scholarly journals A Single-Copter UWB-Ranging-Based Localization System Extendable to a Swarm of Drones

Drones ◽  
2021 ◽  
Vol 5 (3) ◽  
pp. 85 ◽  
Author(s):  
Christoph Steup ◽  
Jonathan Beckhaus ◽  
Sanaz Mostaghim
Keyword(s):  
Nonlinear Optimization ◽  
High Precision ◽  
Precision Measurement ◽  
Gradient Descent ◽  
Optimization Problem ◽  
Single Type ◽  
Localization System ◽  
Gradient Descent Algorithm ◽  
Update Rate ◽  
Anchor Points

This paper presents a single-copter localization system as a first step towards a scalable multihop drone swarm localization system. The drone was equipped with ultrawideband (UWB) transceiver modules, which can be used for communication, as well as distance measurement. The location of the drone was detected based on fixed anchor points using a single type of UWB transceiver. Our aim is to create a swarm localization system that enables drones to switch their role between an active swarm member and an anchor node to enhance the localization of the whole swarm. To this end, this paper presents our current baseline localization system and its performance regarding single-drone localization with fixed anchors and its integration into our current modular quadcopters, which was designed to be easily extendable to a swarm localization system. The distance between each drone and the anchors was measured periodically, and a specially tailored gradient descent algorithm was used to solve the resulting nonlinear optimization problem. Additional copter and wireless-specific adaptations were performed to enhance the robustness. The system was tested with a Vicon system as a position reference and showed a high precision of 0.2 m with an update rate of <10 Hz. Additionally, the system was integrated into the FINken copters of the SwarmLab and evaluated in multiple outdoor scenarios. These scenarios showed the generic usability of the approach, even though no accurate precision measurement was possible.

scholarly journals Harbor Aquaculture Area Extraction Aided with an Integration-Enhanced Gradient Descent Algorithm

2021 ◽  
Vol 13 (22) ◽  
pp. 4554
Author(s):  
Yafeng Zhong ◽  
Siyuan Liao ◽  
Guo Yu ◽  
Dongyang Fu ◽  
Haoen Huang
Keyword(s):  
High Precision ◽  
Gradient Descent ◽  
Supervised Classification ◽  
Spectral Feature ◽  
Likelihood Method ◽  
Support Vector ◽  
Gradient Descent Algorithm ◽  
Aquaculture Area ◽  
Feature Information ◽  
Extraction Performance

In this study, the harbor aquaculture area tested is Zhanjiang coast, and for the remote sensing data, we use images from the GaoFen-1 satellite. In order to achieve a superior extraction performance, we propose the use of an integration-enhanced gradient descent (IEGD) algorithm. The key idea of this algorithm is to add an integration gradient term on the basis of the gradient descent (GD) algorithm to obtain high-precision extraction of the harbor aquaculture area. To evaluate the extraction performance of the proposed IEGD algorithm, comparative experiments were performed using three supervised classification methods: the neural network method, the support vector machine method, and the maximum likelihood method. From the results extracted, we found that the overall accuracy and F-score of the proposed IEGD algorithm for the overall performance were 0.9538 and 0.9541, meaning that the IEGD algorithm outperformed the three comparison algorithms. Both the visualized and quantitative results demonstrate the high precision of the proposed IEGD algorithm aided with the CEM scheme for the harbor aquaculture area extraction. These results confirm the effectiveness and practicality of the proposed IEGD algorithm in harbor aquaculture area extraction from GF-1 satellite data. Added to that, the proposed IEGD algorithm can improve the extraction accuracy of large-scale images and be employed for the extraction of various aquaculture areas. Given that the IEGD algorithm is a type of supervised classification algorithm, it relies heavily on the spectral feature information of the aquaculture object. For this reason, if the spectral feature information of the region of interest is not selected properly, the extraction performance of the overall aquaculture area will be extremely reduced.

scholarly journals A New Hybrid Optimizer for Global Optimization Based on a Comparative Study Remarks of Classical Gradient Descent Variants

2021 ◽  
Vol 9 (3) ◽  
pp. 630-664
Author(s):  
Mouad Touarsi ◽  
Driss Gretete ◽  
Abdelmajid Elouadi
Keyword(s):  
Global Optimization ◽  
Gradient Descent ◽  
Optimization Problem ◽  
Local Minima ◽  
Test Functions ◽  
Empirical Comparison ◽  
Test Function ◽  
R Language ◽  
Gradient Descent Algorithm ◽  
Range Of Values

In this paper, we present an empirical comparison of some Gradient Descent variants used to solve globaloptimization problems for large search domains. The aim is to identify which one of them is more suitable for solving an optimization problem regardless of the features of the used test function. Five variants of Gradient Descent were implemented in the R language and tested on a benchmark of five test functions. We proved the dependence between the choice of the variant and the obtained performances using the khi-2 test in a sample of 120 experiments. Those test functions vary on convexity, the number of local minima, and are classified according to some criteria. We had chosen a range of values for each algorithm parameter. Results are compared in terms of accuracy and convergence speed. Based on the obtained results,we defined the priority of usage for those variants and we contributed by a new hybrid optimizer. The new optimizer is testedin a benchmark of well-known test functions and two real applications are proposed. Except for the classical gradient descent algorithm, only stochastic versions of those variants are considered in this paper.

