Dear Editor,This letter presents techniques to simplify dataset generation for instance segmentation of raw meat products,a critical step toward automating food production lines.Accurate segmentation is essential for ...Dear Editor,This letter presents techniques to simplify dataset generation for instance segmentation of raw meat products,a critical step toward automating food production lines.Accurate segmentation is essential for addressing challenges such as occlusions,indistinct edges,and stacked configurations,which demand large,diverse datasets.To meet these demands,we propose two complementary approaches:a semi-automatic annotation interface using tools like the segment anything model(SAM)and GrabCut and a synthetic data generation pipeline leveraging 3D-scanned models.These methods reduce reliance on real meat,mitigate food waste,and improve scalability.Experimental results demonstrate that incorporating synthetic data enhances segmentation model performance and,when combined with real data,further boosts accuracy,paving the way for more efficient automation in the food industry.展开更多
Deep Learning has been widely used to model soft sensors in modern industrial processes with nonlinear variables and uncertainty.Due to the outstanding ability for high-level feature extraction,stacked autoencoder(SAE...Deep Learning has been widely used to model soft sensors in modern industrial processes with nonlinear variables and uncertainty.Due to the outstanding ability for high-level feature extraction,stacked autoencoder(SAE)has been widely used to improve the model accuracy of soft sensors.However,with the increase of network layers,SAE may encounter serious information loss issues,which affect the modeling performance of soft sensors.Besides,there are typically very few labeled samples in the data set,which brings challenges to traditional neural networks to solve.In this paper,a multi-scale feature fused stacked autoencoder(MFF-SAE)is suggested for feature representation related to hierarchical output,where stacked autoencoder,mutual information(MI)and multi-scale feature fusion(MFF)strategies are integrated.Based on correlation analysis between output and input variables,critical hidden variables are extracted from the original variables in each autoencoder's input layer,which are correspondingly given varying weights.Besides,an integration strategy based on multi-scale feature fusion is adopted to mitigate the impact of information loss with the deepening of the network layers.Then,the MFF-SAE method is designed and stacked to form deep networks.Two practical industrial processes are utilized to evaluate the performance of MFF-SAE.Results from simulations indicate that in comparison to other cutting-edge techniques,the proposed method may considerably enhance the accuracy of soft sensor modeling,where the suggested method reduces the root mean square error(RMSE)by 71.8%,17.1%and 64.7%,15.1%,respectively.展开更多
Spectrum prediction is considered as a key technology to assist spectrum decision.Despite the great efforts that have been put on the construction of spectrum prediction,achieving accurate spectrum prediction emphasiz...Spectrum prediction is considered as a key technology to assist spectrum decision.Despite the great efforts that have been put on the construction of spectrum prediction,achieving accurate spectrum prediction emphasizes the need for more advanced solutions.In this paper,we propose a new multichannel multi-step spectrum prediction method using Transformer and stacked bidirectional LSTM(Bi-LSTM),named TSB.Specifically,we use multi-head attention and stacked Bi-LSTM to build a new Transformer based on encoder-decoder architecture.The self-attention mechanism composed of multiple layers of multi-head attention can continuously attend to all positions of the multichannel spectrum sequences.The stacked Bi-LSTM can learn these focused coding features by multi-head attention layer by layer.The advantage of this fusion mode is that it can deeply capture the long-term dependence of multichannel spectrum data.We have conducted extensive experiments on a dataset generated by a real simulation platform.The results show that the proposed algorithm performs better than the baselines.展开更多
