Acquisition System Research Articles

Evaluation of atmospheric-plasma-source absorption mode Fourier transform Orbitrap mass spectrometry for chlorinated paraffin mixtures

Chlorinated paraffins (CP) are complex molecular mixtures occurring in a wide range of isomers and homologs of environmental hazards, whose analytical complexity demand advanced mass spectrometry (MS) methods for their characterization. The reported formation of chlorinated olefins (COs) and other transformation products during CP biotransformation and degradation can alter the MS analysis, increasing the high resolution required to distinguish CPs from their degradation products. An advanced setup hyphenating a plasma ionization source and an external high-performance data acquisition and processing system to the legacy hybrid LTQ Orbitrap XL mass spectrometer is reported. First, the study demonstrated the versatility of a liquid sampling atmospheric pressure glow discharge, as a soft ionization technique, for CP analysis. Second, enhanced resolution and sensitivity provided by the absorption mode Fourier transform spectral representation on this legacy mass spectrometer are shown. The developed Orbitrap-based platform allowed the detection of new isotopic clusters and CPs and COs to be distinguished at medium resolution (setting 30,000 at m/z 400, ~ 400 ms transients), and even chlorinated di-olefins (CdiOs) at higher resolution (setting 100,000 at m/z 400, ~ 1500 ms transients). Overall, such proof-of-principle instrumental improvements are promising for environmental and analytical research in the field of CP analysis.Graphical

Collision adhesion law of oil Droplets-Bubbles with different particle sizes in free floating

The treatment of oily wastewater has become a serious problem in the late stage of oilfield development. At the same time, it is of great significance to the improvement of ecological environment. As the key process of oil-bearing wastewater treatment, the study on the binding and adhesion law of oil droplets and bubbles in flotation and its related mechanism can provide reference for its application. Using a double high-speed camera acquisition system, the floating process and collision adhesion law of oil droplets and bubbles in a vertical transparent circular tube were experimentally studied, and the collision adhesion process of oil droplets (0.721 ∼ 3.759 mm) and bubbles (0.797 ∼ 2.886 mm) in different diameters were analysed. It is concluded that the oil droplet and the bubble collide with each other, and then the bubble slides along the surface of the oil droplet, and a neck shape appears at the end of the contact site, and then the neck increases with the diffusion of the oil droplet to form an oil-bubble adhesion body. And the process of elastic drags and contraction separation of the mixture is also demonstrated. It is found that the combination of oil droplets and bubbles with different diameters will have two types of collision adhesion modes, which oil droplet type and oil film type, respectively. Therefore, the diameter ratio of oil droplets and bubbles is a key factor, and when the diameter ratio is greater than 0.75, the adhesion mode of the adhesive body changes from unstable oil droplet type to more stable oil film type.

Analysis of Aluminum Alloy Double-Pulse MIG Welding Arc Signal Characteristics Based on Broadband Mode Decomposition

Welding voltage and current in arc signals are directly related to arc stability and welding quality. Process experiments with different parameters were organized according to the orthogonal experimental design method by constructing an aluminum alloy double-pulse metal inert gas (MIG) welding arc electric signal test platform. The data acquisition system of the aluminum alloy MIG welding process was established to obtain real-time arc signal information reflecting the welding process. The aluminum alloy’s collected double-pulse arc current signals are decomposed adaptively by broadband mode decomposition (BMD). The direct current (DC) signal, pulse signal, distortion signal, ripple signal, and noise signal are separated and extracted, and the composite multiscale fuzzy entropy (CMFE) is calculated for the component set of the electrical signal. The experimental results show that the current waveform obtained by the double-pulse MIG welding current signal is consistent with the corresponding weld forming diagram. Simultaneously, the composite multiscale fuzzy entropy is calculated for the arc characteristic parameters. The rationality of matching process parameters and arc stability of aluminum alloy's double-pulse MIG welding were evaluated.

A Modified EMD Technique for Broken Rotor Bar Fault Detection in Induction Machines.

Induction machines (IMs) are commonly used in various industrial sectors. It is essential to recognize IM defects at their earliest stage so as to prevent machine performance degradation and improve production quality and safety. This work will focus on IM broken rotor bar (BRB) fault detection, as BRB fault could generate extra heating, vibration, acoustic noise, or even sparks in IMs. In this paper, a modified empirical mode decomposition (EMD) technique, or MEMD, is proposed for BRB fault detection using motor current signature analysis. A smart sensor-based data acquisition (DAQ) system is developed by our research team and is used to collect current signals wirelessly. The MEMD takes several processing steps. Firstly, correlation-based EMD analysis is undertaken to select the most representative intrinsic mode function (IMF). Secondly, an adaptive window function is suggested for spectral operation and analysis to detect the BRB fault. Thirdly, a new reference function is proposed to generate the fault index for fault severity diagnosis analytically. The effectiveness of the proposed MEMD technique is verified experimentally.

