Perform Data Acquisition Research Articles

Research Progress of Dangerous Driving Behavior Recognition Methods Based on Deep Learning

In response to the rising frequency of traffic accidents and growing concerns regarding driving safety, the identification and analysis of dangerous driving behaviors have emerged as critical components in enhancing road safety. In this paper, the research progress in the recognition methods of dangerous driving behavior based on deep learning is analyzed. Firstly, the data collection methods are categorized into four types, evaluating their respective advantages, disadvantages, and applicability. While questionnaire surveys provide limited information, they are straightforward to conduct. The vehicle operation data acquisition method, being a non-contact detection, does not interfere with the driver’s activities but is susceptible to environmental factors and individual driving habits, potentially leading to inaccuracies. The recognition method based on dangerous driving behavior can be monitored in real time, though its effectiveness is constrained by lighting conditions. The precision of physiological detection depends on the quality of the equipment. Then, the collected big data are utilized to extract the features related to dangerous driving behavior. The paper mainly classifies the deep learning models employed for dangerous driving behavior recognition into three categories: Deep Belief Network (DBN), Convolutional Neural Network (CNN), and Recurrent Neural Network (RNN). DBN exhibits high flexibility but suffers from relatively slow processing speeds. CNN demonstrates excellent performance in image recognition, yet it may lead to information loss. RNN possesses the capability to process sequential data effectively; however, training these networks is challenging. Finally, this paper concludes with a comprehensive analysis of the application of deep learning-based dangerous driving behavior recognition methods, along with an in-depth exploration of their future development trends. As computer technology continues to advance, deep learning is progressively replacing fuzzy logic and traditional machine learning approaches as the primary tool for identifying dangerous driving behaviors.

Real-time monitoring of multiparameter in 1,3-propanediol production process with near-infrared spectroscopy

1,3-propanediol (1,3-PDO) is a significant product of fermentation, with glycerol serving as the primary substrate in most cases. Bioprocess control based on real-time information of feedstock and main products is crucial for reducing the cost of production. However, rapid quantification of 1,3-PDO and glycerol remains challenging due to their highly similar molecular structures. In this study, the feasibility of near-infrared (NIR) spectroscopy to monitor 1,3-PDO, glycerol, acetate, and butyrate concentrations in the fermentation process using strain Clostridium pasteurianum was evaluated. NIR spectra were acquired through at-line measurement involving sampling and ex-situ analysis or on-line measurement with a fiber optic probe immersed in fermentation broth, integrated with Partial Least Squares (PLS) regression to establish calibration models on a laboratory-scale and pilot-scale. The best PLS regressions of 1,3-PDO, glycerol, acetate, and butyrate with two measurement approaches provided excellent performance, with the root-mean-squared errors of prediction (RMSEP) of 1.656g/L, 1.502g/L, 0.746g/L, and 0.557g/L in at-line measurement and 1.113g/L, 1.581g/L, 0.415g/L, and 0.526g/L in on-line measurement. The cross-scale application performance of at-line measurement was evaluated by an external fermentation trial and an acceptable result was achieved. At-line measurement technique represents a superior choice for the optimization of fermentation process since the robustness across varying fermentation scales and its applicability in multiple bioreactors. Thus, a calibration model developed for one bioreactor is likely to be used in other bioreactors, which enables the reduction of modeling costs. On-line measurement technique, owing to its automated operation and frequent data acquisition, enables real-time monitoring and precise control of the fermentation process, thereby reducing cost and improving production efficiency.

A Study on the Timing Sensitivity of the Transient Dose Rate Effect on Complementary Metal-Oxide-Semiconductor Image Sensor Readout Circuits.

