scholarly journals Sensitivity to Calibrated Parameters

2021 ◽  
pp. 1-30
Author(s):  
Thomas H. Jørgensen
Keyword(s):  
Life Cycle ◽  
Low Cost ◽  
Economic Models ◽  
Systematic Investigation ◽  
Model Parameters ◽  
Empirical Application ◽  
Life Cycle Savings

Abstract A common approach to estimation of dynamic economic models is to calibrate a sub-set of model parameters and keep them fixed when estimating the remaining parameters. Calibrated parameters likely affect conclusions based on the model but estimation time often makes a systematic investigation of the sensitivity to calibrated parameters infeasible. I propose a simple and computationally low-cost measure of the sensitivity of parameters and other objects of interest to the calibrated parameters. In the main empirical application, I revisit the analysis of life-cycle savings motives in Gourinchas and Parker (2002) and show that some estimates are sensitive to calibrations.

DURACORR

Alloy Digest ◽  
1997 ◽  
Vol 46 (5) ◽  
Keyword(s):  
Fracture Toughness ◽  
Stainless Steel ◽  
Corrosion Resistance ◽  
Life Cycle ◽  
Physical Properties ◽  
Tensile Properties ◽  
Life Cycle Cost ◽  
Low Cost ◽  
Bend Strength ◽  
Steel Company

Abstract Duracorr is low-cost, utilitarian 11% Cr stainless steel with more corrosion resistance and life-cycle cost advantages than weathering steels. The steel may be used where a combination of abrasion and corrosion resistance is required. This datasheet provides information on composition, physical properties, microstructure, hardness, tensile properties, and bend strength as well as fracture toughness. It also includes information on corrosion resistance as well as joining. Filing Code: SS-680. Producer or source: Lukens Steel Company.

scholarly journals IMAGE NETWORK GENERATION OF UNCALIBRATED UAV IMAGES WITH LOW-COST GPS DATA

2016 ◽  
Vol XLI-B3 ◽  
pp. 31-37 ◽  
Author(s):  
Shan Huang ◽  
Zuxun Zhang ◽  
Jianan He ◽  
Tao Ke
Keyword(s):  
Coordinate System ◽  
Low Cost ◽  
Reference Image ◽  
Model Parameters ◽  
Camera Model ◽  
Orientation Data ◽  
Network Generation ◽  
Free Network ◽  
Uav Images ◽  
Image Network

The use of unmanned air vehicle (UAV) images acquired by a non-metric digital camera to establish an image network is difficult in cases without accurate camera model parameters. Although an image network can be generated by continuously calculating camera model parameters during data processing as an incremental structure from motion (SfM) methods, the process is time consuming. In this study, low-cost global position system (GPS) information is employed in image network generation to decrease computational expenses. Each image is considered as reference, and its neighbor images are determined based on GPS coordinates during processing. The reference image and its neighbor images constitute an image group, which is used to generate a free network through image matching and relative orientation. Data are then transformed from the free network coordinate system of each group into the GPS coordinate system by using the GPS coordinates of each image. After the exterior elements of each image are determined in the GPS coordinate system, the initial image network is established. Finally, self-calibration bundle adjustment constrained by GPS coordinates is conducted to refine the image network. The proposed method is validated on three fields. Results confirm that the method can achieve good image network when accurate camera model parameters are unavailable.

scholarly journals Design and control of a low-cost autonomous profiling float

2020 ◽  
Vol 21 (5) ◽  
pp. 512
Author(s):  
Thomas Le Mézo ◽  
Gilles Le Maillot ◽  
Thierry Ropert ◽  
Luc Jaulin ◽  
Aurélien Ponte ◽  
...  
Keyword(s):  
Kalman Filter ◽  
State Feedback ◽  
Design Method ◽  
Low Cost ◽  
Model Parameters ◽  
Design Rules ◽  
Profiling Float ◽  
Loop Design ◽  
Depth Control ◽  
And Control

This paper presents the development made around the SeaBot, a new low-cost profiling float design for shallow water. We introduce a simplified dynamical model of the float and propose a state feedback depth controller coupled with an Extended Kalman Filter (EKF) to estimate model parameters. We show experimental results of the depth control that validate the model and the controller. We finally propose a loop design method to build low-cost floats by highlighting key design choices along with design rules.

scholarly journals Model-Based Autonomous Navigation with Moment of Inertia Estimation for Unmanned Aerial Vehicles

