Real-time Experiments Research Articles

Deep-learning-enhanced digital twinning of complex composite structures and real-time mechanical interaction

Digital twins are undergoing growth that enables highly informative and scaleable interaction between physical objects and virtual twins, which is of great significance to the life cycle analysis of composites. Real-time fine evolution is challenging due to vast combinations of input features and high-resolution calculated variables. Here, we systematically demonstrate an AI-based methodology for digital twinning of complex composite structures. First, three types of deep neural networks are created with optionally used autoencoders as surrogate models, with architectures and data processing inspired by the rule-of-mixture of composites. Second, the prediction accuracy and efficiency are evaluated quantitatively and qualitatively, demonstrating the feasibility of predicting 3D displacement and stress fields directly from sensing data of temperature, pressure and loading displacement, and the optimal architecture is selected to be the evolving digital twin. Finally, the real-time interactive experiments are relayed to the digital twin and demonstrated that it can interact with physical objects and evolve online with high accuracy. These results indicate that the computational time can be reduced by 3∼6 orders of magnitude with high information intensity and scalability compared with conventional numerical and experimental methods, which opens up the avenues for the cost-effective and efficient development of digital twin services for composites.

Evidence of exploitative competition between honey bees and native bees in two California landscapes.

Human-mediated species introductions provide real-time experiments in how communities respond to interspecific competition. For example, managed honey bees Apis mellifera (L.) have been widely introduced outside their native range and may compete with native bees for pollen and nectar. Indeed, multiple studies suggest that honey bees and native bees overlap in their use of floral resources. However, for resource overlap to negatively impact resource collection by native bees, resource availability must also decline, and few studies investigate impacts of honey bee competition on native bee floral visits and floral resource availability simultaneously. In this study, we investigate impacts of increasing honey bee abundance on native bee visitation patterns, pollen diets, and nectar and pollen resource availability in two Californian landscapes: wildflower plantings in the Central Valley and montane meadows in the Sierra. We collected data on bee visits to flowers, pollen and nectar availability, and pollen carried on bee bodies across multiple sites in the Sierra and Central Valley. We then constructed plant-pollinator visitation networks to assess how increasing honey bee abundance impacted perceived apparent competition (PAC), a measure of niche overlap, and pollinator specialization (d'). We also compared PAC values against null expectations to address whether observed changes in niche overlap were greater or less than what we would expect given the relative abundances of interacting partners. We find clear evidence of exploitative competition in both ecosystems based on the following results: (1) honey bee competition increased niche overlap between honey bees and native bees, (2) increased honey bee abundance led to decreased pollen and nectar availability in flowers, and (3) native bee communities responded to competition by shifting their floral visits, with some becoming more specialized and others becoming more generalized depending on the ecosystem and bee taxon considered. Although native bees can adapt to honey bee competition by shifting their floral visits, the coexistence of honey bees and native bees is tenuous and will depend on floral resource availability. Preserving and augmenting floral resources is therefore essential in mitigating negative impacts of honey bee competition. In two California ecosystems, honey bee competition decreases pollen and nectar resource availability in flowers and alters native bee diets with potential implications for bee conservation and wildlands management.

Retraction: Cyclo(His-Pro) up-regulates heme oxygenase 1 via activation of Nrf2-ARE signalling.

The above article, published online on 16th October 2009 in Wiley Online Library (wileyonlinelibrary.com) and corrected in 2014 with a Corrigendum (JNeurochem 128(6): 975, URL: https://onlinelibrary.wiley.com/doi/10.1111/j.1471-4159.2011.07368.x) correcting Figure 3 of the original publication, has been retracted by agreement between the journal's Editor-in-Chief, Andrew Lawrence, the International Society for Neurochemistry and John Wiley & Sons Ltd. The retraction has been agreed due to concerns raised regarding possible image manipulation of Figure 5b. Corresponding author Alba Minelli as well as all other authors have been contacted to clarify their respective contribution to the article, and provide the original scanned films upon request for the editorial team's review. Corresponding author Alba Minelli confirmed the following author contributions in the corrected version (J Neurochem. 2014 Mar;128(6):975) as the final version of the published manuscript which all authors had read and approved: A. Minelli (contact): substantial contribution to conception and design, drafting the article, data interpretation, C .Conte: real-time PCR and gene silencing experiments, data acquisition and analysis, S. Grottelli: data acquisition and analysis, I. Bellezza: drafting the article, data acquisition and analysis, C. Emiliani: revising the article critically for important intellectual content, J. Bolaños: revising the article critically for important intellectual content. Since the original data could not be retrieved to dispel the new concerns regarding Figure 5 of the original publication, the journal no longer has confidence in the results and conclusions drawn. REFERENCE Minelli, A., Conte, C., Grottelli, S., Bellezza, I., Emiliani, C., & Bolaños, J. P. (2009). Cyclo(his-pro) up-regulates heme oxygenase 1 via activation of Nrf2-ARE signalling. Journal of Neurochemistry, 111(4), 956-966. https://doi.org/10.1111/j.1471-4159.2009.06376.x.