Complementary Metal-Oxide-Semiconductor (CMOS) image sensors (CISs), known for their high integration, low cost, and superior performance, have found widespread applications in satellite and space exploration. However, the readout circuits of pixel arrays are vulnerable to functional failures in complex or intense radiation environments, particularly due to transient γ radiation. Using Technology Computer-Aided Design (TCAD) device simulations and Simulation Program with Integrated Circuit Emphasis (SPICE) circuit simulations, combined with a double-exponential current source fault injection method, this study investigates the transient dose rate effect (TDRE) on a typical readout circuit of CISs. It presents the variations in the photoelectric signal under different dose rates and at different occurrence moments of the TDRE. The results show that, under low dose rates, the CIS readout circuit can still perform data acquisition and digital processing, with the photoelectric signal exhibiting some sensitivity to the occurrence moment. At high dose rates, however, the photoelectric signal not only remains sensitive to the occurrence moment but also shows significant discreteness. Further analysis of the CIS readout circuit sequence suggests that the occurrence moment is a critical factor affecting the circuit's performance and should not be overlooked. These findings provide valuable insights and references for further research on the TDRE in circuits.

Sustainable hybrid renewable energy management system for a community in island: A model approach utilising Hybrid Optimization of Multiple Energy Resources optimization and priority setting‐based Supervisory Control and Data Acquisition operation

Sustainable hybrid renewable energy management system for a community in island: A model approach utilising Hybrid Optimization of Multiple Energy Resources optimization and priority setting‐based Supervisory Control and Data Acquisition operation

Research on the discrimination and classification of multi-modal fault bearing based on multi-attribute decision-making mechanism

In order to solve the problem of low fault diagnosis rate of friction torque in upright placement, this paper proposes a fault diagnosis method based on multi-attribute decision-making mechanism to accurately evaluate the fault damage degree of bearing friction torque test bench. We detect defects by utilizing non-destructive, non-contact infrared thermal imaging technology to perform data acquisition and testing in six situations, including non-destructive, damaged inner ring, damaged outer ring, marble-defect, lack of lubrication, and damaged inner and outer ring bearings. Then, the infrared data of different bearings are transformed in a standardized form, and the decision data is mapped to the Pythagorean fuzzy information space. The correlation between the decision data are considering, the PA operator and the BM operator are used for data processing to achieve the accurate distinction of different faulty bearings. Finally, qualitative and quantitative methods are used to analyze the decision results, and the effectiveness of the method is explained. The variation of the overall singular value of each object in the decision result of the proposed method is less than 0.002, while the data fluctuation of WA, WG, HM and DHM related decision methods is 0.005, and there is a singular value greater than 0.02. In addition, sensitivity analysis provides a feasible scheme for parameter optimization and optimal parameter determination.

Implementation of an experimental set-up for insertion loss measurements of sonic crystal acoustic screens

The use of metamaterials for the implementation of noise control devices in the context of environmental acoustics is a topic that has gained more and more attention in recent years. The complex design of such devices and the difficulty of their construction in real-world applications complicates the acquisition of experimental data related to their acoustical performance. Numerical simulation tools are used to simulate the behaviour of these novel noise control devices in their design phase and help to validate the designs by comparing them with experimental data. This data, usually acquired under controlled conditions, holds significant importance in confirming the increasingly precise numerical models that have been developed over the years to simulate the behaviour of these physical systems. In this paper, an experimental set-up that enables acquisition of acoustical performance data for a particular metamaterial-based noise control device, consisting of networks of isolated scatterers, is presented. This prototype possesses several distinctive characteristics that render it particularly interesting, including its adaptability, ease of use, speed of obtaining results, and, particularly, its economic viability in relation to the tests that can be conducted in an anechoic chamber.

Intelligent textile information collection and early warning system with TCN-GRU

With the tide of Industry 4.0, the information transformation of the Internet of Things in the textile industry is in full swing. In order to reduce the failure rate of textile machines and improve the economic benefits of textile mills, an intelligent textile information collection and early warning system based on TCN-GRU is designed in this article. The system consists of three parts: loom operation data acquisition, data processing and abnormal early warning. First of all, the system designs a wireless equipment information acquisition device according to the requirements of collecting and reporting the main work information such as weaving machine parameters and rotational speed. Then the original collected data are preprocessed and input to the TCN-GRU fusion network model for training to build the loom working current prediction model. Through the comparison of the similarity between the actual detection value and the predicted value of the model output, the early warning threshold is determined by calculating the sliding residual combined with the window statistics method, and the abnormal trend of loom operation is captured before the fault actually occurs. In order to achieve early warning of loom faults. The actual test results show that the system is effective in the loom work information collection and fault early warning, and is significant to improve the efficiency of textile production.

