Data Acquisition Research Articles

A deep transfer learning model for online monitoring of surface roughness in milling with variable parameters

Surface roughness is crucial for the functional and aesthetic properties of mechanical components and must be carefully controlled during machining. However, predicting it under varying machining parameters is challenging due to limited experimental data and fluctuating factors like tool wear and vibration. This study develops a deep transfer learning model that incorporates the correlation alignment method and tool wear to enhance model generalization and reduce data acquisition costs. It utilizes multi-sensor data and the ResNet18 with a convolutional block attention module (CBAM-ResNet) to extract features with improved generalization and accuracy for monitoring milled surface roughness under varying conditions. The performance of the model is evaluated from different perspectives. First, the proposed model achieves high accuracy with fewer than 500 experimental samples from the target domain by using the CORAL module in the CBAM-ResNet model. This demonstrates the model's strong generalization capability by minimizing second-order statistical discrepancies between different datasets. Second, ablation experiments reveal a significant reduction in test error when incorporating CORAL and tool wear, highlighting their contributions to improved model generalization. Integrating tool wear information significantly reduces test errors across various transfer conditions, as it reflects changes in cutting force, vibration, and built-up edge formation. Third, comparisons with existing deep transfer models further emphasize the advantages of the proposed approach in improving model generalization. In summary, the proposed surface roughness model, which incorporates tool wear and multi-sensor signal features as inputs and employs feature transfer and CBAM-ResNet, demonstrates superior generalization and accuracy across various machining parameters.

Real‐time reliability evaluation method for IGBT module in MMC based on junction temperature and bond wire failure online monitoring

AbstractThe lack of an MMC reliability evaluation approach that incorporates data on essential components' health monitoring in the already related research prevents reliability from being further improved. Therefore, this article proposes a real‐time reliability evaluation method of the multi‐state IGBT module in MMC through iterative calculation of the Markov chain model. The degradation process of the IGBT module is divided into multiple states based on the monitored bond wire failure, and the failure rate of the IGBT module is updated in real time using the accumulated monitoring data of junction temperature. The proposed method can more accurately reflect the real state of MMC compared with the binary state evaluation method. Besides, a multi‐channel high‐speed data acquisition board is designed to measuring the gate voltage and collector voltage of the IGBT for the health evaluation of IGBT modules in MMC; the typically measured results validate the effectiveness of the data acquisition board.

A Decentralized Digital Watermarking Framework for Secure and Auditable Video Data in Smart Vehicular Networks

Thanks to the rapid advancements in Connected and Automated Vehicles (CAVs) and vehicular communication technologies, the concept of the Internet of Vehicles (IoVs) combined with Artificial Intelligence (AI) and big data promotes the vision of an Intelligent Transportation System (ITS). An ITS is critical in enhancing road safety, traffic efficiency, and the overall driving experience by enabling a comprehensive data exchange platform. However, the open and dynamic nature of IoV networks brings significant performance and security challenges to IoV data acquisition, storage, and usage. To comprehensively tackle these challenges, this paper proposes a Decentralized Digital Watermarking framework for smart Vehicular networks (D2WaVe). D2WaVe consists of two core components: FIAE-GAN, a novel feature-integrated and attention-enhanced robust image watermarking model based on a Generative Adversarial Network (GAN), and BloVA, a Blockchain-based Video frames Authentication scheme. By leveraging an encoder–noise–decoder framework, trained FIAE-GAN watermarking models can achieve the invisibility and robustness of watermarks that can be embedded in video frames to verify the authenticity of video data. BloVA ensures the integrity and auditability of IoV data in the storing and sharing stages. Experimental results based on a proof-of-concept prototype implementation validate the feasibility and effectiveness of our D2WaVe scheme for securing and auditing video data exchange in smart vehicular networks.

Popular science books in Russian libraries: Status, problems and prospects

The authors discuss the findings of the study “Popular science books in Russian libraries”. The purpose of the study was to get data on the existence, quantity and relevancy of these publications in the libraries. The authors cite the results of the survey and provide their conclusions on the libraries’ managing their popular science collections and related user services. The digital survey was based on Yandex Forms service and comprised quantitative data acquisition and analysis. The authors conclude that with the government’s focusing on science popularization among the population, the libraries act proactively, promote popular science literature and hold various events. However, further system organizational and methodological support is needed to build the unified system of straight interdepartmental control of popular science literature publishing and relevancy, and to improve libraries performance in this area. The authors specify several problems, in particular, lacking the common approach to publishing and using of popular science literature. The solutions lie in new approaches toward typological classification of this targeted publications and target user audience.