Porous and chemically robust MIL-100(V) MOF as an efficient cathode material for zinc‑ion batteries

Rechargeable zinc-ion batteries (ZIBs) have received wide attentiveness nowadays due to having several functional superiorities such as high energy density, cost-effectiveness, lesser toxicity, environmental benignity, and superior safety. Herein, we evaluated the potentiality of a 3D highly porous MIL-100(V) MOF as an efficient cathode material in ZIB. The constructed framework exhibited a maximum specific capacity of ∼362 mA h g−1 at 0.2 A g−1 current density and retained ∼ 95.45% initial capacitance with no significant loss of coulombic efficiency (CE) even after 3500 consecutive cycles. Moreover, the MIL-100(V)//Zn CR-2032-coin cell delivered high energy density of ∼ 195 W h kg−1 at 0.2 A g−1 current density, which could be attributed to the efficient immobilization of Zn2+ ions within the confined pore channels. Mechanism investigation of the Zn2+-storage/release process was verified by several ex-situ studies. A real-time experiment and slow zinc dendrite formation made MIL-100(V) MOF promising for next-generation energy storage devices.

Performance Evaluation of Brushless Direct Current Motor Control Methods through Low-Cost Microcontroller-Based Real-Time Experiments

Brushless direct current (BLDC) motors are high efficiency synchronous motors that are employed in a variety of applications due to prominent features such as long operational life, low maintenance requirements, and great dynamic response. BLDC motors are driven by energizing the motor stator windings with an inverter circuit. To commute the inverter switches, the trapezoidal (120°) method is generally used by considering the back electromagnetic force induced on unenergized phase of BLDC. Furthermore, depending on the application requirements, alternative commutation modes (CM) such as 180°, 150°, and sinusoidal-based approaches are utilized. In the literature, the performance comparison of some CMs was studied for two-phase on operation and three-phase on operation mode as a switching pattern. However, only a few research assessed the performance of two or three commutation modes simultaneously. In this study, the performance comparison of pulse width modulation (PWM) based commutation modes are examined by considering 120°, 180°, 150° modes, and sinusoidal PWM. In this scope, it is the first time that direct digital synthesis (DDS) is addressed as a BLDC control algorithm in a performance comparison study. In experimental studies, a simple and a low-cost drive circuit is designed to acquire the case results. According to the results, the proposed DDS-based sine commutation method is more efficient than other commutation methods and it has lower power consumption at both low and high speeds also a wider operable speed range.

Stability analysis of hybrid synchronization controller based grid forming control

Synchronization units are vital for the stable operation of grid-tied converters in generation and transmission. There are two main synchronization units, i.e., phase-locked loop (PLL) and power synchronization controller (PSC), with the corresponding drawbacks in weak and strong grid respectively. Recently, the hybrid synchronization controller (HSC) is proposed in order to make up the deficiencies of PLL and PSC in weak and strong grid respectively. This paper deduces the detailed impedance characteristics of PSC and HSC and compares their cons and pros to show the stability mechanism of HSC. Moreover, two types of HSCs are also presented and compared in this article with slight difference in structure. It is found that the impedance of Type II based HSC has smaller amplitude and better phase margin than PSC around fundamental frequency, while Type I based HSC has larger amplitude and less phase margin than PSC. Type II based HSC can improve the stability and Type I based HSC can worsen the stability compared with PSC. Realtime experiments are carried out to verify this.

