and performing charge sharing according to change in grayscale of the input pixel data.

The invention discloses a SerDes interconnection structure and method between bare chips. The bare chips are arranged in the packaging body or on the packaged PCB according to the set clock direction, and each bare chip is provided with a plurality of pairs of TX / RX signal channels. Each pair of TX / RX signal channels comprises a TX pin, a calculation module and an RX pin, and the TX pins and the RX pins are positioned on the same side of the bare chips, so that forward data and reverse data can be transmitted among the bare chips according to a set clock direction. Forward data is sent out from the starting bare chip and then directly transmitted to the end point bare chip according to the set hour hand direction, and reverse data is sent out from the end point bare chip and then transmitted to the starting bare chip after passing through all the bare chips except the starting bare chip and the end point bare chip according to the set hour hand direction. According to the invention, by changing a conventional SerDes interconnection structure between bare chips, the bandwidth utilization rate and the system performance are improved, and the delay is reduced.

The invention relates to the technical field of brain disease monitoring, in particular to a clinical research data safety equipment wearing state monitoring method and system based on artificial intelligence. Comprising the following steps: S1, acquiring body information and a brain disease type of a patient, and acquiring a wearable equipment type at the same time; s2, acquiring brain physiological data of the patient in real time through the wearable equipment, monitoring state data at the same time of data acquisition of the wearable equipment, and performing data anomaly threshold setting on the brain physiological data according to the state data in combination with a brain disease type; the abnormal threshold value is dynamically adjusted through the difference between the real-time state data and the standard state of the wearable equipment, the worse the equipment state is, the larger the allowed physiological data fluctuation range is, false alarms caused by non-pathological factors such as poor equipment contact and insufficient electric quantity are avoided, the abnormal threshold value is automatically adjusted, and false positive data are reduced.

The invention provides a real-time data classification and hierarchical encryption system and method based on deep learning, and the method comprises the steps: dividing real-time data into a plurality of independent data frames, recognizing a data attribute corresponding to each independent data frame, constructing a positive data representation corresponding to the real-time data according to the data attributes, dividing the real-time data into corresponding data sets according to positive data representation, constructing a random ciphertext sequence based on data attributes to perform encryption training on corresponding independent data frames, and constructing initially encrypted real-time data according to an encryption effect corresponding to each independent data frame, according to the method, a plurality of random encrypted modes contained in a data set are selected to locally encrypt the initially encrypted real-time data for a plurality of times, the security level of the real-time data is determined and stored, and different data can be classified and then correspondingly encrypted, so that the security of the data needing to be encrypted is guaranteed, the processing of non-encrypted data is reduced, and the security of the data needing to be encrypted is improved. And the security of the to-be-encrypted data is fully guaranteed.

An autonomous vehicle (AV) can utilize quantitative data, such as data received from sensors with known intrinsic parameters (e.g., positive data) to determine a second subsequent action from a set of possible actions, using inputs such as an original subsequent action. To improve the decision-making process of the second subsequent action, the AV can utilize qualitative data, such as data received from a data storage system (e.g., negative data). Qualitative data can be text or image based, such as from a crash report or sensor data missing sufficient intrinsic parameters to make a quantitative evaluation. By weighting the quantitative data against the qualitative data, the decision-making process of the AV towards its high-level goal (such as moving toward a parking lot) can be more efficient, such as ensuring lower levels goals are met. The decision process can utilize multi-modal large language models and latent space models to weight data appropriately.

The invention provides a ground test method and system for cable interference between double super platform cabins. The method comprises the following steps: establishing constraint connection structures at two ends of a test cable group; sensors are arranged at the two ends of the cable group, and the tension value of the cable at the preset position is collected in real time by changing the poses of the two ends of the cable group; executing a forward cooperative displacement test to enable the end part of the cable to displace along the forward direction of the coordinate axis to obtain forward data; executing a negative cooperative displacement test to enable the end part of the cable to displace along the positive direction of the coordinate axis to obtain negative data; grouping the cables of the same type, and re-executing the forward cooperative displacement test to obtain grouped data; and analyzing and fitting positive data, negative data and grouped data of the mechanical data, constructing a model, verifying the validity of the test system through sensor data consistency, and outputting a test result. The method can be flexibly applied to cable interference estimation between double super platform cabins in various forms, and provides a basis for compensation control.

