scholarly journals Influence of Trajectory and Dynamics of Vehicle Motion on Signal Patterns in the WIM System

Sensors ◽  
2021 ◽  
Vol 21 (23) ◽  
pp. 7895
Author(s):  
Artur Ryguła ◽  
Andrzej Maczyński ◽  
Krzysztof Brzozowski ◽  
Marcin Grygierek ◽  
Aleksander Konior
Keyword(s):  
Measurement Errors ◽  
Piezoelectric Sensors ◽  
Strain Gauges ◽  
Measurement Reliability ◽  
Class A ◽  
Vehicle Motion ◽  
Axle Loads ◽  
Selection Of

This paper presents the analyses of the signals recorded by the main sensors of a WIM test station in the cases of abnormal runs (i.e., runs with the changes of trajectory or the dynamics of vehicle motion). The research involved strain gauges which are used for measuring the weight of vehicles, inductive loops, as well as piezoelectric sensors used, inter alia, to detect twin wheels and to determine where a vehicle passes through a station. Since the designers intend the station to be able to implement the direct enforcement function, the selection of runs deviating from the normative ones constitutes an important issue for the assessment of the measurement reliability. The study considered the location of the trajectory of the runs, the dynamics (acceleration/braking) and the trajectory changes. The change in the amplitude and the value of the signal recorded by the strain gauges as a function of the location (position) of the contact between sensor and tires is a noteworthy observation which indicates the need to monitor this parameter in automatic WIM systems. Other tests also demonstrated the influence of the analysed driving parameters on the recorded results. However, by equipping the WIM station with a set of duplicate strain gauges, the measurement errors of the gross weight and axle loads are normally within the accuracy limits of class A(5) stations. Only in the case of accelerating/decelerating, does the error in measuring the load of a single axle reach several per cent.

scholarly journals Where Do You Sit in Class? A Study of Spatial Positioning During Two Courses of Different Duration

2017 ◽  
Vol 1 (1) ◽  
pp. 1-7 ◽  
Author(s):  
Gilles Clément ◽  
Angie Bukley
Keyword(s):  
Academic Programs ◽  
Lecture Hall ◽  
Objective Data ◽  
Class A ◽  
Seating Location ◽  
Spatial Positioning ◽  
Confined Environment ◽  
Hourly Basis ◽  
Crowded Environment ◽  
Selection Of

The objective of this study was to study the selection of seat location by individuals in a group in a confined environment and to identify the factors leading people to prefer one location to another. We analyzed the seating location of students in a lecture hall over the course of two academic programs of different durations (19 days and 44 days). The goal was to determine the rate at which participants would settle into a specific seat location. Unobtrusive photography was used to collect objective data on an hourly basis. Results showed that in both courses participants began to settle into a specific location from the second day of class. Twenty percent of the participants had settled after 4-7 days or 15.5 hours in class. Settling continued for the duration of the shorter course. However, in the longer course settling stopped after 28.5 days on average. The plateau in the number of settlers depended on the number of days, not on the time actually spent in class. At the end of the longer course 52.5% of the participants had settled, compared to 38.9% in the shorter course. Settling into the same seat location can be interpreted as a strategy to establish a personal territory. These results indicate that about half of a cohort expresses the need for establishing a personal territory when in a confined and crowded environment, and this process takes about one month.

scholarly journals A development of cloud top height retrieval using thermal infrared spectra observed with GOSAT and comparison with CALIPSO data

2016 ◽  
Vol 9 (5) ◽  
pp. 1981-1992 ◽  
Author(s):  
Yu Someya ◽  
Ryoichi Imasu ◽  
Naoko Saitoh ◽  
Yoshifumi Ota ◽  
Kei Shiomi
Keyword(s):  
Measurement Errors ◽  
Thermal Infrared ◽  
Atmospheric Conditions ◽  
Cloud Detection ◽  
Low Cloud ◽  
Atmospheric Profile ◽  
Slicing Method ◽  
Cloud Tops ◽  
Spectral Channels ◽  
Selection Of

