scholarly journals Application of Levenberg-Marguardt Algorithm for Prime Radio Propagation Wave Attenuation Modelling in Typical Urban, Suburban and Rural Terrains

2021 ◽  
Vol 13 (3) ◽  
pp. 35-42
Author(s):  
Joseph Isabona ◽  
◽  
Divine O. Ojuh
Keyword(s):  
Wave Attenuation ◽  
Signal Propagation ◽  
Absolute Error ◽  
Initial Guess ◽  
Propagation Loss ◽  
High Approximation ◽  
Complex Task ◽  
Maximum Absolute Error ◽  
Loss Model ◽  
Marquardt Algorithm

The desire to achieve an adaptive prognostics regression learning processes of physical and empirical phenomenon is a complex task and open problem in radio frequency telecommunication engineering. One key method to solving such complex task or problems is by means of numerical based optimisation algorithms. The Levenberg–Marquardt algorithm (LMA) is an efficient nonlinear parametric machine learning based modelling algorithm with optimal, fast, and accurate convergence speed. This paper proposes and demonstrates the real-time application of the LMA in developing a log-distance like propagation loss model based on received radio strength measurements conducted over deployed long term evolution (LTE) eNodeBs antennas in three different propagation areas. The LTE eNodeB signal propagation areas were selected to reflect typical urban, suburban and rural terrains which represent urban, suburban and rural terrains. The heights of the three eNodeBs are 30, 28 and 32m respectively and each operate at 2.6GHz carrier frequency with 10MHz channel bandwidths. The resultant outcome of the proposed propagation loss modelling using LMA indicates a high approximation efficacy over the popular Gauss-Newton algorithm (GNA) modelling method, which has been used to benchmark the process. Precisely, the developed propagation loss model using LMA method attained lower maximum absolute error (MABE) of 7.73, 14.57and 10.53 for urban, suburban and rural terrains compared to the ones developed by GNA which yielded 15.19, 16.59 and 13.05 MABE values. The improved approximation performance of the LMA over the GNA can be ascribed to its capacity handle multiple free parameters and attain optimum solution irrespective of the selected values of initial guess parameters.

Error Compensation Techniques for Fixed-Width Array Multiplier Design — A Technical Survey

2016 ◽  
Vol 26 (03) ◽  
pp. 1730003 ◽  
Author(s):  
S. Balamurugan ◽  
P. S. Mallick
Keyword(s):  
Error Compensation ◽  
Absolute Error ◽  
Mean Square ◽  
Maximum Absolute Error ◽  
Comprehensive Review ◽  
Related Research ◽  
Error Formula ◽  
Error Metrics ◽  
Array Multiplier ◽  
Future Work

This paper provides a comprehensive review of various error compensation techniques for fixed-width multiplier design along with its applications. In this paper, we have studied different error compensation circuits and their complexities in the fixed-width multipliers. Further, we present the experimental results of error metrics, including normalized maximum absolute error [Formula: see text], normalized mean error [Formula: see text] and normalized mean-square error [Formula: see text] to evaluate the accuracy of fixed-width multipliers. This survey is intended to serve as a suitable guideline and reference for future work in fixed-width multiplier design and its related research.

Spatial Dynamic Analysis of a Mangeove Patch Based on Unmanned Aerial Vehicle: A Case Study in Pear Bay in Guangxi, China

2021 ◽  
Author(s):  
Xin Lin ◽  
Chungan Li ◽  
Mei Zhou ◽  
Wenhai Liang ◽  
Biao Li
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Spatial Dynamics ◽  
Absolute Error ◽  
Maximum Error ◽  
Canopy Height ◽  
Accurate Information ◽  
Maximum Absolute Error ◽  
Patch Area ◽  
Maximum Increase ◽  
Aerial Vehicle

Abstract This study investigated the short-term spatial variability of an mangrove patch, located in the Pearl Bay in Guangxi, China. Unmanned aerial vehicle (UAV) imagery covering the period from March 2015 to October 2017 were used and the following models were developed: two annual ultra-high resolution spatial resolution digital orthophoto maps (DOMs), two digital elevation models (DEMs), two digital surface models (DSMs), two canopy height models (CHMs), and a canopy height difference model (d-CHM). Using these models, the spatial dynamics of the extent and canopy height of the patch were analyzed. The resolution of the DOMs was 0.1 m, with an average geometrical error of 0.17 m and a maximum error of 0.44 m. The resolutions of DEMs, DSMs, CHMs, d-CHM were all 1 m. The average elevation errors of CHM in 2015 and 2017 were 0.002 m and -0.001 m, respectively, with maximum absolute errors of 0.034 m and 0.030 m, respectively. The average elevation error of d-CHM was -0.003 m and the maximum absolute error was 0.036 m, and the data quality were rated as good. From 2015 to 2017, the area of the mangrove patch increased from 8.16 ha to 8.79 ha, with an average annual increase of 3.7%. Specifically, the areas of expansion, shrinkage, and maximum seaward expansion were 6356 m2, 19 m2, and 24 m, respectively. The driving factor for the variability was natural processes. Stand canopy height exhibited a particular trend of decrease from northwest to southeast (horizontal; parallel to the seawall) and from the land to the sea (vertically; perpendicular to the seawall). From 2015 to 2017, 88.2% of the patch area showed increased canopy height, with an average increase of 0.78 m and a maximum increase of 3.2 m. In contrast, 11.8% of the patch area showed decreased canopy height with a maximum decrease of 3.1 m. The main reason for the decrease in canopy height was the death of trees caused by serious insect plagues. On the other hand, the reason for the increase in height could be attributed to the natural growth of mangrove trees, but further studies are required to verify the cause. UAV remote sensing has an incomparable advantage over traditional methods in that it provides extremely detailed and highly accurate information for in-depth study of the spatial evolution of mangrove patches, which would significantly contribute towards the protection and management of mangroves.

