基于多机器学习模型的变电站调试检修自动测试方法研究

为了提高变电站调试检修自动测试方法的智能水平,减少人工运维调试工作,提出一种构建LightGBM机器学习模型对变电站调试检修自动测试结果进行智能分析的方法。首先,构建LightGBM机器学习模型并对其进行参数调优和训练;然后采用变电站调试检修自动测试获取的数据对LightGBM机器学习模型进行测试;同时,构建XGBoost机器学习模型作为实验对照组,采用同样的实验方法对其进行训练与测试;最后,对比两种机器学习模型的综合性能。实验结果表明:LightGBM机器学习模型的拟合效果更好;XGBoost机器学习模型对自动检测方法故障类别预测出错数据的分析正确率最高为90.1%;而LightGBM机器学习模型的判断正确率维持在95%以上,最高达到了96.9%。可知在对变电站调试检修自动测试结果进行智能分析时,选择的LightGBM机器学习模型都更加适合,性能更稳定,能够实现提高变电站调试检修自动测试方法智能水平的目的。

Discrimination for minimal hepatic encephalopathy based on Bayesian modeling of default mode network

In order to classify the minimal hepatic encephalopathy （MHE） patients from healthy controls, the independent component analysis （ICA） is used to generate the default mode network （DMN） from resting-state functional magnetic resonance imaging （fMRI）. Then a Bayesian voxel- wised method, graphical-model-based multivariate analysis （GAMMA）, is used to explore the associations between abnormal functional integration within DMN and clinical variable. Without any prior knowledge, five machine learning methods, namely, support vector machines （SVMs）, classification and regression trees （ CART ）, logistic regression, the Bayesian network, and C4.5, are applied to the classification. The functional integration patterns were alternative within DMN, which have the power to predict MHE with an accuracy of 98%. The GAMMA method generating functional integration patterns within DMN can become a simple, objective, and common imaging biomarker for detecting MIIE and can serve as a supplement to the existing diagnostic methods.