Method For Measurement Of Density Research Articles

Comparative Analysis of YOLOv8 and HSV Methods for Traffic Density Measurement

Traffic density measurement is a critical component in traffic management and urban planning. This study addresses the challenge of accurately measuring traffic density by comparing the performance of the YOLOv8 segmentation method with the traditional HSV method. At the beginning of the abstract, we clearly present the problem of accurately measuring traffic density. The primary objective is to highlight the strengths and limitations of each method in terms of accuracy and reliability in traffic density estimation. The choice of segmenting the asphalt area rather than vehicle objects is justified by the need to understand how different segmentation approaches affect traffic density measurements. The HSV method involves converting images to the HSV color space, creating masks for specific areas, and measuring traffic density based on the asphalt area. This method, while straightforward, may not accurately capture the dynamic nature of vehicle movement. In contrast, the YOLOv8 segmentation method utilizes a deep learning approach to detect and segment vehicles, providing potentially more precise measurements. Experimental results from three locations demonstrate varying levels of traffic density. The YOLOv8 method results in a graph with a wavy pattern, reflecting the more detailed detection of vehicles. Conversely, the HSV method produces a linear pattern, indicating a more consistent but potentially less detailed measurement. Quantitative analysis shows that Location 2 has a higher traffic density compared to Locations 1 and 3, as indicated by the average number of detected vehicles per frame. This study provides a comprehensive understanding of the differences between HSV and YOLOv8 segmentation methods for traffic density measurement. The findings suggest that while YOLOv8 offers more detailed and dynamic detection, the HSV method provides a simpler yet effective approach for certain applications.

Development and validation of an electrochemical method for electrolyte density measurement and stratification assessment in lead batteries

RESUMO Este estudo apresenta o desenvolvimento e a validação de um novo método eletroquímico para medir a densidade do eletrólito e avaliar a estratificação em baterias de chumbo. A metodologia proposta baseia-se na diferença de potencial entre dois eletrodos, sendo um composto por PbO2 e o outro por Pb, ambos preparados e caracterizados através de voltametria cíclica. A formação dos eletrodos e sua morfologia foram confirmadas por difração de raios X (DRX) e microscopia eletrônica de varredura (MEV), que revelaram a presença de estruturas tridimensionais características. Os testes com soluções de eletrólito de densidade conhecida demonstraram uma excelente correlação entre a diferença de potencial medida e a densidade real do eletrólito, com uma precisão de ±0,001 g/cm3 em relação às medições realizadas com um densímetro digital portátil. A aplicação prática do método em baterias de chumbo, realizadas em uma bateria comercial de 60Ah, validou a técnica proposta, mostrando uma correlação significativa com os dados obtidos por equipamentos comerciais. A estratificação do eletrólito é uma questão crítica em baterias de chumbo, e o método desenvolvido fornece uma ferramenta eficaz e de baixo custo para monitorar este fenômeno. A técnica pode ser aplicada em diversas pesquisas para melhorar o desempenho e a durabilidade das baterias de chumbo.

Accurate density measurement of a small solid sample using a combination of hydrostatic weighing and pressure-of-flotation methods

Abstract A new high-precision density measurement method for small non-silicon samples was developed. Hydrostatic weighing (HW) and the pressure-of-flotation method (PFM) are high-precision techniques for determination solid sample densities. However, in HW, the uncertainty of the density measurement is quite large when the volume of the sample is much smaller than that of a 1 kg silicon sample owing to the uncertainty of the auxiliary weights during the measurement. Additionally, the PFM is not applicable for measuring the density of non-silicon materials. We developed a new method that combines HW and the PFM. We prepared a small 45 g Si3N4 sphere sample with a diameter of 30 mm. By employing two silicon discs with these masses that were nearly equivalent to the apparent masses of the sample, the same auxiliary weights were used for both the standards and the sample. The new method was validated by measuring the density of the 45 g Si3N4 sphere with a relative standard uncertainty of 4.6 × 10−6, which is significantly lower than that obtained by traditional HW methods. This approach demonstrates substantial improvements in the precision of small-sample density measurements, thus offering a robust alternative to conventional techniques.

Enhancing the Opportunistic Bone Status Assessment Using Radiomics Based on Dual-Energy Spectral CT Material Decomposition Images.

