235 results about "Localization system" patented technology

A system for AI-supported leak detection and localization in water distribution infrastructures, consisting of: a large number of distributed sensor nodes mounted along a water pipe, each sensor node comprising the following: a pressure sensor configured to measure local hydraulic pressure fluctuations within the pipeline with a resolution of at least 0.01 bar; a flow sensor configured to measure the volume flow within the pipeline with an accuracy of at least ±0.5% of the measured value; an acoustic sensor configured to detect vibration signatures caused by leaks in a frequency range between 50 Hz and 20 kHz; an embedded microcontroller with integrated analog-to-digital conversion circuitry for digitizing sensor outputs; a wireless communication module configured to transmit time-synchronized sensor data to a cloud-based processing platform; and a local energy subsystem with a rechargeable battery and an optional circuit for generating photovoltaic energy; the cloud-based processing platform includes an artificial intelligence engine that comprises the following: a data acquisition module configured to receive and decode the transmitted sensor data and to perform time alignment; a supervised learning module that is trained on historical data of flagged leaks and non-leaks to classify incoming sensor patterns; an unsupervised learning module configured to detect anomalies by modeling normal operating baselines of the pipeline; and a topology-aware localization module configured to determine leak coordinates using the topology of the pipeline network, modeling the propagation of hydraulic waves, and estimating the arrival time difference from multi-node acoustic detections; and wherein the system is configured to provide real-time leak alerts and georeferenced visualization via a remote monitoring interface.

A context localization system provides relevant local context to a user query to reduce hallucinations and / or inaccuracies for a generative AI system. In embodiments, a corpus of data may be accessed to provide relevant local context. A user query may be used to obtain relevant portions of the local data, which may then be summarized and combined with the user query to form an engineered prompt that include the relevant local context. The engineered prompt is then provided to a generative AI system. In some embodiments, the engineered prompt may allow for determining user sentiment. Other embodiments may be described and / or claimed.

The invention relates to the field of biomedicine, and particularly discloses an epileptic focus artificial intelligence positioning system and method based on magnetic resonance and electroencephalography, and the system comprises the following contents: a data collection module is used for synchronously collecting T1 weighted magnetic resonance images and electroencephalogram signals of scalp; the data preprocessing module is used for performing brain tissue segmentation on the magnetic resonance image, obtaining structural features of each brain region, constructing vectors including whole brain structural features, and obtaining magnetic resonance structural feature vectors; artifacts of the electroencephalogram signals are removed, electroencephalogram features of all brain areas are extracted through time-frequency analysis, a matrix containing whole electroencephalogram physiological features is constructed, and the electrophysiological features are obtained; the cross-modal confidence coefficient dynamic evaluation module is used for establishing a bidirectional constraint rule to perform confidence coefficient calibration on the magnetic resonance structure feature vector and the electrophysiological feature; a positioning model construction and training module; a positioning result output module; according to the technical scheme, noise can be reduced during magnetic resonance and electroencephalogram fusion, and the epileptic focus positioning precision is high.

The invention relates to the technical field of material nondestructive testing, in particular to a CFRP wing skin damage positioning system and method based on multi-modal signal processing and Bayesian optimization DSCN, and the method comprises the steps: carrying out permutation entropy analysis and Higuchi fractal dimension analysis on a signal data set received by a sensor; performing time-frequency feature extraction on the sensor receiving signal data set deviating from the reference; performing damage feature screening on the signal data in the effective time period; training the DSCN structure to obtain an initial CFRP wing skin damage positioning prediction model; performing hyper-parameter optimization on the initial CFRP wing skin damage positioning prediction model according to a Bayesian algorithm; and performing damage positioning prediction on a to-be-detected sample according to the final CFRP wing skin damage positioning prediction model to obtain a corresponding CFRP wing skin damage positioning result. According to the method, effective damage features can be accurately and efficiently extracted from complex signals, and the accuracy and efficiency of CFRP wing skin damage positioning are remarkably improved.

An electrical grid fault localization system including: a plurality of current sensors (measurement devices) electrically connected to the electrical grid; a memory for storing a representation of the electrical grid as a plurality of geographical markers, and for storing a plurality of sets of expected time data, each one of the plurality of sets of expected time data associated with each one of the plurality of geographical markers, each member of the set of expected time data based on an expected signal propagation time from, at least one of the plurality of geographical markers to at least one current sensor; a processor communicative with the plurality of current sensors and the memory, the processor configured to receive measured time data of a fault event from, the plurality of sensors to generate a set of fault event time data and to match the set of fault event time data to at least one of the plurality of sets of expected time data and identify in g / outputting at least one matched geographical marker. Methods and computer-readable media directed to electrical grid fault localization are also described.

