scholarly journals Research on Performance Prediction Model of Impeller-Type Breather

2019 ◽  
Vol 9 (17) ◽  
pp. 3504
Author(s):  
Xiaobin Zhang ◽  
Weibing Zhu ◽  
Lei Qian ◽  
Miao Li
Keyword(s):  
Prediction Model ◽  
Performance Prediction ◽  
Separation Efficiency ◽  
Operating Condition ◽  
Factors Affecting ◽  
Mean Error ◽  
The Mean ◽  
Main Factors ◽  
Experimental Values ◽  
Impeller Type

To investigate the characteristics of separation and resistance of an impeller-type breather in an aeroengine lubrication system, orthogonal test design is used in calculation of the operating condition. Also, phase coupling of the RNG(Renormalization Group) k − ε model and the DPM model (Discrete Phase Model) is used in calculating the selected operating condition. Through analysis of the results, combined with dimensional analysis, it shows the significance of various influencing factors and the optimal level. Based on this, a general formed dimensionless group equation is established for comprehensive separation efficiency, breather separation efficiency, and ventilation resistance. Also, through the least squares method, the performance prediction model of the breather is obtained considering five operating conditions and six structural parameters. The theoretical calculation of separation efficiency and ventilation resistance of an impeller-type breather can be performed. The results show that: the main factors affecting the separation efficiency are the rotating speed and the number of impeller blades; the main factors affecting the ventilation resistance are the ventilation rate and the diameter of the vent hole; the variation trends of the calculated values of the performance prediction model and the experimental values are consistent. The mean error of the comprehensive separation efficiency is 0.97% and the mean error of the ventilation resistance is 11.73%. The calculated values and the experimental values remain consistent, which proves that this performance prediction model can provide references to the assessment and the design of an impeller breather.

scholarly journals Paleolimnology of Lake Yamanaka as reflected on particle-size distribution

1970 ◽  
Vol 14 ◽  
pp. 35-42 ◽  
Author(s):  
Danda Pani Adhikari
Keyword(s):  
Particle Size ◽  
Grain Size ◽  
Size Distribution ◽  
Central Japan ◽  
Size Frequency Distribution ◽  
Factors Affecting ◽  
Mean Grain Size ◽  
The Mean ◽  
Main Factors ◽  
Lake Size

A 17.63 m long bore-hole core extracted from the deepest part of Lake Yamanaka, one of the Fuji-five Lakes at the northeasternfoot of Mount Fuji, central Japan, composed of sediment with intercalations of scoria fallout deposits. The sediment of the upper11.4 m was investigated for grain-size distribution by using a laser diffraction particle size analyser. The mean grain-size profileshowed various degrees of fluctuations, both short-and long-terms, and the size-frequency distribution revealed unimodal-trimodalmixing of sediments. Changes in lake size and water depth appear to be the main factors affecting the variability in the grain-sizedistribution and properties. The lake level appears low during 7000–5000 cal BP and 2800–1150 cal BP and relatively high during5000–2800 cal BP and 1150 cal BP– present.DOI: http://dx.doi.org/10.3126/bdg.v14i0.5437Bulletin of the Department of Geology Vol.14 2011, pp.35-42 

Prediction on China's Energy Demand Based on RBF Neural Network Model

2013 ◽  
Vol 805-806 ◽  
pp. 1421-1424
Author(s):  
Xue Feng ◽  
Wuyunbilige Bao ◽  
Ben Ha
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Neural Network Model ◽  
Energy Demand ◽  
Rbf Neural Network ◽  
Bp Network ◽  
Factors Affecting ◽  
Rbf Network ◽  
Network Prediction ◽  
Main Factors

Choose factors which influence the energy demand by the method of path analysis, build radial basis function (RBF) neural network model to predict energy demand in China. The RBF neural network is trained with the actual data of the main factors affecting energy demand during 1989-2003 and energy demand during 1993-2007 as learning sample with a good fitting effect. After testing network with the actual data of the main factors affecting energy demand during 2004-2007 and energy demand during 2008-2011, higher prediction accuracy can be obtained. By comparison with the BP network, RBF network prediction model outperforms BP network prediction model, finally RBF network is applied to make prediction of energy consumption for the year 2013-2015.

