Advanced Data Acquisition System Research Articles

Study of Avicennan unani drug saad kufi (Cyperus scariosus R.Br) for cardiac activity on isolated Langendorff rat heart

Cardioprotective Unani drug, Saad Kufi (Cyperus scariosus R.Br.), mentioned by the intellectual colossus Ibn Sina (Avicenna) a thousand years back in his book, “Kitab al-Adviya al-Qalbia” and still widely used by Unani physicians, was selected for experimental study. The main objective of the study was to explore the trial drug ex vivo for validation, hemodynamic elucidation, and molecular mechanism identification for the improvement of Unani therapeutics and to identify a safe and effective novel cardiovascular drug for mainstream medicine. Five doses of 50% ethanolic extract of the physiochemically standardized root were studied in a normal rat heart, on a semi-automated Langendorff apparatus supported with an advanced data acquisition system, perfused with carbogenated non-recirculating Kreb’s-Hensleit solution, at constant flow mode, by continuous recording. The effect of 6 repetitions (n = 6) of each dose was studied with the following parameters on the Lab chart pro version: heart rate, left ventricular developed pressure, cardiac work, and coronary pressure. The significance of the difference was determined by the student’s “t” test. Results of all doses of Saad Kufi showed a significant increase in the above parameters. The maximum increase was observed by Dose III. It could be concluded that Unani’s clinical use of Saad Kufi (C. scariosus R.Br) as cardioprotective was validated and higher doses were indicated to be clinically optimal. Hemodynamically and molecular mechanism-wise, it is indicated to be a sympathomimetic or inotropic-like agent and possibly a direct vasodilator. Since existing long-term treatment of heart failure is not done by cardiac stimulants or positive inotropic and chronotropic agents, the possible future clinical demonstration of therapeutic improvement in heart failure will bring Saad Kufi as a novel drug in mainstream medicine.

Revolutionizing smart grid-ready management systems: A holistic framework for optimal grid reliability

Existing energy management systems are becoming increasingly insecure and inefficient due to the rapid adoption of smart grid technology. Current research indicates that effectively managing dynamic energy flows, adjusting to changing needs, and protecting against new cyber threats remain significant challenges for the smart grid system. An advanced and comprehensive plan for managing smart grids is therefore required, capable of addressing these delicate and multifaceted problems. The proposed framework addresses these challenges through unifying several key aspects, it includes an advanced data acquisition system that captures real-time data from various grid sources, enabling comprehensive energy monitoring and dynamic flow analysis. By integrating predictive algorithms, the framework provides precise energy demand forecasting, which is essential for adaptive grid management. A significant contribution is the incorporation of an AI-based module for diagnostics and prognostics, which leverages machine learning techniques to shift from reactive to proactive maintenance strategies. The optimal power flow (OPF) optimization module represents a central component of the framework. It employs advanced computational methods to ensure efficient and cost-effective power distribution, particularly in grids incorporating renewable energy sources. Additionally, the architectural framework is strengthened by a robust cybersecurity module designed to safeguard against a wide range of cyber threats, maintaining the integrity of both operational and consumer data. This paper also addresses practical implementation challenges such as compatibility with existing infrastructure, investment costs, and the need for specialized training. This solution represents a new benchmark for smart grid operations, ensuring more sustainable and efficient energy systems.

Establishment of correlation for the pressure fluctuations on the low-head Francis turbine in the draft tube cone from the model test

Pressure fluctuation is an essential metric for assessing the effectiveness of the functioning of a hydraulic turbine. During the operation of the hydraulic turbine, pressure fluctuations occur under varied load conditions. An advanced data acquisition system and measurement setup were designed to investigate the pressure fluctuation characteristics in the Francis turbine model with three pressure heads. The operating points for pressure fluctuations investigations ranged from lower part load to overload condition. The plant sigma condition is studied extensively under three different heads, i.e. 30.45 m, 31.98 m and 38.78 m. The miniature-type pressure transducers are used for the investigations. Three pressure sensors, one in the inlet of the spiral case, second and third in the draft tube upstream and downstream respectively, are installed. The measurements were conducted in different operating conditions considering the prototype output power of 16.7 MW, 20.3 MW, 27.8 MW, 34.3 MW and 40.4 MW. A correlation has been developed to represent the variation of draft tube cone pressure fluctuations concerning head and output power.

