Emulation System Research Articles

Convolution Neural Network-Assisted Smart Fluorescent-Tongue Based on Lanthanide Ion-Induced Forming MOF/HOF Composite for Differentiation of Flavor Compounds and Wine Identification.

Wine flavor is a vital quality characteristic in wine, influenced by those flavor components with low sensory thresholds. It is crucial to recognize and classify the wine components related to their flavor contribution. The integration of fluorescent sensors and artificial intelligence shows huge potential in flavor recognition by emulation of the gustatory perception system. Meanwhile, achieving information identification of wine based on multiple information barcodes has hopeful applications in anticounterfeiting. In this study, we present a simple method in which organic linkers are weaved into a hydrogen-bonded organic framework (HOF) for the available transformation of a metal-bonded organic framework (MOF) induced by lanthanide ions (Ln3+). The fluorescent Ln-MOF/HOF composite exhibits high sensitivity, rapid response, and good recyclability for detecting seven flavor compounds in wine, including tannic acid, ionone, vanillin, anethole, anisaldehyde, hydroxybenzaldehyde, and 4-hydroxy-2-methylacetophenone. Depending on its satisfactory detectability, a novel strategy is provided in which a fluorescent sensor is able to function as a smart fluorescent-tongue (F-tongue) by the aid of convolutional neural network to differentiate these seven flavor compounds. In addition, the Ln-MOF/HOF composite has been used to prepare multiple information barcodes for wine information identification on the basis of dynamic fluorescence response toward tannic acid. The mimetic gustatory perception system developed in this study may offer a promising strategy for flavor recognition in food and further food anticounterfeiting.

Feature extraction-reduction and machine learning for fault diagnosis in PV panels

With the rapid expansion and installation of Photovoltaic (PV) power plants, developing a proper Fault Detection and Diagnosis (FDD) strategy has become a significant issue within the objective to organize and plan for maintenance and reduce repair time, which in turn ensures the availability of operated systems. In this context, supervised Machine Learning (ML) algorithms are undergoing extensive development and their performances are analyzed in terms of different metrics including computation time detection accuracy, precision, recall, and F1-score in addition to False Alarm and missed detection rates. However, such algorithms generally require the extraction of significant features to handle irrelevant and redundant features while reducing the computational burden. Considering that different dimensionality reduction methods directly impact the FDD performances, this paper presents a novel Data Dimensionality Reduction Strategy (DDRS) based on a proposed algorithm for Information Gain (IG) score optimal threshold determination. This strategy also relies on Principal Component Analysis (PCA) to obtain the optimal subset which represents the lowest dimensional feature in the training and testing stages of different supervised ML algorithms. FDD performances are then evaluated by using faulty and non-faulty datasets acquired experimentally from a Grid-connected PV system (GPVS) emulator. The experimental results show the efficiency of the proposed strategy and its ability to improve the FDD based on supervised ML algorithms.

Highly Efficient Three-Phase Bi-Directional SiC DC–AC Inverter for Electric Vehicle Flywheel Emulator

Flywheels are nowadays a solution for the dynamic charging of electric vehicles since they act as transient energy storage. The need for a top efficient reversible power converter for the flywheel system is crucial to assure high dynamic performance. The paper presents the design of a 50 kW highly efficient reversible three-phase DC–AC inverter involving the most recent silicon carbide metal oxide semiconductor field effect transistors, and its experimental validation on a home-made emulator. Highest efficiency in reversible mode, compactness, and thermal enhancement are the targeted objectives that have been achieved. The power converter prototype evaluated on an original pulse width modulation testing-bench is able to emulate the working of the flywheel system. High frequency pulse width modulation switching, speed cycle operating, and thermal losses are evaluated. In addition, an efficiency above 99% for the converter has been attained, enabling robust functioning of the flywheel system emulator to perform specific charging profiles for electric vehicles.

Identification of high-power density source requirement for electric vehicle during on-road conditions

This paper describes various setups for testing electric vehicle (EV) on-road operating conditions. The significance of a high-power density energy source during EV acceleration and deceleration is highlighted in this work. The drive motor (DM) draws a large current during acceleration, the magnitude of which is determined by the acceleration time and vehicle parameters. Also, the source must capture all of the regenerative power from the DM during deceleration. The battery life will be reduced if it is used as an energy source for acceleration. Furthermore, due to its low power–density, the battery cannot quickly store regenerative power. To meet the aforementioned requirements, a high power–density source, such as an ultracapacitor (UC), is required. The simulation results as well as hardware results are shown for EV emulation systems.

