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A computational compensation method for fabric panels of tensioned membrane structures using a shape optimization method based on gradientless algorithms

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INTERNATIONAL JOURNAL OF SOLIDS AND STRUCTURES 2017年 112卷 16-24页

作者： Dinh, T. D. Rezaei, A. Linthout, T. Mollaert, M. Van Hemelrijck, D. Van Paepegem, W. Univ Ghent Dept Mat Sci & Engn Tech Lane Ghent Sci PkCampus A B-9052 Ghent Belgium Vrije Univ Brussel Dept Architectural Engn Pl Laan 2 B-1050 Brussels Belgium Vrije Univ Brussel Dept Mech Mat & Construct Pl Laan 2 B-1050 Brussels Belgium

Tensioned fabric membrane structures, in which a coated fabric material is used as both cladding and supporting structure, are classified as unconventional structures and demand a dedicated design method. In this paper, we propose a method to combine different stages in design and analysis of these structures within the shape optimization framework. The proposed method facilitates the incorporation of various sources of nonlinearities in the analysis of tensioned membrane structures. Especially, the amount of fabric panels compensation when using simple material model for the coated fabric, i.e., linear orthotropic elasticity, as is widely used in practice, is presented. The novelty of the proposed method lies on the postulation of a stress-free 3-D intermediate configuration, which is built from flat fabric panels. The goal is to find an optimum shape of this 3-D intermediate configuration such that the stress distribution in the resulting structure, which is formed by deforming the 3-D intermediate configuration, remains at desired levels. A nonlinear finite element analysis is employed to identify these stress fields in the resulting structures. In this analysis, material and geometrical nonlinearities are taken into account. Moreover, the nonlinear kinematical interaction between the boundary cables and the membrane is also considered. Hence the stress fields on the resulting structures are expected to be reliable and close to the physical ones. In this work, different gradientless optimization algorithms, viz., genetic algorithm, pattern search, and bayesian, are considered. The obtained results show that the stress levels in the resulting structures obtained from the proposed method are close to the desired ones, and the bayesian optimization algorithm has the best performance among the considered algorithms. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： Tensioned fabric membrane structure Integrated design and analysis Shape optimization bayesian algorithm Sliding cables Computational compensation of fabric panels

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Biohydrogen from food waste: Modeling and estimation by machine learning based super learner approach

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INTERNATIONAL JOURNAL OF HYDROGEN ENERGY 2023年第49期48卷 18586-18600页

作者： Sultana, Nahid Hossain, S. M. Zakir Aljameel, Sumayh S. Omran, M. E. Razzak, S. A. Haq, B. Hossain, M. M. Imam Abdulrahman Bin Faisal Univ Coll Comp Sci & Informat Technol Dept Comp Sci Dammam 31441 Saudi Arabia Univ Bahrain Coll Engn Dept Chem Engn Zallaq 32038 Bahrain King Fahd Univ Petr & Minerals Dept Chem Engn Dhahran Saudi Arabia King Fahd Univ Petr & Minerals Ctr Membranes & Water Secur Dhahran Saudi Arabia King Fahd Univ Petr & Minerals Dept Petr Engn Dhahran Saudi Arabia King Fahd Univ Petr & Minerals Ctr Refining & Adv Chem Dhahran Saudi Arabia

This study demonstrated the application of a hybrid bayesian algorithm (BA) and support vector regression (SVR) as a potential super-learner tool (BA-SVR) to predict biohydrogen production from food waste-originated feedstocks. The novelty of the present approach, as compared to the existing response surface methodology (RSM), includes (i) hybridization of BA with SVR for modeling of biohydrogen production and minimization of biomethane formation, (ii) performance evaluation and comparison of the developed BA-SVR models with the existing RSM models based on the several indicators such as coefficient of determination (R2), relative error (RE), mean absolute error (MAE), mean absolute per-centage error (MAPE), and root mean square error (RMSE), (iii) analysis of the robustness of the model and (iv) testing generalization ability. The calculated values of these indicators suggested that the proposed super leaner models demonstrated better performance pre-dicting the biohydrogen and biomethane (products) responses than those using the existing RSM models -as reported in Rafieenia et al. 2019 [45]. The estimated low errors for biohydrogen: MAE = 0.5919, RMSE = 0.592, MAPE = 11.1387;for biomethane: MAE = 0.2681, RMSE = 0.2688, MAPE = 0.3708, signifie the reliable model predictions. The BA-SVR model also provided high adj R2 (>0.99 for both biohydrogen and biomethane), indicating an excellent fitting of the model. Concerning the MAPE, the proposed BA-SVR models for both the biohydrogen and biomethane responses showed superior performances (as compared to the RSM models) with a performance enhancement of 64.16% and 98.81%, respectively. (c) 2023 Hydrogen Energy Publications LLC. Published by Elsevier Ltd. All rights reserved.

