Data Acquisition System Research Articles

External Data Systems Enable Enhanced (and Sustainable) Fourier Transform Mass Spectrometry Imaging for Legacy Hybrid Linear Ion Trap-Orbitrap Platforms.

Legacy Fourier transform (FT) mass spectrometers provide robust platforms for bioanalytical mass spectrometry (MS) yet lack the most modern performance capabilities. For many laboratories, the routine investment in next generation instrumentation is cost prohibitive. Field-based upgrades provide a direct path to extend the usable lifespan of MS platforms which may be considered antiquated based on performance specifications at the time of manufacture. Here we demonstrate and evaluate the performance of a hybrid linear ion trap (LTQ)-Orbitrap mass spectrometer that has been enhanced via an external high-performance data acquisition and processing system to provide true absorption mode FT processing during an experimental acquisition. For the application to mass spectrometry imaging, several performance metrics have been improved including mass resolving power, mass accuracy, and dynamic range to provide an FTMS system comparable to current platforms. We also demonstrate, perhaps, the unexpected ability of these legacy platforms to detect usable time-domain signals up to 5 s in duration to achieve a mass resolving power 8× higher than the original platform specification.

Energy and time characterization of the newly acquired clover detector in Jordan

A comprehensive overview of the Balqarad clover detector acquired by Al-Balqa Applied University in Jordan is given in detail. The detector consists of four high-purity germanium crystals, accompanied by a composite scintillation active shield. Through meticulous testing involving a range of radioactive sources such as 137Cs, 60Co, and 241Am, we evaluate the performance of the clover detector. The associated digital data acquisition system incorporates specialized and unconventional time registers, operating in event-by-event mode. Our objective is to elucidate the energy and time characteristics exhibited by the new system under different operational modes, employing specifically designed offline software. Our findings encompass vital aspects. These aspects include the determination of the optimal time window value for processing data obtained from gamma detection measurements. Another issue that has been considered is the analysis of hit pattern distribution within each individual crystal. Additionally, we calculate the addback factor for the 60Co source, accounting for gamma energies of 1173 keV and 1332 keV while also considering the influence of time window increment.

Intelligent Identification of Surrounding Rock Grades Based on a Self-Developed Rock Drilling Test System

The classification of surrounding rock is crucial for formulating safe tunnel construction plans and support measures. However, the complex geological environment of tunnels presents a challenge in obtaining accurate drilling parameters for rock mass classification. This paper presents the development of a rock drilling testing system, which includes a propulsion speed acquisition system, oil pressure acquisition system, air pressure acquisition system, and an automatic data acquisition system. This system enables real-time, high-precision automatic collection and storage of parameters such as propulsion speed, with data collected twice per second for each parameter. Leveraging the Qingdao Metro Line 6 as a case study, we conducted rock mass drilling and constructed a rock mass classification database. By employing kernel density estimation and Pearson correlation analysis, we quantified the correlation between rock mass classification and the drilling parameters. The results indicated that relying on a single drilling parameter is insufficient for accurately determining rock mass classification. Both impact pressure and rotational pressure showed the strongest correlation with rock mass classification, each with a correlation coefficient below −0.8 (indicating a strong negative correlation). Outlier values of drilling parameters were excluded using the interval method. Based on the remaining data, we established an intelligent rock mass classification model using the random forest algorithm. This model demonstrated good accuracy and generalization performance, with an average accuracy exceeding 0.9. The proposed rock drilling testing system, combined with the intelligent rock mass classification model, forms an integrated system for the intelligent identification of rock mass grades. This system has significant implications for the intelligent and safe construction of drill-and-blast tunnels.