The invention provides a supply chain cost anomaly AI positioning system based on multi-mode depth tracing, and relates to the technical field of cost anomaly tracing. According to the supply chain cost anomaly AI positioning system based on multi-modal depth tracing, a three-layer architecture is adopted, a dynamic threshold algorithm and a multi-level association map are combined, real-time detection and root positioning of cost anomaly are achieved, and the three-layer structure is data acquisition, dynamic modeling and visual tracing. The system has the advantages that dynamic adaptability is improved, reasonable fluctuation and abnormal risks are accurately distinguished, the system breaks through the rigid limitation of a traditional static rule, the judgment threshold value is dynamically adjusted by fusing market dynamics (such as bulk commodity prices and policy adjustment) and real-time service data, and a supplier-production-logistics-storage association graph is constructed, so that the risk of abnormal risks is accurately distinguished. And the conduction path of the cost abnormity in each link of the supply chain is visually displayed.

The invention discloses a degradation scene-oriented multi-residual fusion laser radar positioning method, which comprises the following steps of: firstly, performing state prediction by adopting an iterative extended Kalman filtering framework and an IMU (Inertial Measurement Unit), and constructing three complementary observation models of a global map matching residual, a local point-to-plane geometry residual and a luminosity residual; secondly, designing a degradation sensing mechanism based on a covariance ellipsoid, representing absolute and relative degradation degrees through a condition number and an information entropy respectively, realizing quantitative evaluation of system observability, and dynamically adjusting fusion weights of observation residuals; meanwhile, a self-adaptive weight strategy based on luminosity Jacobi intensity is introduced; and finally, performing anomaly detection through deviation comparison between the IMU predicted pose and the IEKF estimated pose, inhibiting pose jump, and ensuring continuity of a positioning time sequence. The method effectively overcomes the challenges of geometric constraint deficiency, positioning drift accumulation and the like of the LiDAR positioning system in the geometric degradation environment, does not need to adjust parameters for a specific scene, and improves the precision, robustness and real-time performance of global positioning in the degradation environment.

Systems and methods described herein include implementation of road surface-based localization techniques for advanced vehicle features and control methods including advanced driver assistance systems (ADAS), lane drift detection, passing guidance, bandwidth conservation and caching based on road data, vehicle speed correction, suspension and vehicle system performance tracking and control, road estimation calibration, and others.

The invention discloses a concept for improved position and orientation determination of a plurality of objects each marked with at least one optoelectronically detectable marker, in which holographic optical elements are introduced into two-dimensional flat markers, at which ambient light is reflected back with sufficiently high intensity even at high scattering angles relative to the surface normal of the two-dimensional flat markers.For this purpose, it is proposed to detect light reflected by holographic optical elements (HOE; 21, 22, 23) applied to the markers (20) in an optoelectronic sensor (32) and to generate a two-dimensional reflection image, and to detect, in an image processing device (34), on the basis of the spectrally resolved local intensity distribution of the two-dimensional reflection image detected by the optoelectronic sensor (32), differences between the reflection behavior of the optoelectronically detectable markers (20) of the current position and orientation of the plurality of objects (100; 200) and the reflection behavior of the optoelectronically detectable markers (20) in a reference configuration of the plurality of objects (100; 200), and to detect deviations in the overall configuration of the plurality of objects (100; 200) relative to the optoelectronic detection device (30) by evaluating the detected differences. determine.

A vehicle localization system and method includes acquiring, from a first sensor, first data indicative of a first navigation state of a vehicle; acquiring, from a second sensor, second data indicative of an environment in the vicinity of the vehicle; generating, using a processor communicatively coupled to the first sensor and the second sensor, a second navigation state based on matching the second data to a known map of the environment; and, generating, using the processor, a current navigation state based on the first navigation state and the second navigation state.

A system is provided for localizing an autonomous work vehicle within mapped agricultural environments when GPS may be unavailable. Range sensors capture LiDAR images that are segmented by a trained model into features such as trunks and canopy. Trunk points are clustered to compute centroids, which are used to estimate left and right tree lines and a lane centerline relative to the vehicle. This sensor-derived centerline is compared with georeferenced lane and row geometry stored in a digital map. A particle filter maintains multiple pose hypotheses, propagates them using vehicle odometry, and weights them according to alignment between the lane centerline relative to the vehicle and the mapped lane geometry for the particle. Resampling and averaging of high-weight hypotheses produces an adjusted pose that corrects for lateral and heading errors. The adjusted pose serves as input to a navigation controller that issues steering and speed commands, enabling accurate autonomous operation.

