Extreme learning machine (ELM) is a feedforward neural network-based machine learning method that has the benefits of short training times, strong generalization capabilities, and will not fall into local minima. Howe...Extreme learning machine (ELM) is a feedforward neural network-based machine learning method that has the benefits of short training times, strong generalization capabilities, and will not fall into local minima. However, due to the traditional ELM shallow architecture, it requires a large number of hidden nodes when dealing with high-dimensional data sets to ensure its classification performance. The other aspect, it is easy to degrade the classification performance in the face of noise interference from noisy data. To improve the above problem, this paper proposes a double pseudo-inverse extreme learning machine (DPELM) based on Sparse Denoising AutoEncoder (SDAE) namely, SDAE-DPELM. The algorithm can directly determine the input weight and output weight of the network by using the pseudo-inverse method. As a result, the algorithm only requires a few hidden layer nodes to produce superior classification results when classifying data. And its combination with SDAE can effectively improve the classification performance and noise resistance. Extensive numerical experiments show that the algorithm has high classification accuracy and good robustness when dealing with high-dimensional noisy data and high-dimensional noiseless data. Furthermore, applying such an algorithm to Miao character recognition substantiates its excellent performance, which further illustrates the practicability of the algorithm.展开更多
To enhance the accuracy and efficiency of bridge damage identification,a novel data-driven damage identification method was proposed.First,convolutional autoencoder(CAE)was used to extract key features from the accele...To enhance the accuracy and efficiency of bridge damage identification,a novel data-driven damage identification method was proposed.First,convolutional autoencoder(CAE)was used to extract key features from the acceleration signal of the bridge structure through data reconstruction.The extreme gradient boosting tree(XGBoost)was then used to perform analysis on the feature data to achieve damage detection with high accuracy and high performance.The proposed method was applied in a numerical simulation study on a three-span continuous girder and further validated experimentally on a scaled model of a cable-stayed bridge.The numerical simulation results show that the identification errors remain within 2.9%for six single-damage cases and within 3.1%for four double-damage cases.The experimental validation results demonstrate that when the tension in a single cable of the cable-stayed bridge decreases by 20%,the method accurately identifies damage at different cable locations using only sensors installed on the main girder,achieving identification accuracies above 95.8%in all cases.The proposed method shows high identification accuracy and generalization ability across various damage scenarios.展开更多
Software defect prediction plays an important role in software quality assurance.However,the performance of the prediction model is susceptible to the irrelevant and redundant features.In addition,previous studies mos...Software defect prediction plays an important role in software quality assurance.However,the performance of the prediction model is susceptible to the irrelevant and redundant features.In addition,previous studies mostly regard software defect prediction as a single objective optimization problem,and multi-objective software defect prediction has not been thoroughly investigated.For the above two reasons,we propose the following solutions in this paper:(1)we leverage an advanced deep neural network-Stacked Contractive AutoEncoder(SCAE)to extract the robust deep semantic features from the original defect features,which has stronger discrimination capacity for different classes(defective or non-defective).(2)we propose a novel multi-objective defect prediction model named SMONGE that utilizes the Multi-Objective NSGAII algorithm to optimize the advanced neural network-Extreme learning machine(ELM)based on state-of-the-art Pareto optimal solutions according to the features extracted by SCAE.We mainly consider two objectives.One objective is to maximize the performance of ELM,which refers to the benefit of the SMONGE model.Another objective is to minimize the output weight norm of ELM,which is related to the cost of the SMONGE model.We compare the SCAE with six state-of-the-art feature extraction methods and compare the SMONGE model with multiple baseline models that contain four classic defect predictors and the MONGE model without SCAE across 20 open source software projects.The experimental results verify that the superiority of SCAE and SMONGE on seven evaluation metrics.展开更多
