[Objective]Accurate prediction of tomato growth height is crucial for optimizing production environments in smart farming.However,current prediction methods predominantly rely on empirical,mechanistic,or learning-base...[Objective]Accurate prediction of tomato growth height is crucial for optimizing production environments in smart farming.However,current prediction methods predominantly rely on empirical,mechanistic,or learning-based models that utilize either images data or environmental data.These methods fail to fully leverage multi-modal data to capture the diverse aspects of plant growth comprehensively.[Methods]To address this limitation,a two-stage phenotypic feature extraction(PFE)model based on deep learning algorithm of recurrent neural network(RNN)and long short-term memory(LSTM)was developed.The model integrated environment and plant information to provide a holistic understanding of the growth process,emploied phenotypic and temporal feature extractors to comprehensively capture both types of features,enabled a deeper understanding of the interaction between tomato plants and their environment,ultimately leading to highly accurate predictions of growth height.[Results and Discussions]The experimental results showed the model's ef‐fectiveness:When predicting the next two days based on the past five days,the PFE-based RNN and LSTM models achieved mean absolute percentage error(MAPE)of 0.81%and 0.40%,respectively,which were significantly lower than the 8.00%MAPE of the large language model(LLM)and 6.72%MAPE of the Transformer-based model.In longer-term predictions,the 10-day prediction for 4 days ahead and the 30-day prediction for 12 days ahead,the PFE-RNN model continued to outperform the other two baseline models,with MAPE of 2.66%and 14.05%,respectively.[Conclusions]The proposed method,which leverages phenotypic-temporal collaboration,shows great potential for intelligent,data-driven management of tomato cultivation,making it a promising approach for enhancing the efficiency and precision of smart tomato planting management.展开更多
For the multi-mode radar working in the modern electronicbattlefield, different working states of one single radar areprone to being classified as multiple emitters when adoptingtraditional classification methods to p...For the multi-mode radar working in the modern electronicbattlefield, different working states of one single radar areprone to being classified as multiple emitters when adoptingtraditional classification methods to process intercepted signals,which has a negative effect on signal classification. A classificationmethod based on spatial data mining is presented to address theabove challenge. Inspired by the idea of spatial data mining, theclassification method applies nuclear field to depicting the distributioninformation of pulse samples in feature space, and digs out thehidden cluster information by analyzing distribution characteristics.In addition, a membership-degree criterion to quantify the correlationamong all classes is established, which ensures classificationaccuracy of signal samples. Numerical experiments show that thepresented method can effectively prevent different working statesof multi-mode emitter from being classified as several emitters,and achieve higher classification accuracy.展开更多
There are multiple operating modes in the real industrial process, and the collected data follow the complex multimodal distribution, so most traditional process monitoring methods are no longer applicable because the...There are multiple operating modes in the real industrial process, and the collected data follow the complex multimodal distribution, so most traditional process monitoring methods are no longer applicable because their presumptions are that sampled-data should obey the single Gaussian distribution or non-Gaussian distribution. In order to solve these problems, a novel weighted local standardization(WLS) strategy is proposed to standardize the multimodal data, which can eliminate the multi-mode characteristics of the collected data, and normalize them into unimodal data distribution. After detailed analysis of the raised data preprocessing strategy, a new algorithm using WLS strategy with support vector data description(SVDD) is put forward to apply for multi-mode monitoring process. Unlike the strategy of building multiple local models, the developed method only contains a model without the prior knowledge of multi-mode process. To demonstrate the proposed method's validity, it is applied to a numerical example and a Tennessee Eastman(TE) process. Finally, the simulation results show that the WLS strategy is very effective to standardize multimodal data, and the WLS-SVDD monitoring method has great advantages over the traditional SVDD and PCA combined with a local standardization strategy(LNS-PCA) in multi-mode process monitoring.展开更多