Healthcare networks prove to be an urgent issue in terms of intrusion detection due to the critical consequences of cyber threats and the extreme sensitivity of medical information.The proposed Auto-Stack ID in the st...Healthcare networks prove to be an urgent issue in terms of intrusion detection due to the critical consequences of cyber threats and the extreme sensitivity of medical information.The proposed Auto-Stack ID in the study is a stacked ensemble of encoder-enhanced auctions that can be used to improve intrusion detection in healthcare networks.TheWUSTL-EHMS 2020 dataset trains and evaluates themodel,constituting an imbalanced class distribution(87.46% normal traffic and 12.53% intrusion attacks).To address this imbalance,the study balances the effect of training Bias through Stratified K-fold cross-validation(K=5),so that each class is represented similarly on training and validation splits.Second,the Auto-Stack ID method combines many base classifiers such as TabNet,LightGBM,Gaussian Naive Bayes,Histogram-Based Gradient Boosting(HGB),and Logistic Regression.We apply a two-stage training process based on the first stage,where we have base classifiers that predict out-of-fold(OOF)predictions,which we use as inputs for the second-stage meta-learner XGBoost.The meta-learner learns to refine predictions to capture complicated interactions between base models,thus improving detection accuracy without introducing bias,overfitting,or requiring domain knowledge of the meta-data.In addition,the auto-stack ID model got 98.41% accuracy and 93.45%F1 score,better than individual classifiers.It can identify intrusions due to its 90.55% recall and 96.53% precision with minimal false positives.These findings identify its suitability in ensuring healthcare networks’security through ensemble learning.Ongoing efforts will be deployed in real time to improve response to evolving threats.展开更多
Carbonaceous material has attracted much attention in the application of sodium-ion batteries(SIBs)anode.However,sluggish reaction kinetics and structure stability impede the application.Therefore,a stacked layered su...Carbonaceous material has attracted much attention in the application of sodium-ion batteries(SIBs)anode.However,sluggish reaction kinetics and structure stability impede the application.Therefore,a stacked layered sulfur-carbon complex with long-chain C–S_(x)–C bond(M-SC-S)is prepared.The layered structure ensures structural stability,and long-chain C–S_(x)–C bond expanding interlayer spacing boosts facile Na+diffusion.When assembled into cells,a high-quality solid-electrolyte interphase film would be formed due to a good match between the M-SC-S electrode and ether electrolyte.Moreover,an electrochemical activation process would happen between the Cu current collector and proper S-doped electrode material to in-situ form Cu_(2)S.The formation of Cu_(2)S in active material can not only provide more active sites for sodium storage and enhance pseudo-capacitance,but also reinforce the electrode/current collector interface and decrease the interfacial transfer resistance for rapid Na+kinetics.The synergistic effect of structure design and interface engineering optimizes the sodium storage system.Thus,the M-SC-S electrode delivers an excellent cyclic performance(321.6 mAh g^(−1)after 1000 cycles at 2 A g^(−1)with a capacity retention rate of 97.4%)and good rate capability(282.8 mAh g^(−1)after 4000 cycles even at a high current density of 10 A g^(−1)).The full cell also has an impressive cyclic performance(151.4 mAh g^(−1)after 500 cycles at 0.5 A g^(−1)).展开更多
In the context of intelligent manufacturing,the modern hot strip mill process(HSMP)shows characteristics such as diversification of products,multi-specification batch production,and demand-oriented customization.These...In the context of intelligent manufacturing,the modern hot strip mill process(HSMP)shows characteristics such as diversification of products,multi-specification batch production,and demand-oriented customization.These characteristics pose significant challenges to ensuring process stability and consistency of product performance.Therefore,exploring the potential relationship between product performance and the production process,and developing a comprehensive performance evaluation method adapted to modern HSMP have become an urgent issue.A comprehensive performance evaluation method for HSMP by integrating multi-task learning and stacked performance-related autoencoder is proposed to solve the problems such as incomplete performance indicators(PIs)data,insufficient real-time acquisition requirements,and coupling of multiple PIs.First,according to the existing Chinese standards,a comprehensive performance evaluation grade strategy for strip steel is designed.The random forest model is established to predict and complete the parts of PIs data that could not be obtained in real-time.Second,a stacked performance-related autoencoder(SPAE)model is proposed to extract the deep features closely related to the product performance.Then,considering the correlation between PIs,the multi-task learning framework is introduced to output the subitem ratings and comprehensive product performance rating results of the strip steel online in real-time,where each task represents a subitem of comprehensive performance.Finally,the effectiveness of the method is verified on a real HSMP dataset,and the results show that the accuracy of the proposed method is as high as 94.8%,which is superior to the other comparative methods.展开更多
