Data Acquisition Techniques Research Articles

Guidelines for the monitoring and management of iron overload in patients with haemoglobinopathies and rare anaemias.

Guidelines for the monitoring and management of iron overload in patients with haemoglobinopathies and rare anaemias.

Efficient Drone-Based Rare Plant Monitoring Using a Species Distribution Model and AI-Based Object Detection

Monitoring rare plant species is used to confirm presence, assess health, and verify population trends. Unmanned aerial systems (UAS) are ideal tools for monitoring rare plants because they can efficiently collect data without impacting the plant or endangering personnel. However, UAS flight planning can be subjective, resulting in ineffective use of flight time and overcollection of imagery. This study used a Maxent machine-learning predictive model to create targeted flight areas to monitor Geum radiatum, an endangered plant endemic to the Blue Ridge Mountains in North Carolina. The Maxent model was developed with ten environmental layers as predictors and known plant locations as training data. UAS flight areas were derived from the resulting probability raster as isolines delineated from a probability threshold based on flight parameters. Visual analysis of UAS imagery verified the locations of 33 known plants and discovered four previously undocumented occurrences. Semi-automated detection of plant species was explored using a neural network object detector. Although the approach was successful in detecting plants in on-ground images, no plants were identified in the UAS aerial imagery, indicating that further improvements are needed in both data acquisition and computer vision techniques. Despite this limitation, the presented research provides a data-driven approach to plan targeted UAS flight areas from predictive modeling, improving UAS data collection for rare plant monitoring.

UAV (Unmanned Aerial Vehicle) for Landslide Analysis Case Study in Grenggeng Village, Kebumen District, Central Java

Remote sensing technology has developed rapidly; one of them is data acquisition techniques using UAV (Unmanned Aerial Vehicle). With high-resolution aerial photographs, an unmanned aerial vehicle (UAV) can be a flexible, cost-effective, and accurate monitoring of landslide technique. This research aimed to determine and test the utilization of unmanned aerial vehicles (UAVs) in congested areas. Data was collected at Grenggeng Village, Kebumen Regency, using unmanned aerial vehicles cruising altitude of 90 – 110 meters above ground level and a spatial resolution of 5 – 10 cm over a 0.200 km2 area. In November 2020, the research site will be a landslide area with similar rock lithology to the Halang Formation’s sandstone and claystone layers. Direct field observations revealed the geological structures involved and the rock lithology that produced the slip field, seepage, and the sorts of vegetation that the community had planted. According to aerial photography data, the relief appears to be a straight-line pattern in the direction of the geological structure, the slope of the layers, and different vegetation. Aerial photography using UAV can also be used to carry out rehabilitation and reconstruction techniques.

RMisbeta: A robust missing value imputation approach in transcriptomics and metabolomics data

Transcriptomics and metabolomics data often contain missing values or outliers due to limitations of the data acquisition techniques. Most of the statistical methods require complete datasets for downstream analysis. A number of methods have been developed for missing value imputation using the classical mean and variance based on maximum likelihood estimators, which are not robust against outliers. Consequently, the performance of these methods deteriorates in the presence of outliers. Hence precise imputation of missing values and outliers handling are both concurrently important. Therefore, in this paper, we developed a robust iterative approach using robust estimators based on the minimum beta divergence method, which simultaneously impute missing values and outliers. We investigate the performance of the proposed method in a comparison with six frequently used missing value imputation methods such as Zero, KNN, robust SVD, EM, random forest (RF) and weighted least square approach (WLSA) through feature selection using both simulated and real datasets. Ten performance indices were used to explore the optimal method such as Frobenius norm (FOBN), accuracy (ACC), sensitivity (SN), specificity (SP), positive predictive value (PPV), negative predictive value (NPV), detection rate (DR), misclassification error rate (MER), the area under the ROC curve (AUC) and computational runtime. Evaluation based on both simulated and real data suggests the superiority of the proposed method over the other traditional methods in terms of various rates of outliers and missing values. The suggested approach also keeps almost equal performance in absence of outliers with the other methods. The proposed method is accurate, simple, and consumes lower computational time compared to the other methods. Therefore, our recommendation is to apply the proposed procedure for large-scale transcriptomics and metabolomics data analysis. The computational tool has been implemented in an R package, which is publicly available from https://CRAN.R-project.org/package=rMisbeta.