Mill vibration is a common problem in rolling production,which directly affects the thickness accuracy of the strip and may even lead to strip fracture accidents in serious cases.The existing vibration prediction mode...Mill vibration is a common problem in rolling production,which directly affects the thickness accuracy of the strip and may even lead to strip fracture accidents in serious cases.The existing vibration prediction models do not consider the features contained in the data,resulting in limited improvement of model accuracy.To address these challenges,this paper proposes a multi-dimensional multi-modal cold rolling vibration time series prediction model(MDMMVPM)based on the deep fusion of multi-level networks.In the model,the long-term and short-term modal features of multi-dimensional data are considered,and the appropriate prediction algorithms are selected for different data features.Based on the established prediction model,the effects of tension and rolling force on mill vibration are analyzed.Taking the 5th stand of a cold mill in a steel mill as the research object,the innovative model is applied to predict the mill vibration for the first time.The experimental results show that the correlation coefficient(R^(2))of the model proposed in this paper is 92.5%,and the root-mean-square error(RMSE)is 0.0011,which significantly improves the modeling accuracy compared with the existing models.The proposed model is also suitable for the hot rolling process,which provides a new method for the prediction of strip rolling vibration.展开更多
In order to solve difficult detection of far and hard objects due to the sparseness and insufficient semantic information of LiDAR point cloud,a 3D object detection network with multi-modal data adaptive fusion is pro...In order to solve difficult detection of far and hard objects due to the sparseness and insufficient semantic information of LiDAR point cloud,a 3D object detection network with multi-modal data adaptive fusion is proposed,which makes use of multi-neighborhood information of voxel and image information.Firstly,design an improved ResNet that maintains the structure information of far and hard objects in low-resolution feature maps,which is more suitable for detection task.Meanwhile,semantema of each image feature map is enhanced by semantic information from all subsequent feature maps.Secondly,extract multi-neighborhood context information with different receptive field sizes to make up for the defect of sparseness of point cloud which improves the ability of voxel features to represent the spatial structure and semantic information of objects.Finally,propose a multi-modal feature adaptive fusion strategy which uses learnable weights to express the contribution of different modal features to the detection task,and voxel attention further enhances the fused feature expression of effective target objects.The experimental results on the KITTI benchmark show that this method outperforms VoxelNet with remarkable margins,i.e.increasing the AP by 8.78%and 5.49%on medium and hard difficulty levels.Meanwhile,our method achieves greater detection performance compared with many mainstream multi-modal methods,i.e.outperforming the AP by 1%compared with that of MVX-Net on medium and hard difficulty levels.展开更多
To address the difficulties in fusing multi-mode sensor data for complex industrial machinery, an adaptive deep coupling convolutional auto-encoder (ADCCAE) fusion method was proposed. First, the multi-mode features e...To address the difficulties in fusing multi-mode sensor data for complex industrial machinery, an adaptive deep coupling convolutional auto-encoder (ADCCAE) fusion method was proposed. First, the multi-mode features extracted synchronously by the CCAE were stacked and fed to the multi-channel convolution layers for fusion. Then, the fused data was passed to all connection layers for compression and fed to the Softmax module for classification. Finally, the coupling loss function coefficients and the network parameters were optimized through an adaptive approach using the gray wolf optimization (GWO) algorithm. Experimental comparisons showed that the proposed ADCCAE fusion model was superior to existing models for multi-mode data fusion.展开更多
