Rapid Prototyping System Research Articles

RSVP for VPSA : A Meta Design Study on Rapid Suggestive Visualization Prototyping for Visual Parameter Space Analysis.

Visual Parameter Space Analysis (VPSA) enables domain scientists to explore input-output relationships of computational models. Existing VPSA applications often feature multi-view visualizations designed by visualization experts for a specific scenario, making it hard for domain scientists to adapt them to their problems without professional help. We present RSVP, the Rapid Suggestive Visualization Prototyping system encoding VPSA knowledge to enable domain scientists to prototype custom visualization dashboards tailored to their specific needs. The system implements a task-oriented, multi-view visualization recommendation strategy over a visualization design space optimized for VPSA to guide users in meeting their analytical demands. We derived the VPSA knowledge implemented in the system by conducting an extensive meta design study over the body of work on VPSA. We show how this process can be used to perform a data and task abstraction, extract a common visualization design space, and derive a task-oriented VisRec strategy. User studies indicate that the system is user-friendly and can uncover novel insights.

Lure: A simulator for networks of batteryless intermittent nodes

The emerging paradigm of batteryless intermittent sensor networks (BISNs) presents new challenges for researchers of low-power wireless systems and protocols. The nature of these challenges exacerbates the difficulty of evaluating networks of physical sensor nodes, making simulation an even more important component in evaluating performance metrics, such as communication throughput and delay, for BISN designs. To our knowledge, existing simulators and analytical models do not meet the unique needs of BISN research; therefore, we have created a new open-source BISN simulator named Lure. Lure is designed from the ground-up for simulation of batteryless intermittent systems and networks. Written in Python, Lure is powerful, flexible, highly configurable, and supports rapid prototyping of new protocols, systems, and applications, with a low learning curve. In this paper, we present Lure and validate it with experimental data to show that Lure can accurately reflect the reality of BISNs. We then demonstrate the process of applying Lure to research questions in select case studies.

LExCI: A framework for reinforcement learning with embedded systems

Advances in artificial intelligence (AI) have led to its application in many areas of everyday life. In the context of control engineering, reinforcement learning (RL) represents a particularly promising approach as it is centred around the idea of allowing an agent to freely interact with its environment to find an optimal strategy. One of the challenges professionals face when training and deploying RL agents is that the latter often have to run on dedicated embedded devices. This could be to integrate them into an existing toolchain or to satisfy certain performance criteria like real-time constraints. Conventional RL libraries, however, cannot be easily utilised in conjunction with that kind of hardware. In this paper, we present a framework named LExCI, the Learning and Experiencing Cycle Interface, which bridges this gap and provides end-users with a free and open-source tool for training agents on embedded systems using the open-source library RLlib. Its operability is demonstrated with two state-of-the-art RL-algorithms and a rapid control prototyping system.

ANFIS-based improved GWO: Rapid prototyping of low-power solar energy system under fast-changing solar radiation conditions

The increasing need for energy, coupled with progress in technology, has emphasized the importance of renewable energy sources around the world. Solar energy systems harness the power of sunlight to generate electricity, offering a renewable and environmentally friendly alternative to conventional energy sources. Maximum Power Point Tracking (MPPT) algorithms are employed to achieve optimal efficiency from the PV systems. This paper introduces a new method for controlling Maximum Power Point (MPP) in battery charging systems using an Adaptive Neuro-Fuzzy Inference System (ANFIS)-based improved Grey Wolf Optimization (ANFIS-GWO) algorithm. The study involves the application and evaluation of the IGWO algorithm under fast-changing solar radiation conditions (1000 w/m2, 600 w/m2, 800 w/m2) and constant ambient temperature (25 °C). An analysis was performed to evaluate the behavior of the system in the laboratory environment using rapid prototyping. The proposed IGWO algorithm demonstrates improved accuracy compared to traditional methods in the current literature. The study aims to enhance the efficiency of MPPT control in photovoltaic (PV) systems, offering a potential solution for optimizing performance in different solar radiation conditions.

Efficient Reinforcement Learning for Real-Time Hardware-Based Energy System Experiments

In the context of urgent climate challenges and the pressing need for rapid technology development, Reinforcement Learning (RL) stands as a compelling data-driven method for controlling real-world physical systems. However, RL implementation often entails time-consuming and computationally intensive data collection and training processes, rendering them inefficient for real-time applications that lack non-real-time models. To address these limitations, real-time emulation techniques have emerged as valuable tools for the lab-scale rapid prototyping of intricate energy systems. While emulated systems offer a bridge between simulation and reality, they too face constraints, hindering comprehensive characterization, testing, and development. In this research, we construct a surrogate model using limited data from simulated systems, enabling an efficient and effective training process for a Double Deep Q-Network (DDQN) agent for future deployment. Our approach is illustrated through a hydropower application, demonstrating the practical impact of our approach on climate-related technology development.

