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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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Reliability prediction and its validation for nuclear power units in service

Jinyuan SHI,Yong WANG

《能源前沿（英文）》 2016年 第10卷 第4期   页码 479-488 doi: 10.1007/s11708-016-0425-7

摘要： In this paper a novel method for reliability prediction and validation of nuclear power units in service is proposed. The equivalent availability factor is used to measure the reliability, and the equivalent availability factor deducting planed outage hours from period hours and maintenance factor are used for the measurement of inherent reliability. By statistical analysis of historical reliability data, the statistical maintenance factor and the undetermined parameter in its numerical model can be determined. The numerical model based on the maintenance factor predicts the equivalent availability factor deducting planed outage hours from period hours, and the planed outage factor can be obtained by using the planned maintenance days. Using these factors, the equivalent availability factor of nuclear power units in the following 3 years can be obtained. Besides, the equivalent availability factor can be predicted by using the historical statistics of planed outage factor and the predicted equivalent availability factor deducting planed outage hours from period hours. The accuracy of the reliability prediction can be evaluated according to the comparison between the predicted and statistical equivalent availability factors. Furthermore, the reliability prediction method is validated using the nuclear power units in North American Electric Reliability Council (NERC) and China. It is found that the relative errors of the predicted equivalent availability factors for nuclear power units of NERC and China are in the range of –2.16% to 5.23% and –2.15% to 3.71%, respectively. The method proposed can effectively predict the reliability index in the following 3 years, thus providing effective reliability management and maintenance optimization methods for nuclear power units.

关键词： nuclear power units in service     reliability     reliability prediction     equivalent availability factors    

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Position-varying surface roughness prediction method considering compensated acceleration in milling

《机械工程前沿（英文）》 2021年 第16卷 第4期   页码 855-867 doi: 10.1007/s11465-021-0649-z

摘要： Machined surface roughness will affect parts’ service performance. Thus, predicting it in the machining is important to avoid rejects. Surface roughness will be affected by system position dependent vibration even under constant parameter with certain toolpath processing in the finishing. Aiming at surface roughness prediction in the machining process, this paper proposes a position-varying surface roughness prediction method based on compensated acceleration by using regression analysis. To reduce the stochastic error of measuring the machined surface profile height, the surface area is repeatedly measured three times, and Pauta criterion is adopted to eliminate abnormal points. The actual vibration state at any processing position is obtained through the single-point monitoring acceleration compensation model. Seven acceleration features are extracted, and valley, which has the highest R-square proving the effectiveness of the filtering features, is selected as the input of the prediction model by mutual information coefficients. Finally, by comparing the measured and predicted surface roughness curves, they have the same trends, with the average error of 16.28% and the minimum error of 0.16%. Moreover, the prediction curve matches and agrees well with the actual surface state, which verifies the accuracy and reliability of the model.

关键词： surface roughness prediction     compensated acceleration     milling     thin-walled workpiece    

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Trend prediction technology of condition maintenance for large water injection units

Xiaoli XU, Sanpeng DENG

《机械工程前沿（英文）》 2010年 第5卷 第2期   页码 171-175 doi: 10.1007/s11465-009-0091-0

摘要： Trend prediction technology is the key technology to achieve condition-based maintenance of mechanical equipment. Large-sized water injection units are key equipment in oilfields. The traditional preventive maintenance is not economical and cannot completely avoid vicious accidents. To ensure the normal operation of units and save maintenance costs, trend prediction technology is studied to achieve condition-based maintenance for water injection units. The main methods of the technology are given, the trend prediction method based on neural network is put forward, and the expert system based on the knowledge is developed. The industrial site verification shows that the proposed trend prediction technology can reflect the operating condition trend change of the water injection units and provide technical means to achieve condition-based predictive maintenance.