Polarization position angle standard stars: a reassessment of θ and its variability for seventeen stars based on a decade of observations

ABSTRACT Observations of polarization position angle ($\theta$) standards made from 2014 to 2023 with the High Precision Polarimetric Instrument (HIPPI) and other HIPPI-class polarimeters in both hemispheres are used to investigate their variability. Multiband data were first used to thoroughly recalibrate the instrument performance by bench-marking against carefully selected literature data. A novel co-ordinate difference matrix (CDM) approach – which combines pairs of points – was then used to amalgamate monochromatic ($g^\prime$ band) observations from many observing runs and re-determine $\theta$ for 17 standard stars. The CDM algorithm was then integrated into a fitting routine and used to establish the impact of stellar variability on the measured position angle scatter. The approach yields variability detections for stars on long time-scales that appear stable over short runs. The best position angle standards are $\ell$ Car, o Sco, HD 154445, HD 161056, and $\iota ^1$ Sco, which are stable to $\le$0.123$^\circ$. Position angle variability of 0.27–0.82$^\circ$, significant at the 3$\sigma$ level, is found for 5 standards, including the Luminous Blue Variable HD 160529 and all but one of the other B/A-type supergiants (HD 80558, HD 111613, HD 183143, and 55 Cyg), most of which also appear likely to be variable in polarization magnitude (p) – there is no preferred orientation for the polarization in these objects, which are all classified as $\alpha$ Cygni variables. Despite this we make six key recommendations for observers – relating to data acquisition, processing and reporting – that will allow them to use these standards to achieve $\lt $ 0.1$^\circ$ precision in the telescope position angle with similar instrumentation, and allow data sets to be combined more accurately.

Integration of airborne geophysical data for the characterization of the geothermal system in Valle del Cura, high central Andes

The Valle del Cura region, located in the High Central Andes, over the Pampean flat-slab segment, exhibits several geothermal manifestations suggesting structural rather than magmatic controls. However, the structural and favorable conditions of this geothermal system have not been studied in detail yet. By integrating high-resolution airborne geophysical data with existing geological information using GIS tools, we can better characterize the structural controls of the thermal areas. The application of the upward continuation filter and tilt-angle derivative to the reduced to pole magnetic anomaly map allowed us to delineate structural lineaments that correlate well with known faults in the area. Euler deconvolution successfully identified local structures controlling the subsurface water upwelling and provided reliable depth estimations. Results indicate that the Despoblados area is characterized by NW-oriented deep structural controls reaching depths of up to 5 km. In contrast, the Bañitos-Gollete and the northern thermal areas are controlled by the intersection of N-S with E-W shallow structures at less than 3 km deep. Aeroradiometric data provided additional information for the characterization of several lithological units based on the dominance of radioelements (K, Th and U) in near-surface rocks by using individual and ternary maps. In addition, estimations of radiogenic heat production were crucial for assessing the potential energy of geothermal resources in the region. In particular, higher average values were obtained for igneous and metamorphic basement which may contribute to the geothermal gradient and temperature distribution at depth. Further studies, including new geophysical data acquisition and field verification, are still needed to fully describe the regional and local structures involved in these geothermal areas.

Analysis of Multi-Dimensional Layers in Historic Districts Based on Theory of the Historic Urban Landscape: Taking Shenyang Fangcheng as an Example

