Research on the Relation of Thunderstorm Days and Lightning Density in Jiangsu Province

2013 ◽  
Vol 807-809 ◽  
pp. 699-703
Author(s):  
Zhong Jiang Yang ◽  
Tian Zhang ◽  
Ming Xue Feng ◽  
Xue Jiao ◽  
Bin Bin Lin
Keyword(s):  
Comparative Analysis ◽  
Correlation Coefficient ◽  
Relative Error ◽  
Jiangsu Province ◽  
Average Relative Error ◽  
Density Data ◽  
Actual Application ◽  
Standard Equation ◽  
Weather Stations ◽  
Curve Equation

Thunderstorm days are used to show the lightning frequency happened somewhere. Through comparative analysis on the thunderstorm days data of 66 weather stations in Jiangsu province and lightning data of ADTD lightning monitoring, it presents that the correlation coefficient r became the largest at 11 km range, and the longest distance of thunderstorm artificial observation is 11-14 km. According to the study of lightning activities in Jiangsu province to get the statistical fitting function curve equation by thunderstorm days and ground flash density data. Test the actual application effect of fitting equation by calculating the average relative error of fitting equation and standard equation, then make sure the actual thunderstorm days in Jiangsu province and the cloud to ground flash density formula is Ng=0.0224 Td1.48.

Research of System of Trailer Axle’s Function Test

2010 ◽  
Vol 44-47 ◽  
pp. 941-945
Author(s):  
Wei Bin Wu ◽  
Tian Sheng Hong ◽  
Xu Xiang Chen ◽  
Shi Ting Yang ◽  
Kai Cheng Chen
Keyword(s):  
Correlation Coefficient ◽  
Relative Error ◽  
Visual Inspection ◽  
Average Relative Error ◽  
Entire System ◽  
Guide Rail ◽  
Hardware System ◽  
Function Test

Axle is one of the most important parts related to the safety of trailer’s operation on the railroad. In China, the checking of axles is mainly through visual inspection and hammer examinations. The construction of an automation hardware system was simulated by Pro/E, including support, guide rail, sensor, AC servomotor and servo driver conformed to the model. Next, for controlling the operation of the entire system, LabVIEW was used to write a program and to control the rotation of servomotor. The pressure heads controlled by linkage system were utilized to load the axle; meanwhile, through the PCI of NI, the data of pressure and displacement was collected. After calibration, the relative error of pinpointing sensor was 5.207%, and the average relative error was about 1.4%. Analyzed by SPSS, the voltage-load formula's correlation coefficient R is greater than 0.994, while the significance is less than 0.05, showing the regression of great significance. Finally, the capabilities of the simulated axle, such as fatigue, stiffness, intensity and stress, were analyzed carefully.

scholarly journals Predicting Indoor Temperature Distribution Based on Contribution Ratio of Indoor Climate (CRI) and Mobile Sensors

Buildings ◽  
2021 ◽  
Vol 11 (10) ◽  
pp. 458
Author(s):  
Yanan Zhao ◽  
Zihan Zang ◽  
Weirong Zhang ◽  
Shen Wei ◽  
Yingli Xuan
Keyword(s):  
Temperature Distribution ◽  
Relative Error ◽  
Prediction Accuracy ◽  
Prediction Method ◽  
Mobile Sensors ◽  
Average Relative Error ◽  
Contribution Ratio ◽  
Mobile Sensor ◽  
Indoor Climate ◽  
Indoor Temperature

In practical building control, quickly obtaining detailed indoor temperature distribution is necessary for providing satisfying personal comfort and improving building energy efficiency. The aim of this study is to propose a fast prediction method for indoor temperature distribution without knowing the thermal boundary conditions in practical applications. In this method, the index of contribution ratio of indoor climate (CRI), which represents the independent contribution of each heat source to the temperature distribution, has been combined with the air temperature collected by one mobile sensor at the height of the working area. Based on a typical office model, the effectiveness of using mobile sensors was discussed, and the influence of its acquisition height and acquisition distance on the prediction accuracy was analyzed as well. The results showed that the proposed prediction method was effective. When the sensors fixed on the wall were used to predict the indoor temperature distribution, the maximum average relative error was 27.7%, whereas when the mobile sensor was used to replace the fixed sensors, the maximum average relative error was 4.8%. This indicates that using mobile sensors with flexible acquisition location can help promote both reliability and accuracy of temperature prediction. In the human activity area, data from a set of mobile sensors were used to predict the temperature distribution at four heights. The prediction accuracy was 2.1%, 2.1%, 2.3%, and 2.7%, respectively. However, the influence of acquisition distance of mobile sensors on prediction accuracy cannot be ignored. The distance should be large enough to disperse the distribution of the acquisition points. Due to the influence of airflow, some distance between the acquisition points and the room boundaries should be given.