关键词： water injection units     condition-based maintenance     trend prediction    

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An energy consumption prediction approach of die casting machines driven by product parameters

《机械工程前沿（英文）》   页码 868-886 doi: 10.1007/s11465-021-0656-0

摘要： Die casting machines, which are the core equipment of the machinery manufacturing industry, consume great amounts of energy. The energy consumption prediction of die casting machines can support energy consumption quota, process parameter energy-saving optimization, energy-saving design, and energy efficiency evaluation; thus, it is of great significance for Industry 4.0 and green manufacturing. Nevertheless, due to the uncertainty and complexity of the energy consumption in die casting machines, there is still a lack of an approach for energy consumption prediction that can provide support for process parameter optimization and product design taking energy efficiency into consideration. To fill this gap, this paper proposes an energy consumption prediction approach for die casting machines driven by product parameters. Firstly, the system boundary of energy consumption prediction is defined, and subsequently, based on the energy consumption characteristics analysis, a theoretical energy consumption model is established. Consequently, a systematic energy consumption prediction approach for die casting machines, involving product, die, equipment, and process parameters, is proposed. Finally, the feasibility and reliability of the proposed energy consumption prediction approach are verified with the help of three die casting machines and six types of products. The results show that the prediction accuracy of production time and energy consumption reached 91.64% and 85.55%, respectively. Overall, the proposed approach can be used for the energy consumption prediction of different die casting machines with different products.

关键词： die casting machine     energy consumption prediction     product parameters    

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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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Machine learning-based solubility prediction and methodology evaluation of active pharmaceutical ingredients

《化学科学与工程前沿（英文）》 2022年 第16卷 第4期   页码 523-535 doi: 10.1007/s11705-021-2083-5

摘要： Solubility has been widely regarded as a fundamental property of small molecule drugs and drug candidates, as it has a profound impact on the crystallization process. Solubility prediction, as an alternative to experiments which can reduce waste and improve crystallization process efficiency, has attracted increasing attention. However, there are still many urgent challenges thus far. Herein we used seven descriptors based on understanding dissolution behavior to establish two solubility prediction models by machine learning algorithms. The solubility data of 120 active pharmaceutical ingredients (APIs) in ethanol were considered in the prediction models, which were constructed by random decision forests and artificial neural network with optimized data structure and model accuracy. Furthermore, a comparison with traditional prediction methods including the modified solubility equation and the quantitative structure-property relationships model was carried out. The highest accuracy shown by the testing set proves that the ML models have the best solubility prediction ability. Multiple linear regression and stepwise regression were used to further investigate the critical factor in determining solubility value. The results revealed that the API properties and the solute-solvent interaction both provide a nonnegligible contribution to the solubility value.

关键词： solubility prediction     machine learning     artificial neural network     random decision forests    

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

《结构与土木工程前沿（英文）》   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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The prediction of adsorption isotherms of ester vapors on hypercrosslinked polymeric adsorbent

Liuyan WU,Lijuan JIA,Xiaohan LIU,Chao LONG

《环境科学与工程前沿（英文）》 2016年 第10卷 第3期   页码 482-490 doi: 10.1007/s11783-015-0826-6

摘要： Adsorption isotherms of methyl acetate, ethyl acetate, propyl acetate, isopropyl acetate and ethyl propionate on hypercrosslinked polymeric resin (ND-100) were measured at 303K, 318K and 333K,respectively, and well fitted by Dubinin–Astakhov (DA) equation. The plots of the adsorbed volume ( ) versus the adsorption potential ( ) at three different temperatures all fell basically onto one single curve for every ester. A predicted model based on DA equation was obtained on the basis of adsorption equilibrium data of methyl acetate, ethyl acetate and ethyl propionate at 318K. The model equation successfully predicted the adsorption isotherms of methyl acetate, ethyl acetate and ethyl propionate on ND-100 at 303K, and 333K, and also gave accurate predictive results for adsorption isotherms of the other two ester compounds (propyl acetate and isopropyl acetate) on ND-100 at 303K, 318K and 333K. The results proved the effectiveness of DA model for predicting the adsorption isotherms of ester compounds onto ND-100. In addition, the relationship between physico-chemical properties of adsorbates and their adsorption properties was also investigated. The results showed that molecular weight, molar volume and molar polarizability had good linear correlations with the parameter (which represents adsorption characteristic energy) of DA equation.