Capacitor-less 2T0C dynamic random-access memory(DRAM)employing oxide semiconductors(OSs)as a channel has great potential in the development of highly scaled three dimensional(3D)-structured devices.However,the use of...Capacitor-less 2T0C dynamic random-access memory(DRAM)employing oxide semiconductors(OSs)as a channel has great potential in the development of highly scaled three dimensional(3D)-structured devices.However,the use of OS and such device structures presents certain challenges,including the trade-off relationship between the field-effect mobility and stability of OSs.Conventional 4-line-based operation of the 2T0C enlarges the entire cell volume and complicates the peripheral circuit.Herein,we proposed an IGO(In-Ga-O)channel 2-line-based 2T0C cell design and operating sequences comparable to those of the conventional Si-channel 1 T1C DRAM.IGO was adopted to achieve high thermal stability above 800℃,and the process conditions were optimized to simultaneously obtain a high μFE of 90.7 cm^(2)·V^(-)1·s^(-1),positive Vth of 0.34 V,superior reliability,and uniformity.The proposed 2-line-based 2T0C DRAM cell successfully exhibited multi-bit operation,with the stored voltage varying from 0 V to 1 V at 0.1 V intervals.Furthermore,for stored voltage intervals of 0.1 V and 0.5 V,the refresh time was 10 s and 1000 s in multi-bit operation;these values were more than 150 and 15000 times longer than those of the conventional Si channel 1T1C DRAM,respectively.A monolithic stacked 2-line-based 2T0C DRAM was fabricated,and a multi-bit operation was confirmed.展开更多
The adoption of deep learning-based side-channel analysis(DL-SCA)is crucial for leak detection in secure products.Many previous studies have applied this method to break targets protected with countermeasures.Despite ...The adoption of deep learning-based side-channel analysis(DL-SCA)is crucial for leak detection in secure products.Many previous studies have applied this method to break targets protected with countermeasures.Despite the increasing number of studies,the problem of model overfitting.Recent research mainly focuses on exploring hyperparameters and network architectures,while offering limited insights into the effects of external factors on side-channel attacks,such as the number and type of models.This paper proposes a Side-channel Analysis method based on a Stacking ensemble,called Stacking-SCA.In our method,multiple models are deeply integrated.Through the extended application of base models and the meta-model,Stacking-SCA effectively improves the output class probabilities of the model,leading to better generalization.Furthermore,this method shows that the attack performance is sensitive to changes in the number of models.Next,five independent subsets are extracted from the original ASCAD database as multi-segment datasets,which are mutually independent.This method shows how these subsets are used as inputs for Stacking-SCA to enhance its attack convergence.The experimental results show that Stacking-SCA outperforms the current state-of-the-art results on several considered datasets,significantly reducing the number of attack traces required to achieve a guessing entropy of 1.Additionally,different hyperparameter sizes are adjusted to further validate the robustness of the method.展开更多
【目的】为了解决滑坡易发性评价模型中最优超参数组合难以确定的问题。【方法】本文引入一种蜘蛛蜂优化算法用于寻找机器学习模型的最优超参数组合,通过蜘蛛蜂优化算法(Spider Wasp Optimizer,SWO)对随机森林(Random Forest,RF)、轻量...【目的】为了解决滑坡易发性评价模型中最优超参数组合难以确定的问题。【方法】本文引入一种蜘蛛蜂优化算法用于寻找机器学习模型的最优超参数组合,通过蜘蛛蜂优化算法(Spider Wasp Optimizer,SWO)对随机森林(Random Forest,RF)、轻量的梯度提升机(Light Gradient Boosting Machine,LightGBM)、CatBoost(Categorical Boosting)模型进行超参数优化,得到模型最优超参数组合值,进而构建滑坡易发性评价模型。在此基础上,将SWO优化后的上述机器学习模型,采用Stacking集成方法进行模型组合。对比各模型评价结果,筛选得到最优滑坡易发性模型,并采用SHAP(SHapley Additive exPlanations)算法对最优模型评价结果进行可解释性分析。【结果】本文以黑龙江省亚雪公路沿线边坡为例,采用SWO优化算法对上述机器学习模型超参数组合寻优后,SWO-LightGBM、SWO-CatBoost和SWO-RF分别较优化前的模型AUC(Area Under the Curve)值提高2.4%、1.6%、2.2%,这表明SWO算法有效增强了机器学习模型整体性能,即滑坡易发性预测精度。其SWO-LightGBM模型表现最优,其AUC值达到0.939。4个Stacking模型评价结果AUC值在0.924~0.935之间,均低于SWO-LightGBM模型结果。最后,对SWO-LightGBM模型进行可解释性分析可知,坡度、距道路距离、年平均降雨量、距河流距离对滑坡易发性的贡献较大。【结论】本研究通过蜘蛛蜂优化算法寻找最优超参数组合,使模型的预测精度和结果准确性得到了有效提升。展开更多
基金supported by European Union’s Horizon Europe research and innovation programme,project AGILEHAND(Smart Grading,Handling and Packaging Solutions for Soft and Deformable Products in Agile and Reconfigurable Lines)(101092043).
