Selection Of Measurement Points Research Articles

Research on Human Exposure to Transport-Induced Vibration in Buildings

The analysis of human perception of vibrations in buildings is a critical aspect of structural engineering, particularly as urbanization intensifies and the proximity of vibration sources to buildings increases. This paper addresses the frequent errors in the assessment and diagnosis of the impact of vibrations on building occupants. Despite stringent standards and detailed methodologies, misinterpretations and incorrect implementations of these guidelines are common, leading to flawed diagnostic studies. These errors often stem from the misuse of measurement equipment, inappropriate selection of measurement points, and a general lack of comprehensive education on vibration analysis. National guidelines, although largely based on ISO standards, vary significantly, contributing to inconsistent practices across Europe. The dominant sources of urban vibrations include vehicle traffic, particularly heavy trucks and rail vehicles, which significantly impact both building structures and human comfort. This paper reviews the methodologies for measuring and interpreting vibrations, emphasizing the importance of correct sensor placement and data analysis. It highlights the necessity of integrating vibrational comfort into building design, considering both external and internal vibration sources. The study also explores the effectiveness of different evaluation methods, such as the RMS and VDV methods, and the impact of various weighting functions on the analysis results. The findings underscore the need for improved education and standardization in the field to ensure accurate assessments and enhance the vibrational comfort of building occupants.

Comparative Analysis of Measurement Points for Structural Damage Identification in Free-Free Aluminium Beam using Response Surface Methodology and FRF Curvature

This research paper presents a comprehensive comparative analysis of measurement points for structural damage identification in free-free aluminium beams using Response Surface Methodology (RSM) and Frequency Response Function (FRF) curvature. The primary objective is to determine the optimal measurement points for establishing a relationship between the input parameters and response features, thus forming a surrogate model. The investigation focuses on analyzing the influence of the number of measurement points on RSM's capability to identify structural damage. Three distinct Design of Experiment (DOE) techniques are explored: central composite design (CCD), Box-Behnken design (BBD), and D-optimal design, each incorporating different numbers of measurement points (10 elements, 15 elements, and 20 elements). The study's results highlight BBD's strong capability in detecting damage with fewer measurement points, while CCD design accurately identifies damage in finer measurement points. The findings reveal a clear trend: as the number of measurement points increases, the model becomes more refined, yielding higher stiffness reduction factor (SRF) values. The results underscore the significance of selecting an appropriate number of measurement points to enhance RSM's efficiency in identifying structural damage, thereby optimizing damage identification processes. This study contributes valuable insights into the selection of measurement points for effective structural damage identification, offering practical implications for engineering applications.

Research on the temperature measurement point selection technology for multi-degree-of-freedom thermal error compensation of lathe spindle

Abstract In order to solve the problem of difficulty in selecting temperature measurement points caused by the inconsistent axial and radial thermal deformation trends of CNC lathes, a multi-degree-of-freedom joint point selection method is proposed. The Ant Colony Optimization (ACO) algorithm is employed to identify a combination of temperature measurement points. Based on this combination, thermal errors in both directions are modeled, with modeling accuracy serving as the condition for updating ant pheromones. The process iterates until the optimal temperature measurement point combination is determined. Experimental results demonstrate that the temperature measurement points selected using this method exhibit significantly improved fitting accuracy and stability for various operating conditions compared to traditional point selection methods.

Thermal error modeling method of truss robot based on GA-LSTM

PurposeThe selection of sensitive temperature measurement points is the premise of thermal error modeling and compensation. However, most of the sensitive temperature measurement point selection methods do not consider the influence of the variability of thermal sensitive points on thermal error modeling and compensation. This paper considers the variability of thermal sensitive points, and aims to propose a sensitive temperature measurement point selection method and thermal error modeling method that can reduce the influence of thermal sensitive point variability.Design/methodology/approachTaking the truss robot as the experimental object, the finite element method is used to construct the simulation model of the truss robot, and the temperature measurement point layout scheme is designed based on the simulation model to collect the temperature and thermal error data. After the clustering of the temperature measurement point data is completed, the improved attention mechanism is used to extract the temperature data of the key time steps of the temperature measurement points in each category for thermal error modeling.FindingsBy comparing with the thermal error modeling method of the conventional fixed sensitive temperature measurement points, it is proved that the method proposed in this paper is more flexible in the processing of sensitive temperature measurement points and more stable in prediction accuracy.Originality/valueThe Grey Attention-Long Short Term Memory (GA-LSTM) thermal error prediction model proposed in this paper can reduce the influence of the variability of thermal sensitive points on the accuracy of thermal error modeling in long-term processing, and improve the accuracy of thermal error prediction model, which has certain application value. It has guiding significance for thermal error compensation prediction.