关键词： Biohydrogen Biomethane Food waste bayesian algorithm Support vector regression Machine learning

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A prototype precipitation retrieval algorithm over land using passive microwave observations stratified by surface condition and precipitation vertical structure

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JOURNAL OF GEOPHYSICAL RESEARCH-ATMOSPHERES 2015年第11期120卷 5295-5315页

作者： You, Yalei Wang, Nai-Yu Ferraro, Ralph Univ Maryland Cooperat Inst Climate & Satellite College Pk MD 20742 USA NOAA IMSG STAR NESDIS College Pk MD USA NOAA NESDIS STAR College Pk MD USA

A prototype precipitation retrieval algorithm over land has been developed by utilizing 4year National Mosaic and Multi-Sensor Quantitative Precipitation Estimation and Special Sensor Microwave Imager/Sounder coincident data sets. One of the unique features of this algorithm is using the ancillary parameters (i.e., surface type, surface temperature, land elevation, and ice layer thickness) to stratify the single database into many smaller but more homogeneous databases, in which both the surface condition and precipitation vertical structure are similar. It is found that the probability of detection (POD) increases about 8% and 12% by using stratified databases for rainfall and snowfall detection, respectively. In addition, by considering the relative humidity at lower troposphere and the vertical velocity at 700 hPa in the precipitation detection process, the POD for snowfall detection is further increased by 20.4% from 56.0% to 76.4%. The better result is evident in both ends of the retrieved rain rate when the stratified databases are used, especially when the rain rate is greater than 30 mm/h. Similarly, the retrieved snowfall rate using stratified databases also outperforms that using single database. The correlation between retrieved and observed rain rates from stratified databases is 0.63, while it is 0.42 using the single database. The root-mean-square error is reduced by 50.3% from 2.07 to 0.98 by using stratified databases. The retrieved snow rates from stratified database are also better correlated with observations and possess smaller root-mean-square error. Additionally, the precipitation overestimation from the single database over the western United States is largely mitigated when the stratified databases are utilized. It is further demonstrated that over the majority of the stratified databases, the relationship between precipitation rate and brightness temperature is much closer to that from the corresponding category in the validation databases,

关键词： precipitation retrieval database stratification bayesian algorithm

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Web-Based Biomedical Literature Mining

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Journal of Shanghai Jiaotong university(Science) 2012年第4期17卷 494-499页

作者：安建福薛惠平陈瑛吴建国章鲁 Department of Biomedical Engineering Basic Medical CollegeShanghai Jiaotong University School of Medicine Information and Resource Center Shanghai Jiaotong University School of Medicine Division of Gastroenterology and Hepatology Renji HospitalShanghai Jiaotong University School of Medicine Department of Nuclear Medicine Renji HospitalShanghai Jiaotong University School of Medicine

With an upsurge in biomedical literature,using data-mining method to search new knowledge from literature has drawing more attention of *** this study,taking the mining of non-coding gene literature from the network database of PubMed as an example,we first preprocessed the abstract data,next applied the term occurrence frequency(TF) and inverse document frequency(IDF)(TF-IDF) method to select features,and then established a biomedical literature data-mining model based on bayesian ***,we assessed the model through area under the receiver operating characteristic curve(AUC),accuracy,specificity,sensitivity,precision rate and recall *** 1 000 features are selected,AUC,specificity,sensitivity,accuracy rate,precision rate and recall rate are 0.868 3,84.63%,89.02%,86.83%,89.02% and 98.14%,*** results indicate that our method can identify the targeted literature related to a particular topic effectively.

