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Reinforcement Schedules

Reinforcement Schedules

Positive reinforcement is a powerful method for teaching new behaviors to both animals and humans. B.F. Skinner demonstrated this with his experiments using rats in a Skinner box. When a rat pressed a lever, it received a food pellet. This immediate reward encouraged the rat to repeat the behavior. This method, where a reward follows every instance of the behavior, is known as continuous reinforcement. It is highly effective for establishing new behaviors quickly.
Once a behavior is learned,...

Distributed Loads: Problem Solving

Distributed Loads: Problem Solving

Beams are structural elements commonly employed in engineering applications requiring different load-carrying capacities. The first step in analyzing a beam under a distributed load is to simplify the problem by dividing the load into smaller regions, which allows one to consider each region separately and calculate the magnitude of the equivalent resultant load acting on each portion of the beam. The magnitude of the equivalent resultant load for each region can be determined by calculating...

Statically Indeterminate Problem Solving

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...

Reinforcement

Reinforcement

Positive and negative reinforcement are key concepts in operant conditioning, a learning process where the consequences of a behavior affect the likelihood of that behavior being repeated.
Positive reinforcement occurs when a behavior is followed by the presentation of a rewarding stimulus, increasing the frequency of that behavior. For example:

Short-distance Transport of Resources

Short-distance Transport of Resources

Short-distance transport refers to transport that occurs over a distance of just 2-3 cells, crossing the plasma membrane in the process. Small uncharged molecules, such as oxygen, carbon dioxide, and water, can diffuse across the plasma membrane on their own. In contrast, ions and larger molecules require the assistance of transport proteins due to their charge or size. Transport across membranes also occurs within individual cells, playing a variety of essential roles for the plant as a whole.

Machines: Problem Solving I

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...

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相关实验视频

Updated: May 14, 2025

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Published on: September 8, 2023

在IoT应用程序的边缘,基于联合增强学习的动态资源分配和任务调度.

Saroj Mali¹, Feng Zeng¹, Deepak Adhikari²

¹School of Computer Science and Engineering, Central South University, Changsha 410083, China.

Sensors (Basel, Switzerland)

|April 12, 2025

概括

此摘要是机器生成的。

本研究介绍了一种边缘计算算法,用于物联网 (IoT) 任务卸载,使用混合预测模型提高性能和能源效率. 它还建议为联合学习提供深度决定性政策梯度 (D4PG),在动态环境中提高准确性和隐私.

关键词:

边缘计算是一种边缘计算.联合学习的联合学习联合强化学习学习物联网的东西互联网.强化学习是一种强化学习.

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Last Updated: May 14, 2025

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

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科学领域:

边缘计算边缘计算
物联网 (IoT) 的物联网 (IoT) 的物联网.
机器学习机器学习
联邦学习学习 (Federated Learning) 是一种学习方式.

背景情况:

为物联网应用程序优化边缘计算环境中的资源配置和能源消耗至关重要.
现有的任务卸载算法经常与动态资源可用性和数据分布作斗争.
确保边缘设备的联合学习模型中的隐私和公平性存在重大挑战.

研究的目的:

为物联网边缘计算开发一种高效的任务卸载算法,以提高性能并降低能源消耗.
提出一种混合预测模型,将双向长期短期存储器 (BiLSTM) 和门式循环单元 (GRU) 结合起来,并关注资源使用预测.
引入基于深度决定性政策梯度 (D4PG) 的联合学习算法,以实现动态的用户设备参与,重点关注准确性,效率和隐私.

主要方法:

利用谷歌集群轨迹进行算法开发,并使用EdgeSimPy进行模拟.
开发了一个混合预测模型,集成BiLSTM,GRU层和注意力机制.
在EMNIST和Crop Prediction数据集上实现并将基于D4PG的联合学习算法与DQN,DDQN,Dueling DQN和Dueling DDQN进行比较.

主要成果:

提出的任务卸载算法表现优于最适合,首次适合和最不适合的算法,确保稳定的边缘服务器功耗.
基于D4PG的联合学习在作物预测数据集上实现了92.86%的准确性和高F1分数 (0.9192在非IID EMNIST,0.82在IID EMNIST).
与现有方法相比,混合卸载算法证明了边缘节点的功耗波动减少.

结论:

开发的混合预测模型和任务卸载算法显著提高了物联网边缘计算中的性能和能源效率.
基于D4PG的联合学习方法在动态边缘环境中提供了卓越的准确性,效率和隐私保护.
这项研究为稳定的能源使用和分布式边缘系统中增强的数据处理提供了强大的解决方案.