Flow-Field Inference for Turbulent Exhale Flow Measurement.

Vision-based pulmonary diagnostics present a unique approach for tracking and measuring natural breathing behaviors through remote imaging. While many existing methods correlate chest and diaphragm movements to respiratory behavior, we look at how the direct visualization of thermal CO2 exhale flow patterns can be tracked to directly measure expiratory flow. In this work, we present a novel method for isolating and extracting turbulent exhale flow signals from thermal image sequences through flow-field prediction and optical flow measurement. The objective of this work is to introduce a respiratory diagnostic tool that can be used to capture and quantify natural breathing, to identify and measure respiratory metrics such as breathing rate, flow, and volume. One of the primary contributions of this work is a method for capturing and measuring natural exhale behaviors that describe individualized pulmonary traits. By monitoring subtle individualized respiratory traits, we can perform secondary analysis to identify unique personalized signatures and abnormalities to gain insight into pulmonary function. In our study, we perform data acquisition within a clinical setting to train an inference model (FieldNet) that predicts flow-fields to quantify observed exhale behaviors over time. Expiratory flow measurements capturing individualized flow signatures from our initial cohort demonstrate how the proposed flow field model can be used to isolate and analyze turbulent exhale behaviors and measure anomalous behavior. Our results illustrate that detailed spatial flow analysis can contribute to unique signatures for identifying patient specific natural breathing behaviors and abnormality detection. This provides the first-step towards a non-contact respiratory technology that directly captures effort-independent behaviors based on the direct measurement of imaged CO2 exhaled airflow patterns.

Hardware and Software Design of Programmable Medium and High-Speed Data Acquisition (DAQ) Board of Fiber Optic Signal for Partial Discharge Acquisition

The anti-electromagnetic interference capability of partial discharge (PD) acoustic signal conversion and collection circuits severely restrict the sensitivity of PD detection. The data acquisition (DAQ) systems available in the current market are costly and have limited functionality, making it difficult to satisfy the acquisition requirements for PD detection. This paper proposes a medium to high-speed fiber optic signal acquisition board with an adjustably controlled sampling rate and filter cutoff frequency. The circuit achieves a higher signal-to-noise (SNR) ratio by distributing the noise in each part of the signal acquisition chain reasonably. The temperature characteristics of the acquisition module are improved by utilizing the programmable T-type structure for transimpedance amplification of photocurrent. The DAQ card performs data acquisition and processing using STM32H743 internal ADC and caches data in bulk with an SRAM and SD card. A data uploading method based on time reference has been proposed, which enables full, effective information signal upload through a low-cost transmission interface. The research ultimately achieves a stable sampling of three channels at 1 MSps, SNR of 63 dB, and programmable gain amplification of the photocurrent with 0–60 dB. Finally, the system is used for PD acoustic signal acquisition in the frequency range of 20 Hz to 100 kHz.

Drilling dynamics measurement of drilling motors and its application in recognition of motor operation states through machine learning