To improve the traffic scheduling capability in operator data center networks,an analysis prediction and online scheduling mechanism(APOS)is designed,considering both the network structure and the network traffic in t...To improve the traffic scheduling capability in operator data center networks,an analysis prediction and online scheduling mechanism(APOS)is designed,considering both the network structure and the network traffic in the operator data center.Fibonacci tree optimization algorithm(FTO)is embedded into the analysis prediction and the online scheduling stages,the FTO traffic scheduling strategy is proposed.By taking the global optimal and the multi-modal optimization advantage of FTO,the traffic scheduling optimal solution and many suboptimal solutions can be obtained.The experiment results show that the FTO traffic scheduling strategy can schedule traffic in data center networks reasonably,and improve the load balancing in the operator data center network effectively.展开更多
The in-service life of turbine blades directly affects the on-wing lifetime and operating cost of aircraft engines.It would be essential to accurately evaluate the remaining useful life of turbine blades for safe engi...The in-service life of turbine blades directly affects the on-wing lifetime and operating cost of aircraft engines.It would be essential to accurately evaluate the remaining useful life of turbine blades for safe engine operation and reasonable maintenance decision-making.In this paper,a machine learning-based mechanism with multiple information fusion is proposed to predict the remaining useful life of high-pressure turbine blades.The developed method takes account of the in-service operating factors such as the high-pressure rotor speed and exhaust gas temperature,as well as the engine operating environments and performance degradation.The effectiveness of this method is demonstrated on simulated test cases generated by an integrated blade creep-life assessment model,which comprises engine performance,blade stress,thermal,and creep life estimation models.The results show that the proposed method provides a prospective result for in-service life evaluation of turbine blades and is of significance to evaluating the engine on-wing lifetime and making a reasonable maintenance plan.展开更多
目的:基于数据挖掘技术探讨内服中药治疗重症肺炎的用药规律。方法:通过全面检索中国知网、万方、维普、中国生物医学文献数据库、PubMed、Web of Science等数据库中的文献,收集自建库至2024年3月15日符合内服中药治疗重症肺炎的相关文...目的:基于数据挖掘技术探讨内服中药治疗重症肺炎的用药规律。方法:通过全面检索中国知网、万方、维普、中国生物医学文献数据库、PubMed、Web of Science等数据库中的文献,收集自建库至2024年3月15日符合内服中药治疗重症肺炎的相关文献,用Excel软件记录所有中药处方,并进行性味、归经和功效分析,运用R语言进行频次、关联规则和聚类分析,从而探讨内服中药治疗重症肺炎的用药规律。结果:共筛选出中药处方172首,涉及中药173味。进行频次分析,居于前10位的中药依次为甘草、黄芩、苦杏仁、石膏、大黄、茯苓、半夏、桑白皮、桔梗、陈皮。中药四气以寒为主,其次是温、平,五味以甘、苦为主,其次是辛,归经以肺经为主,其次是胃经、脾经。按功效分类居于前3位的依次为化痰止咳平喘药、清热药和补虚药。通过关联规则分析可知,浙贝母–黄芩为支持度最高的药物组合。通过聚类分析,得到5个中药组合。结论:中医学认为,重症肺炎为本虚标实之证,治疗以扶正祛邪为准则。临床多见痰热壅肺证,治以清热解毒、宣肺化痰,辅以补气活血、通腑等。临床用药多为化痰止咳平喘药、清热药和补虚药。展开更多
文摘[Objective]Accurate prediction of tomato growth height is crucial for optimizing production environments in smart farming.However,current prediction methods predominantly rely on empirical,mechanistic,or learning-based models that utilize either images data or environmental data.These methods fail to fully leverage multi-modal data to capture the diverse aspects of plant growth comprehensively.[Methods]To address this limitation,a two-stage phenotypic feature extraction(PFE)model based on deep learning algorithm of recurrent neural network(RNN)and long short-term memory(LSTM)was developed.The model integrated environment and plant information to provide a holistic understanding of the growth process,emploied phenotypic and temporal feature extractors to comprehensively capture both types of features,enabled a deeper understanding of the interaction between tomato plants and their environment,ultimately leading to highly accurate predictions of growth height.[Results and Discussions]The experimental results showed the model's ef‐fectiveness:When predicting the next two days based on the past five days,the PFE-based RNN and LSTM models achieved mean absolute percentage error(MAPE)of 0.81%and 0.40%,respectively,which were significantly lower than the 8.00%MAPE of the large language model(LLM)and 6.72%MAPE of the Transformer-based model.In longer-term predictions,the 10-day prediction for 4 days ahead and the 30-day prediction for 12 days ahead,the PFE-RNN model continued to outperform the other two baseline models,with MAPE of 2.66%and 14.05%,respectively.[Conclusions]The proposed method,which leverages phenotypic-temporal collaboration,shows great potential for intelligent,data-driven management of tomato cultivation,making it a promising approach for enhancing the efficiency and precision of smart tomato planting management.
