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In signal processing, signals are classified based on various characteristics: continuous-time versus discrete-time, periodic versus aperiodic, analog versus digital, and causal versus noncausal. Each category highlights distinct properties crucial for understanding and manipulating signals.
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Three identical single-phase transformers can be configured to form a three-phase transformer connection, which involves high-voltage and low-voltage windings. The high-voltage windings are denoted by capital letters A-B-C, while the low-voltage windings are labeled with lowercase letters a-b-c, representing their respective phases. This notation helps distinguish between the high and low voltage sides of the transformer.
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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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Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
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Updated: Jun 23, 2025

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
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Multimodal Transformer Model Using Time-Series Data to Classify Winter Road Surface Conditions.

Yuya Moroto1, Keisuke Maeda2, Ren Togo3

  • 1Graduate School of Information Science and Technology, Hokkaido University, N-14, W-9, Kita-ku, Sapporo 060-0814, Japan.

Sensors (Basel, Switzerland)
|June 19, 2024
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Summary

This study introduces a multimodal Transformer model for detecting and predicting winter road conditions using time-series data. The model effectively integrates various data sources, improving accuracy for road surface condition monitoring.

Keywords:
deep learningmultimodal analysistime-series processingtransformerwinter road surface condition

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Area of Science:

  • Artificial Intelligence
  • Computer Vision
  • Transportation Engineering

Background:

  • Previous methods for road surface condition analysis often use simple integration of multiple data types like images and auxiliary data.
  • While multimodal approaches improve performance, effective integration of heterogeneous data remains a challenge.

Purpose of the Study:

  • To propose a novel multimodal Transformer model for enhanced detection and prediction of winter road surface conditions.
  • To improve the integration of heterogeneous data modalities for more accurate road condition analysis.

Main Methods:

  • Utilized a multimodal Transformer architecture incorporating time-series data.
  • Implemented a cross-attention mechanism for effective feature integration and compensation between modalities.
  • Incorporated time-series processing to capture temporal changes in road surface conditions.

Main Results:

  • The proposed method demonstrated effectiveness in both detecting and predicting winter road surface conditions.
  • Experimental results confirmed the superiority of the enhanced modality integration technique.
  • The model achieved improved representational ability of integrated features.

Conclusions:

  • The multimodal Transformer model offers a significant advancement in analyzing and predicting winter road surface conditions.
  • Effective integration of heterogeneous data through cross-attention and time-series analysis is crucial for improving performance.
  • The approach holds promise for enhancing road safety and traffic management during winter conditions.