Efficient Implementation of Spontaneous Calcium Oscillations in the Central Nervous System on Reconfigurable Digital Boards

Biological systems in case of real-time state and also large-scale simulation approach are interesting and challenge-based due to different aspects of nonlinear mathematical modeling that can describe the interactions of biological blocks. Thus, hardware circuit designing of these basic blocks in the Central Nervous System (CNS) can be an important field in case of achieving high performance neuromorphic system emulator. This paper presents a high-speed, low-cost, and efficient digital circuit for emulating the plausible calcium-dynamic-based model of astrocyte which has spontaneous oscillations. The nonlinear high-cost functions of the complex astrocyte model are reformulated using the power-2 based low-cost terms using optimized exhaustive search algorithm. Subsequently, the proposed model is simulated in case of validating the presented model and new optimized functions. Finally, the proposed model is physically realized in hardware case using Virtex 4 FPGA platform to test and validate final circuits. FPGA implementation results confirmed the ability of the design to emulate biological cell behaviours in detail with high accuracy. The proposed hardware consumes maximum <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$2\%$</tex-math> </inline-formula> of the all resources of a Virtex 4 board. Additionally, timing analysis and synthesize report represent that the proposed model works in a high frequency of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\rm 371.56$</tex-math> </inline-formula> MHz. Moreover, to validate the results of implementation, the proposed model is compared with the original model and other similar works in terms of accuracy, speed-up, and maximum number of implemented astrocyte.

Hyper-differential sensitivity analysis with respect to model discrepancy: Optimal solution updating

A common goal throughout science and engineering is to solve optimization problems constrained by computational models. However, in many cases a high-fidelity numerical emulation of systems cannot be optimized due to code complexity and computational costs which prohibit the use of intrusive and many query algorithms. Rather, lower-fidelity models are constructed to enable intrusive algorithms for large-scale optimization. As a result of the discrepancy between high and low-fidelity models, optimal solutions determined using low-fidelity models are frequently far from true optimality. In this article we introduce a novel approach that uses post-optimality sensitivities with respect to model discrepancy to update the optimization solution. Limited high-fidelity data is used to calibrate the model discrepancy in a Bayesian framework which in turn is propagated through post-optimality sensitivities of the low-fidelity optimization problem. Our formulation exploits structure in the post-optimality sensitivity operator to achieve computational scalability. Numerical results demonstrate how an optimal solution computed using a low-fidelity model may be significantly improved with limited evaluations of a high-fidelity model.

Accurate wind energy harvest assessment using a comprehensive ultra-large-wind-turbine emulation

A comprehensive 5 MW wind turbine emulator's design and implementation are designed and investigated thoroughly in this paper. A separately excited DC motor set under the control of a current-controlled drive forms the basis of the suggested emulation. The emulation algorithms that simulate the features of the 5 MW wind turbine are run on a digital signal processor (DSP). These algorithms regulate the DC motor's current, which in turn regulates the torque. The results obtained demonstrate that the system was successful in accurately simulating the transient and steady-state characteristics of the 5 MW wind turbine. The emulator system was initially tested and validated with simulation results using MATLAB/Simulink code attached to both the FAST and TurbSim models. Furthermore, step and stochastic wind profiles were used to experimentally verify and test it. Nearly all of the power that is available in the wind turbine system is successfully obtained using the maximum power point tracking technique, which is used to find the best operating point. The created wind turbine emulator can be used to precisely assess energy harvesting by examining various mechanical and electrical properties of the Permanent Magnet Synchronous Generator (PMSG) vastly used in ultra-large wind turbine nacelles.

