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Hybrid deep learning model for risk prediction of fracture in patients with diabetes and osteoporosis

《医学前沿（英文）》 2022年 第16卷 第3期   页码 496-506 doi: 10.1007/s11684-021-0828-7

摘要： The fracture risk of patients with diabetes is higher than those of patients without diabetes due to hyperglycemia, usage of diabetes drugs, changes in insulin levels, and excretion, and this risk begins as early as adolescence. Many factors including demographic data (such as age, height, weight, and gender), medical history (such as smoking, drinking, and menopause), and examination (such as bone mineral density, blood routine, and urine routine) may be related to bone metabolism in patients with diabetes. However, most of the existing methods are qualitative assessments and do not consider the interactions of the physiological factors of humans. In addition, the fracture risk of patients with diabetes and osteoporosis has not been further studied previously. In this paper, a hybrid model combining XGBoost with deep neural network is used to predict the fracture risk of patients with diabetes and osteoporosis, and investigate the effect of patients’ physiological factors on fracture risk. A total of 147 raw input features are considered in our model. The presented model is compared with several benchmarks based on various metrics to prove its effectiveness. Moreover, the top 18 influencing factors of fracture risks of patients with diabetes are determined.

关键词： XGBoost     deep neural network     healthcare     risk prediction    

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Liquefaction prediction using support vector machine model based on cone penetration data

Pijush SAMUI

《结构与土木工程前沿（英文）》 2013年 第7卷 第1期   页码 72-82 doi: 10.1007/s11709-013-0185-y

摘要： A support vector machine (SVM) model has been developed for the prediction of liquefaction susceptibility as a classification problem, which is an imperative task in earthquake engineering. This paper examines the potential of SVM model in prediction of liquefaction using actual field cone penetration test (CPT) data from the 1999 Chi-Chi, Taiwan earthquake. The SVM, a novel learning machine based on statistical theory, uses structural risk minimization (SRM) induction principle to minimize the error. Using cone resistance ( ) and cyclic stress ratio ( ), model has been developed for prediction of liquefaction using SVM. Further an attempt has been made to simplify the model, requiring only two parameters ( and maximum horizontal acceleration ), for prediction of liquefaction. Further, developed SVM model has been applied to different case histories available globally and the results obtained confirm the capability of SVM model. For Chi-Chi earthquake, the model predicts with accuracy of 100%, and in the case of global data, SVM model predicts with accuracy of 89%. The effect of capacity factor ( ) on number of support vector and model accuracy has also been investigated. The study shows that SVM can be used as a practical tool for prediction of liquefaction potential, based on field CPT data.

关键词： earthquake     cone penetration test     liquefaction     support vector machine (SVM)     prediction    

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A novel hybrid model for water quality prediction based on VMD and IGOA optimized for LSTM

《环境科学与工程前沿（英文）》 2023年 第17卷 第7期 doi: 10.1007/s11783-023-1688-y

摘要：

● A novel VMD-IGOA-LSTM model has proposed for the prediction of water quality.

关键词： Water quality prediction     Grasshopper optimization algorithm     Variational mode decomposition     Long short-term memory neural network    

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A 3D sliced-soil–beam model for settlement prediction of tunnelling using the pipe roofing method in

《结构与土木工程前沿（英文）》 2023年 第17卷 第12期   页码 1934-1948 doi: 10.1007/s11709-023-0038-2

摘要： The pipe roofing method is widely used in tunnel construction because it can realize a flexible section shape and a large section area of the tunnel, especially under good ground conditions. However, the pipe roofing method has rarely been applied in soft ground, where the prediction and control of the ground settlement play important roles. This study proposes a sliced-soil–beam (SSB) model to predict the settlement of ground due to tunnelling using the pipe roofing method in soft ground. The model comprises a sliced-soil module based on the virtual work principle and a beam module based on structural mechanics. As part of this work, the Peck formula was modified for a square-section tunnel and adopted to construct a deformation mechanism of soft ground. The pipe roofing system was simplified to a three-dimensional Winkler beam to consider the interaction between the soil and pipe roofing. The model was verified in a case study conducted in Shanghai, China, in which it provided the efficient and accurate prediction of settlement. Finally, the parameters affecting the ground settlement were analyzed. It was clarified that the stiffness of the excavated soil and the steel support are the key factors in reducing ground settlement.

