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To establish a reference for developing an automated insertion strategy based on axial force, as well as an active axial force control strategy during the insertion stage for robot friction stir welding. This research specifically focuses on the insertion phase of stationary shoulder friction stir welding using the 6061-T6 aluminum alloy. It investigates the effects of various parameters, including spindle rotation speed, insertion depth of the stirring pin, insertion speed, and spindle insertion angle, on the axial force experienced during the insertion phase. The experiments were conducted using a ZK3000-500-A industrial robot equipped with a static shoulder friction stir welding spindle system. The axial force was measured using a force transducer and data acquisition system, recording real-time force signals throughout the welding process. The methodology involves varying the insertion depth from 7 to 11 mm, spindle rotation speed from 1000 to 3000 rpm, insertion speed from 10 to 50 mm/min, and spindle insertion angles of 0°, 2.5°, and 5°. The welding process parameters were systematically varied while keeping other conditions constant to isolate the effect of each parameter. The results indicate that the robotic friction stir welding insertion process can be divided into four distinct stages: initial rise, transition, secondary rise, and stabilization. Furthermore, the change in the axial force value exhibits an approximately linear relationship with the insertion depth. It was observed that the axial force increased linearly with increasing insertion depth during the stable stage. Higher spindle speed results in an earlier initiation and termination of the transition stage. Among the investigated parameters, the insertion speed has the most significant impact on the trend of the axial force change. A higher spindle speed not only facilitated a quicker insertion but also resulted in a more rapid initial force increase. Specifically, a higher insertion speed leads to an increased rate of force increase, a decreased stabilized value, and a shorter transition time. On the other hand, the spindle insertion angle does not exert a significant influence on the trend of the force change. This study establishes a basis for future enhancements in force control strategies. [ABSTRACT FROM AUTHOR]

Prior to the establishment of a coal mine, a high-resolution three-dimensional (3D) seismic survey was carried out. For seismic exploration of steep coal seams with an inclination angle more than 45°, the selection of the data gathering system is essential. The time disparity that remains when the traditional acquisition technology acquires 3D seismic data will be rather significant. The relationship between coal seam inclination, remaining time difference, and reflection coefficient is investigated in light of the presence of steeply inclined coal seams. When the direction of the generation surveying line is parallel to the coal seam, it is suggested as a vertical data acquisition system. To create a vertical data gathering system, the point is situated on the down-dipping side of the coal seam and a predetermined distance is kept between shooting points and detection points. A vertical data collection system was used to implement 3D seismic data acquisition in the Muli Coalfield in northern Qinghai. As a solid foundation for the future processing and analysis of 3D seismic data, valuable observation data with high resolution were successfully obtained. The study came to the conclusion that a vertical data collecting system is helpful in promoting effective coal mine output. [ABSTRACT FROM AUTHOR]

This paper presents a system and model for data acquisition and augmentation in OLED panel defect detection to improve detection efficiency. It addresses the challenges of data scarcity, data acquisition difficulties, and classification of different defect types. The proposed system acquires a hypothetical base dataset and employs an image generation model for data augmentation. While image generation models have been instrumental in overcoming data scarcity, time and cost constraints in various fields, they still pose limitations in generating images with regular patterns and detecting defects within such data. Even when datasets are available, the precise definition and classification of different defect types becomes imperative. In this paper, we investigate the feasibility of using an image generation model to generate pattern images for OLED panel defect detection and apply it for data augmentation. In addition, we introduce an OLED panel defect data acquisition system, improve the limitations of data augmentation, and address the challenges of defect detection data augmentation using image generation models. [ABSTRACT FROM AUTHOR]

Wheels are a critical component of railway infrastructure and work as the load carrier of the train. However, defective wheels pose a serious risk to safety that can gravely jeopardize people's safety. There is a significant risk of injury or death from defective wheels, endangering the lives of individuals. In this research, FaultSeg dataset is presented for automatic train wheel defect detection for railway transportation around the world. The FaultSeg consists of 829 manually annotated images of faulty wheels acquired by an indigenously developed wayside data acquisition system. Expert Annotators have manually annotated three classes of potential defects: Cracks/Scratches, Shelling, and Discoloration. To assess the practicality of the FaultSeg dataset for training and testing advanced deep learning (DL) models, the dataset was used to train and evaluate the YOLOv9 instance segmentation algorithm. The model achieves an approximate score of 87% accuracy. These results showcase the usability of the FaultSeg dataset in automatic inspection systems and data driven predictive maintenance strategies to safeguard and ensure the safety of railway transportation. [ABSTRACT FROM AUTHOR]

Polyethylene pipelines have been widely used in the construction of urban pipelines. Due to the non-magnetic and non-conductive properties of polyethylene pipelines, traditional metal pipe positioning techniques are unable to effectively detect polyethylene pipelines. To improve the positioning accuracy of polyethylene pipelines, this paper proposes a pipeline positioning method considering the attenuation characteristics of acoustic wave. The proposed method combines active acoustic positioning technology with the resonance characteristics of pipelines, which can accurately locate the horizontal position and depth of pipelines simultaneously. Firstly, based on the theory of acoustic wave attenuation, the solution of the acoustic wave equation in non-ideal media is derived. Then, the amplitude attenuation characteristics of acoustic waves in a buried polyethylene pipeline are analyzed using finite element simulation. By observing the maximum amplitude of acoustic signals, the horizontal direction of buried polyethylene pipelines can be effectively determined. To determine the depth of buried pipelines, the resonance phenomenon is utilized and the attenuation of acoustic waves is considered to obtain the mathematical model. The largest error of simulation results is 2% for horizontal direction, and 7.8% for depth position, indicating the proposed method can effectively locate the buried pipeline. Finally, a complete pipeline excitation and data acquisition system is established through an on-site experiment, and the experiments are conducted under various burial depth conditions. The experimental results show that the horizontal positioning error and depth positioning error are both less than 8%, indicating the proposed method can accurately locate the buried pipeline. [ABSTRACT FROM AUTHOR]

Ultrasonic sensing may become a useful technique for distance measurement and object detection when optical visibility is not available. However, the research on detecting multiple target objects and locating their coordinates is limited. This makes it a valuable topic. Reflection signal data obtained from a single ultrasonic sensor may be just enough for the measurements of distance and reflection strength. On the other hand, if extracted properly, a scanned set of signal data by the same sensor holds a significant amount of information about the surrounding geometries. Evaluating this dataset from a single sensor scanning can be a perfect application for convolutional neural networks (CNNs). This study proposes an imaging technique based on a scanned dataset obtained by a single low-cost ultrasonic sensor. So that images are suitable for desired outputs in a CNN, a 3D printer is converted to an ultrasonic image scanner and automated to perform as a data acquisition system for the desired datasets. A deep learning model demonstrated by this work extracts object features using convolutional neural networks (CNNs) and performs coordinate estimation using regression layers. With the proposed solution, by training a reasonable amount of obtained data, 90% accuracy was achieved in the classification and position estimation of multiple objects with the CNN algorithm as a result of converting the signals obtained from ultrasonic sensors into images. [ABSTRACT FROM AUTHOR]