Smart grid is envisaged as a power grid that is extremely reliable and flexible.The electrical grid has wide-area measuring devices like Phasor measurement units(PMUs)deployed to provide real-time grid information and...Smart grid is envisaged as a power grid that is extremely reliable and flexible.The electrical grid has wide-area measuring devices like Phasor measurement units(PMUs)deployed to provide real-time grid information and resolve issues effectively and speedily without compromising system availability.The development and application of machine learning approaches for power system protection and state estimation have been facilitated by the availability of measurement data.This research proposes a transmission line fault detection and classification(FD&C)system based on an auto-encoder neural network.A comparison between a Multi-Layer Extreme Learning Machine(ML-ELM)network model and a Stacked Auto-Encoder neural network(SAE)is made.Additionally,the performance of the models developed is compared to that of state-of-the-art classifier models employing feature datasets acquired by wavelet transform based feature extraction as well as other deep learning models.With substantially shorter testing time,the suggested auto-encoder models detect faults with 100% accuracy and classify faults with 99.92% and 99.79%accuracy.The computational efficiency of the ML-ELM model is demonstrated with high accuracy of classification with training time and testing time less than 50 ms.To emulate real system scenarios the models are developed with datasets with noise with signal-to-noise-ratio(SNR)ranging from 10 dB to 40 dB.The efficacy of the models is demonstrated with data from the IEEE 39 bus test system.展开更多
目的针对肿瘤患者经外周静脉置入中心静脉导管(peripherally inserted central catheter,PICC)相关血流感染(central line associated bloodstream infection,PICC-CLABSI)风险预测问题,利用Logistic回归和极限学习机(extreme learning ...目的针对肿瘤患者经外周静脉置入中心静脉导管(peripherally inserted central catheter,PICC)相关血流感染(central line associated bloodstream infection,PICC-CLABSI)风险预测问题,利用Logistic回归和极限学习机(extreme learning machine,ELM)分别建立预测模型并验证其预测效果。方法回顾性收集2019年1月至2023年3月在山西省某三级甲等综合医院肿瘤科接受PICC置管的1146例患者的临床病历资料,将2019年1月至2021年12月收集的786例PICC置管患者的临床病历资料作为建模组,将2022年1月2023年3月收集的360例患者的临床资料作为验证组。采用χ^(2)检验对建模组数据进行分析,将有统计学意义的变量进行Logistic回归分析,构建风险预测模型,并绘制列线图,采用Hosmer-Lemeshow检验和受试者工作特征(receiver operating characteristic,ROC)曲线下面积(area under the curve,AUC)评价该预测模型的拟合度及预测效果;将Logistic回归分析具有统计学意义的危险因素作为ELM预测模型的输入参数,肿瘤患者PICC-CLABSI发生风险作为输出参数,构建ELM预测模型;利用验证组数据对预测性能进行比较。结果(有)糖尿病史、化疗次数(≥3次)、维护周期(>7day)、维护地点(院外)、白细胞计数(<3.5×10^(9)/L)、白蛋白(<40g/L)是肿瘤患者发生经外周静脉置入中心静脉导管相关血流感染的风险因素(均P<0.05)。Logistic风险预测模型的Hosmer-Lemeshow检验结果显示:χ^(2)=5.201,P=0.736,AUC为0.860(95%CI:0.799~0.922),灵敏度是0.893,特异度是0.704,正确率为72.8%,表明模型具有良好的预测能力。ELM预测模型的决定系数为0.823,均方误差为0.051,模型的拟合度良好,正确率为74.5%,表明模型具有良好的预测能力。结论在Logistic回归分析筛选指标基础上建立的Logistic风险预测模型与ELM模型均具有较高的预测精度,可为临床医护人员筛查肿瘤PICC相关血流感染高危患者提供参考。展开更多
文摘Extreme learning machine (ELM) is a feedforward neural network-based machine learning method that has the benefits of short training times, strong generalization capabilities, and will not fall into local minima. However, due to the traditional ELM shallow architecture, it requires a large number of hidden nodes when dealing with high-dimensional data sets to ensure its classification performance. The other aspect, it is easy to degrade the classification performance in the face of noise interference from noisy data. To improve the above problem, this paper proposes a double pseudo-inverse extreme learning machine (DPELM) based on Sparse Denoising AutoEncoder (SDAE) namely, SDAE-DPELM. The algorithm can directly determine the input weight and output weight of the network by using the pseudo-inverse method. As a result, the algorithm only requires a few hidden layer nodes to produce superior classification results when classifying data. And its combination with SDAE can effectively improve the classification performance and noise resistance. Extensive numerical experiments show that the algorithm has high classification accuracy and good robustness when dealing with high-dimensional noisy data and high-dimensional noiseless data. Furthermore, applying such an algorithm to Miao character recognition substantiates its excellent performance, which further illustrates the practicability of the algorithm.
基金The National Natural Science Foundation of China(No.52361165658,52378318,52078459).
文摘To enhance the accuracy and efficiency of bridge damage identification,a novel data-driven damage identification method was proposed.First,convolutional autoencoder(CAE)was used to extract key features from the acceleration signal of the bridge structure through data reconstruction.The extreme gradient boosting tree(XGBoost)was then used to perform analysis on the feature data to achieve damage detection with high accuracy and high performance.The proposed method was applied in a numerical simulation study on a three-span continuous girder and further validated experimentally on a scaled model of a cable-stayed bridge.The numerical simulation results show that the identification errors remain within 2.9%for six single-damage cases and within 3.1%for four double-damage cases.The experimental validation results demonstrate that when the tension in a single cable of the cable-stayed bridge decreases by 20%,the method accurately identifies damage at different cable locations using only sensors installed on the main girder,achieving identification accuracies above 95.8%in all cases.The proposed method shows high identification accuracy and generalization ability across various damage scenarios.