Single-Point and Surface Quality Assessment Algorithm in Continuous Production with the Use of 3D Laser Doppler Scanning Vibrometry System.

In the current economic situation of many companies, the need to reduce production time is a critical element. However, this cannot usually be carried out with a decrease in the quality of the final product. This article presents a possible solution for reducing the time needed for quality management. With the use of modern solutions such as optical measurement systems, quality control can be performed without additional stoppage time. In the case of single-point measurement with the Laser Doppler Vibrometer, the measurement can be performed quickly in a matter of milliseconds for each product. This article presents an example of such quality assurance measurements, with the use of fully non-contact methods, together with a proposed evaluation criterion for quality assessment. The proposed quality assurance algorithm allows the comparison of each of the products' modal responses with the ideal template and stores this information in the cloud, e.g., in the company's supervisory system. This makes the presented 3D Laser Vibrometry System an advanced instrumentation and data acquisition system which is the perfect application in the case of a factory quality management system based on the Industry 4.0 concept.

Innovative impact testing machine for enhancing impact related research in Australia

This paper summarises the development of a state-of-art impact testing machine for simulating impacts such as vehicular crashes or debris impacts onto structures. The machine has a 200 kg pneumatically powered projectile which can travel horizontally within the barrel of the machine with a maximum velocity of 50 m/s to impact the target structure. The maximum kinetic energy that can be generated by the projectile is 125 kJ by using different combinations of mass and velocity. The diameter of the projectile is 214 mm, and its impacting face can be changed to different shapes, such as flat circle, flat square or an elliptical nose to suit different impact scenarios. An innovative braking mechanism incorporating a crush tube is attached within the barrel to ensure safety when the projectile fails to be restrained by the impact. The crush tube can absorb the maximum imparted by the moving projectile. An advanced data acquisition system is installed to collect quantitative and qualitative test data during a period of 50 ms to 1 s. Two high-speed digital image correlation (DIC) cameras are attached and synchronised with the operation of the impact testing machine to record the images at the rate of 50,000 frames per second. Outputs in terms of strains, deformations, accelerations of the target structure with a record of damage history can be analysed using this 3D DIC technique. The paper also briefly presents the first application of this machine for impact testing masonry wall structures.

Machine Learning Based Approach to a Crane Load Estimation

Abstract In the presence of increasing demands for safety and efficiency of material handling systems, the development of advanced supervisory control, monitoring, data acquisition and diagnostic systems is involved, especially for large industrial cranes. The important part of such systems is the continuous monitoring of a crane load. The crane load monitoring system proposed in the paper is based on a fuzzy model that estimates a payload mass transferred by a crane based on measuring the crane girder deflection and trolley position. The model was identified using the fuzzy subtractive clustering and least mean square with the data collected during experiments carried out on the laboratory scaled overhead crane.

Migration from the traditional to the smart factory in the die-casting industry: Novel process data acquisition and fault detection based on artificial neural network

Although die-casting is one of the most popular mass production processes of precise metal parts, the manufacturing environment of the die-casting factory remains at the traditional level. In this study, we developed three core technologies to realize a smart-factory platform for die-casting industry: 1) a novel cost-effective product-tracking technology to obtain high-quality process data providing individual product information, 2) an advanced process data acquisition system that considers process failure, and 3) a fault detection module based on an artificial neural network. Our newly developed systems for the die-casting process were verified using 1500 test production. Based on the pilot production data, we developed a fault detection module with the pre-processing of time series temperature and pressure measurement data. The developed fault detection module shows 96.9 % accuracy for untrained data. The technologies developed in this study are expected to be a promising smart-factory platform to reduce the defect rate and production cost in die-casting industry.

