6results 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 application relates to the field of heat-sensitive ceramic materials, in particular to a high-stability rare earth-containing six-component low-resistance high-B-value NTC heat-sensitive ceramic material and a preparation method thereof. The raw material scheme of the NTC heat-sensitive ceramic material comprises trimanganese tetraoxide, nickel protoxide, reinforcing material, lanthanum trioxide, combined functional material and trimolybdenum tetraoxide. The final heat-sensitive ceramic material prepared by the application successfully breaks the technical bottleneck that the resistivity and the B value are restricted to each other in traditional NTC heat-sensitive ceramic materials, realizes good unification of low resistance and high sensitivity, significantly improves the accuracy and response speed of temperature detection; meanwhile, the long-term stability of the material under harsh service conditions such as high-temperature storage, humid heat environment and cold and hot impact is greatly improved, the resistance value drift is effectively inhibited, and the strict requirements of high-reliability scenes such as automobile electronics, industrial control and new energy battery management systems on long-term precise monitoring of temperature sensors can be met.

The application discloses a kind of ammonia nitrogen micro quick on-line detection device and detection method in environment water body, belong to ammonia nitrogen concentration detection technical field, the device with salicylate spectrophotometry as detection principle, integrated sequential injection system, ammonia nitrogen conversion reaction system, chromogenic reaction system and detection system, realize ammonia nitrogen on-line conversion, effectively improve the anti-matrix interference ability of salicylate spectrophotometry analysis ammonia nitrogen.Salicylate spectrophotometry is used as detection principle in the device, integrated sequential injection system, ammonia nitrogen conversion reaction system, chromogenic reaction system and detection system, realize ammonia nitrogen on-line conversion, effectively improve the anti-matrix interference ability of salicylate spectrophotometry analysis ammonia nitrogen.Ammonia gas absorption chromogenic process does not use gas-permeable membrane, avoid the pollution and replacement problem of gas-permeable membrane.The device quantitative analysis uses absorbance instantaneous change rate as quantitative basis, does not need to wait for chromogenic reaction to end, saves analysis time.The device has the advantages of simple operation, fast, miniaturization, reagent consumption is less, anti-matrix interference ability is strong, etc., is suitable for the on-line detection of ammonia nitrogen in surface water, domestic sewage and other water quality.

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.