The invention discloses a data transmission method and device, an electronic device, equipment and a storage medium. The method comprises the following steps: a sending end determines to-be-transmitted target bit data in current to-be-transmitted first target byte data, outputs electric levels with opposite symbols to a positive data line and a negative data line according to the target bit data, pulls down a clock line, and a receiving end samples the positive data line and the negative data line when detecting a falling edge of the clock line, so as to obtain a to-be-transmitted first target byte data; and determining the received bit data according to the sampling result and pulling up the clock line. According to the technical scheme, a timer does not need to be used for data logic judgment in data transmission, the receiving end is triggered by the falling edge of the clock line to conduct data judgment and reading, the data response speed can be effectively increased, the data transmission speed is increased, the positive data line and the negative data line are adopted to output levels with opposite symbols, and the data transmission efficiency is improved. The transmission distance can be increased, the anti-interference capability in a complex electromagnetic environment can be improved, and the accuracy of data transmission is ensured.

A computer system automatically tests a network communication model by predicting whether particular traffic (whether actual or simulated) should be allowed on the network, and then estimating the accuracy of the network communication model based on the prediction. Such an estimate may be generated even before the model has been applied to traffic on the network. For example, steps can include observing positive data associated with a network; generating a network communication model based on the positive data; generating negative data based on the network communication model; calculating a precision of the network communication model based on the network communication model and the negative data; and calculating an accuracy of the network communication model based on one or more of the precision of the network communication model, or the network communication model and the positive data.

A computing server retrieves a list of custom-defined categories of a database and accesses a plurality of training samples for training a machine-learned encoder model. The computing server trains the machine-learned encoder model that generates embeddings of data instances. The machine-learned encoder model is trained to separate a plurality of embeddings of positive data instances belong to the target category from a plurality of embeddings of negative data instances. The computing server receives a target data instance that is to be imported to the third- party data platform and generates features of the target data instance to prepare the target data instance for further processing. The computing server applies the machine-learned encoder model to the target data instance to determine an assignment of a category from the list of custom-defined categories. The computing server exports the target data instance including the determined assignment to the third-party data platform.

The present disclosure relates to the field of fault detection technology, and provides a fault intelligent detection method, device, equipment and medium, the method comprising: respectively obtaining a positive data set, a negative data set and an observation data set of a weak current device; aligning and arranging the data sets in order to obtain a data matrix; calculating the data difference value of each column in the data matrix, and taking the column with the largest data difference value as the benchmark feature column; arranging the other remaining columns in descending order according to the correlation coefficient, and filling them to both sides of the benchmark feature column in sequence, thereby constructing a feature significance matrix; using a first sliding window and a second sliding window to scan the data set of the feature significance matrix and calculate the window difference; after completing the whole row scan, accumulating the difference according to the weight, and triggering a fault warning when the accumulated difference exceeds a threshold value. By adopting the above scheme, for weak current devices that are sensitive to signal changes, the fault type of the weak current device can be detected in real time and accurately.

A data driver according to an embodiment and a display device including the data driver are disclosed. The data driver includes: a voltage output circuit including a first amplifier and a second amplifier, the first amplifier being configured to amplify input positive pixel data and output the amplified positive pixel data as a positive data voltage, and the second amplifier being configured to amplify negative pixel data and output the amplified negative pixel data as a negative data voltage; a selection circuit configured to selectively connect the first amplifier and the second amplifier to a first data line and a second data line, and to connect the first data line and the second data line before the polarity of the data voltage applied to the first data line and the second data line is reversed; and a control circuit configured to change the charge sharing time for connecting the first data line and the second data line and performing charge sharing according to the grayscale change of the input pixel data.

