15results about How to "Guaranteed recognition accuracy" patented technology

This invention belongs to the field of materials testing technology and discloses a deep learning-based method and system for identifying the interface of SiC modified asphalt. It acquires multi-dimensional interface images through a layered time-series acquisition method using four devices: optical microscope, SEM, EDS-Mapping, and Raman spectrometer. Adaptive algorithms are used for targeted denoising, normalization, and registration, while attention mechanisms and weighted fusion strategies are combined to enhance interface features. Simultaneously, a multi-condition dataset containing a high proportion of minor defects is constructed to achieve deep fusion and accurate identification of multi-modal features, eliminating subjective errors from manual judgment and improving the fine-grained accuracy and detection efficiency of SiC modified asphalt interface identification. The U-Net model is improved for specific scenarios and designed to be lightweight. By optimizing the encoder-decoder structure, introducing a dual-channel attention mechanism and a condition-adaptive branch, and combining a loss function, the feature extraction capability of interface boundaries and minor defects is enhanced.

This invention discloses a hybrid quantization method for image recognition models based on bit-flipping attacks, comprising: Step 1, performing initial quantization on the image recognition model to convert it into an initial quantized model; starting from the first layer of the initial quantized model, performing bit-flipping attacks layer by layer, and recording the sensitivity of each layer under attack; forming a sensitivity set of the initial quantized model from the sensitivities of all layers; constructing an optimization objective function and determining optimization constraints, and determining the optimal hybrid precision quantization strategy for the image recognition model based on the optimization objective function and the optimization constraints; Step 3, setting the bit width for storing the parameters of each layer of the image recognition model according to the optimal hybrid precision quantization strategy, thereby obtaining the optimal hybrid quantization model; Step 4, acquiring the image to be recognized and performing image recognition using the optimal hybrid quantization model.

This invention discloses a lightweight few-shot incremental learning method for power line defects. The method includes: first, constructing an inspection image dataset containing basic and incremental categories, and designing a lightweight feature extraction network; training the network using the basic category data to generate prototype vectors for each category, forming an initial prototype classification model; in the incremental stage, constructing incremental prototypes for a small number of samples of the new category, and introducing a prototype transfer mechanism based on inter-class tension, adaptively adjusting the new prototype based on the geometric similarity and discrimination conflict strength between the new and existing prototypes to alleviate category conflicts; simultaneously, introducing an adaptive feature fusion mechanism that perceives discrimination uncertainty, dynamically fusing the discrimination results of the main and auxiliary branches to improve recognition stability. This invention, employing the above-mentioned lightweight few-shot incremental learning method for power line defects, can achieve efficient and stable power line defect recognition in scenarios with few samples and continuously expanding categories.

The utility model relates to the technical field of paper currency identification, and discloses an adjustable multi-currency automatic identification and classified storage mechanism, which comprises a machine body, a first conveying device is arranged in the machine body, a second conveying device is arranged on the lower side of the first conveying device, a third conveying device is arranged on the lower side of the second conveying device, and a third conveying device is arranged on the lower side of the third conveying device. An inclined plate is fixedly connected between vertical plates on the two sides of the machine body, and through cooperation of a suction fan, a second connecting pipe, a rotating flange, a conveying roller, an air suction opening, an arc-shaped plate and other structures, single paper money stripping is achieved through controllable adsorption force (dynamic adjustment can be conducted according to paper money materials). And the adsorbed banknotes are conveyed to the upper side of the second conveying device, so that the recognition accuracy of the subsequent recognition module on the banknotes is effectively improved, and the separation effect is improved more remarkably.

The utility model relates to early warning equipment technical field, and disclose an emergency lighting system based on wisdom building, including prop, the circumference of prop is fixedly connected with disc, the top of disc is fixedly connected with the ring of teeth, the circumference of prop rotatably connected with the board of turning, the bottom of board of turning is fixedly connected with the round angle board, the inner wall of round angle board rotatably connected with output shaft, the circumference of output shaft is fixedly connected with gear, the top of board of turning is fixedly connected with detector, the front of board of turning is fixedly connected with photovoltaic board, the present application can carry out the detection of larger range between building, when detecting the fire, can give the signal to the detection center and carry out the early warning and open the lighting lamp simultaneously and carry out the emergency lighting.

