199 results about "Event forecasting" patented technology

A method for short-term prediction of storm cells in aircraft using a modified weather radar system, and a modified weather radar system embodying the method. The storm cell prediction method including the operations of accessing first and second weather radar images generated relative to the aircraft and having a similar relationships to the aircraft; mapping the first weather radar image onto the second weather radar image; comparing the first and second weather radar images; forecasting information describing a weather condition represented by the first and second weather radar images; retrieving a phase of flight of the aircraft; and generating a warning as a function of the forecast information describing a weather condition and the aircraft phase of flight.

A computer implemented system for automatically recording and generating predictive outputs relating to a medical procedure is described. The system is augmented with biometric sensory data from a biometric sensor coupled to a body of a healthcare practitioner. The biometric sensory data is processed to obtain one or more time-synchronized data objects providing a proxy to an estimated stress level associated with the healthcare practitioner, and the one or more time-synchronized data objects are utilized to identify abnormality-related durations of time encapsulated in the form of time-based metadata tags. The time-based metadata tags are utilized to automatically modify characteristics of the recording or generating of predictive outputs to temporarily consume more computational processing resources during the abnormality-related durations of time.

A system and method for collecting and analyzing electronic discussion messages to categorize the message communications and the identify trends and patterns in pre-determined markets. The system comprises an electronic data discussion system wherein electronic messages are collected and analyzed according to characteristics and data inherent in the messages. The system further comprises a data store for storing the message information and results of any analyses performed. Objective data is collected by the system for use in analyzing the electronic discussion data against real-world events to facilitate trend analysis and event forecasting based on the volume, nature and content of messages posted to electronic discussion forums.

In an approach to incident prediction and response, one or more computer processors receive one or more alerts corresponding to an incident. The one or more computer processors aggregate the one or more alerts with additional data corresponding to the incident. The one or more computer processors feed the aggregated data into a prediction model, where training of the prediction model uses associated independent stacked Restricted Boltzmann Machines utilizing one or more supervised methods and one or more unsupervised methods. The one or more computer processors determine, based, at least in part, on one or more calculations by the prediction model, at least one probability of the incident. The one or more computer processors determine whether the at least one probability exceeds a pre-defined threshold. In response to determining the at least one probability exceeds a pre-defined threshold, the one or more computer processors send at least one notification.

Systems and methods for anticipation of touch events in a computing device are provided. The computing device may include a multi-touch sensitive display including a sensor configured to sense a position and/or movement of a hand. A graphical user interface (GUI) may be displayed, having a state including a plurality of possible touch inputs. The computing device may further include a touch event prediction module configured to compute one or more anticipated touch inputs based on the sensed hand position and/or movement, and the state of the GUI with the plurality of possible user inputs. The computing device may further include a preprocessing module configured to preprocess data for each anticipated touch input, and upon the detection of an actual touch input received from the user that matches one of the anticipated touch inputs, displaying the preprocessed data for the actual touch input on the GUI.

A graphical user interface for a financial activity network that includes a central managing system connected to a plurality of participant terminals. Rules governing operation of the financial activity are stored for future reference. A participant provides investment information such as a map location for the predicted strike by the natural event and, optionally, one or more secondary parameters relating to the natural event, such as the time interval between the time of investment and the time of all event strike and/or the severity of the event strike according to an established scale. The graphical user interface provides the user with the ability to select options and view data from a screen display, with recalculations of data being displayed on an ongoing basis, reflecting the user's current selections.

Systems and devices including an imaging sensor to capture video sequences in an environment having safety concerns therein. The systems and devices further including a processor to generate driving series based on observations from the video sequences, and generate predictions of future events based on the observations using a dual-stage attention-based recurrent neural network (DA-RNN). The DA-RNN includes an input attention mechanism to extract relevant driving series, an encoder to encode the extracted relevant driving series into hidden states, a temporal attention mechanism to extract relevant hidden states, and a decoder to decode the relevant hidden states. The processor further generates a signal for initiating an action to machines to mitigate harm to items.

The invention discloses a chronic disease condition change event prediction device based on a recurrent neural network, and the device comprises a memory, a processor, and a computer program, a preprocessing module and a chronic disease condition change event prediction model are stored in the memory, and the prediction model comprises a preprocessing module, a condition feature extraction module,and a classification module. When the processor executes a computer program, the following steps are realized: receiving long-term longitudinal data generated by multiple hospitalization of a patient, performing data preprocessing on the number by the preprocessing module, and reconstructing the data of each hospitalization into a feature vector as a to-be-tested data set; Taking the to-be-detected data set as input, extracting disease characteristics by a disease characteristic extraction module, and inputting the disease characteristics into a classification module; And enabling the classification module to output the prediction probability of various events indicating that the illness state changes. The prediction device can predict the event that the chronic disease patient has markeddisease condition change in the target time window, thereby assisting the doctor to formulate reasonable diagnosis and treatment measures and reducing the medical expenditure.

The invention discloses a method for predicting big event traffic dynamic simulation jamming based on vehicle license plate recognition data, and particularly relates to the technical field of intelligent traffic. The method comprises the following steps of adopting simulation software to construct an urban road network model; using the vehicle license plate recognition data to calibrate road section simulation parameters; counting the vehicle license plate recognition data to obtain driving track data of each vehicle; using the vehicle license plate recognition data to count the road sectionflow of every 15 minutes within an assigned time duration; adopting a probabilistic principle component analysis model to complete the interpolation of missing data of all clamping openings and predicting the completed data to obtain the future traffic state; judging whether the standardized root-mean-square error between the history traffic simulation state and the future traffic state is 10% ofa preset threshold value or not. By means of the method for predicting big event traffic dynamic simulation jamming based on the vehicle license plate recognition data, the simulation software is utilized to dynamically allocate the vehicles to obtain the future big event traffic state and output the corresponding big event predicting result. The method has the advantages of having a high automated degree and an accurate predicting result.