A method includes receiving audio data corresponding to an utterance and generating a pair of positive audio data examples. Here, each positive audio data example includes a respective augmented copy of the received audio data. For each respective positive audio data example, the method includes generating a respective sequence of encoder outputs and projecting the respective sequence of encoder outputs for the positive data example into a contrastive loss space. The method also includes determining a L2 distance between each corresponding encoder output in the projected sequences of encoder outputs for the positive audio data examples and determining a per-utterance consistency loss by averaging the L2 distances. The method also includes generating corresponding speech recognition results for each respective positive audio data example. The method also includes updating parameters of the speech recognition model based on a respective supervised loss term and the per-utterance consistency loss.

A display device includes a display panel, a drive controller, and a data driver. The drive controller processes input image data according to a variable input frequency and generates a data signal with a variable frame length. The data driver converts the data signal into a data voltage and outputs the data voltage to the display panel. The drive controller determines a variable frequency mode and generates an asymmetric data signal in the variable frequency mode, including a positive data signal and a negative data signal for the same grayscale value that are asymmetric with respect to a common voltage.

A classification device (10) creates a loss function that decreases the better the classification performance for imbalanced data of a classification model for a test data distribution using unlabeled data that is data based on the test data distribution and does not have binary labels that represent a positive class and a negative class, unlabeled data that is data based on a training data distribution and does not have the binary labels, and labeled data (for example, positive data) that is data based on the training data distribution and has the binary label for the positive class. The classification device (10) updates the parameters of the classification model so as to reduce the loss function.

The embodiment of the invention discloses a method for determining factor items influencing authentication and a related device, which can be applied to various scenes such as cloud technology, artificial intelligence, intelligent traffic, auxiliary driving and the like. According to the method, other factor data are controlled to be unchanged in a variable control mode, and only the factor data corresponding to the to-be-verified factor item is changed, so that the front data which cannot fail in authentication and the back data which can fail in authentication in the theoretical authentication process are obtained. If the simulation authentication result identifies that both the front data and the back data pass the authentication, determining that the to-be-verified factor item is a factor item which does not affect the authentication for the target operation; and if the simulation authentication result identifies that the front data passes the authentication and the back data does not pass the authentication, determining that the to-be-verified factor item is a factor item influencing the authentication for the target operation. Therefore, through the simulated authentication result obtained through multiple times of authentication, whether the authentication for the target operation is influenced by the to-be-verified factor item under the combined action of the multiple factor items can be judged, so that the authentication process is transparent.

The present disclosure relates to a data classification method and device, electronic equipment and storage medium, and relates to the field of insurance and finance technology. The present disclosure solves the problem of low accuracy of data classification results caused by large differences in data volume of positive data and negative data. The method comprises: determining a confidence interval according to the confidence of a plurality of target positive historical data; the confidence represents the correlation degree of the position distribution of each target positive historical data and other target positive historical data in the plurality of target positive historical data; the confidence interval represents the distribution interval of the target positive historical data with a confidence higher than a preset confidence; and classifying the data to be classified according to the confidence interval to determine abnormal data and / or non-abnormal data in the data to be classified.

The application discloses an inter-chip communication method and a storage medium, and relates to the technical field of communication. The inter-chip communication method comprises the following steps: packing slave machine data through a first communication protocol layer of the slave machine to obtain a first protocol package; packing the first protocol package through a first data link layer of the slave machine to obtain a first data package; pulling a positive data pin of the slave machine to a low level, so that the host machine detects the low level and reads the slave machine data according to the low level; and sending the first data package to the host machine. The inter-chip communication method can guarantee the timeliness of data transmission, and makes data interaction more rapid.

