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Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
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Associative learning is a fundamental concept in behavioral psychology, wherein a connection is established between two stimuli or events, leading to a learned response. This process is critical in understanding how behaviors are acquired and modified. Conditioning, the mechanism through which associations are formed, can be divided into two main types: classical conditioning and operant conditioning, each elucidating different aspects of associative learning.
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Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
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Deep learning on multi-view sequential data: a survey.

Zhuyang Xie1,2, Yan Yang1,2, Yiling Zhang1,2

  • 1School of Computing and Artificial Intelligence, Southwest Jiaotong University, Chengdu, 611756 China.

Artificial Intelligence Review
|December 5, 2022
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Summary
This summary is machine-generated.

Deep learning advances address challenges in analyzing large multi-view sequential data (MvSD). This review covers data types, methods, and applications for MvSD, offering future research directions.

Keywords:
Deep neural networksMulti-viewSequential dataSpatio-temporal

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Area of Science:

  • Data Science
  • Machine Learning
  • Artificial Intelligence

Background:

  • Increasingly large volumes of multi-source data are collected chronologically, termed multi-view sequential data (MvSD).
  • Traditional machine learning struggles with the scale and complexity of MvSD, necessitating advanced feature representation.
  • Existing frameworks lack generality for mining multi-view relationships and integrating information.

Purpose of the Study:

  • To introduce common data types constituting MvSD (point, sequence, graph, raster).
  • To summarize technical challenges associated with MvSD analysis.
  • To review deep learning advancements for MvSD and discuss feature representation.

Main Methods:

  • Review of recent deep learning techniques applied to MvSD.
  • Analysis of how neural networks represent and learn features from MvSD.
  • Exploration of MvSD applications across various domains.

Main Results:

  • Deep learning offers effective solutions for handling large-scale MvSD.
  • Neural networks can learn complex features and relationships within MvSD.
  • MvSD has broad applications in intelligent transportation, healthcare, and more.

Conclusions:

  • Deep learning is crucial for unlocking the potential of MvSD.
  • Further research is needed to develop general frameworks for MvSD analysis.
  • MvSD analysis holds significant promise for diverse scientific and industrial applications.