37results about How to "Raise the level of concern" patented technology

The invention discloses a history teaching demonstration time shaft which comprises an outer line cover, wherein a fixing sleeve is fixedly connected with the inner wall of the outer line cover; a telescopic sleeve rod is fixedly connected with the inner wall of the fixing sleeve; a pull ring is fixedly connected with one end, far away from the fixing sleeve, of the telescopic sleeve rod; a moveable sleeve ring is moveably connected with the surface of the telescopic sleeve rod; a fixed sleeve ring is fixedly connected with the surface of the telescopic sleeve rod; connecting transverse platesare fixedly connected with one sides of the moveable sleeve ring and the fixed sleeve ring. By adopting the history teaching demonstration time shaft, due to the adoption of the fixing sleeve, the telescopic sleeve rod is fixed on the outer line cover, the pull ring is matched with the fixed sleeve ring and the connecting transverse rod, and the moveable sleeve ring can be moved on the telescopicsleeve rod together when the telescopic sleeve rod is extended; as a rotating shaft and a fixing rod are combined through a bearing, a file roll can be rolled downwards, then a history teacher does not need to draw a time shaft on a blackboard for multiple times, and due to a dynamic demonstration mode, the attention degrees of students can be improved.

This patent discloses an image recognition method based on an improved Focal loss function. On the basis of the existing Focal loss function, the modulation factor of the Focal loss function is improved, so that the function pays more attention to difficult samples, and the The attention of simple samples is relatively reduced; then, based on the convolutional neural network model based on the Focal loss function, the remaining negative sample sets are predicted, all difficult samples are screened, and divided into N equal parts, which are added to the original training set respectively, N new training sets are formed, then multiple models are trained, and the final predicted image label result is determined according to the voting selection of the N models. On the basis of the original Focal loss function, the present invention pays more attention to difficult samples and improves the generalization ability of the model; in the case of too many negative samples, the original model is used to screen out difficult samples and add them to the training set, not only The accuracy of the model is improved, the available samples are fully utilized, and the amount of computation when training the model is reduced.

An interaction advertisement system relates to the electronic advertisement technology field, which aims to solve the technical problems that the advertisement content is conveniently updated, and the remote control of the advertisement spread is realized. The advertisement system comprises an advertisement player terminal and a server background, wherein the advertisement player terminal is provided with a main controller and a main processor of a media processor which are respectively connected with the terminal internal memory, a terminal external memory, a starting ROM, a wireless networkinterface, a sound card chip, an LCD screen, a keyboard and a power supply of the main processor, and the server background comprises a management module for playing video files, a program playing menu management module, a program playing menu management module, a playing log view module, a working state monitoring module and an on-off control module. The invention can increase the advertisement variety and the coverage percentage of the advertisement, improves the concern extent of receivers, is convenient for the updating of the advertisement, and can realize the characteristics of remote control, remote updating, remote diagnosis and remote monitoring of the function of the advertisement spread and the like.

The present disclosure relates to a method, apparatus, and electronic device for customizing presentation content based on user characteristics. The method includes: when it is detected that the state of the terminal meets a set condition, acquiring a user feature; calling a preset demonstration model, and determining at least one application APP corresponding to the user feature through the demonstration model; For the sorting of at least one application APP, the content corresponding to the at least one application APP is demonstrated through the demo APP. In this embodiment, based on the user characteristics of each user, at least one application APP for the user can be determined, and the content corresponding to these application APPs can better match the actual needs of the user, thereby increasing the attention of each user to the product. It is beneficial to improve the probability of the user to experience the product, which in turn helps to increase the user's desire to buy.

The invention discloses a satellite fault diagnosing method based on an AdaBoost algorithm and belongs to the technical field of satellite fault diagnosis. The satellite fault diagnosing method based on the AdaBoost algorithm is aimed to solve the problem that the fault diagnosing method of an existing Naive Bayes system is weak to diagnose a little of fault states. The satellite fault diagnosing method based on the AdaBoost algorithm reasonably uses the integrated AdaBoost algorithm for the existing Naive Bayes fault diagnosing system and includes that changing the weight of a sample after each training, to be specific, changing the value of an event type characteristic of a misclassified sample, and improving the concern extent of the fault diagnosing system for the characteristic; building a new correspondence matrix between the state and the event characteristic, training a classifier again, and integrating the trained classifier into a new fault diagnosing classifier. The satellite fault diagnosing method based on the AdaBoost algorithm is suitable for the satellite fault diagnosing field.