Systems and methods for extracting rule lists from tree ensembles are provided. A system extracts first stage candidate rules from individual trees. The system identifies the first stage candidate rules that satisfy a precision threshold and places those rules in a solution set. Subsequently, a determination is made whether a further stage is needed based on whether a predetermined number of positive data samples of the data set are covered by the solution set. In the further stage, the system generates next stage candidate rules from previous stage candidate rules that have not been pruned and identifies the next stage candidate rules that satisfy the precision threshold, placing those rules in the solution set. A simplified rule list is generated by identifying a minimum subset of rules in the solution set that covers the positive data samples within the precision threshold.

The invention relates to the field of fire image recognition and the field of fire image monitoring, and discloses an electric vehicle fire recognition system based on image enhancement, which comprises a picture sample acquisition module, an intelligent sampling module, a dynamic layout combination module, an edge data processing module, a high-quality sample recognition module and an enhanced data set output module. The dynamic layout combination module is used for asymmetrically combining the randomly selected i pictures, and the edge data processing module filters low-quality samples through color recognition. According to the invention, a fire image sample and a false alarm data sample are input, and a sample set and a label set which are identified as fire images are output through data processing of an edge data processing module and a high-quality sample identification module. According to the system, the model learns the fine difference between the fire and the non-fire in the same field of view, easily-confused feature information contained in misinformation data is fully mined, the situation that the model has insufficient discrimination capability on similar features is avoided, and the fire false alarm rate is greatly reduced.

The present invention discloses an intelligent data mining method and system based on machine learning, which relates to the field of data mining technology. The method comprises: obtaining the data mining type and data receiving rate of the first data mining to obtain the first mining item and the first receiving item; performing data mining based on the first receiving item to obtain the load rate of the target mining device to obtain the first load item; detecting the data mining type, and when the data mining type changes, obtaining the changed data mining type and data receiving rate. When the data mining type changes, the present invention adjusts the mining form of different data mining types according to the load rate, such as front data mining and background data mining, stores the data of the background data mining, and releases the stored data for replacement at an appropriate time by detecting the data mining type, thereby achieving the effect that the data mining type can be taken over at the fastest speed after turning back.

Methods, systems, and computer-readable media for identifying true positive data within a set of blast exposure data. An equation fit is applied to generate one or more equations corresponding to portions of pressure data within the set of blast exposure data. The one or more equations are compared to the pressure data to determine if respective portions of the blast exposure data relates to true positive data.

An assembly of electronic components for reception of data using an optical fiber wherein data is received in bursts, the assembly comprising: a photodiode; a transimpedance amplifier coupled to said photodiode, the gain of the transimpedance amplifier being adjusted based on a level of a gain control signal: a received input signal sensor configured to sense a received input signal level and provide the gain control signal, the gain control signal being varied according to the received input signal level; wherein the received input signal level is sensed via a sampling circuit arrangement; a comparator; and an adjustor for adjusting a low-frequency behavior and a high-frequency behavior of the comparator so that the detection of both positive data symbol level transitions and negative data symbol level transitions in the received signal have identical behavior within accepted engineering tolerances.

A method includes receiving first driving data associated with a first vehicle, receiving second driving data associated with one or more vehicles around the first vehicle, creating training data by labeling the first driving data as positive data and treating the second driving data as unlabeled, and using the training data to train a classifier to predict whether driving data input to the classifier is positive or unlabeled.

The application discloses a charging method, comprising: a charging head outputs a preset voltage; detecting the level value of a positive data line terminal of the charging head; determining the charging voltage according to the level value and outputting the charging voltage. The application also discloses a charging head, a portable device and a computer readable storage medium. The application determines the charging voltage according to the level value of the positive data line terminal of the charging head, which simplifies the charging steps.

Methods, systems, and computer-readable media for identifying true positive data within a set of blast exposure data. An equation fit is applied to generate one or more equations corresponding to portions of pressure data within the set of blast exposure data. The one or more equations are compared to the pressure data to determine if respective portions of the blast exposure data relates to true positive data.