Analytical and numerical optimization of gravimetric networks: a case study from Mount Etna, Italy

2020 ◽  
Author(s):  
Mehdi Nikkhoo ◽  
Eleonora Rivalta ◽  
Daniele Carbone ◽  
Flavio Cannavò
Keyword(s):  
Analytical Solutions ◽  
Numerical Optimization ◽  
Optimal Solution ◽  
Optimization Techniques ◽  
Mount Etna ◽  
Mass Change ◽  
Target Zone ◽  
Magmatic Fluids ◽  
The Impact ◽  
Different Parts

<p>The transport of magma and magmatic fluids is a key process behind the occurrence, duration and intensity of volcanic crises. Volcano gravimetry allows for unequivocal inference of the location and mass of accumulated or removed magmatic fluids at volcanoes. This task is best accomplished through collecting gravity time series at multiple stations simultaneously. The performance of individual gravimeters and the configuration of the gravimetric array, however, determine the threshold of detectable mass change and the ability of the array to minimize the uncertainty on the inferred quantities.</p><p>We utilize numerical optimization techniques to design a network including one absolute quantum gravimeter (AQG), two superconducting relative gravimeters (iGRAVs) and several microelectromechanical system (MEMS) relative gravimeters at Mount Etna. We also develop analytical solutions for simple design problems. We show that the analytical solutions are essential for validating the numerical optimization procedure. We provide practical details and caveats that should be considered in similar gravimetric network optimizations. These include 1) specifying the target zone of the network by using the history of mass transport, 2) accounting for the relative importance of  different parts of the target zone, 3) accounting for logistic and instrumental constraints in the optimizations  4) calibrating the objective functions associated with various optimizations, 5) analyzing the network sensitivities to different parts of the target zone and identifying blind zones and  6) calculating the optimal number of gravimeters as a function of the sensor sensitivity and accuracies. We show that our optimal solution for Mount Etna provides an improved detection power across the target zone as compared to an equally spaced network of gravimeters with the same existing constraints, surface topography and sensor sensitivities. Furthermore, this optimal solution ensures that a certain range of mass change anywhere in the target zone can be sensed by a given minimum number of gravimeters and at the same time minimizes the impact of random observation errors on the inferred quantities.</p>

scholarly journals Empirical Stochastic Model of Multi-GNSS Measurements

Sensors ◽  
2021 ◽  
Vol 21 (13) ◽  
pp. 4566
Author(s):  
Dominik Prochniewicz ◽  
Kinga Wezka ◽  
Joanna Kozuchowska
Keyword(s):  
Stochastic Model ◽  
Density Ratio ◽  
Functional Model ◽  
Optimal Solution ◽  
Global Navigation Satellite Systems ◽  
Unknown Parameters ◽  
Short Baseline ◽  
Stochastic Parameters ◽  
Dependent Model ◽  
The Impact

The stochastic model, together with the functional model, form the mathematical model of observation that enables the estimation of the unknown parameters. In Global Navigation Satellite Systems (GNSS), the stochastic model is an especially important element as it affects not only the accuracy of the positioning model solution, but also the reliability of the carrier-phase ambiguity resolution (AR). In this paper, we study in detail the stochastic modeling problem for Multi-GNSS positioning models, for which the standard approach used so far was to adopt stochastic parameters from the Global Positioning System (GPS). The aim of this work is to develop an individual, empirical stochastic model for each signal and each satellite block for GPS, GLONASS, Galileo and BeiDou systems. The realistic stochastic model is created in the form of a fully populated variance-covariance (VC) matrix that takes into account, in addition to the Carrier-to-Noise density Ratio (C/N0)-dependent variance function, also the cross- and time-correlations between the observations. The weekly measurements from a zero-length and very short baseline are utilized to derive stochastic parameters. The impact on the AR and solution accuracy is analyzed for different positioning scenarios using the modified Kalman Filter. Comparing the positioning results obtained for the created model with respect to the results for the standard elevation-dependent model allows to conclude that the individual empirical stochastic model increases the accuracy of positioning solution and the efficiency of AR. The optimal solution is achieved for four-system Multi-GNSS solution using fully populated empirical model individual for satellite blocks, which provides a 2% increase in the effectiveness of the AR (up to 100%), an increase in the number of solutions with errors below 5 mm by 37% and a reduction in the maximum error by 6 mm compared to the Multi-GNSS solution using the elevation-dependent model with neglected measurements correlations.

scholarly journals Improving PV Resilience by Dynamic Reconfiguration in Distribution Grids: Problem Complexity and Computation Requirements

