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Related Concept Videos

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
Machines: Problem Solving II01:30

Machines: Problem Solving II

Machines are complex structures consisting of movable, pin-connected multi-force members that work together to transmit forces. Consider a lifting tong carrying a 100 kg load. It comprises movable sections DAF and CBG linked together with member AB.
Machines: Problem Solving I01:22

Machines: Problem Solving I

A toggle clamp is a mechanical device commonly used for holding and clamping objects in various applications, such as woodworking, metalworking, and assembly operations. Consider a toggle clamp subjected to a force of 200 N at the handle. The vertical clamping force can be calculated, provided the dimensions of the toggle clamp are known.
The toggle clamp system is a machine structure consisting of movable, pin-connected multi-force members that form a stabilized system to transmit forces. The...
Statically Indeterminate Problem Solving01:16

Statically Indeterminate Problem Solving

Statically indeterminate problems are those where statics alone can not determine the internal forces or reactions. Consider a structure comprising two cylindrical rods made of steel and brass. These rods are joined at point B and restrained by rigid supports at points A and C. Now, the reactions at points A and C and the deflection at point B are to be determined. This rod structure is classified as statically indeterminate as the structure has more supports than are necessary for maintaining...
Ampere-Maxwell's Law: Problem-Solving01:17

Ampere-Maxwell's Law: Problem-Solving

A parallel-plate capacitor with capacitance C, whose plates have area A and separation distance d, is connected to a resistor R and a battery of voltage V. The current starts to flow at t = 0. What is the displacement current between the capacitor plates at time t? From the properties of the capacitor, what is the corresponding real current?
To solve the problem, we can use the equations from the analysis of an RC circuit and Maxwell's version of Ampère's law.
For the first part of the problem,...
Optimization Problems01:26

Optimization Problems

Optimization problems often involve identifying maximum or minimum values under specific constraints. A well-known example is determining the longest horizontal pipe that can be moved around a right-angled corner, where a 3-meter-wide hallway meets a 2-meter-wide hallway. This scenario, common in architectural design and industrial transport, can be understood conceptually through geometric and trigonometric reasoning.To visualize the problem, consider the pipe as a straight line that touches...

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Related Experiment Video

Updated: Jun 18, 2026

The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy
11:53

The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy

Published on: October 14, 2017

Machine learning-assisted high-speed combinatorial optimization with Ising machines for dynamically changing

Yohei Hamakawa1, Tomoya Kashimata2, Masaya Yamasaki2

  • 1Corporate Laboratory, Toshiba Corporation, Kawasaki, Japan. yohei.hamakawa.r13@mail.toshiba.

Nature Communications
|June 16, 2026
PubMed
Summary
This summary is machine-generated.

Embedded Ising machines solve dynamic combinatorial optimization problems quickly. This method reduces latency and eliminates runtime parameter tuning for practical applications like TDMA scheduling.

Related Experiment Videos

Last Updated: Jun 18, 2026

The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy
11:53

The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy

Published on: October 14, 2017

Area of Science:

  • Quantum computing
  • Combinatorial optimization
  • Machine learning

Background:

  • Ising machines show potential for solving complex optimization problems rapidly.
  • Practical deployment faces challenges like system latency and parameter tuning for dynamic problems.

Purpose of the Study:

  • To develop a combinatorial optimization method using embedded Ising machines for high-speed, parameter-free problem-solving.
  • To address limitations of current Ising machines in real-world, dynamic applications.

Main Methods:

  • Customized simulated bifurcation-based Ising machine algorithm and circuit architecture for model compression and accelerated computation.
  • Developed a machine learning model to estimate optimal parameters using extensive training data.

Main Results:

  • Demonstrated a method for solving diverse problems at high speed without runtime parameter tuning.
  • Achieved a speed advantage over conventional methods in Time Division Multiple Access (TDMA) scheduling for wireless networks.
  • Showcased adaptability to dynamic problem changes.

Conclusions:

  • Embedded Ising machines offer a viable solution for high-speed, dynamic combinatorial optimization.
  • The developed approach overcomes latency and parameter tuning challenges for practical deployment.
  • This method shows significant promise for applications in wireless networks and beyond.