The invention relates to a correcting control method with a function of automatically identifying colors. According to the whole design, color identification is performed according to the difference of color collecting values of a TCS230 color sensor under different filter plates, and the automatic selection of TCS230 filter plates is realized by establishing a two-dimensional fuzzy control table. Without increasing any hardware resource cost, the personal error is reduced, operators are convenient to use and the operation difficulty of a controller is reduced.

Correcting control method with function of automatically identifying colors

The invention discloses an online real-time control method for an intelligent seismic reduction structure. The online real-time control method includes steps of predicting a next-moment displacement response and a next-moment speed response of the intelligent seismic reduction structure via a neural network in real time and inputting the displacement response into a fuzzy controller; enabling the fuzzy controller to select control current of a magneto-rheological damper according to measured seismic acceleration excitation and the predicted displacement response in real time; computing according to the speed response and the control current of the magneto-rheological damper to obtain control force of the magneto-rheological damper; and measuring actual seismic responses and actual seismic acceleration excitation of the intelligent seismic reduction structure. The seismic responses include a displacement response and a speed response. The online real-time control method has the advantages that problems of inherent time lag and the requirement on instantaneous parameter determination in a procedure for selecting control current of a magneto-rheological damper by the traditional control strategy are solved, the control current is accurately and timely selected, and accordingly the magneto-rheological damper plays an effective role in the aspect of controlling vibration of the structure.

Online real-time control method for intelligent seismic reduction structure

The invention relates to the field of color display and color management, in particular to a characterizing method applicable to screens of mobile terminals such as mobile phones or tablet computers under different illumination conditions. A PVCR-PEC (piecewise variation in chromaticity value ratio of primaries-partition compensation error) model is built by the aid of the characterizing method. A PVCR prediction model and a PEC model are coupled with each other to form the PVCR-PEC model, chromatic values of primary colors are accurately predicted by the PVCR model, chromatic three-stimulus values corresponding to optional RGB (red, green and blue) input values can be computed on the basis of hypothesis of channel independence, and the PEC model is used for compensating prediction errors of the PVCR model. The characterizing method has the advantages that service characteristics of the mobile terminals in different environments and chromatic characteristics of the display screens of the mobile terminals are sufficiently considered, the chromatic three-stimulus values of colors of samples can be accurately predicted, and accordingly the accuracy of display colors of the mobile terminals in the different environments can be guaranteed.

Characterizing method applicable to display screens of mobile terminals

The invention provides an LCD color deviation correction method. According to the method, a BP neural network based on genetic algorithm is introduced, wherein the genetic algorithm is used for performing rapid global searching on an initial weight value and a threshold value of the neural network, and thus a defect that conventional BP algorithm is easy to be trapped into a local minimum is overcome, a rate of convergence is fastened, and the generalization ability of the network is improved. Better quantification classification accuracy is provided by adopting Lab color space values as input output values of the neural network than directly adopting RGB color space vectors, and thus the LCD color deviation can be automatically corrected, and the LCD can have better color reproduction ability.

LCD color deviation correction method

The invention discloses a color space conversion method based on an ELM extreme learning machine. The color space conversion method is characterized by comprising the steps of: step 1, adopting a professional measuring instrument to measure XYZ parameter values of different RGB driving values output through a display, and establishing a sample set; step 2, establishing a single hidden layer neural network model according to the sample set established in step1 and output expected values, generating parameters of hidden layer nodes randomly with a given hidden layer output function and given number of the hidden layer nodes, and calculating a hidden layer output matrix to obtain an optimal weight of a single hidden layer neural network; and step 3, establishing an RGB-to-XYZ color space conversion model according to the neural network training results obtained in step 2. The color space conversion method based on the ELM extreme learning machine realizes the optimization and simplification of a neural network structure, allows the network obtained after training to be reestablished quickly, and is used for realizing calculation of RGB-to-XYZ conversion in practical engineering.

Color space conversion method based on ELM extreme learning machine

The invention discloses a color space transition method based on the fuzzy logic and the neural network, comprising steps of selecting sampling points of input color space, collecting modeling data of the sampling points, creating a fuzzy nerve color space transition model, and inputting the modeling data of the sampling points into the fuzzy nerve color space transition model to accomplish the transition of the color space The color space transition method based on the fuzzy logic and the neural network of the invention makes full use of fuzzy identification on advantage of rapidity in WAN firstly, and the input color space is divided into a plurality of subspaces; then the invention also makes full use of neural network on advantages of strong ability and high precision on self-adaption and identification in local subspace and reasonably achieves the complementary advantages between fuzzy and neural network arithmetic, thereby greatly improving the processing speed of model conversion and effectively improving the precision of model conversion

Color space transition method based on fuzzy logic and neural network

The present invention discloses a color space converting method based on a fuzzy theory, which comprises the following steps: input and output equipment is adjusted to guarantee the equipment to be normally used; according to color space needing to be changed, sampling points are selected, and the modeling data of the sampling points is acquired; input and output color space fuzzy subsets are divided, and fuzzy subset subjection degree functions are selected; at the same time, fuzzy control rules are established, each fuzzy control rule has a fuzzy implicit relationship correspondingly, and the combination of the fuzzy implicit relationships of all the fuzzy control rules is the chief fuzzy implicit relationship of the system; and the chief fuzzy implicit relationship of the system is clearly treated so as to obtain an output value. The color space converting method based on the fuzzy theory uses an artificial intelligence fuzzy set theory and fuzzy logical deduction, so that the conversion of the color space is completed. The method has the advantages of simple step and high data conversion precision.

Color space transition method based on fuzzy theory

Popular Tasks used by Researchers

How can I help with your research?

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

We encountered an error while loading the panel. Please try refreshing the page.

Something went wrong

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

Anyone with the link can view this chat. You can switch it back to private anytime.

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Yi Shi

Author Tools

Chat about Author

Papers

Color space transition method based on fuzzy logic and neural network

Color space transition method based on fuzzy theory