In one embodiment, a method that can be performed on a system, is provided for predictive booking or reservations based on historical aggregation and events. In one embodiment, the method comprises surveying prices paid for traveler purchases or reservations to identify price increases for travel purchases or reservations, wherein the price increases include a increase in a price of a travel purchase during an identified period of time; identifying a correlation between identified price increases for travel purchases or reservations and identified events; predicting of future price increases in select travel bookings based on the identified correlation between the identified price increases for travel purchases or reservations and identified events; and determining whether to purchase or reserve a set of travel bookings based on the predicting of the future price increases.

The invention discloses a wind power climbing event probability prediction method and system based on a Bayesian network, and the method comprises the steps: mining the dependency relationship betweena wind power climbing event and related meteorological influence factors such as wind speed, wind direction, temperature, air pressure, humidity, and the like, and building a Bayesian network topological structure with the highest fitting degree with sample data; quantitatively describing a conditional dependency relationship between the climbing event and each meteorological factor, estimating the value of each conditional probability in a conditional probability table at each node of the Bayesian network, and forming a Bayesian network model for predicting the wind power climbing event together with a Bayesian network topological structure; deducing the probability of occurrence of each state of the climbing event according to the numerical weather forecast information of the mastered prediction time; the value of the corresponding conditional probability at each node is adaptively adjusted, so that the inferred conditional probability result of each state of the climbing event is optimized, and the compromise between the reliability and the sensitivity of the prediction result is realized.

A financial activity network includes a central managing system connected to a plurality of participant terminals. Rules governing operation of the financial activity are stored for future reference. A participant provides investment information such as a map location for the predicted strike by the natural peril event and, optionally, one or more secondary parameters relating to the natural peril event, such as the time interval between the time of investment and the time of an event strike and/or the severity of the event strike according to an established scale. In one example, an external objective independent information source is consulted, with the external objective independent information source providing monitoring, interpretation and derived determination of parameters pertaining to the natural peril event. Methods and articles of manufacture are also disclosed.

The embodiment of the invention provides a method for training an event prediction model, the method can be applied to a transfer learning scene, and data isolation and privacy security protection ofa source domain participant and a target domain participant are realized by setting a neutral server, wherein the source domain participant deploys a source domain feature extractor, the target domainparticipant deploys a target domain feature device, and a model sharing part in an event prediction model is deployed in a neutral server and specifically comprises a sharing feature extractor, a graph neural network and a classification network. For any participant, feature extraction is performed on a sample in a local domain by utilizing a feature extractor of the local domain to obtain localdomain feature representations, and the local domain feature representation is processed by using the current parameters of the model sharing part obtained from the server to obtain a corresponding event classification result, model updating based on the event classification result and the local domain sample is performed, and an updating result of the model sharing part is uploaded to the serverto enable the server to perform centralized updating.

Embodiments of the present invention relate to a method and system for data protection. A data protection method comprises: receiving at least one event prediction message from at least one message source, the at least one event prediction message being associated with an event that is predicted to occur in a future period of time; analyzing information, which is relevant to the event, included in the at least one event prediction message, so as to determine a risk level of the event with respect to the data to be protected; and determining a data protection operation at least based on the risk level and a predetermined event handling policy. There is further disclosed a corresponding data protection system. According to the embodiments of the present invention, it is enabled to voluntarily, dynamically, and flexibly handle high-risk events potentially damaging data, thereby better guaranteeing data security.

The invention belongs to the technical field of wind power generation, and especially relates to a system and a method for predicting a wind farm output power climbing event. The system comprises a data reading module, an experiment module, a wind farm output power climbing event influencing factor analysis module, and a wind farm output power climbing event predicting and warning module. The method comprises the following steps: acquiring parameter information related to a wind power climbing event, and designing experiments to explore the way in which each factor influences the wind power climbing event, the order of priority of the factors, and the horizontal combination of the factors under the greatest risk of wind power climbing; selecting model input and optimizing a prediction model according to the order of priority of the factors to improve the prediction accuracy greatly; and providing wind power climbing event alarm for the power system according to the horizontal combination of the factors under the greatest risk of wind power climbing. The wind power climbing event prediction system provided by the invention is universal, the model input can be optimized for all wind plants, and the prediction accuracy is guaranteed.

Systems and methods are disclosed for a future event prediction. Embodiments include capturing spatiotemporal data pertaining to activities, wherein the activities include a plurality of events, and employing an augmented-hidden-conditional-random-field (a-HCRF) predictor to generate a future event prediction based on a parameter-vector input, hidden states, and the spatiotemporal data. Methods therein utilize a graph including a first node associated with random variables corresponding to a future event state, a second node associated with random variables corresponding to spatiotemporal input data, a first group of nodes, each node therein associated with random variables corresponding to a subset of the spatiotemporal input data, a second group of nodes, each node therein associated with random variables corresponding to a hidden-state; wherein the edges connect the first node with the second node, the first node with the second group of nodes, and the first group of nodes with the second group of nodes.