Data-driven thermal error modeling based on a novel method of temperature measuring point selection

Data-driven thermal error modeling based on a novel method of temperature measuring point selection

The Optimal Acoustic Measurement Point Selection for UHV Reactors in Substations Based on Equivalent Impedance and Information Entropy Theory

The Optimal Acoustic Measurement Point Selection for UHV Reactors in Substations Based on Equivalent Impedance and Information Entropy Theory

Optimal measurement point selection for resonant ultrasound spectroscopy of complex-shaped specimens using principal component analysis

Traditional resonant ultrasound spectroscopy (RUS) uses the natural vibrations, or normal modes, of an object to determine the elastic properties of the object’s constituent material. When measuring the resonances of a sample, the measurement point is often selected based on user experience and intuition. This approach is often adequate when the sample has a simple geometry, but can be insufficient when testing complex-shaped samples. In such cases, multiple measurement points are used and the responses superimposed to obtain an overall resonance response. This approach can be effective but is time-intensive and can be still subject to error due to the possibility of missing modes. This paper introduces a method to automatically locate a minimal number of measurement points to extract sufficient modal information based on principal component analysis. The goal is to reduce the arbitrary nature of measurement point selection and measurement time while retaining the significant spectral information required for an accurate inversion. The efficacy of the method is demonstrated on a sample of relatively simple geometry as well as a part of greater complexity. The inversion results suggest that the modal information extracted from the few selected points is sufficient to yield accurate elastic constants.

Trans-conductance amplifier calibration protocol establishment – metrology ambiguities, constraints, and measurement uncertainty propagation analysis

The primary dilemma, when establishing calibration protocol for testing of any measurement transducer, is whether the concrete unit is going to be regarded as a standalone instrument, or it will be treated as a part of an integrated measurement system. The first approach would provide results that are applicable for broader further usage. The data obtained if the unit is calibrated within the measurement system, may be further used only for performance evaluation of the overall system. The concrete ambiguity is related to the appropriate quantity selection on the basis of which the measurements will be carried out, i.e., whether the measurand is the transducer’s output signal or its transformation coefficient. The determination of the quantity, which is supposed to be recorded, opens a further debate about the appropriate selection of measurement points and the uncertainty evaluation procedure, both in terms of dominant influential factors determination, and in overall uncertainty budget distribution modelling adequacy. In the concrete contribution, an originally developed protocol for high current trans-conductance amplifier examination will be presented, taking into account both perspectives for examination.

The Use of Mosses in Biomonitoring of Air Pollution in the Terrestrial Environment: A Review

Abstract Environmental biomonitoring is an excellent supplement to instrumental methods of environmental quality assessment. The use of biological methods has many advantages (relatively low cost, independence from the source of energy in the field etc.). The limitation in the use of bioindication methods is the inability to compare the results with legal environmental pollution standards. Mosses are commonly used in air pollution monitoring. Mosses exhibit most of the characteristics attributed to effective bioindicators. The advantage of mosses over other bioindicators comes from the fact that the result depends only on-air pollution due to the lack of practical contact with the soil. Mosses are used in air pollution biomonitoring in two basic methods: naturally growing or transplanted to the researched area. They are most often used to assess air pollution by metals and other elements, but also by various organic compounds. A prerequisite for successful application of these methods is strict adherence to a recognised methodology and standardisation of all activities, such as random selection of measurement points. The article presents a critical analysis of the advantages and disadvantages of biomonitoring of air pollutants, along with a final recommendation for their use, provided that appropriate methodological rigor is maintained. An important advantage of mosses in biomonitoring is their relatively practical ease of application and interpretation of results, resulting in their widespread use.