Control of Multivariable Coupling System Based on Improved PID Neural Networks

2012 ◽  
Vol 157-158 ◽  
pp. 386-389
Author(s):  
Jun Kang ◽  
Wen Jun Meng
Keyword(s):  
Neural Networks ◽  
High Precision ◽  
Gradient Descent ◽  
Pso Algorithm ◽  
Initial Structure ◽  
Descent Algorithm ◽  
Coupling System ◽  
Gradient Descent Algorithm ◽  
Short Time

The paper analyzes the characteristics and the situation of the control of multivariable coupling system, combining improved PID neural networks with PSO algorithm, and finally designs a suitable controller model. To achieve a controller model, an initial structure of PID neural networks is established in the first place. Then weights in the network is initialized by PSO algorithm and optimized by improved gradient descent algorithm. A simulation, MIMO coupling system, prove this controller has such characteristics with short time and high precision. The research of the paper provides a new idea and approach for control of complex coupling system.

High precision measurement of the SOS surface thickness in the rough phase

1991 ◽  
Vol 1 (12) ◽  
pp. 1669-1673 ◽  
Author(s):  
Hans Gerd Evertz ◽  
Martin Hasenbusch ◽  
Mihail Marcu ◽  
Klaus Pinn ◽  
Sorin Solomon
Keyword(s):  
High Precision ◽  
Precision Measurement ◽  
High Precision Measurement ◽  
Surface Thickness

scholarly journals Revisiting the dynamic interactions between economic growth and environmental pollution in Italy: evidence from a gradient descent algorithm

2021 ◽  
Author(s):  
Marco Mele ◽  
Cosimo Magazzino ◽  
Nicolas Schneider ◽  
Floriana Nicolai
Keyword(s):  
Economic Growth ◽  
Gradient Descent ◽  
Statistical Data ◽  
Stochastic Gradient Descent ◽  
Policy Recommendations ◽  
Dynamic Interactions ◽  
Gradient Descent Algorithm ◽  
The Relationship ◽  
Main Strand ◽  
Yearly Data

AbstractAlthough the literature on the relationship between economic growth and CO2 emissions is extensive, the use of machine learning (ML) tools remains seminal. In this paper, we assess this nexus for Italy using innovative algorithms, with yearly data for the 1960–2017 period. We develop three distinct models: the batch gradient descent (BGD), the stochastic gradient descent (SGD), and the multilayer perceptron (MLP). Despite the phase of low Italian economic growth, results reveal that CO2 emissions increased in the predicting model. Compared to the observed statistical data, the algorithm shows a correlation between low growth and higher CO2 increase, which contradicts the main strand of literature. Based on this outcome, adequate policy recommendations are provided.

scholarly journals BATCH PROCESSING OF TREE-RING SAMPLES FOR RADIOCARBON ANALYSIS

Radiocarbon ◽  
2020 ◽  
pp. 1-13
Author(s):  
Alexandra Fogtmann-Schulz ◽  
Sabrina G K Kudsk ◽  
Florian Adolphi ◽  
Christoffer Karoff ◽  
Mads F Knudsen ◽  
...  
Keyword(s):  
High Precision ◽  
Tree Rings ◽  
Precision Measurement ◽  
Radiocarbon Dating ◽  
The Other ◽  
High Precision Measurement ◽  
Machine Performance ◽  
High Throughput Method ◽  
Ca 50 ◽  
Low Activity

ABSTRACT We here present a comparison of methods for the pretreatment of a batch of tree rings for high-precision measurement of radiocarbon at the Aarhus AMS Centre (AARAMS), Aarhus University, Denmark. The aim was to develop an efficient and high-throughput method able to pretreat ca. 50 samples at a time. We tested two methods for extracting α-cellulose from wood to find the most optimal for our use. One method used acetic acid, the other used HCl acid for the delignification. The testing was conducted on background 14C samples, in order to assess the effect of the different pretreatment methods on low-activity samples. Furthermore, the extracted wood and cellulose fractions were analyzed using Fourier transform infrared (FTIR) spectroscopy, which showed a successful extraction of α-cellulose from the samples. Cellulose samples were pretreated at AARAMS, and the graphitization and radiocarbon analysis of these samples were done at both AARAMS and the radiocarbon dating laboratory at Lund University to compare the graphitization and AMS machine performance. No significant offset was found between the two sets of measurements. Based on these tests, the pretreatment of tree rings for high-precision radiocarbon analysis at AARAMS will henceforth use HCI for the delignification.

scholarly journals Adaptive Gradient Estimation Stochastic Parallel Gradient Descent Algorithm for Laser Beam Cleanup

Photonics ◽  
2021 ◽  
Vol 8 (5) ◽  
pp. 165
Author(s):  
Shiqing Ma ◽  
Ping Yang ◽  
Boheng Lai ◽  
Chunxuan Su ◽  
Wang Zhao ◽  
...  
Keyword(s):  
Solid State ◽  
Adaptive Optics ◽  
Gradient Descent ◽  
Solid State Laser ◽  
Gradient Estimation ◽  
State Laser ◽  
Gradient Descent Algorithm ◽  
Stochastic Parallel Gradient Descent ◽  
High Output ◽  
Improved Algorithm

For a high-power slab solid-state laser, obtaining high output power and high output beam quality are the most important indicators. Adaptive optics systems can significantly improve beam qualities by compensating for the phase distortions of the laser beams. In this paper, we developed an improved algorithm called Adaptive Gradient Estimation Stochastic Parallel Gradient Descent (AGESPGD) algorithm for beam cleanup of a solid-state laser. A second-order gradient of the search point was introduced to modify the gradient estimation, and it was introduced with the adaptive gain coefficient method into the classical Stochastic Parallel Gradient Descent (SPGD) algorithm. The improved algorithm accelerates the search for convergence and prevents it from falling into a local extremum. Simulation and experimental results show that this method reduces the number of iterations by 40%, and the algorithm stability is also improved compared with the original SPGD method.

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