The invention discloses a highway pavement pit slot real-time identification and positioning system based on computer vision, and the method comprises the following steps: obtaining multi-modal sensor data of a to-be-detected pavement, the multi-modal sensor data at least comprising a visible light image; inputting the multi-modal sensor data into a pre-trained target detection model; outputting a detection result containing the target position and category through the target detection model; wherein the target detection model is an improved model based on a YOLO architecture. According to the real-time recognition and positioning system for the highway pavement potholes based on the computer vision, the target positioning accuracy can be comprehensively improved, better adaptability is achieved for positioning of small targets, irregular targets and complex scenes, meanwhile, stable convergence of the model training process can be promoted, negative effects caused by gradient fluctuation are reduced, and the recognition accuracy of the highway pavement potholes is improved. The consistency and reliability of detection results are effectively enhanced, and more solid performance support is provided for target detection tasks in different scenes.

Systems and methods for determining a location based on image data are provided. A method can include receiving, by a computing system, a query image depicting a surrounding environment of a vehicle. The query image can be input into a machine-learned image embedding model and a machine-learned feature extraction model to obtain a query embedding and a query feature representation, respectively. The method can include identifying a subset of candidate embeddings that have embeddings similar to the query embedding. The method can include obtaining a respective feature representation for each image associated with the subset of candidate embeddings. The method can include determining a set of relative displacements between each image associated with the subset of candidate embeddings and the query image and determining a localized state of a vehicle based at least in part on the set of relative displacements.

To specify the position of a movable body even in a situation where it is difficult to specify the position based on the received radio wave intensity of a beacon signal.SOLUTION: The method includes generating reception data including an area ID of an area in which a receiver is disposed, a received radio wave intensity of a beacon signal, and a reception time; performing image analysis on image data of an imaged area to detect the presence or absence of a movable body in the imaged area, and identifying a movable body ID of the movable body when the movable body is detected; generating image analysis data including the area ID of the area, the presence or absence of the movable body, the movable body ID of the movable body when the movable body is present, and the imaged time; and determining the presence or absence of the movable body using the received radio wave intensity and specifying the presence area based on the strength of the received radio wave intensity when the presence area can be specified, and specifying the presence area based on the image analysis data when the presence area cannot be specified.SELECTED DRAWING: Figure 8

The invention belongs to the technical field of medicine, and discloses a pulmonary nodule intraoperative positioning system based on a flexible array sensor, which comprises the flexible array sensor arranged at a to-be-detected part of a subject and used for collecting a compression signal and obtaining stress distribution; the nodule area detection unit is used for determining a nodule area through clustering analysis according to the stress distribution; the morphological optimization unit is used for performing morphological optimization on the determined nodule region; the feature extraction unit is used for extracting related geometric features based on the optimized nodule region; and the visualization unit is used for visually displaying the stress distribution, the optimized nodule region and related geometric features. The method can assist doctors in accurately positioning the pulmonary nodules, and meanwhile, quantitative features of the nodules are provided.

Systems and methods are disclosed that facilitate object pose determination via keypoint detection. A machine learning algorithm is employed to facilitate voting-based estimation of the locations of at least three keypoints. In some example embodiments, the machine learning algorithm is trained, based on reference intensity-depth images of an object, to determine a radial distance between a keypoint and a 3D scene location associated with each pixel. At inference, the algorithm is employed to process an intensity-depth image to determine radial distance estimates for each pixel, and these radial distances are employed to increment an accumulator space, such that a sphere is generated in the accumulator space for each pixel, centered on the 3D scene location, and a keypoint location is determined by a peak in the accumulator space, with multiple keypoints enabling pose determination. The methods are adapted to other imaging modalities, including non-depth images and point cloud datasets.

Embodiments include systems and methods for managing safety in a multi-sensor environment. Aspects include receiving data from a first localization system and a second localization system to identify a last known consistent location or an inconsistent area for each of a set of objects detected in an environment, identifying one or more hazards within the environment, and determining a maximum speed for each of the set of objects. Aspect also includes evaluating whether any of the set of objects are capable of entering a hazardous area of the one or more hazards within a threshold time and based on a determination that an object of the set of objects is capable of entering the hazardous area of the one or more hazards within the threshold time, performing a safety action.