Energies ◽  
2021 ◽  
Vol 14 (4) ◽  
pp. 830
Author(s):  
Filipe F. C. Silva ◽  
Pedro M. S. Carvalho ◽  
Luís A. F. M. Ferreira
Keyword(s):  
Distributed Generation ◽  
Optimal Solution ◽  
Dynamic Reconfiguration ◽  
Scheduling Problem ◽  
Low Carbon ◽  
Distribution Grid ◽  
Medium Voltage ◽  
Classical Computing ◽  
The Impact ◽  
Distribution Grids

The dissemination of low-carbon technologies, such as urban photovoltaic distributed generation, imposes new challenges to the operation of distribution grids. Distributed generation may introduce significant net-load asymmetries between feeders in the course of the day, resulting in higher losses. The dynamic reconfiguration of the grid could mitigate daily losses and be used to minimize or defer the need for network reinforcement. Yet, dynamic reconfiguration has to be carried out in near real-time in order to make use of the most updated load and generation forecast, this way maximizing operational benefits. Given the need to quickly find and update reconfiguration decisions, the computational complexity of the underlying optimal scheduling problem is studied in this paper. The problem is formulated and the impact of sub-optimal solutions is illustrated using a real medium-voltage distribution grid operated under a heavy generation scenario. The complexity of the scheduling problem is discussed to conclude that its optimal solution is infeasible in practical terms if relying upon classical computing. Quantum computing is finally proposed as a way to handle this kind of problem in the future.

scholarly journals Exploring optimal control of epidemic spread using reinforcement learning

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Abu Quwsar Ohi ◽  
M. F. Mridha ◽  
Muhammad Mostafa Monowar ◽  
Md. Abdul Hamid
Keyword(s):  
Reinforcement Learning ◽  
Economic Crisis ◽  
Transmission Rate ◽  
Optimal Solution ◽  
Short Length ◽  
Reproduction Rate ◽  
Economic Factors ◽  
Epidemic Spread ◽  
The Impact ◽  
Individual Human

AbstractPandemic defines the global outbreak of a disease having a high transmission rate. The impact of a pandemic situation can be lessened by restricting the movement of the mass. However, one of its concomitant circumstances is an economic crisis. In this article, we demonstrate what actions an agent (trained using reinforcement learning) may take in different possible scenarios of a pandemic depending on the spread of disease and economic factors. To train the agent, we design a virtual pandemic scenario closely related to the present COVID-19 crisis. Then, we apply reinforcement learning, a branch of artificial intelligence, that deals with how an individual (human/machine) should interact on an environment (real/virtual) to achieve the cherished goal. Finally, we demonstrate what optimal actions the agent perform to reduce the spread of disease while considering the economic factors. In our experiment, we let the agent find an optimal solution without providing any prior knowledge. After training, we observed that the agent places a long length lockdown to reduce the first surge of a disease. Furthermore, the agent places a combination of cyclic lockdowns and short length lockdowns to halt the resurgence of the disease. Analyzing the agent’s performed actions, we discover that the agent decides movement restrictions not only based on the number of the infectious population but also considering the reproduction rate of the disease. The estimation and policy of the agent may improve the human-strategy of placing lockdown so that an economic crisis may be avoided while mitigating an infectious disease.

scholarly journals Time-jerk optimal trajectory planning of hydraulic robotic excavator

2021 ◽  
Vol 13 (7) ◽  
pp. 168781402110346
Author(s):  
Yunyue Zhang ◽  
Zhiyi Sun ◽  
Qianlai Sun ◽  
Yin Wang ◽  
Xiaosong Li ◽  
...  
Keyword(s):  
Angular Velocity ◽  
Trajectory Planning ◽  
Optimal Trajectory ◽  
High Efficiency ◽  
Optimal Solution ◽  
Optimal Time ◽  
Target Point ◽  
Robotic Excavator ◽  
The Impact ◽  
Optimal Trajectory Planning

Due to the fact that intelligent algorithms such as Particle Swarm Optimization (PSO) and Differential Evolution (DE) are susceptible to local optima and the efficiency of solving an optimal solution is low when solving the optimal trajectory, this paper uses the Sequential Quadratic Programming (SQP) algorithm for the optimal trajectory planning of a hydraulic robotic excavator. To achieve high efficiency and stationarity during the operation of the hydraulic robotic excavator, the trade-off between the time and jerk is considered. Cubic splines were used to interpolate in joint space, and the optimal time-jerk trajectory was obtained using the SQP with joint angular velocity, angular acceleration, and jerk as constraints. The optimal angle curves of each joint were obtained, and the optimal time-jerk trajectory planning of the excavator was realized. Experimental results show that the SQP method under the same weight is more efficient in solving the optimal solution and the optimal excavating trajectory is smoother, and each joint can reach the target point with smaller angular velocity, and acceleration change, which avoids the impact of each joint during operation and conserves working time. Finally, the excavator autonomous operation becomes more stable and efficient.