关键词： bayesian algorithm term occurrence frequency(TF) and inverse document frequency(IDF)(TFIDF) data-mining

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PHENYTOIN DOSAGE PREDICTIONS IN PEDIATRIC-PATIENTS

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CLINICAL PHARMACOKINETICS 1989年第4期16卷 254-260页

作者： YUEN, GJ LATIMER, PT LITTLEFIELD, LC MACKEY, RW OREGON STATE UNIV COLL PHARM CORVALLIS OR 97331 USA UNIV TEXAS COLL PHARM AUSTIN TX 78712 USA UNIV TEXAS HLTH SCI CTR DEPT PEDIAT SAN ANTONIO TX 78284 USA

Phenytoin dosing in paediatric patients is complicated both by alterations in patient requirements due to growth and maturation changes and by the capacity-limited characteristics of phenytoin metabolism. This study examines 2 pharmacokinetic methods to adjust phenytoin dosage based on a single dosing-rate/steady-state serum phenytoin concentration pair. A bayesian forecaster and a fixed parameters [rate of metabolism (Vmax)] method were examined with previously published sets of a priori parameter estimates. The fixed Vmax method was utilised with the parameter derived from native Japanese (method 1), US Caucasian (method 2) and European (method 3) patients. The bayesian forecaster used a priori parameter estimates obtained from native Japanese (method 4) and European (method 5) patients. Each method was examined retrospectively in 34 paediatric patients with a total of 48 predictions possible. Measures of absolute predictability, bias (mean error, % dose) and precision (root mean squared error, % dose), were -3.58/12.2, -1.51/12.2, 4.06/9.96, -4.38/13.2, and -3.10/11.5, for methods 1, 2, 3, 4 and 5, respectively. There was no significant difference among the 5 methods. However, the bayesian algorithm tended to be more robust over a broad range of situations, providing predictions in all cases. The fixed Vmax methods could not provide predictions in every case. Finally, all methods had a significant number of overpredictions of dosage. Poorer results were observed when prediction of steady-state serum concentrations were performed, partly due to the retrospective nature of the study. We conclude that close monitoring of patients, regardless of the method chosen to adjust dosage, is recommended.

关键词： Root Mean SQuared Error bayesian algorithm Mean Error Maximal Metabolic Rate Concentration Pair

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Application of 3-algorithm ANN programming to predict the strength performance of hydrated-lime activated rice husk ash treated soil

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MULTISCALE AND MULTIDISCIPLINARY MODELING EXPERIMENTS AND DESIGN 2021年第4期4卷 259-274页

作者： Onyelowe, Kennedy C. Iqbal, Mudassir Jalal, Fazal E. Onyia, Michael E. Onuoha, Ifeanyichukwu C. Michael Okpara Univ Agr Dept Civil Engn Umudike Nigeria Kampala Int Univ Dept Civil & Mech Engn Kampala Uganda Shangai Jiao Tong Univ Sch Naval Architecture Ocean & Civil Engn Shanghai Key Lab Digital Maintenance Bldg & Infra Shanghai Peoples R China Shanghai Jiao Tong Univ Dept Civil Engn Shanghai Peoples R China Univ Nigeria Fac Engn Dept Civil Engn Nsukka Nigeria Fed Univ Technol Owerri Dept Environm Technol Owerri Nigeria