The invention discloses a localized system migration adaptation method and device based on a multi-dimensional dependency graph. The method comprises the following steps: firstly, collecting cross-level dependency clues of a to-be-migrated system, and generating a dependency event sequence; then constructing a dependency graph based on the event sequence, and extracting a graph primitive used for describing an event association structure from the graph; generating an adaptive mapping tensor by using the map primitive to quantify the interface difference, instruction path offset and time sequence tolerance interval of each technical component in different localized target environments, and forming adaptive resistance distribution representing migration difficulty; on this basis, inferring the adaptation trend and possible blocking position of the system dependency chain, and obtaining a key dependency chain set which needs to be emphatically processed; and generating a structured migration rule according to the set and converting the structured migration rule into an executable migration instruction sequence, thereby realizing high efficiency and controllability of system migration. According to the method, the accuracy and the automation degree of localization migration are remarkably improved through the synergistic effect of the multi-dimensional dependency map and the tensorization adaptation analysis.

In various examples, probabilistic-based techniques may be used to predict intended paths of machines through an environment. For instance, various input data from perception systems, localization systems, mapping systems, and / or other sources of data may be used to determine occupant intent and compute scores associated with road segments in an environment. The scores may indicate a probability that certain road segments are part of the occupant's intended path for the machine, and the scores may be aggregated for each of the road segments across multiple instances of receiving an analyzing the input data. In some instances, a highest scoring road segment(s) may be selected as part of a predicted path of the machine. For instance, at a junction(s) where multiple road segments meet, the highest scoring road segment(s) may be selected as the predicted path.

Systems and methods are disclosed in which a playback device transmits a first sound signal including a predetermined waveform. In one example, the playback device receives a second sound signal including at least one reflection of the first sound signal. The second sound signal is processed to determine a location of a person relative to the playback device, and a characteristic of audio reproduction by the playback device is selected, based on the determined location of the person.

The invention discloses an epileptic seizure detection and positioning system and method combining time convolution and Transform network, and the system comprises a data obtaining module which is configured to be used for obtaining a to-be-detected multi-channel electroencephalogram signal through an electrode, a multi-channel electroencephalogram amplifier and an A / D converter which are connected in sequence, dividing the multi-channel electroencephalogram signal into a plurality of multi-channel electroencephalogram signal segments according to time; the data processing module is configured to be used for extracting time-frequency characteristics of each multi-channel electroencephalogram signal segment through S transformation and performing dimension reduction processing; the epileptic seizure detection module is configured to be used for inputting the multi-channel electroencephalogram signal fragments subjected to dimension reduction processing into the trained TCN-Transformer model to obtain an epileptic seizure detection result; and the epilepsy positioning module is configured to calculate the weight of each channel based on the epilepsy seizure probability score when the epilepsy seizure is detected, generate the final activation vector of each channel based on the weight, and realize the positioning and visualization of the epilepsy seizure based on the activation vector. According to the method, the multi-scale time-frequency-space characteristics from the multi-channel EEG data can be more comprehensively captured, so that the accuracy and robustness of epileptic seizure detection and focus positioning are improved.

Various embodiments of the present invention are directed to a leak localization system configured to identify the presence of a leak within a pipe system and locate the same to a high degree of accuracy. At least one embodiment of such a leak localization system may comprise an emitting device configured in connection with an amplification device for the propagation of an acoustic pulse throughout the pipe. Distally disposed in relation to such emitting device may be at least one frequency sensing device configured to detect the frequency of the acoustic pulse, with the intent of determining any changes thereto. Such frequency sensing device(s) may be configured to generate sensor data which may used to localize an existing leak. Such sensor data may be communicated to a control center via an interconnected control system, wherein the localized leak may subsequently be communicated to the owner of the pipe for remediation.

The invention discloses a lightweight security protection method for file permission access in a localization scene, and belongs to the technical field of information security and localization system security. According to the method, the attack process is effectively delayed and automatic attacks are blocked through three aspects of technologies, including file access interception and underlying authority control, virtual Shell hiding and induction, and external command hiding and path migration. Meanwhile, a set of user-oriented file permission access and Shell obtaining and approving system is formulated, a set of file permission access management and control mechanism is formed, and safety energizing and force applying are carried out on a localized system. According to the lightweight security protection method, the limitation of a traditional method is broken through, a security policy is embedded into a file system and a command path, the lightweight security protection method is suitable for a domestic system in a specific scene, and on the premise that normal users and operation and maintenance process availability are guaranteed, attack chain execution can be effectively delayed, automatic attacks are prevented, and system security is enhanced.