The accelerated process of urbanisation in China is resulting in a decline in and threat to the historic landscape of historic districts. This study is based on the theory of historic urban landscapes and employs a multi-dimensional layers research framework for historic districts. It adopts a single case study and a research method that combines quantitative and qualitative methods. The stratification elements of Fangcheng in Shenyang are identified and summarised, the process of stratification is analysed, and the stratification patterns and laws are summarised through the acquisition and collection of multivariate data. The findings of this study indicate that the stratification elements of the Fangcheng Historic District have undergone five distinct phases of stratification evolution. The resulting stratification pattern can be summarised as follows: newborn, preserve, override, juxtaposition and decession. The spatial elements are layered in the following pattern: The historic landscape can be conceptualised as comprising four layers: (1) the layering of the historic landscape with large public buildings as the anchor point; (2) the layering of the historic landscape with the spatial pattern as the skeleton; (3) the layering of the historic landscape with the iconic buildings as the nodes; and (4) the layering of the historic landscape with the correlative elements as the substrate. The law of value element layering primarily reflects the principles of concentration, diversity and adaptation. In light of the urban historic landscape theory, the reconstruction and restoration strategies, integration of old and new and adaptive conservation of historic landscapes are proposed to offer novel insights and guidance for the conservation of the historic landscape in the Fangcheng Historic District.

Development and Validation of a Point Cloud Data Processing Algorithm for Obstacle Recognition in Double Hull Block

<i>Shipyards have recently been experiencing severe labor shortages, prompting increased adoption of production automation systems to mitigate this issue. This paper introduces a point cloud data acquisition and processing system designed to support automation operations within double-hull block environments. The acquisition system utilizes LiDAR sensors and is built as a portable device capable of conducting 360-degree scans inside double-hull blocks. The processing system integrates the RANSAC algorithm for plane recognition and a voxelization algorithm for object detection, enabling accurate identification of obstacles within the double-hull block. To validate the system, a full-scale test bench was constructed to replicate actual working conditions. Experimental results indicated that the system could detect the positions of various obstacles within the test bench with an accuracy of up to 100 mm, which is sufficient for the implementation of automation systems. The findings from this research are expected to facilitate the adoption of production automation in shipyards, enhancing productivity and addressing labor shortages in the industry.</i>

Low‐cost animal tracking using Bluetooth low energy beacons on a crowd‐sourced network

Abstract Animal tracking has opened the door to address many fundamental questions in ecology and conservation. Whilst historically animals have been tracked as a means to understand their large‐scale movements, such as migration, there is now a greater focus on using tracking to study movements over smaller scales, individual variation in movement or how movements shape social network structure. With this shift in focus also comes different tracking needs, including the need to track larger numbers of individuals. Tracking studies all face some technological limitations. For example, GPS and other active tracking solutions can collect fine‐scale movement data, but have a high cost per tag, limiting the number of individuals that can be followed. They also have high low‐energy costs of data acquisition and download, limiting time periods over which data can be collected. Low‐energy passive (e.g. PIT) or active (e.g. reverse GPS) tags can overcome these limitations, but instead require animals to remain within a bounded study area or to come into close proximity to detectors. Here we describe one solution that can overcome many current limitations by employing the massive global network of personal mobile phones as gateways for tracking animals using Bluetooth low‐energy (BLE) beacons. In areas with medium to high density of people, these simple‐to‐make beacons can provide regular updates of position over long time periods (battery life 1–3 years). We describe how to use off‐the‐shelf components to produce BLE beacons that weigh c. 5–6 g and cost <$7USD. Using field‐testing, we then show that beacons are capable of producing high‐frequency tracking data that can be used to build home ranges or to detect spatiotemporal co‐occurrences among individuals. BLE beacons are a low cost, low‐energy solution for studying organisms (e.g. birds, mammals and reptiles) living and moving in urban landscapes. Their low weight and small size makes them particularly well‐suited for tracking smaller species. When combined with fixed gateways, their use can also be extended to non‐urban habitats. Their high accessibility is likely to make them an attractive solution for many research projects.

Advancements and Challenges in Antenna Design and Rectifying Circuits for Radio Frequency Energy Harvesting.

The proliferation of smart devices increases the demand for energy-efficient, battery-free technologies essential for sustaining IoT devices in Industry 4.0 and 5G networks, which require zero maintenance and sustainable operation. Integrating radio frequency (RF) energy harvesting with IoT and 5G technologies enables real-time data acquisition, reduces maintenance costs, and enhances productivity, supporting a carbon-free future. This survey reviews the challenges and advancements in RF energy harvesting, focusing on far-field wireless power transfer and powering low-energy devices. It examines miniaturization, circular polarization, fabrication challenges, and efficiency using the metamaterial-inspired antenna, concentrating on improving diode nonlinearity design. This study analyzes key components such as rectifiers, impedance matching networks, and antennas, and evaluates their applications in biomedical and IoT devices. The review concludes with future directions to increase bandwidth, improve power conversion efficiency, and optimize RF energy harvesting system designs.