scholarly journals Numerical rainfall simulation with different spatial and temporal evenness by using WRF multi-physics ensembles

2016 ◽  
Author(s):  
Jiyang Tian ◽  
Jia Liu ◽  
Chuanzhe Li ◽  
Fuliang Yu
Keyword(s):  
Relative Error ◽  
Wrf Model ◽  
Temporal Distribution ◽  
Northern China ◽  
Rainfall Simulation ◽  
Average Relative Error ◽  
Storm Events ◽  
Cumulative Rainfall ◽  
Study Sites

Abstract. The Weather Research and Forecasting (WRF) model is used in this study to simulate six storm events in two semi-humid and semi-arid catchments of Northern China. The six storm events are classified into four types based on the rainfall evenness in the spatial and temporal dimensions. Two microphysics, two planetary boundary layers (PBL) and three cumulus parameterizations are combined to develop 12 physical ensembles for rainfall generation. The WRF model performs the best for Type 1 event with relatively even distributions of rainfall in both space and time. The average relative error (ARE) for the cumulative rainfall amount is 16.98 %. For the spatial rainfall simulation, the lowest root mean square error (RMSE) is found with event II (0.3989) which has the most even spatial distribution, and for the temporal simulation the lowest RMSE is found with event I (1.0171) which has the most even temporal distribution. It is found to be the most difficult to reproduce the very convective storm with uneven spatiotemporal distributions (Type 4 event) and the average relative error (ARE) for the cumulative rainfall amounts is up to 68.07 %. The RMSE results of Event III with the most uneven spatial and temporal distribution are 0.9363 for the spatial simulation and 2.7769 for the temporal simulation, which are much higher than the other storms. The general performance of the current WRF physical parameterisations is discussed. The Betts-Miller-Janjic (BMJ) is found to be unsuitable for rainfall simulation in the study sites. For Type 1, 2, and 4 storms, ensemble 4 performs the best. For Type 3 storms, ensemble 5 and 7 are the better choice. More guidance is provided for choosing among the physical parameterisations for accurate rainfall simulations of different storm types in the study area.

scholarly journals MENYELESAIKAN VEHICLE ROUTING PROBLEM MENGGUNAKAN ALGORITMA FUZZY EVOLUSI

2018 ◽  
Vol 7 (3) ◽  
pp. 252
Author(s):  
I PUTU ARYA YOGA SUMADI ◽  
I PUTU EKA NILA KENCANA ◽  
LUH PUTU IDA HARINI
Keyword(s):  
Evolutionary Algorithm ◽  
Vehicle Routing ◽  
Relative Error ◽  
Vehicle Routing Problem ◽  
Average Relative Error ◽  
Routing Problem ◽  
Capacitated Vehicle Routing Problem ◽  
Optimal Value ◽  
Evolution Algorithm ◽  
Capacitated Vehicle

The purpose of this research is to know the performance of Fuzzy Evolutionary Algorithm in solving one type of Vehicle Routing Problem that is Capacitated Vehicle Routing Problem (CVRP). There are 8 different CVRP data to be solved. The performance of the algorithm can be determined by comparing the value obtained by AFE with the optimal value of the data. The result of this research is fuzzy evolution algorithm yields the best average relative error from all data for distance that is equal to 69,5855% and for minimum vehicle equal to 26%.

