16 results about "Track disease" patented technology

The invention discloses a dynamic trajectory construction and visual mapping method for association of mental disorder brain network damage and systemic system diseases, and belongs to the field of artificial intelligence medical application. According to the method, high-resolution MRI images, biomarkers and clinical information of major mental disorder patients are collected, and the influence of factors such as age, gender, medication and diagnosis on the braingut axis and the cardio-cerebral axis is evaluated through multi-modal data fusion. By constructing a disease dynamic trajectory model, brain structures and function change modes corresponding to different mental disorders are identified. Large-scale samples are analyzed through machine learning, potential risks and protection factors are extracted, and a visual tool is developed to visually display changes of the brain under different disease systems. A closed-loop feedback mechanism is established through follow-up visit, the disease progress and the intervention effect are dynamically tracked, and key evaluation indexes are identified. According to the invention, theoretical basis and practical guidance are provided for early screening, precise intervention and personalized treatment of mental disorders, and the diagnosis and treatment accuracy and efficiency are remarkably improved.

The application discloses a railway track disease detection method based on coordinate attention, comprising the following steps: acquiring a railway track image, performing adaptive pyramid scaling and normalization processing, and expanding a data set based on a Mosaic data enhancement method; adopting a lightweight backbone network based on FasterNet to extract multi-scale features of the railway track image; strengthening disease area related channel features based on an MSCAM multi-scale convolution attention module; connecting a coordinate attention CoordAtt module after the output of the MSCAM multi-scale convolution attention module to form a fused feature enhancement module, and obtaining disease area related channel features after association; fusing a perception loss on the basis of an original loss function of YOLOv8 to form a joint loss function, optimizing disease texture alignment in a deep disease area feature space, and forming enhanced features; inputting the features into a detection head to output disease categories, a bounding box and a confidence, and completing railway track disease detection. The application also discloses a system, an electronic device and a computer readable storage medium.

The invention relates to a double-track vehicle-mounted multi-source track disease detection system, which comprises two detection devices connected side by side, each detection device comprises: a frame body, the bottom of which is provided with a walking mechanism used for walking on a steel rail; the imaging assembly is arranged on the frame body and is used for imaging a steel rail to be detected and acquiring image data of the steel rail to be detected; the ultrasonic probe is arranged on the frame body and is used for scanning a to-be-detected steel rail and acquiring ultrasonic scanning data of the to-be-detected steel rail; the processing device is in signal connection with the imaging assembly and the ultrasonic probe, and the processing device obtains the image data and the ultrasonic scanning data and judges the disease state of the steel rail to be detected in combination with the image data and the ultrasonic scanning data. The steel rail detection efficiency and detection accuracy can be improved.

The application discloses a kind of mobile loading devices for track disease identification, it is related to track disease identification technical field, including mobile assembly, mobile assembly includes base, the bottom of base is provided with support frame, support frame is sequentially arranged with several groups, and the sliding support frame is slidably installed in support frame, the positioning assembly for positioning is arranged in the side of support frame, and the third mounting seat is installed in the bottom of sliding support frame.The application can drive the mobile wheel to contact the track and rotate by setting the mobile assembly, so as to conveniently complete the movement of the device, the rotation of the control wheel can contact the two sides of the track, so as to complete clamping and fixing, effectively reinforce the mobile wheel, avoid the mobile wheel from falling off the track during movement, assist during movement, avoid instability, shaking and overturning during movement, ensure normal use of the device, and avoid affecting loading accuracy and safety.

The invention provides a multi-domain feature fusion track disease identification method, which belongs to the technical field of data processing, and comprises the following steps: collecting vibration acceleration signals of a track in a normal state and a plurality of disease states, carrying out multi-domain basic feature extraction on the collected vibration acceleration signals, and carrying out feature coding on the extracted multi-domain basic features to obtain a multi-domain feature fusion track disease identification result; obtaining a track disease detection data set; inputting the track disease detection data set into a CNN-LSTM multi-domain feature fusion network, performing multi-domain depth feature extraction, and fusing the extracted multi-domain depth features to obtain multi-domain fusion depth features; and taking the multi-domain fusion depth feature as input, and outputting an orbit state and a probability value thereof through a full connection layer and a Softmax function. Therefore, efficient and accurate recognition of various track diseases is realized through multi-domain feature fusion, and the problems of poor multi-disease recognition capability and insufficient feature extraction in track disease recognition in the prior art are effectively solved.

