资源类型

期刊论文 549

年份

2024 1

2023 44

2022 47

2021 42

2020 29

2019 23

2018 21

2017 35

2016 24

2015 29

2014 30

2013 25

2012 18

2011 15

2010 20

2009 43

2008 15

2007 21

2006 16

2005 10

展开 ︾

关键词

影响因素 4

神经网络 4

地震预测 3

安全系数 3

预测 3

COVID-19 2

SARS 2

人工智能 2

储层预测 2

机器学习 2

热释放速率 2

1)模型 1

2型糖尿病 1

Anderson 模型 1

BNCT医院中子照射器 1

CO2 捕集 1

CPU-GPU异构；多核；共享内存；访存调度 1

Colebrook隐式方程 1

GDP 1

展开 ︾

检索范围：

排序： 展示方式：

Modeling, simulation, and prediction of global energy indices: a differential approach

Stephen Ndubuisi NNAMCHI, Onyinyechi Adanma NNAMCHI, Janice Desire BUSINGYE, Maxwell Azubuike IJOMAH, Philip Ikechi OBASI

《能源前沿（英文）》 2022年 第16卷 第2期   页码 375-392 doi: 10.1007/s11708-021-0723-6

摘要： Modeling, simulation, and prediction of global energy indices remain veritable tools for econometric, engineering, analysis, and prediction of energy indices. Thus, this paper differentially modeled, simulated, and non-differentially predicated the global energy indices. The state-of-the-art of the research includes normalization of energy indices, generation of differential rate terms, and regression of rate terms against energy indices to generate coefficients and unexplained terms. On imposition of initial conditions, the solution to the system of linear differential equations was realized in a Matlab environment. There was a strong agreement between the simulated and the field data. The exact solutions are ideal for interpolative prediction of historic data. Furthermore, the simulated data were upgraded for extrapolative prediction of energy indices by introducing an innovative model, which is the synergy of deflated and inflated prediction factors. The innovative model yielded a trendy prediction data for energy consumption, gross domestic product, carbon dioxide emission and human development index. However, the oil price was untrendy, which could be attributed to odd circumstances. Moreover, the sensitivity of the differential rate terms was instrumental in discovering the overwhelming effect of independent indices on the dependent index. Clearly, this paper has accomplished interpolative and extrapolative prediction of energy indices and equally recommends for further investigation of the untrendy nature of oil price.

关键词： energy indices     differential model     normalization     simulation     inflation/deflation     predictive factor and prediction rate    

HTML PDF 收藏

Abnormal glycosylated hemoglobin as a predictive factor for glucose metabolism disorders in antipsychotic

XU Leping, JI Juying, DUAN Yiyang, SHI Hui, ZHANG Bin, SHAO Yaqin, SUN Jian

《医学前沿（英文）》 2007年 第1卷 第3期   页码 316-319 doi: 10.1007/s11684-007-0061-z

摘要： The aim of this study was to observe the changes in glucose metabolism after antipsychotic (APS) therapy, to note the influencing factors, as well as to discuss the relationship between the occurrence of glucose metabolism disorders of APS origin and abnormal glycosylated hemoglobin (HbAc) levels. One hundred and fifty-two patients with schizophrenia, whose fasting plasma glucose (FPG) and 2-h plasma glucose (2hPG) in the oral glucose tolerance test (2HPG) were normal, were grouped according to the HbAc levels, one normal and the other abnormal, and were randomly enrolled into risperidone, clozapine and chlorpromazine treatment for six weeks. The FPG and 2hPG were measured at the baseline and at the end of the study. In the group with abnormal HbA1c and clozapine therapy, 2HPG was higher after the study [(9.5±1.8) mmol/L] than that before the study [(7.2±1.4) mmol/L] and the difference was statistically significant (〈0.01). FPG had no statistically significant difference before and after the study in any group (〉0.05). HbAc levels and drugs contributing to 2HPG at the end of study had statistical cross-action (〈0.01). In the abnormal HbAc group, 2HPG after the study was higher in the clozapine treatment group [(9.5±1.8) mmol/L] than in the risperidone treatment group [(7.4±1.7) mmol/L] and the chlorpromazine treatment group [(7.3±1.6) mmol/L]. The differences were statistically significant (〈0.01). In the normal HbAc group there was no statistically significant difference before and after the study in any group (〉0.05). 2HPG before [(7.1±1.6) mmol/L] and after the study [(8.1±1.9) mmol/L] was higher in the abnormal HbAc group than in the normal HbAc group [(6.2±1.4) mmol/L (6.5±1.4) mmol/L] with the difference being statistically significant (〈0.01 〈0.001). As compared with normal HbAc group, the relative risk (RR) of glucose metabolism disease occurrence was 4.7 in the abnormal HbAc group with the difference being statistically significant (〈0.001). Patients with abnormal HbAc are more likely to have a higher risk of having glucose metabolism disorders after APS treatment.