Investigation of current density and temperature distribution characteristics under transient conditions based on a 108 cm2 segmented proton exchange membrane fuel cell

Visualizing the electrochemical and temperature distribution characteristics inside proton exchange membrane fuel cell (PEMFC) should greatly contribute to the product development and performance optimization. In the study, a customized 27-segment 108 cm2 fuel cell was fabricated based on the printed circuit board methodology to achieve the local current density and local voltage measurement. In addition, 27 thermocouples were embedded in the segmented cathode graphite gas flow plate for local temperature measurement. Detailed information of fuel cell core components, multi-channel data acquisition system, external temperature control system, and uncertainty analysis was presented. According to the test results of 4000 s current rising conditions and 2400 s current decreasing conditions, the measurement deviation of total current between data acquisition system and TOYO test station is 2.4%–3.2%. Meanwhile, the deviation of output voltage ranges from 0.1% to 2.6%. To quantify the degree of distribution non-uniformity, the range and standard deviation of local parameter values as well as contour map are demonstrated. The non-uniformity of local current density and local temperature becomes more significant under higher operating current conditions. Furthermore, the degree of parameter non-uniformity is more noticeable under current rising conditions compared with current decreasing conditions. The transient increase of operating current leads to the sudden overshoot phenomena of local current range, and the change of local current density among all segments is instantaneously finished. However, the range of current demonstrates relatively smooth transition trends when reactant flow rate rises. Higher temperature area appears in the middle of membrane electrode assembly, especially in the position with higher local current density. The parameter distribution and transition characteristics could provide helpful guidance for fuel cell optimization and diagnosis.

Experimental study on the elastic response characteristics of a rotating body under an oblique water impact load

In this study, an integrated experimental model was designed and a data acquisition system was established to capture the structural response of an elastic rotating body throughout the oblique water impact process. This established experimental methodology offers a dependable approach for gathering structural response data during the water impact process. The axial impact loads and effective stress on the rotating body throughout water impact were measured using acceleration sensor and strain gauge rosettes installed in the water impact zone of the structure, while a high-speed camera system captured the evolution of the flow field. Results from the arbitrary Lagrangian-Eulerian (ALE) numerical model were compared with those from repeated experiments, revealing good agreements. The article conducted water impact tests at different impact initial conditions, analyzing the response of the body to the impact load during water entry process across different water impact velocities and angles. Observations during the oblique water impact process revealed that the stress response at the center of the rotating body impact surface is approximately equal to that of the first water impact point, with this effect becoming more pronounced at higher velocities. This study contributes valuable experimental data for numerical investigations into the water impact of elastic structures.

Demonstration of neutron time-of-flight diffraction with an event-mode imaging detector

Neutron diffraction beamlines have traditionally relied on deploying large detector arrays of 3He tubes or neutron-sensitive scintillators coupled with photomultipliers to efficiently probe crystallographic and microstructure information of a given material. Given the large upfront cost of custom-made data acquisition systems and the recent scarcity of 3He, new diffraction beamlines or upgrades to existing ones demand innovative approaches. This paper introduces a novel Timepix3-based event-mode imaging neutron diffraction detector system as well as first results of a silicon powder diffraction measurement made at the HIPPO neutron powder diffractometer at the Los Alamos Neutron Science Center. Notably, these initial measurements were conducted simultaneously with the 3He array on HIPPO, enabling direct comparison. Data reduction for this type of data was implemented in the MAUD code, enabling Rietveld analysis. Results from the Timepix3-based setup and HIPPO were benchmarked against McStas simulations, showing good agreement for peak resolution. With further development, systems such as the one presented here may substantially reduce the cost of detector systems for new neutron instrumentation as well as for upgrades of existing beamlines.

