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Multi-input and Multi-variable systems01:22

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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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IoT and Deep Learning-Based Farmer Safety System.

Yudhi Adhitya1, Grathya Sri Mulyani1, Mario Köppen1

  • 1Department of Computer Science and Systems Engineering (CSSE), Graduate School of Computer Science and Systems Engineering, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka-shi 820-8502, Fukuoka, Japan.

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Summary
This summary is machine-generated.

This study uses wearable devices and Hierarchical Temporal Memory (HTM) to detect farmer accidents. The system achieved 88% accuracy on validation data, showing potential for safer farming environments.

Keywords:
cascade classifierfarming activity monitoringhierarchical temporal memoryprobability predictionquaternionsmart farmingtime series dataset

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Area of Science:

  • Agricultural Safety
  • Internet of Things (IoT)
  • Machine Learning

Background:

  • Farming labor is inherently hazardous, leading to injuries and fatalities.
  • Ensuring farmer safety is crucial for economic development and well-being.
  • Traditional safety measures require enhancement with technological solutions.

Purpose of the Study:

  • To develop and validate a system for detecting farmer accidents using wearable sensors.
  • To apply Hierarchical Temporal Memory (HTM) for analyzing time-series sensor data.
  • To assess the feasibility of IoT-based safety monitoring in rural farming environments.

Main Methods:

  • Utilized wearable devices as Internet of Things (IoT) subsystems to collect sensor data.
  • Applied the Hierarchical Temporal Memory (HTM) classifier to quaternion features representing 3D rotation.
  • Evaluated performance using validation and Farming-Pack motion capture (mocap) datasets.

Main Results:

  • The validation dataset achieved 88.00% accuracy, 0.99 precision, 0.04 recall, and 0.09 F-Score.
  • The Farming-Pack mocap dataset yielded 54.00% accuracy, 0.97 precision, 0.50 recall, and 0.66 F-Score.
  • Performance metrics indicate the system's effectiveness in identifying accident patterns.

Conclusions:

  • The proposed computational framework using wearable devices and HTM is feasible for real-time farmer safety monitoring.
  • The system effectively addresses constraints in time-series datasets for rural environments.
  • This technology offers a viable solution for enhancing agricultural safety and reducing accidents.