关键词： pipe roofing method     soft ground     numerical simulation     settlement prediction     simplified calculation     parametric analysis    

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A hybrid spatial-temporal deep learning prediction model of industrial methanol-to-olefins process

《化学科学与工程前沿（英文）》 2024年 第18卷 第4期 doi: 10.1007/s11705-024-2403-7

摘要： Methanol-to-olefins, as a promising non-oil pathway for the synthesis of light olefins, has been successfully industrialized. The accurate prediction of process variables can yield significant benefits for advanced process control and optimization. The challenge of this task is underscored by the failure of traditional methods in capturing the complex characteristics of industrial processes, such as high nonlinearities, dynamics, and data distribution shift caused by diverse operating conditions. In this paper, we propose a novel hybrid spatial-temporal deep learning prediction model to address these issues. Firstly, a unique data normalization technique called reversible instance normalization is employed to solve the problem of different data distributions. Subsequently, convolutional neural network integrated with the self-attention mechanism are utilized to extract the temporal patterns. Meanwhile, a multi-graph convolutional network is leveraged to model the spatial interactions. Afterward, the extracted temporal and spatial features are fused as input into a fully connected neural network to complete the prediction. Finally, the outputs are denormalized to obtain the ultimate results. The monitoring results of the dynamic trends of process variables in an actual industrial methanol-to-olefins process demonstrate that our model not only achieves superior prediction performance but also can reveal complex spatial-temporal relationships using the learned attention matrices and adjacency matrices, making the model more interpretable. Lastly, this model is deployed onto an end-to-end Industrial Internet Platform, which achieves effective practical results.

关键词： methanol-to-olefins     process variables prediction     spatial-temporal     self-attention mechanism     graph convolutional network    

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Improved analytical model for residual stress prediction in orthogonal cutting

null

《机械工程前沿（英文）》 2014年 第9卷 第3期   页码 249-256 doi: 10.1007/s11465-014-0310-1

摘要：

The analytical model of residual stress in orthogonal cutting proposed by Jiann is an important tool for residual stress prediction in orthogonal cutting. In application of the model, a problem of low precision of the surface residual stress prediction is found. By theoretical analysis, several shortages of Jiann’s model are picked out, including: inappropriate boundary conditions, unreasonable calculation method of thermal stress, ignorance of stress constraint and cyclic loading algorithm. These shortages may directly lead to the low precision of the surface residual stress prediction. To eliminate these shortages and make the prediction more accurate, an improved model is proposed. In this model, a new contact boundary condition between tool and workpiece is used to make it in accord with the real cutting process; an improved calculation method of thermal stress is adopted; a stress constraint is added according to the volume-constancy of plastic deformation; and the accumulative effect of the stresses during cyclic loading is considered. At last, an experiment for measuring residual stress in cutting AISI 1045 steel is conducted. Also, Jiann’s model and the improved model are simulated under the same conditions with cutting experiment. The comparisons show that the surface residual stresses predicted by the improved model is closer to the experimental results than the results predicted by Jiann’s model.

关键词： residual stress     analytical model     orthogonal cutting     cutting force     cutting temperature    

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Fracture model for the prediction of the electrical percolation threshold in CNTs/Polymer composites

Yang SHEN, Pengfei HE, Xiaoying ZHUANG

《结构与土木工程前沿（英文）》 2018年 第12卷 第1期   页码 125-136 doi: 10.1007/s11709-017-0396-8

摘要： In this paper, we propose a 3D stochastic model to predict the percolation threshold and the effective electric conductivity of CNTs/Polymer composites. We consider the tunneling effect in our model so that the unrealistic interpenetration can be avoided in the identification of the conductive paths between the CNTs inside the polymer. The results are shown to be in good agreement with reported experimental data.