Artificial neural network (ANN) method has been applied in the present work to predict the California bearing ratio (CBR), unconfined compressive strength (UCS), and resistance value (R) of expansive soil treated with recycled and activated composites of rice husk ash. Pavement foundations suffer from poor design and construction, poor material handling and utilization and management lapses. The evolutions of soft computing techniques have produced various algorithms developed to overcome certain lapses in performance. Three of such algorithms from ANN are Levenberg-Muarquardt Backpropagation (LMBP), bayesian Programming (BP), and Conjugate Gradient (CG) algorithms. In this work, the expansive soil classified as A-7-6 group soil was treated with hydrated-lime activated rice husk ash (HARHA) in varying proportions between 0.1 and 12% by weight of soil at the rate of 0.1% to produce 121 datasets. These were used to predict the behavior of the soil's strength parameters (CBR, UCS and R) utilizing the evolutionary hybrid algorithms of ANN. The predictor parameters were HARHA, liquid limit (w(L)), (plastic limit (w(P)), plasticity index (I-P), optimum moisture content (w(OMC)), clay activity (A(C)), and (maximum dry density (delta(max)). A multiple linear regression (MLR) was also conducted on the datasets in addition to ANN to serve as a check and linear validation mechanism. MLR and ANN methods agreed in terms of performance and fit at the end of computing and iteration. However, the response validation on the predicted models showed a good correlation above 0.9 and a great performance index. Comparatively, the LMBP algorithm yielded an accurate estimation of the results in lesser iterations than the bayesian and the CG algorithms, while the bayesian technique produced the best result with the required number of iterations to minimize the error. And finally, the LMBP algorithm outclassed the other two algorithms in terms of the predicted models' accuracy.

关键词： Soft computing Artificial intelligence Artificial neural network (ANN) Machine learning in geotechnics Back-propagation algorithm Levenberg– muarquardt algorithm bayesian algorithm Conjugate gradient algorithm Sustainable construction materials

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Method for Collision Relationship of Hydraulic Supports Considering Multilayer Sensing Errors

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IEEE SENSORS JOURNAL 2024年第19期24卷 30895-30908页

作者： Wang, Yirong Xie, Jiacheng Zheng, Ziying Wang, Yichen Wang, Xuewen Taiyuan Univ Technol Coll Mech & Vehicle Engn Taiyuan 030024 Shanxi Peoples R China Taiyuan Univ Technol Shanxi Key Lab Fully Mechanized Coal Min Equipment Taiyuan 030024 Shanxi Peoples R China

To address the issues of low accuracy and insufficient information in the pose measurement of hydraulic support caused by complex working conditions, this article proposes a method for pose optimization and collision resolution. First, based on the structure and significance, the pose is divided into "three layers of base pose-one layer of internal pose." A mapping model of each layer's pose and sensing error is established to accurately resolve errors. Then, the bayesian algorithm is employed to infer the single pose under a normal distribution. A multimodel system is constructed through five steps: updating measurement, updating error, prior calculation, bayesian estimation, and pose correction, to optimize the complete pose of support. Subsequently, three highly credible virtual support scenes are constructed. Sensor information is input into the pose optimization scene to generate the pose distribution, which is synchronized to the collision-solving scene to visualize and solve collision probabilities. This probability is then fed back to the monitoring scene for warning judgment. Finally, various conditions are set for pose optimization and collision-solving experiments. The results show that the accuracy of the optimized support pose is enhanced by an average of 11.57% and the collision between supports is accurately simulated. This verifies the accuracy of the theoretical research and the bayesian algorithm. The approach is beneficial to warn the timely abnormal situation warnings, enhancing the reliability of underground support, and providing an important reference to support following control and adjustment.

关键词： Sensors Accuracy Hydraulic systems Bayes methods Monitoring Optimization Nonhomogeneous media bayesian algorithm collision hydraulic support optimization pose error sensor virtual reality (VR)

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Color image segmentation for bladder cancer diagnosis

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MATHEMATICAL AND COMPUTER MODELLING 1998年第2期27卷 103-120页

作者： Chen, CH Lee, JD Wang, JR Mao, CW Chang Gung Univ Dept Elect Engn Tao Yuan 333 Taiwan Natl Cheng Kung Univ Dept Elect Engn Tainan 701 Taiwan

In this paper, a new color image segmentation method is proposed to extract the region of bladder tumor from a color bladder image. In the past, the diagnosis of bladder tumors mainly relies upon cystoscopic examination with an in vivo staining technique. This manner heavily depends on the interpreter's experience and often results in errors in diagnosis. Instead of previous method, we use methylene blue in vivo staining combined with color segmentation techniques to improve the accuracy of the diagnosis of bladder tumors. The segmentation task can be decomposed into two stages. First, cluster detection combined with probabilistic relaxation is used to extract the clusters of specified colors from the HLS color space. Then, in order to refine the chromatic properties, the bayesian algorithm is employed to reject the false region from the clusters obtained in the first stage. Experimental results show that the proposed method can segment the bladder tumor successfully. The technique could serve as an auxiliary tool for doctors or researchers in the diagnosis of bladder tumors.

