Imperfect slope measurements drive overestimation in a geometric cone model of lake and reservoir depth

Inland Waters ◽  
2022 ◽  
pp. 1-11
Author(s):  
Jemma Stachelek ◽  
Patrick J. Hanly ◽  
Patricia A. Soranno
Keyword(s):  
Cone Model

Conical SNA using fuzzy k-medoids based on user experience

2021 ◽  
pp. 002072092098849
Author(s):  
Poonam Rani ◽  
MPS Bhatia ◽  
Devendra K Tayal
Keyword(s):  
Social Networks ◽  
Social Network ◽  
User Experience ◽  
Three Dimensional ◽  
Experimental Results ◽  
Dynamic Parameters ◽  
Clustering Method ◽  
Cone Model ◽  
Intelligent Approach ◽  
Conical Model

The paper presents an intelligent approach for the comparison of social networks through a cone model by using the fuzzy k-medoids clustering method. It makes use of a geometrical three-dimensional conical model, which astutely represents the user experience views. It uses both the static as well as the dynamic parameters of social networks. In this, we propose an algorithm that investigates which social network is more fruitful. For the experimental results, the proposed work is employed on the data collected from students from different universities through the Google forms, where students are required to rate their experience of using different social networks on different scales.

scholarly journals Correction to: Experimental investigation of blunt cone model at hypersonic Mach number 7.25

2021 ◽  
Vol 43 (7) ◽  
Author(s):  
Saiprakash Mani ◽  
C. Senthilkumar ◽  
G. Kadam Sunil ◽  
Singh Prakash Rampratap ◽  
V. Shanmugam ◽  
...  
Keyword(s):  
Experimental Investigation ◽  
Mach Number ◽  
Blunt Cone ◽  
Cone Model

Modeling of coronal mass ejections that caused particularly large geomagnetic storms using ENLIL heliosphere cone model

Space Weather ◽  
2011 ◽  
Vol 9 (6) ◽  
pp. n/a-n/a ◽  
Author(s):  
A. Taktakishvili ◽  
A. Pulkkinen ◽  
P. MacNeice ◽  
M. Kuznetsova ◽  
M. Hesse ◽  
...  
Keyword(s):  
Coronal Mass Ejections ◽  
Geomagnetic Storms ◽  
Cone Model

scholarly journals Theoretical and Experimental Results on the Recovery of Potato Processing Residuals by Anaerobic Digestion

2019 ◽  
Vol 70 (7) ◽  
pp. 2524-2529 ◽  
Author(s):  
Andreea D. Dima ◽  
Carmen Mateescu ◽  
Oana C. Parvulescu ◽  
Eduard M. Lungulescu ◽  
Nicoleta O. Nicula
Keyword(s):  
Anaerobic Digestion ◽  
Gompertz Model ◽  
Economic Feasibility ◽  
Potato Processing ◽  
Biomethane Potential ◽  
Potato Waste ◽  
Cone Model ◽  
Treatment Technologies ◽  
Negative Environmental Impact ◽  
Modified Gompertz Model

Anaerobic digestion of organic matter with high moisture content has proven to be a suitable method for disposal of wet organic residuals with several advantages compared to other treatment technologies. This paper aimed at evaluating the theoretical and experimental biomethane potential of food processing residuals that are responsible for negative environmental impact, with exemplification for the potato processing waste. The biomethane potential is a useful parameter to assess the economic efficiency of anaerobic digestion processes as it can considerably influence the efficiency and the economic feasibility of the energy recovery technologies. Both experimental and theoretical biomethane potentials of potato waste in mesophilic anaerobic digestion as well as biodegradability of the substrate were estimated. Moreover, effects of microalgal extract addition on the digestion of potato waste were examined in an attempt to stimulate the anaerobic digestion. Cone model and a modified Gompertz model were used to predict the dynamics of biomethane production.

scholarly journals Geometric Modelling of Octagonal Lamp Poles

2014 ◽  
Vol XL-5 ◽  
pp. 145-150 ◽  
Author(s):  
T. O. Chan ◽  
D. D. Lichti
Keyword(s):  
Point Cloud ◽  
Geometric Model ◽  
Real Data ◽  
Point Clouds ◽  
Circular Cone ◽  
Basic Point ◽  
The Real ◽  
Cone Model ◽  
Proposed Model ◽  
Pole Point