关键词： electrical percolation     CNTs/Polymer composites     fracture model     electric conductivity     tunnelling effects    

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Enhanced wear prediction of tunnel boring machine disc cutters for accurate remaining useful life estimationusing a hybrid model

《结构与土木工程前沿（英文）》 2024年 第18卷 第4期   页码 642-662 doi: 10.1007/s11709-024-1058-2

摘要： In tunnel construction with tunnel boring machines (TBMs), accurate prediction of the remaining useful life (RUL) of disc cutters is critical for timely maintenance and replacement to avoid delays and cost overruns. This paper introduces a novel hybrid model, integrating fundamental and data-driven approaches, to enhance wear prediction of TBM disc cutters and enable accurate RUL estimation. The fundamental model is improved by incorporating composite wear mechanisms and load estimation techniques, showcasing superior prediction accuracy compared to single-mechanism models. Additionally, the hybrid model innovatively incorporates a data-driven supplementary residual term into the improved fundamental model, leading to a high-performance wear prediction model. Using actual field data from a highway tunnel project in Shenzhen, the performance of the hybrid model is rigorously tested and compared with pure fundamental and data-driven models. The hybrid model outperforms the other models, achieving the highest accuracy in predicting TBM disc cutter wear (mean absolute error (MAE) = 0.53, root mean square error (RMSE) = 0.64). Furthermore, this study thoroughly analyzes the hybrid model’s generalization capability, revealing significant impacts of geological conditions on prediction accuracy. The model’s generalization capability is also improved by expanding and updating the data sets. The RUL estimation results provided by the hybrid model are straightforward and effective, making it a valuable tool by which construction staff can monitor TBM disc cutters.

关键词： tunnel boring machine     disc cutter     wear prediction     remaining useful life     field data     hybrid model    

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Performance prediction of switched reluctance generator with time average and small signal models

Jyoti KOUJALAGI, B. UMAMAHESWARI, R. ARUMUGAM

《能源前沿（英文）》 2013年 第7卷 第1期   页码 56-68 doi: 10.1007/s11708-012-0216-8

摘要： This paper presents the complete mathematical model and predicts the performance of switched reluctance generator with time average and small signal models. The complete mathematical model is developed in three stages. First, a switching model is developed based on quasi-linear inductance profile. Next, based on the switching behaviour, a time average model is obtained to measure the difference between the excitation and generation time in each switching cycle. Finally, to track control voltage and current wave shapes, a small signal model is designed. The effectiveness of the complete multilevel model combining electrical machine, power converter, load and control with programming language is demonstrated through simulations. A PI controller is used for controlling the voltage of the generator. The results presented show that the controller exhibits accurate tracking control of load voltage under different operating conditions. This demonstrates that the proposed model is able to perform an accurate control of the generated output voltage even in transient situations. The simulation is performed to choose the control parameters and study the performance of switched reluctance generator prior to its actual implementation. Initial experimental results are presented using NI-Data acquisition card to control the output power according to load requirements.

关键词： generator     reluctance     switching model     small signal model     time average model    

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Vibration-based crack prediction on a beam model using hybrid butterfly optimization algorithm with artificial

Abdelwahhab KHATIR; Roberto CAPOZUCCA; Samir KHATIR; Erica MAGAGNINI

《结构与土木工程前沿（英文）》 2022年 第16卷 第8期   页码 976-989 doi: 10.1007/s11709-022-0840-2

摘要： Vibration-based damage detection methods have become widely used because of their advantages over traditional methods. This paper presents a new approach to identify the crack depth in steel beam structures based on vibration analysis using the Finite Element Method (FEM) and Artificial Neural Network (ANN) combined with Butterfly Optimization Algorithm (BOA). ANN is quite successful in such identification issues, but it has some limitations, such as reduction of error after system training is complete, which means the output does not provide optimal results. This paper improves ANN training after introducing BOA as a hybrid model (BOA-ANN). Natural frequencies are used as input parameters and crack depth as output. The data are collected from improved FEM using simulation tools (ABAQUS) based on different crack depths and locations as the first stage. Next, data are collected from experimental analysis of cracked beams based on different crack depths and locations to test the reliability of the presented technique. The proposed approach, compared to other methods, can predict crack depth with improved accuracy.