scholarly journals Role of FCBF Feature Selection in Educational Data Mining

2020 ◽  
Vol 39 (4) ◽  
pp. 772-778
Author(s):  
Maryam Zaffar ◽  
Manzoor Ahmad Hashmani ◽  
K.S. Savita ◽  
Syed Sajjad Hussain Rizvi ◽  
Mubashar Rehman
Keyword(s):  
Data Mining ◽  
Feature Selection ◽  
Prediction Model ◽  
Student Performance ◽  
Performance Prediction ◽  
Prediction Models ◽  
Educational Data Mining ◽  
Action Plans ◽  
Factors Affecting ◽  
Academic Organization

The Educational Data Mining (EDM) is a very vigorous area of Data Mining (DM), and it is helpful in predicting the performance of students. Student performance prediction is not only important for the student but also helpful for academic organization to detect the causes of success and failures of students. Furthermore, the features selected through the students’ performance prediction models helps in developing action plans for academic welfare. Feature selection can increase the prediction accuracy of the prediction model. In student performance prediction model, where every feature is very important, as a neglection of any important feature can cause the wrong development of academic action plans. Moreover, the feature selection is a very important step in the development of student performance prediction models. There are different types of feature selection algorithms. In this paper, Fast Correlation-Based Filter (FCBF) is selected as a feature selection algorithm. This paper is a step on the way to identifying the factors affecting the academic performance of the students. In this paper performance of FCBF is being evaluated on three different student’s datasets. The performance of FCBF is detected well on a student dataset with greater no of features.

Wastage of conserved fodder when feeding livestock

2010 ◽  
Vol 50 (6) ◽  
pp. 400 ◽  
Author(s):  
C. R. Stockdale
Keyword(s):  
Beef Cattle ◽  
Dairy Cattle ◽  
Present Review ◽  
Factors Affecting ◽  
Storage Method ◽  
The Mean ◽  
Main Factors ◽  
Wet Weather ◽  
The Cost ◽  
The Impact

The objective of the present review was to establish levels of conserved fodder wastage when feeding livestock (sheep, beef cattle, dairy cattle) under various conditions and using various feed-out systems, and to determine the factors affecting wastage. The mean wastage of hay recorded in the literature reviewed was 17% of the DM offered, but the range was from 4 to 77%. The main factors affecting the degree of wastage were storage method, packaging method, method of feeding out, amount of fodder on offer and its palatability and/or quality and the impact of wet weather. Although the emphasis was on hay, the principles should also apply to silage. If wastage was 40% rather than 5%, the cost of feeding conserved fodder to livestock would be a third greater than producers might expect or budget on.

scholarly journals Factors Affecting Health-Promoting Behaviors among Nursing Students

2020 ◽  
Vol 17 (17) ◽  
pp. 6291
Author(s):  
Younghui Hwang ◽  
Jihyun Oh
Keyword(s):  
Nursing Students ◽  
Health Concern ◽  
Health Perceptions ◽  
Health Promoting Behaviors ◽  
Factors Affecting ◽  
Related Information ◽  
Health Related ◽  
Health Promoting ◽  
The Mean ◽  
Main Factors

Health-promoting behaviors help prevent chronic illness. Health-promoting behaviors of nursing students can affect not only their own health, but also the health of their future patients, for whom they can act as role models. Nursing students should participate in health-promoting behaviors; however, nursing students often have unhealthy behaviors. This study aimed to investigate the factors affecting health-promoting behaviors in nursing students. A descriptive, self-report survey of 304 nursing students from three universities in South Korea was conducted. Subjects’ general characteristics, health perceptions, health concerns, and health-promoting behaviors were collected. Of the total participants, 90.1% were female and the mean age was 20.4 years. The mean score for health-promoting behaviors was 2.47, higher than the midpoint. The mean for the subscale of physical activity among health-promoting behaviors was the lowest. The main factors affecting health-promoting behaviors were gender, health perceptions, health concern, and time per week spent searching online for health-related information. The main factors affecting physical activity were gender, health concern, and time per week spent searching online for health-related information. Based on the study findings, it is recommended that a program to empower nursing students to perform health-promoting behaviors be incorporated into the nursing education curriculum with regard to unique needs based on gender. Specifically, it would be effective to develop programs that are easily accessible via the Internet.

scholarly journals A Performance-Prediction Model for PIC Applications on Clusters of Symmetric MultiProcessors: Validation with Hierarchical HPF+OpenMP Implementation

2003 ◽  
Vol 11 (2) ◽  
pp. 159-176
Author(s):  
Sergio Briguglio ◽  
Beniamino Di Martino ◽  
Gregorio Vlad
Keyword(s):  
Prediction Model ◽  
Performance Prediction ◽  
High Performance ◽  
Symmetric Multiprocessors ◽  
Particle In Cell ◽  
Decomposition Strategies ◽  
Experimental Values ◽  
High Level ◽  
Parallelization Efficiency ◽  
A Performance