DRR-based acoustic detection model for estimating room shape

An acoustic detection model based on the direct-to-reverberant energy ratio (DRR) is proposed to estimate room shape with audible sound. Besides the direct propagation, the first-order reflection is considered in the calculation of the DRR in this work. Thus, the direct-to-reflected acoustic path ratio is estimated in the proposed model to compute the reflection point, which is used to calculate the pose estimate of the reflector. The mapping of the proposed model is completed by fusing the pose estimates of walls with the spatio-temporal diversity. An initialization process is developed to obtain the initial value of the detection model. An acquisition system working under audible frequencies is built. The experimental results show that even with a low signal-to-diffuse noise ratio (−10 dB), the error of the proposed method is still less than 0.5 degrees in angle and 0.03 m in distance, demonstrating the robustness of the proposed model under the environment with multipath reflections.

Digital modelling method of coal-mine roadway based on millimeter-wave radar array

The roadway space of coal mine is narrow, and the illumination is low and uneven. Dynamic mining is accompanied by dust and water mist. The accuracy and reliability of roadway data collected by vision and laser sensors are poor. Based on this, a digital modeling method of coal mine roadway based on millimeter-wave radar array is proposed. Firstly, aiming at the problem of complex environmental interference, combined with the characteristics of small amount of single frame data of millimeter-wave point cloud, a multi-layer filtering noise reduction processing and dynamic subgraph registration method of millimeter-wave point cloud is proposed to filter out interference points and realize single radar point cloud registration. Secondly, aiming at the problem that a single millimeter-wave radar cannot scan the complete roadway information at one time, combined with the characteristics of small elevation field of view of millimeter-wave radar, a millimeter-wave radar array acquisition system is built, and an improved iterative closest point (ICP) registration algorithm based on point cloud features is established to construct the roadway point cloud fusion model. Finally, aiming at the problem of uneven and sparse point cloud after array fusion, a Poisson surface reconstruction method based on point cloud density weighted interpolation is proposed to refine the roadway structure and realize the accurate reconstruction of digital roadway model. The experimental results show that the digital modeling method of millimeter-wave radar array can accurately obtain the information of roadway surrounding rock, the density of roadway point cloud is increased by 22.4%, and the average absolute error percentage of the width and height of the reconstructed roadway model is only 0.82% and 0.72%, which provides a new research method for the reconstruction of underground roadway and an important basis for the digital modeling method of coal mine roadway.

Siderophore-Conjugated Antifungals: A Strategy to Potentially Cure Fungal Infections.

Fungi pose a global threat to humankind due to the increasing emergence of multi-drug-resistant fungi. There is a rising incidence of invasive fungal infections. Due to the structural complexity of fungal cell membranes, only a few classes of antifungal agents are effective and have been approved by the U.S. FDA. Hence, researchers globally are focusing on developing novel strategies to cure fungal infections. One of the potential strategies is the "Trojan horse" approach, which uses the siderophore-mediated iron acquisition (SIA) system to scavenge iron to deliver potent antifungal agents for therapeutics and diagnostics. These siderophore conjugates chelate to iron and are taken up through siderophore-iron transporters, which are overexpressed exclusively on microbes such as bacteria or fungi, but not mammalian cells. Our comprehensive review delves into recent advancements in the design of siderophore-conjugated antifungal agents to gain fungal cell entry. Notably, our focus extends to unraveling the intricate relationship between the structure of natural siderophores or siderophore-like molecules and the resulting antifungal activity. By exploring these design strategies, we aim to contribute to the ongoing discourse on combating drug-resistant fungal infections and advancing the landscape of antifungal theranostics.

Optimizing water supply systems in developing regions: a sustainable approach using ESCO model system for urban water supply in Dehradun, India

The critical importance of water makes it an indispensable natural resource. In context of urban infrastructure, particularly in the developing world, supplying water to urban households poses a notable challenge. Optimizing water supply systems (WSS) is one of the most arduous challenges in addressing this issue, primary contributors being energy and water losses during supply process. This paper focuses on the method adopted for minimizing losses in WSSs and creating a self-sustainable model for water supply in the urban settlement of Dehradun, India. The solution entails deployment of Energy Savings Companies (ESCO) service delivery model and the utilization of Supervisory Control and Data Acquisition (SCADA) system. This approach, thus far has yielded saving in both revenue, energy and water consumption at source, potentially generating saving that would enable covering the operational and maintenance changes for the established system for around 10 years.