Integrating machine learning algorithms leveraged by advanced data acquisition systems is emerging as a pivotal approach in predictive maintenance. This paper presents the deployment of such an integration on an industrial air compressor unit. This research combines updated concepts from the Internet of Things, machine learning, multi-sensor data collection, structured data mining, and cloud-based data analysis. To this end, temperature, pressure, and flow rate data were acquired from sensors in contact with the compressor. The observed data were sent to the Structured Query Language database. Then, a Linear Regression model was fitted to the training data, and the optimized model was stored for real-time inference. Afterward, structured data were passed through the model, and if the data exceeded the determined threshold, a warning email was sent to an operator. Adopting the Internet of Things enhances surveillance for specialists, decreasing the failure and damage probabilities. The model achieved 98% accuracy in the Mean Squared Error metric for our regression model. By analyzing the gathered data, the implemented system demonstrates the capabilities to predict potential equipment failures with promising accuracy, facilitating a shift from reactive to proactive maintenance strategies. The findings reveal substantial potential for improvements in maintenance efficiency, equipment uptime, and cost savings. [ABSTRACT FROM AUTHOR]

In animal facilities, monitoring and controlling the thermal environment are essential in ensuring productivity and sustainability. However, many production units face challenges in implementing and maintaining effective thermal monitoring and control systems. Given the need for Smart Livestock Farming systems, this study aimed to develop and validate an easy-to-use, low-cost embedded system (ESLC) for the real-time monitoring of dry-bulb air temperature (Tdb, in °C) and relative humidity (RH, in %) in animal production facilities. The ESLC consists of data collection/transmission modules and a server for Internet of Things (IoT) data storage. ESLC modules and standard recording sensors (SRS) were installed in prototype animal facilities. Over 21 days, their performance was evaluated based on the Data Transmission Success Rate (DTSR, in %) and Data Transmission Interval (DTI, in minutes). Additionally, agreement between the ESLC modules and the SRS was assessed using the daily mean root mean square error (RMSE) and mean relative error (RE) across different Tdb and RH ranges. The ESLC successfully collected and transmitted data to the cloud server, achieving an average DTSR of 94.04% and a predominant DTI of one minute. Regarding measurement agreement, distinct daily mean RMSE values were obtained for Tdb (0.26–2.46 °C) and RH (4.37–16.20%). Furthermore, four sensor modules exhibited mean RE values below 3.00% across all Tdb ranges, while all sensor modules showed progressively increasing mean RE values as RH levels rose. Consequently, calibration curves were established for each sensor module, achieving a high correlation between raw and corrected values (determination coefficient above 0.98). It was concluded that the ESLC is a promising solution for thermal monitoring in animal facilities, enabling continuous and reliable data collection and transmission. [ABSTRACT FROM AUTHOR]

The Large Area Detector (LAD) is one of the science payloads of the enhanced X-ray Timing and Polarimetry (eXTP) mission. The LAD is a spectral-timing instrument with a broad energy response, covering a range from 2 to 30 keV, a good timing resolution of better than 10 µs, and an expected energy resolution of 260 eV at 6 keV. The LAD consists of 640 large-area multi-anode Silicon Drift Detectors (SDDs). Given the large number of detectors, the LAD uses a modular design. Each module comprises sixteen detectors, and each detector is equipped with dedicated Front-End Electronics (FEE), interfacing with two separate Module Back-End Electronics (MBEEs). Each MBEE is designed to process the data from 1,792 anode channels in 8 FEEs (224 anode channels per FEE), performing the energy reconstruction and time tagging for X-ray events. The MBEE uses the European Field Programmable Gate Array (FPGA) from NanoXplore™, based on a pipeline concept, which reduces dead time, making the LAD suitable for higher flux X-ray detection, and it can handle a sustained flux of >500 mCrab and a continuous flux of >15 Crab for up to 300 minutes (Feroci et al. 2018). Additionally, the MBEE serves as the central hub for configuring the module's electronics, including the FEEs, the Power Supply Unit (PSU), and the MBEE itself, and it is also responsible for collecting housekeeping data to monitor the system's status. The prototype MBEE was designed, manufactured, and programmed with FPGA firmware using VHDL. The basic functional test was conducted in this paper, and the results indicated that the MBEE could be operated in different modes to perform the functions mentioned above. Analysis and testing show that it can transmit event packets-containing timing tag, event type, position ID, and energy information-at a baud rate of 2 Mbps with an event loss fraction of 1.5%. [ABSTRACT FROM AUTHOR]

The logging-while-drilling (LWD) acoustic logging instrument is pivotal in unconventional oil and gas exploration, and in providing real-time assessments of subsurface formations. The acquisition system, a core component of the LWD acoustic logging suite, is tasked with capturing, transmitting, and processing acoustic signals from the formation, which directly affects the accuracy and timeliness of the logging data. Recognizing the constraints of current LWD acquisition systems, including limited data collection capabilities and inadequate precision, this study introduces an FPGA-based multi-node data acquisition system for LWD acoustic logging. This system increases sampling density and data accuracy, leading to a more comprehensive collection of formation information. The multi-node acquisition system is composed primarily of a main control circuit board and several acquisition circuit boards, all connected via an RS485 bus. The Field-Programmable Gate Array (FPGA) is utilized to develop the acquisition circuit board's firmware, offering adjustable control over parameters, such as the AD7380's operational mode, sampling rate, and depth, facilitating real-time and concurrent acquisition and storage of formation acoustic signals. The main control board communicates with the acquisition boards via the RS485 bus, issuing commands to enable autonomous data collection and transfer from each board, thus enhancing the system's reliability and scalability. Experimental results confirm the system's capacity to efficiently capture waveform signals and upload them in real-time, underscoring its dependability and timeliness. The findings suggest that the system is capable of high-speed, real-time acquisition and processing of acoustic signals, offering robust technical support for the continued application of LWD acoustic logging instruments. [ABSTRACT FROM AUTHOR]

This work presents an in-depth study on the system identification of a servo-valve controlled hydraulic cylinder operating under variable load. This research addresses the growing demand for improved control systems (enhancing time response, settling time, and precision) in variable load hydraulic actuators, such as those used in blade pitching systems of wind turbines. The paper begins by detailing the experimental setup, followed by the development of the system's mathematical model, a fourth-order transfer function (TF). The experimental data collected by a proposed data acquisition system are used for the dynamic identification of the hydraulic setup using periodical signals as commands. All possible combinations of TFs up to order 8 are identified. After an initial visual preselection of the 15 most accurate ones, analyses comparing quality indicators between the measured (experimental) and the TF (simulated) step and sinusoidal responses are conducted to determine the most accurate TF. The paper concludes with the presentation and analysis of the dynamic model, identified as being a fourth-order TF, which replicates the system dynamics with the greatest fidelity. It provides an identification methodology with significant potential for industry practitioners aiming to improve, optimize, and enhance control strategies for variable load hydraulic actuators. [ABSTRACT FROM AUTHOR]