Drilling motors are widely used in unconventional oil and gas exploration. Due to the increased non-productive time and drilling costs brought about by accidental damage to drilling motors, predictive maintenance for drilling motors is necessary to optimize asset utilization. However, service companies face significant challenges in achieving predictive maintenance: operational data acquisition, automated statistics analysis, and drilling state recognition. This paper presents a miniature vibration recorder, an automatic statistical analysis method, and a layered recognition algorithm to resolve these challenges and improve tool maintenance efficiency. The designed recorder can be installed in the catch of a conventional mud motor to record drilling dynamics over a drilling motor's entire operation cycle. Time-series data from the recorder can be used to automatically generate operation statistics, mitigating the costs incurred by manual data analysis. The layered recognition algorithm then enables the automatic identification of drilling operation states, i.e., surface, downhole non-drilling, downhole sliding, and downhole rotation. The solutions were validated by deploying the recorder in drilling field runs and analyzing recorded data using the associated design software, yielding a functional data collection, automatic data statistical analysis, and operation state recognition accuracy of 95%. Through achieving improved data collection and analysis, the recorder and software introduced in this work can notify motor owners of the detailed operation history of their tools and enable informed preventive maintenance.

High Elevation Radiation Array (HERA) detectors for airborne thunderstorm investigations

A high-energy atmospheric physics phenomenon, referred to as a terrestrial gamma ray flash (TGF), is associated with lightning and produces large bursts of energetic photon radiation. TGFs will be investigated using a suite of gamma-ray instruments designed and constructed to fly on ten United States Air Force (USAF) WC-130J Hurricane Hunter aircraft as part of an aircrew ionization study led by the Air Force Institute of Technology (AFIT) and the United States Air Force School of Aerospace Medicine (USAFSAM), in cooperation with the 53rd Weather Reconnaissance Squadron (WRS). Each instrument consists of one NaI and one plastic detector, a GPS timing device, and an instrument computer that performs data acquisition. High Elevation Radiation Array (HERA) detectors will be employed to maximize the chances of observing TGFs near their source and to gain a better understanding of their origin, mechanism, ubiquity, and to assess potential hazards posed to military and commercial aircrew and passengers. The HERA program, deployed on 10 separate Air Force aircraft over a multi-year campaign, will result in thousands of observational flight hours and be the largest concerted effort to date to observe TGFs in situ through aircraft observations. In this paper, we give an overview of the scientific goals of this campaign and how the HERA instruments have been designed to meet those goals. We include a detailed description of the HERA instrument, along with mass model and signal processing simulations.

Performance Enhancement of the Polarimetric Fibre Optical Current Sensor at JET Using Polarisation Optimisation.

To achieve optimal operation of the polarimetry-based FOCS, the light polarisation state at the input of the sensing fibre part must be close to a linear one. In the case of a FOCS deployed on a tokamak, the Joint European Torus (JET) in the present work, the long fibre optics link between the laser source and the sensing fibre modifies the polarisation in an unpredictable way, making it unclear which source polarisation state is to be set. A method for performing the necessary polarisation adjustment in a systematic way is proposed based on the FOCS analysis. The method requires performing data acquisition at two different input polarisations. Based on these measurements, the optimal laser source polarisation can be found. The method was experimentally verified using laboratory set-up and then successfully demonstrated with the FOCS installed at JET.

Curating Multimodal Satellite Imagery for Precision Agriculture Datasets with Google Earth Engine

In the era of modern agriculture, satellite imagery has been widely used to monitor crops, one of which is paddy. This paper tries to describe the vegetation indices, climate, and soil index features related to paddy plants and curates a collection of satellite imagery on the Google Earth Engine (GEE). This paper reveals how GEE can be used to collect and process multimodal satellite imagery to form a precision agriculture dataset. The objective of this study is to establish a comprehensive precision agriculture dataset by leveraging multimodal satellite imagery to monitor paddy crops. The data collected as a dataset originates from 306 locations in Karawang Regency, Indonesia, during the 2019-2020 period. In the first step, we identify the relevant features essential for paddy crop analysis. Subsequently, we carefully select image collections within GEE based on these features. Afterward, we perform data acquisition and necessary preprocessing through the Google Colab environment. The results showed that satellite imagery from Sentinel-2 outperforms Landsat 8 in terms of spatial and temporal resolution. Apart from that, the generated dataset successfully captures the growth patterns of paddy plants.

