How to mine gold without digging

2019 ◽  
Vol 06 (01) ◽  
pp. 1950009
Author(s):  
Kevin Guo ◽  
Tim Leung ◽  
Brian Ward
Keyword(s):  
Optimal Control ◽  
Kalman Filter ◽  
Real Options ◽  
Factor Loadings ◽  
Proposed Model ◽  
Two Factors ◽  
Replicating Portfolio

This paper examines the main drivers of the returns of gold miner stocks and ETFs during 2006–2017. We solve a combined optimal control and stopping problem to demonstrate that gold miner equities behave like real options on gold. Inspired by our proposed model, we construct a method to dynamically replicate gold miner stocks using two factors: the spot gold ETF and market equity portfolio. Furthermore, through each firm’s factor loadings on the replicating portfolio, we dynamically infer the firm’s implied leverage parameters of our model using the Kalman Filter. We find that our approach can explain a significant portion of the drivers of firm implied gold leverage. We posit that gold miner companies hold additional real options which help mitigate firm downside volatility, but these real options contribute to lower returns relative to the replicating portfolio when gold returns are positive.

Real-time SOC Estimation of the EKF and UKF Algorithms Under Different Working Conditions

2021 ◽  
pp. 1-29
Author(s):  
Xiongbin Peng ◽  
Yuwu Li ◽  
Wei Yang ◽  
Akhil Garg
Keyword(s):  
Kalman Filter ◽  
Working Conditions ◽  
Unscented Kalman Filter ◽  
Stress Test ◽  
Estimation Method ◽  
Influential Factor ◽  
Recursive Least Squares ◽  
Maximum Deviation ◽  
Soc Estimation ◽  
Proposed Model

Abstract In the battery thermal management system (BMS), the state of charge (SOC) is a very influential factor, which can prevent overcharge and over-discharge of the lithium-ion battery (LIB). This paper proposed a battery modeling and online battery parameter identification method based on the Thevenin equivalent circuit model (ECM) and recursive least squares (RLS) algorithm. The proposed model proved to have high accuracy. The error between the ECM terminal voltage value and the actual value basically fluctuates between ±0.1V. The extended Kalman filter (EKF) algorithm and the unscented Kalman filter (UKF) algorithm were applied to estimate the SOC of the battery based on the proposed model. The SOC experimental results obtained under dynamic stress test (DST), federal urban driving schedule (FUDS), and US06 cycle conditions were analyzed. The maximum deviation of the SOC based on EKF was 1.4112%~2.5988%, and the maximum deviation of the SOC based on UKF was 0.3172%~0.3388%. The SOC estimation method based on UKF and RLS provides a smaller deviation and better adaptability in different working conditions, which makes it more implementable in a real-world automobile application.

scholarly journals The Optimal Felling Rate in the Palm Oil Plantation System

MATEMATIKA ◽  
2019 ◽  
Vol 35 (1) ◽  
pp. 95-104
Author(s):  
Mohd Ismail Abd Aziz ◽  
Noryanti Nasir ◽  
Akbar Banitalebi
Keyword(s):  
Optimal Control ◽  
Palm Oil ◽  
Oil Palm ◽  
Oil Production ◽  
Real Data ◽  
Palm Trees ◽  
Environmental Friendly ◽  
Proposed Model ◽  
Carbon Absorption ◽  
Plantation System

Successful palm oil plantation should have high returns profit, clean and environmental friendly. Since oil palm trees have a long life and it takes years to be fully grown, controlling the felling rate of the palm oil trees is a fundamental challenge. It needs to be addressed in order to maximize oil production. However, a good arrangement of the felling palm oil trees may still affect the amount of carbon absorption. The objective of this study is to develop an optimal felling model of the palm oil plantation system taking into account both oil production and carbon absorption. The model facilitates in providing the optimal control of felling rate that results in maximizing both oil production and carbon absorption. With this aim, the model is formulated considering palm oil biomass, carbon absorption rate, oil production rate and the average prices of carbon and oil palm. A set of real data is used to estimate the parameters of the model and numerical simulation is conducted to highlight the application of the proposed model. The resulting parameter estimation is solved that leads to an optimal control of felling rate problem.

scholarly journals Multimodal Capacitated Hub Location Problems with Multi-Commodities: An Application in Freight Transport

2020 ◽  
Vol 2020 ◽  
pp. 1-9 ◽  
Author(s):  
Alan Osorio-Mora ◽  
Francisco Núñez-Cerda ◽  
Gustavo Gatica ◽  
Rodrigo Linfati
Keyword(s):  
Latin American ◽  
Location Problems ◽  
Transport Cost ◽  
Mixed Integer ◽  
Hub Location ◽  
Proposed Model ◽  
Multimodal Transport ◽  
Two Factors ◽  
Commercial Solver

Hub location problems (HLPs) support decision making on multimodal transport strategic planning. It is related to the location of hubs and the allocation of origin/destination (O/D) flow in a system. Classical formulations assume that these flows are predefined paths and direct delivery is not available. This applied research presents a mixed integer linear programming (MILP) model for a capacitated multimodal, multi-commodity HLP. Furthermore, an application on the export process in a Latin American country is detailed. The new proposed model, unlike the traditional HLP, allows direct shipment, and its O/D flows are part of the decision model. Situations with up to 100 nodes, six products, and two transport modes are used, working with initial and projected flows. All instances can be solved optimally using the commercial solver, Gurobi 7.5.0, in computational times less than a minute. Results indicate that only one hub is profitable for the case study, both for the initial and projected scenarios. The installation of a hub generates transport savings over 1% per year. Two factors affect the location decision: low concentration and distance between the hubs and destinations. Long distances involve an exhaustive use of trains instead of trucks, which leads to lower transport cost per unit.