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The adoption rate of battery electric vehicles (EVs) is rapidly increasing. Electric vehicles differ significantly from conventional internal combustion engine vehicles and vary widely across different manufacturers. Emergency responders (ERs) and recovery personnel may have less experience with EVs and lack timely access to critical information such as the extent of the stranded energy present, high-voltage safety hazards, and post-crash handling procedures in a user-friendly manner. This paper presents a software/hardware interactive tool named Electric Vehicle Information for Incident Response Solutions (EVIRS) to aid in the quick access to emergency response and recovery information. The current prototype of EVIRS identifies EVs using the VIN or Make, Model, and Year, and offers several useful features for ERs and recovery personnel. These features include integration and easy access to emergency response procedures tailored to an identified EV, vehicle structural schematics, the quick identification of battery pack specifications, and more. For EVs that are not severely damaged, EVIRS can perform calculations to estimate stranded energy in the EV's battery and discharge time for various power loads using either EV dashboard information or operational data accessed through the CAN interface. Knowledge of this information may be helpful in the post-crash handling, management, and storage of an EV. The functionality and accuracy of EVIRS were demonstrated through laboratory tests using a 2021 Ford Mach-E and associated data acquisition system. The results indicated that when the remaining driving range was used as an input, EVIRS was able to estimate the pack voltage with an error of less than 3 V. Conversely, when pack voltage was used as an input, the estimated state of charge (SOC) error was less than 5% within the range of 30–90% SOC. Additionally, other features, such as retrieving emergency response guides for identified EVs and accessing lessons learned from archived incidents, have been successfully demonstrated through EVIRS for quick access. EVIRS can be a valuable tool for emergency responders and recovery personnel, both in action and during offline training, by providing crucial information related to assessing EV/battery safety risks, appropriate handling, de-energizing, transport, and storage in an integrated and user-friendly manner. [ABSTRACT FROM AUTHOR]

Effective cutting tool condition monitoring (TCM) is critical for achieving precision, cost efficiency, and minimizing unplanned downtime. This study proposes a sophisticated sensor fusion framework for accurate tool fault prediction during machining. Experimental data were collected while turning AISI 410-grade steel bars with uncoated carbide inserts under dry-cutting conditions. Force and vibration signals were captured across five tool health states (one healthy and four faulty) using a sensor network and data acquisition systems. The raw signals were decomposed using discrete wavelet transform, and key statistical features were extracted. Three distinct input datasets are constructed: Dataset I comprises statistical parameters extracted exclusively from the force signals, Dataset II consists of statistical parameters derived from the vibration signals, and Dataset III integrates the individual statistical parameters from both force and vibration signals through feature-level fusion. These datasets are then utilized for training ML classifiers (Support Vector Machine, Random Forest, and Naive Bayes) to perform feature learning and subsequent classification. Among the considered classifiers, the RF classifier yielded better classification accuracies of 96% and 97% while discriminating among the tool health scenarios through dataset I and II. Also, the RF and SVM classifiers achieved a classification accuracy of 98% and 88% in distinguishing tool health scenarios for dataset III. This method demonstrates exceptional suitability for real-time, in situ fault diagnostics and provides a strong foundation for developing online TCM systems, advancing the objectives of Industry 4.0 and smart manufacturing. [ABSTRACT FROM AUTHOR]

A robust experimental-based vehicle drive system model and parameters tuned to match the real-world performance of the electro-mechanical systems is presented. The model is based on battery electric vehicle systems, focusing on the individual components of the drive system. The model includes motors, a battery pack, a driveline, and a DC-DC converter. Based on the collected empirical data using an on-board data acquisition system, and fine-tuning the critical vehicle parameters to align the simulation with the empirical data, stationary and drive cycle tests were designed and performed to replicate standard day-to-day driving conditions. Performance metrics such as the power consumption, the state of charge and the range were employed for the model validation. The validated model was then used for the performance study at different operating temperatures. The results of the study demonstrate that the model successfully mirrored the performance of the powertrain under various conditions and lined up with existing research work, providing a valuable tool for further research and development in battery electric vehicle technology. [ABSTRACT FROM AUTHOR]

The Purinoreceptor 7 (P2X7R) has become a promising drug target in many cardiovascular diseases, including coronary artery disease, since prolonged activation of P2X7R could promote vascular dysfunction, atherosclerosis, and thrombosis. Thus, we aimed to study the effects of P2X7R activation on vascular relaxation responses of the human left internal mammary artery (LIMA). Sections of redundant human LIMA were cut into 3-mm wide rings,, suspended in 20-mL organ baths containing physiologic salt solution, and attached to an isometric force transducer connected to a computer-based data acquisition system. Long-term (60 min) incubation with specific P2X7R agonist Bz-ATP caused significant reductions in relaxation responses of LIMA to ATP and acetylcholine, which were reversed by selective P2X7R antagonists Brilliant Blue G or AZ11645373, whereas there were no changes in relaxation responses to endothelium-independent vasodilators isoprenaline, cAMP analog 8-Br-cAMP, and nitric oxide donor sodium nitroprusside. The impairment in relaxant responses of LIMA to endothelium-dependent vasodilators following activation of P2X7R for the long-term may contribute to postoperative LIMA vasospasm and hypertension. Modulation of P2X7R activity with selective agents may represent a new potential therapeutic approach in patients undergoing coronary artery bypass grafting surgery. [ABSTRACT FROM AUTHOR]

A ready-to-use spectrometer-based product, which focuses on data acquisition using a BeagleBone board and a Hamamatsu C12666MA spectrometer module, is presented. The device meets stringent requirements, including the ability to measure the visible light spectrum over a wide range of intensities, being compact and lightweight, and having customizable electronics to suit different application needs. The system's primary component is a Hamamatsu C12666MA spectrometer module with a measurement range of 341 nm to 780 nm, which is supplemented by supporting electronics such as a microcontroller and an analog-to-digital converter. The development encompasses hardware design, the fabrication of a control board, and software development for spectral acquisition and visualization. The software controls the spectral measurement process and facilitates data processing and analysis. The results demonstrate that the designed system can accurately capture spectra and fulfill the specified requirements. Additionally, this work investigates and evaluates the potential migration of the data acquisition system to Field-Programmable Gate Array technology. Such a migration offers several advantages, including real-time processing, parallel data handling capabilities, reduced latency, and greater flexibility in adapting to various spectrometer configurations, as well as the possibility to work in a synchronized way with other devices. These improvements would significantly expand the system's potential applications in real-time spectroscopy and other demanding optical measurement tasks. The proposed system thus provides a foundation for future enhancements, which could exploit Field-Programmable Gate Array technology, potentially revolutionizing the efficiency and application scope of portable spectrometry devices. [ABSTRACT FROM AUTHOR]

Landslides present a significant global hazard, resulting in substantial socioeconomic losses and casualties each year. Traditional monitoring approaches, such as geodetic, geotechnical, and geophysical methods, have limitations in providing early warning capabilities due to their inability to detect precursory subsurface deformations. In contrast, the acoustic emission (AE) technique emerges as a promising alternative, capable of capturing the elastic wave signals generated by stress-induced deformation and micro-damage within soil and rock masses during the early stages of slope instability. This paper provides a comprehensive review of the fundamental principles, instrumentation, and field applications of the AE method for landslide monitoring and early warning. Comparative analyses demonstrate that AE outperforms conventional techniques, with laboratory studies establishing clear linear relationships between cumulative AE event rates and slope displacement velocities. These relationships have enabled the classification of stability conditions into "essentially stable", "marginally stable", "unstable", and "rapidly deforming" categories with high accuracy. Field implementations using embedded waveguides have successfully monitored active landslides, with AE event rates linearly correlating with real-time displacement measurements. Furthermore, the integration of AE with other techniques, such as synthetic aperture radar (SAR) and pore pressure monitoring, has enhanced the comprehensive characterization of subsurface failure mechanisms. Despite the challenges posed by high attenuation in geological materials, ongoing advancements in sensor technologies, data acquisition systems, and signal processing techniques are addressing these limitations, paving the way for the widespread adoption of AE-based early warning systems. This review highlights the significant potential of the AE technique in revolutionizing landslide monitoring and forecasting capabilities to mitigate the devastating impacts of these natural disasters. [ABSTRACT FROM AUTHOR]