Evolution Law of Acoustic–Thermal Effect of Freeze–Thaw Sandstone Failure Based on Coupling of Multivariate Monitoring Information

The instability and failure of rock that has been frozen and thawed cause serious rock engineering accidents in cold regions. Exploring the precursor information of freeze–thaw rock failure is of great theoretical value and engineering significance. Real-time uniaxial compression acoustic thermal monitoring experiments were conducted on freeze–thaw sandstone, and non-contact rock fracture precursor warning indicators were proposed. According to the coupled analysis of acoustic–thermal monitoring information, a precursor information chain for freeze–thaw rocks was established in time and space, and the spatiotemporal evolution of damage and acoustic thermal effects of freeze–thaw sandstone under compressive load was studied. The freeze–thaw cycle enhances the sensitivity of acoustic–thermal precursor information. Significant synchronous changes in ring count often occur during the rapid expansion period of damage, which can provide an essential reference for the occurrence and intensification of damage. The sequence of precursor warning information during the process of freeze–thaw sandstone compression failure is in the order of thermal infrared temperature → acoustic emission ringing count → acoustic emission energy → infrared thermal image. Thermal infrared temperature and acoustic emission precursor information can help in prioritizing early warning of rock damage in terms of time. At the same time, thermal image anomalies can predict potential fracture areas of rocks.

Flexible transceiver for an access network: a multicarrier entropy loading approach

To cope with the extraordinary bandwidth demand, an optical access network based on point-to-multipoint (P2MP) topology has been widely applied. Recently, flexible transceivers have been investigated to maximize the optical power budget of optical network units for increasing the capacity of passive optical networks. In this work, we experimentally demonstrate a low-cost, high-bandwidth P2MP solution based on multicarrier modulations and probabilistic constellation shaping technology by reusing a 10G class directly modulated laser and avalanche photodetector for the first time to our knowledge. The proposed P2MP solution is capable of achieving fine granularity data rate adjustment from 12.5 Gb/s up to 63 Gb/s. We discuss the hardware implementation aspect of the proposed multicarrier entropy loading flexible transceiver. By means of a real-time transmission experiment, we show that the proposed P2MP solution can achieve a 29 dB power budget at a symmetric 53 Gb/s line rate, which makes it well positioned to support non-residential P2MP applications such as passive optical local area networks and fiber-to-the-office.

Gallic and ferulic acids suppress proteolytic activities and volatile trimethylamine production in the food-borne spoiler Rahnella aquatilis KM05.

Rahnella aquatilis is a recognized microbial threat that alters the sensory properties of seafood. The high frequency with which R. aquatilis is isolated from fish has prompted a search for alternative preservatives. In this work, in vitro and fish-based ecosystem (RS) approaches were used to validate the antimicrobial effects of gallic (GA) and ferulic (FA) acids against R. aquatilis KM05. The results were compared with data describing the response of KM05 to sodium benzoate (SB). Bioinformatics data of the whole genome was used to analyze the potential for fish spoilage by KM05 in detail, and the results revealed the main physiological characteristics that underlie reduced seafood quality. In the KM05 genome, the most abundantly enriched GO terms were 'metabolic process', 'organic substance metabolic process' and 'cellular process'. Through an evaluation of the Pfam annotations, 15 annotations were found to be directly involved in the proteolytic activity of KM05. Peptidase_M20 was the most abundantly represented (abundance value of 14 060). Proteins representing the CutC family (abundance value of 427) indicated the potential for KM05 degradation of TMAO. Subinhibitory concentrations of GA and FA suppressed the proteolytic activities of KM05 both in vitro and in RS medium by an average of 33-45%. These results were confirmed by RT-qPCR experiments, which also showed that the expression levels of genes involved in proteolytic activities and volatile TMA production were also decreased. Phenolic compounds can be used as potential food additives for preventing quality deterioration of fish products. This article is protected by copyright. All rights reserved.

The method of assessing and improving the user experience of subscribers in software-configured networks based on the use of machine learning

Evolutionary processes, which primarily affected computer technologies, led to the appearance of several types of computer networks, representing a set of computer devices combined into one system. The main purpose of such a system is user access to shared resources and the ability to exchange data between subscribers during work. Such networks are called software-configurable networks - SDN. SDN networks have long been the basis for building carrier-class telecommunication networks. However, they have a certain number of shortcomings that must be eliminated. The object of the study is the process of evaluating and improving the user experience of subscribers in software-configured networks. The subject of the study is a method of evaluating and improving the user experience of subscribers in software-configured networks based on the use of machine learning. The purpose of the work is to develop a model and a corresponding method for assessing the quality of the user experience of subscribers of SDN networks. As a result of the research, for the first time, a method of evaluating and improving the user experience of subscribers of SDN networks was developed based on the use of machine learning. The method consists in sequentially conducting an automated survey of users, measuring indicators of subscriber service quality, selecting and building a regression model from a set of defined models, and managing the user experience according to the measured parameters of SDN subscriber service quality. The developed method, in contrast to the known ones, makes it possible to improve the quality of the user experience in real time. Conclusions. The research of existing mechanisms for managing the user experience of subscribers and the analysis of regression models for the possibility of using them to establish the relationship between network parameters and user experience allowed to develop a generalized model for evaluating and improving the user experience of subscribers of SDN networks, based on the use of machine learning, and to develop an algorithm works of the method. The developed method makes it possible to build accurate models of the relationship of QoE and QoS parameters and increases the quality of the user experience of subscribers of SDN networks by up to 10%.