文摘Dear Editor,This letter presents techniques to simplify dataset generation for instance segmentation of raw meat products,a critical step toward automating food production lines.Accurate segmentation is essential for addressing challenges such as occlusions,indistinct edges,and stacked configurations,which demand large,diverse datasets.To meet these demands,we propose two complementary approaches:a semi-automatic annotation interface using tools like the segment anything model(SAM)and GrabCut and a synthetic data generation pipeline leveraging 3D-scanned models.These methods reduce reliance on real meat,mitigate food waste,and improve scalability.Experimental results demonstrate that incorporating synthetic data enhances segmentation model performance and,when combined with real data,further boosts accuracy,paving the way for more efficient automation in the food industry.
基金supported by the National Key Research and Development Program of China(2023YFB3307800)National Natural Science Foundation of China(62394343,62373155)+2 种基金Major Science and Technology Project of Xinjiang(No.2022A01006-4)State Key Laboratory of Industrial Control Technology,China(Grant No.ICT2024A26)Fundamental Research Funds for the Central Universities.
文摘Deep Learning has been widely used to model soft sensors in modern industrial processes with nonlinear variables and uncertainty.Due to the outstanding ability for high-level feature extraction,stacked autoencoder(SAE)has been widely used to improve the model accuracy of soft sensors.However,with the increase of network layers,SAE may encounter serious information loss issues,which affect the modeling performance of soft sensors.Besides,there are typically very few labeled samples in the data set,which brings challenges to traditional neural networks to solve.In this paper,a multi-scale feature fused stacked autoencoder(MFF-SAE)is suggested for feature representation related to hierarchical output,where stacked autoencoder,mutual information(MI)and multi-scale feature fusion(MFF)strategies are integrated.Based on correlation analysis between output and input variables,critical hidden variables are extracted from the original variables in each autoencoder's input layer,which are correspondingly given varying weights.Besides,an integration strategy based on multi-scale feature fusion is adopted to mitigate the impact of information loss with the deepening of the network layers.Then,the MFF-SAE method is designed and stacked to form deep networks.Two practical industrial processes are utilized to evaluate the performance of MFF-SAE.Results from simulations indicate that in comparison to other cutting-edge techniques,the proposed method may considerably enhance the accuracy of soft sensor modeling,where the suggested method reduces the root mean square error(RMSE)by 71.8%,17.1%and 64.7%,15.1%,respectively.
基金supported in part by the National Natural Science Foundation of China under Grants 62231015,62427801in part by Jiangsu Province Frontier Leading Technology Basic Research Project BK20232030.
文摘Spectrum prediction is considered as a key technology to assist spectrum decision.Despite the great efforts that have been put on the construction of spectrum prediction,achieving accurate spectrum prediction emphasizes the need for more advanced solutions.In this paper,we propose a new multichannel multi-step spectrum prediction method using Transformer and stacked bidirectional LSTM(Bi-LSTM),named TSB.Specifically,we use multi-head attention and stacked Bi-LSTM to build a new Transformer based on encoder-decoder architecture.The self-attention mechanism composed of multiple layers of multi-head attention can continuously attend to all positions of the multichannel spectrum sequences.The stacked Bi-LSTM can learn these focused coding features by multi-head attention layer by layer.The advantage of this fusion mode is that it can deeply capture the long-term dependence of multichannel spectrum data.We have conducted extensive experiments on a dataset generated by a real simulation platform.The results show that the proposed algorithm performs better than the baselines.
基金funded by Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2025R319),Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia and Prince Sultan University for covering the article processing charges(APC)associated with this publicationResearchers Supporting Project Number(RSPD2025R1107),King Saud University,Riyadh,Saudi Arabia.
