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基于人工智能技术的森林火灾监测方法研究
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作者 王宝鹏 《中国科技期刊数据库 工业A》 2021年第4期430-432,共3页
为了减少森林火灾造成的损失,有必要在火灾发生初期对其进行尽可能多的火情监测,因此,提出一种基于人工智能技术的电网森林火灾监测方法研究。基于人工智能技术数据分析模型,森林火灾监测系统具体包括人机交互模块、监测模块、温度异常... 为了减少森林火灾造成的损失,有必要在火灾发生初期对其进行尽可能多的火情监测,因此,提出一种基于人工智能技术的电网森林火灾监测方法研究。基于人工智能技术数据分析模型,森林火灾监测系统具体包括人机交互模块、监测模块、温度异常诊断模块和数据显示模块;在监测算法上构建了卷积神经网络模型确定森林温度异常类别的层次,并基于熵值法区分最终的温度异常属性特征。测试结果显示,提出方法的温度异常信号电流测试、电压测试过程均不存在温度异常波动,数值采样方差值能够被控制在0.01以内。 展开更多
关键词 森林火灾 预人工智能技术 卷积神经网络 熵值法
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Low Leakage Power Sequential Circuits Using Multi-Vth at Nano-Scale Transistor
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作者 Abdoul Rjoub Hassan Almanasrah 《Journal of Energy and Power Engineering》 2013年第1期193-205,共13页
Leakage power is the dominant source of power dissipation for Sub-100 nm VLSI (very large scale integration) circuits. Various techniques were proposed to reduce the leakage power at nano-scale; one of these techniq... Leakage power is the dominant source of power dissipation for Sub-100 nm VLSI (very large scale integration) circuits. Various techniques were proposed to reduce the leakage power at nano-scale; one of these techniques is MTV (multi-threshold voltage) In this paper, the exact and optimal value of threshold voltage (Vth) for each transistor in any sequential circuit in the design is found, so that the value of the total leakage current in the design is at the minimum. This could be achieved by applying AI (artificial intelligence) search algorithm. The proposed algorithm is called LOAIS (leakage optimization using AI search). LOAIS exploits the total slack time of each transistor's location and their contributions in the leakage current. It is introduced by AI heuristic search algorithms under 22 nm BSIM4 predictive technology model. The proposed approach saves around 80% of the sub-threshold leakage current without degrading the performance of the circuit. 展开更多
关键词 Component artificial intelligence leakage current low power mtflti-threshold technique NANOTECHNOLOGY SPICEparameters.
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