Single-sensor-based dynamic response reconstruction of blades under base excitation

Blades in the turbo-machinery are inevitably excited by base excitation in addition to unbalanced excitation of the rotor during operation. Accurate measurements of the dynamic response under base excitation are especially critical, which feasibly provides essential guidance on vibration fatigue analysis and remaining life prediction of blades. However, due to some practical factors such as the restriction of the sensor installation position and the inability to directly obtain the external load information, there are still significant challenges in obtaining dynamic response of blades at critical positions under base excitation. In this paper, a novel method is proposed to reconstruct the dynamic response of blades under base excitation, where the measurement of only one sensor is adopted. The method comprises mostly of two steps. The measured signal is first divided into single-frequency response components using empirical mode decomposition with intermittency criteria (EMDIC), and the response components are then classified into two groups depending on whether the frequency is the blade's natural frequency. Then, by combing the time-domain and frequency-domain response transmissibility matrices under base excitation (RTMBE) derived in this paper, the dynamic response of the blade can be reconstructed, whether the blade is in the resonant or non-resonant state. This paper presents numerical simulation and experimental research on a titanium alloy fan blade. The effects of single-and multi-frequency excitation, measurement noise, and measuring point selection on the results of response reconstruction are all investigated. The results demonstrate the feasibility of the response reconstruction method presented in this work for the dynamic response reconstruction of blades under base excitation.

An Improved Hyperbolic Method and Its Application to Property Inversion in Martian Tianwen-1 GPR Data

On 15th May 2021, the Tianwen-1 (TW-1) successfully landed on the surface of Mars within the southern Utopia Planitia. It delivered a rover named Zhurong equipped with ground penetrating radar (GPR), a device that can investigate the thickness and structure of the geological layers below the martian surface. The hyperbola method is commonly used for extracting dielectric property variations with depth from GPR data to aid the interpretation of the subsurface structure and material composition. This study analyzes the advantages and drawbacks of three hyperbola methods with different geometric models. Next, it proposes a new method aiming to use the highly precise, yet complex geometric model and address issues caused by its solving process. We also analyze the influencing factors contributing to measurement errors and design corresponding criteria for measurement point selection to mitigate errors. The average error of the proposed method is less than 5% for a depth of up to 9 m. We employed the proposed method to obtain the dielectric constant distribution in the shallow surface layer of the TW-1 landing zone to a depth of up to 16 m. The dielectric constant is mainly concentrated between 2 and 8 m and increases gradually with depth. Below 2 m, the dielectric constant is about 5.1 and the density is about 2.5 g/cm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> . The stratification and density variation can be inferred from the dielectric constant distribution diagram.

Thermal Parameters Inversion Method for Concrete Dam Based on Optimal Temperature Measuring Point Selecting

Concrete thermal parameters in a natural pouring environment are essential inputs for simulating the temperature field of a concrete dam. This paper proposes a two-stage thermal parameters inversion method for a concrete dam based on optimal temperature measuring point selection to improve the accuracy of parameters. Firstly, a selection method of optimal measuring point for thermal parameters inversion is presented and the temperature response sensitivity of measuring points when the parameters disturb is taken as the critical evaluation index. And then, an inversion model is established based on support vector regression (SVR) and particle swarm optimization (PSO). Finally, the proposed method is applied to the thermal parameter inversion of a concrete dam. The results show that the proposed method is effective for improving the inversion accuracy and obtaining accurate parameters. The average error of the inversion results based on the SVR-PSO model is 28.54% lower than that of the genetic algorithm optimization using a back propagation neural network (BPNN-GA). Besides that, the average error of the inversion results based on the optimal measurement points is 35.57% lower than that of the nonoptimized ones.