关键词： damage prediction     ANN     BOA     FEM     experimental modal analysis    

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基于修正Anderson 模型的冲击载荷下地基振动响应预测方法

房波

《中国工程科学》 2014年 第16卷 第11期   页码 96-102

摘要：

提出了一个预测潜在冲击载荷下振动效应的理论模型与现场实测相结合的综合预测方法。通过一系列具有针对性的室外重锤冲击振动试验，以及现场实测数据对Anderson 模型进行了验证并修正，然后利用修正的Anderson 模型预测冲击荷载的振动效应。将预测结果和现场试验结果进行对比分析，结果表明：预测结果与实测结果吻合较好。

关键词： 预测方法     冲击载荷     振动效应     Anderson 模型    

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Geological risk prediction under uncertainty in tunnel excavation using online learning and hidden Markovmodel

《工程管理前沿（英文）》 doi: 10.1007/s42524-024-0082-1

摘要： The accurate estimation of geological risks is essential for preventing geohazards, and ensuring efficient and safe construction processes. This study proposes a method, the online hidden Markov model (OHMM), which combines online learning with the hidden Markov model to estimate geological risks. The OHMM is tailored for the continuous nature of observational data, allowing it to adaptively update with each new piece of data. To address the challenge of limited data in the early stages of construction, we use pre-construction borehole samples as additional data. This approach extends the short sequence of observed data to match the length of a complete sequence through an observation extension mechanism. The effectiveness of the OHMM, equipped with this observation extension mechanism, is demonstrated in a case study that models geological risks for a tunnel excavation project in Singapore. The OHMM outperforms traditional methods, including the hidden Markov model, long short-term memory network, neural network, and support vector machine, in predicting geological risks ahead of the tunnel boring machine. Notably, the OHMM can accurately forecast geological risks in areas yet to be constructed, using limited observational and site investigation data. This research advances geological risk prediction models by offering an online updating capability for tunnel excavation and construction projects. It enables early-stage risk prediction and provides long-term forecasts with minimal historical data requirements, maximizing the use of site investigation data.

关键词： geological risk prediction     machine learning     online learning     hidden Markov model     borehole logging    

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A hybrid deep learning model for robust prediction of the dimensional accuracy in precision milling of

《机械工程前沿（英文）》 2022年 第17卷 第3期 doi: 10.1007/s11465-022-0688-0

摘要： The use of artificial intelligence to process sensor data and predict the dimensional accuracy of machined parts is of great interest to the manufacturing community and can facilitate the intelligent production of many key engineering components. In this study, we develop a predictive model of the dimensional accuracy for precision milling of thin-walled structural components. The aim is to classify three typical features of a structural component—squares, slots, and holes—into various categories based on their dimensional errors (i.e., “high precision,” “pass,” and “unqualified”). Two different types of classification schemes have been considered in this study: those that perform feature extraction by using the convolutional neural networks and those based on an explicit feature extraction procedure. The classification accuracy of the popular machine learning methods has been evaluated in comparison with the proposed deep learning model. Based on the experimental data collected during the milling experiments, the proposed model proved to be capable of predicting dimensional accuracy using cutting parameters (i.e., “static features”) and cutting-force data (i.e., “dynamic features”). The average classification accuracy obtained using the proposed deep learning model was 9.55% higher than the best machine learning algorithm considered in this paper. Moreover, the robustness of the hybrid model has been studied by considering the white Gaussian and coherent noises. Hence, the proposed hybrid model provides an efficient way of fusing different sources of process data and can be adopted for prediction of the machining quality in noisy environments.

关键词： precision milling     dimensional accuracy     cutting force     convolutional neural networks     coherent noise    

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Two-phase early prediction method for remaining useful life of lithium-ion batteries based on a neural

《能源前沿（英文）》 doi: 10.1007/s11708-023-0906-4

摘要： Lithium-ion batteries (LIBs) are widely used in transportation, energy storage, and other fields. The prediction of the remaining useful life (RUL) of lithium batteries not only provides a reference for health management but also serves as a basis for assessing the residual value of the battery. In order to improve the prediction accuracy of the RUL of LIBs, a two-phase RUL early prediction method combining neural network and Gaussian process regression (GPR) is proposed. In the initial phase, the features related to the capacity degradation of LIBs are utilized to train the neural network model, which is used to predict the initial cycle lifetime of 124 LIBs. The Pearson coefficient’s two most significant characteristic factors and the predicted normalized lifetime form a 3D space. The Euclidean distance between the test dataset and each cell in the training dataset and validation dataset is calculated, and the shortest distance is considered to have a similar degradation pattern, which is used to determine the initial Dual Exponential Model (DEM). In the second phase, GPR uses the DEM as the initial parameter to predict each test set’s early RUL (ERUL). By testing four batteries under different working conditions, the RMSE of all capacity estimation is less than 1.2%, and the accuracy percentage (AP) of remaining life prediction is more than 98%. Experiments show that the method does not need human intervention and has high prediction accuracy.