Wind turbine gearbox condition monitoring using AI-enabled virtual indicators*

Abstract At present, the condition monitoring of wind turbine gearbox mostly involves selecting monitoring indicators in advance and establishing normal behavior models based on deep learning. However, single physical monitoring indicator cannot fully characterize the operating status of the wind turbine gearbox. To address the above issues, a wind turbine gearbox condition monitoring method using convolutional neural network (CNN)-long and short term memory network (LSTM)-autoencoder (AE) enabled virtual indicators is proposed in this paper. This method first establishes a cleaning principle to preprocess the data of supervisory control and data acquisition (SCADA) system and screen out effective SCADA data further. A CNN-LSTM-AE monitoring model was trained using SCADA data during normal operating process, various characteristic parameters of the gearbox are integrated to construct the AI-enabled virtual indicators, and meanwhile warning thresholds was determined based on the probability density distribution of virtual indicators to analyze the operating status of the gearbox. Finally, the proposed method was validated using real SCADA data from two wind turbines in a cooperated wind farm with my group. Compared with a single physical indicator, the virtual indicator enables to detect gearbox early faults 5 d in advance, indicating that the proposed method can effectively alert wind turbine gearbox failures.

Spatially-Resolved Freestream Velocity Measurements At The NASA Langley 31-Inch Mach 10 Air Tunnel Using FLEET

Freestream velocity measurements in the NASA Langley 31-inch Mach 10 wind tunnel are reported in this paper using Femtosecond Laser Electronic Excitation Tagging (FLEET). The freestream measurements acquired during the January 2023 test campaign were the first direct measurement of freestream velocity in this hypersonic wind tunnel facility. Spatial distributions of time-averaged and instantaneous velocity measurements were obtained at all three typical wind tunnel freestream unit Reynolds number conditions of Re∞ /L = 1800000 [1/m] , 3600000 [1/m], and 6400000 [1/m], though the current paper focuses on centerline measurements for the three Re∞ /L and spatial distributions for one Re∞ /L. Measured values for time-averaged velocity and mean of the instantaneous velocity at the wind tunnel centerline agree within 5 m/s or 0.4% of the calculated velocity from the facility data acquisition system. Measurements acquired at locations away from the wind tunnel centerline reveal the spatial extent of the core flow of the hypersonic facility.

Computer-Vision-Oriented Adaptive Sampling in Compressive Sensing.

Compressive sensing (CS) is recognized for its adeptness at compressing signals, making it a pivotal technology in the context of sensor data acquisition. With the proliferation of image data in Internet of Things (IoT) systems, CS is expected to reduce the transmission cost of signals captured by various sensor devices. However, the quality of CS-reconstructed signals inevitably degrades as the sampling rate decreases, which poses a challenge in terms of the inference accuracy in downstream computer vision (CV) tasks. This limitation imposes an obstacle to the real-world application of existing CS techniques, especially for reducing transmission costs in sensor-rich environments. In response to this challenge, this paper contributes a CV-oriented adaptive CS framework based on saliency detection to the field of sensing technology that enables sensor systems to intelligently prioritize and transmit the most relevant data. Unlike existing CS techniques, the proposal prioritizes the accuracy of reconstructed images for CV purposes, not only for visual quality. The primary objective of this proposal is to enhance the preservation of information critical for CV tasks while optimizing the utilization of sensor data. This work conducts experiments on various realistic scenario datasets collected by real sensor devices. Experimental results demonstrate superior performance compared to existing CS sampling techniques across the STL10, Intel, and Imagenette datasets for classification and KITTI for object detection. Compared with the baseline uniform sampling technique, the average classification accuracy shows a maximum improvement of 26.23%, 11.69%, and 18.25%, respectively, at specific sampling rates. In addition, even at very low sampling rates, the proposal is demonstrated to be robust in terms of classification and detection as compared to state-of-the-art CS techniques. This ensures essential information for CV tasks is retained, improving the efficacy of sensor-based data acquisition systems.