Virtual technical analysis of archaeological textiles by synchrotron microtomography

Archaeological textile remains from Antiquity are rare due to their perishable nature. In certain cases, the conservation of the morphology and/or of chemical signatures can be exquisite. Detailed archaeological information on the social and cultural life of past societies, such as on funerary practices, can then be deduced from their study. In temperate climates, these cases of exceptional preservation mainly involve textiles closely associated with corroded metal artefacts (i.e. copper or iron based), through a process called mineralisation. An essential step in understanding these textiles is the study of morphometric parameters documenting their manufacturing processes, which is carried out by archaeologists under the name of technical analysis. In this article, we show how the use of high-resolution synchrotron-based microtomography (μCT) can contribute to performing a non-invasive 3D technical analysis of archaeological textiles and overpass several limitations of the conventional techniques. We show that several standard parameters (e.g. weave type, type of yarn, direction of twist, thread count) characterising the manufacturing of these ancient textiles can be determined, while some of them are very difficult to obtain using optical and electronic microscopies. In addition, the statistical representativeness of the data can be examined. The comparison between the three approaches – manual measurements either from surface microscopy or from selected virtual sections and automated analysis on 3D volumes – leads to question the differences between the quantitative results obtained. While the manual selection of measurement points is inherently associated to perceptual biases, the automated process may also come with limitations and biases which we detail in order to make the method more robust for future use. We analyse these implications by studying a textile from the locality of Le Paradis in Creney-près-Troyes (Aube, France, 5th century BC), taken as a typical case of fragmentary textile difficult to analyse by standard analytical methods.

Statistics-Based Measuring Point Selection for Monitoring the Thermal Deformation of a Workpiece in End-Milling

The deformation of the thermal workpiece in the end-milling process has a significant effect on the accuracy of machining. In-process direct measurement of workpiece deformation is difficult because process disturbances occur during machining. On the other hand, local temperatures of the workpiece can be easily and accurately measured using common measuring methods. This study aims to develop a monitoring method for workpiece deformations. A sensor-configured thermal simulation is proposed by combining local temperature measurements with thermal simulations to estimate the thermal states of the workpiece in small-lot production. Furthermore, an empirical modeling method is introduced to estimate the workpiece deformation from measured temperatures, thereby accelerating process time. A reliable estimation requires the selection of appropriate measuring points. Using multiple linear regression (MLR), a statistics-based selection method is proposed to establish a relationship between thermal deformation and temperatures of measuring points in various machining situations. During the end-milling process, the predicted time-series of deformations at the machining point and temperatures of the measuring points are regarded as output variables and input variables, respectively, in the finite element method (FEM)-based thermal simulation. The number of measuring points is determined by evaluating Akaike information criterion (AIC), and effective measuring points are selected using the p-value index. The proposed systematic construction method is evaluated using simulation-based case studies. The constructed temperature-based model for measuring workpiece deformation corresponded well to the FEM simulation. Therefore, the constructed model can represent workpiece deformation with the minimum number of measuring points.

LPWAN Coverage Assessment Planning Without Explicit Knowledge of Base Station Locations

An assessment of radio network coverage, usually in the form of a measurement campaign, is essential for multibase-station (multi-BS) network deployment and maintenance. It can be conducted by a network operator or its served consumers. However, the number of measurement points and their locations may not be known in advance for an efficient and accurate evaluation. The main goal of this study is to propose a new methodology for understanding the selection of measurement points during coverage and signal quality assessment. It is particularly tailored to multi-BS low-power wide-area network (LPWAN) deployments without explicit knowledge of BS locations. To this aim, we first conduct a large-scale measurement campaign for three popular LPWAN technologies, namely, narrowband IoT (NB-IoT), Sigfox, and LoRaWAN. Utilizing this baseline data, we develop a procedure for identifying the minimum set of measurement points for the coverage assessment with a given accuracy as well as study which interpolation algorithms produce the lowest approximation error. Our results demonstrate that a random choice of measurement points is on par with their deterministic selection. Out of the candidate interpolation algorithms, the Kriging method offers attractive performance in terms of the absolute error for NB-IoT deployments. In contrast, for Sigfox and LoRaWAN infrastructures, less complex techniques, such as the natural neighbor, linear interpolation, or inverse-distance weighting, can achieve comparable (and occasionally even better) accuracy levels.