Calibration of central catadioptric cameras based on the projection properties between the right and oblique cones.

Central catadioptric cameras with a single effective viewpoint can easily transform the original image into a perspective image. In this study, we proposed a calibration algorithm for central catadioptric cameras. A vanishing line was recovered by the images of two sphere images (or line images) and mirror contour based on the properties of right and oblique cones. Subsequently, the intrinsic parameters were solved by the image of circular points and the orthogonal vanishing points. Finally, the distortion coefficient was estimated based on the geometric properties of the right cone. The simulation and real-time experiments conducted proved the effectiveness and feasibility of the proposed algorithm.

Simple method for detecting sleep episodes in rats ECoG using machine learning

In this paper we propose a new method for the automatic recognition of the state of behavioral sleep (BS) and waking state (WS) in freely moving rats using their electrocorticographic (ECoG) data. Three-channels ECoG signals were recorded from frontal left, frontal right and occipital right cortical areas. We employed a simple artificial neural network (ANN), in which the mean values and standard deviations of ECoG signals from two or three channels were used as inputs for the ANN. Results of wavelet-based recognition of BS/WS in the same data were used to train the ANN and evaluate correctness of our classifier. We tested different combinations of ECoG channels for detecting BS/WS.Our results showed that the accuracy of ANN classification did not depend on ECoG-channel. For any ECoG-channel, networks were trained on one rat and applied to another rat with an accuracy of at least 80 %. Itis important that we used a very simple network topology to achieve a relatively high accuracy of classification. Our classifier was based on a simple linear combination of input signals with some weights, and these weights could be replaced by the averaged weights of all trained ANNs without decreases in classification accuracy. In all, we introduce a new sleep recognition method that does not require additional network training. It is enough to know the coefficients and the equations suggested in this paper. The proposed method showed very fast performance and simple computations, therefore it could be used in real time experiments. It might be of high demand in preclinical studies in rodents that require vigilance control or monitoring of sleep-wake patterns.

Climate-responsive machine learning-based control of switchable glazing towards human-centric lighting

This work presents a climate-responsive control of switchable glazing using machine learning models. Here carried out experimental investigations in daylight artificial light integrated room on Polymer-dispersed liquid crystals (PDLCs) for collecting data towards modelling. A support vector machine learning classification algorithm is designed to model the transparency change in PDLC. A tunable LED luminaire is used to adjust the brightness and circadian effectiveness in the test room. The real-time implementation of the model gives the states of switchable glazing under various climate conditions. Real-time experimentation is carried out to verify the results. It is observed that the Interior light colour characteristics were satisfactory under different PDLC states. Correlated colour temperature (CCT) and circadian stimulus (CS) were similar to daylight. In the available literature, simulation results show PDLC as satisfactory for visual comfort. However, the experimental investigations and the prediction models give a range of sunlight on the window and solar altitude for which it satisfactorily works; outside this range, PDLC acts as a luminous source. The feasibility of PDLC in all window orientations is also simulated. The Simulink model can give the states of switchable glazing by optimising visual comfort, thermal comfort and energy effectiveness.

Development of an intelligent dual-beam gamma densitometer for real-time recognition of two-phase flow regime in horizontal pipe

This work includes the hardware and software development of a high sampling rate dual-beam gamma densitometer for real-time two-phase flow monitoring. The designed densitometer consists of two fast SiPM-based gamma detectors and a custom embedded system for digital signal processing and running flow regime recognition neural network. An exclusive LSTM deep neural network has been developed to classify flow regimes into six classes including stratified, annular, bubbly, wavy, slug, and plug. Therefore, the proposed design is superior to the traditional approaches by (i), unifying signal processing and flow identification units into a single cost-effective embedded system; (ii), reducing the need for time-consuming signal preprocessing steps from conventional to feed-forward neural networks, and (iii), real-time identification of the flow due to the high refresh rate of the data processing chain. The optimal neural network model has been obtained through comprehensive sensitivity analyses to have the best interpolation and generalization. The experimental evaluations showed that the optimized model recognizes the flow regime based on 6 s of gamma data sequence with an accuracy of ∼92%–97% depending on the flow regime in the pipeline. Furthermore, the real-time experiment proved the successful performance of the densitometer to trace the flow regime across the two-phase flow map.