The invention relates to the technical field of railway detection, and particularly discloses a track disease image detection method and device, and the method comprises the steps: collecting and inputting a labeled ballastless track image sequence in real time; dynamically processing the ballastless track image sequence through multi-level feature extraction to determine multi-scale disease features of the ballastless track image sequence; multi-scale disease features are optimized and aligned in real time through a dynamic feature alignment method, and semantic information of the multi-scale disease features is integrated through a cross-modal attention method; dynamically generating a pixel-level probability distribution map and a boundary heat map of the multi-scale disease features based on a boundary perception segmentation head, and determining segmentation loss and boundary loss based on a multi-task supervision strategy to construct a detection model; detecting and outputting a disease detection result updated in real time based on the detection model, and dynamically adjusting the image acquisition frequency according to the detection result.

The application discloses a reusable high-speed railway disease maintenance training device for ballastless track and a method thereof, and belongs to the technical field of track traffic engineering equipment. The device is composed of a base plate, a track plate module, an adjusting and driving module, a grouting lifting module, a detachable support module and a sensing and data acquisition system. The track plate module is arranged above the base plate. The adjusting and driving module can adjust the height and posture of the track plate, and forms a controllable grouting cavity between the track plate and the base plate. The grouting lifting module is used for grouting into the cavity and lifting the track plate. The detachable support module can be used for pouring and forming a concrete support structure, and can be quickly disassembled and reassembled to reset the device. The sensing system can collect training parameters in real time and complete feedback control and quantitative evaluation. The application can completely simulate the whole process of lifting, grouting and supporting and pouring of the ballastless track, realize the repeatable simulation of the disease working condition, significantly reduce the training cost and structural loss, and is suitable for track engineering teaching and training and operation and maintenance process demonstration.

In view of the problems of the track irregularity and disease monitoring system on the market, such as the need for manual cooperation, low intelligence and integration, poor dynamic monitoring and sensing effect, high labor cost and the like, the present application discloses a track irregularity and disease intelligent detection and positioning method, which collects and analyzes the data collected by the three-camera camera and GPS sensor installed on the track inspection vehicle, on the one hand, the track is three-dimensionally reconstructed and the track irregularities such as triangular pits, high and low and level are detected and positioned through the elevation analysis of the point cloud; on the other hand, the image target deep learning detection model is used to detect and position the diseases such as peeling, fish scale injury, layer cracking and wave abrasion on the track, so as to realize the accurate and rapid detection and analysis of the track irregularity and disease, and solve the problem of rapid detection and positioning of track diseases during the operation of the track inspection vehicle. Compared with manual detection, the present application is more reliable, efficient and safe.

Systems and methods for tracking an evolution of a disease in a colon of a patient over time are configured for operations including receiving video data representing a colon of a patient; segmenting the video data into segments representing portions of a colon of the patient; extracting, from the video data based on the segmenting, a set of features representing locations in the colon of the patient; and registering the feature vector as representing the colon of the patient for tracking the evolution of the disease in the colon of the patient. The system can be configured to predict disease progression predict drug dosage for patients.

The invention discloses a real-time track disease detection method and device. The method comprises the steps that a multi-modal image and positioning and attitude determination data of a track area are collected in real time through a GPU detection device; performing fusion and enhancement processing on the multi-modal image to obtain an enhanced image; inputting the enhanced image into a disease detection-based network, and identifying a disease target in real time; performing time sequence verification on disease targets in continuous multiple frames, and eliminating transient false detection based on a cross-frame Boolean voting result to confirm a stable disease target; risk assessment and grading are carried out based on the pixel coordinates and the widths of the disease targets; fusing the pixel coordinates of the stable disease target with positioning and attitude determination data, and mapping to a GNSS / RTK coordinate system; and uploading structured data containing the disease information and the positioning result to a cloud end in real time, and automatically triggering an alarm and maintenance order sending process based on the risk level.