Abstract. An algorithm based on CO2 slicing, which has been used for cirrus cloud detection using thermal infrared data, was developed for high-resolution radiance spectra from satellites. The channels were reconstructed based on sensitivity height information of the original spectral channels to reduce the effects of measurement errors. Selection of the reconstructed channel pairs was optimized for several atmospheric profile patterns using simultaneous studies assuming a cloudy sky. That algorithm was applied to data by the Greenhouse gases Observing SATellite (GOSAT). Results were compared with those obtained from the space-borne lidar instrument on-board Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO). Monthly mean cloud amounts from the slicing generally agreed with those from CALIPSO observations despite some differences caused by surface temperature biases, optically very thin cirrus, multilayer structures of clouds, extremely low cloud tops, and specific atmospheric conditions. Comparison of coincident data showed good agreement, except for some cases, and revealed that the improved slicing method is more accurate than the traditional slicing method. Results also imply that improved slicing can detect low-level clouds with cloud top heights as low as approximately 1.5 km.

A model of the distribution and metabolism of corticotropin-releasing factor

1988 ◽  
Vol 254 (1) ◽  
pp. E104-E112
Author(s):  
B. Candas ◽  
J. Lalonde ◽  
M. Normand
Keyword(s):  
Measurement Errors ◽  
Compartment Model ◽  
Corticotropin Releasing Factor ◽  
Data Sets ◽  
Data Set ◽  
Rapid Injection ◽  
Three Compartment Model ◽  
Rival Models ◽  
Sampling Schedule ◽  
Selection Of

The aim of this study is the selection of the number of compartments required for a model to represent the distribution and metabolism of corticotropin-releasing factor (CRF) in rats. The dynamics of labeled rat CRF were measured in plasma for seven rats after a rapid injection. The sampling schedule resulted from the combination of the two D-optimal sampling sets of times corresponding to both rival models. This protocol improved the numerical identifiability of the parameters and consequently facilitated the selection of the relevant model. A three-compartment model fits adequately to the seven individual dynamics and better represents four of them compared with the lower-order model. It was demonstrated, using simulations in which the measurement errors and the interindividual variability of the parameters are included, that his four-to-seven ratio of data sets is consistent with the relevance of the three-compartment model for every individual kinetic data set. Kinetic and metabolic parameters were then derived for each individual rat, their values being consistent with the prolonged effects of CRF on pituitary-adrenocortical secretion.

Statistical Methodologies for Reliability Assessment of Gas Turbine Measurements

2003 ◽  
Author(s):  
M. Pinelli ◽  
M. Venturini ◽  
M. Burgio
Keyword(s):  
Data Processing ◽  
Mathematical Models ◽  
Gas Turbine ◽  
Measurement Errors ◽  
Field Data ◽  
Reliability Assessment ◽  
Field Measurements ◽  
Measurement Reliability ◽  
Student Distribution ◽  
Analysis Models

All measurements, although taken as accurately as possible, are subjected to uncertainty. So the analysis of errors and uncertainty is crucial in all applications since such errors need to be estimated and, when possible, reduced. In particular, when gas turbine mathematical models based on the processing of field measurements (such as the Gas Path Analysis models) are used, the evaluation of measurement reliability is a key point. In fact, it has been demonstrated that these kinds of techniques are sensitive to measurement errors: thus, tools for field data processing to evaluate the presence of the so-called outliers are advisable. In this paper, some statistical methodologies for the assessment of the reliability of the measurements taken on a gas turbine are presented. The methodologies, taken from literature and used for historical measurements, are discussed. Moreover, a new methodology, based on a modified t-Student distribution, is proposed.

Assessment of the Robustness of Gas Turbine Diagnostics Tools Based on Neural Networks

2006 ◽  
Author(s):  
R. Bettocchi ◽  
M. Pinelli ◽  
P. R. Spina ◽  
M. Venturini ◽  
G. A. Zanetta
Keyword(s):  
Neural Networks ◽  
Gas Turbine ◽  
Measurement Errors ◽  
Health State ◽  
Artificial Intelligence Technique ◽  
Boundary Load ◽  
Set Up ◽  
Operating Points ◽  
Range Of Variation ◽  
Selection Of

The paper deals with the set-up and the application of an Artificial Intelligence technique based on Neural Networks (NNs) to gas turbine diagnostics, in order to evaluate its capabilities and its robustness. The data used for both training and testing the NNs were generated by means of a Cycle Program, calibrated on a Siemens V94.3A gas turbine. Such data are representative of operating points characterized by different boundary, load and health state conditions. The analyses carried out are aimed at the selection of the most appropriate NN structure for gas turbine diagnostics, by evaluating NN robustness with respect to: • interpolation capability and accuracy in the presence of data affected by measurement errors; • extrapolation capability in the presence of data lying outside the range of variation adopted for NN training; • accuracy in the presence of input data corrupted by bias errors; • accuracy when one input is not available. This situation is simulated by replacing the value of the unavailable input with its nominal value.