Discovering dynamics and parameters of nonlinear oscillatory and chaotic systems from partial observations

Despite rapid progress in data acquisition techniques, many complex physical, chemical, and biological systems remain only partially observable, thus posing the challenge to identify valid theoretical models and estimate their parameters from an incomplete set of experimentally accessible time series. Here, we combine sensitivity methods and ranked-choice model selection to construct an automated hidden dynamics inference framework that can discover predictive nonlinear dynamical models for both observable and latent variables from noise-corrupted incomplete data in oscillatory and chaotic systems. After validating the framework for prototypical FitzHugh-Nagumo oscillations, we demonstrate its applicability to experimental data from squid neuron activity measurements and Belousov-Zhabotinsky reactions, as well as to the Lorenz system in the chaotic regime. Published by the American Physical Society 2024

Evaluating the performance of multi-omics integration: a thyroid toxicity case study.

Multi-omics data integration has been repeatedly discussed as the way forward to more comprehensively cover the molecular responses of cells or organisms to chemical exposure in systems toxicology and regulatory risk assessment. In Canzler etal. (Arch Toxicol 94(2):371-388. https://doi.org/10.1007/s00204-020-02656-y ), we reviewed the state of the art in applying multi-omics approaches in toxicological research and chemical risk assessment. We developed best practices for the experimental design of multi-omics studies, omics data acquisition, and subsequent omics data integration. We found that multi-omics data sets for toxicological research questions were generally rare, with no datasets comprising more than two omics layers adhering to these best practices. Due to these limitations, we could not fully assess the benefits of different data integration approaches or quantitatively evaluate the contribution of various omics layers for toxicological research questions. Here, we report on a multi-omics study on thyroid toxicity that we conducted in compliance with these best practices. We induced direct and indirect thyroid toxicity through Propylthiouracil (PTU) and Phenytoin, respectively, in a 28-day plus 14-day recovery oral rat toxicity study. We collected clinical and histopathological data and six omics layers, including the long and short transcriptome, proteome, phosphoproteome, and metabolome from plasma, thyroid, and liver. We demonstrate that the multi-omics approach is superior to single-omics in detecting responses at the regulatory pathway level. We also show how combining omics data with clinical and histopathological parameters facilitates the interpretation of the data. Furthermore, we illustrate how multi-omics integration can hint at the involvement of non-coding RNAs in post-transcriptional regulation. Also, we show that multi-omics facilitates grouping, and we assess how much information individual and combinations of omics layers contribute to this approach.

Design and evaluation of a gas, temperature profiling and data acquisition system to monitor the performance of a batch fixed dome biogas digester

Recovering energy from biomass and treating organic waste can be done effectively through a biogas digester system. However, monitoring the performance and control of the system to produce biogas is possible through the online process. Therefore, this study developed a novel low-cost gas and temperature profiling system (GTPS) and data acquisition system (DAS) used in biogas digester. The GTPS is designed to monitor and record the amount of methane (CH4) and carbon dioxide (CO2), the main constituent of biogas, from 0 to 100% and to measure the slurry temperature in the digester using a K-type thermocouple. To test the developed monitoring system, the biogas yield (%) and temperature were monitored, and the result obtained agreed with that reported and obtained in the literature (50–70% and 30−40%) for CH4 and CO2, respectively, followed by the desired mesophilic temperature condition for biogas production (20–35 °C). The study employed an RS-232/RS-485 converter for data communication and a low-cost thermocouple module (isolated analog input) that was installed in the data acquisition system. Interestingly, the application of the converter equipment has never been used in any study on monitoring a biogas digester.