Solid waste shape description and generation based on spherical harmonics and probability density function

Transport and separation processes of solid waste can only be modelled successfully with discrete element methods in case the shape of the particles can be described accurately. The existing techniques for morphological data acquisition, such as computed tomography, laser scanning technique, optical interferometer, stereo photography and structured light technique, are laborious and require a large amount of realistic solid waste samples. Therefore, there is a pressing need for an alternative method to describe the shape of solid waste particles and to generate multiple variations of particles with almost similar shapes. In this paper, a new method to describe solid waste particles is proposed that is frequency-based and uses spherical harmonics (SHs). Additionally, a new shape generation method is introduced that uses the shape description of a single particle to generate an array of related shapes based on a probability density function with a dimensionless control factor η. The newly proposed methods were successfully applied to describe the complex shapes of pieces of metal and plastic scrap. The shapes of these pieces of scrap can be described adequately with 15° of SH expansion and the overall divergence is within 0.1 mm. Five different values for η were tested, which generated shapes with the same distribution as the original particle. Rising levels of η cause the morphological variation of the generated particles to increase. These new methods improve the modelling of transportation and separation processes.

Nonlinear ultrasonic testing and data analytics for damage characterization: A review

Nondestructive testing and evaluation (NDT&E) are commonly used in the industry for their ability to identify damage and assess material conditions. Ultrasonic testing (UT) is one of the most popular NDT&E techniques. A variant of ultrasonic testing known as nonlinear ultrasonic testing (NUT) has some advantages over conventional (linear) UT as it is more sensitive to damages in their early stages; even at the microscopic levels. Furthermore, the nonlinear characteristics of ultrasonic waves can be correlated to several material properties. In the last two decades, the NUT method has been investigated from two aspects, namely the direct (modeling) problem and the inverse (NUT testing) problem. The direct problem aims to establish the nonlinear mechanism and analyze the behavior of wave-damage interaction. The inverse problem is investigated under three headings: (1) data acquisition with NUT techniques, (2) signal pre-processing and feature extraction, and (3) parameter analysis for damage characterization. The conventional data analytical methods extract nonlinear features from noisy signals and build a damage index to characterize damages. However, damage index-based analyzing model can be challenging, as other factors affect the overall system nonlinearity such as complex specimen geometry, different damage characteristics, varying ambient conditions, and measurement uncertainties. To overcome these shortcomings, machine learning (ML) methods appear promising for the analysis of complex nonlinear ultrasonic signals by exploiting data mining and pattern recognition capabilities. Therefore, this paper aims to provide a comprehensive review of the state-of-the-art ML-enriched NUT for damage characterization. Other NUT-based technologies are also reviewed, including modeling of wave-damage interaction, different NUT techniques for data acquisition, signal pre-processing methods, and damage index-based parameter analysis strategies for damage characterization. Major emphasis is placed on the application of ML methods for NDT&E applications. Additionally, future research trends on data augmentation, complex damage characterization, and baseline-free methods using NUT are also discussed.

Analysis and management of structural deformations through parametric models and HBIM workflow in architectural heritage

Maintaining the structure and ornaments of historical buildings is essential to preserve cultural heritage in any society. Thus, dating the state and the evolution of the elements requires a special treatment, including the application of both advanced numerical analysis and non-invasive data acquisition techniques. In the latter, making digital copies of geometries through 3D reconstruction models is of great interest to compare and analyse structural evolutionary data accurately. For this purpose, appropriate software with containers of information correlated to the parametric elements in a BIM environment should be used. However, it is imperative to advance from static to dynamic models to collect the structural transformations caused by both the pass of time and other factors. The methodology followed in this paper is based on the experimentation by creating digital twins. The portico of a courtyard in a historical building from the 18th century was used as test bed. Based on the bibliographical recommendations, the terrestrial laser scanner is applied as a technique to acquire accurate data. The point cloud is used as a referential auxiliary to survey the model in the BIM platform with the Revit software. To assess the quality of the model built and to analyse the structural deviations between the parametric model and the actual geometry, the Dynamo script is used. To validate the experimentation, structural deviations are measured using both the parametric model and the point cloud with CloudCompare, a software for data treatment. The results were very positive because the deviation between the data obtained by Dynamo© and CloudCompare in the most unfavourable construction unit was between 0.5 and 1.17 cm, so these techniques are highly appropriate to review visual records and to analyse structural deviations. This new approach presents a new gap in the 3D reconstruction to date and control architectural structures, particularly in historical buildings.