关键词： significant difference     occurrence     hemoglobin     risperidone treatment     abnormal    

HTML PDF 收藏

Realtime prediction of hard rock TBM advance rate using temporal convolutional network (TCN) with tunnel

Zaobao LIU; Yongchen WANG; Long LI; Xingli FANG; Junze WANG

《结构与土木工程前沿（英文）》 2022年 第16卷 第4期   页码 401-413 doi: 10.1007/s11709-022-0823-3

摘要： Real-time dynamic adjustment of the tunnel bore machine (TBM) advance rate according to the rock-machine interaction parameters is of great significance to the adaptability of TBM and its efficiency in construction. This paper proposes a real-time predictive model of TBM advance rate using the temporal convolutional network (TCN), based on TBM construction big data. The prediction model was built using an experimental database, containing 235 data sets, established from the construction data from the Jilin Water-Diversion Tunnel Project in China. The TBM operating parameters, including total thrust, cutterhead rotation, cutterhead torque and penetration rate, are selected as the input parameters of the model. The TCN model is found outperforming the recurrent neural network (RNN) and long short-term memory (LSTM) model in predicting the TBM advance rate with much smaller values of mean absolute percentage error than the latter two. The penetration rate and cutterhead torque of the current moment have significant influence on the TBM advance rate of the next moment. On the contrary, the influence of the cutterhead rotation and total thrust is moderate. The work provides a new concept of real-time prediction of the TBM performance for highly efficient tunnel construction.

关键词： hard rock tunnel     tunnel bore machine advance rate prediction     temporal convolutional networks     soft computing     construction big data    

HTML PDF 收藏

Short-term prediction of influent flow rate and ammonia concentration in municipal wastewater treatment

Shuai MA, Siyu ZENG, Xin DONG, Jining CHEN, Gustaf OLSSON

《环境科学与工程前沿（英文）》 2014年 第8卷 第1期   页码 128-136 doi: 10.1007/s11783-013-0598-9

摘要： The prediction of the influent load is of great importance for the improvement of the control system to a large wastewater treatment plant. A systematic data analysis method is presented in this paper in order to estimate and predict the periodicity of the influent flow rate and ammonia (NH ) concentrations: 1) data filtering using wavelet decomposition and reconstruction; 2) typical cycle identification using power spectrum density analysis; 3) fitting and prediction model establishment based on an autoregressive model. To give meaningful information for feedforward control systems, predictions in different time scales are tested to compare the corresponding predicting accuracy. Considering the influence of the rainfalls, a linear fitting model is derived to estimate the relationship between flow rate trend and rain events. Measurements used to support coefficient fitting and model testing are acquired from two municipal wastewater treatment plants in China. The results show that 1) for both of the two plants, the periodicity affects the flow rate and NH concentrations in different cycles (especially cycles longer than 1 day); 2) when the flow rate and NH concentrations present an obvious periodicity, the decreasing of prediction accuracy is not distinct with increasing of the prediction time scales; 3) the periodicity influence is larger than rainfalls; 4) the rainfalls will make the periodicity of flow rate less obvious in intensive rainy periods.