PARAMETRIC ANALYSIS OF A GRINDING PROCESS USING THE ROUGH SETS THEORY

With continuous automation of the manufacturing industries and the development of advanced data acquisition systems, a huge volume of manufacturing-related data is now available which can be effectively mined to extract valuable knowledge and unfold the hidden patterns. In this paper, a data mining tool, in the form of the rough sets theory, is applied to a grinding process to investigate the effects of its various input parameters on the responses. Rotational speed of the grinding wheel, depth of cut and type of the cutting fluid are grinding parameters, and average surface roughness, amplitude of vibration and grinding ratio are the responses. The best parametric settings of the grinding parameters are also derived to control the quality characteristics of the ground components. The developed decision rules are quite easy to understand and can truly predict the response values at varying combinations of the considered grinding parameters.

Orion Prepares for Space with Advanced Data Acquisition System

NASA's Orion Crew Vehicle will transport astronauts to the Moon and beyond with its structural integrity proved using the most advanced technology available

9 Kw VRFB Experimental Facility: High Current Polarization Tests and Energy Analyses

A Vanadium redox flow battery test facility of industrial size (IS-VRFB) has been recently built at the Energy Storage and Conversion Laboratory of University of Padua in order to investigate the performance potential of scaling-up from small-scale single-cell bench experiments. The facility is built around a 40-cell 600-cm­ 2 active-area stack and two 550-litre tanks. The facility is provided with advanced electronic bidirectional power supply, multi-physics instrumentation and data acquisition and control system. This paper reports on the experimental results obtained so far. Two working modes were tested, i.e. fast and steady-state discharge, representative of real operative conditions, such as primary frequency regulation and peak shaving, respectively. During discharge tests at SOC=0.9 on a passive load, currents as high as 400 A (665 mA cm­– 2) where measured at a specific flow rate of 20.5×10–3 cm s–1, with a steady state value in excess of 350 A (of 583 mA cm–2). Maximum power exceeded 9 kW (370 mW cm–2 per cell), with a steady state value approaching 8 kW (330 mW cm–2 per cell). Energy losses measurement showed a round trip efficiency (RTE) in excess of 70%. This performance required a proper programming of the control strategies provided by the system supervisor. References Alotto, M. Guarnieri and F. Moro, “Redox flow batteries for the storage of renewable energy: A review,” Renew Sus. Energ Rev. 29, (2014): 325–335.Guarnieri, A. Trovò, A. D’Anzi, P. Alotto, “Developing vanadium redox flow technology on a 9-kW 26-kWh industrial scale test facility: design review and early experiments”, Appl Energy, 230 (2018) 1425-1434. DOI: 10.1016/j.apenergy.2018.09.021.Trovò, G. Marini, A. Sutto, P. Alotto, M. Giomo, F. Moro, M. Guarnieri, “Standby thermal model of a vanadium redox flow battery stack with crossover and shunt-current effects”, Appl Energy, 240 (2019) 893-906.Trovò, A. Saccardo, M. Giomo, F. Moro, M. Guarnieri, “Thermal modeling of industrial-scale vanadium redox flow batteries in high-current operations”, J Power Sources, in print. Figure 1

