201 results about "Severity level" patented technology

An apparatus for storing data records associated with a medical monitoring event in a data structure. An implanted device obtains data and stores the data in the data record in a first data structure that is age-based. Before an oldest data record is lost, the oldest data record may be stored in a second data structure that is priority index-based. The priority index may be determined by a severity level and may be further determined by associated factors. The implanted device may organize, off-load, report, and/or display a plurality of data records based on an associated priority index. Additionally, the implanted device may select a subset or composite of physiologic channels from the available physiologic channels based on a selection criterion.

Certain embodiments of the present invention provide a system for vulnerability detection and scoring with threat assessment including an analysis engine adapted to perform at least one of automated and semi-automated analysis of a computing system of at least one of known threats, vulnerabilities, and risk factors. The analysis engine is further adapted to determine a security score for the computing system based on the analysis and a schedule indicating a severity level for each threat, vulnerability, and risk factor.

Method for transmitting a warning signal to a driver of a driven vehicle regarding an impending collision with a moving and/or stationary object in the vicinity of the driven vehicle. The method comprises the following steps of providing the driven vehicle with means for obtaining updated data regarding, position, velocity vector and predicted moving path of the objects; selecting a series of one or more time horizons having decreasing or increasing duration; for the longest of the selected time horizons: generating a linear velocity object (LVO) and/or non-linear velocity object (NLVO) of each of the objects; selecting a sampling time interval Δt, during which an LVO and/or NLVO is generated; determining a range of feasible velocity vector changes for the driven vehicle that are attainable within a performance time interval ΔT; repeatedly providing the driver, after each Δt, with information regarding feasible velocity vector changes for the performance time interval; sensing, estimating or assuming dynamic changes parameters representing the movement of the driven vehicle within the performance time interval, and whenever required, generating a warning signal with an escalating severity level that reflects the relative imminence of collision with the objects and that corresponds to the longest time horizon; repeating the steps above, while each time generating an updated LVO and/or NLVO for a subsequent sampling time interval, until reaching another selected time horizon which is shorter than a previously selected time horizon and another selected time horizon, until collision is unavoidable.

A method and apparatus is provided for fault notification based on a severity level. The method comprises detecting a fault associated with a processing tool that is adapted to process one or more workpieces, determining a fault severity level of the detected fault and selecting at least one user to notify of the fault based on the severity level of the fault.

A computer system assesses the overall risk for different technologies for an organization. Technologies may be evaluated by obtaining severity levels and environmental risk scores for the vulnerabilities associated with the technologies. Each severity level measures a possible risk level of a corresponding vulnerability, while each environmental risk score is based on the organization's environment. Technology risk scores are then determined from the severity levels and the environmental risk scores. Each technology may then be categorized from a statistical distribution of the technology risk scores. An indexed risk score for each technology may also be determined based on time trending variables. Inputs may be a number of vulnerabilities, blended advisory/severity scores, and a standard deviation of the blended advisory/severity scores, and the results then provide behavior forecasting of the technologies. Further evaluation of the technologies may be performed to determine a risk versus reward model for the different technologies.

A method for conveying both a risk and the degree of risk of a severe weather at a particular geographic location is disclosed. The method involves receiving a location of interest from a requestor, forecasting a risk of severe weather for a specific time period associated with the location, computing a degree of severity level of the risk of severe weather for the location, and generating a report containing the risk forecast and the computed severity level that can be delivered or provided to the requestor. The severity level of the risk of severe weather is provided in an easy to understand graphic or other format.

A method of predicting severity of a potential collision of a vehicle and an object. The method includes determining a probability of the potential collision. An elicitation signal is directed and transmitted to the object from the vehicle when the probability of the potential collision is greater than a threshold value. A response signal is received onboard the vehicle from a device situated on the object in response to the elicitation signal. The response signal includes a type associated with the object. A severity level of the potential collision is predicted based on the type.