Mesoscale Modeling of Distributed Water Systems Enables Policy Search

AbstractIt is widely acknowledged that distributed water systems (DWSs), which integrate distributed water supply and treatment with existing centralized infrastructure, can mitigate challenges to water security from extreme events, climate change, and aged infrastructure. However, it is unclear which are beneficial DWS configurations, i.e., where and at what scale to implement distributed water supply. We develop a mesoscale representation model that approximates DWSs with reduced backbone networks to enable efficient system emulation while preserving key physical realism. Moreover, system emulation allows us to build a multiobjective optimization model for computational policy search that addresses energy utilization and economic impacts. We demonstrate our models on a hypothetical DWS with distributed direct potable reuse (DPR) based on the City of Houston's water and wastewater infrastructure. The backbone DWS with greater than link and node reductions achieves satisfactory approximation of global flows and water pressures, to enable configuration optimization analysis. Results from the optimization model reveal case‐specific as well as general opportunities, constraints, and their interactions for DPR allocation. Implementing DPR can be beneficial in areas with high energy intensities of water distribution, considerable local water demands, and commensurate wastewater reuse capacities. The mesoscale modeling approach and the multiobjective optimization model developed in this study can serve as practical decision‐support tools for stakeholders to search for alternative DWS options in urban settings.

“Scan-(pre)Plan-Treat” Workflow for Bone Metastases Using the Ethos Therapy System: A Single-Center, In Silico Experience

PurposeTo report on the accuracy of automated delineation, treatment plan quality, and duration of an in-silico “scan-(pre)plan-treat” (SPT) workflow for vertebral bone metastases using a 1 × 8 Gy regimen. Method and MaterialsThe cloud-based emulator system of the Ethos therapy system was used to adapt an organ-at-risk-sparing preplan created on the diagnostic CT to the anatomy-of-the-day using the cone beam CT made before treatment. ResultsSPT using the Ethos emulator system resulted in relatively good coverage of the PTV and acceptable dose to the OAR. Delivery time and plan homogeneity was the best for 7-field IMRT plan template. ConclusionsA SPT workflow formula results in a highly conformal treatment delivery while maintaining an acceptable timeframe for the patient on the treatment couch.

INDUCTIVE COUPLER FOR BATTERY CHARGING SYSTEM OF HEAVY ELECTRIC VEHICLES

This paper proposes a robust fault-tolerant control (FTC) for the dual three-phase (DTP) induction machines under failures controlled by a higher-order sliding mode control strategy. However, the DTP induction machine is increasingly used because of better reliability and a supply division. A passive and an active FTC law have been designed and tested on DTP. The proposed method not only realizes the FTC and the fault elimination but also provides a possible solution for emulating a traction system using a direct continue machine (DCM) supplied by a four-quadrant chopper. Therefore, the emulation system is based on a controlled DCM, which imposes the same behavior of the mechanical power train of an electric vehicle to the DTP. Simulation results are given to verify the robustness and good performance of the proposed fault-tolerant control scheme.

NONLINEAR FAULT TOLERANT CONTROL OF DUAL THREE-PHASE INDUCTION MACHINES BASED ELECTRIC VEHICLES

This paper proposes a robust fault-tolerant control (FTC) for the dual three-phase (DTP) induction machines under failures and which is controlled by a higher-order sliding mode control strategy. However, the DTP induction machine is increasingly used because of its advantages such as better reliability and supply division, both passive and active FTC laws have been designed and tested on DTP. The proposed method not only realizes the FTC and the fault elimination as well but also provides a possible solution for emulating a traction system using a direct continue machine (DCM) supplied by a four-quadrant chopper. Therefore, the emulation system is based on a controlled DCM, which imposes the same behavior of the mechanical power train of an electric vehicle to the DTP. Simulation results are given to verify the robustness and good performance of the proposed fault-tolerant control scheme.

Efficient Federated DRL-Based Cooperative Caching for Mobile Edge Networks

Edge caching has been regarded as a promising technique for low-latency, high-rate data delivery in future networks, and there is an increasing interest to leverage Machine Learning (ML) for better content placement instead of traditional optimization-based methods due to its self-adaptive ability under complex environments. Despite many efforts on ML-based cooperative caching, there are still several key issues that need to be addressed, especially to reduce computation complexity and communication costs under the optimization of cache efficiency. To this end, in this paper, we propose an efficient cooperative caching (FDDL) framework to address the issues in mobile edge networks. Particularly, we propose a DRL-CA algorithm for cache admission, which extracts a boarder set of attributes from massive requests to improve the cache efficiency. Then, we present an lightweight eviction algorithm for fine-grained replacements of unpopular contents. Moreover, we present a Federated Learning-based parameter sharing mechanism to reduce the signaling overheads in collaborations. We implement an emulation system and evaluate the caching performance of the proposed FDDL. Emulation results show that the proposed FDDL can achieve a higher cache hit ratio and traffic offloading rate than several conventional caching policies and DRL-based caching algorithms, and effectively reduce communication costs and training time.