Initial Study of the Onsite Measurement of Flow Sensors on Turbine Blades (SOTB)

This paper presents a new framework using MEMS flow sensors on turbine blades (SOTB) to investigate unsteady flow features of a rotating wind turbine. Self-heating flow sensors were implemented by the U18 complementary metal-oxide semiconductor (CMOS) MEMS foundry provided by Taiwan Semiconductor Research Institute (TSRI). Flow sensor chips with a size of 1.5 mm × 1.5 mm were parylene-coated, output via a wireless data acquisition system (WDAQ), and mounted at the root, middle and tip of a 1.2 m diameter semi-rigid turbine blade of a 400 W horizontal axis wind turbine (HAWT). The instantaneous angles of attack (AOAs) of the SOTB were found to be 46~62°, much higher than the general stall AOA of 15°, but were accurate considering the normal detection of the flow sensors. The computational fluid dynamics (CFD) simulation of the HAWT was also compared with the SOTB output. The onsite measurement herein revealed that the 3D secondary flow increment, mostly obvious near the middle part of the turbine blades, degraded both the sensor and the turbine performance and initially justified the onsite measurement application.

DATIV-Remote Enhancement of Smart Aerosol Measurement System Using Raspberry Pi-Based Distributed Sensors.

Enclosed public spaces are hotspots for airborne disease transmission. To measure and maintain indoor air quality in terms of airborne transmission, an open source, low cost and distributed array of particulate matter sensors was developed and named Dynamic Aerosol Transport for Indoor Ventilation, or DATIV, system. This system can use multiple particulate matter sensors (PMSs) simultaneously and can be remotely controlled using a Raspberry Pi-based operating system. The data acquisition system can be easily operated using the GUI within any common browser installed on a remote device such as a PC or smartphone with a corresponding IP address. The software architecture and validation measurements are presented together with possible future developments.

ARIMAX Modelling: Response of Hass Avocado Respiration Rate to Environmental Factors

This research explores how random events influence the respiration rate in Hass avocado beyond deterministic models in order to develop better strategies for extending its shelf life. Understanding these factors can enhance the accuracy of postharvest management strategies. The Autoregressive Integrated Moving Average (ARIMA) model with exogenous variables (ARIMAX) is an alternative stochastic probability model which is capable of modeling complex, externally influenced phenomena such as respiration. This study aimed to elucidate the effect of three exogenous variables, namely temperature, relative humidity, and ambient illumination, on the respiration rate of Hass avocado fruits. Data on the respiration rate and exogenous variables were obtained using sensors coupled to a data acquisition system in a prototype of continuous airflow. The Box–Jenkins methodology was employed to construct the ARIMA models. The temperature, relative humidity, ambient illumination, and respiration rate variables were adjusted to the ARIMA models (3,1,2), ARIMA (1,1,2), ARIMA (1,1,2), and ARIMA (1,1,3), respectively. The ARIMAX (1,1,3) models were obtained from the pre-whitened respiration rate series. The impact detected in the transfer functions indicates increases in the respiration rate of 0.34%, 1.52%, and 0.99% for each unit increase in the temperature, relative humidity, and ambient illumination variables, respectively. In this regard, ARIMAX modeling is reliable for explaining the physiological response of Hass avocado fruits due to external factors. In future research, it is intended to extrapolate this stochastic modeling procedure to measure the effect of dynamic loads on the respiratory metabolism of fruits during transportation, where there is a considerable loss in the quality of fresh products.

3D network-on-chip data acquisition system mapping based on reinforcement learning and improved attention mechanism

The three-dimensional Network-on-Chip (NoC) data acquisition system is designed to create a time-interleaved data acquisition system using NoC technology. In the design of NoC application systems, optimizing the mapping algorithm can effectively reduce network communication latency. Aiming at the mapping challenge of a large number of functional IP nodes in 3D NoC data acquisition system, the reinforcement learning and improved attention mechanism mapping algorithm (RA-Map) is proposed. The RA-Map mapping algorithm employs node function encoding and node position encoding to express the properties of an IP node in the task graph preprocessing. The local attention mechanism is used in the mapping network encoder, and the fusion of dynamic key node information is proposed in the decoder. The mapping result evaluation network achieves unsupervised training of the mapping network. These targeted improvements improve the quality of the mapping. Experimental results show that the RA-Map mapping algorithm can effectively model the IP core mapping. Compared with the DPSO algorithm and SA algorithm, the average communication cost of RA-Map mapping algorithm is reduced by 6.5 % and 8.5 %, respectively.