Transient Analysis of Flow Unsteadiness And Machine Instabilities In a Centrifugal Compressor at Surge and Second Quadrant Operation

Abstract Instabilities in turbomachinery operation originate from intrinsic flow unsteadiness, sudden variations of operating conditions, and interactions with the surrounding system, affecting both system stability and machine reliability. The flow unsteadiness, presenting a characteristic behaviour, requires a deep understanding of the underlying flow fundamentals, a careful analysis using advanced experimental methods and a clear distinction on how the system might lead to instabilities. The identified approach addresses deep surge and second quadrant characterization. The analysis presented here benefits from the responsiveness and flexibility of the experimental test rig, fine tuning of the system layout, the instrumentation installation, and the transient tests procedure. The main focus area is the time-frequency analysis of flow and pressure signals at transients, thus providing a proper reconstruction of flow mechanisms and instability evolution. Valuable techniques include: short-time fast Fourier transform, wavelet analysis, and Wigner-Ville distribution; coherence between flow and pressure, to trace waves propagation, is considered too. These, presented outlining their strengths and weaknesses, are evaluated considering real time field applicability with regards to computational resources too. The experimental campaigns and data processing are presented, with a test matrix covering a wide range of parameters, including system layout and surge volume size, rotational speeds, flow rates ranging from nominal point to full second quadrant operation. These data are vital for system modeling validation for a full compressor-system digital twin. Results indicate the importance of ensuring reliable data acquisition and analysis, with adequate instrumentation range, accuracy, synchronization, responsiveness and positioning.

Heat Capacity of Fish Oil at High Temperatures and Pressures

This paper reports new results from studies of the isobaric heat capacities of OMEGA-3 “950” fish oil provided by SOLGAR INC (USA) at high temperatures and high pressures. The measurements were carried out on a differential scanning calorimeter (ITs-400) with an automatic data acquisition system in the temperature range from 298.15 to 473.15 K and at pressures from 0.098 to 39.2 MPa. The coverage factor for the 95% confidence level for a two-way coverage interval is assumed to be 2. The expanded measurement uncertainty of heat capacity is estimated to be 2.4%, pressure is estimated to be 0.05% and temperature is estimated to be 15 mK. Experimental data on the fish oil isobaric heat capacity as a function of temperature and pressure are approximated by the correlation equation proposed in the work. At atmospheric pressure, the deviations between those calculated by the correlation equation and the current measured data on isobaric heat capacity are within the range of average absolute deviations (AAD) = 0.22% (standard deviation St. Dev = 0.28% and maximum deviation Max. Dev = 0.39%). Additionally, a comparison was made of the state parameters obtained during the experiment and the literature data at the studied parameters.

Continuous Viscoelasticity Measurement of Cell Spheroids via Microfluidic Electrical Aspiration.

Measurement of viscoelastic characteristics of cells cultured in three-dimensional (3D) is critical to study many biological processes including tissue and organ growth. In this article, we present a unique electrical aspiration method to measure the viscoelastic properties of cell spheroids. A microfluidic sensor was created to demonstrate this method. Unlike the traditional optical aspiration method, the aspiration of the cell spheroids is monitored via monitoring the dynamic electrical resistance change of a symmetrical microfluidic aspiration channel. We first used the microfluidic device to measure the viscoelastic properties of two types of biological tissues derived from calfskin and porcine left ventricular myocardium. The equilibrium elastic modulus and creep time constants were measured to be 148.1 ± 24.1 kPa and 76.7 ± 3.5 s and 64.5 ± 7.7 kPa and 31.4 ± 2.7 s respectively, which matched well with reported data. The test validated the principle of the electrical aspiration method. Next, we applied the method for measuring cell spheroids made of human mesenchymal stem cells at different culture stages. The equilibrium elastic modulus and apparent viscosity decreased with increasing culture time. Compared to optical aspiration methods, this microfluidic electrical aspiration method has no reliance on transparent materials and image processing, which thus allows simple setup, fast data acquisition and analysis. The use of a symmetric aspiration channel and the linear half-space model enable measurements of a large number of viscoelastic properties via a single measurement with higher accuracy. This method will enable high throughput, continuous viscoelastic measurement of cell spheroids as well as other 3D cell culture models in flow conditions without the need for any optical measurements.