This invention discloses a method for detecting subsidence using simple linear iterative clustering, applicable to the road infrastructure industry. It can simply, accurately, and efficiently acquire subsidence information on asphalt pavements. Step 1: Scaling the image; Step 2: Dividing the image into multiple small regions using simple linear iterative clustering, significantly reducing the relative weight of spatial and gray-level distance to obtain a reasonable number and shape of regions; Step 3: Analyzing the hierarchical relationships between regions based on their geometric features to establish inclusion pairs and filtering out non-target regions; Step 4: Performing local planar fitting on the periphery of the remaining bottom-level sub-regions to eliminate the influence of pavement slope on subsidence depth calculation; Step 5: For the remaining bottom-level sub-regions, calculating the gray-level difference between each point on the outer edge of the sub-region and the center of the sub-region, and determining whether it is a subsidence area based on the proportion of depth differences exceeding a threshold. This invention can quickly segment images and identify potential subsidence areas.

This application discloses a detection device and its control method, control device, and readable storage medium, relating to the field of equipment detection technology. The control method of the detection device includes: controlling a current generator to send a first current signal to a current transformer and a current processor respectively; controlling the current processor to receive a second current signal sent by the current transformer, the second current signal being a feedback current signal after the first current signal passes through the current transformer; when the current processor receives the first and second current signals, controlling the current processor to determine the current difference between the first and second current signals; and determining the performance state of the current transformer based on the current difference. This application improves the detection accuracy of the detection device for current transformers.

The application discloses a kind of unmanned aerial vehicle cluster multi-view target identification method based on adaptive segmentation inference, applied to mobile communication and artificial intelligence fusion technical field, for the computing resource limited, communication bandwidth limited and real-time requirement higher problem that unmanned aerial vehicle cluster faces when executing target identification task in complex dynamic environment, the application first constructs a kind of distributed inference system framework based on unmanned aerial vehicle cluster, including a cluster head node and multiple member nodes, uses multi-view convolutional neural network as target identification model;Then an optimization model with the minimum system inference delay as the target is constructed, the model segmentation position is determined for each member node respectively, and the application method realizes low latency, high efficiency target identification, and is suitable for emergency rescue, maritime search and rescue, battlefield reconnaissance, intelligent inspection and complex environment perception and other application scenarios.

This application relates to the field of privacy protection technology and discloses a face recognition privacy protection system and method, comprising: an edge device dynamically generating noise from an acquired original face image to obtain a target perturbation image, and sending the target perturbation image to a corresponding edge server; the edge server training a local model based on the target perturbation image, generating local model parameters, and uploading the local model parameters to a central server; the central server aggregating the local model parameters to obtain updated global model parameters, and distributing the global model parameters to the edge servers; the edge server updating the local model based on the global model parameters, and performing face recognition on the perturbation image sent by the edge device according to the updated local model. This achieves end-to-end privacy protection while ensuring face recognition accuracy, eliminating the risk of original face data leakage.

This application relates to a text processing method, apparatus, computer device, and storage medium. The method includes: acquiring text to be parsed; converting the text to be parsed into a text string; the text string including prompt information and answer information from the text to be parsed; filling the prompt information and answer information from the text string into a prompt template according to a pre-determined prompt template; inputting the filled prompt template into a pre-trained reinforcement learning agent; outputting the answer information from the prompt template; and converting the answer information into a prediction result of the reinforcement learning agent. This method can process the text to be parsed into a standardized format using prompt templates, ensuring that the reinforcement learning agent can handle new scenarios with unlabeled or poorly labeled text, allowing the reinforcement learning agent to adapt to new text processing scenarios and guaranteeing the recognition accuracy of new texts to be parsed.