基金supported by the National Natural Science Foundation of China(61371172)the International S&T Cooperation Program of China(2015DFR10220)+1 种基金the Ocean Engineering Project of National Key Laboratory Foundation(1213)the Fundamental Research Funds for the Central Universities(HEUCF1608)
文摘For the multi-mode radar working in the modern electronicbattlefield, different working states of one single radar areprone to being classified as multiple emitters when adoptingtraditional classification methods to process intercepted signals,which has a negative effect on signal classification. A classificationmethod based on spatial data mining is presented to address theabove challenge. Inspired by the idea of spatial data mining, theclassification method applies nuclear field to depicting the distributioninformation of pulse samples in feature space, and digs out thehidden cluster information by analyzing distribution characteristics.In addition, a membership-degree criterion to quantify the correlationamong all classes is established, which ensures classificationaccuracy of signal samples. Numerical experiments show that thepresented method can effectively prevent different working statesof multi-mode emitter from being classified as several emitters,and achieve higher classification accuracy.
基金Project(61374140)supported by the National Natural Science Foundation of China
文摘There are multiple operating modes in the real industrial process, and the collected data follow the complex multimodal distribution, so most traditional process monitoring methods are no longer applicable because their presumptions are that sampled-data should obey the single Gaussian distribution or non-Gaussian distribution. In order to solve these problems, a novel weighted local standardization(WLS) strategy is proposed to standardize the multimodal data, which can eliminate the multi-mode characteristics of the collected data, and normalize them into unimodal data distribution. After detailed analysis of the raised data preprocessing strategy, a new algorithm using WLS strategy with support vector data description(SVDD) is put forward to apply for multi-mode monitoring process. Unlike the strategy of building multiple local models, the developed method only contains a model without the prior knowledge of multi-mode process. To demonstrate the proposed method's validity, it is applied to a numerical example and a Tennessee Eastman(TE) process. Finally, the simulation results show that the WLS strategy is very effective to standardize multimodal data, and the WLS-SVDD monitoring method has great advantages over the traditional SVDD and PCA combined with a local standardization strategy(LNS-PCA) in multi-mode process monitoring.
基金Project(2023JH26-10100002)supported by the Liaoning Science and Technology Major Project,ChinaProjects(U21A20117,52074085)supported by the National Natural Science Foundation of China+1 种基金Project(2022JH2/101300008)supported by the Liaoning Applied Basic Research Program Project,ChinaProject(22567612H)supported by the Hebei Provincial Key Laboratory Performance Subsidy Project,China。
文摘Mill vibration is a common problem in rolling production,which directly affects the thickness accuracy of the strip and may even lead to strip fracture accidents in serious cases.The existing vibration prediction models do not consider the features contained in the data,resulting in limited improvement of model accuracy.To address these challenges,this paper proposes a multi-dimensional multi-modal cold rolling vibration time series prediction model(MDMMVPM)based on the deep fusion of multi-level networks.In the model,the long-term and short-term modal features of multi-dimensional data are considered,and the appropriate prediction algorithms are selected for different data features.Based on the established prediction model,the effects of tension and rolling force on mill vibration are analyzed.Taking the 5th stand of a cold mill in a steel mill as the research object,the innovative model is applied to predict the mill vibration for the first time.The experimental results show that the correlation coefficient(R^(2))of the model proposed in this paper is 92.5%,and the root-mean-square error(RMSE)is 0.0011,which significantly improves the modeling accuracy compared with the existing models.The proposed model is also suitable for the hot rolling process,which provides a new method for the prediction of strip rolling vibration.
基金National Youth Natural Science Foundation of China(No.61806006)Innovation Program for Graduate of Jiangsu Province(No.KYLX160-781)Jiangsu University Superior Discipline Construction Project。
文摘In order to solve difficult detection of far and hard objects due to the sparseness and insufficient semantic information of LiDAR point cloud,a 3D object detection network with multi-modal data adaptive fusion is proposed,which makes use of multi-neighborhood information of voxel and image information.Firstly,design an improved ResNet that maintains the structure information of far and hard objects in low-resolution feature maps,which is more suitable for detection task.Meanwhile,semantema of each image feature map is enhanced by semantic information from all subsequent feature maps.Secondly,extract multi-neighborhood context information with different receptive field sizes to make up for the defect of sparseness of point cloud which improves the ability of voxel features to represent the spatial structure and semantic information of objects.Finally,propose a multi-modal feature adaptive fusion strategy which uses learnable weights to express the contribution of different modal features to the detection task,and voxel attention further enhances the fused feature expression of effective target objects.The experimental results on the KITTI benchmark show that this method outperforms VoxelNet with remarkable margins,i.e.increasing the AP by 8.78%and 5.49%on medium and hard difficulty levels.Meanwhile,our method achieves greater detection performance compared with many mainstream multi-modal methods,i.e.outperforming the AP by 1%compared with that of MVX-Net on medium and hard difficulty levels.