The Selection of Thermistors for the Temperature Measurement Gear

1970 ◽  
Vol 111 (5) ◽  
pp. 59-62
Author(s):  
J. Leskauskaite ◽  
A. Dumcius
Keyword(s):  
Temperature Measurement ◽  
Measurement Errors ◽  
Numerical Data ◽  
Optimum Selection ◽  
Selection Of

The parameters represented on manufacturer datasheet are usually insufficient for the optimum selection of thermistors. Some manufacturers give generalized numerical data of R(T) dependences. Using this data it is possible to select successfully the most suitable thermistor and equation for describing of T(R) dependence. It is shown that by selecting four points in the generalized characteristic it is possible to calculate the coefficients of the approximation equation. By the application of these coefficients in the calculations the temperature measurement errors can be decreased by an order. The results of calculations and experiment are given. Ill. 4, bibl. 8, tabl. 5 (in English; abstracts in English and Lithuanian).http://dx.doi.org/10.5755/j01.eee.111.5.357

scholarly journals Multi-Sensor Retrieval of Aerosol Optical Properties for Near-Real-Time Applications Using the Metop Series of Satellites: Concept, Detailed Description, and First Validation

2021 ◽  
Vol 14 (1) ◽  
pp. 85
Author(s):  
Michael Grzegorski ◽  
Gabriele Poli ◽  
Alessandra Cacciari ◽  
Soheila Jafariserajehlou ◽  
Andriy Holdak ◽  
...  
Keyword(s):  
Real Time ◽  
First Generation ◽  
Retrieval Algorithm ◽  
Aerosol Model ◽  
Class A ◽  
Spatial Performance ◽  
Good Consistency ◽  
Real Time Applications ◽  
Selection Of

The Polar Multi-Sensor Aerosol product (PMAp) is based on the synergistic use of three instruments from the Metop platform, GOME-2, AVHRR, and IASI. The retrieval algorithm includes three major steps: a pre-identification of the aerosol class, a selection of the aerosol model, and a calculation of the Aerosol Optical Depth (AOD). This paper provides a detailed description of the PMAp retrieval, which combines information provided by the three instruments. The retrieved AOD is qualitatively evaluated, and a good temporal as well as spatial performance is observed, including for the transition between ocean and land. More quantitatively, the performance is evaluated by comparison to AERONET in situ measurements. Very good consistency is also observed when compared to other space-based data such as MODIS or VIIRS. The paper demonstrates the ability of this first generation of synergistic products to derive reliable AOD, opening the door for the development of synergistic products from the instruments to be embarked on the coming Metop Second Generation platform. PMAp has been operationally distributed in near-real-time since 2014 over ocean, and 2016 over land.

scholarly journals Selection of medications in comorbidity

2020 ◽  
Vol 10 (1) ◽  
pp. 57-60
Author(s):  
F. I. Belialov
Keyword(s):  
Adverse Effects ◽  
Positive Influence ◽  
Comorbid Disease ◽  
New Classification ◽  
Drug Classification ◽  
Class A ◽  
Class D ◽  
Class B ◽  
Comorbid Diseases ◽  
Selection Of

New classification divides medications on five classes by influence on comorbid diseases and conditions and rates drug’s effects as favourable (A), possible (B), neutral (C), undesirable (D), and unfavourable (X). Class A includes drugs used in treatment of comorbid disease, class B embraced drugs with positive influence, class C includes drugs without significant influence or contradictory influence, class D consist of drugs with possible nonsevere adverse effects, and class X includes drugs with severe adverse effects. The more universal drug classification according to influence on comorbid diseases can include and unite other classifications. Classification may help unify marks of positive and negative influences drugs on comorbidity and help practitioners in selection of effective and safe treatment.

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