[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
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
目的:构建基于人工智能的高血压性脑出血医疗文本信息自动识别系统,快速识别和分析患者临床信息,高效地输出正确的诊疗方案。方法:基于国内外最新高血压性脑出血诊疗指南,经多位高年资神经外科医生和专业人工智能团队共同讨论,构建基于...目的:构建基于人工智能的高血压性脑出血医疗文本信息自动识别系统,快速识别和分析患者临床信息,高效地输出正确的诊疗方案。方法:基于国内外最新高血压性脑出血诊疗指南,经多位高年资神经外科医生和专业人工智能团队共同讨论,构建基于语言表征模型和专家模块的高血压性脑出血医疗文本信息自动识别及决策系统(即H系统)。随后将收集到的高血压性脑出血病例分为训练集、测试集和验证集,以数据库中病例的真实治疗方案为金标准,先总体评价H系统的准确性,再将其与神经外科医生进行对比,分析H系统的判读效率。结果:在测试集中,H系统所输出的治疗方案的准确率为94.0%(91.5%~96.5%),特异度为91.8%(86.3%~97.3%),灵敏度为95.5%(89.3%~98.2%),曲线下面积(area under the curve,AUC)值为0.936(0.922~0.950)(P=0.000);在验证集中,H系统所输出的治疗方案的准确率为93.3%(89.5%~97.1%),特异度为89.9%(83.4%~96.4%),灵敏度为95.8%(92.3%~99.3%),AUC值为0.928(0.891~0.966)(P=0.000)。在处理同样的70例病例时,H系统用时(334.60±4.46)s,而神经外科医生用时(12 550.28±95.45)s;在50 min内,H系统处理的病例数为(383±3)例,而神经外科医生处理的病例数为(11±4)例。结论:本研究所构建的H系统能够对高血压性脑出血患者的急诊病例进行自动识别和分析,并快速输出准确的诊疗方案,可协助医生对高血压脑出血进行急诊诊疗。展开更多
Experimentation data of perspex glass sheet cutting, using CO2 laser, with missing values were modelled with semi-supervised artificial neural networks. Factorial design of experiment was selected for the verification...Experimentation data of perspex glass sheet cutting, using CO2 laser, with missing values were modelled with semi-supervised artificial neural networks. Factorial design of experiment was selected for the verification of orthogonal array based model prediction. It shows improvement in modelling of edge quality and kerf width by applying semi-supervised learning algorithm, based on novel error assessment on simulations. The results are expected to depict better prediction on average by utilizing the systematic randomized techniques to initialize the neural network weights and increase the number of initialization. Missing values handling is difficult with statistical tools and supervised learning techniques; on the other hand, semi-supervised learning generates better results with the smallest datasets even with missing values.展开更多
Taking advantage of the new standard HTML5,we designed an online tool called a browser/server-based glaucoma image database builder(BGIDB)for the demarcation of the optic disk and cup’s ellipse-like boundaries.The B-...Taking advantage of the new standard HTML5,we designed an online tool called a browser/server-based glaucoma image database builder(BGIDB)for the demarcation of the optic disk and cup’s ellipse-like boundaries.The B-spline interpolation algorithm is used,and a specially designed algorithm is proposed for classifying the disease grade according to the disc damage likelihood scale criterion,which is correlated strongly with the glaucoma process by quantity.This tool exhibits the best performance with a low overlapping error of 4.34%for the optic disk demarcation and 8.31%for the optic cup demarcation.It also has preferable time-consuming as compared to other tools and is a cross-platform system.This tool has already been utilized in building the ophthalmic image database in the cooperation of Center for Ophthalmic Imaging Research and The Second Xiangya Hospital.展开更多
The traditional prediction methods of element yield rate can be divided into experience method and data-driven method.But in practice,the experience formulae are found to work only under some specific conditions,and t...The traditional prediction methods of element yield rate can be divided into experience method and data-driven method.But in practice,the experience formulae are found to work only under some specific conditions,and the sample data that are used to establish data-driven models are always insufficient.Aiming at this problem,a combined method of genetic algorithm(GA) and adaptive neuro-fuzzy inference system(ANFIS) is proposed and applied to element yield rate prediction in ladle furnace(LF).In order to get rid of the over reliance upon data in data-driven method and act as a supplement of inadequate samples,smelting experience is integrated into prediction model as fuzzy empirical rules by using the improved ANFIS method.For facilitating the combination of fuzzy rules,feature construction method based on GA is used to reduce input dimension,and the selection operation in GA is improved to speed up the convergence rate and to avoid trapping into local optima.The experimental and practical testing results show that the proposed method is more accurate than other prediction methods.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.