Real-time MHD feedback control system in Keda Torus eXperiment

This article describes the feedback control system in magnetic confinement devices, aiming to mitigate magnetohydrodynamic (MHD) instabilities and optimize plasma confinement within the Keda Torus eXperiment (KTX). The system includes saddle sensors as the input signal, a real-time control system as the core controller, a digital power amplifier (DPA) array as the actuator, and saddle coils as the brake to restrain the MHD instabilities. A flexible, distributed real-time control system based on field-programmable gate array (FPGA) has been developed and implemented for MHD control in KTX. This is the first time FPGA has been used to verify presupposed magnetic field mode, perform data acquisition, real-time calculation, and real-time feedback in KTX. It fulfills the real-time requirements of the experiment completely. The system is presently utilized in MHD control experiments, successfully achieving experiments at the current stage that reach the limit of Ohmic discharge, with good reproducibility.

Automated classification of electroluminescence images using artificial neural networks in correlation to solar cell performance parameters

The quality control in solar cell production relies on the acquisition of quantitative performance data (I–V-data) together with imaging diagnostics such as electroluminescence (EL) imaging. The classification of the solar cells into different quality groups based on the performance parameters or the identification of specific defects in the EL-images, such as cracks, is rather straightforward and well established. These techniques are extended in our work as we analyze the quantitative correlation between the two approaches. As a specific example, a quality issue related to the contacting of the solar cells during the solar simulator measurement is considered. We demonstrate that a reliable and fully automated classification of the images with respect to the considered quality issue can be achieved by our artificial intelligence (AI) approach. This approach is different from the majority of state-of-the-art approaches as it does not consider specific image objects or features, such as cracks or spots, but rather predicts an image category for the entire image. The performance of the developed supervised image classification approach based on a neural network is analyzed quantitatively. We show that a clear quantitative correlation between cell I–V-data and AI-predicted EL-image classes can be established. In this way, an early-warning procedure can be implemented to detect this performance related issue using the AI-analysis of the EL-images even when its appearance is rather weakly pronounced and before it can be discovered in the I–V-data. We also present results on the impact of the training data set on the performance of the neural network and key parameters describing the quality of the network.

Highly response gas sensor based the Au-ZnO films processed by combining magnetron sputtering and Ar plasma treatment

The excellent and promising gas sensors not only have high response, but also can be easily integrated with other semiconductor devices to form an intelligent chip. In order to realize this goal, an effective strategy is proposed to combine the magnetron sputtering and Ar plasma treatment. As a result, a high-performance sensor based on Au-ZnO films is achieved at the optimal technology parameter, with high response (Ra/Rg) of 190 to 100 ppm isopropanol (IPA), rapid response/recovery speed of 1 s/18 s, and low detection limit of 100 ppb at 300 °C. Moreover, the mechanisms of the improvement on the sensing properties of the as-fabricated sensor are discussed. The present work provides new ideas for the future development of integrating gas sensors with functional circuits to form a smart chip that can perform data acquisition, processing and storage.

Commissioning and Operation of the Upgraded Belle II DAQ System With PCI-Express-Based High-Speed Readout

The Belle II experiment and the SuperKEKB collider are designed to operate under a higher luminosity compared to that of Belle for the improvement on rare <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">B</i> meson decay study and new physics search. In order to break the bottleneck of bandwidth and to improve the stability in the operation of the Belle II Data Acquisition (DAQ) system, a new PCI-Express-based readout system has been developed. The new system includes a PCI-Express-based high-speed readout board (PCIe40), which was originally developed for the upgrades of the LHCb and ALICE experiments, the PCIe40 firmware, the slow control and readout software running on a readout PC. The new readout system’s commissioning with most of the Belle II sub-detectors has been performed, and the readout upgrade is complete for the particle-identification detectors and the neutral kaon and muon detector in Belle II, which has been operating stably with the new system in the beam collision “Physics runs”. The results of the commissioning and the performance of the global DAQ operation will be reported.

