20 results about "Reliability criterion" patented technology

Device for navigating and / or guiding and / or stabilizing a vehicle (200), namely a wheelchair, the device (80) comprising: - at least one first sensor, namely an inertial navigation unit (11), which is designed and arranged to detect at least one first body part of a passenger of the vehicle (200) and to output first sensor signals (S1); - at least one image-based second sensor (21, 22) designed and arranged to detect at least the first body part of the passenger and / or its features and to output second sensor signals (S2); and - a control unit (100) that is designed to, a) to receive the first and second sensor signals; b) to determine, at least on the basis of the first sensor signals, first control signals (ST1) for controlling the vehicle (200); c) to determine, based at least on the second sensor signals, whether the first control signals (ST1) meet at least the first reliability criteria; d) to enter a safety mode if the control unit (100) determines that the first control signals (ST1) do not meet at least the first reliability criteria, where reliability criteria define general or individualized thresholds in relation to measurable physical conditions of the passenger.

Systems and methods for providing a memory sub-system controller that selectively performs extrinsic defect scan operations on certain word line groups of a memory sub-system are described. The controller performs an extrinsic defect scan operation on a set of predetermined portions of a set of memory components. The controller, in response to performing the extrinsic defect scan operation, stores a map that identifies a subset of the predetermined portions of the set of memory components having a reliability value that fails to satisfy a reliability criterion. The controller, in response to storing the map, performs one or more subsequent extrinsic defect scan operations on the identified subset of the predetermined portions of the set of memory components having the reliability value that fails to satisfy the reliability criterion.

The application discloses a millimeter wave radar point cloud enhancement method for trajectory smoothness evaluation and occlusion compensation, and belongs to the technical field of radar signal processing. The method performs clustering and inter-frame association on original radar point cloud data, and constructs a target motion trajectory. A trajectory smoothness index is calculated. From three dimensions of velocity variation, acceleration variation and motion direction variation, product type aggregation evaluation is performed based on human motion physical constraint parameters, and two-dimensional verification is performed in combination with a signal-to-noise ratio. For real targets and suspicious targets, when occlusion occurs, virtual points are generated frame by frame based on Kalman filter prediction steps, and are dynamically calculated by an environmental complexity factor and adaptively adjusted according to the occlusion time and target speed. A position standard deviation is used as a reliability criterion, and reliable virtual points are inserted into the trajectory to compensate for data loss. The application can effectively eliminate ghost false targets and maintain trajectory continuity when occlusion occurs.

Systems and methods for providing a memory sub-system controller that selectively performs extrinsic defect scan operations on certain word line groups of a memory sub-system are described. The controller performs an extrinsic defect scan operation on a set of predetermined portions of a set of memory components. The controller, in response to performing the extrinsic defect scan operation, stores a map that identifies a subset of the predetermined portions of the set of memory components having a reliability value that fails to satisfy a reliability criterion. The controller, in response to storing the map, performs one or more subsequent extrinsic defect scan operations on the identified subset of the predetermined portions of the set of memory components having the reliability value that fails to satisfy the reliability criterion.

Aspects of the present disclosure configure a system component, such as a memory sub-system controller, to modify pulses used to program memory components. The controller receives a request to program data in an individual memory component of a set of memory components. The controller computes a plurality of memory reliability criteria associated with the individual memory component and compares the plurality of memory reliability criteria to one or more threshold values. The controller selects a program pulse used to program the data to the individual memory component based on a result of comparing the plurality of memory reliability criteria to the one or more threshold values.

A method includes receiving, by a user device from a network node, a reliability criteria; providing, by the user device, one or more measurements or context information as inputs to a machine learning model; receiving, by the user device from the machine learning model, a predicted action based on the inputs; determining, by the user device, whether the reliability criteria is met for the predicted action; performing, by the user device, the predicted action if the predicted action meets the reliability criteria; and if the predicted action does not meet the reliability criteria: obtaining a simulated reward for the predicted action; and training or updating the machine learning model based on the simulated reward for the predicted action.