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.

Tabletop experiment to determine the center of percussion of a baseball bat

The center of percussion is an essential physics concept with a variety of practical applications. For any handheld object there is a point that, once a force is applied to it, results in no acceleration at the hands because the translational and angular accelerations from the force cancel each other out. This point is referred to as the center of percussion. Although the concept of the center of percussion is applicable to a variety of different areas, this paper focuses on America’s favorite pastime; the sport of baseball. The center of percussion of a baseball bat, also referred to as the “sweet spot”, is the optimal spot for making contact with a moving baseball. This paper demonstrates the development of a tabletop model for visually explaining this process of identifying the center of percussion to an audience. The setup is an instrumented experiment with an integrated data acquisition system to collect, visualize, and analyze the data for the demonstration of the process to identify a baseball bat’s center of percussion.

Assessment of UHF Frequency Range for Failure Classification in Power Transformers.

Ultrahigh-frequency (UHF) sensing is one of the most promising techniques for assessing the quality of power transformer insulation systems due to its capability to identify failures like partial discharges (PDs) by detecting the emitted UHF signals. However, there are still uncertainties regarding the frequency range that should be evaluated in measurements. For example, most publications have stated that UHF emissions range up to 3 GHz. However, a Cigré brochure revealed that the optimal spectrum is between 100 MHz and 1 GHz, and more recently, a study indicated that the optimal frequency range is between 400 MHz and 900 MHz. Since different faults require different maintenance actions, both science and industry have been developing systems that allow for failure-type identification. Hence, it is important to note that bandwidth reduction may impair classification systems, especially those that are frequency-based. This article combines three operational conditions of a power transformer (healthy state, electric arc failure, and partial discharges on bushing) with three different self-organized maps to carry out failure classification: the chromatic technique (CT), principal component analysis (PCA), and the shape analysis clustering technique (SACT). For each case, the frequency content of UHF signals was selected at three frequency bands: the full spectrum, Cigré brochure range, and between 400 MHz and 900 MHz. Therefore, the contributions of this work are to assess how spectrum band limitation may alter failure classification and to evaluate the effectiveness of signal processing methodologies based on the frequency content of UHF signals. Additionally, an advantage of this work is that it does not rely on training as is the case for some machine learning-based methods. The results indicate that the reduced frequency range was not a limiting factor for classifying the state of the operation condition of the power transformer. Therefore, there is the possibility of using lower frequency ranges, such as from 400 MHz to 900 MHz, contributing to the development of less costly data acquisition systems. Additionally, PCA was found to be the most promising technique despite the reduction in frequency band information.

Data acquisition system for OLED defect detection and augmentation of system data through diffusion model

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.

Disruptive innovations and defence procurement: a study of the Brazilian Navy Case

ABSTRACT This paper deals with the study of the challenges that disruptive technologies represent for industrial defence strategies and procurement policies. It focuses on the incorporation of drones into military arsenals and will be based on a study of the Brazilian Navy case. Drones are a disruptive technology both for the defence industrial base and for their implications for military tactics and the organisational structure of the armed forces. The analysis addresses the main difficulties encountered in incorporating these technologies into traditional military structures and acquisition systems. We explore the ways in which defence procurement adapts (or fails to adapt) to the emergence of disruptive technologies: are traditional military organisations and procurement structures capable of introducing disruptive technologies and engaging in the organisational and operating practices required to do so? Some lessons are drawn from this experience: even when the military relevance of drone technology is widely acknowledged and even officially recognized, procurement strategies and practices have not been able to shift from their focus on incumbent suppliers and conventional systems.

Influence of the peel on online detecting soluble solids content of pomelo using Vis-NIR spectroscopy coupled with chemometric analysis

The application of Visible-near infrared spectroscopy (Vis-NIRs) to internal quality detection of pomelo with large sizes and thick peels is still challenging. Considering that the peel has a large mass ratio in pomelo and a great added value in industrial processing, a postharvest processing mode of grading after peeling without affecting consumption of pomelos may be an approach to avoid adverse influence of peel on soluble solids content (SSC) evaluation. In this study, we investigated the performance variation of Vis-NIRs for SSC online determination of pomelos with and without peel for the first time. An online detection system for pomelo spectra acquisition was developed, and the spectral characteristic difference of the intact and peeled pomelos was analyzed. Subsequently, the performance of calibration models was gradually improved and comparatively analyzed by various spectral processing and wavelength selection methods. Furthermore, convolutional neural network (CNN) was utilized to explore its ability for feature extraction. The results showed that combined with standard normal variate (SNV) and second order detrending and changeable-size moving window algorithms, the partial least squares regression (PLSR) model using spectra of peeled pomelos achieved the best prediction results. Specifically, the model attained a determination coefficient of prediction (Rp2) of 0.88, a root mean square error of prediction (RMSEP) of 0.294%, and a residual predictive deviation (RPD) value of 2.57. This study demonstrates that the peel has a significantly negative effect on the prediction performance and the CNN could be an alternative to conventional PLSR method. Our work may open new avenues for the internal quality assessment of agro-products with complex tissue structure.