Evaluation of rock cutting performance of conical cutting tool based on commonly measured rock properties

Efficiency of rock cutting process plays a critical role in performance of mechanical excavation units. The composition of cutting forces (normal and drag force acting on cutting tools) and the total force (FT), specific energy (SE), and percent of fine material (FM) produced in cutting process are important indicators of efficient cutting process. The other key factors in assessment of machine performance are tool wear, energy consumption, dust production, and machine maintenance, availability, and utilization. In this study, small scale linear cutting experiments were performed with a conical pick on thirteen sedimentary and metamorphic weak to medium strength rock samples at a range of 0.5 to 6 mm cutting depths in unrelieved cutting mode. FT was measured by using a 3D dynamometer and recorded by the data acquisition system, and FM was determined by sieve analysis. Finally, SE was calculated using both the cutting force signal and the volume of the cuttings for each test. Subsequently, an analysis of the effective cutting geometry was performed based on cutting depth, using the specific energy as an indicator of cutting efficiency. Statistical and regression analysis was used to correlate FT, SE, and FM with the rock properties and cutting geometry. The results revealed that the uniaxial compressive strength, Schmidt rebound number, and density are the main parameters that affect FT and SE, and the brittleness index is the main parameter that affects FM. A nonlinear predictive model is introduced that offers a reasonable estimate of FT, SE, and FM to assist engineers in determining the effective operational cutting geometry for a given rock type for unrelieved cuts.

SiPM and readout electronics for the JUNO-TAO Central Detector

The Taishan Antineutrino Observatory (TAO) is a satellite experiment of the Jiangmen Underground Neutrino Observatory (JUNO). TAO consists of a spherical ton-level Gadolinium-doped Liquid Scintillator detector and its main purpose is the precise measurement of the reactor antineutrino spectrum by detection of light produced in v̅e + p⟶ e + + n reaction, as a reference for JUNO. About 4,500 photoelectrons per MeV could be detected by instrumenting the sphere surface (∼10 m2) with state-of-the-art Silicon PhotoMultipliers (SiPMs), resulting in a sub-percent energy resolution. In this work we present the implemented architecture of the readout electronics based on low-noise, high-speed Front-End Boards (FEBs) connected to a 50×50 mm2 SiPM Hamamatsu tile, composed by 32 SiPM elements of 12×6 mm2 each, divided into two independent output channels. The overall 4,024 FEBs will be supplied through eight custom flanges that have to bring in about 1.5 kW. On the same flanges the 8,048 output signal cables are distributed and routed to the Front-End Controllers (FECs), based on Virtex Ultrascale FPGAs, able to manage up to eight 16-channel ADCs, for a total of 128 channels on a single FEC, with a maximum sampling rate of 250 MHz with 12-bit resolution. A dedicated trigger and data-acquisition system will filter and record occurring events, rejecting dark count events. We report the results of the characterization for the pre-production FEBs batch, following the main figures of merit defined for the experiment, showing single photoelectron resolution better than 13% and dynamic range up to 250 photoelectrons.

Design of wireless transmission system for intelligent agricultural greenhouse data acquisition based on modbus communication protocol

Abstract Traditional agricultural greenhouses rely too much on manual labor and have a poor ability to adjust the key factors that affect crop growth, such as temperature, humidity, and illumination, resulting in low production efficiency and unstable product quality. Based on the MODBUS communication protocol, a set of intelligent agricultural greenhouse data acquisition wireless transmission systems is designed in this paper. Intelligent instruments, sensors, wireless transmission equipment, and smart control equipment (PLC) have been introduced in agricultural greenhouses. These sensors and instruments automatically collect data and wirelessly transmit it to the intelligent control system in the control room, which is located a few kilometers away. The intelligent control system processes the data and generates various control instructions which are then transmitted to the executive mechanism in agricultural greenhouses for implementation. We complete automatic adjustment of temperature, humidity, illuminance, and other parameters and operations. The intelligent, large-scale, and unmanned agricultural greenhouses are realized. It greatly improves the production efficiency and product quality of crops.