Anisotropic diffusion filters for flow-dependent variational data assimilation of sea surface temperature

It is well known that the background error covariance matrix in the data assimilation is anisotropic and varied with the flow field, that is, flow-dependent. However, it is generally assumed to be isotropic and static for ease of implementation. Therefore, to enhance the quality of the background error covariance, two easy-to-implement, flow-dependent three-dimensional variational analysis (3DVar) schemes, the advection–diffusion filter (ADF) and the Smagorinsky diffusion filter (SDF), are tentatively proposed. These serve as alternative schemes for the traditional methods that are based on the isotropic assumption. First, a correctness test of the gradient for the ADF is performed to verify the validity of the adjoint code. Then, the response analysis for the 1D advection–diffusion​ equation is investigated to validate the anisotropic characteristic of the ADF with various flow velocities. Second, the two flow-dependent 3DVar schemes are verified via single-observation assimilation experiments. Compared to the existing diffusion filter with a static diffusion coefficient, both new schemes show a flow-dependent capability in the assimilation experiment because they successfully spread the observational information anisotropically. Finally, a retroactive real-time forecast experiment framework based on a 3D baroclinic primitive equation ocean model is used to investigate the effects of the ADF/SDF on the analysis and forecast accuracy of sea surface temperature (SST). While the traditional variational assimilation method with an isotropic diffusion filter generally reduces initial field errors, both the ADF and SDF can further enhance the accuracy of the initial fields in strong current areas. In addition, better forecast skills can be obtained with the two flow-dependent schemes in the East China Sea.

A Hybrid Islanding Detection Method Based on Lissajous Pattern Having Robust Performance Under Various Power Quality Scenarios

A fast, accurate, robust, and two-staged islanding detection technique (IDT) is proposed. It is the hybridization of second-order general integrator-frequency locked loop (SOGI-FLL), Lissajous pattern (LP), and active power absorption-cum-reactive power injection via dc-bus connected battery unit-based inverter. The LP is used to identify islanding condition on the basis of frequency variation of fundamental voltage. Here, the measured ac voltage signal is preprocessed by SOGI-FLL to obtain fundamental bus voltage under various power quality scenarios. This assures robust islanding detection by removing uncertainty, and thus, reliability is improved. The uncertainty effects due to the partial shading condition of the PV module (source-side disturbance) and weak grid-connected condition are avoided by dc-bus connected battery storage unit. Thus, it removes above-said limitations of the existing scheme. The LP-based detection within the nondetection zone is expedited due to active and reactive powers variation via controlling of the inverter and charging operation of the dc-bus connected battery unit. During the reactive power injection by solar inverter, active power is absorbed by dc-bus connected battery unit. In effect, an active power output of the inverter is reduced. Here, the reactive power injection at the reduced active power output condition of the inverter accelerates frequency variation allowing rapid islanding detection. The real-time experiments are carried out using Typhoon-HIL tools to compare the proposed method with other works reported in the literature. Improved performances under diversified scenarios are found in the proposed case.

A path to precision cosmology: synergy between four promising late-universe cosmological probes

In the next decades, it is necessary to forge new late-universe cosmological probes to precisely measure the Hubble constant and the equation of state of dark energy simultaneously. In this work, we show that the four novel late-universe cosmological probes, 21 cm intensity mapping (IM), fast radio burst (FRB), gravitational wave (GW) standard siren, and strong gravitational lensing (SGL), are expected to be forged into useful tools in solving the Hubble tension and exploring dark energy. We propose that the synergy of them is rather important in cosmology. We simulate the 21 cm IM, FRB, GW, and SGL data based on the hypothetical observations of the Hydrogen Intensity and Real-time Analysis eXperiment, the Square Kilometre Array, the Einstein Telescope, and the Large Synoptic Survey Telescope, respectively. We find that the four probes have different parameter dependencies in cosmological constraints, so any combination of them can break the degeneracies and thus significantly improve the constraint precision. The joint 21 cm IM+FRB+GW+SGL data can provide the constraint errors of σ(Ωm) = 0.0022 and σ(H 0) = 0.16 km s-1 Mpc-1 in the ΛCDM model, which meet the standard of precision cosmology, i.e., the constraint precision of parameters is better than 1%. In addition, the joint data give σ(w) = 0.020 in the wCDM model, and σ(w 0) = 0.066 and σ(wa ) = 0.25 in the w 0 wa CDM model, which are better than the constraints obtained by the CMB+BAO+SN data. We show that the synergy between the four late-universe cosmological probes has magnificent prospects.