The mechanical properties of a final product are directly influenced by the solidification process, chemical composition heterogeneity, and the thermal variables during solidification. This study aims to analyze the influence of solidification thermal variables on the microstructure, hardness, and phase distribution of the CuMn11Al8Fe3Ni3. The alloy was directionally and upward solidified from a temperature of 1250 °C. Heat extraction occurred through a water-cooled AISI 1020 steel interface. The thermal variables were recorded using a data acquisition system, with temperature monitored at seven different positions, where cooling rates varied from 13.03 °C/s at the closest position to 0.23 °C/s at the farthest. The Brinell hardness decreased from 199 HB at the highest cooling rate position to 184 HB at the slowest cooling rate position. This indicates that higher cooling rates increase the hardness of the alloy, which can be attributed to the stabilization of the metastable β phase with refined and equiaxial grains due to iron addition. Vickers microhardness was observed in regions subjected to slower cooling (244 HV) compared to faster cooling regions (222 HV). Therefore, the correlation between solidification thermal variables and alloy properties provides valuable insights into the relationship between microstructure and the properties of the CuMn11Al8Fe3Ni3 alloy. [ABSTRACT FROM AUTHOR]

This study presents the design; modeling; fabrication by fused deposition modeling, including annealing; and experimental study of 3D-printed cantilever beam force sensors composed of standard and electrically conductive PLA. To facilitate the design of 3D-printed force sensors for a range of applications, an analytical model of the sensitivity based on geometric parameters and material properties is presented. Four identical force sensors, with respect to materials, geometric parameters, and annealing processes, were fabricated to investigate the impact that annealing has on their zero-force output (or balance), sensitivity to applied forces, and sensitivity to varying ambient temperatures. Of the four sensors, one was excluded from the experimental study because its output exceeded the measurement range of the data acquisition system. For the remaining three sensors, the standard deviation in their balance, which was measured in 1-degree increments from 24 to 30 ∘ C, decreased by a factor of 4.9 on average as a result of annealing. Annealing also decreased the percent difference between the predicted and measured sensitivities by a factor of 3.8 on average (across sensors and temperatures). Lastly, the uncertainty in the measured force, pooling results from the ambient temperatures of 24 and 30 ∘ C, decreased as a result of annealing by an average factor of 4.3. These results demonstrate annealing as an effective post-processing method for 3D-printed force sensors to increase temperature compensation and overall sensor performance. [ABSTRACT FROM AUTHOR]

In this work, a sensing unit on a Printed Circuit Board (PCB) has been designed to house hall sensors. Hall sensors are used to capture leakage flux signals from magnetized wire ropes for Non-Destructive Testing (NDT) of wire ropes. Various parameters are optimized before designing PCB, such as: Lift-off, orientation of sensor, number of hall sensors. Hall sensor signals are captured using DAQ card of NI® and program is written in LABVIEW® software. System is designed for: reducing noise in acquired signal (using RC filter), decreasing the number of channels for data acquisition system, reducing system cost and maintain signal fidelity. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to discuss the effect of earthquake (EQ) preparation on changes in meteorological parameters. The two physical quantities of temperature (T)/relative humidity (Hum) and atmospheric chemical potential (ACP) have been investigated with the use of the Japanese meteorological "open" data of AMeDAS (Automated Meteorological Data Acquisition System), which is a very dense "ground-based" network of meteorological stations with higher temporal and spatial resolutions than the satellite remote sensing open data. In order to obtain a clearer identification of any seismogenic effect, we have used the AMeDAS station data at local midnight (LT = 01 h) and our initial target EQ was chosen to be the famous 1995 Kobe EQ of 17 January 1995 (M = 7.3). Initially, we performed conventional statistical analysis with confidence bounds and it was found that the Kobe station (very close to the EQ epicenter) exhibited conspicuous anomalies in both physical parameters on 10 January 1995, just one week before the EQ, exceeding m (mean) + 3σ (standard deviation) in T/Hum and well above m + 2σ in ACP within the short-term window of one month before and two weeks after an EQ. When looking at the whole period of over one year including the day of the EQ, in the case of T/Hum only we detected three additional extreme anomalies, except in winter, but with unknown origins. On the other hand, the anomalous peak on 10 January 1995 was the largest for ACP. Further, the spatial distributions of the anomaly intensity of the two quantities have been presented using about 40 stations to provide a further support to the close relationship of this peak with the EQ. The above statistical analysis has been compared with an analysis with recent machine/deep learning methods. We have utilized a combinational use of NARX (Nonlinear Autoregressive model with eXogenous inputs) and Long Short-Term Memory (LSTM) models, which was successful in objectively re-confirming the anomalies in both parameters on the same day prior to the EQ. The combination of these analysis results elucidates that the meteorological anomalies on 10 January 1995 are considered to be a notable precursor to the EQ. Finally, we suggest a joint examination of our two meteorological quantities for their potential use in real short-term EQ prediction, as well as in the future lithosphere–atmosphere–ionosphere coupling (LAIC) studies as the information from the bottom part of LAIC. [ABSTRACT FROM AUTHOR]

Timely damage detection on a mechanical system can prevent the appearance of catastrophic damage in it, as well as allow for better scheduling of its maintenance and repair process. For this purpose, multiple signal analysis methods have been developed to help identify anomalies in a system, through quantities such as vibrations or deformations in its critical components. In most applications, however, these data may be scarce or inexistent, hindering the overall process. For this purpose, a novel approach for damage detection and identification on elevator systems is developed in this work, where vibration data obtained through physical measurements and high-fidelity multibody dynamics models are combined with deep learning algorithms. High-quality training data are first generated through multibody dynamics simulations and are then combined with healthy state vibration measurements to train an ensemble of autoencoders and convolutional neural networks for damage detection and classification. A dedicated data acquisition system is then developed and integrated with an elevator cabin, allowing for condition monitoring through this novel methodology. The results indicate that the developed framework can accurately identify damages in the system, hinting at its potential as a powerful structural health monitoring tool for such applications, where manual damage localization would otherwise be considerably time-consuming. [ABSTRACT FROM AUTHOR]

The BeEST experiment is a precision laboratory search for physics beyond the standard model that measures the electron capture decay of 7 Be implanted into superconducting tunnel junction (STJ) detectors. For Phase-III of the experiment, we constructed a continuously sampling data acquisition system to extract pulse shape and timing information from 16 STJ pixels offline. Four additional pixels are read out with a fast list-mode digitizer, and one with a nuclear MCA already used in the earlier limit-setting phases of the experiment. We present the performance of the data acquisition system and discuss the relative advantages of the different digitizers. [ABSTRACT FROM AUTHOR]

