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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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Online machine learning for stream wastewater influent flow rate prediction under unprecedented emergencies

《环境科学与工程前沿（英文）》 2023年第17卷第12期 doi: 10.1007/s11783-023-1752-7

摘要：

● Online learning models accurately predict influent flow rate at wastewater plants.

关键词： Wastewater prediction Data stream Online learning Batch learning Influent flow rates

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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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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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Dynamic prediction of moving trajectory in pipe jacking: GRU-based deep learning framework

《结构与土木工程前沿（英文）》 doi: 10.1007/s11709-023-0942-5

摘要： The moving trajectory of the pipe-jacking machine (PJM), which primarily determines the end quality of jacked tunnels, must be controlled strictly during the entire jacking process. Developing prediction models to support drivers in performing rectifications in advance can effectively avoid considerable trajectory deviations from the designed jacking axis. Hence, a gated recurrent unit (GRU)-based deep learning framework is proposed herein to dynamically predict the moving trajectory of the PJM. In this framework, operational data are first extracted from a data acquisition system; subsequently, they are preprocessed and used to establish GRU-based multivariate multistep-ahead direct prediction models. To verify the performance of the proposed framework, a case study of a large pipe-jacking project in Shanghai and comparisons with other conventional models (i.e., long short-term memory (LSTM) network and recurrent neural network (RNN)) are conducted. In addition, the effects of the activation function and input time-step length on the prediction performance of the proposed framework are investigated and discussed. The results show that the proposed framework can dynamically and precisely predict the PJM moving trajectory during the pipe-jacking process, with a minimum mean absolute error and root mean squared error (RMSE) of 0.1904 and 0.5011 mm, respectively. The RMSE of the GRU-based models is lower than those of the LSTM- and RNN-based models by 21.46% and 46.40% at the maximum, respectively. The proposed framework is expected to provide an effective decision support for moving trajectory control and serve as a foundation for the application of deep learning in the automatic control of pipe jacking.

关键词： dynamic prediction moving trajectory pipe jacking GRU deep learning

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

《机械工程前沿（英文）》 2021年第16卷第4期页码 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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Ensemble unit and AI techniques for prediction of rock strain

Pradeep T; Pijush SAMUI; Navid KARDANI; Panagiotis G ASTERIS

《结构与土木工程前沿（英文）》 2022年第16卷第7期页码 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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GLOBAL GENOMIC PREDICTION IN HORTICULTURAL CROPS: PROMISES, PROGRESS, CHALLENGES AND OUTLOOK

Craig HARDNER, Satish KUMAR, Dorrie MAIN, Cameron PEACE

《农业科学与工程前沿（英文）》页码 353-355 doi: 10.15302/J-FASE-2021387

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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

Online machine learning for stream wastewater influent flow rate prediction under unprecedented emergencies

期刊论文

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

期刊论文

Reliability prediction and its validation for nuclear power units in service

Jinyuan SHI,Yong WANG

期刊论文

Dynamic prediction of moving trajectory in pipe jacking: GRU-based deep learning framework

期刊论文

Trend prediction technology of condition maintenance for large water injection units

Xiaoli XU, Sanpeng DENG

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

Prediction of the shear wave velocity

Amoroso SARA

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

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

期刊论文

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

期刊论文

Ensemble unit and AI techniques for prediction of rock strain

Pradeep T; Pijush SAMUI; Navid KARDANI; Panagiotis G ASTERIS

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

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

Craig HARDNER, Satish KUMAR, Dorrie MAIN, Cameron PEACE

期刊论文

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