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End Point Prediction: Gran Plot01:07

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A Gran plot is used to predict the equivalence volume or endpoint of a potentiometric or acid-base titration without reaching the endpoint. Typically, titration data is collected as a function of the titrant's volume up to a point less than the equivalence volume and then transformed into a linear format. The straight line is extended to the x-axis, indicating the necessary titrant volume to achieve the equivalence point.
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Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
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Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,
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Big Data Analysis and Prediction System Based on Improved Convolutional Neural Network.

Xuegong Du1, Xiaojun Cao1, Rui Zhang1

  • 1College of Information Engineering, Lanzhou University of Finance and Economics, Lanzhou 730020, China.

Computational Intelligence and Neuroscience
|March 21, 2022
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Summary
This summary is machine-generated.

This study introduces an improved convolutional neural network (CNN) for big data analysis and vehicle environment detection. The enhanced CNN significantly boosts data mining accuracy and reduces training time compared to traditional methods.

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Area of Science:

  • Artificial Intelligence
  • Computer Science
  • Data Science

Background:

  • Big data analysis and prediction systems are crucial for various applications.
  • Convolutional Neural Networks (CNNs) are increasingly used for environmental detection in vehicles.
  • Existing CNN approaches face challenges with long training times and low accuracy.

Purpose of the Study:

  • To develop a big data analysis and prediction system using an improved CNN.
  • To enhance the accuracy and efficiency of CNNs for vehicle environment detection.
  • To address limitations of traditional CNNs in processing camera data for vehicle control.

Main Methods:

  • Utilized continuous template matching technology for big data analysis.
  • Implemented information fusion processing with matching related detection, frequent item detection, and association rule feature extraction.
  • Employed clustering methods for cloud service portfolio big data classification and mining.
  • Proposed an improved CNN architecture for processing camera data.

Main Results:

  • The improved CNN demonstrated a 12.43% and 21.76% higher data mining accuracy than traditional methods.
  • The number of iteration steps was reduced, indicating higher mining timeliness.
  • The proposed network structure effectively improved training speed and accuracy.
  • Experimental results confirm faster training speed and higher accuracy of the improved CNN.

Conclusions:

  • The improved CNN offers superior performance in big data mining and vehicle environment detection.
  • The enhanced network structure provides a viable solution for overcoming the limitations of traditional CNNs.
  • This research contributes to more efficient and accurate AI-driven systems in automotive applications.