Comparative Study of GPR Acquisition Methods for Shallow Buried Object Detection

This paper investigates the use of ground-penetrating radar (GPR) technology for detecting shallow buried objects, utilizing an air-coupled stepped frequency continuous wave (SFCW) radar system that operates within a 2 GHz bandwidth starting at 500 MHz. Different GPR data acquisition methods for air-coupled systems are compared, specifically down-looking, side-looking, and circular acquisition strategies, employing the back projection algorithm to provide focusing of the acquired GPR data. Experimental results showed that the GPR can penetrate up to 0.6 m below the surface in a down-looking mode. The developed radar and the back projection focusing algorithm were used to acquire data in the side-looking and circular mode, providing focused images with a resolution of 0.1 m and detecting subsurface objects up to 0.3 m below the surface. The proposed approach transforms B-scans of the GPR-based data into 2D images. The provided approach has significant potential for advancing shallow object detection capabilities by transforming hyperbola-based features into point-like features.

Electrochemical Acid-Base Transport Limitation Principle for Low Electroactive Analyte Sensing in Wastewater Monitoring.

Electrochemical sensing (ES) is crucial for improving data acquisition in wastewater treatment, but obtaining the signal for a low electroactive analyte is challenging. Here, we propose an electrochemical acid-base transport limitation (eABTL) principle for inertness-based sensing, offering a new insight into generating ES signals from an interfacial transport process rather than electron transfer. This principle enables potential ES application for various weak acids and bases (WABs) without reactions themselves. We established an eABTLP method for detecting orthophosphate in solutions as a proof of concept, demonstrating commendable accuracy and precision, and a wide detection range from 10 μM to over 300 mM. Endogenous interferences were identified using 23 weak acids, indicating no significant endogenous interfering factors in typical wastewaters. Of them, volatile fatty acids are the main interference, but their effect can be eliminated by adjusting pH above 6.0. Exogenous factors like anions, cations, ion strength, temperature, organic load, and dissolved oxygen were examined, and most of their effects can be ignored by maintaining consistent analytical procedures between calibration curve and sample. Furthermore, measurement of wastewater samples confirmed the applicability toward domestic wastewater and demonstrated its wide applicability when combined with digestion pretreatment. Given the merits of inertness-based sensing, the eABTL-based methods have the potential to be a crucial part of ES techniques for environmental and industrial monitoring.

Automated detection of motion artifacts in brain MR images using deep learning.

Quality assessment, including inspecting the images for artifacts, is a critical step during magnetic resonance imaging (MRI) data acquisition to ensure data quality and downstream analysis or interpretation success. This study demonstrates a deep learning (DL) model to detect rigid motion in T1-weighted brain images. We leveraged a 2D convolutional neural network (CNN) trained on motion-synthesized data for three-class classification and tested it on publicly available retrospective and prospective datasets. Grad-CAM heatmaps enabled the identification of failure modes and provided an interpretation of the model's results. The model achieved average precision and recall metrics of 85% and 80% on six motion-simulated retrospective datasets. Additionally, the model's classifications on the prospective dataset showed 93% agreement with the labeling of a radiologist a strong inverse correlation (-0.84) compared to average edge strength, an image quality metric indicative of motion. This model is aimed at inline automatic detection of motion artifacts, accelerating part of the time-consuming quality assessment (QA) process and augmenting expertise on-site, particularly relevant in low-resource settings where local MR knowledge is scarce.

Environmental Conditions Associated with Four Index Cases of Pacific Oyster Mortality Syndrome (POMS) in Crassostrea gigas in Australia Between 2010 and 2024: Emergence or Introduction of Ostreid herpesvirus-1?

Warm water temperature is a risk factor for recurrent mass mortality in farmed Pacific oysters Crassostrea gigas caused by Ostreid herpesvirus-1, but there is little information on environmental conditions when the disease first appears in a region-the index case. Environmental conditions between four index cases in Australia (2010, 2013, 2016 and 2024) were compared to provide insight into possible origins of the virus. Each index case was preceded by unusually low rainfall and higher rates of temperature change that could increase oyster susceptibility through thermal flux stress. Water temperature alone did not explain the index cases, there being no consistency in sea surface, estuary or air temperatures between them. Tidal cycles and chlorophyll-a levels were unremarkable, harmful algae were present in all index cases and anthropogenic environmental contamination was unlikely. The lack of an interpretable change in the estuarine environment suggests the recent introduction of OsHV-1; however, viral emergence from a local reservoir cannot be excluded. Future events will be difficult to predict. Temperature flux and rainfall are likely important, but they are proxies for a range of undetermined factors and to identify these, it will be necessary to develop comprehensive protocols for data acquisition during future index cases.