关键词： lithium-ion batteries     RUL prediction     double exponential model     neural network     Gaussian process regression (GPR)    

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Regional seismic-damage prediction of buildings under mainshock–aftershock sequence

Xinzheng LU, Qingle CHENG, Zhen XU, Chen XIONG

《工程管理前沿（英文）》 2021年 第8卷 第1期   页码 122-134 doi: 10.1007/s42524-019-0072-x

摘要： Strong aftershocks generally occur following a significant earthquake. Aftershocks further damage buildings weakened by mainshocks. Thus, the accurate and efficient prediction of aftershock-induced damage to buildings on a regional scale is crucial for decision making for post-earthquake rescue and emergency response. A framework to predict regional seismic damage of buildings under a mainshock–aftershock (MS–AS) sequence is proposed in this study based on city-scale nonlinear time-history analysis (THA). Specifically, an MS–AS sequence-generation method is proposed to generate a potential MS–AS sequence that can account for the amplification, spectrum, duration, magnitude, and site condition of a target area. Moreover, city-scale nonlinear THA is adopted to predict building seismic damage subjected to MS–AS sequences. The accuracy and reliability of city-scale nonlinear THA for an MS–AS sequence are validated by as-recorded seismic responses of buildings and simulation results in published literature. The town of Longtoushan, which was damaged during the Ludian earthquake, is used as a case study to illustrate the detailed procedure and advantages of the proposed framework. The primary conclusions are as follows. (1) Regional seismic damage of buildings under an MS–AS sequence can be predicted reasonably and accurately by city-scale nonlinear THA. (2) An MS–AS sequence can be generated reasonably by the proposed MS–AS sequence-generation method. (3) Regional seismic damage of buildings under different MS–AS scenarios can be provided efficiently by the proposed framework, which in turn can provide a useful reference for earthquake emergency response and scientific decision making for earthquake disaster relief.

关键词： regional seismic damage prediction     city-scale nonlinear time-history analysis     mainshock–aftershock sequence     multiple degree-of-freedom (MDOF) model     2014 Ludian earthquake    

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标题 作者 时间 类型 操作

Hybrid deep learning model for risk prediction of fracture in patients with diabetes and osteoporosis

期刊论文

Liquefaction prediction using support vector machine model based on cone penetration data

Pijush SAMUI

期刊论文

A novel hybrid model for water quality prediction based on VMD and IGOA optimized for LSTM

期刊论文

A 3D sliced-soil–beam model for settlement prediction of tunnelling using the pipe roofing method in

期刊论文

A hybrid spatial-temporal deep learning prediction model of industrial methanol-to-olefins process

期刊论文

Improved analytical model for residual stress prediction in orthogonal cutting

null

期刊论文

Fracture model for the prediction of the electrical percolation threshold in CNTs/Polymer composites

Yang SHEN, Pengfei HE, Xiaoying ZHUANG

期刊论文

Enhanced wear prediction of tunnel boring machine disc cutters for accurate remaining useful life estimationusing a hybrid model

期刊论文

Performance prediction of switched reluctance generator with time average and small signal models

Jyoti KOUJALAGI, B. UMAMAHESWARI, R. ARUMUGAM

期刊论文

Vibration-based crack prediction on a beam model using hybrid butterfly optimization algorithm with artificial

Abdelwahhab KHATIR; Roberto CAPOZUCCA; Samir KHATIR; Erica MAGAGNINI

期刊论文

基于修正Anderson 模型的冲击载荷下地基振动响应预测方法

房波

期刊论文

Geological risk prediction under uncertainty in tunnel excavation using online learning and hidden Markovmodel

期刊论文

A hybrid deep learning model for robust prediction of the dimensional accuracy in precision milling of

期刊论文

Two-phase early prediction method for remaining useful life of lithium-ion batteries based on a neural

期刊论文

Regional seismic-damage prediction of buildings under mainshock–aftershock sequence

Xinzheng LU, Qingle CHENG, Zhen XU, Chen XIONG

期刊论文