A method and a system for setting health monitoring warning thresholds of long-span arch bridges based on reliability criteria are provided. The method includes establishing a performance function based on structural ultimate responses and warning thresholds of the long-span arch bridge; determining target reliability indices, setting initial values of random variables and warning thresholds, and defining convergence tolerance errors; calculating gradient values of the performance function, computing scaling factors based on the gradient values, obtaining failure points of the performance function through the scaling factors combined with a conjugate search strategy, and updating warning thresholds at the failure points; iterating until errors become less than or equal to the convergence tolerance errors to establish reliability criteria for warning threshold configuration; and deriving warning thresholds corresponding to target reliability indices according to the reliability criteria.

A method includes receiving, by a user device from a network node, a reliability criteria; providing, by the user device, one or more measurements or context information as inputs to a machine learning model; receiving, by the user device from the machine learning model, a predicted action based on the inputs; determining, by the user device, whether the reliability criteria is met for the predicted action; performing, by the user device, the predicted action if the predicted action meets the reliability criteria; and if the predicted action does not meet the reliability criteria: obtaining a simulated reward for the predicted action; and training or updating the machine learning model based on the simulated reward for the predicted action.

The present application belongs to the technical field of power system reliability evaluation and virtual power plant operation optimization, and provides a virtual power plant heterogeneous resource regulation capacity evaluation method and system. The technical scheme is that, for multiple source uncertainty variables, an empirical Copula function is obtained by fitting historical data, and a time series scenario set is generated based on the empirical Copula function and a trained stacked auto-encoder model. Based on the generated time series scenario set, the regulation capacity of all uncertainty scenarios is calculated, and a regulation capacity sample set is constructed based on the calculated regulation capacity of all uncertainty scenarios. Based on the constructed regulation capacity sample set, a multi-confidence level regulation curve library is constructed. The multi-confidence level regulation curve library is embedded as a constraint condition in the intraday rolling scheduling, and the power supply reliability criterion is set to calculate the power supply reliability, and the probabilistic evaluation result of the power supply reliability is obtained. It can adapt to the access demand of different types and different sizes of distributed energy resources.

The invention relates to a training system and a training method for training an artificial neural network, in particular for medical applications, with patient-specific features. The patient-specific artificial neural network training system comprises an input interface, configured to receive image data from a patient, a computing device, further comprising a classification module, a separator module and a training module, and an output interface, configured to output a diagnosis signal, wherein the classification module is configured to acquire as input the received image data and generate a first classification signal for each image of the image data, wherein the separator module is configured to, based on the first classification signal, separate the received image data in at least a first dataset and a second dataset according to a reliability criterion, and wherein the training module is configured to use the first dataset or only a part thereof as a training dataset to train an artificial neural network.

Programming techniques disclosed herein may be used to recover from potential plane and / or die level failures. The memory system may detect evidence of a plane or die level failure that impacts memory operations performed in a target region of the blocks if a first programming technique is used in the target region. For example, the memory system may note that a significant number of blocks have memory operation failures in the target region. The storage system switches to a second programming technique for programming the target region responsive to detecting evidence of a plane or die level failure. The blocks may be tested for a reliability criterion after programming using the second programming technique in the target region of the block. The second programming technique may overcome the plane or die level failure such that blocks may continue to be used with the second programming technique.

Various embodiments provide for a method for determining whether positioning data should be collected and used as training data to train an Artificial Intelligence / Machine Learning (AI / ML) model. A signaling mechanism can be provided where a network node specifies which positioning measurements data should be collected for training purposes and which should be discarded by providing the criteria for data collection to UEs or gNBs. A first network node can determine a tolerance for ground truth labeling (512), and then based on the tolerance, determine one or more reliability criteria for the positioning data (514). The first network node can provide the request for positioning data and reliability criteria to a second network node (516), which can collect the positioning data based on the criteria (518), and then provide the collected positioning data to the first network node (522), which can then use the positioning data as training data.

Systems and methods for providing a memory sub-system controller that selectively performs extrinsic defect scan operations on certain word line groups of a memory sub-system are described. The controller performs an extrinsic defect scan operation on a set of predetermined portions of a set of memory components. The controller, in response to performing the extrinsic defect scan operation, stores a map that identifies a subset of the predetermined portions of the set of memory components having a reliability value that fails to satisfy a reliability criterion. The controller, in response to storing the map, performs one or more subsequent extrinsic defect scan operations on the identified subset of the predetermined portions of the set of memory components having the reliability value that fails to satisfy the reliability criterion.