Low latency optical-based mode tracking with machine learning deployed on FPGAs on a tokamak.

Active feedback control in magnetic confinement fusion devices is desirable to mitigate plasma instabilities and enable robust operation. Optical high-speed cameras provide a powerful, non-invasive diagnostic and can be suitable for these applications. In this study, we process high-speed camera data, at rates exceeding 100 kfps, on in situ field-programmable gate array (FPGA) hardware to track magnetohydrodynamic (MHD) mode evolution and generate control signals in real time. Our system utilizes a convolutional neural network (CNN) model, which predicts the n = 1 MHD mode amplitude and phase using camera images with better accuracy than other tested non-deep-learning-based methods. By implementing this model directly within the standard FPGA readout hardware of the high-speed camera diagnostic, our mode tracking system achieves a total trigger-to-output latency of 17.6 μs and a throughput of up to 120 kfps. This study at the High Beta Tokamak-Extended Pulse (HBT-EP) experiment demonstrates an FPGA-based high-speed camera data acquisition and processing system, enabling application in real-time machine-learning-based tokamak diagnostic and control as well as potential applications in other scientific domains.

Application of entertainment and fitness robots based on game interaction in sports training data analysis

This study aims to explore the application of sports data analysis of recreational fitness robots in sports training. By introducing interactive elements of games, recreational fitness robots can provide more interesting and challenging fitness experience, thus attracting more people to participate in sports training. Research and design and develop a game interactive entertainment fitness robot, with action recognition and user interaction functions, can perceive the user’s actions and make corresponding responses. When the user interacts with the robot, the sensor continuously collects and records data, establishes a data acquisition and storage system, analyzes and processes the collected interactive data, identifies the user’s movement pattern, assesses the user’s physical fitness level, and identifies potential improvement points. According to the user’s physical condition and training objectives, the appropriate exercise plan and game interaction mode are designed to provide better training results and entertainment experience. It is found that the game-based interactive entertainment fitness robot can effectively improve the user’s exercise motivation and participation in sports training. Through the analysis of sports data, we can develop personalized training plans according to the specific needs and goals of users, so as to achieve better training results.

A gait phase recognition method for obstacle crossing based on multi-sensor fusion

The analysis of human motion patterns and gait phase detection holds significant importance for both human motion analysis and precise control of exoskeleton devices. Presently, research on gait during obstacle crossing primarily focuses on the medical rehabilitation domain, with limited studies concerning gait phase analysis during obstacle crossing. This study conducts a detailed analysis of gait data during obstacle crossing and proposes a CNN-PCA-LSTM algorithm that fuses multi-sensor data to accurately identify gait phases when crossing obstacles in the horizontal direction. A wearable multi-sensor data acquisition system was designed, utilizing flexible capacitive pressure sensors to collect pressure data from the soles of both feet and IMUs to gather foot motion data. Kinematic simulation analyses of human obstacle-crossing motions were performed using Anybody software to determine various gait phases, validating the efficacy of the simulation model. Subsequently, one-dimensional and two-dimensional convolutional neural networks were separately employed to extract features from sole pressure data and foot motion data. Principal Component Analysis (PCA) was utilized to reduce the dimensionality of these feature datasets, which were then inputted into LSTM networks. Finally, the Softmax function was employed for the classification and recognition of gait phases when crossing obstacles in the horizontal direction. The experimental results indicate that employing the CNN-PCA-LSTM algorithm integrating multiple sensor data achieves a recognition accuracy of 97.91 % for identifying gait phases during horizontal obstacle crossing.

A Novel Scalable Digital Data Acquisition System for Industrial Condition Monitoring

A Novel Scalable Digital Data Acquisition System for Industrial Condition Monitoring