Retrospective data were collected from 307 patients who underwent both quantitative computed tomography (QCT) and full-abdomen DEsCT scans at our institution. Based on QCT measurements, patients were divided into three categories: normal bone mineral density (BMD), osteopenia, and osteoporosis. Using the abdominal DEsCT data, six types of MD images were reconstructed, including HAP (Water), HAP (Fat), Ca (Water), Ca (Fat), Fat (Ca), and Fat (HAP). Patients were randomly divided into a training cohort (n = 214) and a validation cohort (n = 93) at a ratio of 7:3. Focusing on the L1 to L3 vertebrae, density values from the six MD images were measured. Six density value models and six radiomics models were constructed using a random forest (RF) classifier. The performance of these models in assessing bone status was evaluated using the receiver operating characteristic (ROC) curves, and the DeLong test was employed to compare performance differences between the models. The macro-area under the curve (AUC) values for the density value models based on HAP (Water), HAP (Fat), Ca (Water), and Ca (Fat) MD images were 0.870, 0.870, 0.847, and 0.765, respectively, which outperformed those of Fat (Ca) (AUC = 0.623) and Fat (HAP) (AUC = 0.618) density value models. In the comparison of radiomics models, the trends of model performance were consistent with the density value models across the six MD images. However, the models based on HAP (Water), Ca (Water), HAP (Fat), Ca (Fat), Fat (Ca), and Fat (HAP) images exhibited superior performance than those of the density value models with the corresponding MD images, with values of 0.946, 0.941, 0.934, 0.926, 0.831, and 0.824, respectively. Bone status assessment can be accurately conducted using density values from HAP (Water), HAP (Fat), Ca (Water), and Ca (Fat) MD images. However, radiomics models derived from MD images surpass traditional density measurement methods in evaluating bone status, highlighting their superior diagnostic potential.

Coal and Gangue Detection Networks with Compact and High-Performance Design.

The efficient separation of coal and gangue remains a critical challenge in modern coal mining, directly impacting energy efficiency, environmental protection, and sustainable development. Current machine vision-based sorting methods face significant challenges in dense scenes, where label rewriting problems severely affect model performance, particularly when coal and gangue are closely distributed in conveyor belt images. This paper introduces CGDet (Coal and Gangue Detection), a novel compact convolutional neural network that addresses these challenges through two key innovations. First, we proposed an Object Distribution Density Measurement (ODDM) method to quantitatively analyze the distribution density of coal and gangue, enabling optimal selection of input and feature map resolutions to mitigate label rewriting issues. Second, we developed a Relative Resolution Object Scale Measurement (RROSM) method to assess object scales, guiding the design of a streamlined feature fusion structure that eliminates redundant components while maintaining detection accuracy. Experimental results demonstrate the effectiveness of our approach; CGDet achieved superior performance with AP50 and AR50 scores of 96.7% and 99.2% respectively, while reducing model parameters by 46.76%, computational cost by 47.94%, and inference time by 31.50% compared to traditional models. These improvements make CGDet particularly suitable for real-time coal and gangue sorting in underground mining environments, where computational resources are limited but high accuracy is essential. Our work provides a new perspective on designing compact yet high-performance object detection networks for dense scene applications.

RETRACTED: Theoretical development and validation of asphalt concrete density measurement using non-contact interdigital coplanar capacitive sensor

Asphalt concrete (AC) density serves as a critical criterion for quality control and quality assurance in asphalt pavement construction. Current non-destructive testing (NDT) approaches have shown potential for AC density measurement, but they present disadvantages such as inefficiency and unstable accuracy. This paper developed a novel NDT approach for AC density measurement based on non-contact interdigital coplanar capacitive sensor (ICCS). The feasibility of this approach was evaluated through both numerical and experimental validations. Firstly, the theoretical model of non-contact ICCS for AC density measurement was established based on conformal mapping technique, partial capacitance technique, and dielectric mixing model. Secondly, numerical simulations were performed to validate the developed theoretical model under two scenarios. Thirdly, laboratory experiments were conducted to measure capacitance data and predict density for three common types of ACs. Finally, the error of non-contact ICCS was discussed and compared with that of other NDT instruments. The results indicated that the developed theoretical model could establish the relationship between AC density and capacitance value, which provided the basis for AC density measurement using non-contact ICCS. It was also observed that the capacitance data obtained by the proposed model closely matched those computed by numerical simulations. The maximum errors between theoretical and numerical data were 4.10% and 1.08% for two cases respectively, which proved the feasibility of the theoretical model. Furthermore, experimental results indicated that the capacitance value of AC specimens increased by about 1 pF as the rolling time increases due to a decrease in the volume of air in AC, which reaffirms the validity of AC density measurement using non-contact ICCS. Additionally, the non-contact ICCS demonstrated excellent accuracy with a maximum error of 6.69%, which is comparable to the current mainstream NDT approaches. And it has the advantages of lower cost, simpler data processing and higher efficiency. The findings of this paper offer a new and reliable method and tool for non-destructive and large-scale AC density measurement.

