4results about How to "Increase sensitivity" patented technology

The application provides an image sensor and a manufacturing method thereof, and relates to the technical field of semiconductors. The image sensor provided by the application constructs a light absorption structure through a groove array and is arranged upstream of a light sensing structure, so that the light absorption structure increases the absorption of an incident light beam through the light absorption characteristics of the groove and guides the incident light beam to the light sensing structure, thereby improving the absorption of the incident light beam by the light sensing structure. The groove array is constructed based on a light filter upstream of the light sensing structure, so that the absorption of the light beam filtered by the light filter is specialized, the absorption capacity of the light sensing structure for the light beam filtered by the light filter is further improved, and the sensitivity of the image sensor to light is improved.

The invention provides a semi-supervised relation extraction method for aircraft fault data, which comprises the following steps of: 1, relation classification: extracting semantic information capable of representing a relation between entities from a text so as to realize relation extraction between the entities; the method comprises the following steps: firstly, mining context semantic information of a fault text by using a text encoder; utilizing a relation classifier to realize relation extraction of the unstructured fault data; step 2, pseudo label generation: a pseudo label generation module is adopted to generate pseudo labels for the unlabeled relational data, so that training samples of the relational data are effectively expanded, and the classification performance of a relational classification module is improved; in order to improve the quality of pseudo labels generated by the pseudo label generation module, a meta-learning mechanism is introduced, and the verification loss of the parameters of the relation classification module is used as a meta-learning target to update the parameters of the pseudo label generation module; 3, self-adaptive pseudo label selection is carried out, the step aims to select a pseudo label with higher quality from pseudo label data generated by a pseudo label generation module, and then the performance of relation extraction is improved. According to the method, semi-supervised relation extraction for the unstructured fault data of the airplane can be realized.

The application discloses a wind farm unit reconstruction and energy storage configuration collaborative planning method and system, and relates to the technical field of new energy planning. The method comprises the following steps: obtaining equipment parameters and cost parameters of wind farm unit reconstruction and energy storage configuration, and respectively constructing a technical model and a cost model of the unit reconstruction and the energy storage configuration based on an operation performance optimization target; a wind farm grid connection examination and auxiliary service income calculation model embedded with time sequence operation simulation is constructed, and a quantitative mapping of technical features to income of the wind farm grid connection examination and auxiliary service income calculation model is utilized; based on the quantitative mapping relationship of the technical features to the income, a double-layer optimization framework of flexibility improvement considering investment cost and income is constructed according to the technical model and the cost model of the unit reconstruction and the energy storage configuration. The application can systematically improve the flexibility level and market competitiveness of the wind farm while ensuring safe and stable operation of the wind farm.

The invention provides a deep learning-based household garbage rapid identification method and system. The method comprises the following steps of S1, acquiring a real-time image of a working area of a conveyor belt; s2, performing saliency enhancement processing according to the obtained real-time image to obtain a saliency enhancement feature map; s3, foreground recognition is carried out according to the saliency enhancement feature map, and a foreground recognition result is obtained; s4, segmenting garbage pictures according to the foreground recognition result, and performing classification recognition according to the obtained garbage pictures to obtain a garbage classification result; s5, mechanical arm grabbing position information is obtained according to the foreground recognition result; and S6, a control instruction is generated according to the grabbing position information of the mechanical arm and the corresponding garbage classification result so as to control the mechanical arm to grab the garbage and throw the garbage into the corresponding classification area. According to the invention, the accuracy of garbage classification identification and throwing is improved.