Industrial application data acquisition systems can be sources of vast amounts of data. The seismic surveys conducted by oil and gas companies result in enormous datasets, often exceeding terabytes of data. The storage and communication demands these data require can only be achieved through compression. Careful consideration must be given to minimize the reconstruction error of compressed data caused by lossy compression. This paper investigates the combination of principal component analysis (PCA), discrete wavelet transform (DWT), thresholding, quantization, and entropy encoding to compress such datasets. The proposed method is a lossy compression algorithm tuned by evaluating the reconstruction error in frequency ranges of interest, namely 0–20 Hz and 15–65 Hz. The PCA compression and decompression acts as a noise filter while the DWT drives the compression. The proposed method can be tuned through threshold and quantization percentages and the number of principal components to achieve compression rates of up to 31:1 with reconstruction residues energy of less than 4 % in the frequency ranges of 0–20 Hz, 15–65 Hz, and 60–105 Hz. [ABSTRACT FROM AUTHOR]

Performance evaluation of avionics software in conjunction with flight hardware is a critical process carried out using a specialized Hardware-In-Loop Simulation (HILS) platform. This platform integrates essential flight subsystems, such as actuators and navigation systems, to validate their performance under real-time conditions. A unique facility, the Flight Motion Simulator (FMS), plays a vital role in testing the dynamic behavior of navigation systems. However, challenges arise due to the physical separation of critical equipment like the FMS and actuator setups from the main HILS Test-bed, necessitating their integration across large distances. To address this, a remote simulation Test-bed has been designed and developed utilising the emerging MIL-STD 1773 protocol with fiber optics-based communication. This approach ensures real-time data transfer with minimal latency, preserving the high-performance requirements of HILS. The Fiber Optics Data Acquisition System (FODAS) facilitates seamless integration of remote flight subsystems with the HILS Test-bed, eliminating delays associated with relocating equipment and re-establishing setups. Additionally, it enables the connection of flight subsystems directly from integration hangers, enhancing testing efficiency and flexibility. This research outlines the design and development methodology of the MIL-STD 1773-based FODAS system integrated with the HILS Test-bed. It further provides performance analysis, advantages, and practical results from its implementation, demonstrating the system's capability to overcome existing limitations while improving operational efficiency. [ABSTRACT FROM AUTHOR]

Cavitation, defined by the formation and collapse of vapor bubbles in a liquid, presents a significant challenge to the reliable operation of axial pumps. Timely detection of cavitation is essential for preventing damage and optimizing pump performance. This research aims to evaluate detection techniques for cavitation in axial pumps through the analysis of vibration and acoustic signals. We utilized costeffective sensors and data acquisition systems, including embedded accelerometer sensors and smartphone-based microphones, to capture these signals. Our study involves a detailed analysis of vibration and acoustic data collected under various operating conditions, with a particular focus on the pump's optimal efficiency point. By employing three directional axes for vibration data acquisition, we achieved a comprehensive examination of the cavitation phenomenon. Signal processing techniques, such as feature extraction in the frequency domain, were used to identify distinct operating conditions as cavitation developed. Additionally, convolutional features were applied to assess the classification accuracy when datasets were converted into spectrograms. This research includes a thorough comparison of classification algorithms and different directional axes to provide insights into the effectiveness of the detection methods. The findings demonstrate the feasibility of detecting cavitation in real operating conditions using vibration signals, while highlighting challenges associated with using low-cost commercial acoustic data for cavitation detection. This introduction sets the stage for an indepth exploration of the methodology, results, and implications of our study on early cavitation detection in axial irrigation pumps. The analysis of acoustic and vibration signals yielded similar results in detecting cavitation. Key indicators, such as peak-to-peak, RMS, and variation values, were effective metrics for cavitation detection. Frequency-based analysis in both the broadband range (2 kHz - 10 kHz) and the low-frequency range (0 - 1 kHz) revealed clear trends related to cavitation presence. Time-domain analysis of vibration signals proved effective for detecting and diagnosing cavitation in axial pumps. Using mean and peak values for vibration and acoustic amplitude analysis in the frequency domain provided additional insights for predicting cavitation. [ABSTRACT FROM AUTHOR]

Frequent hacker attacks and network viruses pose a serious threat to network security, and traditional static and passive defense technologies can no longer meet the high security demands of networks. This paper proposes a computer network security intrusion detection algorithm based on the optimization of convolutional neural networks through transfer learning. Initially, a one-dimensional convolutional neural network (1D-CNN(T)) is trained to form a stable model. Subsequently, in the transfer learning phase, a target function is constructed, experimental data is acquired, and 1D-CNN(E) is trained. The network parameters are fine-tuned using a co-gradient algorithm, and features are extracted from the experimental data, with connections being dropped during the process to reduce overfitting. Based on this algorithm, an intrusion detection system is designed, which is modularly constructed to provide a complete intrusion detection solution. The paper also provides a detailed analysis of modules such as data acquisition, preprocessing, feature extraction, and neural network classifiers. Finally, the performance of the proposed convolutional neural network-based intrusion detection algorithm, which leverages transfer learning, is validated through a neural network model experiment. This algorithm is compared with other classical classification algorithms commonly used in intrusion detection, including RF, AlexNet, LeNet-5, CNN, and BiLSTM, each serving as a classifier. The experimental results demonstrate that the proposed classification algorithm achieves an accuracy of 83.58% and a recall rate of 84.49%. Compared to the RF algorithm, the proposed method exhibits improvements of 8.87% in accuracy and 9% in recall rate. When benchmarked against the AlexNet model, the accuracy and F1-Measure of the proposed algorithm are enhanced by 6.56% and 7.26%, respectively. The classifier in TLCNN-IKNN achieves the highest classification accuracy using weighted Euclidean distance, with an accuracy rate of 99.07% for detecting COMBO attacks in the Bashlite botnet, 97.02% for detecting Junk attacks, 97.71% for detecting Scan attacks, 98.08% for detecting TCP attacks, and 100.00% for detecting UDP attacks. These findings underscore the effectiveness of the transfer learning-based intrusion detection algorithm in boosting the detection rate of intrusion detection systems and reducing false positive rates, thereby establishing its high practical application value. [ABSTRACT FROM AUTHOR]

A high-performance data acquisition and processing system within a spectrometer provides a powerful guarantee for obtaining high-precision data in ground-based Fourier-transform infrared greenhouse-gas spectroscopy. Addressing the challenge of accurate interferogram sampling in Fourier-transform spectroscopy, a dual-channel interferogram acquisition method was designed. Dual-channel analog-to-digital converters, acquiring interferograms at different gains, enable high dynamic range and high-resolution acquisition of infrared interferometric signals; the analog-to-digital converter channel of low-gain interferograms mainly captures data near the zero-optical-range-difference spike, and the analog-to-digital converter channel of high-gain interferograms mainly acquires the weak signals from the two flanks. The simulation results, circuit design, and correction method between the two channels of the method are given. Finally, in the ground-based Fourier-transform infrared greenhouse-gas spectrometer for experimental applications, the experiment shows that under the same measurement conditions, the carbon dioxide column concentration-measurement accuracy is improved by 2.096 times, and the dual-channel interferometric data acquisition method can significantly enhance the data retrieval accuracy. [ABSTRACT FROM AUTHOR]