文摘目的:构建基于人工智能的高血压性脑出血医疗文本信息自动识别系统,快速识别和分析患者临床信息,高效地输出正确的诊疗方案。方法:基于国内外最新高血压性脑出血诊疗指南,经多位高年资神经外科医生和专业人工智能团队共同讨论,构建基于语言表征模型和专家模块的高血压性脑出血医疗文本信息自动识别及决策系统(即H系统)。随后将收集到的高血压性脑出血病例分为训练集、测试集和验证集,以数据库中病例的真实治疗方案为金标准,先总体评价H系统的准确性,再将其与神经外科医生进行对比,分析H系统的判读效率。结果:在测试集中,H系统所输出的治疗方案的准确率为94.0%(91.5%~96.5%),特异度为91.8%(86.3%~97.3%),灵敏度为95.5%(89.3%~98.2%),曲线下面积(area under the curve,AUC)值为0.936(0.922~0.950)(P=0.000);在验证集中,H系统所输出的治疗方案的准确率为93.3%(89.5%~97.1%),特异度为89.9%(83.4%~96.4%),灵敏度为95.8%(92.3%~99.3%),AUC值为0.928(0.891~0.966)(P=0.000)。在处理同样的70例病例时,H系统用时(334.60±4.46)s,而神经外科医生用时(12 550.28±95.45)s;在50 min内,H系统处理的病例数为(383±3)例,而神经外科医生处理的病例数为(11±4)例。结论:本研究所构建的H系统能够对高血压性脑出血患者的急诊病例进行自动识别和分析,并快速输出准确的诊疗方案,可协助医生对高血压脑出血进行急诊诊疗。
文摘Experimentation data of perspex glass sheet cutting, using CO2 laser, with missing values were modelled with semi-supervised artificial neural networks. Factorial design of experiment was selected for the verification of orthogonal array based model prediction. It shows improvement in modelling of edge quality and kerf width by applying semi-supervised learning algorithm, based on novel error assessment on simulations. The results are expected to depict better prediction on average by utilizing the systematic randomized techniques to initialize the neural network weights and increase the number of initialization. Missing values handling is difficult with statistical tools and supervised learning techniques; on the other hand, semi-supervised learning generates better results with the smallest datasets even with missing values.
基金Projects(61672542,61573380)supported by the National Natural Science Foundation of ChinaProject(2016zzts055)supported by Fundamental Research Funds for the Central Universities,China
文摘Taking advantage of the new standard HTML5,we designed an online tool called a browser/server-based glaucoma image database builder(BGIDB)for the demarcation of the optic disk and cup’s ellipse-like boundaries.The B-spline interpolation algorithm is used,and a specially designed algorithm is proposed for classifying the disease grade according to the disc damage likelihood scale criterion,which is correlated strongly with the glaucoma process by quantity.This tool exhibits the best performance with a low overlapping error of 4.34%for the optic disk demarcation and 8.31%for the optic cup demarcation.It also has preferable time-consuming as compared to other tools and is a cross-platform system.This tool has already been utilized in building the ophthalmic image database in the cooperation of Center for Ophthalmic Imaging Research and The Second Xiangya Hospital.
基金Projects(2007AA041401,2007AA04Z194) supported by the National High Technology Research and Development Program of China
文摘The traditional prediction methods of element yield rate can be divided into experience method and data-driven method.But in practice,the experience formulae are found to work only under some specific conditions,and the sample data that are used to establish data-driven models are always insufficient.Aiming at this problem,a combined method of genetic algorithm(GA) and adaptive neuro-fuzzy inference system(ANFIS) is proposed and applied to element yield rate prediction in ladle furnace(LF).In order to get rid of the over reliance upon data in data-driven method and act as a supplement of inadequate samples,smelting experience is integrated into prediction model as fuzzy empirical rules by using the improved ANFIS method.For facilitating the combination of fuzzy rules,feature construction method based on GA is used to reduce input dimension,and the selection operation in GA is improved to speed up the convergence rate and to avoid trapping into local optima.The experimental and practical testing results show that the proposed method is more accurate than other prediction methods.