From research desk to policy makers and community: MUHAS research symposia and policy briefs targeting the stakeholders

2020 ◽  
Vol 31 (1) ◽  
pp. 120-122
Author(s):  
Hendry R. Sawe ◽  
Bruno F. Sunguya ◽  
Eligius F. Lyamuya
Keyword(s):  
Research Output ◽  
Research Work ◽  
Health Impact ◽  
Scientific Journals ◽  
Child Transmission ◽  
Policy Makers ◽  
One Year ◽  
The Impact ◽  
Mother To Child ◽  
Different Parts

All too frequent, valuable research output and scholarly materials from expensively conducted research work in different parts of the world end up in research desks, academic libraries, and scientific journals. Muhimbili University of Health and Allied Science (MUHAS) through the Directorate of Research and Publications initiated a series of symposia that aim to disseminate the evidence generated by the researchers to the policy makers and the community. In two of the six conducted University-wide symposia in the last one year, MUHAS produced two important policy briefs summarizing the impact of MUHAS research in two important—though distinct areas of local and global health impact—Elimination of Mother to Child Transmission (EMTCT) of HIV, and Diarrhea diseases.

An Alternative Design Approach for Vehicle Crash Safety Using Crash Pulse Optimization

2000 ◽  
Author(s):  
R. J. Yang ◽  
C. H. Tho ◽  
C. C. Gearhart ◽  
Y. Fu
Keyword(s):  
Time Domain ◽  
Numerical Optimization ◽  
Piecewise Linear ◽  
Linear Representation ◽  
Optimization Techniques ◽  
Safety Requirements ◽  
Crash Safety ◽  
Design Variables ◽  
Piecewise Linear Representation ◽  
Occupant Injury

Abstract This paper presents an approach, based on numerical optimization techniques, to identify an ideal (5 star) crash pulse and generate a band of acceptable crash pulses surrounding that ideal pulse. This band can be used by engineers to quickly determine whether a design will satisfy government and corporate safety requirements, and whether the design will satisfy the requirements for a 5 star crash rating. A piecewise linear representation of the crash pulse with two plateaus is employed for its conceptual simplicity and because such a pulse has been shown to be sufficient for reproducing occupant injury behavior when used as input into MADYMO models. The piecewise linear crash pulse is parameterized with 7 design variables (5 for time domain and 2 for acceleration domain) in the optimization process. A series of sample runs are conducted to validate that pulses falling within the acceptable crash pulse band do in fact satisfy 5 star requirements.

Pulp mill process closure: a review of global technology developments and mill experiences in the 1990s

2004 ◽  
Vol 50 (3) ◽  
pp. 183-194 ◽  
Author(s):  
S.C. Stratton ◽  
P.L. Gleadow ◽  
A.P. Johnson
Keyword(s):  
Manufacturing Industry ◽  
Pollution Prevention ◽  
Optimal Solution ◽  
Pulp Mill ◽  
Thermomechanical Pulp ◽  
Pulp Mills ◽  
Oxygen Delignification ◽  
Manufacturing Operations ◽  
The Impact ◽  
Mill Process

The impact of effluent discharges continues to be an important issue for the pulp manufacturing industry. Considerable progress has been made in pollution prevention to minimize waste generation, so-called manufacturing “process closure.” Since the mid-1980s many important technologies have been developed and implemented, many of these in response to organochlorine concerns. Zero effluent operation is now a reality for a few bleached chemi-thermomechanical pulp (BCTMP) pulp mills. In kraft pulp manufacturing, important developments include widespread adoption of new cooking techniques, oxygen delignification, closed screening, improved process control, new bleaching methods, and systems that minimize pulping liquor losses. Coupled to this is a commitment to reduce water use and maximize reuse of in-mill process streams. Some companies pursued bleach plant closure, and many have been successful in eliminating a portion of their bleaching wastewaters. However, the difficulties inherent in closing bleach plants are considerable. For many mills the optimal solution has been found to be a high degree of closure coupled with external biological treatment of the remaining process effluent. No bleach plants at papergrade bleached kraft mills are known to be operating effluent-free on a continuous basis. This paper reviews the important worldwide technological developments and mill experiences in the 1990s that were focused on minimizing environmental impacts of pulp manufacturing operations.

scholarly journals 17Α-METHYLTESTOSTERONE (ANABOLIC ANDROGENIC STEROIDS) ALTERS ACETYLCHOLINESTERASE ENZYME ACTIVITY IN DIFFERENT PARTS OF THE BRAIN IN FEMALE MICE, MUS MUSCULUS

2018 ◽  
Vol 11 (5) ◽  
pp. 410
Author(s):  
Sachin B Patil ◽  
Laxmi S Inamdar
Keyword(s):  
Enzyme Activity ◽  
Ache Activity ◽  
Negative Impact ◽  
Memory Loss ◽  
Anabolic Androgenic Steroids ◽  
Female Mice ◽  
Neuronal Transmission ◽  
Androgenic Steroids ◽  
The Impact ◽  
Different Parts