An exemplary method includes displaying a graphical user interface configured to facilitate identification of one or more defects in a software instance, receiving data representative of at least one instruction via the graphical user interface to associate one of a plurality of defect severity levels each having one of a plurality of weighted values with each of the one or more defects, and generating a product quality index corresponding to the software instance and that indicates a quality level of the software instance compared to a substantially defect-free software instance. In some examples, the generation of the product quality index is based on the defect severity levels and the weighted values associated with the one or more defects.

A data processing technique is provided. In one embodiment, a computer-implemented method includes accessing reference deviation maps for a plurality of disease types. The reference deviation maps may include subsets of maps associated with severity levels of respective disease types, and a disease severity score may be associated with each severity level. The method may include selecting patient severity levels for multiple disease types based on the subsets of reference deviation maps. Also, the method may include automatically calculating a combined patient disease severity score based at least in part on the disease severity scores associated with the selected patient severity levels, and may include outputting a report based at least in part on the combined patient disease severity score. Additional methods, systems, and manufactures are also disclosed.

A method encompasses a damage evaluation session which may be initiated by an evaluation system user using a user device such as a smartphone. The evaluation session allows the user to identify a damaged section of a damaged product, and then displays a properly sized representation of the damaged section at the user device. This displayed representation of the damaged section is associated with a grid which defines a number of grid segments with each grid segment located over a respective portion of the representation. The method includes receiving a damage severity level input for one or more of the grid segments. Based upon the damage severity level input for the one or more grid segments, damage estimate data is retrieved from a repair data store and applied to produce an overall repair cost estimate for the damaged section.

Methods and apparatus for storing data records associated with a medical monitoring event in a data structure. An implanted device obtains data and stores the data in the data record in a first data structure that is age-based. Before an oldest data record is lost, the oldest data record may be stored in a second data structure that is priority index-based. The priority index may be determined by a severity level and may be further determined by associated factors. The implanted device may organize, off-load, report, and/or display a plurality of data records based on an associated priority index. Additionally, the implanted device may select a subset or composite of physiologic channels from the available physiologic channels based on a selection criterion.

The invention discloses a failure mode effects and criticality analysis method adopting two RPNs (risk priority numbers). The method particularly comprises the steps as follows: grading according to the prevention level and the detection difficulty respectively to obtain failure mode severity and occurrence quantitative indexes, namely an RPN 1 and an RPN 2, wherein the RPN 1 is O*S*P, the RPN 2 is O*S*D, P is the prevention level, O is the failure mode occurrence rank, S is the severity level, and D is the detection difficulty level; and comparing the RPN 1 with the RPN 2 according to the numerical sizes of the two RPNs to obtain failure mode sorting and selection modes. According to the method, the detection difficulty level of failure modes and the prevention level of design improvement to the failure modes are considered when the RPNs are calculated, so that the failure modes are sorted from the aspects of prevention and detection by the obtained two RPNs, the situation of neglection of design improvement due to good detection measure is avoided, and a more comprehensive basis is provided for product design improvement.

A computer receives a system event initiated by an initiating client associated with a user. The system event comprises a plurality of data elements associated with respective ones of a plurality of system event attributes. It is determined that the system event fails to conform to the security policy. A greatest of a plurality of significance factors assigned to a plurality of data groups that are associated with the plurality of data elements of the system event is determined. If the system event does not match a special attention rule, the greatest of the plurality of significance factors is assigned to the system event as a severity level. If the system event matches the special attentions rule, then a greater of the greatest of the plurality of significance factors and the pre-assigned severity level is assigned to the system event as a severity level.

This document discloses a system and method for consolidating threat intelligence data for a computer and its related networks. Massive volumes of raw threat intelligence data are collected from a plurality of sources and are partitioned into a common format for cluster analysis whereby the clustering of the data is done using unsupervised machine learning algorithms. The resulting organized threat intelligence data subsequently undergoes a weighted asset based threat severity level correlation process. All the intermediary network vulnerabilities of a particular computer network are utilized as the critical consolidation parameters of this process. The final processed intelligence data gathered through this high speed automated process is then formatted into predefined formats prior to transmission to third parties.