Agent-Based Decentralized Model Predictive Control for Plants With Multiple Identical Actuators

This article proposes a decentralized model predictive control (DMPC) algorithm without communication for systems consisting of multiple identical, independent actuators acting on a single central plant. The particular system design is relevant for applications where modularity is paramount and for highly dynamic systems, e.g., battery emulator systems controlled by multiple dc–dc converter modules. Each agent, consisting of an actuator and a DMPC, controls a virtually scaled version of the plant to implicitly consider the effects of other agents. The set of DMPCs achieves the same plant performance as a corresponding centralized model predictive controller (CMPC) in unconstrained operation. Also, the states of the independent agents converge toward the globally optimal CMPC solution. This is obtained by dividing the CMPC’s objective function into local objective functions related to the subsystems. The optimality and stability of the DMPC in unconstrained operation are shown analytically. The stability of control input-constrained operation is analyzed by computing the region of attraction. Numerical studies of a battery emulator system compare the performance of the DMPC with the global optimum in detail.

Wind turbine emulator control improvement using nonlinear PI controller for wind energy conversion system: Design and real‐time implementation

SummaryWind turbine emulators (WTE) have become a necessity for testing, developing, and improving design and control strategies in the renewable energy domain. The aim of this paper is to realize an experimental standalone wind energy conversion system emulator (WECSE) with improved torque and current control strategy using a nonlinear PI controller. The prototype was developed with a separately excited DC motor to simulate the wind turbine by providing the required speed and torque for power generation using a directly driven wound‐rotor synchronous generator and a power conversion system controlled by a modified drift‐free Perturb and Observe (P&amp;O) Maximum Power Point Tracking algorithm (MPPT). The DC motor torque is controlled by an nonlinear PI controller regulator for an estimated current reference through a chopper driven by a dSPACE control board where the MATLAB/Simulink platform is used for wind turbine simulation. The proposed method was validated by experimental tests for different wind speeds and was compared with the conventional method. The experimental results have demonstrated that the control, emulation, and MPPT performances of the proposed method are significantly better.

Flexible and Low-Cost Emulation of Control Behaviors for Testing and Teaching of AC Microgrid

The fast development of distributed generations enables the microgrid a popular solution for the construction of the modern power grid, where the control behaviors of power electronics converters play a crucial role. Under this scenario, the emulation of microgrid control behaviors is becoming an emerging need for the testing and teaching of the AC microgrid. However, conventional approaches, such as the dynamic simulation test and the Power-Hardware-In-Loop, are still costly or bulky to flexibly recreate the correct characteristics of microgrid including different layers of controls and the interactions among multiple converters. The dynamic simulation test is bulky and costly to emulate various types of control behaviors since all physical components in the test system may need to be adjusted. The high cost of Power-Hardware-In-Loop is mainly caused by the high-performance real-time simulator and power amplifier. In this paper, a novel emulation system is proposed for the testing of the AC microgrid. A low-cost circuit configuration, which includes two face-to-face connected DC–AC converters and some passive loads, is introduced with the possibility of flexibly emulating most of the typical control schemes in an AC microgrid. In addition, a user interface for the real-time operation and measurement of the hardware platform is introduced on a host computer to further facilitate the testing process. Finally, various control schemes in microgrids, including the voltage control, current control, droop control, and secondary control, are validated in the experiment setup based on the proposed emulation approach.

Familiar and unfamiliar speaker recognition assessment and system emulation for cochlear implant users.

In the area of speech processing, human speaker identification under naturalistic environments is a challenging task, especially for hearing-impaired individuals with cochlear implants (CIs) or hearing aids (HAs). Motivated by the fact that electrodograms reflect direct CI stimulation of input audio, this study proposes a speaker identification (ID) investigation using two-dimensional electrodograms constructed from the responses of a CI auditory system to emulate CI speaker ID capabilities. Features are extracted from electrodograms through an identity vector (i-vector) framework to train and generate identity models for each speaker using a Gaussian mixture model-universal background model followed by probabilistic linear discriminant analysis. To validate the proposed system, perceptual speaker ID for 20 normal hearing (NH) and seven CI listeners was evaluated with a total of 41 different speakers and compared with the scores from the proposed system. A one-way analysis of variance showed that the proposed system can reliably predict the speaker ID capability of CI (F[1,10] = 0.18, p = 0.68) and NH (F[1,20] = 0, p = 0.98) listeners in naturalistic environments. The impact of speaker familiarity is also addressed, and the results show a reduced performance for speaker recognition by CI subjects using their CI processor, highlighting limitations of current speech processing strategies used in CIs/HAs.