A performance-prediction model is presented, which describes different hierarchical workload decomposition strategies for particle in cell (PIC) codes on Clusters of Symmetric MultiProcessors. The devised workload decomposition is hierarchically structured: a higher-level decomposition among the computational nodes, and a lower-level one among the processors of each computational node. Several decomposition strategies are evaluated by means of the prediction model, with respect to the memory occupancy, the parallelization efficiency and the required programming effort. Such strategies have been implemented by integrating the high-level languages High Performance Fortran (at the inter-node stage) and OpenMP (at the intra-node one). The details of these implementations are presented, and the experimental values of parallelization efficiency are compared with the predicted results.

scholarly journals Spatio-temporal variability in the Cladocera assemblage of a subtropical hypersaline lagoon

2022 ◽  
Vol 82 ◽  
Author(s):  
J. C. L. Rosa ◽  
L. L. Batista ◽  
W. M. Monteiro-Ribas
Keyword(s):  
Diversity Index ◽  
Salinity Gradient ◽  
Global Scale ◽  
Hypersaline Environments ◽  
Factors Affecting ◽  
Factors Influencing ◽  
The Mean ◽  
Main Factors ◽  
Spatio Temporal ◽  
Hypersaline Lagoon

Abstract Cladocera represent an important zooplankton group because of their seasonal prominence in terms of abundance and their contribution in controlling primary production (phytoplankton). On a global scale, there are few studies on Cladocera in hypersaline environments. The present work aims to evaluate the spatio-temporal variation of the Cladocera assemblage across a salinity gradient in the habitats of the Araruama Lagoon. Samples were collected in random months over a period of four years at 12 fixed stations in the Araruama Lagoon using a WP2 plankton net equipped with a flow meter. Our results do not reveal significant influence of the tide and seasonal variation as factors affecting the Cladocera assemblage. Five Cladocera species were found in the Araruama Lagoon, only in stations 11 and 12 where they reached an average of 1,799 ± 3,103 ind. m-3. The mean of the Shannon Diversity Index was 0.45 ± 0.2. The species that stood out in terms of frequency and abundance were: Penilia avirostris (frequency of occurrence: 71%), followed by Pseudevadne tergestina (41%). The same species also stood out in terms of relative abundance, Penilia avirostris (87%) and Pseudevadne tergestina (11%). The absence of Cladocera in the innermost parts of the lagoon suggests that their entrance to these locations is possibly inhibited by the salinity and temperature gradient of the lagoon, being the main factors influencing the dynamics of the Cladocera assemblages.

scholarly journals Electricity Demand Prediction of Smart Grid in Smart Cities Using K-means Algorithm

2021 ◽  
Author(s):  
Shurui Wang ◽  
Aifeng Song ◽  
Yufeng Qian
Keyword(s):  
Big Data ◽  
Prediction Model ◽  
Error Rate ◽  
Smart City ◽  
Smart Cities ◽  
Development Planning ◽  
Electricity Demand ◽  
Demand Prediction ◽  
Mean Error ◽  
The Mean

Abstract The aims are to unify big data management among various departments in smart city construction, establish a centralized data collection and sharing system, and provide fast and convenient big data services for smart applications in various fields. The national grid industry is taken as the research object. A new electricity demand prediction model is proposed based on smart city big data’s characteristics. This model integrates meteorological, geographic, demographic, corporate, and economic information to form an intelligent big database. The K-mean algorithm mines and analyzes the data to optimize the electricity user information. The electricity prediction model is established using the Backpropagation (BP) neural network algorithm. The electricity market is evaluated through an in-depth exploration of data relationships to verify the effectiveness of the model proposed. Results demonstrate that the K-mean algorithm can significantly improve electricity user segmentation accuracy, separate the different regional electricity consumption, and categorize different electricity users. The electricity demand network model constructed can significantly improve the prediction accuracy, and the mean error rate is 3.2671%. The model’s training time improved by the additional momentum factor is significantly reduced, and the mean error rate is 2.13%. The above results can provide a theoretical and practical basis for electricity demand prediction and personalized marketing, as well as development planning for the electricity sector.

scholarly journals Soil Moisture Estimation and Its Influencing Factors Based on Temporal Stability on a Semiarid Sloped Forestland