9 Kw VRFB Experimental Facility: Early Results from a Multichannel Electrochemical Impedance Spectroscopy

A Vanadium redox flow battery test facility of industrial size (IS-VRFB) has been recently built at the Energy Storage and Conversion Laboratory of University of Padua in order to investigate the performance potential of scaling-up from small-scale single-cell bench experiments. The facility is built around a 40-cell 600-cm­ 2 active-area stack and two 550-litre tanks. The facility is provided with advanced electronic bidirectional power supply, multiphysics instrumentation and data acquisition and control system. This paper reports on the commissioning of a multichannel Electrochemical Impedance Spectroscopy (EIS) system, tailored for such large stack, capable of operating in the range 0-30 kHz, with bias currents up to 400 A. Results from early experimental campaign will be also reported, which show the lack of uniformity among cells and how differences enlarge with increasing battery load conditions. Results can be instrumental in addressing the design of flow patterns inside a large stack. References Alotto, M. Guarnieri and F. Moro, “Redox flow batteries for the storage of renewable energy: A review,” Renew Sus. Energ Rev. 29, (2014): 325–335.Guarnieri, A. Trovò, A. D’Anzi, P. Alotto, “Developing vanadium redox flow technology on a 9-kW 26-kWh industrial scale test facility: design review and early experiments”, Appl Energy, 230 (2018) 1425-1434. DOI: 10.1016/j.apenergy.2018.09.021.Trovò, G. Marini, A. Sutto, P. Alotto, M. Giomo, F. Moro, M. Guarnieri, “Standby thermal model of a vanadium redox flow battery stack with crossover and shunt-current effects”, Appl Energy, 240 (2019) 893-906.Trovò, A. Saccardo, M. Giomo, F. Moro, M. Guarnieri, “Thermal modeling of industrial-scale vanadium redox flow batteries in high-current operations”, J Power Sources, in print. Figure 1

OpenCL Implementation of an Adaptive Disruption Predictor Based on a Probabilistic Venn Classifier

The ability and flexibility of the open computing language (OpenCL) for task parallelization in heterogeneous computing platforms [field-programmable gate array (FPGA), CPU, and GPU] are remarkable advantages when designing advanced data acquisition and processing systems. The use of FPGA devices in data acquisition devices increases the capabilities of traditional data acquisition systems, thus allowing the implementation of complex mathematical algorithms with the acquired data. This paper shows an OpenCL implementation of an adaptive probabilistic disruption predictor for fusion devices implemented in a Cyclone V SoC device with an ARM processor, the FPGA logic, and an analog to digital converter. This paper describes the methodology used, the hardware/software system architecture, and the implementation results. The work highlights the critical aspects involved in designing these OpenCL-based systems and, in particular, the implementation of the board support package. This paper also presents the aspects such as the significant differences in the design flow concept between FPGA and GPU OpenCL implementations and how to optimize the FPGA implementation with OpenCL. The results show that it is possible to make predictions with computation times shorter than $500~\mu \text{s}$ when using this low-cost SoC.

Fractal study on Saraswati supercluster

The study of galaxies and superclusters is a major field of interest of astrophysicists. With the development of modern advanced telescopes and data acquisition systems, scientists could gather more information about the universe. Investigations using Sloan 2.5-m telescope, the multi-fiber spectrograph and the galaxy redshift data recorded enabled the discovery of Saraswati—an extremely massive and giant supercluster. Fractal analysis is an established powerful mathematical technique for the analysis and study of complex systems extending from microcosm to macrocosm. Fractal dimension is related to the complexity of the system. In the present work, an attempt has been made to analyze the recently reported supercluster Saraswati by box-counting method. It is found that fractal dimension is very high for the supercluster and low for the voids. The fractal dimension of 1.75–2 in the Saraswati region suggests a sheet-like morphology to it. Complexity mapping is carried out by plotting contour plot of fractal dimension and percentage occupancy plot, which enable greater understanding of the distribution of galaxies in the universe.