This invention relates to the field of pipeline exploration technology and discloses an underground pipeline detection device, including an equipment box and a frame slidably installed at the bottom of the equipment box. The frame includes two sets of connecting rods and two sets of connecting rods, which are connected end to end by bearing hinges to form a rhomboid closed frame. An adjustment component is provided inside the equipment box. The adjustment component drives the four-bar linkage of the rhomboid closed frame to achieve stepless expansion and contraction of the frame width. This invention, by setting an adjustable-width frame, forms a balanced force support through the cooperation of the rhomboid closed frame and triangularly distributed universal wheels, which can avoid deviation and ensure detection accuracy. Through the linkage of knobs, gear columns, and tooth grooves, the support rod can slide smoothly along the slide groove. It can be adapted to narrow passage scenarios such as old residential building corridors and urban village alleys without disassembly, solving the problem of traditional fixed-width carts being unable to enter.

This invention discloses a cross-layer semantic fusion traffic identification method, system, device, and medium, relating to the field of network security. The method includes collecting raw data from the network observation side and the semantic side and converting it into encrypted traffic vectors and log vectors carrying consistent timestamps; mapping the log vectors and encrypted traffic vectors to a unified space; calculating the semantic similarity between the log vectors and encrypted traffic vectors; constructing a time proximity weight based on the consistent timestamps; coordinating the time proximity weights and semantic similarity to generate a cross-layer association matrix; constructing a cross-layer heterogeneous inference graph using the unified vector representation and the cross-layer association matrix; mapping the target directed path extracted from the cross-layer heterogeneous inference graph to a target state vector; matching the target state vector with a reference vector; and determining the encrypted traffic identification result based on the matching result to improve the reliability of encrypted network communication.

This utility model provides a mobile payment QR code scanning box, belonging to the technical field of QR code scanning boxes. The mobile payment QR code scanning box includes a scanning platform body and a scanning dock inside the scanning platform body. Its features include: a protective component on the scanning surface of the scanning platform body, several reinforcing ribs on both sides of the outer wall, a maintenance door on the back, two integrally formed anti-slip strips on both sides of the bottom of the scanning platform body, and a mounting groove in the middle. A counterweight is magnetically attached inside the mounting groove, protruding from the groove and magnetically fixed to an external tabletop. The protective component on the top of the scanning platform body, the reinforcing ribs on the side walls, and the maintenance door on the back, combined with the mounting groove and counterweight, effectively solve the problems existing in current mobile payment QR code scanning boxes regarding fixing methods, scanning end protection, and dustproof performance.

The present disclosure discloses a human action recognition method and device based on a neural network and a medium, belonging to the technical field of machine learning, comprising: acquiring first sensing data through a plurality of sensors placed on different parts of the human body; setting second weights for each sensor according to the feature vectors of the first sensing data through a fusion optimization network and a sensor weight generation network; further obtaining feature weight values through a feature weight network; obtaining a first human action recognition result and calculating a loss value through a classification network according to the feature weight values to train the network; and identifying the human action of second sensing data to be identified according to the trained neural network model. The fusion optimization network explores the correlation between the internal channels of the sensor, fully utilizes the data of each channel in a sensor, reduces the redundancy of the data based on the dual selection of the sensor and the feature, reduces the sensor data, and at the same time ensures the final recognition accuracy and recognition stability.

The application discloses an image recognition method and device based on image frame features, and relates to the technical field of image recognition. The method comprises the following steps: constructing a global image recognition model and a global dynamic frame extraction model on a cloud server, and deploying the global image recognition model and the global dynamic frame extraction model to an edge computing gateway; performing migration fine-tuning on the edge computing gateway to obtain a scene image recognition model and a scene dynamic frame extraction model; performing frame processing on a video stream; performing feature extraction and fusion by using the scene image recognition model to obtain fused features; performing dynamic frame extraction by using the scene dynamic frame extraction model according to the fused features to obtain to-be-recognized image data of continuous frames after frame extraction; and performing image recognition on the to-be-recognized image data of continuous frames after frame extraction by using the scene image recognition model to obtain an image recognition result. The method solves the problems of data islands, insufficient model generalization ability, high computing resource requirement and difficult model deployment and maintenance in the prior art.