Foam density measurement using a 3D scanner

In this work, we used a 3D scanner for the volume measurement of foamed samples, a long-standing problem in the density evaluation of foams. The 3D scanning density measurement method can be selected as an alternative to or in combination with well-established, classical methods that involve the use of instruments like a caliper, a pycnometer, or other devices based on displacement or flotation principles. In particular, the classical methods show some limitations when the foamed sample geometry is irregular, when the polymer is highly hygroscopic, and when it has open porosities. We have tested numerous foamed samples of different sizes, shapes, densities, materials, and morphologies. We utilized different 3D scanner configurations for their volume measurement and compared the results with geometrical and displacement methods, when possible. Results showed that the proposed method is highly accurate, reproducible, and simple, although some specific precautions should be put in place to avoid misinterpretation by the shape-reconstructing software.

LNG density measurement by gravimetric method

Density is a critical parameter in the liquefied natural gas (LNG) industry, which necessitates the study of measurement methods. Standard methods for calculating LNG density are employed in downstream LNG custody transfer, but it still faces challenges in density measurement in the upstream, such as in LNG plants and LNG refueling station. In this paper, a gravimetric density measurement method was established for LNG plants, which is SI-traceable as it relies on volume and mass. A home-made sampling device was developed and employed to obtain the LNG volume and mass. The principle involves trapping LNG in a known-volume quantitative tube within the sampling device. The LNG is then transferred to a vacuum sampling cylinder and weighed using a mass compactor. This approach not only provides representative samples but also enables compositional analysis while performing density calculations according to ISO 6578. Feasibility and practicality tests of this novel method were conducted in an LNG plant. The test results indicate that the reproducibility is 0.6 %(RSD), and the relative expanded uncertainty is 2.0 % (k = 2). Compared to the revised Klosek and McKinley method (RKM), it shows a relative difference of −0.3 % to 1.5 %, demonstrating the applicability to LNG plants.

Validation of a new gamma ray soil bulk density sensor

AbstractSoil compaction and soil bulk density are key soil properties affecting soil health and soil ecosystem services like crop production, water retention and purification and carbon sequestration. The standard method for soil bulk density measurements using Kopecky rings is very labour intensive, time consuming and leaves notable damage to the field. Accurate data on bulk density are therefore scarce. To enable large‐scale data collection, we tested a new portable gamma ray sensor (RhoC) for in situ field and dry bulk density measurements up to 1 m depth. In this first validation study, measurements with the RhoC‐sensor were compared with classic ring sampling. Measurements were made in two agricultural fields in the Netherlands (a sandy clay loam and a sandy soil), with large variation in subsoil compaction. At 10 locations within each field, three soil density profiles were made. Each profile comprised six depth measurements (every 10 cm from 10 to 60 cm depth) using the RhoC‐sensor and Kopecky rings, resulting in 30 pairwise profiles and 180 measurements in total per field. At an average soil density of 1.5 g/cm3, the relative uncertainty was 9% for the Kopecky rings and 15% for the RhoC‐sensor. Because the RhoC‐sensor is easy and quick to use, the higher relative uncertainty can easily be compensated for by making additional measurements per location. In conclusion, the RhoC‐sensor allows a reliable quantitative in situ assessment of both field and dry bulk density. This provides the much‐needed possibility for rapid and accurate assessment of soil compaction. The acquisition of this data supports the calculation of soil organic carbon stocks and is indispensable for (national) soil monitoring, to assess soil health and to inform sustainable land management practices for sustained or improved soil health and provision of soil ecosystem services, such as requested in the proposed EU Directive on Soil Monitoring and Resilience.