Screwing process analysis using multivariate statistical process control

Screws are widely used for parts joining in industry. The definition of effective monitoring strategies for screwing processes can help to prevent or significantly reduce ineffective procedures, defective screwing and downtime. Monitoring several correlated variables simultaneously in order to detect relevant changes in manufacturing processes is an increasingly frequent practice furthered by advanced data acquisition systems. However, the monitoring approaches currently used do not consider the multivariate nature of the screwing processes. This paper presents the results of a study performed in an automotive electronics assembly line. Screwing process data concerning torque and rotation angle were analyzed using multivariate statistical process control based on principal component analysis (MSPC-PCA). The main purpose was to extract relevant information from a high number of correlated variables in order to early detect undesirable changes in the process performance. A PCA model was defined based on three principal components. The physical meaning of each component was identified, and underlying causes were inferred based on technical knowledge about the process. Monitoring tools, such as score plots and multivariate control charts allowed to detect the defective screwing cases included in the analyzed data set. Furthermore, eight periods of instability were identified. Considering that the out-of-control signals detected in these periods mainly correspond to delays at the beginning of the tightening operation, four potential causes to explain this behavior were ascertained and analyzed. This research allowed to acquire a deeper understanding on the screwing process behavior and about the causes with higher impact on its stability. Due to its flexibility and versatility, it is considered that this approach can be applied to effectively monitor screwing processes in the assembly of different product types either periodically or in real-time.

Natural Thermal Convection in a Vertical Water-Filled Cylinder: Infrared Thermography Investigation

Abstract —Temperature logging furnishes the essential part of geothermal data. Its applications are progressively expanding due to advanced temperature loggers and data acquisition systems that ensure precise and stable measurements at high spatial and temporal resolution. However, it may be hard to achieve the full effect of the available logging facilities because of noisy temperature oscillations produced by natural convection in water-filled boreholes. A new laboratory method is suggested to study the structure of convection flows and their thermal effect by infrared thermography at conditions close to those of real temperature logging. Thermographic cameras image temperature anomalies on the outer wall of a water-filled pipe which are imprints of the convection processes in the water column. The temperature gradient on the pipe wall maintains flow of warm air ascending from a toroidal heater. It is shown experimentally using a pipe of 20 mm inner diameter that convection of fluid in the pipe forms a helical system rotating around a vertical axis at Rayleigh numbers in a range of 280 to 2800. As the Rayleigh numbers increase from 280 to 2800, the helical pitch decreases from 270 to 130 mm while the angular velocity increases from 0.7 × 10–2 to 3.4 × 10–2 rad/s. The experiment confirms the theoretically predicted dependence of standard deviation of temperature fluctuation on the temperature gradient and inner radius of the logged borehole: σT = 3G·r.

Securing the Smart Grid: A Comprehensive Compilation of Intrusion Detection and Prevention Systems

The smart grid (SG) paradigm is the next technological leap of the conventional electrical grid, contributing to the protection of the physical environment and providing multiple advantages such as increased reliability, better service quality, and the efficient utilization of the existing infrastructure and the renewable energy resources. However, despite the fact that it brings beneficial environmental, economic, and social changes, the existence of such a system possesses important security and privacy challenges, since it includes a combination of heterogeneous, co-existing smart, and legacy technologies. Based on the rapid evolution of the cyber-physical systems (CPS), both academia and industry have developed appropriate measures for enhancing the security surface of the SG paradigm using, for example, integrating efficient, lightweight encryption and authorization mechanisms. Nevertheless, these mechanisms may not prevent various security threats, such as denial of service (DoS) attacks that target on the availability of the underlying systems. An efficient countermeasure against several cyberattacks is the intrusion detection and prevention system (IDPS). In this paper, we examine the contribution of the IDPSs in the SG paradigm, providing an analysis of 37 cases. More detailed, these systems can be considered as a secondary defense mechanism, which enhances the cryptographic processes, by timely detecting or/and preventing potential security violations. For instance, if a cyberattack bypasses the essential encryption and authorization mechanisms, then the IDPS systems can act as a secondary protection service, informing the system operator for the presence of the specific attack or enabling appropriate preventive countermeasures. The cases we study focused on the advanced metering infrastructure (AMI), supervisory control and data acquisition (SCADA) systems, substations, and synchrophasors. Based on our comparative analysis, the limitations and the shortcomings of the current IDPS systems are identified, whereas appropriate recommendations are provided for future research efforts.