Comparative Analysis of Two Volumetrical Ultrafiltration Monitors for Hemodialysis

1986 ◽  
Vol 9 (3) ◽  
pp. 163-166
Author(s):  
J.H.M. Berden ◽  
J.M.P. Wokke ◽  
R.A.P. Koene
Keyword(s):  
Comparative Analysis ◽  
Correlation Coefficient ◽  
Hollow Fiber ◽  
Clinical Setting ◽  
Parallel Flow ◽  
The Other ◽  
Dialysis Modality ◽  
Volumetric Measurement ◽  
Dialysate Flow ◽  
Regression Correlation

Controlled ultrafiltration (UF) during hemodialysis may prevent dialysis associated hypotension. A prerequisite for controlled ultrafiltration is an accurate measurement of ultrafiltration. Volumetric measurement is the best currently available method for this purpose. In this study we compared in a clinical setting two volumetric ultrafiltration monitors (UFM): one device constructed in our hospital using oval flowmeters (UFM-N) and the other using electromagnetic flow transducers (UFM-G: UFM 10-2, Gambro Lund Sweden). The UF measurements of both UFM's were compared with UF calculated from bedscales weight monitoring and standard scales determinations. During dual needle hemodialysis (n = 8) with a hollow fiber dialyzer the accuracy of the UFM-N was 91% and that of the UFM-G 97%. During dual needle dialysis with a parallel flow dialyzer the UFM-N appeared to be more sensitive for pulsatile changes in the dialysate flow due to the greater compliance of this type of dialyzer. The accuracy of the UFM-N in this setting was 80%, while that of the UFM-G was 87% (n = 11). During single needle dialysis with a parallel flow dialyzer (n = 14) only the UFM-G was tested and it measured UF with an accuracy of 92%. Finally the UFM-G can control UF actively by adjusting the TMP to obtain a given UF rate. The accuracy of the UFM-G in this setting was 94%, and the lineair regression correlation coefficient between planned UF and actually obtained UF was 0.974 (n - 61). In conclusion volumetric monitoring of UF is accurate and reliable, but its accuracy is dependent on the type of dialyzer used. The UFM-G proved to be useful in every dialysis modality tested, while the UFM-N can be used in dual-needle dialysis using hollow fiber dialyzers.

scholarly journals Numerical rainfall simulation with different spatial and temporal evenness by using a WRF multiphysics ensemble

2017 ◽  
Vol 17 (4) ◽  
pp. 563-579 ◽  
Author(s):  
Jiyang Tian ◽  
Jia Liu ◽  
Denghua Yan ◽  
Chuanzhe Li ◽  
Fuliang Yu
Keyword(s):  
Relative Error ◽  
Wrf Model ◽  
Temporal Distribution ◽  
Northern China ◽  
Rainfall Simulation ◽  
Average Relative Error ◽  
Storm Events ◽  
Cumulative Rainfall ◽  
Physical Parameterizations

Abstract. The Weather Research and Forecasting (WRF) model is used in this study to simulate six storm events in two semi-humid catchments of northern China. The six storm events are classified into four types based on the rainfall evenness in the spatial and temporal dimensions. Two microphysics, two planetary boundary layers (PBL) and three cumulus parameterizations are combined to develop an ensemble containing 16 members for rainfall generation. The WRF model performs the best for type 1 events with relatively even distributions of rainfall in both space and time. The average relative error (ARE) for the cumulative rainfall amount is 15.82 %. For the spatial rainfall simulation, the lowest root mean square error (RMSE) is found with event II (0.4007), which has the most even spatial distribution, and for the temporal simulation the lowest RMSE is found with event I (1.0218), which has the most even temporal distribution. The most difficult to reproduce are found to be the very convective storms with uneven spatiotemporal distributions (type 4 event), and the average relative error for the cumulative rainfall amounts is up to 66.37 %. The RMSE results of event III, with the most uneven spatial and temporal distribution, are 0.9688 for the spatial simulation and 2.5327 for the temporal simulation, which are much higher than the other storms. The general performance of the current WRF physical parameterizations is discussed. The Betts–Miller–Janjic (BMJ) scheme is found to be unsuitable for rainfall simulation in the study sites. For type 1, 2 and 4 storms, member 4 performs the best. For type 3 storms, members 5 and 7 are the better choice. More guidance is provided for choosing among the physical parameterizations for accurate rainfall simulations of different storm types in the study area.