Accuracy of Phantomless Calibration of Routine Computed Tomography Scans for Opportunistic Osteoporosis Screening in the Spine Clinic.

Diagnostic accuracy study. To establish a simple method of phantomless bone mineral density (BMD) measurement by using preoperative lumbar Computed Tomography (CT) scans, and compare the accuracy of reference tissue combinations to diagnose low BMD against uncalibrated Hounsfield units (HUs). HUs are used as a measure of BMD; however, associations between HU and T-scores vary widely. Quantitative CT (qCT) scans are more accurate, but they require density calibration with an object of known density (phantom), which limits feasibility. As an emerging technique, phantomless (internal) calibration of routine CT scans may provide a good opportunity for screening. Patients who were scheduled to undergo lumbar surgery, with a preoperative CT scan, and a dual-energy x-ray absorptiometry (DXA) scan within six months were included. Four tissues were selected for calibration: subcutaneous adipose (A), erector spinae (ES), psoas (P) and aortic blood (AB). The HUs of these tissues were used in linear regression against ground-truth values. Calibrations were performed by using two different internal tissues at a time to maintain simplicity and in-office applicability.Volumetric bone mineral densities (vBMD) derived from internally calibrated CT scans were analyzed for new threshold values for low bone density. Areas under the curve (AUC) were calculated with 95% confidence intervals (CI). 45 patients were included (M/F=10/35, mean age:63.3). Calibrated vBMDs had stronger correlations with DXA T-scores when compared with HUs, with L2 exhibiting the highest coefficients. Calibration by using A and ES with the threshold of 162mg/cm3 had a sensitivity of 90% in detecting low BMD (AUC=0.671). This novel method allows simple, in-office calibration of routine preoperative CT scans without the use of a phantom. Calibration using adipose and erector spinae with a threshold of 162mg/cm3 is proposed for low bone density screening with high sensitivity (90%). Level III.

Microwave Method of Tubular Plasma Density Measurement for Relativistic Microwave Oscillator

Microwave Method of Tubular Plasma Density Measurement for Relativistic Microwave Oscillator

Study of Temporal Dynamics of Urban Heat Island Surface in Padang West Sumatra, Indonesia

Padang as the capital of the province, is a strategic area and also the center of the economy. Annual population growth affects changes in land use from vegetated land to built-up areas. An increase in barren land will trigger an increase in temperature. SUHI is a temperature phenomenon that occurs on the surface resulting from the increase in temperature. SUHI can be observed through surface temperature data or Land Surface Temperature. This study aims to identify changes in land surface temperature that are affected by changes in land use in the form of building density conditions. In analyzing this using Landsat 7 ETM+ imagery in 2001, 2006, 2011, 2016, and 2020. The building density measurement method LST transformations to measure surface temperature and helps the Surface Urban Heat Island phenomenon. The results of the analysis showed that there was an increase in the building density of the city of Padang over a period of 20 years. This phenomenon affects the surface temperature, indicating that the surface temperature has increased by around 0.47°C. The highest temperature from 2001-2020 occurred in 2016, with the highest temperature of 36°C.

Natural Fibers Density Measurement Methods: Factors Affecting Density Result in Helium Gas Pycnometry

ABSTRACT Fiber density can be used to estimate composite weight and to evaluate fiber content for property predictions in fiber-reinforced composites. Yet, natural fibers don’t have fixed density value given on datasheet and their density measurement is affected by various factors. This study compares various standard natural fiber’s density mearusing methods and identifies determinant factors to, then, estimate the density of Enset fibers by using the optimal combination. The advantages and disadvantages of five standard methods were compared considering the characteristics of natural fibers’ and methods’ effectiveness where Helium gas Pycnometry is selected afterward. Standard calibration sphere and synthetic fiber with known density is measured for validation. The study analyzed the impact of variables on result’s precision and accuracy. Then, Density of Enset fiber have been measured with varied factors including uniformity and duration purging, volume cell type and amount of fiber in a cell, and fiber size. The result showed that fluctuation is minimized, and accuracy and precision is enhanced for the larger volume cell, larger mass of fiber in a cell, smaller size fiber and longer uniform purging. Finally, measured density of Enset using the best combination of factors is 1.38 ± 0.01 g/cm3.