文摘Healthcare networks prove to be an urgent issue in terms of intrusion detection due to the critical consequences of cyber threats and the extreme sensitivity of medical information.The proposed Auto-Stack ID in the study is a stacked ensemble of encoder-enhanced auctions that can be used to improve intrusion detection in healthcare networks.TheWUSTL-EHMS 2020 dataset trains and evaluates themodel,constituting an imbalanced class distribution(87.46% normal traffic and 12.53% intrusion attacks).To address this imbalance,the study balances the effect of training Bias through Stratified K-fold cross-validation(K=5),so that each class is represented similarly on training and validation splits.Second,the Auto-Stack ID method combines many base classifiers such as TabNet,LightGBM,Gaussian Naive Bayes,Histogram-Based Gradient Boosting(HGB),and Logistic Regression.We apply a two-stage training process based on the first stage,where we have base classifiers that predict out-of-fold(OOF)predictions,which we use as inputs for the second-stage meta-learner XGBoost.The meta-learner learns to refine predictions to capture complicated interactions between base models,thus improving detection accuracy without introducing bias,overfitting,or requiring domain knowledge of the meta-data.In addition,the auto-stack ID model got 98.41% accuracy and 93.45%F1 score,better than individual classifiers.It can identify intrusions due to its 90.55% recall and 96.53% precision with minimal false positives.These findings identify its suitability in ensuring healthcare networks’security through ensemble learning.Ongoing efforts will be deployed in real time to improve response to evolving threats.
基金supported by the Key Research and Development Program of Wuhan(2025010102030005)the National Nature Science Foundation of Jiangsu Province(BK20221259)。
文摘Carbonaceous material has attracted much attention in the application of sodium-ion batteries(SIBs)anode.However,sluggish reaction kinetics and structure stability impede the application.Therefore,a stacked layered sulfur-carbon complex with long-chain C–S_(x)–C bond(M-SC-S)is prepared.The layered structure ensures structural stability,and long-chain C–S_(x)–C bond expanding interlayer spacing boosts facile Na+diffusion.When assembled into cells,a high-quality solid-electrolyte interphase film would be formed due to a good match between the M-SC-S electrode and ether electrolyte.Moreover,an electrochemical activation process would happen between the Cu current collector and proper S-doped electrode material to in-situ form Cu_(2)S.The formation of Cu_(2)S in active material can not only provide more active sites for sodium storage and enhance pseudo-capacitance,but also reinforce the electrode/current collector interface and decrease the interfacial transfer resistance for rapid Na+kinetics.The synergistic effect of structure design and interface engineering optimizes the sodium storage system.Thus,the M-SC-S electrode delivers an excellent cyclic performance(321.6 mAh g^(−1)after 1000 cycles at 2 A g^(−1)with a capacity retention rate of 97.4%)and good rate capability(282.8 mAh g^(−1)after 4000 cycles even at a high current density of 10 A g^(−1)).The full cell also has an impressive cyclic performance(151.4 mAh g^(−1)after 500 cycles at 0.5 A g^(−1)).
基金supported by the National Natural Science Foundation of China(NSFC)under Grants(Nos.U21A20483,62373040 and 62273031).
文摘In the context of intelligent manufacturing,the modern hot strip mill process(HSMP)shows characteristics such as diversification of products,multi-specification batch production,and demand-oriented customization.These characteristics pose significant challenges to ensuring process stability and consistency of product performance.Therefore,exploring the potential relationship between product performance and the production process,and developing a comprehensive performance evaluation method adapted to modern HSMP have become an urgent issue.A comprehensive performance evaluation method for HSMP by integrating multi-task learning and stacked performance-related autoencoder is proposed to solve the problems such as incomplete performance indicators(PIs)data,insufficient real-time acquisition requirements,and coupling of multiple PIs.First,according to the existing Chinese standards,a comprehensive performance evaluation grade strategy for strip steel is designed.The random forest model is established to predict and complete the parts of PIs data that could not be obtained in real-time.Second,a stacked performance-related autoencoder(SPAE)model is proposed to extract the deep features closely related to the product performance.Then,considering the correlation between PIs,the multi-task learning framework is introduced to output the subitem ratings and comprehensive product performance rating results of the strip steel online in real-time,where each task represents a subitem of comprehensive performance.Finally,the effectiveness of the method is verified on a real HSMP dataset,and the results show that the accuracy of the proposed method is as high as 94.8%,which is superior to the other comparative methods.
基金supported by the Technology Innovation Program(Grant Nos.20017382 and 20023023)funded by the Ministry of Trade,Industry&Energy(MOTIE,Republic of Korea)supported by a National Research Foundation of Korea(NRF)grant funded by the Korean Government(MSIT)(Grant No.RS-2023-00260527).