Aim: Anabolic androgenic steroids (AAS) are synthetic derivatives of the male sex hormone testosterone. Androgens and anabolic steroids have been used for therapeutic purpose with few exceptions. However, the abuse of AAS is a remarkably prevalent problem, particularly among athletes and adolescents. Supraphysiological doses of AAS exert profound effects on mental state and behaviors such as depression, anxiety, aggressiveness, and cognitive deterioration.Objective: In the present investigation, we studied the impact of one of the AAS compounds, i.e., 17α-methyltestosterone on acetylcholinesterase (AChE) enzyme activity in different brain parts of mice, namely, forebrain, hippocampus, midbrain, and hindbrain.Methods: The adult female mice were assigned to four experimental groups to which different doses of 17α-MT (0.5, 5.0 and 7.5 mg/kg bwt, respectively) were administrated s.c. for 30 days. A significant increase in AChE activity in forebrain and midbrain (low and medium dose treatment) suggests a reduction of cholinergic neurotransmission efficiency due to decrease in acetylcholine levels in trans-synaptic cleft. Further, concurrent reduction in AChE activity was observed in whole brain, hippocampus, and hindbrain of 17α-MT-treated mice suggests the impairment in neuronal transmission. Since the regulation of cholinergic system through acetylcholine hydrolysis has been largely attributed to AChE activity, a significant reduction in its activity may lead to stress-related anxiety, memory loss with some cognitive and behavioral aspects in the mice.Conclusion: Based on the observed results, we propose that 17α-MT, an alkylated steroid compound, has a negative impact on AChE enzyme activity in different parts of mice brain, leading to impairment in neuronal transmission.

scholarly journals Decision Support Tool for Offshore Wind Farm Vessel Routing under Uncertainty

Energies ◽  
2018 ◽  
Vol 11 (9) ◽  
pp. 2190 ◽  
Author(s):  
Rafael Dawid ◽  
David McMillan ◽  
Matthew Revie
Keyword(s):  
Wind Farm ◽  
Real Life ◽  
Synthetic Data ◽  
Optimal Solution ◽  
Decision Support Tool ◽  
Offshore Wind ◽  
Offshore Wind Farm ◽  
Support Tool ◽  
Trade Offs ◽  
The Impact

This paper for the first time captures the impact of uncertain maintenance action times on vessel routing for realistic offshore wind farm problems. A novel methodology is presented to incorporate uncertainties, e.g., on the expected maintenance duration, into the decision-making process. Users specify the extent to which these unknown elements impact the suggested vessel routing strategy. If uncertainties are present, the tool outputs multiple vessel routing policies with varying likelihoods of success. To demonstrate the tool’s capabilities, two case studies were presented. Firstly, simulations based on synthetic data illustrate that in a scenario with uncertainties, the cost-optimal solution is not necessarily the best choice for operators. Including uncertainties when calculating the vessel routing policy led to a 14% increase in the number of wind turbines maintained at the end of the day. Secondly, the tool was applied to a real-life scenario based on an offshore wind farm in collaboration with a United Kingdom (UK) operator. The results showed that the assignment of vessels to turbines generated by the tool matched the policy chosen by wind farm operators. By producing a range of policies for consideration, this tool provided operators with a structured and transparent method to assess trade-offs and justify decisions.

scholarly journals Evolutionary Algorithms for Constrained Parameter Optimization Problems

1996 ◽  
Vol 4 (1) ◽  
pp. 1-32 ◽  
Author(s):  
Zbigniew Michalewicz ◽  
Marc Schoenauer
Keyword(s):  
Nonlinear Programming ◽  
Evolutionary Algorithms ◽  
Evolutionary Computation ◽  
Numerical Optimization ◽  
Optimization Problems ◽  
Optimization Techniques ◽  
Test Cases ◽  
Nonlinear Constraints ◽  
Nonlinear Programming Problem ◽  
General Nonlinear Programming

Evolutionary computation techniques have received a great deal of attention regarding their potential as optimization techniques for complex numerical functions. However, they have not produced a significant breakthrough in the area of nonlinear programming due to the fact that they have not addressed the issue of constraints in a systematic way. Only recently have several methods been proposed for handling nonlinear constraints by evolutionary algorithms for numerical optimization problems; however, these methods have several drawbacks, and the experimental results on many test cases have been disappointing. In this paper we (1) discuss difficulties connected with solving the general nonlinear programming problem; (2) survey several approaches that have emerged in the evolutionary computation community; and (3) provide a set of 11 interesting test cases that may serve as a handy reference for future methods.

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