10results about How to "Achieve lossless" patented technology

The invention belongs to the related technical field of intelligent sportswear, and particularly relates to a solar AI intelligent sportswear, which comprises an outdoor jacket main body, at least three flexible amorphous silicon film power generation modules, an energy management module, an energy storage unit, an electricity load and a flexible conductive circuit for interconnecting the flexible amorphous silicon film power generation modules, the energy management module, the energy storage unit and the electricity load. The flexible amorphous silicon film power generation modules are optimally arranged in high-sunlight areas such as the rear of the brim and the shoulders and are combined with the garment through the hot-pressing composite technology, sewing needle holes are avoided, the original elasticity, soft touch feeling and appearance smoothness of the garment are kept, and the service life of the garment is prolonged. The non-sensibility of the power generation function and the non-destructive effect of the clothes movement function are realized; according to the power generation module, ETFE / PI'sandwich 'packaging and ultrasonic welding edge sealing are adopted, and a waterproof moisture-permeable hot melt adhesive composite technology is matched, so that multi-layer protection is jointly formed.

The invention discloses a method for detecting potential electric leakage defects of an LED device, and relates to the technical field of semiconductor photoelectric device detection. Aiming at the technical pain point that recessive microdefects (such as a metal migration channel caused by a V-shaped pit and dislocation) cannot be identified by the existing single spectroscopic detection, the invention provides a detection logic of reference parameter acquisition, defect stress excitation, parameter acquisition after excitation and differential analysis and judgment. Specific stress is applied to primarily screened qualified products, ion migration at potential defects is excited in an oriented mode, and hidden electric leakage is dominated. Potential defective products are accurately rejected by calculating parameter drift distances (VFL, IR) before and after stress. The method is not only suitable for interactive screens, but also suitable for high-reliability scenes such as vehicle-mounted display and high-end illumination, and can effectively intercept hidden lamp beads causing'caterpillar 'failure or early light attenuation, thereby remarkably improving the product yield and the terminal reliability.

According to the aging detection method for the vehicle sealing strip, the electronic equipment and the vehicle, when the aging detection condition is met, aging detection is carried out, interference of the environment inside and outside the vehicle can be reduced, a compartment acoustic cavity is formed in the space in the vehicle, and the aging detection accuracy is guaranteed. A low-frequency sweep-frequency signal is played to excite a compartment acoustic cavity, a transfer function representing acoustic characteristics in a vehicle is determined in combination with a synchronously collected sound pressure response signal, and lossless, online and quantitative evaluation of the aging state of the vehicle door sealing strip is realized by analyzing the change of the transfer function relative to a health baseline.

This invention provides a non-destructive characterization method for hollow anti-resonant optical fibers based on multimodal deep learning. The specific implementation path includes: (1) acquiring microscopic images (image modalities) of the end face of hollow anti-resonant optical fibers covering differentiated microstructures and their corresponding original transmission spectral data to construct a multimodal original sample set; (2) performing feature extraction and standardization processing on the multimodal data; (3) building a cascaded deep learning model integrating a visual feature capture subnetwork and a transmission spectral feature fusion regression subnetwork; (4) implementing joint training, using a convolutional neural network (CNN) to automatically extract the geometric feature parameters of the fiber core and cladding microstructures in the microscopic images, and fusing them with the transmission spectrum features, constructing a mapping relationship through a deep neural network (DNN); (5) inputting the microscopic image and transmission spectral data of the optical fiber under test into the model to achieve non-destructive prediction of loss characteristics. This invention features high characterization accuracy, fast speed, and zero material loss.

This application provides a method and system for diagnosing transformer winding deformation based on transient harmonic trajectory maps, belonging to the field of power equipment fault detection technology. The method includes acquiring the time-domain electrical signal during transformer operation; monitoring sudden changes in the time-domain electrical signal based on a preset rate of change threshold to determine if a transient event has occurred; when a transient event occurs, extracting the time-domain electrical signal from the time period before and after the current moment to obtain the transient time-domain signal; performing multi-scale decomposition processing on the transient time-domain signal to obtain transient harmonic components; extracting features from the transient harmonic components and plotting a fault trajectory map based on the extracted feature parameters; and using a trained diagnostic model to perform fault assessment on the fault trajectory map based on the health fingerprint trajectory map of the transformer during its commissioning period to obtain the winding deformation fault diagnosis result. This application can reduce human intervention and achieve online monitoring and accurate fault diagnosis of transformer winding deformation through time-frequency analysis and trajectory imaging technology.