Sensors ◽  
2019 ◽  
Vol 19 (11) ◽  
pp. 2467 ◽  
Author(s):  
Hery Mwenegoha ◽  
Terry Moore ◽  
James Pinchin ◽  
Mark Jabbal
Keyword(s):  
Unmanned Aerial Vehicles ◽  
Navigation System ◽  
Process Model ◽  
Unscented Kalman Filter ◽  
Low Cost ◽  
Moment Of Inertia ◽  
Model Parameters ◽  
Main Process ◽  
Model Based ◽  
Aerial Vehicles

The dominant navigation system for low-cost, mass-market Unmanned Aerial Vehicles (UAVs) is based on an Inertial Navigation System (INS) coupled with a Global Navigation Satellite System (GNSS). However, problems tend to arise during periods of GNSS outage where the navigation solution degrades rapidly. Therefore, this paper details a model-based integration approach for fixed wing UAVs, using the Vehicle Dynamics Model (VDM) as the main process model aided by low-cost Micro-Electro-Mechanical Systems (MEMS) inertial sensors and GNSS measurements with moment of inertia calibration using an Unscented Kalman Filter (UKF). Results show that the position error does not exceed 14.5 m in all directions after 140 s of GNSS outage. Roll and pitch errors are bounded to 0.06 degrees and the error in yaw grows slowly to 0.65 degrees after 140 s of GNSS outage. The filter is able to estimate model parameters and even the moment of inertia terms even with significant coupling between them. Pitch and yaw moment coefficient terms present significant cross coupling while roll moment terms seem to be decorrelated from all of the other terms, whilst more dynamic manoeuvres could help to improve the overall observability of the parameters.

Convergence of Facilities in Low Cost Operation Areas

2008 ◽  
pp. 303-314
Author(s):  
Toru Higuchi ◽  
Marvin Troutt
Keyword(s):  
Life Cycle ◽  
Economies Of Scale ◽  
Low Cost ◽  
Manufacturing Facilities ◽  
The Cost

In this chapter, the convergence of manufacturing facilities is discussed. Very little room is left for the differentiation of products in the late standardized stage. Although companies source globally to reduce the cost, they should cut down their cost even further. In addition, the demand for a product begins to decline sharply at the end of the life cycle because of the saturation of the market or the emergence of alternative products. As a result, companies should make the most of economies of scale in a low cost operation area. Companies converge their manufacturing facilities into low cost operation areas or withdraw completely from the market.

scholarly journals Intelligent Path Recognition against Image Noises for Vision Guidance of Automated Guided Vehicles in a Complex Workspace

2019 ◽  
Vol 9 (19) ◽  
pp. 4108 ◽  
Author(s):  
Wu ◽  
Sun ◽  
Zou ◽  
Xiao ◽  
Zhai
Keyword(s):  
Robot Navigation ◽  
Low Cost ◽  
Estimation Method ◽  
Automated Guided Vehicles ◽  
Guidance System ◽  
Mobile Robot Navigation ◽  
Model Parameters ◽  
Uniform Illumination ◽  
Vision Guidance ◽  
Open Question

Applying computer vision to mobile robot navigation has been studied more than twodecades. The most challenging problems for a vision-based AGV running in a complex workspaceinvolve the non-uniform illumination, sight-line occlusion or stripe damage, which inevitably resultin incomplete or deformed path images as well as many fake artifacts. Neither the fixed thresholdmethods nor the iterative optimal threshold methods can obtain a suitable threshold for the pathimages acquired on all conditions. It is still an open question to estimate the model parameters ofguide paths accurately by distinguishing the actual path pixels from the under- or oversegmentationerror points. Hence, an intelligent path recognition approach based on KPCA–BPNNand IPSO–BTGWP is proposed here, in order to resist the interferences from the complexworkspace. Firstly, curvilinear paths were recognized from their straight counterparts by means of apath classifier based on KPCA–BPNN. Secondly, an approximation method based on BTGWP wasdeveloped for replacing the curve with a series of piecewise lines (a polyline path). Thirdly, a robustpath estimation method based on IPSO was proposed to figure out the path parameters from a set ofpath pixels surrounded by noise points. Experimental results showed that our approach caneffectively improve the accuracy and reliability of a low-cost vision-guidance system for AGVs in acomplex workspace.

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