scholarly journals Method of Measuring Liquid Water Mass Fraction of Snow by Alcohol Solution

1986 ◽  
Vol 32 (112) ◽  
pp. 538-539 ◽  
Author(s):  
D. Fisk
Keyword(s):  
Liquid Water ◽  
Water Mass ◽  
Field Measurements ◽  
Absolute Error ◽  
Liquid Water Content ◽  
Snow Sample ◽  
Maximum Absolute Error ◽  
Water Fraction ◽  
Single Operator ◽  
Temperature Depression

Abstracta method of making field measurements of the liquid water fraction of snow has been developed in which a snow sample is dissolved in methanol to produce a temperature depression. The depression is linearly related to the liquid water content of the snow sample. a single operator can perform four to five measurements per hour with a maximum absolute error of 1.0%.

scholarly journals Distributed Localization Based on Signal Propagation Loss for Underwater Sensor Networks

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 112985-112995 ◽  
Author(s):  
Gang Qiao ◽  
Chen Zhao ◽  
Feng Zhou ◽  
Niaz Ahmed
Keyword(s):  
Sensor Networks ◽  
Signal Propagation ◽  
Propagation Loss ◽  
Underwater Sensor Networks

Effects of Thermo-Physically Initial Properties on Inverse Heat Conduction Problem

2013 ◽  
Vol 284-287 ◽  
pp. 733-737
Author(s):  
Sung Deok Hong ◽  
Mi Gyung Cho ◽  
Chan Soo Kim ◽  
Cheol Ho Bai ◽  
Sung Yull Hong ◽  
...  
Keyword(s):  
Heat Conduction ◽  
Analytical Solution ◽  
Heat Conduction Problem ◽  
Damping Ratio ◽  
Initial Guess ◽  
Convergence Time ◽  
Theoretical Equation ◽  
Inverse Heat Conduction ◽  
Marquardt Algorithm ◽  
Infinite Slab

The Levenberg-Marguardt algorithm is used to study effects on convergence for inverse heat conduction in the unsteady state. In this model, the finite volume method is usedto obtain anestimated temperature, which is necessary for minimizing inverse error. To validate the model, constant thermal conductivity (k) and heat capacity (ρCpC) are identified from a semi-infinite slab subjected to constant heat flux. These properties are inserted into the theoretical equation for a semi-infinite slab, and an analytical solution is obtained by solving the theoretical equation including the two identified properties. The analytical solution and the identified resultare in very good agreement. Three simulations were performed to investigate the sensitivity of computation time and conversion to initial thermo-physical values by changing three different damping ratios of the Levenberg-Marquardt algorithm. Our results show that agood initial guessallowsgood convergence, but convergence time decreases as the value of damping ratio decreases.A poor initial guess results in more convergence time, and causes divergence when a small damping ratio is used. Once the simulation converges, our model shows that results areobtained within an error of 0.01%.

Prediction Model of Micum Strength for Iron Ore Sinter

2011 ◽  
Vol 402 ◽  
pp. 476-479
Author(s):  
Wei Wang ◽  
Zhi Hui Xu ◽  
Long Long Yang ◽  
Zheng Liang Xue ◽  
Dong Nan Zhao ◽  
...  
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Absolute Error ◽  
Maximum Absolute Error ◽  
Important Indicator ◽  
The Neural Network ◽  
Iron Ore Sinter ◽  
Real Value ◽  
The Law

Micum strength is an important indicator of quality of sinter; BP artificial neural network model is built to predict the strength of sinter drum. The neural network use the main factors that influence the sinter drum as input data, and output is Micum strength. Experiment results shows that the maximum absolute error between the Micum strength predicted by neural network and real value from the sinter plant is 0.3346, and the average absolute error is 0.1154. These prove that the prediction is accuracy. In addition, because of the "black box" characteristic of the neural network model, the neural network model can not give the law of how the various factors affect the micum strength of sinter ore, this paper also uses the model to analysis the law of how TFe, SiO2 content affect the micum strength. The results not only consist with the sintering theory, but also verify the validity of the model.

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