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Calibrating Range Measurements of Lidars Using Fixed Landmarks in Unknown Positions
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We consider the problem of calibrating range measurements of a Light Detection and Ranging (lidar) sensor that is dealing with the sensor nonlinearity and heteroskedastic, range-dependent, measurement error. We solved the calibration problem without using additional hardware, but rather exploiting assumptions on the environment surrounding the sensor during the calibration procedure. More specifically we consider the assumption of calibrating the sensor by placing it in an environment so that its measurements lie in a 2D plane that is parallel to the ground. Then, its measurements come from fixed objects that develop orthogonally w.r.t. the ground, so that they may be considered as fixed points in an inertial reference frame. Moreover, we consider the intuition that moving the distance sensor within this environment implies that its measurements should be such that the relative distances and angles among the fixed points above remain the same. We thus exploit this intuition to cast the sensor calibration problem as making its measurements comply with this assumption that “fixed features shall have fixed relative distances and angles”. The resulting calibration procedure does thus not need to use additional (typically expensive) equipment, nor deploy special hardware. As for the proposed estimation strategies, from a mathematical perspective we consider models that lead to analytically solvable equations, so to enable deployment in embedded systems. Besides proposing the estimators we moreover analyze their statistical performance both in simulation and with field tests. We report the dependency of the MSE performance of the calibration procedure as a function of the sensor noise levels, and observe that in field tests the approach can lead to a tenfold improvement in the accuracy of the raw measurements.

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Publication Date
Wed Jul 01 2015
Journal Name
Journal Of Engineering
Enhancing the Performance of Piezoelectric Energy Harvesters Using Permanent Magnets
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A cantilevered piezoelectric beam with a tip mass at its free end is a common energy harvester configuration. This paper introduces a new principle of designing such a harvester which increases the generated voltage without changing the natural frequency of the harvester: The attraction force between two permanent magnets is used to add stiffness to the system. This magnetic stiffening counters the effect of the tip mass on the natural frequency. Three setups incorporating piezoelectric bimorph cantilevers of the same type in different mechanical configurations are compared theoretically and experimentally to investigate the feasibility of this principle. Theoretical and experimental results show that magnetically stiffe

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Publication Date
Sat Jan 01 2022
Journal Name
International Journal Of Mechanical Engineering
Improving the Performance of the Vehicle Suspension System Using Inerter
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Publication Date
Thu Oct 01 2020
Journal Name
Journal Of Engineering
Strengthening of RC Beam with Large Square Opening Using CFRP
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The use of essential services in modern constructions, such pipes, and ducts, became important, placing these pipes and ducts underneath the soffit of the beam. They made a ceiling sandwich, and that causes to reduce the height of the floor, so the presence of the opening in the beam saves the height of the floor.  In this paper, the investigation of the beam response of reinforced concrete simply supported rectangle beams with square web openings is presented, including a number of the web openings (two, four, and eight), in addition to its use in strengthening the member at the openings (when the beam is planned before casting, internal deformation steel bar is used, and in case of the opening is existing in the b

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Publication Date
Wed Jan 01 2020
Journal Name
Journal Of Craniofacial Surgery
Radiographic Assessment of Nasopalatine Canal Using Cone Beam Computed Tomography
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Publication Date
Tue Dec 01 2015
Journal Name
Journal Of Engineering
Modeling and Control of Fuel Cell Using Artificial Neural Networks
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This paper includes an experimental study of hydrogen mass flow rate and inlet hydrogen pressure effect on the fuel cell performance. Depending on the experimental results, a model of fuel cell based on artificial neural networks is proposed. A back propagation learning rule with the log-sigmoid activation function is adopted to construct neural networks model. Experimental data resulting from 36 fuel cell tests are used as a learning data. The hydrogen mass flow rate, applied load and inlet hydrogen pressure are inputs to fuel cell model, while the current and voltage are outputs. Proposed model could successfully predict the fuel cell performance in good agreement with actual data. This work is extended to developed fuel cell feedback

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Publication Date
Wed Mar 30 2016
Journal Name
College Of Islamic Sciences
The link of destination Using the evidence Analytical Fundamental Study
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This paper seeks to study the link between the fundamentalist evidence based on the observance of governance and interests and the ranks of the three legitimate purposes (necessary, need and detailed). The researcher followed the descriptive-analytical approach. The study reached important results, including that the measurement relates to the three ranks, but predominantly attached to measure the meaning of the need and the need, and the measurement of the semi-formal and semi-predominance improvement. Reclamation is considered by the majority of scholars to be authentic if it is related to the necessity and the need, and that it is not acceptable to improve only by a witness who recommends it. The excuses relate to Hajji and Tahini, no

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Publication Date
Sat May 01 2021
Journal Name
Journal Of Physics: Conference Series
The Prediction of COVID 19 Disease Using Feature Selection Techniques
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Abstract<p>COVID 19 has spread rapidly around the world due to the lack of a suitable vaccine; therefore the early prediction of those infected with this virus is extremely important attempting to control it by quarantining the infected people and giving them possible medical attention to limit its spread. This work suggests a model for predicting the COVID 19 virus using feature selection techniques. The proposed model consists of three stages which include the preprocessing stage, the features selection stage, and the classification stage. This work uses a data set consists of 8571 records, with forty features for patients from different countries. Two feature selection techniques are used in </p> ... Show More
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Publication Date
Mon Mar 30 2009
Journal Name
Iraqi Journal Of Chemical And Petroleum Engineering
Extraction of atropine from Datura Innoxia using liquid membrane Technique
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Selective recovery of atropine from Datura innoxia seeds was studied. Applying pertraction in a rotating film contactor (RFC) the alkaloid was successfully recovered from native aqueous extracts obtained from the plant seeds. Decane as a liquid membrane and sulfuric acid as a stripping agent were used. Pertraction from native liquid extracts provided also a good atropine refinement, since the most of co-extracted from the plant species remained in the feed or membrane solution. Solid–liquid extraction of atropine from Datura innoxia seeds was coupled with RF-pertraction in order to purify simultaneously the extract obtained from the plant. Applying the integrated process, proposed in this study, a product containing 92.6% atropine was

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Publication Date
Wed Jan 15 2020
Journal Name
Iraqi Journal Of Laser
Hole Drilling of High Density Polyethylene using Nd:YAG Pulsed Laser
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This paper presents the theoretical and experimental results of drilling high density
polyethylene sheet with thickness of 1 mm using millisecond Nd:YAG pulsed laser. Effects of laser
parameters including laser energy, pulse duration and peak power were investigated. To describe and
understand the mechanism of the drilling process Comsol multiphysics package version 4.3b was used to
simulate the process. Both of the computational and experimental results indicated that the drilling
process has been carried out successfully and there are two phases introduced in the drilling process,
vaporization and melting. Each portion of these phases depend on the laser parameters used in the
drilling process

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Publication Date
Tue Dec 05 2023
Journal Name
Baghdad Science Journal
Processing of Polymers Stress Relaxation Curves Using Machine Learning Methods
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Currently, one of the topical areas of application of machine learning methods is the prediction of material characteristics. The aim of this work is to develop machine learning models for determining the rheological properties of polymers from experimental stress relaxation curves. The paper presents an overview of the main directions of metaheuristic approaches (local search, evolutionary algorithms) to solving combinatorial optimization problems. Metaheuristic algorithms for solving some important combinatorial optimization problems are described, with special emphasis on the construction of decision trees. A comparative analysis of algorithms for solving the regression problem in CatBoost Regressor has been carried out. The object of

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