JADAS: a customizable automated data acquisition system and its application to ice-embedded single particles.

Performance evaluation of PV powered solar tunnel dryer integrated with a mobile alert system for shrimp drying

Cryo-electron microscopy is becoming an increasingly powerful tool for solving the structure of protein complexes and has the potential to address problems that cannot be solved using other methods. The field however suffers from several major disadvantages related to the time required to acquire, process and analyze the data and the tedium of using the current prevailing methods. We have for some time been working towards the goal of developing a system that will result in a 3D map of a macromolecular structure automatically and within hours of inserting a specimen into a transmission electron microscope. We propose that these automated methods for data collection and analysis will have a significant impact in transferring the cryo-electron microscopy technology to the general biological community as well as in increasing the volume of data that can be collected during a single session at the microscope. The Leginon system that we have developed is designed to emulate all of the decisions and actions of a highly trained microscopist in collecting data from a vitreous ice specimen. These include identifying suitable areas of vitreous ice at low magnification, determining the presence and location of specimen on the grid, automatically adjusting imaging parameters (focus, astigmatism) under low dose conditions and acquiring images at high magnification to either film or a digital camera.

A system of computer programs and hardware was developed by the Instrumentation Branch of the Morgantown Energy Technology Center (METC) to provide data acquisition and control features for research projects at the site. The Automatic Data Acquisition and Control System (ADACS) has the capability of servicing up to eight individual projects simultaneously, providing data acquisition, data feedback, and process control where needed. Several novel software features - including a data table driven program, extensive feedback in real time, free format English commands, and high reliability - were incorporated to provide these functions.

Digital phased array antennas (DPAAs) have been widely used in modern radar systems. The flexible production and testing of large-scale DPAAs are becoming a hot research topic. The dedicated calibration elements are passive because they do not have transmitter and receiver modules. These elements cannot be used to test DPAAs. In this paper, we design a multichannel automatic test and data acquisition system for DPAAs. Our motivation is to use the system to verify the correctness and stability of an entire DPAA. The system can achieve snapshot data acquisition of 1152 optical fibers, and it can also process echo data in real time by digital beam forming (DBF) and record the processing results. At present, the system has been put into production for testing of large-scale DPAAs, and it can test the DPAA flexibly and steadily.

A novel multidisciplinary course was developed by the authors that integrates knowledge gained and tools acquired from the introductory freshman, circuits, mechanics and C/C++ courses and is built around the concepts associated with automated data acquisition systems. This lab intensive course uses a suite of data acquisition equipment located in a new Computer Aided Teaching Lab. The presentation format is two lectures and 1 three hour lab per week. The lectures are designed to cover material that directly supports the labs. Labs early in the course explore the subsystems of an automated data acquisition system. The students then learn the operation of the parallel port on the personal computer write C/C++ programs to control the parallel port, learn the operation of analog to digital and digital to analog converters, and learn the use of the C/C++ commands provided for controlling these subsystems. The students learn these concepts while doing typical experiments dealing with the measurement of temperature, strain, and the evaluation of a temperature controller.

Finding the optimal parameters in laser processing applications can be time-consuming given the large parameter space and various sources of error. This problem is exacerbated by day-to-day variation in laser beam characteristics and a large variety of materials that need to be processed. The ideal laser processing system should be “smart,” meaning that it can sense changes in the environment, make proper adjustment, and predict parameters for new materials. As a step toward this goal, we propose a method to efficiently extract the areas of a large number of laser-induced damages in situ using an automated data acquisition system that can control laser parameters, motorized stage movement, image capturing/processing, and feature extraction. The damage areas are extracted and compared with direct measurements. Damage areas are fed into an artificial neural network (ANN) for prediction. Various ANN structures and training functions are tested to create the optimal ANN for prediction. ANN predictions were found to be capable enough to accurately model and optimize the laser processing parameters that were investigated. With the capability of collecting a large amount of usable data in a short period of time, this acquisition system can be used to train sophisticated ANNs for complicated tasks such as quality control and failure prediction.