BQ-Bank: A Quantum Software for Finance and Banking

The power of quantum computing may bring a revolution in finance and banking. Here, we present quantum software, BQ-Bank for option pricing, Value at Risk, portfolio optimization, and others. BQ-Bank can be run on a real quantum computer such as superconducting system or on an emulation system based on a classical computer with an interface. BQ-Bank, such as other quantum types of software, represents a new generation of the toolbox that likely brings disruptive innovations to the financial industry and banking market in the future. BQ-Bank also provides the classical Monte Carlo solution, so that users can compare their quantum results with classical ones directly. Our simulation results for a variety of examples show the superiority of quantum solutions.

Modeling and Analysis of the Active Surface System for the Large Single-Dish Sub-mm Telescope

Enlarging antenna diameter while maintaining an accurate reflective surface is important in the development of next-generation single-dish sub-mm telescopes. In this article, we propose an emulation and optimization method of the active surface system (EOMASS) for large single-dish sub-mm telescopes. The proposed method involves three main steps: 1) building an active surface system model (ASSM) based on the whole finite element model (FEM) of the large single-dish sub-mm telescope, which includes the main reflector, realistic adjustment models of actuators, backup structure and so on; 2) emulating the reflective surface deformation compensation under gravity based on the initial distribution of the actuators with the ASSM, in consideration of the manufacturing error of panels; and 3) further optimizing the number and distribution of actuators based on the illumination function, and predicting the performance of the sub-mm antenna due to the limited number of faulty actuators. As an application example, a 60 m single-dish sub-mm telescope is analyzed under gravity with the proposed EOMASS. The results demonstrate that the proposed EOMASS is an effective and advanced method, providing a feasible precision design of the active surface system for a large single-dish sub-mm telescope.

Early surge detection in a mGT plant coupled with large volumes

The present work features post-processing methods applied to vibro-acoustic data acquired from a T100 micro gas turbine (mGT) plant coupled with different volume interposed plenums. Such experimental campaign was conducted by relying on a test bench developed at the University of Genoa for hybrid systems emulation. Nonetheless, the obtained results can be generalized to all advanced cycles in which a mGT is coupled with further external elements which cause an increase of plant overall volume size. Since in this case a 100 kW mGT was employed, the interposed vessel was placed between heat recovery system outlet and combustor inlet, such as in common cases relevant to small size plants.Post-processing techniques carried out on acoustic and vibrational measurements can make available innovative diagnostic tools and predictive solutions by relying on appropriate instability indicators which are defined basing only on microphone and accelerometer experimental data.The main results presented in this work are relevant to rotating stall and incipient surge proper identification. Such investigation has been performed to increase the knowledge about such dangerous compressor working conditions; indeed, energy systems characterized by significant interposed volumes coupled with centrifugal compressors feature issues relevant to structural damaging due to surge and rotating stall.

Situational interest of students with autism spectrum disorder using context personalization in physical education

Physical education can enable students with autism spectrum disorder (ASD) to live positive experiences and adopt a healthy lifestyle. However, motivation may be a critical factor in their success. In this perspective, the context personalization approach could be valuable to support the interests and motivation of students with ASD in physical education. This qualitative interpretative research documents the Situational Interest dimensions of four high school male students with ASD (14 years old, boys) following a team sports unit conceived and personalized by their physical education teacher to meet their interest in video games. After the unit, the four students and the teacher realized a semi-structured interview. According to thematic analyses, students&#8217; situational interest was elicited. The dimensions of instant enjoyment, challenge and novelty were predominant. The emulation system, as a part of the context personalization related to video games, ends up in every dimension and sheds original light on the motivation conditions. In light of the present results, the context personalization approach, based on the interests and preferences of students, is a promising avenue to support interest in PE. However, differences between the students&#8217; and the teachers&#8217; perspectives underline the need to consider students&#8217; voices regarding their interest in physical education.