A risk framework is described for computing risk. One or more persons may be grouped into classes of stakeholders. Different types of risk may be classified into risk categories. Within each risk category, events contributing to that type of risk may be determined and assigned a weight/severity value and a likelihood of occurrence value. Risk likelihood may be computed for each risk category by summing the products of weight/severity value and likelihood of occurrence value for each event within the risk category. A severity level may be determined and assigned with respect to each class of stakeholders regarding the risk categories, and a risk score may be computed for each stakeholder class and risk category combination. An aggregate risk likelihood may be computed by summing and/or weighting the risk likelihoods computed for each risk category.

The present invention is a method for providing hazard alerting for an aircraft via an airborne weather radar system. The method includes performing multiple weather radar scans at various tilt angles via the system and receiving radar returns via a receiver. The returns are stored in a memory of the system and merged with ground clutter suppression algorithms for eliminating ground returns from the stored returns and for creating a weather image based on remaining returns included in the stored returns. The image is provided via a weather radar display of the system, and includes an alert icon and an alert message when a hazard is present within an area proximal to the aircraft and is at or above a severity level threshold. The alert icon and alert message collectively indicate a type, severity level and location of the hazard in azimuth relative to a heading of the aircraft.

A system and methods, thereof, monitor biomarkers from a patient suffering from neural injury in real-time. The system comprises a quartz crystal microbalance wherein a capture molecule specific for certain biomarkers is diagnostic of the type of neural injury, location of neural injury and the degree of severity of neural injury. The system, in particular, provides real-time continuous monitoring of a patient.

The invention discloses a road ponding image detection early warning method based on a hybrid model, belongs to the technical field of big data, realizes the real-time ponding detection of different monitoring cameras on a road based on deep learning image characteristics, generates the ponding severity level data, and solves the defects of complex installation, easy damage and the like of a traditional detection instrument. An interactive area configuration mode is adopted to adapt to monitoring cameras in different scenes. According to the method, the deep neural network characteristics andthe improved ResNet residual network are adopted, the spatio-temporal characteristics of the monitoring video are combined, the ponding confidence is calculated in a mixed mode, so that the detectionaccuracy is improved, and the requirement for the actual application accuracy is met based on the monitoring video condition.

The invention discloses a Jetson TK1 based rice weed unmanned aerial vehicle monitoring system and a monitoring method thereof. The system comprises an unmanned aerial vehicle and a ground control station, wherein the unmanned aerial vehicle is provided with a Jetson TK1 based flight control module, a wireless transparent transmission module and an image collection module, the first wireless transparent transmission module and the image collection module are electrically connected with the flight control module respectively, the image collection module comprises a GOPRO camera and an image collection card, the ground control station comprises a PC terminal ground station and a second wireless transparent transmission module, the second wireless transparent transmission module is electrically connected with the PC terminal ground station, and the ground control station is in wireless communication connection with the flight control module through the first wireless transparent transmission module and the second wireless transparent transmission module. The Jetson TK1 based rice weed unmanned aerial vehicle monitoring system disclosed by the invention can analyze a collected real-time image in real time, thus improving the working efficiency, and the distribution and severity level of the weeds are displayed on an offline map of the ground control station, thereby being intuitiveand convenient.

The present invention discloses a convolutional neural network-based asphalt pavement crack classification and recognition method. Road cracks are classified according to different repair strategies of cracks of different widths and shapes; sample pictures are marked correspondingly and preprocessed so as to train a constructed convolutional neural network; and the trained convolutional neural network is adopted to classify crack information in pictures, and the severity levels of cracks are divided according to the widths and shapes of the cracks. Since the crack information in the pictures is automatically classified according to a pre-classification mode, and the severity levels of the cracks are divided, and therefore, efficiency of crack identification can be improved, road maintenance and repair work can be greatly facilitated; the convolutional neural network algorithm is used as a classifier to classify the road cracks; the convolutional neural network is a layered neural network composed of convolutional layers and sampling layers which are alternately distributed; and the convolutional neural network can learn features from training data implicitly and has greater advantages in the classification of cracks with no regular and significant features.