The extensive deployment of escalators has greatly improved travel convenience; however, significant concerns have been raised due to the increasing frequency of safety incidents in recent years. Ensuring the safe operation of escalators and detecting faults in a timely manner have become critical concerns for both manufacturers and maintenance personnel. Traditional periodic inspections are resource-intensive and increasingly deemed inadequate due to the growing diversity and number of escalators. In this article, a data acquisition and transmission system for the main drive shaft bearing of the escalator, based on the Internet of Things (IoT), is designed using the main drive shaft bearing as an example. Additionally, a fault classification model combining a deep autoencoder (DAE) and Bidirectional Long Short-Term Memory Network (BiLSTM) is proposed. The experimental results of this study demonstrate that the DAE-BiLSTM-based fault diagnosis model provides accurate fault detection and early warnings, achieving an accuracy rate exceeding 99%, while significantly reducing the computational costs and training time. [ABSTRACT FROM AUTHOR]

A calibration procedure with 22Na radioactive sources for the counters of a gamma-nuclear transition spectrometer, which is fully integrated in the infrastructure of the Hyperon+ setup, including trigger and data acquisition systems, is proposed and implemented. [ABSTRACT FROM AUTHOR]

As the new generation of power load management systems is gradually being implemented, there is an increasing demand for accuracy in power load forecasting. To meet this demand, a new load forecasting method of the power system based on multi-index clustering optimization is proposed in this paper. Firstly, the sensor technology is fully utilized to realize the data acquisition of the power system and the real-time monitoring of key parameters, providing more accurate and timely data for the load forecasting model. Secondly, the real-time data collected by the sensor is combined with the K-means clustering algorithm, which is improved, and the optimal number of clusters is selected by evaluating the clustering effect of multiple indicators. Then, an improved Particle Swarm Optimization-Back Propagation (PSO-BP) neural network prediction model is built by combining the ability of the BP neural network to solve complex nonlinear function approximation and the global optimization ability of the PSO algorithm. The model's parameters are adjusted flexibly to improve the prediction accuracy and response speed. Finally, the model is compared with the BP neural network to verify the optimization effect. The experimental results of the simulation test show that the proposed method can effectively ensure the reliability of data transmission, realize the effective classification and aggregation of heterogeneous data, provide accurate load prediction results quickly, and improve robustness. [ABSTRACT FROM AUTHOR]

Background: Traffic crashes are the leading cause of death globally for people aged 5–29 years, with 90% of mortality occurring in low- and middle-income countries (LMICs). The STABLE (Slashing Two-wheeled Accidents by Leveraging Eyecare) trial was designed to determine whether providing spectacles could reduce risk among young myopic motorcycle users in Vietnam. Methods: This investigator-masked, stepped-wedge, cluster randomised naturalistic driving trial will recruit 625 students aged 18–23 years, driving ≥ 50 km/week, with ≥ 1-year driving experience and using motorcycles as their primary means of transport, in 25 clusters of 25 students in Ho Chi Minh City, Vietnam. Motorcycles of consenting students who have failed self-testing on the WHOeyes app will be fitted with Data Acquisition Systems (DAS) with video cameras and accelerometers. Video clips (± 30 s) of events flagged by the accelerometer will be reviewed for crash and near-crash events per 1000 km driven (main outcome). Five clusters of 25 students will be randomly selected every 12 weeks to undergo ocular examination and an estimated 40% of these will have bilateral spherical equivalent < − 0.5 D, and better-eye presenting distance visual acuity < 6/12, correctable bilaterally to ≥ 6/7.5. They will be given free distance spectacles and their driving data before receiving spectacles will be analysed as the control condition and subsequent data as the intervention condition. Secondary outcomes include visual function, cost-effectiveness and self-reported crash events. Discussion: STABLE will be the first randomised trial of vision interventions and driving safety in a LMIC. Trial registration: ClinicalTrials.gov, NCT05466955. Initial registration: 20 July 2022, most recent update: 9 July 2024. [ABSTRACT FROM AUTHOR]

There are various commercially available multifunctional electronics laboratory test equipment on the market that integrates an oscilloscope, signal generator, and logic analyzer. The price point for all these devices typically exceeds USD 250. To lower this cost to around USD 100, the data acquisition components of these devices need to be re-evaluated. In this paper, we present the design of an analog front-end (AFE) for low-cost multifunctional laboratory equipment, mainly for electronics students. The primary objective was to meet student requirements while ensuring minimal cost, maximum functionality, and high robustness. Through our research, we optimized our AFE design to achieve these objectives effectively. Our approach addresses key limitations found in existing designs, as we developed four-stage attenuation and three-stage op-amp gain architecture. This arrangement optimizes signal clarity and achieves high bandwidth while maintaining a cost-effective framework suitable for educational applications. With this study, a 20 MHz bandwidth, ±40 V input voltage range, volt/div range from 5 mV to 10 V, and minimum amplitude resolution of 9.7 µV were achieved in the design of the analog front-end and data acquisition unit. The achieved bandwidth and linearity enable designed AFE to accommodate applications such as ultrasonic discrete TGC (Time Gain Compensation). [ABSTRACT FROM AUTHOR]

This study highlights the increasing demand for battery-operated applications, particularly electric vehicles (EVs), necessitating the development of more efficient Battery Management Systems (BMS), particularly lithium-ion (Li-ion) batteries used in energy storage systems (ESS). This research addresses some of the key limitations of current BMS technologies, with a focus on accurately predicting the remaining useful life (RUL) of batteries, which is a critical factor for ensuring operational efficiency and sustainability. Real-time data are collected from sensors via an Internet of Things (IoT) device and processed using Arduino Nano, which extracts values for input into a Long Short-Term Memory (LSTM) model. This model employs the National Aeronautics and Space Administration (NASA) Li-battery dataset and current, voltage temperature, and cycle values to predict the battery RUL. The proposed model demonstrates significant forecasting precision, attaining a root mean square error (RMSE) of 0.01173, outperforming all comparative models. This improvement facilitates more effective decision-making in BMS, particularly in resource allocation and adaptability to transient conditions. However, the practical implementation of real-time data acquisition systems at a scale and across diverse environments remains challenging. Future research will focus on enhancing the generalizability of the model, expanding its applicability to broader datasets, and automating data ingestion to minimize integration challenges. These advancements are aimed at improving energy efficiency in both industrial and residential applications in accordance with the Sustainable Development Goals (SDGs) of the UN. [ABSTRACT FROM AUTHOR]

Measuring tractor performance parameters is crucial for validating manufacturer claims and selecting a suitable tractor. Field studies often face high costs and inaccuracies due to external factors and limited parameter measurement. A chassis dynamometer serves as an effective simulator for tractor movement on the ground. This research focused on designing and constructing an in-situ test system (chassis dynamometer) with practical features for measuring parameters at various speeds, loads, and conditions. An accurate data acquisition system records unlimited data based on relations between drawbar traction, speed, vertical load on driving wheels, and the distance to the center of gravity. To evaluate the system, the traction performance of an MF285 tractor was tested at different load and speed levels. The drawbar pull and dynamic axle load of the 2WD tractor were measured, with a maximum traction of about 11 kN. This study successfully designed and implemented an in-situ test system to assess the traction and balance capacities of conventional tractors by applying maximum load on the driving wheels. [ABSTRACT FROM AUTHOR]