关键词： influent load prediction     wavelet de-noising     power spectrum density     autoregressive model     time-frequency analysis     wastewater treatment    

HTML PDF 收藏

Evaluation and prediction of slope stability using machine learning approaches

《结构与土木工程前沿（英文）》 2021年 第15卷 第4期   页码 821-833 doi: 10.1007/s11709-021-0742-8

摘要： In this paper, the machine learning (ML) model is built for slope stability evaluation and meets the high precision and rapidity requirements in slope engineering. Different ML methods for the factor of safety (FOS) prediction are studied and compared hoping to make the best use of the large variety of existing statistical and ML regression methods collected. The data set of this study includes six characteristics, namely unit weight, cohesion, internal friction angle, slope angle, slope height, and pore water pressure ratio. The whole ML model is primarily divided into data preprocessing, outlier processing, and model evaluation. In the data preprocessing, the duplicated data are first removed, then the outliers are filtered by the LocalOutlierFactor method and finally, the data are standardized. 11 ML methods are evaluated for their ability to learn the FOS based on different input parameter combinations. By analyzing the evaluation indicators R 2, MAE, and MSE of these methods, SVM, GBR, and Bagging are considered to be the best regression methods. The performance and reliability of the nonlinear regression method are slightly better than that of the linear regression method. Also, the SVM-poly method is used to analyze the susceptibility of slope parameters.

关键词： slope stability     factor of safety     regression     machine learning     repeated cross-validation    

HTML PDF 收藏

The predictive value of chromosome 8p deletion for metastasis of hepatocellular carcinoma: a summary

QIN Lunxiu, TANG Zhaoyou, GUAN Xinyuan, YE Qinghai, JIA Huliang, REN Ning

《医学前沿（英文）》 2008年 第2卷 第3期   页码 211-215 doi: 10.1007/s11684-008-0041-y

摘要： Hepatocellular carcinoma (HCC) represents an extremely poor prognostic cancer, which is mainly due to the high frequency of metastasis/recurrence after surgical operation. Exploring the molecular mechanisms involved in HCC metastasis could be helpful in the prediction and early diagnosis of HCC recurrence and could also provide new therapeutic targets for HCC metastasis. In the recent decade, we analyzed the genomic aberrations of the clinical specimens, as well as the metastatic models and cell lines of human HCC to identify the genetic markers related to HCC metastasis and to verify their clinical values in the prediction and control of metastasis of HCC. Using the comparative genomic hybridization (CGH) technique, we compared the differences of chromosomal aberrations between primary HCC tumors and their matched metastatic lesions, and found that chromosome 8p deletions might contribute to HCC metastasis. This novel finding was further confirmed by comparison between nude mice models of HCC with different metastatic potentials. By the more sensitive genome-wide microsatellite analysis, 8p deletion was defined to 8p23.3 and 8p11.2, which are two likely regions harboring metastasis-related genes of HCC. Using ‘8p-specific’ microarrays, two novel metastatic suppressors ( and ) were identified, and were proven to suppress invasion and metastasis of HCC. Clinical studies indicate that 8p deletion detected in HCC or circulating plasma DNA of patients is a useful predictor for metastatic recurrence and prognosis, even for patients with early stage HCC. These novel findings are regarded as important advances in the study of the molecular mechanisms of HCC metastasis, which provide not only a holistic view on the molecular cytogenetic bases of HCC metastasis, but also candidate regions for further study to identify metastatic suppressor genes.

关键词： sensitive genome-wide     prediction     genome-wide microsatellite     frequency     Hepatocellular carcinoma    

HTML PDF 收藏

Unconfined compressive strength prediction of soils stabilized using artificial neural networks and support

Alireza TABARSA, Nima LATIFI, Abdolreza OSOULI, Younes BAGHERI

《结构与土木工程前沿（英文）》 2021年 第15卷 第2期   页码 520-536 doi: 10.1007/s11709-021-0689-9

摘要： This study aims to improve the unconfined compressive strength of soils using additives as well as by predicting the strength behavior of stabilized soils using two artificial-intelligence-based models. The soils used in this study are stabilized using various combinations of cement, lime, and rice husk ash. To predict the results of unconfined compressive strength tests conducted on soils, a comprehensive laboratory dataset comprising 137 soil specimens treated with different combinations of cement, lime, and rice husk ash is used. Two artificial-intelligence-based models including artificial neural networks and support vector machines are used comparatively to predict the strength characteristics of soils treated with cement, lime, and rice husk ash under different conditions. The suggested models predicted the unconfined compressive strength of soils accurately and can be introduced as reliable predictive models in geotechnical engineering. This study demonstrates the better performance of support vector machines in predicting the strength of the investigated soils compared with artificial neural networks. The type of kernel function used in support vector machine models contributed positively to the performance of the proposed models. Moreover, based on sensitivity analysis results, it is discovered that cement and lime contents impose more prominent effects on the unconfined compressive strength values of the investigated soils compared with the other parameters.