The structure of fully-developed turbulence in a smooth pipe has been studied via wavenumber spectra for various friction velocities, namely, u τ,=0.61 and 1.2 m/s (the corresponding Reynolds numbers based on centerline velocity and pipe radius being respectively 134,000 and 268,000) at various distances from the wall, namely y + = 70, 200,400 and 1,000. For each distance from the wall, correlations of the longitudinal component of turbulence were obtained simultaneously in seven narrow frequency bands by using an automated data acquisition system which jointly varied the longitudinal (x) and transverse (z) separations of two hot-wire probes. The centre frequencies of the bandpass filters used correspond to a range of nondimensional frequencies ω + from 0.005 to 0.21. By taking Fourier transforms of these correlations, three-dimensional power spectral density functions and hence wavenumber spectra have been obtained at each y + with nondimensional frequency ω + and nondimensional longitudinal and transverse wavenumbers k x + and k z + as the independent variables. The data presented in this form show the distribution of turbulence intensity among waves of different size and inclination. The data reported here cover a wave size range of over 100, spanning a range of wave angles from 2° to 84°. The effects of friction velocity and Reynolds number on the distribution of waves, their lifetimes and convection velocities are also discussed.

Precision water management and crop growth monitoring are essential where water is a scarce, especially in desert soils. The purpose of the study was to control the irrigation and real-time image acquisition for monitoring the rice cultivation inside the net house under the UAE desert soil. An automated data acquisition system was constructed, installed, and tested in the experimental site at Al-Foah, Al-Ain. Soil water content sensors were placed in the different depths of desert soils, and an automatic irrigation logic was implemented to maintain the average of 30% desired water content level in desert soils. The irrigation rate was controlled based on the sensor data and the on/off of the pump and valves. When the average soil water content percentage level exceeds 30%, the pump and solenoid valve automatically turned off and vice versa. A Raspberry Pi operating system was used to control the irrigation, and a Raspberry Pi camera system was used to capture the real-time images for monitoring the rice growth and development. A web server was developed to upload and display the sensor values and images using python programing language through the embedded Wi-Fi network service. The web-based monitoring system was allowed to monitor the rice field situation from anywhere and download data from the site. The existing irrigation technique would help to grow the rice in UAE desert soil environments.

Current reliability approaches were designed to assess and quantify the reliability associated with complex systems such as nuclear power plants (NPPs). These approaches are generally based on classical Boolean logic structures such as event trees (ETs) and fault trees (FTs) [Rausand, 2020]. The outcome obtained by combining FTs and ETs is the set of minimal cuts sets (MCSs), with each MCS representing a unique combination of BEs that leads to an undesired outcome (e.g., core damage). Probabilistic evaluation of a MCS is performed by evaluating the product of the probability values associated with each BE. A relevant factor here is that the probability values associated with BEs used in the plant models are updated at least every 4 years based on past operational experience and through the use of a Bayesian statistical process [Siu, 1998]. Hence, the probability value of a BE associated with a physical asset (e.g., a centrifugal pump or motor-operated valve) in no way reflects that asset’s actual condition and performance.
 This fact plays a major role in the application of plant reliability models to support risk-informed decisions. With the particular goal of reducing operation and maintenance costs, existing NPPs are moving from corrective and periodic maintenance toward new types of predictive maintenance strategies [Agarwal, 2021]. This transition is designed such that maintenance is conducted only when the asset requires it (i.e., prior to undergoing imminent failure). And though these benefits cannot be achieved through actual reliability modelling methods and currently employed reliability data, they can be achieved by employing asset-monitoring sensors, automated data acquisition systems, data analysis methods, and improved decision-making processes. Combined, these resources can provide precise information on the health of an asset, track its degradation trends, and estimate its expected failure time. Based on such information, maintenance operations can be scheduled and performed for each asset on an as-needed basis. This dynamic context of predictive maintenance operations requires new methods of data analysis, the propagation of asset health information from the asset level to the system level, and the optimization of plant resources.
 This paper provides an alternative reliability approach designed for a predictive maintenance context in which a direct link is created between monitoring data and decision-making. Rather than thinking of reliability in terms of system/asset probability of failure, we propose a reliability mindset based on the concept of margin [Mandelli, 2023]. An asset’s health is quantified by determining its margin, based on the asset’s current and historical monitoring data. The margin values of the monitored asset are then propagated through system reliability models (e.g., FTs or reliability block diagrams) to identify the assets that are more critical to guarantee system operation. We show how a margin-based approach can be used assess asset health, based solely on current and historic monitoring data (e.g., condition-based, anomaly detection, diagnostic, and prognostic data) [Xingang, 2021]. A margin-based approach directly addresses the limitations of classical reliability modelling approaches and provides a snapshot of system health—given the availability of monitoring data. These two different approaches are designed to address different types of decisions: classical reliability models support static decisions (e.g., a set frequency of periodic maintenance or surveillance operations) based on past operational experience, whereas a margin-based approach directly supports dynamic decisions involving maintenance operations that should only be performed when necessary, based on monitoring data (i.e., a predictive maintenance context).