With the in-depth development of marine ranching construction, environmental monitoring platforms have been widely used in the monitoring of mariculture environments in marine ranches. In view of the problem of cable breakage and damage that occurs during the deployment and recovery process of the data acquisition pod of the marine environmental monitoring platform used in production due to the influence of wind, waves and currents. A pod anti-rolling device for offshore platforms is designed. Based on Newton's second law, a dynamic model of the pod anti-rolling device under the excitation of rolling and pitching of the offshore platform is established and numerical simulation analysis was carried out using Matlab/Simulink software, and the changes in the swing amplitude of the pod with and without the anti-rolling device were compared. The results show that in the case of having an anti-rolling device, the swing amplitude of the inplane angle of the pod can be reduced by about 66. 9%, and the swing amplitude of the out-of-plane angle can be reduced by about 69%. The average anti-rolling ratio is 67. 95%. Based on the existing theory, an experimental prototype was built and tested for verification. The test results show that the pod anti-rolling device has an obvious restraining effect on the swing amplitude of the pod. The swing amplitude of the in-plane angle can be reduced by about 73. 5%. The research shows that the pod anti-rolling device has important practical guiding significance for improving the reliability and safety of the data acquisition system of the marine ranch monitoring platform. [ABSTRACT FROM AUTHOR]

Le modernisme médical prompt à promouvoir le soin le plus juste, le plus personnalisé exploite le champ de l'ethnicité et de la racialité. Si l'ambition d'un soin adapté, au plus près des attentes du patient et au plus proche des impératifs déontologiques d'un soin consciencieux, dévoué et fondés sur les données acquises de la science domine, elle se heurte aux grands principes de non-discrimination que garantit notre Constitution. The Medical modernism pattern, eagerly promoting the fairest, the most personalized care, exploits the field of ethnicity and raciality. If assuming the utmost adapted care, as close as possible to the expectations of the patient and as close as possible to the ethical imperatives of conscientious, dedicated care based on data acquired from science dominates, it comes up against the main principles of non-discrimination assessed by the Constitution. [ABSTRACT FROM AUTHOR]

The digitalization level of the new power system driven by "dual carbon" is increasing, leading to a growth in the amount of data that need to be acquired. This has intensified the contradiction between data volume and acquisition capacity. Therefore, it is urgent to study compressed data acquisition methods for power systems based on data compression. In this regard, a novel compressed data acquisition method based on chaotic compressive measurement with the compressed sensing principle is proposed. Firstly, the advantages of applying compressed sensing are analyzed for data acquisition in power systems, and the key issues that need to be addressed are identified. Subsequently, a chaotic map is sampled based on the basic requirements of the measurement matrix in compressed sensing, and the chaotic compressive measurement matrix is constructed and optimized based on the sampling results. Next, the sparse data difference of the power system is used as the compression target for the optimized chaotic measurement matrix, and an acquisition process is designed to recover the complete power data from a small amount of compressed data. Finally, the proposed method is validated in a case study, and the results demonstrate that the method is correct and effective. [ABSTRACT FROM AUTHOR]

The Industrial Internet of Things (IIoT) revolutionizes both industrial and residential operations by integrating AI (artificial intelligence)-driven analytics with real-time monitoring, optimizing energy usage, and significantly enhancing energy efficiency. This study proposes a secure IIoT framework that simultaneously predicts both active and reactive loads while also incorporating anomaly detection. The system is optimized for real-time deployment on an edge server, such as a single-board computer (SBC), as well as on a cloud or centralized server. It ensures secure and reliable industrial operations by integrating smart data acquisition systems with real-time monitoring, control, and protective measures. We propose a Temporal Convolutional Networks-Gated Recurrent Unit-Attention (TCN-GRU-Attention) model to predict both active and reactive loads, which demonstrates superior performance compared to other conventional models. The performance metrics for active load forecasting are 0.0183 Mean Squared Error (MSE), 0.1022 Mean Absolute Error (MAE), and 0.1354 Root Mean Squared Error (RMSE), while for reactive load forecasting, the metrics are 0.0202 (MSE), 0.1077 (MAE), and 0.1422 (RMSE). Furthermore, we introduce an optimized Isolation Forest model for anomaly detection that considers the transient conditions of appliances when identifying irregular behavior. The model demonstrates very promising performance, with the average performance metrics for all appliances using this Isolation Forest model being 95% for Precision, 98% for Recall, 96% for F1 Score, and nearly 100% for Accuracy. To secure the entire system, Transport Layer Security (TLS) and Secure Sockets Layer (SSL) security protocols are employed, along with hash-encoded encrypted credentials for enhanced protection. [ABSTRACT FROM AUTHOR]

The CONUS+ experiment aims to detect coherent elastic neutrino-nucleus scattering (CE ν NS) of reactor antineutrinos on germanium nuclei in the fully coherent regime, continuing on this way the CONUS physics program started at the Brokdorf nuclear power plant, Germany. The CONUS+ setup is installed in the nuclear power plant in Leibstadt, Switzerland, at a distance of 20.7 m from the 3.6 GW thermal power reactor core. The CE ν NS signature will be measured with the same four point-contact high-purity germanium (HPGe) detectors produced for the former experiment, however refurbished and with optimized low energy thresholds of about 160 eV ee . To suppress the background in the CONUS+ detectors, the passive and active layers of the original CONUS shield were modified such to fit better to the significantly changed background conditions at the new experimental location. New data acquisition and monitoring systems were developed. A direct network connection between the experiment and the Max-Planck-Institut für Kernphysik (MPIK) makes it possible to control and monitor data acquisition in real time. The impact of all these modifications is discussed with particular emphasis on the resulting CE ν NS signal prediction for the first data collection phase of CONUS+. Prospects of the planned upgrade in a second phase integrating new larger HPGe detectors are also discussed. [ABSTRACT FROM AUTHOR]

Generating aerial shoreline segmentation masks can be a daunting task, often requiring manual labeling or correction. This is further problematic because neural segmentation models require decent and abundant data for training, requiring even more manpower to automate the process. In this paper, we propose utilizing Unmanned Surface Vehicles (USVs) in an automated shoreline segmentation system on satellite imagery. The remotely controlled vessel first collects above- and underwater shoreline information using light detection and ranging (LiDAR) and multibeam echosounder (MBES) measuring instruments, resulting in a geo-referenced 3D point cloud. After cleaning and processing these data, the system integrates the projected map with an aerial image of the region. Based on the height values of the mapped points, the image is segmented. Finally, post-processing methods and the k-NN algorithm are introduced, resulting in a complete binary shoreline segmentation mask. The obtained data were used for training U-Net-type segmentation models with pre-trained backbones. The InceptionV3-based model achieved an accuracy of 96% and a dice coefficient score of 93%, demonstrating the effectiveness of the proposed system as a source of data acquisition for training deep neural networks. [ABSTRACT FROM AUTHOR]