A numerical model for solving time delay and phase inconsistency problem in the data acquisition system of very long baseline interferometry

AbstractWe developed an advanced data acquisition system for the very long baseline interferometry used for deep‐space detection in recent years. However, there are serious time delay and phase inconsistency problems in the data acquisition system, which greatly increase the complexity of subsequent processors and reduce measurement accuracy. To solve the problem, we establish a numerical model to analyze the specific reasons and propose a novel scheme to achieve a much better performance by optimizing the mixed digital direct synthesizer (DDS) for frequencies. We find that the DDS module cannot output an accurate phase and correct this inaccuracy by changing the original design of DDS module. Simulation and measurement results show that the redesign of DDS can remove the inconsistency of time delay and phases and improve the mixed performance.

Implementation of an Advanced Control and Data Acquisition System for the 100 T Pulsed Magnet at WHMFC

The 100 T pulsed magnetic field project is under development at the Wuhan National High Magnetic Field Center, Wuhan, China. Using the NI CompactRIO hardware and LabVIEW software platforms, a control and data acquisition system (CADAS) for the 100 T pulsed high magnetic field facility is designed and implemented. This paper briefly introduces the design of the control system and presents the aspects of the implementation. The CADAS developed in this paper communicates with three types of power supply systems and controls their discharge coordinately, and records discharge waveforms. It has been put into test, and 67.6 T pulsed magnetic field has been generated by a test three-coil magnet.

Advanced Data Acquisition System for Wind Energy Applications

Today renewable energies, particularly wind energy are important to meet 24 hour energy demands while keeping car­bon emissions low. As the cost of renewable energies are high, improving their efficiency is a key factor to reduce energy prices. High quality experimental data is essential to develop deeper understanding of the existing systems and to improve their efficiency.This paper introduces an unmanned airborne data acquisition system that can measure properties around wind-turbines to pro­vide new insight into aerodynamic performance and loss mech­anisms and to provide validation data for wind-turbine design methods. The described system is a flexible and portable platform for collecting high quality data from existing full-scale wind-tur­bine installations. This allows experiments to be conducted with­out scaling and with real-world boundary conditions.The system consists of two major parts: the unmanned flying platform (UAV) and the data acquisition system (DAQ). For the UAV a commercially available unit is selected, which has the ability to fly a route autonomously with sufficient preci­sion, the ability to hover, and sufficient load capacity to carry the DAQ system. The DAQ system in contrast, is developed in-house to achieve a high quality data collection capability and to increase flexibility.

An automated approach for real time diagnostics of electrical and optical parameters for ESD events at triple junction

Abstract Environment consisting of charged particles leads to an electrostatic discharge (arcs) on the surface of dielectric material. Simulating such environment in laboratory requires the expertise of plasma physics, vacuum technology and advanced instrumentation. Various electrical and optical discharge parameters are important for understanding arc behavior and developing a mitigation technique against it’s deteriorating effects like change in material properties and decrease in substrate’s life. A real time controller can be used for measuring all these parameters. In order to measure feeble arcs of few microseconds, its voltage and current signals are measured using high speed digitizer. Arc voltage signal is used to trigger a CCD camera for capturing arc image and hence locating its position on the substrate’s surface. Both electrical and optical signals are measured simultaneously with high precision. Gradual rise in surface potential which is responsible for ESD, can be measured by scanning entire substrate’s surface at regular time intervals. A non-contact capacitive Trek probe which is carried by a stage controller is used for this measurement. For these measurements, an advanced data acquisition, logging and analysis system is developed using LabVIEW platform. The scope of this paper is limited to on-line measurement of electrical and optical signals of an ESD event using RT and transferring them to host system. A specific case of discharges on solar panel is considered for experimental validation of the developed algorithm.