Systems and techniques for localization of disturbances are illustrated. One embodiment includes a method for detecting a disturbance in an area of operation, the area of operation including a plurality of transmitter-receiver pairs. The method obtains, based on a first plurality of signal transmissions between the plurality of transmitter-receiver pairs, at least one baseline channel impulse response (CIR) measurement. The method obtains, based on a second plurality of signal transmissions between the plurality of transmitter-receiver pairs, at least one additional CIR measurement; The method obtains, based on the at least one additional CIR measurement and the at least one baseline CIR measurement, at least one residual CIR measurement. The method derives, based on a sufficient similarity between the at least one residual CIR measurement and at least one particular key of a plurality of keys, localization data for at least one disturbance detected within the area of operation.

Systems and methods for improving label localization are disclosed herein. An exemplary system comprises: a plurality of labels configured to transmit data in response to requests; and a gateway that queries the plurality of labels at an initial query rate. The gateway can be configured to: receive an initial request; in response to receiving the initial request, transmit a signal to the plurality of labels causing a first subset of the plurality of labels to transmit the data at a second query rate that differs from the first query rate; and receive the data from a second subset of the plurality of labels at the first query rate.

The invention discloses a concept for improved time-resolved location and / or position determination of an object marked with several optoelectronically detectable markers, in which holographic optical elements are introduced into two-dimensionally planar markers, at which ambient light is reflected back with sufficiently high intensity even at high scattering angles relative to the surface normal of the two-dimensionally planar markers.For this purpose, it is proposed to detect light reflected by holographic optical elements (HOE; 21, 22, 23) applied to the marker (20) in an optoelectronic sensor (32) and to generate a temporal sequence of two-dimensional reflection images, and to determine in an image processing device (34) a time-resolved position and / or orientation of the object relative to the optoelectronic detection device (30) or the environment marked with detectable markers (20) by evaluating the spectral and local intensity distributions in the two-dimensional reflection images detected by the optoelectronic sensor (32).

An autonomous robot localization system for managing a plurality of autonomous robots is disclosed. The autonomous robot localization system includes a plurality of rail paths including a plurality of markers, a plurality of autonomous robots, and a system controller. The plurality of rail paths is used for guiding the plurality of autonomous robots. The plurality of autonomous robots include a plurality of readers, and a plurality of robot controllers. The plurality of readers include a plurality of sensors The plurality of robot controllers generate a plurality of marker patterns for corresponding marker based on assigned presence and absence status of the plurality of marker bits. The system controller manages movements of the plurality of autonomous robots based on a plurality of commands sent to the plurality of robot controllers.

This invention relates to the field of defect detection technology, specifically to a high-voltage cable defect location system and method based on a two-dimensional ultrasonic array. The system includes a signal main peak acquisition module, a lateral deviation calculation module, an excitation timing optimization module, a main peak spatial focusing module, and a focusing area control module. This invention utilizes a multi-element spatial collaborative acquisition method of a two-dimensional ultrasonic array. It constructs a signal spatial mapping relationship through the main peak timing characteristics, combines path difference parameters to achieve fine analysis of the propagation characteristics of multi-source signals, optimizes the excitation timing and array spatial response structure by adjusting the excitation sequence, periodically determines spatial convergence indices, identifies the main peak focusing state of the defect area, and dynamically adjusts the element weight distribution to improve the signal response capability of specific spatial areas. The spatial location of the defect area is thus clearly distinguished, effectively improving the resolution and accuracy of multi-point defect location in complex cable structures.

This invention relates to the field of optical inspection technology, specifically to a micro LED wafer-level rapid defect localization system based on photoluminescence. A broadband pulse excitation module outputs pulsed laser light matching the characteristics of the material under test; a two-dimensional scanning platform carries the wafer and achieves nanometer-level precise positioning; a time-resolved acquisition module collects fluorescence signals and converts them into time-resolved photoluminescence data; a data processing and control module synchronously controls the operation of each module, receives and processes the time-resolved photoluminescence data, extracts the transient luminescence intensity and carrier lifetime of each detection point, compares the two-dimensional feature vector formed by these two parameters with a preset defect feature database, identifies the defect type of each micro LED chip, and generates a wafer-level defect fingerprint spectrum. This system can accurately distinguish different types of failure modes such as epitaxial defects, surface state defects, and poor electrode contact, significantly improving detection efficiency and defect classification accuracy.