文摘To address the difficulties in fusing multi-mode sensor data for complex industrial machinery, an adaptive deep coupling convolutional auto-encoder (ADCCAE) fusion method was proposed. First, the multi-mode features extracted synchronously by the CCAE were stacked and fed to the multi-channel convolution layers for fusion. Then, the fused data was passed to all connection layers for compression and fed to the Softmax module for classification. Finally, the coupling loss function coefficients and the network parameters were optimized through an adaptive approach using the gray wolf optimization (GWO) algorithm. Experimental comparisons showed that the proposed ADCCAE fusion model was superior to existing models for multi-mode data fusion.
基金supported by National Natural Science Foundation of China(No.62163036).
文摘To improve the traffic scheduling capability in operator data center networks,an analysis prediction and online scheduling mechanism(APOS)is designed,considering both the network structure and the network traffic in the operator data center.Fibonacci tree optimization algorithm(FTO)is embedded into the analysis prediction and the online scheduling stages,the FTO traffic scheduling strategy is proposed.By taking the global optimal and the multi-modal optimization advantage of FTO,the traffic scheduling optimal solution and many suboptimal solutions can be obtained.The experiment results show that the FTO traffic scheduling strategy can schedule traffic in data center networks reasonably,and improve the load balancing in the operator data center network effectively.
基金supported in part by National Natural Science Foundation of China(91860139).
文摘The in-service life of turbine blades directly affects the on-wing lifetime and operating cost of aircraft engines.It would be essential to accurately evaluate the remaining useful life of turbine blades for safe engine operation and reasonable maintenance decision-making.In this paper,a machine learning-based mechanism with multiple information fusion is proposed to predict the remaining useful life of high-pressure turbine blades.The developed method takes account of the in-service operating factors such as the high-pressure rotor speed and exhaust gas temperature,as well as the engine operating environments and performance degradation.The effectiveness of this method is demonstrated on simulated test cases generated by an integrated blade creep-life assessment model,which comprises engine performance,blade stress,thermal,and creep life estimation models.The results show that the proposed method provides a prospective result for in-service life evaluation of turbine blades and is of significance to evaluating the engine on-wing lifetime and making a reasonable maintenance plan.
文摘目的:基于数据挖掘技术探讨内服中药治疗重症肺炎的用药规律。方法:通过全面检索中国知网、万方、维普、中国生物医学文献数据库、PubMed、Web of Science等数据库中的文献,收集自建库至2024年3月15日符合内服中药治疗重症肺炎的相关文献,用Excel软件记录所有中药处方,并进行性味、归经和功效分析,运用R语言进行频次、关联规则和聚类分析,从而探讨内服中药治疗重症肺炎的用药规律。结果:共筛选出中药处方172首,涉及中药173味。进行频次分析,居于前10位的中药依次为甘草、黄芩、苦杏仁、石膏、大黄、茯苓、半夏、桑白皮、桔梗、陈皮。中药四气以寒为主,其次是温、平,五味以甘、苦为主,其次是辛,归经以肺经为主,其次是胃经、脾经。按功效分类居于前3位的依次为化痰止咳平喘药、清热药和补虚药。通过关联规则分析可知,浙贝母–黄芩为支持度最高的药物组合。通过聚类分析,得到5个中药组合。结论:中医学认为,重症肺炎为本虚标实之证,治疗以扶正祛邪为准则。临床多见痰热壅肺证,治以清热解毒、宣肺化痰,辅以补气活血、通腑等。临床用药多为化痰止咳平喘药、清热药和补虚药。