关键词： unconfined compressive strength     artificial neural network     support vector machine     predictive models     regression    

HTML PDF 收藏

Heterogeneous length-of-stay modeling of post-acute care residents in the nursing home with competing discharge dispositions

《工程管理前沿（英文）》   页码 577-591 doi: 10.1007/s42524-022-0203-7

摘要： Post-acute care (PAC) residents in nursing homes (NHs) are recently hospitalized patients with medically complex diagnoses, ranging from severe orthopedic injuries to cardiovascular diseases. A major role of NHs is to maximize restoration of PAC residents during their NH stays with desirable discharge outcomes, such as higher community discharge likelihood and lower re/hospitalization risk. Accurate prediction of the PAC residents’ length-of-stay (LOS) with multiple discharge dispositions (e.g., community discharge and re/hospitalization) will allow NH management groups to stratify NH residents based on their individualized risk in realizing personalized and resident-centered NH care delivery. Due to the highly heterogeneous health conditions of PAC residents and their multiple types of correlated discharge dispositions, developing an accurate prediction model becomes challenging. Existing predictive analytics methods, such as distribution-/regression-based methods and machine learning methods, either fail to incorporate varied individual characteristics comprehensively or ignore multiple discharge dispositions. In this work, a data-driven predictive analytics approach is considered to jointly predict the individualized re/hospitalization risk and community discharge likelihood over time in the presence of varied residents’ characteristics. A sampling algorithm is further developed to generate accurate predictive samples for a heterogeneous population of PAC residents in an NH and facilitate facility-level performance evaluation. A real case study using large-scale NH data is provided to demonstrate the superior prediction performance of the proposed work at individual and facility levels through comprehensive comparison with a large number of existing prediction methods as benchmarks. The developed analytics tools will allow NH management groups to identify the most at-risk residents by providing them with more proactive and focused care to improve resident outcomes.

关键词： nursing home     predictive analytics     individualized prediction     competing risks     health outcomes    

HTML PDF 收藏

Elliptical lobe shape gerotor pump design to minimize wear

Mohammad Reza KARAMOOZ RAVARI

《机械工程前沿（英文）》 2011年 第6卷 第4期   页码 429-434 doi: 10.1007/s11465-011-0247-6

摘要：

The gerotor pumps are the most important parts of mechanical equipment that have a vast number of applications in industries and automobiles. Because the gerotor pumps cannot be adjusted for wear so it is important to reduce the wear as much as possible. In this paper first mathematical equations for elliptical lobe shape rotors profile and curvature of them have been derived and then Specific flow and wear rate proportional factor (WRPF) have been formulated. To reach the minimum wear in rotors teeth, the ellipse shape factor is changed for each value of number of outer rotor teeth in a feasible range and wear rate proportional factor has been resulted. Also in order to have better comparison specific flow has been presented. The obtained results have been compared with circular pumps with similar geometrical parameters and show the significant improvement in wear of the rotors with negligible changes in the specific flow.

关键词： gerotor pump     elliptical lobe shape pump     wear rate proportional factor (WRPF)     specific flow    

HTML PDF 收藏

面向维修的机械系统可靠度预测与仿真研究

黄良沛,尹喜云,岳文辉

《中国工程科学》 2007年 第9卷 第12期   页码 69-74

摘要： 机电产品在服役期间因零件失效而产生故障，重组维修破坏了原有的系统可靠性模型，因而需要对设备可靠性问题重新进行研究和评价。基于机电系统中零件的失效时间分布密度函数，研究了在重组维护过程中机电系统服役期间零件年龄结构的分布规律，发展了机电系统可靠性数学模型。通过仿真研究，探讨了系统服役期间年龄结构、可靠度和失效率的发展规律，定量地研究了失效时间分布密度函数的参数对系统可靠度的影响。这对于评估机械系统的可靠性和全生命周期的失效率，制定合理的维修策略具有重要意义。