Spectral Detector Design for Nitrogen and Moisture of Rape

2011 ◽  
Vol 464 ◽  
pp. 241-244
Author(s):  
Zhi Yong Xiang ◽  
Bo Quan Li ◽  
Han Ping Mao ◽  
Xiao Dong Zhang
Keyword(s):  
Correlation Coefficient ◽  
Relative Error ◽  
Light Filter ◽  
Signal Conditioning ◽  
Passive Detection ◽  
Moisture Contents ◽  
Spectral Bands ◽  
Sample Classification ◽  
Four Levels ◽  
Detector Design

For the four spectral bands of rape canopy leaf which is sensitive to nitrogen and moisture contents, a reflection spectral detector was designed to measure nitrogen and moisture of rape, constituted by the adjustable light, filter module, passive detection array, signal conditioning and acquisition modules. Further, a test was carried on to verify performance by several different reflectivity of the plates and four levels of nitrogen and moisture rape. The result shows that correlation coefficient between the contents of nitrogen, moisture and output of detector was 0.78, relative error of the detector was 9.5%, accuracy of sample classification was 65%.

Comparative analysis of the influence of solar radiation screen ageing on temperature measurements by means of weather stations

2013 ◽  
Vol 34 (4) ◽  
pp. 1297-1310 ◽  
Author(s):  
G. Lopardo ◽  
F. Bertiglia ◽  
S. Curci ◽  
G. Roggero ◽  
A. Merlone
Keyword(s):  
Comparative Analysis ◽  
Solar Radiation ◽  
Temperature Measurements ◽  
Weather Stations

scholarly journals Forecast of regional water demand based on NSGAII-FORAGM

2021 ◽  
Author(s):  
Jun Li ◽  
Chunye Liu ◽  
Li Tang
Keyword(s):  
Fractional Order ◽  
Relative Error ◽  
Optimization Model ◽  
Water Demand ◽  
Average Relative Error ◽  
Tertiary Industry ◽  
Wide Range ◽  
Yulin City ◽  
Relative Errors ◽  
Regional Water

Abstract Regional water demand is an important basic data for regional engineering planning, design and management. Making full use of multi-source data and prior knowledge to quickly and economically obtain high-precision regional water demand is of great significance to the optimal allocation of regional water resources. In order to accurately predict the regional water demand, this study took Yulin City as a research area to predict the water demand of the city from 2017 to 2019. Aiming at the oscillating characteristics of the regional water demand sequence and the over-fitting problem of traditional prediction models, this study proposed the non-dominated sorting genetic algorithm II-fractional order reverse accumulative grey model (NSGAII-FORAGM). The regional water demand oscillation sequence was transformed into a monotonically decreasing non-negative sequence. Based on the transformation sequence, an optimization model was constructed according to the two objective functions of ‘maximum (or minimum) order’ and ‘best fit to historical data’, and the NSGAII method were adopted to solve the model. The three model structures of ‘fractional order’, ‘reverse accumulation’ and ‘obtaining order through multi-objective optimization model ‘ were tested based on the water use sequence of the three sectors (industry, tertiary industry and domestic) in Yulin City, and the performance of the method is compared with NSGAII-IORAGM, NSGAII-FOFAGM and SOGA-FORAGM. The results showed that the average relative error of the model established in this study for the simulation of industry, tertiary industry (The tertiary industry is a technical name for the service sector of the economy, which encompasses a wide range of businesses), and domestic was 15.54%, 11.20%, 9.98% respectively. The average relative error of the model established in this study for the prediction of industry, tertiary industry and domestic was 9.46%, 7.9%, 1.8% respectively. For the simulation of water demand sequences in three sections, the simulation average relative errors of the other three models were not absolutely dominant except for the SOGA-FORAGM model. The average relative predicted error by the model in this study was the smallest (The relative errors of the three sequence predictions for industry, tertiary industry and domestic were lower than the relative errors of the optimal results of the comparison model, which were 0.97%, 0.72% and 4.5%, respectively), indicating that the model had certain applicability for the water demand prediction of various sectors (industry, tertiary industry and domestic) in the region compared with other models, and can improve the accuracy of the prediction results.

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