Fused deposition modeling (FDM), a method of additive manufacturing (AM), comprises the extrusion of materials via a nozzle and the subsequent combining of the layers to create 3D-printed objects. FDM is a widely used method for 3D-printing objects since it is affordable, effective, and easy to use. Some defects such as poor infill, elephant foot, layer shift, and poor surface finish arise in the FDM components at the printing stage due to variations in printing parameters such as printing speed, change in nozzle, or bed temperature. Proper fault classification is required to identify the cause of faulty products. In this work, the multi-sensory data are gathered using different sensors such as vibration, current, temperature, and sound sensors. The data acquisition is performed by using the National Instrumentation (NI) Data Acquisition System (DAQ) which provides the synchronous multi-sensory data for the model training. To induce the faults, the data are captured under different conditions such as variations in printing speed, temperate, and jerk during the printing. The collected data are used to train the machine learning (ML) and deep learning (DL) classification models to classify the variation in printing parameters. The ML models such as k-nearest neighbor (KNN), decision tree (DT), extra trees (ET), and random forest (RF) with convolutional neural network (CNN) as a DL model are used to classify the variable operation printing parameters. Out of the available models, in ML models, the RF classifier shows a classification accuracy of around 91% whereas, in the DL model, the CNN model shows good classification performance with accuracy ranging from 92 to 94% under variable operating conditions. [ABSTRACT FROM AUTHOR]

The plastic scintillator detector is widely used to measure pulsed radiation fields, and a high-quality neutron energy response curve is crucial for accurately determining neutron yields. Traditionally, the neutron energy response curve can be obtained by the simulation of the performance of neutron detectors via the knowledge of the light yield of secondary particles or direct measuring energy response. Both methods record signals by a data acquisition system in the counting mode. This work establishes a new model to directly measure the energy response to neutrons of a plastic scintillator detector in current mode with white source spectra. The plastic scintillator detector can be triggered externally. There is no signal loss and dead time, and it is possible to calibrate the neutron detector with a high-intensity neutron source. Efficiencies can be determined at all energies simultaneously, and a rapid calibration of the plastic scintillator detector is available. To validate the experimental results, the theoretical values are obtained by Geant4 simulation, and the results reproduce the shapes of the experimental curves reasonably well. The neutron energy response curves of the detector show that when the thickness of the scintillator is reduced from 3 to 1 mm, the reduction ratio of gamma signal intensity is greater than that of neutron signal intensity, which leads to an increase in the neutron–gamma sensitivity ratio. Meanwhile, the thin scintillator thickness can obtain a somewhat flatter neutron energy response curve compared with other thicknesses. [ABSTRACT FROM AUTHOR]

Light Detection And Ranging (LiDAR) technology has provided an impactful way to capture 3D data. However, consistent mapping in sensing-degenerated and perceptually-limited scenes (e.g. multi-story buildings) or under high dynamic sensor motion (e.g. rotating platform) remains a significant challenge. In this paper, we present OR-LIM, a novel observability-aware LiDAR-inertial-mapping system. Essentially, it combines a robust real-time LiDAR-inertial-odometry (LIO) module with an efficient surfel-map-smoothing (SMS) module that seamlessly optimizes the sensor poses and scene geometry at the same time. To improve robustness, the planar surfels are hierarchically generated and grown from point cloud maps to provide reliable correspondences for fixed-lag optimization. Moreover, the normals of surfels are analyzed for the observability evaluation of each frame. To maintain global consistency, a factor graph is utilized integrating the information from IMU propagation, LIO as well as the SMS. The system is extensively tested on the datasets collected by a low-cost multi-beam LiDAR (MBL) mounted on a rotating platform. The experiments with various settings of sensor motion, conducted on complex multi-story buildings and large-scale outdoor scenes, demonstrate the superior performance of our system over multiple state-of-the-art methods. The improvement of point accuracy reaches 3.39–13.6 % with an average 8.71 % outdoor and correspondingly 1.89–15.88 % with 9.09 % indoor, with reference to the collected Terrestrial Laser Scanning (TLS) map. [ABSTRACT FROM AUTHOR]

Introduction: The complex dynamics of structures under external excitations, with emphasis on the design and analysis of Constrained Layer Damping (CLD) structures was studied using FEM and modified Ross-Kerwin-Ungar (RKU) model. Materials and method: The 5-layer CLD structures consisting of mild steel substrate, NBR-PVC (5-5) 20HAF viscoelastic damping layers (2 no's) paired with Aluminum (configuration-1) and GRP (configuration-2) as constraining layers (2 No's) were fabricated. Series of vibration experiments were conducted on electrodynamic vibration shaker integrated with data acquisition system. Modal and harmonic analysis was also carried out using modified RKU, FEM to predict frequency response and system loss factor for two CLD configurations. Results: It is observed that, two CLD configurations exhibited substantial variation in the system loss factor (0.012 to 0.19), compared to the substrate, which is well below 0.01. Further, the CLD with Aluminum as constraining layer indicated superior damping performance compared to its counterpart GRP. The comparison of results from FEM and modified RKU method with experimental data revealed, deviations of approximately 5–15% and 15–23% respectively indicating relative accuracy of FE simulation data, especially in complex structural scenarios. Conclusion: The study offers valuable insights into the distinct performances of different constraining layers, improves our understanding of the dynamic behavior of multilayer CLD structures. [ABSTRACT FROM AUTHOR]

Understanding the response and damage evolution of structures subjected to multiple impact events is essential for designing resilient structures capable of withstanding complex loading scenarios, such as impacts from hail, gravel or foreign object debris. This article presents the development and characterization of a novel test bench, the "Compressed air multi-cannon", designed specifically for studying the multi-impact behavior of composite materials. This test bench offers advantages over traditional impact testing methods by enabling controlled and adjustable impact parameters, including number of impacts, spatial and temporal lag, energy, angle and impactor dimensions. The primary objective of this work is to provide a detailed description of the test bench design, construction, and validation procedures. Key components such as the pressurized air system, projectile launch mechanism, target mounting arrangement, and data acquisition system are discussed. Experimental methodologies for assessing multi-impact response, specimen preparation, instrumentation, and data analysis techniques are outlined. Through a series of single-impact and multi-impact tests, distinctive damage mechanisms and energy absorption characteristics were observed in composite structures, revealing significant differences in how composites respond under single- and multi-impact conditions. It was found that the single-impact configuration remains particularly critical compared to multi-impact configurations with a high number of impacts. However, further testing is required to determine whether this result holds true under varying impact parameters, highlighting the unique value of this machine for exploring new, realistic questions in the literature. [ABSTRACT FROM AUTHOR]

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Through the description of the methodology of the development of a bite force measurement device it will be shown how interdisciplinary work of Engineers and Health Professionals bring enhance of life quality to general population. Bite force measurement is a reliable exam to check stomatognathic system (SS) conditions. In order to provide a reliable, low cost and do-it-yourself gnathodynamometer a Dentist joined Bioengineering Laboratory (LabBio) at UFMG. The development of a 3D printed resin structure was made using CAD/CAM and tested by means of FEM. A Carbon Nano Tube (CNT) extensometer developed at CTNANO at UFMG to capture the structure deformation were fixed in two geometries that showed good results in FEM simulation and will be tested in an EMIC universal mechanical testing machine (DL1000) equipped with a 1 kN load cell. The electrical response of the extensometers was monitored using a Keithley 2000 digital multimeter (Tektronix), connected to a computer and remotely controlled by a LabView application (View Point Systems), a data storage protocol in a SD card and in the clouds using IoT and an data acquisition system will be tested. In bench tests both geometries showed good results with deformation capturable from 40 N. Data was codified using an Arduino Nano and a program was developed for data acquisition and storage. The interdisciplinary work generates prototypes with promising results in bench tests. The fruit of the team work may generate a toll that improves life quality of population by allowing more people to be tested and lowering health costs. [ABSTRACT FROM AUTHOR]