关键词： hypercrosslinked polymeric adsorbent     adsorption isotherm     ester     prediction    

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Prediction of the shear wave velocity

Amoroso SARA

《结构与土木工程前沿（英文）》 2014年 第8卷 第1期   页码 83-92 doi: 10.1007/s11709-013-0234-6

摘要： The paper examines the correlations to obtain rough estimates of the shear wave velocity from non-seismic dilatometer tests (DMT) and cone penetration tests (CPT). While the direct measurement of is obviously preferable, these correlations may turn out useful in various circumstances. The experimental results at six international research sites suggest that the DMT predictions of from the parameters (material index), (horizontal stress index), (constrained modulus) are more reliable and consistent than the CPT predictions from (cone resistance), presumably because of the availability, by DMT, of the stress history index .

关键词： horizontal stress index     shear wave velocity     flat dilatometer test     cone penetration test    

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GLOBAL GENOMIC PREDICTION IN HORTICULTURAL CROPS: PROMISES, PROGRESS, CHALLENGES AND OUTLOOK

《农业科学与工程前沿（英文）》 2021年 第8卷 第2期

摘要：

Horticultural crops are a major source of high value nutritious food, and new improved cultivars developed through breeding are required for sustainable production in the face of abiotic and biotic stresses, and to deliver novel, premium products to consumers. However, grower confidence in the performance of new germplasm, particularly across environmental variability, is important for commercial adoption and germplasm-environment matching to optimize production.

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Ensemble unit and AI techniques for prediction of rock strain

《结构与土木工程前沿（英文）》   页码 858-870 doi: 10.1007/s11709-022-0831-3

摘要： The behavior of rock masses is influenced by a variety of forces, with measurement of stress and strain playing the most critical roles in assessing deformation. The laboratory test for determining strain at each location within rock samples is expensive and difficult but rock strain data are important for predicting failure of rock material. Many researchers employ AI technology in order to solve these difficulties. AI algorithms such as gradient boosting machine (GBM), support vector regression (SVR), random forest (RF), and group method of data handling (GMDH) are used to efficiently estimate the strain at every point within a rock sample. Additionally, the ensemble unit (EnU) may be utilized to evaluate rock strain. In this study, 3000 experimental data are used for the purpose of prediction. The obtained strain values are then evaluated using various statistical parameters and compared to each other using EnU. Ranking analysis, stress-strain curve, Young’s modulus, Poisson’s ratio, actual vs. predicted curve, error matrix and the Akaike’s information criterion (AIC) values are used for comparing models. The GBM model achieved 98.16% and 99.98% prediction accuracy (in terms of values of R2) in the longitudinal and lateral dimensions, respectively, during the testing phase. The GBM model, based on the experimental data, has the potential to be a new option for engineers to use when assessing rock strain.

关键词： prediction     strain     ensemble unit     rank analysis     error matrix    

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我国地震预测成绩的回顾与统计

高建国

《中国工程科学》 2009年 第11卷 第6期   页码 129-131

摘要：

地震预测预报的难度大，但并非了无痕迹或不可知，我国每次较大的地震都有案例总结。统计表明，近４０ 年来，有７７ 次地震在发生前均有中期、短期甚至临震预测，不能因为汶川地震预测的失败，就全面抹杀中国的地震预报成绩，即我国的地震预测成绩是应予肯定的，汶川地震也并非是无前兆的“怪震”。

关键词： 地震     地震预测     中国近30 多年的地震预报统计    

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Digital-Twin-Enhanced Quality Prediction for the Composite Materials Article