基金This work is supported in part by the National Science Foundation of China(Grant Nos.61672392,61373038)in part by the National Key Research and Development Program of China(Grant No.2016YFC1202204).
文摘Software defect prediction plays an important role in software quality assurance.However,the performance of the prediction model is susceptible to the irrelevant and redundant features.In addition,previous studies mostly regard software defect prediction as a single objective optimization problem,and multi-objective software defect prediction has not been thoroughly investigated.For the above two reasons,we propose the following solutions in this paper:(1)we leverage an advanced deep neural network-Stacked Contractive AutoEncoder(SCAE)to extract the robust deep semantic features from the original defect features,which has stronger discrimination capacity for different classes(defective or non-defective).(2)we propose a novel multi-objective defect prediction model named SMONGE that utilizes the Multi-Objective NSGAII algorithm to optimize the advanced neural network-Extreme learning machine(ELM)based on state-of-the-art Pareto optimal solutions according to the features extracted by SCAE.We mainly consider two objectives.One objective is to maximize the performance of ELM,which refers to the benefit of the SMONGE model.Another objective is to minimize the output weight norm of ELM,which is related to the cost of the SMONGE model.We compare the SCAE with six state-of-the-art feature extraction methods and compare the SMONGE model with multiple baseline models that contain four classic defect predictors and the MONGE model without SCAE across 20 open source software projects.The experimental results verify that the superiority of SCAE and SMONGE on seven evaluation metrics.
文摘Smart grid is envisaged as a power grid that is extremely reliable and flexible.The electrical grid has wide-area measuring devices like Phasor measurement units(PMUs)deployed to provide real-time grid information and resolve issues effectively and speedily without compromising system availability.The development and application of machine learning approaches for power system protection and state estimation have been facilitated by the availability of measurement data.This research proposes a transmission line fault detection and classification(FD&C)system based on an auto-encoder neural network.A comparison between a Multi-Layer Extreme Learning Machine(ML-ELM)network model and a Stacked Auto-Encoder neural network(SAE)is made.Additionally,the performance of the models developed is compared to that of state-of-the-art classifier models employing feature datasets acquired by wavelet transform based feature extraction as well as other deep learning models.With substantially shorter testing time,the suggested auto-encoder models detect faults with 100% accuracy and classify faults with 99.92% and 99.79%accuracy.The computational efficiency of the ML-ELM model is demonstrated with high accuracy of classification with training time and testing time less than 50 ms.To emulate real system scenarios the models are developed with datasets with noise with signal-to-noise-ratio(SNR)ranging from 10 dB to 40 dB.The efficacy of the models is demonstrated with data from the IEEE 39 bus test system.
文摘目的针对肿瘤患者经外周静脉置入中心静脉导管(peripherally inserted central catheter,PICC)相关血流感染(central line associated bloodstream infection,PICC-CLABSI)风险预测问题,利用Logistic回归和极限学习机(extreme learning machine,ELM)分别建立预测模型并验证其预测效果。方法回顾性收集2019年1月至2023年3月在山西省某三级甲等综合医院肿瘤科接受PICC置管的1146例患者的临床病历资料,将2019年1月至2021年12月收集的786例PICC置管患者的临床病历资料作为建模组,将2022年1月2023年3月收集的360例患者的临床资料作为验证组。采用χ^(2)检验对建模组数据进行分析,将有统计学意义的变量进行Logistic回归分析,构建风险预测模型,并绘制列线图,采用Hosmer-Lemeshow检验和受试者工作特征(receiver operating characteristic,ROC)曲线下面积(area under the curve,AUC)评价该预测模型的拟合度及预测效果;将Logistic回归分析具有统计学意义的危险因素作为ELM预测模型的输入参数,肿瘤患者PICC-CLABSI发生风险作为输出参数,构建ELM预测模型;利用验证组数据对预测性能进行比较。结果(有)糖尿病史、化疗次数(≥3次)、维护周期(>7day)、维护地点(院外)、白细胞计数(<3.5×10^(9)/L)、白蛋白(<40g/L)是肿瘤患者发生经外周静脉置入中心静脉导管相关血流感染的风险因素(均P<0.05)。Logistic风险预测模型的Hosmer-Lemeshow检验结果显示:χ^(2)=5.201,P=0.736,AUC为0.860(95%CI:0.799~0.922),灵敏度是0.893,特异度是0.704,正确率为72.8%,表明模型具有良好的预测能力。ELM预测模型的决定系数为0.823,均方误差为0.051,模型的拟合度良好,正确率为74.5%,表明模型具有良好的预测能力。结论在Logistic回归分析筛选指标基础上建立的Logistic风险预测模型与ELM模型均具有较高的预测精度,可为临床医护人员筛查肿瘤PICC相关血流感染高危患者提供参考。