Self-attention convolutional neural network optimized with season optimization algorithm Espoused Chronic Kidney Diseases Diagnosis in Big Data System

Nowadays, Internet of Things (IoT) and cloud platforms are broadly employed in numerous healthcare applications. Instead of using the limited storage and processing power found in mobile devices, the vast amount of data generated by internet of things devices in healthcare sector can be evaluated on a cloud platform. In this article, the Self-Attention Convolutional Neural Network optimized with Season Optimization Algorithm is proposed for Chronic Kidney Disease Diagnosis using IoT and cloud computing in Smart Medical Big Data health care system (SACNN-SOA-CKD-IoT-CC). IoT devices, like wearable and sensors perform data acquisition process. For chronic kidney disease (CKD) diagnostic model, the self-Attention convolutional neural network (SACNN) is applied. But, the SACNN not divulge any optimization systems adoption to calculate the optimal parameters and to make sure the exact categorization of Chronic Kidney Disease (CKD). Therefore, the season optimization algorithm (SOA) is used to optimize SACNN. The proposed approach is implemented in python language its performance is analyzed with performance metrices, like sensitivity, accuracy, recall, f-measure, specificity, network latency, scalability, response time, delay, and accuracy. The proposed SACNN-SOA-CKD-IoT-CC method achieves higher accuracy in CKD dataset of 15.66 %, 21.65 % and 9.64 %, lower error rate of 11.27 %, 8.35 %, and 21.06 % compared to the existing methods, like intelligent internet of things with cloud centric clinical decision support scheme for the prediction of CKD (LR-AME-CKD-IoT-CC), diagnostic prediction method for CKD in internet of things (MLP-SVM-CKD-IoT-CC) and ensemble of deep learning based clinical decision support system for CKD detection in medical internet of things environment (EDL-CDSS-CKD-IoT-CC).

Design and realization of a mobile-automatic calibration system for ventilator testers: oxygen concentration

Ventilators have a vital role in supplying oxygen to the patients experiencing respiratory failure and ensuring the patient’s oxygen saturation to levels above 50% continuously. Therefore, the accuracy of oxygen concentration measurement becomes very important to assure patient safety obtained by testing the ventilator against a ventilator tester. Testing and calibration laboratory use a ventilator tester for routine calibration of health facilities as well as performance and clinical testing for emergency ventilators that developed massively these days. A mobile-ventilator tester calibration system for oxygen levels, especially for concentrations of 30%, 60%, and 90% according to SNI ISO 80601-2-12:2020, is being developed. This calibration system is designed to operate automatically based on Laboratory virtual instruments engineering workbench (LabVIEW) to control the instrumentation system and perform data acquisition of (oxygen concentration) readings. Three concentrations of oxygen gas reference are stored each on 2 L gas reservoirs to ease the mobilization of the system to testing and calibration laboratory. Hopefully, the traceability to International System of Units (SI) for the ventilator tester can be realized with this calibration system, thus can improve the quality of health services and increase the competitiveness of nationally produced medical devices. Although some improvements are required, the prototype of the calibrator works appropriately as intended.

Setting the Tone for Sound Forensic Investigations on Android-Based Social Media Platforms

Abstract: Android Social Media Applications have become a yardstick in facilitating a platform for human socialization on cyber space. They are an inevitable alternative, which is fast replacing most traditional ways that lacks full multimedia interaction adored by many. These applications are of forensic value as they account for most activities helpful in either incriminating or exonerating suspects in cases of adverse events. By default, most social applications store activity data in specific directories they create at the background of the hosting Android devices. Through expertise, this data can be extracted and analyzed to come up with meaningful insights useful in an inquiry of digital evidence interest. This study focused on forensics of Twitter and Clubhouse android based social media applications. The approach taken was to install these applications on emerging Android devices using the Samsung Galaxy S20+ (SMGS20+) and Samsung Galaxy Tab A7 (SMGTA7), populate known test data, perform data acquisition, execute data analysis noting results and then do a comparative analysis of tools and techniques utilized towards provisioning alternative solutions.