Researchers of the future will depend upon computer technology to address questions that previously could not be answered using traditional methods of data acquisition. Recent advances in computer design and software availability have eliminated many of the limitations previously associated with computer use in the laboratory setting. Although computerisation facilitates the acquisition of laboratory data, identification of errors introduced into the measurement is often more difficult. The laboratory computer is only one component of an automated data acquisition system. A system designed to collect physiological data comprises several primary components, including: (a) sensing element; (b) signal amplification/conditioning circuits; (c) analogue display; (d) computer interface; (e) laboratory computer; and (f) acquisition software. The acquisition and conditioning of analogue signals is accomplished using traditional laboratory procedures familiar to the researcher. Converting this analogue signal into a digital format used by the computer, however, represents new techniques. Fundamentals of computerised data acquisition are discussed. An understanding of how an automated data acquisition system is configured and the techniques used to transform the values will assist the user in identifying sources of error introduced into the measurement. Additionally, familiarization with the methods of computerisation will provide insight into future applications of computer technology in the human performance laboratory.

This study illustrates presents a set of the Long-Term Geoelectric Potential (LTGP) measurements that are collected for experimental investigation in Western Greece during a five-year period (1993-1997). During this period, many major destructive earthquake events occurred that caused human casualties and extended material damages. The collection and processing of geoelectric measurements was done by an automated data acquisition system at the Seismological Laboratory of the University of Patras, Greece. This novel study considers seismic activity of this area as a typical linear dynamic system and the dynamic relationship between the magnitude of earthquakes and Long-Term Geoelectric Potential signals is inferred by the Recursive Least Square algorithm. The results are encouraging and show that linear dynamic systems, which are widely used in modern control theory, can describe efficiently the dynamic behavior of seismic activity and become a useful interpretative tool of seismic phenomenon.

The carbon dioxide concentration patterns in a large, high-rise, office building in Ottawa were examined experimentally using an automated data acquisition system. Daily CO2 concentration profiles throughout the building and air change rates, using SF6 as a tracer gas, were measured at minimum outdoor air supply rates during much of a heating season. Of particular interest was how well mixed the indoor air was and how well the CO2 concentrations that were measured in the central ventilation system's return air plenum represented the average CO2 concentration behaviour in the building as a whole. CO 2 concen tration profiles were also measured on individual floor spaces in the building to determine the range of variability in the concentration behaviour. The mea surement results are presented and discussed in the context of demand-con trolled ventilation.

The McCook Reservoir project is a below-grade reservoir to temporarily store combined sanitary overflow (CSO) water from the city of Chicago and surrounding communities. CSO is stored until it can be treated and then discharged into the Chicago sanitary and ship canal (CSSC). The reservoir is being excavated through approximately 15.2 m (50 ft) of soil and 76.2 m (250 ft) of rock, requiring the installation of earthen retaining walls and rock support measures for the near vertical rock wall to achieve the design depths and geometries. In addition, in order to minimize groundwater infiltration and CSO exfiltration, a perimeter cutoff wall and grout curtain have been installed. Due to the project proximity to adjacent roadways, railways, and waterways as well as for onsite project safety, a potential failure modes analysis was performed on the various project features and a surveillance and monitoring plan was developed to monitor for those failure modes that were deemed credible. This monitoring program consists of a series of piezometers, inclinometers, time domain reflectometry, extensometers, crack meters, survey methods, and visual monitoring. An automated data acquisition system (ADAS) is also installed that permits near-real time access to automated instrumentation data. The purpose of this paper is to discuss the lessons learned regarding design and installation considerations for the design, installation, data management, interpretation, and evaluation of the instrumentation.

Exploring high-rise preventive ventilation: Experimental investigation of inter-zone air pressurization with tracer gas analysis

Sea catfish, tested individually for 3-day periods in an automated data acquisition system, displayed a nocturnal pattern of locomotor activity with 62% of the activity occurring during scotophase. In addition, there were two relative maxima of activity at 0700–0900 and 1300–1500 C.S.T. This diel pattern of activity could not be correlated with tidal phenomena in the fish's natural environment and is virtually identical to that exhibited by several freshwater catfish species. Thus, even though sea catfish are a primarily estuarine fish and subject to daily tidal fluctuations, tidal rhythmicity is apparently not used as a cue for diel activity patterns.