文摘Capacitor-less 2T0C dynamic random-access memory(DRAM)employing oxide semiconductors(OSs)as a channel has great potential in the development of highly scaled three dimensional(3D)-structured devices.However,the use of OS and such device structures presents certain challenges,including the trade-off relationship between the field-effect mobility and stability of OSs.Conventional 4-line-based operation of the 2T0C enlarges the entire cell volume and complicates the peripheral circuit.Herein,we proposed an IGO(In-Ga-O)channel 2-line-based 2T0C cell design and operating sequences comparable to those of the conventional Si-channel 1 T1C DRAM.IGO was adopted to achieve high thermal stability above 800℃,and the process conditions were optimized to simultaneously obtain a high μFE of 90.7 cm^(2)·V^(-)1·s^(-1),positive Vth of 0.34 V,superior reliability,and uniformity.The proposed 2-line-based 2T0C DRAM cell successfully exhibited multi-bit operation,with the stored voltage varying from 0 V to 1 V at 0.1 V intervals.Furthermore,for stored voltage intervals of 0.1 V and 0.5 V,the refresh time was 10 s and 1000 s in multi-bit operation;these values were more than 150 and 15000 times longer than those of the conventional Si channel 1T1C DRAM,respectively.A monolithic stacked 2-line-based 2T0C DRAM was fabricated,and a multi-bit operation was confirmed.
基金supported by the Hunan Provincial Natural Science Foundation of China(2022JJ30103)“the 14th Five-Year Plan”Key Disciplines and Application-Oriented Special Disciplines of Hunan Province(Xiangjiaotong[2022]351)the Science and Technology Innovation Program of Hunan Province(2016TP1020).
文摘The adoption of deep learning-based side-channel analysis(DL-SCA)is crucial for leak detection in secure products.Many previous studies have applied this method to break targets protected with countermeasures.Despite the increasing number of studies,the problem of model overfitting.Recent research mainly focuses on exploring hyperparameters and network architectures,while offering limited insights into the effects of external factors on side-channel attacks,such as the number and type of models.This paper proposes a Side-channel Analysis method based on a Stacking ensemble,called Stacking-SCA.In our method,multiple models are deeply integrated.Through the extended application of base models and the meta-model,Stacking-SCA effectively improves the output class probabilities of the model,leading to better generalization.Furthermore,this method shows that the attack performance is sensitive to changes in the number of models.Next,five independent subsets are extracted from the original ASCAD database as multi-segment datasets,which are mutually independent.This method shows how these subsets are used as inputs for Stacking-SCA to enhance its attack convergence.The experimental results show that Stacking-SCA outperforms the current state-of-the-art results on several considered datasets,significantly reducing the number of attack traces required to achieve a guessing entropy of 1.Additionally,different hyperparameter sizes are adjusted to further validate the robustness of the method.
文摘【目的】为了解决滑坡易发性评价模型中最优超参数组合难以确定的问题。【方法】本文引入一种蜘蛛蜂优化算法用于寻找机器学习模型的最优超参数组合,通过蜘蛛蜂优化算法(Spider Wasp Optimizer,SWO)对随机森林(Random Forest,RF)、轻量的梯度提升机(Light Gradient Boosting Machine,LightGBM)、CatBoost(Categorical Boosting)模型进行超参数优化,得到模型最优超参数组合值,进而构建滑坡易发性评价模型。在此基础上,将SWO优化后的上述机器学习模型,采用Stacking集成方法进行模型组合。对比各模型评价结果,筛选得到最优滑坡易发性模型,并采用SHAP(SHapley Additive exPlanations)算法对最优模型评价结果进行可解释性分析。【结果】本文以黑龙江省亚雪公路沿线边坡为例,采用SWO优化算法对上述机器学习模型超参数组合寻优后,SWO-LightGBM、SWO-CatBoost和SWO-RF分别较优化前的模型AUC(Area Under the Curve)值提高2.4%、1.6%、2.2%,这表明SWO算法有效增强了机器学习模型整体性能,即滑坡易发性预测精度。其SWO-LightGBM模型表现最优,其AUC值达到0.939。4个Stacking模型评价结果AUC值在0.924~0.935之间,均低于SWO-LightGBM模型结果。最后,对SWO-LightGBM模型进行可解释性分析可知,坡度、距道路距离、年平均降雨量、距河流距离对滑坡易发性的贡献较大。【结论】本研究通过蜘蛛蜂优化算法寻找最优超参数组合,使模型的预测精度和结果准确性得到了有效提升。