2021 ◽  
Vol 9 ◽  
Author(s):  
Mingzhu Xu ◽  
Guoce Xu ◽  
Yuting Cheng ◽  
Zhiqiang Min ◽  
Peng Li ◽  
...  
Keyword(s):  
Loess Plateau ◽  
Temporal Stability ◽  
Relative Difference ◽  
Root Density ◽  
Sand Content ◽  
The Loess Plateau ◽  
Factors Affecting ◽  
Dry Seasons ◽  
The Mean ◽  
Main Factors

Soil water content (SWC) plays a crucial role in the hydrological cycle and ecological restoration in arid and semi-arid areas. Studying the temporal stability of SWC spatial distribution is a requirement for the dynamic monitoring of SWC and the optimization of water resource management. The SWC in a Pinus tabulaeformis Carr. forest on the slope of the Loess Plateau of China were analyzed in five soil layers (0–100 cm with an interval of 20 cm) in the rainy and dry seasons from July 2014 to November 2017. The mean SWC was estimated and the main factors affecting the temporal stability of the SWC were further analyzed. Results showed that the SWC had strong temporal stability during the two seasons for several consecutive years. The temporal stability of SWC and the number of representative locations varied with season and depth. The elevation, soil total phosphorus (STP), clay, silt, or sand content of the representative locations approached the corresponding mean value of the study area. A single representative location accurately represented the mean SWC for the five depths in the rainy and dry seasons (RMSE <2%; rainy season: 0.81 < R2 < 0.94; dry season: 0.63 < R2 < 0.83; p < 0.01). The mean relative difference (MRD) and the relative difference standard deviation (SDRD) changed with the seasons and were significantly correlated with elevation, root density, and sand and silt content in two seasons (p < 0.05). Elevation, root density, and sand content were the main factors influencing the change of SWC temporal stability in different seasons. The results provide scientific guidance to monitor SWC by using a small number of locations and enrich our understanding of the factors affecting the temporal stability of SWC in the rainy and dry seasons of the Loess Plateau of China.

scholarly journals Prediction of Highway Tunnel Pavement Performance Based on Digital Twin and Multiple Time Series Stacking

2020 ◽  
Vol 2020 ◽  
pp. 1-21
Author(s):  
Gang Yu ◽  
Shuang Zhang ◽  
Min Hu ◽  
Y. Ken Wang
Keyword(s):  
Time Series ◽  
Prediction Model ◽  
Performance Prediction ◽  
Pavement Performance ◽  
Multiple Time ◽  
Multiple Time Series ◽  
Digital Twin ◽  
Highway Tunnel ◽  
The Mean ◽  
Pavement Performance Prediction

The existing pavement performance prediction methods are limited to single-factor predictions, which often face the challenges of high cost, low efficiency, and poor accuracy. It is difficult to simultaneously solve the temporal, spatial, and exogenous dependencies between pavement performance data and maintenance, the service life of highways, the environment, and other factors. Digital twin technology based on the building information modeling (BIM) model, combined with machine learning, puts forward a new perspective and method for the accurate and timely prediction of pavement performance. In this paper, we propose a highway tunnel pavement performance prediction approach based on a digital twin and multiple time series stacking (MTSS). This paper (1) establishes an MTSS prediction model with heterogeneous stacking of eXtreme gradient boosting (XGBoost), the artificial neural network (ANN), random forest (RF), ridge regression, and support vector regression (SVR) component learners after exploratory data analysis (EDA); (2) proposes a method based on multiple time series feature extraction to accurately predict the pavement performance change trend, using the highway segment as the minimum computing unit and considering multiple factors; (3) uses grid search with the k-fold cross validation method to optimize hyperparameters to ensure the robustness, stability, and generalization ability of the prediction model; and (4) constructs a digital twin for pavement performance prediction to realize the real-time dynamic evolution of prediction. The method proposed in this study is applied in the life cycle management of the Dalian highway-crossing tunnel in Shanghai, China. A dataset covering 2010–2019 is collected for real-time prediction of the pavement performance. The prediction accuracy evaluation shows that the mean absolute error (MAE) is 0.1314, the root mean squared error (RMSE) is 0.0386, the mean absolute percentage error (MAPE) is 5.10%, and the accuracy is 94.90%. Its overall performance is better than a single model. The results verify that the prediction method based on digital twin and MTSS is feasible and effective in the highway tunnel pavement performance prediction.

Sign in / Sign up

Export Citation Format

Share Document