Lamp poles are one of the most abundant highway and community components in modern cities. Their supporting parts are primarily tapered octagonal cones specifically designed for wind resistance. The geometry and the positions of the lamp poles are important information for various applications. For example, they are important to monitoring deformation of aged lamp poles, maintaining an efficient highway GIS system, and also facilitating possible feature-based calibration of mobile LiDAR systems. In this paper, we present a novel geometric model for octagonal lamp poles. The model consists of seven parameters in which a rotation about the z-axis is included, and points are constrained by the trigonometric property of 2D octagons after applying the rotations. For the geometric fitting of the lamp pole point cloud captured by a terrestrial LiDAR, accurate initial parameter values are essential. They can be estimated by first fitting the points to a circular cone model and this is followed by some basic point cloud processing techniques. The model was verified by fitting both simulated and real data. The real data includes several lamp pole point clouds captured by: (1) Faro Focus 3D and (2) Velodyne HDL-32E. The fitting results using the proposed model are promising, and up to 2.9 mm improvement in fitting accuracy was realized for the real lamp pole point clouds compared to using the conventional circular cone model. The overall result suggests that the proposed model is appropriate and rigorous.

scholarly journals Calculating Point Cloud Object Volume Using Co-Opposite-Direction Slicing Method

2019 ◽  
Author(s):  
Bin Li ◽  
Xiaowei Bi ◽  
Cheng Peng ◽  
Yong Chen ◽  
Xiaofa Zhao ◽  
...  
Keyword(s):  
Full Advantage ◽  
Opposite Direction ◽  
Point Cloud ◽  
Systematic Errors ◽  
Random Errors ◽  
Improved Method ◽  
Cone Model ◽  
Slicing Method

Although the Slicing Method (SM) is effective for calculating the volume of point cloud objects (PCOs), it is restricted in terms of applicability and practicability because of a certain contingency and directional defects. The Co-Opposite-Direction Slicing Method (CODSM) proposed in this paper is an improved method for calculating PCO volume by increasing parallel (co-opposite-direction) observation and considering the two-way mean as the result. This method takes full advantage of the mutual offsetting of random errors and the compensation of systematic directional errors, which can effectively overcome (or mitigate) the effect of random errors and reduce the effect of systematic errors in SM. In this paper, two typical objects, a cone model and a stone lion base, are the examples for calculating PCO volume using CODSM. The results show that CODSM has all the inherent advantages of SM and effectively weakens the volatility of random errors and the directionality of systematic errors from SM. Therefore, CODSM is a robust configuration upgrade of SM.

scholarly journals An operational solar wind prediction system transitioning fundamental science to operations

2018 ◽  
Vol 8 ◽  
pp. A39 ◽  
Author(s):  
Jingjing Wang ◽  
Xianzhi Ao ◽  
Yuming Wang ◽  
Chuanbing Wang ◽  
Yanxia Cai ◽  
...  
Keyword(s):  
Magnetic Field ◽  
Solar Wind ◽  
Arrival Time ◽  
Space Environment ◽  
Maximum Speed ◽  
Source Surface ◽  
Prediction System ◽  
Fundamental Science ◽  
Cone Model ◽  
The Mean

We present in this paper an operational solar wind prediction system. The system is an outcome of the collaborative efforts between scientists in research communities and forecasters at Space Environment Prediction Center (SEPC) in China. This system is mainly composed of three modules: (1) a photospheric magnetic field extrapolation module, along with the Wang-Sheeley-Arge (WSA) empirical method, to obtain the background solar wind speed and the magnetic field strength on the source surface; (2) a modified Hakamada-Akasofu-Fry (HAF) kinematic module for simulating the propagation of solar wind structures in the interplanetary space; and (3) a coronal mass ejection (CME) detection module, which derives CME parameters using the ice-cream cone model based on coronagraph images. By bridging the gap between fundamental science and operational requirements, our system is finally capable of predicting solar wind conditions near Earth, especially the arrival times of the co-rotating interaction regions (CIRs) and CMEs. Our test against historical solar wind data from 2007 to 2016 shows that the hit rate (HR) of the high-speed enhancements (HSEs) is 0.60 and the false alarm rate (FAR) is 0.30. The mean error (ME) and the mean absolute error (MAE) of the maximum speed for the same period are −73.9 km s−1 and 101.2 km s−1, respectively. Meanwhile, the ME and MAE of the arrival time of the maximum speed are 0.15 days and 1.27 days, respectively. There are 25 CMEs simulated and the MAE of the arrival time is 18.0 h.

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