The precision of gingival recession measurements is increased by an automated curvature analysis method

Background The extent of gingival recession represents one of the most important measures determining outcome of periodontal plastic surgery. The accurate measurements are, thus, critical for optimal treatment planning and outcome evaluation. Present study aimed to introduce automated curvature-based digital gingival recession measurements, evaluate the agreement and reliability of manual measurements, and identify sources of manual variability.MethodsMeasurement of gingival recessions was performed manually by three examiners and automatically using curvature analysis on representative cross-sections (n = 60). Cemento-enamel junction (CEJ) and gingival margin (GM) measurement points selection was the only variable. Agreement and reliability of measurements were analysed using intra- and inter-examiner correlations and Bland–Altman plots. Measurement point selection variability was evaluated with manual point distance deviation from an automatic point. The effect of curvature on manual point selection was evaluated with scatter plots.ResultsBland–Altman plots revealed a high variability of examiner’s recession measurements indicated by high 95% limits of agreement range of approximately 1 mm and several outliers beyond the limits of agreement. CEJ point selection was the main source of examiner’s variability due to smaller curvature values than GM, i.e., median values of − 0.98 mm− 1 and − 4.39 mm− 1, respectively, indicating straighter profile for CEJ point. Scatter plots revealed inverse relationship between curvature and examiner deviation for CEJ point, indicating a threshold curvature value around 1 mm− 1.ConclusionsAutomated curvature-based approach increases the precision of recession measurements by reproducible measurement point selection. Proposed approach allows evaluation of teeth with indistinguishable CEJ that could be not be included in the previous studies.

Measurement of Apparent Building Damage by Using a Single Image

In the proposed detection method, a single image was used for the rapid quantitative measurement of the damage area of a building. By using cross-ratio invariability between real and image coordinates, the damage area and the side lengths of the selected region were measured using the vanishing point, the vanishing line, and a line segment with a known length. Perspective transformation and image binarisation were performed on the selected frame to convert the length into area information. The damage area was obtained rapidly from the proportion of pixels in the damage region to pixels in the selected region. Corner detection and subpixel methods were combined to determine measurement errors because of the selection of measurement points. The experimental results from the example test revealed that quantitative area measurement of surface damage on buildings could be realised using the proposed method.

Soil Respiration May Overestimate or Underestimate in Forest Ecosystems

The inappropriate selection of measurement points and measurement times in an ecosystem may easily lead to the underestimation or overestimation of soil respiration due to spatial and temporal heterogeneity. To assess the law of spatial and temporal heterogeneity and more accurately determine the soil respiration rate, we measured the soil respiration rate of a forest in the plant growing season from 2011 to 2013 on Changbai Mountain in 8 directions and 7 distances from each tree trunk. Neglecting the direction of the measuring point may overestimate or underestimate the soil respiration rate by 29.81% and 26.09%, respectively; neglecting the distance may overestimate or underestimate the soil respiration rate by 41.36% and 20.28%, respectively; and ignoring the measurement time may overestimate and underestimate the soil respiration rate by 41.71% and 57.64%, respectively. In addition, choosing a measurement point in the eastern direction at a 1.8 m distance and conducting the measurement in September may relatively accurately reflect the soil respiration rate of the ecosystem. These findings can deepen our understanding of soil respiration rate heterogeneity and may provide new ideas for improving the measurement method of soil respiration.

Research on Measuring Point Selection for Workpiece Thermal Monitoring

Research on Measuring Point Selection for Workpiece Thermal Monitoring

Selection on Measuring Points of the Surface Roughness on Large Size Ultra-Thin Stainless Steel

Stainless steel will become one of the main substrate materials of flexible large-scale display. The biggest problem of stainless steel as flexible display substrate is that the surface roughness is too large, so it is necessary to be ultra-precision machining on its surface, such as the chemical mechanical polishing (CMP). How to select the most representative measurement points in measuring surface roughness after CMP is the first problem to be solved. Based on the idea of orthogonal design, taking a roll of ultra-thin flexible stainless steel substrate as an example, the method and algorithm of selecting 12 measuring points on a 600mm×100mm rectangular area are obtained. This method avoids the blindness in random selection of measurement points, and reduces the workload of selecting measurement points. It can provide a reference for the selection of measurement points on the large-scale surface after ultra-precision machining.