Results 41 to 50 of about 2,180 (133)
Energy-saving algorithm considering idle light-path prediction in SDM-EON [PDF]
, 2019 To effectively reduce the energy consumption,blocking rate and crosstalk between adjacent cores in a multi-core fiber for space division multiplexing elastic optical network (SDM-EON),an energy-saving algorithm considering idle light-path prediction was ...
Baohua WANG +3 more
core +1 more source
Gaoya dianqi, 2017 为有效克服变压器不完备故障样本数据对故障诊断结果的影响,文中构建了一种基于粗糙集的人工鱼群极限学习机变压器故障诊断方法,该方法首先运用粗糙集对决策表中的16个条件属性进行约简;其次,根据最简规则表对训练样本进行编码,利用已编码的训练样本对极限学习机进行训练,并运用人工鱼群优化方法对极限学习机的权值及阈值进行优化;最后,利用训练好的极限学习机方法对编码好的样本进行故障诊断。该方法将粗糙集在不完整数据方面所具有的优良特性与极限学习机优良的泛化能力有机融合,以有效提高故障诊断精度。经实例对比分析表明 ...
雷帆 +4 more
doaj
Attitude monitoring method for hydraulic support in fully mechanized working face based on PSO-ELM [PDF]
In response to the problems of cumulative errors and inaccurate correction results in the attitude calculation method of hydraulic supports based on inertial measurement units, a fully mechanized working face hydraulic support attitude monitoring method ...
HAO Zhenjie +7 more
core +1 more source
Short-term Wind Speed Forecasting by Combination of Masking Signal-based Empirical Mode Decomposition and Extreme Learning Machine [PDF]
, 2017 针对随空间、时间呈现非平稳、非线性变化的特征,提出基于极限学习机和掩膜经验模态分解的组合短期风速预测方法。首先,风速序列的非平稳性特征对风速预测结果有较大影响,利用掩膜经验模态分解的方法将风速序列分解成对平稳的不同频率的分量,解决其存在的非平稳性问题;其次,为处理极限学习机的输入维数随意性选取问题,对风速序列分解不同频率的分量进行相空间重构;最后,利用ELM神经网络方法对各分量建立预测模型。实验结果表明：该预测方法在短期风速序列预测中取得了理想的预测效果,提高了算法精度,具有先进性和有效性。In ...
徐广玉, 沈少萍, 邱继辉
core +1 more source
A review on coal and gas outburst prediction based on machine learning [PDF]
The safety in the coal-producing mines in China is continuously improving, but coal and gas outburst accidents still occur. The prediction of coal and gas outbursts allows the scientific application of outburst prevention measures, which can ensure the ...
Liang YUAN +4 more
core +1 more source
Internal corrosion rate prediction of natural gas gathering and transportation pipelines in marine environments [PDF]
ObjectiveTo improve the accuracy of internal corrosion rate predictions for natural gas gathering and transportation pipelines in marine environments, evaluate their residual strength, develop anti-corrosion measures, and ensure their safe operation, a ...
Sisi CHEN, Yiqiong GAO, Zhengshan LUO
core +1 more source
Photovoltaic Output Prediction Based on Improved Long Short-Term Memory Network Using White Shark Optimization Algorithm [PDF]
ObjectivesIn order to ensure the safety, stability, and economic operation of the power system after photovoltaic integration, a photovoltaic power prediction model based on improved long short-term memory (LSTM) network using white shark optimization ...
LI Lanqing +5 more
core +1 more source
He jishu在极端相对论重离子碰撞条件下，精确构建有限重子化学势μB区域的量子色动力学（Quantum Chromodynamics，QCD）物质状态方程（Equation of State，EoS）是当前高能核物理研究的核心难题之一。本研究提出一种基于深度学习的准部分子模型，通过构建三个深度神经网络，成功实现了零μB条件下QCD状态方程的高精度重建。同时，经深入分析四阶广义磁化率χ4B在不同温度和μB下的单调性行为，大致限定了QCD临界点可能存在的区间为（T，μB）=（（0.113±0.019） GeV，（0 ...
李 甫鹏, 庞 龙刚, 秦 广友
doaj +1 more source
Gaoya dianqi, 2016 针对变压器差动保护装置易受励磁涌流误动作问题,提出了基于EMD-SVD-KELM与参数优选的励磁涌流辨识方法。首先,以经验模态分解（EMD）和奇异值分解（SVD）为工具,对励磁涌流和故障电流信号进行预处理,提取出识别特征量,并作为后续核函数极限学习机（KELM）学习输入量;然后,因学习机性能受参数C和γ影响较大,以均分训练样本所得多个模型的平均准确率作为适应度评价函数,为KELM参数优选提供评价标准。通过EMTDC仿真计算生成训练样本和测试样本,利用多种优化算法对KELM进行训练和测试。最终 ...
施恂山 +5 more
doaj
Fault diagnosis of ship motor bearings based on multi-domain information fusion and improved ELM [PDF]
ObjectivesAiming at the problems that the symptom parameters from monitoring signals in a single analysis domain fail to fully characterize the operating state of the monitored object, and the model parameters of the Extreme Learning Machine (ELM ...
Chun GE +3 more
core +1 more source