5results about How to "Maximize accuracy" patented technology

The invention discloses a method for constructing, updating and retrieving an action memory library, which belongs to the technical field of computer vision and comprises the following steps of: constructing a training data source with time sequence diversity; initializing an action memory library containing a plurality of learnable prototype matrixes and a double-flow interaction network; memory bank evolution is executed, an action prototype is retrieved by utilizing a query stream, a current memory state is dynamically generated by combining a memory state updating gate mechanism with a historical state, and dynamic memory is injected into a feature space by utilizing memory driving graph convolution; synchronously updating parameters based on multi-target loss, and driving a memory bank to evolve into optimal structured prior; and finally, performing structured reasoning on the to-be-detected sequence by using the optimal memory bank. According to the method, structured priori is constructed by mining a spatio-temporal topology mode of a human body action sequence, and hierarchical memory evolution and double-flow depth interaction are combined, so that the problem of depth ambiguity in a monocular vision task is effectively solved, geometric structure distortion is corrected, and the accuracy of action posture estimation is remarkably improved.

The invention provides a worst execution time path derivation method for an ARINC653 operating system. Only tasks to be tested are reserved in a partition to be tested; from the entrance of the to-be-tested task to top down, performing pile pitching on each branch possibly influencing the program trend in the to-be-tested task; running the to-be-tested task under the target machine, injecting a test case set into the to-be-tested task, and collecting time information of each branch in all pile points; subtracting the execution time of the pile point from the execution time of the to-be-tested task; and obtaining a theoretical worst path and an actual worst path of the to-be-tested project, fitting the bifurcation points of the two paths to obtain a fitting path, running the fitting path under the target machine again, and obtaining a final WCET path. The method solves the problems that a static analysis method is difficult to meet the time margin resource requirement of embedded software operation, a dynamic measurement method is difficult to prove that the obtained result is a real WCET value, and the algorithm threshold is too high.

The invention relates to the technical field of electronic transaction processing, in particular to an air material transaction platform intelligent matching recommendation method and system, and the method comprises the following steps: obtaining an inventory record and manual data, building an interchange edge and a modification edge, constructing an air material configuration state topology network, obtaining a purchase request, and positioning a target state node, and performing breadth-first search on a reverse path, performing accumulative weighting to generate candidate alternative paths, obtaining an access rule to verify airworthiness certificates and life values, retaining compliant alternative paths, and performing ascending arrangement according to the total conversion cost to generate a recommendation list. According to the method, the directed topology network containing the configuration state, the interchange relation and the refitting cost weight is constructed, the breadth-first search is utilized to automatically calculate the composite alternative path, the airworthiness certificate and the life rule are deeply verified, the hidden available inventory is effectively mined, the compliance scheme is generated, sorting is carried out according to the total conversion cost, and the conversion efficiency is improved. And the inventory utilization rate and the transaction matching accuracy are maximized while the compliance risk is eliminated.

The application discloses a federal learning model compression method, a user terminal, a server and a system, and belongs to the technical field of communication. The method comprises the following steps: acquiring a local model corresponding to at least one target application; determining a compression strategy of the local model based on a performance index of the target application; performing compression processing on the local model based on the compression strategy to obtain a local compressed model and a compression identifier corresponding to the local compressed model; uploading the local compressed model and the compression identifier to a server for model aggregation processing, acquiring global model parameters issued by the server; and updating the local model based on the global model parameters. In this way, according to the performance index of the application, a suitable compression strategy is selected for model compression, the communication cost can be reduced on the basis that the federal learning model is deployed to multiple devices, the accuracy of the federal learning model parameters is maximized, and the experience of users is significantly improved.

A building automation system (306) for controlling conditions of a room (310) is described. The building automation system (306) includes room devices (328-332), a first interface (322), a second interface (324), and a management device (e.g., 404-408). The first interface (322) receives a voice command based on a voice utterance detected by a voice-enabled system (302, 304) in the room (310). The second interface (324) receives a hospitality user profile from a hospitality information system (308). The hospitality user profile identifies one or more user parameters associated with the room (310). The management device of the building automation system (306) includes a guest room profile that identifies one or more room parameters associated with the room (310). The management device controls the room devices (328-332) based on the voice command, the hospitality user profile, and the guest room profile.