The invention belongs to the technical field of power system reliability evaluation and virtual power plant operation optimization, and provides a virtual power plant heterogeneous resource regulation capability evaluation method and system. According to the technical scheme, an experience Copula function is obtained through historical data fitting for multi-source uncertainty variables; generating a time sequence scene set based on an experience Copula function and the trained stacked automatic encoder model; on the basis of the generated time sequence scene set, calculating adjustment capabilities of all the uncertainty scenes, and constructing an adjustment capability sample set on the basis of the adjustment capabilities, obtained through calculation, of all the uncertainty scenes; constructing a multi-confidence level adjustment curve library based on the constructed adjustment capability sample set; and the multi-confidence level adjustment curve library is used as a constraint condition to be embedded into intra-day rolling scheduling, power supply reliability calculation is performed in combination with a set power supply reliability criterion, and a power supply reliability probabilistic evaluation result is obtained. And the access requirements of different types and different scales of distributed energy resources can be met.

Programming techniques disclosed herein may be used to recover from potential plane and / or die level failures. The memory system may detect evidence of a plane or die level failure that impacts memory operations performed in a target region of the blocks if a first programming technique is used in the target region. For example, the memory system may note that a significant number of blocks have memory operation failures in the target region. The storage system switches to a second programming technique for programming the target region responsive to detecting evidence of a plane or die level failure. The blocks may be tested for a reliability criterion after programming using the second programming technique in the target region of the block. The second programming technique may overcome the plane or die level failure such that blocks may continue to be used with the second programming technique.

The invention discloses a single dual-polarization radar data self-filling method based on physical constraint and dynamic optimization, and relates to the technical field of data filling. Comprising the following steps: acquiring original observation data, preprocessing the original observation data, and calculating a beam blocking rate; establishing a reliability criterion, and selecting an optimal filling path for each pixel point; when the relative differential phase is determined to be reliable, constructing a core path based on a-relationship; when the differential phase is not reliable but the differential reflectivity is reliable, an auxiliary path is constructed based on a statistical relationship, and data filling is executed hierarchically; and solving a reflectivity field which is finally filled and is physically coherent by minimizing a cost function. Smooth transition of the filling area and the surrounding effective observation area is ensured, and a high-quality data field is formed.

The invention discloses a vehicle-mounted nuclear power system and an energy storage capacity calculation method.The vehicle-mounted nuclear power system comprises a movable nuclear power unit, an energy storage unit, a load unit, a self-power-generation unit and a plurality of load vehicles, and the movable nuclear power unit, the energy storage unit, the load unit and the self-power-generation unit are arranged on the different load vehicles respectively. The movable nuclear power unit, the energy storage unit, the load unit and the self-generating unit are connected in parallel to a bus; the energy storage capacity calculation method comprises the steps of collecting operation data and environment data of the vehicle-mounted nuclear power system in the operation process, constructing an LSTM power prediction model to obtain load prediction data, constructing a power balance equation according to the load prediction data, the power of the movable nuclear power unit, the power of the energy storage unit and the power of the self-generating unit, and calculating the energy storage capacity of the vehicle-mounted nuclear power system. And finally, according to the operation condition and the reliability criterion of the vehicle-mounted nuclear power system, margin analysis is carried out to obtain the energy storage capacity.

A method for controlling an automotive lighting device. The method includes projecting a first light pattern, capturing an image of a region in front of the lighting device, obtaining a luminance map from the captured image, identifying objects in the luminance map and classify them as reliable or not reliable, according to at least one reliability criterion and modifying the first light pattern to modify the luminous intensity in at least one zone intended to project light on a non-reliable object.

Aspects of the present disclosure configure a system component, such as a memory sub-system controller, to modify pulses used to program memory components. The controller receives a request to program data in an individual memory component of a set of memory components. The controller computes a plurality of memory reliability criteria associated with the individual memory component and compares the plurality of memory reliability criteria to one or more threshold values. The controller selects a program pulse used to program the data to the individual memory component based on a result of comparing the plurality of memory reliability criteria to the one or more threshold values.