Resolvin D1 (RvD1) Regulates Porphyromonas Gingivalis Lipopolysaccharide-induced Del-1 and Cytokine Expressions in Human Gingival Fibroblasts

Purpose: To detect the effect of Resolvin D1 (RvD1) on Developmental endothelial locus-1 (Del-1) and cytokine expressions of human gingival fibroblast cells exposed to Porphyromonas gingivalis lipopolysaccharide (P. gingivalis-LPS).
 
 Material and Methods: The effect of RvD1 on cell viability of human gingival fibroblasts exposed to P. gingivalis-LPS were determined by MTT (3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide) assay. Meanwhile, the effect of RvE1 on Del-1 and cytokine (IL-1β, IL-6, IL-8, IL-10, IL-17) expressions of human gingival fibroblasts exposed to P. gingivalis-LPS were studied by real-time PCR experiment, statistical analysis was performed using GraphPad Prism version 5 for Windows.
 
 Results: Cell viability assay results demonstrated that RvD1 concentrations upregulated cell number compared to control group at 24 and at 72 hours. While RvD1 reduced IL-6, IL-8, IL-17 mRNA expression, IL-10 and Del-1 mRNA expression increased as a time- and dose-dependent manner. Also, IL-1β was not affected by RvD1 treatments. 
 
 Conclusion: The increased expression of Del-1 and IL-10 by RvD1 down-regulated the pro-inflammatory cytokine expressions induced by P. gingivalis-LPS in gingival fibroblast. RvD1 displayed regulatory effects on gingival inflammation in P. gingivalis LPS-induced cell culture experiment. In particular, results of study display that Del-1 induced by RvD1 may have therapeutic potential to modulate periodontal inflammation.

Real-Time Interval Type-2 Fuzzy Control of an Unmanned Aerial Vehicle with Flexible Cable-Connected Payload

This study presents the design and real-time applications of an Interval Type-2 Fuzzy PID (IT2-FPID) control system on an unmanned aerial vehicle (UAV) with a flexible cable-connected payload in comparison to the PID and Type-1 Fuzzy PID (T1-FPID) counterparts. The IT2-FPID control has significant stability, disturbance rejection, and response time advantages. To prove and show these advantages, the DJI Tello, a commercial UAV, is used with a flexible cable-connected payload to test the robustness of PID, T1-FPID, and IT2-FPID controllers. First, the optimal coefficients of the compared controllers are found using the Big Bang–Big Crunch algorithm via the nonlinear UAV model without the payload. Second, once optimised, the controllers are tested using several scenarios, including disturbing the payload and the coverage path planning area to examine their robustness. Third, the controller performance results are evaluated according to reference achievement and point-based tracking under disturbances. Finally, the superiority of the IT2-FPID controller is shown via simulations and real-time experiments with a better overshoot, a faster settling time, and good properties of disturbance rejection compared with the PID and the T1-FPID controllers.

Concurrent experimentation in NCSLab: A scalable approach for online laboratories

The scheduling of user access to test rigs is vital for online laboratories, as a good access scheduling scheme can reduce waiting time and improve the utilization of experimentation systems. To enhance the benefits of online laboratories and also improve user experience, a novel system that is constructed based on front-end and back-end separation has been explored in this paper, which is scalable with a complex four-tier architecture supporting both remote and virtual experiments. A concurrent scheme that allows many users to conduct real-time interactive experiments with a single test rig has been implemented. The new system enables massive access for virtual experimentation and saves users from booking sessions in advance or queuing. To verify the effectiveness of the system, a renewable energy system has been developed, thus, users can concurrently conduct experiments with different control algorithms and monitoring interfaces for research and education purposes. A scalability test was conducted, the results of which showed that 94.67% of 300 users can concurrently access the same virtual laboratory, proving the scalability and concurrency features of the proposed system. The system demonstrated a throughput of 499.4 requests per second without errors during a 30-second performance test with 1600 concurrent users, where each user established 10 connections.