Current Trends in Integration of Nondestructive Testing Methods for Engineered Materials Testing.

Material failure may occur in a variety of situations dependent on stress conditions, temperature, and internal or external load conditions. Many of the latest engineered materials combine several material types i.e., metals, carbon, glass, resins, adhesives, heterogeneous and nanomaterials (organic/inorganic) to produce multilayered, multifaceted structures that may fail in ductile, brittle, or both cases. Mechanical testing is a standard and basic component of any design and fabricating process. Mechanical testing also plays a vital role in maintaining cost-effectiveness in innovative advancement and predominance. Destructive tests include tensile testing, chemical analysis, hardness testing, fatigue testing, creep testing, shear testing, impact testing, stress rapture testing, fastener testing, residual stress measurement, and XRD. These tests can damage the molecular arrangement and even the microstructure of engineered materials. Nondestructive testing methods evaluate component/material/object quality without damaging the sample integrity. This review outlines advanced nondestructive techniques and explains predominantly used nondestructive techniques with respect to their applications, limitations, and advantages. The literature was further analyzed regarding experimental developments, data acquisition systems, and technologically upgraded accessory components. Additionally, the various combinations of methods applied for several types of material defects are reported. The ultimate goal of this review paper is to explain advanced nondestructive testing (NDT) techniques/tests, which are comprised of notable research work reporting evolved affordable systems with fast, precise, and repeatable systems with high accuracy for both experimental and data acquisition techniques. Furthermore, these advanced NDT approaches were assessed for their potential implementation at the industrial level for faster, more accurate, and secure operations.

The Highest Peaks of the Mountains: Comparing the Use of GNSS, LiDAR Point Clouds, DTMs, Databases, Maps, and Historical Sources

Advances in remote data acquisition techniques have contributed to the flooding of society with spatial data sets and information. Widely available spatial data sets, including digital terrain models (DTMs) from aerial laser scanning (ALS) data, are finding more and more new applications. The article analyses and compares the heights of the 14 highest peaks of the Polish Carpathians derived from different data sources. Global navigation satellite system (GNSS) geodetic measurements were used as reference. The comparison primarily involves ALS data, and selected peaks’ GNSS measurements carried out with Xiaomi Mi 8 smartphones were also compared. Recorded raw smartphone GNSS measurements were used for calculations in post-processing mode. Other data sources were, among others, global and local databases and models and topographic maps (modern and old). The article presents an in-depth comparison of Polish and Slovak point clouds for two peaks. The results indicate the possible use of large-area laser scanning in determining the maximum heights of mountain peaks and the need to use geodetic GNSS measurements for selected peaks. For the Polish peak of Rysy, the incorrect classification of point clouds causes its height to be overestimated. The conclusions presented in the article can be used in the dissemination of knowledge and to improve positioning methods.

The state-of-the-art methodologies for quality analysis of arc welding process using weld data acquisition and analysis techniques

The state-of-the-art methodologies for quality analysis of arc welding process using weld data acquisition and analysis techniques

Combination of HBIM and UAV photogrammetry for modelling and documentation of forgotten heritage. Case study: Isabel II dam in N\xedjar (Almer\xeda, Spain)

The Isabel II dam is a monumental hydraulic structure built in the middle of the nineteenth century in Spain. In this study, unmanned aerial vehicle (UAV) photogrammetry was used as a data acquisition technique to carry out a survey of the dam’s current state and its surrounding constructions. The point cloud obtained by the photogrammetric process, together with the collected historical in-formation, served as the basis to generate an historic building information model (HBIM) that is the central core containing all the graphical, structural and archaeological information. The HBIM was validated by means of the As-Built for Autodesk Revit®-FARO® plug-in, and shows the high accuracy obtained with respect to the point cloud. The results show that with this methodology it is possible to obtain models representative of reality with an accuracy of ± 0.05 m. In addition, in order to improve the visualization, texture adjustments are made to obtain a photorealistic rendering of the model.