The present invention discloses an AI-powered biometric identity and gait-based disease monitoring system / platform. The system captures gait data through wearable devices, analyzes gait metrics with vertical disease-specific AI models and horizontal pharmacological AI models. The system may also generate biometric identity hashes, and is able to continuously track disease progression through longitudinal AI analysis. The system further integrates drug-gait interaction modeling and secure cloud deployment while generating clinician-ready reporting formats. The system may also be used to continuously assess insurance-related matters such as premiums and policy-related actions.

The invention relates to the technical field of track disease detection, and discloses a regional ballastless track disease detection method, device and equipment and a storage medium, and the method comprises the steps: generating a detection data uploading demand and detection data uploading capability of each detection sub-section according to a ballastless track historical disease database and a communication quality map; and then, detecting the vehicle speed as a core, and combining hardware constraint and a gear switching optimization target to solve an optimal detection strategy so as to guide execution of ballastless track detection driving, detection data uploading and cloud disease detection analysis. Therefore, by optimizing the detection speed of the ballastless track detection vehicle in different detection sub-sections, the problem that detection is stopped or old data is covered due to data accumulation in poor network areas such as tunnel groups and mountainous areas is thoroughly solved while it is ensured that detection is completed within the skylight period, the integrity of detection tasks and the reliability of data are guaranteed, and the detection efficiency is improved. The cloud is helped to quickly analyze and output disease detection results, and a timely basis is provided for track maintenance.

Provided is a method to determine the tissue or cell origin of cfDNA from a sample obtained from a subject and uses thereof to trace tissue damage in disease and disorder in the subject. The method comprises measuring read-level or fragment-level cfDNA methylation at cell type or tissue-specific regions and assigning cfDNA to a cell type or tissue origin. The cell type or tissue specific cfDNA level indicates cell or tissue damage in the subject. Also provided is the use of the present method for developing targeted kits for diagnosis, patient screening,

The invention discloses a railway track disease inspection method based on deep learning. The railway track disease inspection method comprises the following steps: acquiring track detection data in a railway track detection scene and preprocessing the track detection data; performing multi-dimensional element association packaging based on the standardized track detection data set; constructing a track structure region set based on the track detection input unit set; constructing a disease engineering characteristic constraint and carrying out regional association labeling to obtain a constraint binding input set; performing multi-path feature coding and fusion processing on the constraint binding input set through a feature extraction model, and outputting a disease feature representation set; carrying out disease identification and judgment on the disease feature representation set to obtain a disease identification result set; and carrying out structured packaging on the disease identification result set and outputting a track disease inspection result set. The method is based on deep learning and a track structure sensing method, achieves the refined recognition and quantitative evaluation of track diseases, and has the advantages of being high in structure pertinence, high in recognition reliability and good in engineering applicability.

The invention provides a track disease and space line shape integrated inspection robot. The inspection robot comprises a main body; the walking mechanism is used for driving the main body to move; the measuring mechanism is arranged on the main body and is used for acquiring a two-dimensional image and a three-dimensional point cloud and acquiring IMU (Inertial Measurement Unit) data and GNSS (Global Navigation Satellite System) data; the track disease detection module is used for carrying out track disease detection according to the two-dimensional image and the three-dimensional point cloud; and the orbit space linear detection module is used for performing orbit space linear detection according to the IMU data and the GNSS data. The robot can realize detection of track diseases and detection of track space line shapes.

The invention relates to the technical field of railway ballast remote sensing prediction, and discloses a remote sensing image-based track disease prediction system, which comprises a historical track risk screening module, a first risk assessment module and a second risk assessment module, and is characterized in that the first risk assessment module reads a remote sensing image library capable of obtaining remote sensing images in real time to obtain the remote sensing images; the first risk assessment module is used for acquiring a remote sensing image, constructing a plant proximity coefficient of each section of track based on the remote sensing image, sending a first risk instruction and executing a first risk strategy when the plant proximity coefficient is lower than a preset plant proximity risk threshold value, and the second risk assessment module is used for acquiring a railway ballast image for analysis and judgment by calling an unmanned aerial vehicle and acquiring a railway ballast risk early warning coefficient. The system establishes color change area thresholds for different track sections by analyzing historical maintenance data, dynamically adjusts the inspection period, and performs large-range and rapid plant proximity risk screening by using remote sensing images, thereby realizing early warning of diseases such as track ballast pollution and the like.