关键词： color image segmentation bladder tumor diagnosis Probabilistic relaxation bayesian algorithm

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The pharmacokinetics of vancomycin in patients with severe acute pancreatitis

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EUROPEAN JOURNAL OF CLINICAL PHARMACOLOGY 2016年第6期72卷 697-702页

作者： He, Juan Mao, En-Qiang Feng, Jing Jiang, Hui-Ting Yang, Wan-Hua Chen, Er-Zhen Shanghai Jiao Tong Univ Sch Med Ruijin Hosp Dept Pharm 301 Room26 Bldg197 Ruijin 2 Rd Shanghai Peoples R China Shanghai Jiao Tong Univ Sch Med Ruijin Hosp EICU 3 Floor5 Bldg197 Ruijin 2 Rd Shanghai 200030 Peoples R China

The aim of this study was to evaluate the pharmacokinetics of vancomycin in patients with severe acute pancreatitis (SAP). Sixty-seven patients with SAP were included. The FPIA method was used to measure vancomycin serum trough concentrations, and the pharmacokinetic parameters were calculated using the bayesian estimator. Comparisons of mean values were analyzed using SPSS 11.0. The average daily dose of vancomycin was 15.0 +/- 3.7 mg/kg (q 12 h). Sixty-seven trough concentrations were collected. Compared with the recommended standard vancomycin trough concentration (15 mg/L), SAP patients had significantly lower vancomycin trough concentrations (6.1 +/- 3.0 mg/L;p < 0.0001) while the volume of distribution (Vd) and clearance (CL) of vancomycin were significantly increased. Multiple regression analysis revealed that vancomycin trough concentration was strongly correlated not only with age and albumin but also with the duration from SAP onset to vancomycin therapy (p < 0.0001). Stepwise regression analysis revealed that the duration was the most important variable for vancomycin trough concentration (r (2) = 0.456). The relationships between vancomycin trough concentrations and the duration were further evaluated after the 67 patients were stratified into two groups according to the duration from SAP onset to vancomycin therapeutic drug monitoring (TDM) within or over 4 weeks. Early group had much lower trough concentrations compared to late group, and the CL was also significantly increased in the early group. Of these 67 patients, 24 patients made vancomycin dosage adjustment (increased to 18.5 +/- 3.9 mg/kg, q 12 h) and the average trough concentrations increased to 12.6 +/- 3.8 mg/L. The serum trough concentration of vancomycin was significantly reduced in SAP patients. Higher dosage regimens are needed to ensure the clinical effect.

关键词： Vancomycin Severe acute pancreatitis Trough concentration Pharmacokinetics bayesian algorithm Clinical individualization

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Predicting the Law: Artificial Intelligence Findings from the IMF's Central Bank Legislation Database

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COMPUTATIONAL ECONOMICS 2024年 1-31页

作者： Alajmi, Khaled Deodoro, Jose Khan, Ashraf Moriya, Kei Int Monetary Fund Washington DC 20431 USA

Using the 2010, 2015, and 2020/2021 datasets of the IMF's Central Bank Legislation Database (CBLD), we explore artificial intelligence (AI) and machine learning (ML) approaches to analyzing patterns in central bank legislation. Our findings highlight that: (i) a simple Na & iuml;ve Bayes algorithm can link CBLD search categories with a significant and increasing level of accuracy to specific articles and phrases in articles in laws (i.e., predict search classification);(ii) specific patterns or themes emerge across central bank legislation (most notably, on central bank governance, central bank policy and operations, and central bank stakeholders and transparency);and (iii) other AI/ML approaches yield interesting results, meriting further research.

关键词： Central bank legislation Central banking Artificial intelligence Machine learning bayesian algorithm Boolean algorithm Central bank governance Law and economics E58 K00

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