This invention relates to the field of identification technology for precious woods and medicinal materials, particularly a method for non-destructive testing of agarwood based on micro-CT to determine its authenticity and quality. The technical solution includes: acquiring complete three-dimensional data of the agarwood interior through micro-CT non-destructive scanning; extracting characteristic parameters such as the three-dimensional topological structure of the interstitial phloem, the distribution and fullness of agarwood resin, and abnormal structures; identifying authenticity by comparing these parameters with a pre-built database, particularly effective in identifying internal forgery traces such as high-pressure oil injection and mechanical compression; simultaneously, using a multi-parameter fusion model to quantitatively evaluate the spatiotemporal distribution characteristics of agarwood resin and the proportion of resin-containing interstitial phloem to determine its quality grade; achieving comprehensive, objective, and non-destructive testing of agarwood from the inside out, from authenticity to quality, especially suitable for identifying high-end crafts, collectibles, and precious agarwood raw materials, solving the industry pain point that traditional destructive testing methods cannot be applied to intact products.

The invention relates to the technical field of ship engineering, and discloses an instrument protection device for a grating plate, which comprises a protection cover, the protection cover can cover an instrument, and the protection cover comprises an observation plate with an observation port; the at least two elastic supporting legs extend towards the lower part of the observation plate, and a bending transition part is arranged between the two elastic supporting legs; and the clamping parts are arranged at the lower ends of the elastic supporting legs. The elastic supporting feet elastically deform inwards when being extruded by opposite external force, so that the clamping parts get close to each other and are inserted between the grating strips of the grating plate. And after the external force is removed, the elastic supporting legs rebound by virtue of the elastic potential energy of the bending transition parts, and the clamping parts are driven to abut against the outer side and are clamped on the grille bars. According to the instrument protection device for the grating plate, through the mechanical logic of elastic deformation and resilience of the elastic supporting feet, the clamping parts are driven to abut against and be clamped with the grating strips, so that the non-destructive rapid disassembly and assembly effect is achieved, and the installation stability of the instrument protection device can be maintained in the marine environment.

The invention relates to the field of additive manufacturing aftertreatment, in particular to automatic sand cleaning equipment and method for a 3D printing sand core. In the prior art, a traditional mechanical sand cleaning method (high-pressure water jet, shot blasting and the like) is too high in impact energy, the fine structure of a 3D printing sand core is prone to being damaged, the product rejection rate is high, a mainstream manual / semi-mechanical sand cleaning mode is low in efficiency and poor in cleaning effect, and residual loose sand in a complex inner cavity and a blind hole cannot be treated; in order to solve the problems, the invention provides the automatic sand cleaning equipment and method for the 3D printing sand core. Through cooperative operation of the turnover mechanism and the telescopic mechanism, the mode of high-pressure airflow and gravity assisted shakeout is adopted, residual sand in a complex inner cavity is safely and efficiently removed, the industrial pain points that a 3D printing sand mold is difficult to remove sand and prone to damage are solved, and automation, intellectualization and lossless in the sand removing process are achieved.

The application discloses an oil window dismounting tool, and belongs to the technical field of dismounting tools, and mainly aims to realize lossless, safe and efficient dismounting of an oil window glass. The main technical scheme of the application is that the oil window dismounting tool comprises a positioning and supporting mechanism, a lifting driving mechanism and a vacuum adsorption mechanism; the positioning and supporting mechanism is used for abutting against installation base bodies on two sides of an oil window to be dismounted; the lifting driving mechanism is matched with the positioning and supporting mechanism and is used for providing a pulling force in a direction away from the installation base bodies; the vacuum adsorption mechanism is connected with the lifting driving mechanism and is used for adsorbing an oil window glass sheet of the oil window to be dismounted, and under the driving of the lifting driving mechanism, the oil window glass sheet is separated from the installation base bodies.

The invention discloses an automatic fritillaria identification system and method based on hierarchical deep learning, and relates to the technical field of intelligent detection of traditional Chinese medicinal materials. The system comprises a hardware integration and image acquisition module, an image processing and target positioning module and a grading identification algorithm module. Wherein ConvNeXt-is adopted for the category pairs of the Songbei and the Pingbei, which are relatively low in similarity; carrying out classification and identification through a Tiny network; the method comprises the following steps of: performing classification identification by adopting a double-branch ConvNeXt model aiming at furnace shell and illite type pairs with relatively high similarity, respectively extracting local microscopic texture and overall macroscopic morphological characteristics by adopting texture branches and morphological branches which are constructed in parallel, and performing optimization by adopting a joint loss function fusing tuple marginal penalty terms so as to enhance the inter-class discrimination capability. According to the invention, full-process automation from sample feeding to identification result output is realized, the identification precision is high, the speed is high, and the method is suitable for rapid nondestructive batch detection in the circulation link of traditional Chinese medicinal materials.