Air gap flux density measurement of PMSLM based on TMR sensing external stray magnetic field and CNN-LSTM

This paper presents a new non-invasive air gap flux density measurement method for permanent magnet synchronous linear motor (PMSLM) using tunneling magnetoresistance (TMR) sensor and convolutional neural networks-long short-term memory (CNN-LSTM) regression modeling. First, the analytical and finite element models of the air gap magnetic field of PMSLM are established as the data basis. Second, TMR sensor is used to measure the external stray magnetic density. Gramian Angular Field method combined with image similarity matching technology are used to obtain the optimal measurement position of the TMR sensor. Then, a new deep learning regression method as CNN-LSTM is introduced to establish a high-precision mapping model to realize non-invasive high-precision measurement of the air gap magnetic density by “substituting external to internal.” Finally, PMSLM prototype experimental platform with TMR sensor hardware acquisition circuit is built. Comparison confirmatory experiments with Gauss meter can verify the effectiveness and superiority of the proposed method.

Intelligent vineyard blade density measurement method incorporating a lightweight vision transformer

Under the new demand model of Agriculture 4.0, automated spraying is a very complex task in precision agriculture, which needs to be combined with a computerized vision perception system to distinguish the plant leaf density and execute the spraying operation in real-time accordingly. Aiming at the accurate determination of grape leaf density, an image leaf density determination method based on the lightweight Vision Transformer (ViT) architecture is proposed, which designs a fusion data augmentation method containing a dual augmentation spatial extension and weather data augmentation method, where the former adopts the pixel augmentation and spatial augmentation for the original image processing, and the latter realizes the data augmentation from the empirical point of view adapted to the agricultural operation environment, and fuses the two in order to expand the sample capacity of the grape leaf density image, which then enhances the model's generalization ability and robustness. The lightweight ViT model has self-attention that can automatically and efficiently extract high-frequency local feature representations and use the two-branch structure to mix high-frequency and low-frequency information to form grapevine-leaf density features in the region of interest. The semantic analysis of the feature extraction layer is parsed using t-SNE and histogram methods, which improves the transparency of the model from the multidimensional with frequency domain distribution space. The experimental results show that the fusion data augmentation method can effectively improve the model recognition accuracy, and the accuracy of comparing the included data augmentation methods is improved by 0.55 % and 3.46 %, respectively. The accuracy of recognizing all four types of grape leaf densities exceeded 94 %, and the MCC reached 90.39 %. In addition, the proposed lightweight ViT improves the accuracy by at least 0.34 % with FLOPs of only 0.6 G compared to the popular MobileViT. The proposed method of this work has high recognition speed and accuracy, which can provide practical technical support for plant protection spraying robots and improve the profitability of growers based on the reduction of pesticide residues.

Uncertainty in measuring the role of climate change on debris-flow triggering on volcanoes - bulk-density, temperature and moisture analysis at Unzen Volcano (Japan)

As climate change creeps into the 21st century, the intensity of debris flows due to heavy and concentrated rainfall has increased in mountainous regions of Japan and East Asia. However, the relationship between climate change and an increase in debris flows is likely to be non-linear. Rainwater infiltrates more quickly into porous material, and the lack of vegetation cover increases evaporation and the temperature of surface sediments. In addition, periodic gully collapses bring fresh layers of porous material that increase the distance between the surface and the vadose zone. Therefore, to understand the relationship between volcanic debris flows (or lahars), parameters such as density, porosity, temperature, and moisture retention must be captured in detail in both time and space. The aim of this paper is to assess the role of loose, coarse, and porous sediments in lahar triggering. The present study was conducted at Unzen volcano in the Tansandani Guly between 31 May and 1 June 2023, 30 years after the last eruption. The dacite material is composed of a matrix of sand and coarse sand mixed with larger fractions and blocks, therefore traditional density measurement methods could not be applied, and a photogrammetric based method was used. In the field, sets of SfM-MVS photographs were taken before and after digging a hole in the sediments so that the measured mass could be compared to the volume of the hole in the sediments. After the sediments were dried, the dry and wet density, bulk density and porosity of the sample were calculated. When compared to the temperature data collected in the field, the following results were obtained: (1) The porosity of the volcanic material was highest in the lower reaches, followed by the upper reaches, and lowest in the middle reaches. This may be because fine sand washed out of the upstream area by rainfall is currently stored in the midstream area, which may facilitate debris flow generation. In addition, the downstream area has a high porosity, which may be due to the surrounding vegetation preventing the influx of new fine sand from the channel wall. (2) Because of the higher porosity and the lack of organic matter and vegetation cover, the increase in temperature acts more directly on the decrease in water content than in mountainous areas. Consequently, empirical equations for the potential for mudslides in volcanic areas with respect to surface temperature and soil moisture need to be developed for hazard and disaster risk management purposes.