Design and measurements of the diamagnetic loop in Aditya-U tokamak

A diamagnetic diagnostic system is designed, prepared and installed at Aditya Upgrade Tokamak. Also, the raw data is acquired in a specified acquisition system with suitable front-end electronics. The entire system consists of separate loops and is first calibrated in the presence of all coils, which conduct electricity during the plasma discharge. Then, the change in the toroidal magnetic field due to the plasma is measured using the diamagnetic system, and hence, several plasma parameters including poloidal beta, stored energy, etc. are estimated. The measurements were then repeated during various tests of the tokamak plasma, and several interesting facts were observed. The present paper covers the entire concept of designing magnetic diagnostics – from basic considerations to final data interpretation.

Enhanced throughput message streaming methods for DAQ systems of physics experiments

In nuclear and particle experiments, to achieve a real-time software-based data acquisition (DAQ) system, a message streaming platform is adopted as a solution for real-time data streaming transmission, facilitating a decoupled and scalable architecture. As increasing data rate in recent experiments demands higher and higher throughput, existing message streaming platforms lack of high single-node throughput, guarantees of reliability, and sequentiality for high-throughput transmission applications. This paper introduces enhanced throughput message streaming methods for DAQ systems, named DAQMQ. DAQMQ utilizes in-memory storage to store messages during transmission, offering high throughput performance and configurable message retention capability. Reliability in high-throughput transmission is strictly guaranteed by employing a two-phased acknowledgment mechanism. By fast reorganizing message streams in real-time, DAQMQ achieves high-throughput non-blocking sequential transmission. Evaluations conducted on ordinary PCs with 10-Gigabit network connections show that DAQMQ achieves a stable single-node throughput of 9.0 Gbit/s even when handling up to 1024 topics. Furthermore, DAQMQ achieves sequential transmission throughput close to that of non-sequential transmission. Tests with a broker or consumer breakdown during high-throughput transmission demonstrate DAQMQ's reliability and asynchronous transmission ability.

SSR Using Portable sEMG Acquisition System With Electrode Layout Optimization

SSR Using Portable sEMG Acquisition System With Electrode Layout Optimization

Development and application of multi-parameter real-time acquisition system for intelligent secondary equipment

Abstract A multi-parameter real-time collection and analysis system, including intelligent secondary equipment, data collection gateway, and health management backend, has been constructed to solve the problem of current incomplete monitoring data and inability to providential warning of hidden faults of intelligent secondary equipment in substations. The key feature parameters of the device’s functional modules were extracted, and a data collection gateway was developed to achieve IoT access to feature data. A fault identification and warning model for the secondary equipment functional module considering temperature is provided, which realizes the assessment of equipment health status and ensures the stable and reliable operation of the power grid.

Arduino-based real-time data acquisition systems: boosting STEM career interest

Arduino-based real-time data acquisition systems: boosting STEM career interest

Development of Remote Condition Monitoring System for Panauti Hydropower Station

Abstract Condition monitoring is the process of predicting faults in machine through the measurement and analysis of pre-identified parameters. This paper aims to present the features and process of development of a real time condition monitoring system installed at Panauti Hydropower station. In total, seven different sensors including Pressure, Temperature, Guide Vane Position and Tri-axial Vibration sensor were installed at the power plant. The locations of the pressure sensors are at the inlet of the turbine and the outlet of the draft tube. Tri-axial vibration sensor was installed at the turbine shaft to capture the turbine vibrations at different operating conditions. The temperature sensors provide the temperature data from the bearings and housing of a generator. The sensors were connected to the NI-DAQ (data acquisition system) hardware including current measuring module and sound and vibration module integrated with the LabVIEW program. The data acquisition system is capable of recording the pressure pulsations at the outlet of draft tube. The monitoring system processes the data from sensors and uploads the relevant information to the Kathmandu University’s central server. A remote monitoring program in LabVIEW has been developed which uses an Application Programming Interface (API) to retrieve the information from the central server in the real time, enabling the monitoring of the power plant from any location.