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Mechanical systems are analogous to to electrical networks where springs and masses play similar roles to inductors and capacitors, respectively. A viscous damper in mechanical systems functions similarly to a resistor in electrical networks, dissipating energy. The forces acting on a mass in such systems include an applied force in the direction of motion, counteracted by forces from the spring, a viscous damper, and the mass's acceleration. This interplay of forces is mathematically...
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Cognitive psychology is the field of psychology dedicated to examining how people think. It attempts to explain how and why we think the way we do by studying the interactions among human thinking, emotion, creativity, language, and problem-solving, as well as other cognitive processes. Cognitive psychology studies how information is processed and manipulated in remembering, thinking, and knowing.
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The mechanical efficiency of a machine is a fundamental concept that describes how effectively a machine can convert input work into output work. According to this concept, the efficiency of a machine is equal to the ratio of the output work to the input work. An ideal machine, meaning a machine that has no energy losses, has an efficiency of one. This implies that the input work and the output work are equal.
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Control systems are everywhere in contemporary society, influencing diverse applications from aerospace to automated manufacturing. These systems can be found naturally within biological processes, such as blood sugar regulation and heart rate adjustment in response to stress, as well as in man-made systems like elevators and automated vehicles. A control system is essentially a network of subsystems and processes that collaboratively convert specific inputs into desired outputs.
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A Y-connected synchronous generator, grounded through a neutral impedance, is designed to produce balanced internal phase voltages with only positive-sequence components. The generator's sequence networks include a source voltage that is exclusively in the positive-sequence network. The sequence components of line-to-ground voltages at the generator terminals illustrate this configuration.
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Engineering and AI: Advancing the synergy.

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Artificial intelligence (AI) and machine learning (ML) are transforming engineering, but face challenges in accuracy, bias, and safety. This paper explores AI

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Area of Science:

  • Engineering and Computer Science
  • Artificial Intelligence and Machine Learning

Background:

  • AI and ML advancements, fueled by data and computing power, impact various fields.
  • Key challenges include accuracy, trustworthiness, security, bias, interpretability, and performance drift.
  • Impact is limited in data-scarce domains.

Purpose of the Study:

  • Examine the influence of AI and ML on engineering systems.
  • Address safety and performance assurance in AI-driven engineering.
  • Analyze progress and challenges to enhance the engineering-AI synergy.

Main Methods:

  • Literature review of AI/ML applications in engineering.
  • Analysis of current progress and identified challenges.
  • Examination of safety and performance assurance strategies.

Main Results:

  • AI/ML are increasingly integrated into engineering domains like autonomous vehicles and materials discovery.
  • Significant challenges persist regarding reliability, security, and bias in AI/ML systems.
  • The synergy between engineering principles and AI/ML requires further development for robust applications.

Conclusions:

  • Strengthening the engineering-AI synergy is crucial for reliable and safe deployment.
  • Addressing AI/ML challenges is vital for broader adoption in data-limited engineering fields.
  • Future research should focus on robust AI/ML methodologies tailored for engineering applications.