Yucheng Wang, Fei Tao, Ying Zuo, Meng Zhang, Qinglin Qi

《工程（英文）》 2023年 第22卷 第3期   页码 23-33 doi: 10.1016/j.eng.2022.08.019

摘要：

Composite materials are widely used in many fields due to their excellent properties. Quality defects in composite materials can lead to lower quality components, creating potential risk of accidents. Experimental and simulation methods are commonly used to predict the quality of composite materials. However, it is difficult to predict the quality of composite materials accurately due to the uncertain curing environment and incomplete feature space. To address this problem, a digital twin (DT) visual model of a composite material is first constructed. Then, a static autoclave DT virtual model is coupled with a variable composite material DT virtual model to construct a model of the curing process. Features are added to the proposed model by generating simulated data to enhance the quality prediction. An extreme learning machine (ELM) for quality prediction is trained with the generated data. Finally, the effectiveness of the proposed method is verified through result analysis.

关键词： Digital twin     Quality prediction     Composites     Coupling models    

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Dimensionality reduction and prediction of soil consolidation coefficient using random forest coupling

《结构与土木工程前沿（英文）》   页码 224-238 doi: 10.1007/s11709-022-0812-6

摘要： The consolidation coefficient of soil (Cv) is a crucial parameter used for the design of structures leaned on soft soi. In general, the Cv is determined experimentally in the laboratory. However, the experimental tests are time-consuming as well as expensive. Therefore, researchers tried several ways to determine Cv via other simple soil parameters. In this study, we developed a hybrid model of Random Forest coupling with a Relief algorithm (RF-RL) to predict the Cv of soil. To conduct this study, a database of soil parameters collected from a case study region in Vietnam was used for modeling. The performance of the proposed models was assessed via statistical indicators, namely Coefficient of determination (R2), Root Mean Squared Error (RMSE), and Mean Absolute Error (MAE). The proposal models were constructed with four sets of soil variables, including 6, 7, 8, and 13 inputs. The results revealed that all models performed well with a high performance (R2 > 0.980). Although the RF-RL model with 13 variables has the highest prediction accuracy ( R2 = 0.9869), the difference compared with other models was negligible (i.e., R2 = 0.9824, 0.9850, 0.9825 for the cases with 6, 7, 8 inputs, respectively). Thus, it can be concluded that the hybrid model of RF-RL can be employed to predict Cv based on the basic soil parameters.

关键词： soil consolidation coefficient     machine learning     random forest     Relief    

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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

Reliability prediction and its validation for nuclear power units in service

Jinyuan SHI,Yong WANG

期刊论文

Position-varying surface roughness prediction method considering compensated acceleration in milling

期刊论文

Trend prediction technology of condition maintenance for large water injection units

Xiaoli XU, Sanpeng DENG

期刊论文

An energy consumption prediction approach of die casting machines driven by product parameters

期刊论文

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

Pijush SAMUI

期刊论文

Machine learning-based solubility prediction and methodology evaluation of active pharmaceutical ingredients

期刊论文

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

期刊论文

The prediction of adsorption isotherms of ester vapors on hypercrosslinked polymeric adsorbent

Liuyan WU,Lijuan JIA,Xiaohan LIU,Chao LONG

期刊论文

Prediction of the shear wave velocity

Amoroso SARA

期刊论文

GLOBAL GENOMIC PREDICTION IN HORTICULTURAL CROPS: PROMISES, PROGRESS, CHALLENGES AND OUTLOOK

期刊论文

Ensemble unit and AI techniques for prediction of rock strain

期刊论文

我国地震预测成绩的回顾与统计

高建国

期刊论文

Digital-Twin-Enhanced Quality Prediction for the Composite Materials

Yucheng Wang, Fei Tao, Ying Zuo, Meng Zhang, Qinglin Qi

期刊论文

Dimensionality reduction and prediction of soil consolidation coefficient using random forest coupling

期刊论文