关键词： 重组维修；可靠度预测；年龄分布；失效率    

HTML PDF 收藏

Real-time mobile robot teleoperation via Internet based on predictive control

WANG Shihua, XU Bugong, WANG Shihua, ZHOU Yeming, LIU YunHui

《机械工程前沿（英文）》 2008年 第3卷 第3期   页码 299-306 doi: 10.1007/s11465-008-0049-7

摘要： A remote control system that can control a mobile robot in real time via the internet is proposed. To compensate for the network delay and counteract its impact on the teleoperation system, a predictive control scheme based on the modified Smith predictor proposed is selected. To ensure the stability and transparency of the system, a dynamic model manager is designed based on the information exchange between the sensors at the master and slave sides. To precisely predict the time delay, a new timer synchronization algorithm is proposed. To decrease delay- jitter, a new data buffer scheme is performed. Force feedback and a virtual predictive display are introduced to enhance the real-time efficiency of teleoperation. The usefulness and effectiveness of the proposed method and system are proven by teleoperation experiments via the internet over a long distance.

关键词： predictive     feedback     virtual predictive     distance     synchronization algorithm    

HTML PDF 收藏

Multi-timescale optimization scheduling of interconnected data centers based on model predictive control

《能源前沿（英文）》 doi: 10.1007/s11708-023-0912-6

摘要： With the promotion of “dual carbon” strategy, data center (DC) access to high-penetration renewable energy sources (RESs) has become a trend in the industry. However, the uncertainty of RES poses challenges to the safe and stable operation of DCs and power grids. In this paper, a multi-timescale optimal scheduling model is established for interconnected data centers (IDCs) based on model predictive control (MPC), including day-ahead optimization, intraday rolling optimization, and intraday real-time correction. The day-ahead optimization stage aims at the lowest operating cost, the rolling optimization stage aims at the lowest intraday economic cost, and the real-time correction aims at the lowest power fluctuation, eliminating the impact of prediction errors through coordinated multi-timescale optimization. The simulation results show that the economic loss is reduced by 19.6%, and the power fluctuation is decreased by 15.23%.

关键词： model predictive control     interconnected data center     multi-timescale     optimized scheduling     distributed power supply     landscape uncertainty    

HTML PDF 收藏

The CatMath: an online predictive platform for thermal + electrocatalysis

《化学科学与工程前沿（英文）》 2023年 第17卷 第12期   页码 2156-2160 doi: 10.1007/s11705-023-2371-3

摘要： The catalytic volcano activity models are the quantified and visualized tools of the Sabatier principle for heterogeneous catalysis, which can depict the intrinsic activity optima and trends of a catalytic reaction as a function of the reaction descriptors, i.e., the bonding strengths of key reaction species. These models can be derived by microkinetic modeling and/or free energy changes in combination with the scaling relations among the reaction intermediates. Herein, we introduce the CatMath—an online platform for generating a variety of common and industrially important thermal + electrocatalysis. With the CatMath, users can request the volcano models for available reactions and analyze their materials of interests as potential catalysts. Besides, the CatMath provides the function of the online generation of Surface Pourbaix Diagram for surface state analysis under electrocatalytic conditions, which is an essential step before analyzing the activity of an electrocatalytic surface. All the model generation and analysis processes are realized by cloud computing via a user-friendly interface.

关键词： CatMath     catalysis     volcano activity plots     Surface Pourbaix Diagrams     online platform    

HTML PDF 收藏

Extended model predictive control scheme for smooth path following of autonomous vehicles

《机械工程前沿（英文）》 2022年 第17卷 第1期   页码 4-4 doi: 10.1007/s11465-021-0660-4

摘要： This paper presents an extended model predictive control (MPC) scheme for implementing optimal path following of autonomous vehicles, which has multiple constraints and an integrated model of vehicle and road dynamics. Road curvature and inclination factors are used in the construction of the vehicle dynamic model to describe its lateral and roll dynamics accurately. Sideslip, rollover, and vehicle envelopes are used as multiple constraints in the MPC controller formulation. Then, an extended MPC method solved by differential evolution optimization algorithm is proposed to realize optimal smooth path following based on driving path features. Finally, simulation and real experiments are carried out to evaluate the feasibility and the effectiveness of the extended MPC scheme. Results indicate that the proposed method can obtain the smooth transition to follow the optimal drivable path and satisfy the lateral dynamic stability and environmental constraints, which can improve the path following quality for better ride comfort and road availability of autonomous vehicles.