Non-periodic motion reduction in globoidal cam with roller follower mechanism

Non periodic motion has been investigated using power spectrum analysis of Fast Fourier Transform (FFT), Lyapunov exponent parameter using Wolf algorithm, and Phase-plane mapping. A multi degrees of freedom (spring-damper system) are added at the end of the follower stem to reduce the nonlinear dynamic behavior of the follower. In the experiment setup, data acquisition technique with signal processing approach is used through an infrared camera device to capture the nonlinear dynamic movement of the follower. A numerical simulation is done using SolidWorks software. An OPTOTRAK/3020 with an infrared camera device is used to track the follower position experimentally. The peak of nonlinear response of the roller follower is reduced by (20.89%, 47.76%, 58.2%, and 67.16%) after using multi degrees of freedom systems.

X-ray multi-probe data acquisition: A novel technique for laser pump x-ray transient absorption spectroscopy

We report the development and implementation of a novel data acquisition (DAQ) technique for synchrotron-based laser pump X-ray Transient Absorption (XTA) spectroscopy, called X-ray Multi-Probe DAQ (XMP DAQ). This technique utilizes high performance analog to digital converters and home-built software to efficiently measure and process the XTA signal from all x-ray pulses between laser excitations. XMP DAQ generates a set of time resolved x-ray absorption spectra at thousands of different pump-probe time delays simultaneously. Two distinct XMP DAQ schemes are deployed to accommodate different synchrotron storage ring filling patterns. Current Integration (CI) DAQ is a quasi-analog technique that implements a fitting procedure to extract the time resolved absorption intensity from the averaged fluorescence detector response. The fitting procedure eliminates issues associated with small drifts in the voltage baseline and greatly enhances the accuracy of the technique. Photon Counting (PC) DAQ is a binary technique that uses a time resolved histogram to calculate the XTA spectrum. While PC DAQ is suited to measure XTA data with closely spaced x-ray pulses (∼10ns) and a low count rate (<1 detected photon/pulse), CI DAQ works best for widely spaced pulses (tens of ns or greater) with a high count rate (>1 detected photon/pulse). XMP DAQ produces a two-dimensional XTA dataset, enabling efficient quantitative analysis of photophysical and photochemical processes from the sub-nanosecond timescale to 100 μs and longer.

The Effectiveness Of Recruitment Of Civil Services (Asn) In Increasing Performance Of Employees In The Performance Section Of The Province Of Bengkulu

The purpose of this study is to determine the impact of civil servant recruitment on improving the performance of Bengkulu Province civil servant employees. The method used in this study is a descriptive method using a qualitative approach. Data acquisition techniques were conducted by interviewing, observing, and recording documents related to the civil servant recruitment process carried out by Bengkulu local civil servants. The data acquired will be investigated and qualitatively analyzed for all the data collected, and an interview with Duncan's theoretical approach to stress, whose effectiveness can be seen in three indicators: goal achievement, integration, and adaptation Supported by the results of. The results of the field survey came to the correct conclusion as the implementation of labor recruitment carried out by local civil servants in the Bengkulu Province is more transparent and responsible.

Unsupervised Generative Adversarial Network for 3-D Microstructure Synthesis from 2-D Image

Understanding and modelling complex phenomenon in solid oxide fuel cell electrodes require information about their three dimensional microstructures. However, measuring the 3-D micro- and nano-structures requires complex and time-consuming data acquisition technique such as focused ion beam - scanning electron (FIB-SEM) tomography. Meanwhile, obtaining a single 2-D cross-sectional SEM image is relatively simple. A new algorithm for synthetic 3-D microstructure generation from 2-D cross-section is proposed in the present study. The algorithm is based on the generative adversarial network (GAN) composed of a 3-D generator and a 2-D discriminator, which uses only 2-D images as a training data. The applicability of the proposed method is tested on nickel (Ni) – gadolinium doped ceria (GDC) anodes. The synthetic 3-D microstructures are compared with those obtained from FIB-SEM. The proposed method can be easily adapted for any type of materials and imagining techniques.

PERBEDAAN AKTIVITAS BELAJAR SISWA KELAS IV YANG MASUK PAGI DENGAN MASUK SIANG DI SD NEGERI 147 PEKANBARU

The school is one of the conters for learning activities that can be used to develop their potential. There are many types of activities that students can do in school, such as : read, write, take notes, ask questions, practice and express opinions. However, there are schools that implement the morning access system by entering betel. This study aims to describe the differences in the learning activities of Class IV students who enter pekanbaru 147 public elementary schools. The method used is qualitative. Data acquisition techniques by observation with data analysis techniques by collecting data, then reducing data, presenting data, and finally searching for conclusions. The results of this study show that students do more positive activities in the morning, while students do more negative activities in the afternoon. This research is useful to broaden the knowledge and and insights of researchers in the field of education, particularly in student learning activities, and as a material consideration for future researchers