Human Driver Modeling Based on Analytical Optimal Solutions: Stopping Behaviors at the Intersections

2020 ◽  
Vol 1 (1) ◽  
Author(s):  
Jihun Han ◽  
Dominik Karbowski ◽  
Namdoo Kim ◽  
Aymeric Rousseau
Keyword(s):  
Optimal Control ◽  
State Constraint ◽  
Driver Model ◽  
Optimal Solutions ◽  
Driving Behaviors ◽  
Human Driver ◽  
Proposed Model ◽  
Wide Range ◽  
Instrumented Vehicle ◽  
Road Testing

Abstract Safe and energy-efficient driving of connected and automated vehicles (CAVs) must be influenced by human-driven vehicles. Thus, to properly evaluate the energy impacts of CAVs in a simulation framework, a human driver model must capture a wide range of real-world driving behaviors corresponding to the surrounding environment. This paper formulates longitudinal human driving as an optimal control problem with a state constraint imposed by the vehicle in front. Deriving analytically optimal solutions by employing optimal control theory can capture longitudinal human driving behaviors with low computational burden, and adding the state constraint can assist with describing car-following features while anticipating behaviors of the vehicle in front. We also use on-road testing data collected by an instrumented vehicle to validate the proposed human driver model for stop scenarios at intersections. Results show that vehicle stopping trajectories of the proposed model are well matched with those of experimental data.

Generalized software release and testing stop time policy

2019 ◽  
Vol 37 (6/7) ◽  
pp. 1087-1111 ◽  
Author(s):  
Avinash Kumar Shrivastava ◽  
Nitin Sachdeva
Keyword(s):  
Market Competition ◽  
Content Type ◽  
Early Release ◽  
Stop Time ◽  
Proposed Model ◽  
Software Firms ◽  
Generalized Framework ◽  
Two Factors ◽  
Software Release ◽  
The Cost

Purpose Almost everything around us is the output of software-driven machines or working with software. Software firms are working hard to meet the user’s requirements. But developing a fault-free software is not possible. Also due to market competition, firms do not want to delay their software release. But early release software comes with the problem of user reporting more failures during operations due to more number of faults lying in it. To overcome the above situation, software firms these days are releasing software with an adequate amount of testing instead of delaying the release to develop reliable software and releasing software patches post release to make the software more reliable. The paper aims to discuss these issues. Design/methodology/approach The authors have developed a generalized framework by assuming that testing continues beyond software release to determine the time to release and stop testing of software. As the testing team is always not skilled, hence, the rate of detection correction of faults during testing may change over time. Also, they may commit an error during software development, hence increasing the number of faults. Therefore, the authors have to consider these two factors as well in our proposed model. Further, the authors have done sensitivity analysis based on the cost-modeling parameters to check and analyze their impact on the software testing and release policy. Findings From the proposed model, the authors found that it is better to release early and continue testing in the post-release phase. By using this model, firms can get the benefits of early release, and at the same time, users get the benefit of post-release software reliability assurance. Originality/value The authors are proposing a generalized model for software scheduling.

Short Span Compressive Stress-Strain Relation and Model of Molded Pulp Material

2010 ◽  
Vol 450 ◽  
pp. 202-205 ◽  
Author(s):  
Hong Wei Ji ◽  
Huai Wen Wang
Keyword(s):  
Elastic Modulus ◽  
Ultimate Strength ◽  
Loading Rate ◽  
Test Machine ◽  
Stress Strain ◽  
Stress Strain Relation ◽  
Proposed Model ◽  
Two Factors ◽  
Short Span ◽  
Major Factors

Short span compressive experiments of molded pulp specimens were carried out on the SHIMADZU material test machine, resulting in the stress-strain curves. The analytic results indicate that the material density and the loading rate are the two major factors that influence the stress-strain relationships of the molded pulp materials. With the increase of material’s density, elastic modulus and ultimate strength both increase. With the increase of loading rate, elastic modulus decreases whereas ultimate strength increases. By analyzing the test results and the existing models, an improved stress-strain model for molded pulp material, with the two factors taken into consideration, has been proposed. The model coefficients are obtained by fitting against the short span compressive experimental data for the materials with different densities under different loading rate. Comparison made between the experimental results and calculated results indicates that the proposed model can well fit the stress-strain curves of molded pulp.

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