关键词： autonomous vehicles     vehicle dynamic modeling     model predictive control     path following     optimization algorithm    

HTML PDF 收藏

machine-learning-based system identification of dynamical systems under control: application towards the model predictive

《化学科学与工程前沿（英文）》 2022年 第16卷 第2期   页码 237-250 doi: 10.1007/s11705-021-2058-6

摘要： Advanced model-based control strategies, e.g., model predictive control, can offer superior control of key process variables for multiple-input multiple-output systems. The quality of the system model is critical to controller performance and should adequately describe the process dynamics across its operating range while remaining amenable to fast optimization. This work articulates an integrated system identification procedure for deriving black-box nonlinear continuous-time multiple-input multiple-output system models for nonlinear model predictive control. To showcase this approach, five candidate models for polynomial and interaction features of both output and manipulated variables were trained on simulated data and integrated into a nonlinear model predictive controller for a highly nonlinear continuous stirred tank reactor system. This procedure successfully identified system models that enabled effective control in both servo and regulator problems across wider operating ranges. These controllers also had reasonable per-iteration times of ca. 0.1 s. This demonstration of how such system models could be identified for nonlinear model predictive control without prior knowledge of system dynamics opens further possibilities for direct data-driven methodologies for model-based control which, in the face of process uncertainties or modelling limitations, allow rapid and stable control over wider operating ranges.

关键词： nonlinear model predictive control     black-box modeling     continuous-time system identification     machine learning     industrial applications of process control    

HTML PDF 收藏

标题 作者 时间 类型 操作

Modeling, simulation, and prediction of global energy indices: a differential approach

Stephen Ndubuisi NNAMCHI, Onyinyechi Adanma NNAMCHI, Janice Desire BUSINGYE, Maxwell Azubuike IJOMAH, Philip Ikechi OBASI

期刊论文

Abnormal glycosylated hemoglobin as a predictive factor for glucose metabolism disorders in antipsychotic

XU Leping, JI Juying, DUAN Yiyang, SHI Hui, ZHANG Bin, SHAO Yaqin, SUN Jian

期刊论文

Realtime prediction of hard rock TBM advance rate using temporal convolutional network (TCN) with tunnel

Zaobao LIU; Yongchen WANG; Long LI; Xingli FANG; Junze WANG

期刊论文

Short-term prediction of influent flow rate and ammonia concentration in municipal wastewater treatment

Shuai MA, Siyu ZENG, Xin DONG, Jining CHEN, Gustaf OLSSON

期刊论文

Evaluation and prediction of slope stability using machine learning approaches

期刊论文

The predictive value of chromosome 8p deletion for metastasis of hepatocellular carcinoma: a summary

QIN Lunxiu, TANG Zhaoyou, GUAN Xinyuan, YE Qinghai, JIA Huliang, REN Ning

期刊论文

Unconfined compressive strength prediction of soils stabilized using artificial neural networks and support

Alireza TABARSA, Nima LATIFI, Abdolreza OSOULI, Younes BAGHERI

期刊论文

Heterogeneous length-of-stay modeling of post-acute care residents in the nursing home with competing discharge dispositions

期刊论文

Elliptical lobe shape gerotor pump design to minimize wear

Mohammad Reza KARAMOOZ RAVARI

期刊论文

面向维修的机械系统可靠度预测与仿真研究

黄良沛,尹喜云,岳文辉

期刊论文

Real-time mobile robot teleoperation via Internet based on predictive control

WANG Shihua, XU Bugong, WANG Shihua, ZHOU Yeming, LIU YunHui

期刊论文

Multi-timescale optimization scheduling of interconnected data centers based on model predictive control

期刊论文

The CatMath: an online predictive platform for thermal + electrocatalysis

期刊论文

Extended model predictive control scheme for smooth path following of autonomous vehicles

期刊论文

machine-learning-based system identification of dynamical systems under control: application towards the model predictive

期刊论文