UPSCALING AND VALIDATION OF RTK-DIRECT GEOREFERENCED UAV-BASED RGB IMAGE DATA WITH PLANET IMAGERY USING POLYGON GRIDS FOR PASTURE MONITORING

Abstract. The monitoring of managed grasslands with remote sensing methods is becoming more important for spatial decision support. Various remote sensing data acquisition techniques are applied for that purpose in different spatial resolutions ranging from UAV-borne to satellite-based remote sensing. In the last decade, UAV-borne imaging and analysis techniques or in the focus of crop and grassland monitoring and provide very high spatial resolutions. In contrast, satellite data are only available in high to moderate spatial resolutions. In this contribution, we introduce direct georeferenced data acquisition with a Phantom 4 RTK for pasture monitoring and investigate the upscaling of the cm data to satellite resolutions using polygon grids.

Practical Implementation of Structural Health Monitoring in Multi-Story Buildings

This study investigated operational and structural health monitoring (SHM) as well as damage evaluations for building structures. The study involved damage detection and the assessment of buildings by placing sensors and by assuming weak areas, and considered situations of assessment and self-monitoring. From this perspective, advanced sensor technology and data acquisition techniques can systematically monitor a building in real time. Furthermore, the structure’s response and behavior were observed and recorded to predict the damage to the building. In this paper, we discuss the real-time monitoring and response of buildings, which includes both static and dynamic analyses along with numerical simulation studies such as finite element analysis (FEA), and recommendations for the future research and development of SHM are made.

COMBINING TERRESTRIAL SCANNED DATASETS WITH UAV POINT CLOUDS FOR MINING OPERATIONS

Abstract. Surveyors of open cut mining operations employ multiple data acquisition techniques such as the use of Unmanned Aerial Vehicles (UAV), Terrestrial Laser Scanning (TLS) and GNSS positioning for creating 3D surface models. Surveyors, mine planners and geologists are increasingly combining point cloud datasets to achieve more detailed surface models for the use of material reconciliation and volume calculations. Terrestrial Laser Scanning and UAV photogrammetry have enabled large, accurate and time effective data collection and increased computing capacity enables geospatial professionals to create 3D virtual surfaces, through merging UAV point clouds and TLS data combing with GNSS positioning. This research paper investigates the effects of combining data sets for creating 3D surface models from independent spatial data collection methods such as UAV, TLS and GNSS and assess their accuracy for the purpose of volume calculations in mining operation. 3D surface models provide important information for mining operations, planning of resources, material volumes calculation and financial calculations. A case study of two rehabilitation mine sites in Northern Victoria, Australia was selected for this study. Field data were collected using Terrestrial Laser Scanner and UAV. After each dataset was processed and filtered, the data were merged to create surface models. The accuracy of the combined model was assessed comparing height (Z) values using a fishnet point grid of the surfaces. Volumes between surfaces were calculated, and a cost applied to the results based on the current bulk cubic meter (BCM) haulage rates. The outputs from this study will provide scientific contributions to civil and mining industries where the computation of stockpile values is required.

GROUNDWATER POTENTIALS ESTIMATION USING SELF-POTENTIAL METHOD AS AN EARLY PREDICTION OF LAND SUBSIDENCE IN GAMPONG LENGKONG LANGSA CITY

The Self-Potential (SP) method was used to identify changes in the potential groundwater in the area of Gampong Lengkong, Langsa City. The data acquisition technique was carried out with a fixed base technique with a measurement count of four acquisition line. Research is conducted for two months that July and August are the same in order to monitor the changes in the SP value associated with the change of SP anomaly. Interpretation is done qualitatively and quantitatively. Qualitative interpretation through analysis maps of the isopotential contours compared between the first month and the second month of measurements in the field. The interpretation is quantitatively by assuming the source of the subsurface anomaly fix geometry models of cylinders or sphere is done by the inversion modeling using the Levenberg-Marquardt method. The results showed that there was a reduction in SP value in the second month, indicating the potential shortage of groundwater in the region. The decrease in groundwater potential is also shown in the inversion results where the result of the calculation of SP anomaly source parameters in the first month is estimated to be at 7.77 meters to 11.05 meters below the ground in the monthly measurements second.