High-precision alkali-atom density measurement and control methods using light absorption for dual-beam SERF magnetometers

High-precision alkali-atom density measurement and control methods using light absorption for dual-beam SERF magnetometers

Automated Density Measurement of Weft Knitted Fabrics Using Backlight Imaging

This paper proposes a new density measurement algorithm to address the issues of poor applicability and inaccurate results associated with the automatic density measurement algorithm for weft-knitted fabrics. The algorithm involves collecting the transmitted light image of the knitted fabric, calculating the tilt angle using the skewing correction algorithm, and rotating the image to correct the weft skew present therein. The pre-rotated and post-rotated images are then projected vertically and horizontally in grayscale, and the obtained projection curves are used to represent the distribution of loops in vertical and horizontal rows. This study proposed a wave peak coordinate verification algorithm that calculates the coursewise densities and walewise densities of the knitted fabric. In experiments, the proposed density measurement method is found to exhibit an accuracy above 98% when compared with the manual mode.

Experimental Research on Ice Density Measurement Method Based on Ultrasonic Waves

Ice density is an important physical parameter affecting the mechanical properties of ice. Due to bad field environments, the traditional density measurement method cannot achieve continuous monitoring of ice density. Therefore, the authors of this paper propose a new idea: to use the acoustic characteristics of ice to obtain ice density. The acoustic and physical properties of artificially frozen ice samples, with salinity values in the range of 0~8.5‰, were tested using a nonlinear high-energy ultrasonic testing system to explore the relationships among ice density, sound velocity, temperature, and salinity when the temperatures of the ice samples rose from −30 °C to −5 °C. The test results show that the freshwater ice density decreases from 915.5 kg/m3 to 911.9 kg/m3 when the ice temperature rises from −30 °C to −5 °C. The density of saltwater ice varies from 899.8 kg/m3 to 912.9 kg/m3. When the salinity remains the same, the density of an ice sample decreases with an increase in temperature and increases with an increase in sound velocity. When the ice temperature remains the same, the density of saltwater ice increases with an increase in salinity and decreases with an increase in sound velocity. Based on the test results, a prediction model of ice density with respect to sound velocity, temperature, and salinity is established. The root mean square error between the predicted values of the model and the measured values is 0.337 kg/m3, indicating that the prediction accuracy is high.

Deep learning assisted microwave-plasma interaction based technique for plasma density estimation

The electron density is a key parameter to characterize any plasma. Most of the plasma applications and research in the area of low-temperature plasmas (LTPs) are based on the accurate estimations of plasma density and plasma temperature. The conventional methods for electron density measurements offer axial and radial profiles for any given linear LTP device. These methods have major disadvantages of operational range (not very wide), cumbersome instrumentation, and complicated data analysis procedures. The article proposes a deep learning (DL) assisted microwave-plasma interaction-based non-invasive strategy, which can be used as a new alternative approach to address some of the challenges associated with existing plasma density measurement techniques. The electric field pattern due to microwave scattering from plasma is utilized to estimate the density profile. The proof of concept is tested for a simulated training data set comprising a low-temperature, unmagnetized, collisional plasma. Different types of symmetric (Gaussian-shaped) and asymmetrical density profiles, in the range 1016–1019 m−3, addressing a range of experimental configurations have been considered in our study. Real-life experimental issues such as the presence of noise and the amount of measured data (dense vs sparse) have been taken into consideration while preparing the synthetic training data-sets. The DL-based technique has the capability to determine the electron density profile within the plasma. The performance of the proposed DL-based approach has been evaluated using three metrics- structural similarity index, root mean square logarithmic error, and mean absolute percentage error. The obtained results show promising performance in estimating the 2D radial profile of the density for the given linear plasma device and affirms the potential of the proposed machine learning-based approach in plasma diagnostics.