Fuzzy Analytical Processes in Economic Justification of Rapid Prototyping Systems

Fuzzy Analytical Processes in Economic Justification of Rapid Prototyping Systems

DBTL bioengineering cycle for part characterization and refactoring

The Design-Build-Test-Learn (DBTL) cycle is a crucial framework in Synthetic Biology for the development and optimization of biological systems. However, the manual nature of the cycle poses limitations in terms of time and labor. This paper focuses on the application of automation techniques to the DBTL cycle, specifically in the testing and characterization of standard bioparts, which are essential components of genetic circuits. By automating the testing process, throughput, reliability, and reproducibility can be significantly improved. This paper discusses the challenges associated with manual testing methods and explores various automation strategies and technologies that can address these challenges. High-throughput screening methods, laboratory robotics, and data analysis algorithms are key elements in the automation process. As a case study, we utilize the automated DBTL cycle to refactor a biosensor, aiming to enhance its performance. Integrating automation in the DBTL cycle offers numerous advantages, including increased efficiency, standardization, and quality control of bioparts. It also enables the exploration of larger design spaces and rapid prototyping of complex genetic systems. Here, we show the advantages of using the DBTL cycle to refactor a biosensor that presents improved performance and can be readily used in more complex circuits.

Building Bridges: Universitas Multimedia Nusantara's Experience in Strengthening Academic Relationships through a Social Community Media Approach

Universitas Multimedia Nusantara has thousands of academicians and several academic or non-academic organizations. Some of these things allow the potential for increased social relationship connections between the academic community and the intensity of receiving and disseminating information, especially for students. Through a survey of students, there are examples of confirmed problems regarding the receipt and dissemination of information such as the lack of exposure of student work, the location of wall magazines that are difficult to find, information on student emails that are less relevant, time-consuming searches for coursework respondents, and the number of social media accounts of organizations that must be searched on different social media platforms to get information. Therefore, the design and development of a social community media website utilizing the Rapid Application Development method, a rapid prototype system development model with the stages of planning, design workshop, and implementation. The focus of the research is the backend system. The backend system design utilizes draw.io to create flowcharts and supabase schema for entity relationship diagrams, while the system development utilizes javascript with next js as the system development tool while system development utilizes javascript with next js as a framework and supabase as the system database. Backend media website backend of the social community media website was successfully designed and built and received User Acceptance Testing results with an acceptance rate of 88.08% for the perceive usefulness and 88.67% for the perceive satisfaction variable, which means that users strongly agree that the function of the social community media website system has fulfilled the two elements of the variable.

OpenFrame: A modular, sustainable, open microscopy platform with single-shot, dual-axis optical autofocus module providing high precision and long range of operation.

'openFrame' is a modular, low-cost, open-hardware microscopy platform that can be configured or adapted to most light microscopy techniques and is easily upgradeable or expandable to multiple modalities. The ability to freely mix and interchange both open-source and proprietary hardware components or software enables low-cost, yet research-grade instruments to be assembled and maintained. It also enables rapid prototyping of advanced or novel microscope systems. For long-term time-lapse image data acquisition, slide-scanning or high content analysis, we have developed a novel optical autofocus incorporating orthogonal cylindrical optics to provide robust single-shot closed-loop focus lock, which we have demonstrated to accommodate defocus up to ±37μm with <200nm accuracy, and a two-step autofocus mode which we have shown can operate with defocus up to ±68μm. We have used this to implement automated single molecule localisation microscopy (SMLM) in a relatively low-cost openFrame-based instrument using multimode diode lasers for excitation and cooled CMOS cameras.

3D free-assembly modular microfluidics inspired by movable type printing

Reconfigurable modular microfluidics presents an opportunity for flexibly constructing prototypes of advanced microfluidic systems. Nevertheless, the strategy of directly integrating modules cannot easily fulfill the requirements of common applications, e.g., the incorporation of materials with biochemical compatibility and optical transparency and the execution of small batch production of disposable chips for laboratory trials and initial tests. Here, we propose a manufacturing scheme inspired by the movable type printing technique to realize 3D free-assembly modular microfluidics. Double-layer 3D microfluidic structures can be produced by replicating the assembled molds. A library of modularized molds is presented for flow control, droplet generation and manipulation and cell trapping and coculture. In addition, a variety of modularized attachments, including valves, light sources and microscopic cameras, have been developed with the capability to be mounted onto chips on demand. Microfluidic systems, including those for concentration gradient generation, droplet-based microfluidics, cell trapping and drug screening, are demonstrated. This scheme enables rapid prototyping of microfluidic systems and construction of on-chip research platforms, with the intent of achieving high efficiency of proof-of-concept tests and small batch manufacturing.

A system for bioelectronic delivery of treatment directed toward wound healing

The development of wearable bioelectronic systems is a promising approach for optimal delivery of therapeutic treatments. These systems can provide continuous delivery of ions, charged biomolecules, and an electric field for various medical applications. However, rapid prototyping of wearable bioelectronic systems for controlled delivery of specific treatments with a scalable fabrication process is challenging. We present a wearable bioelectronic system comprised of a polydimethylsiloxane (PDMS) device cast in customizable 3D printed molds and a printed circuit board (PCB), which employs commercially available engineering components and tools throughout design and fabrication. The system, featuring solution-filled reservoirs, embedded electrodes, and hydrogel-filled capillary tubing, is assembled modularly. The PDMS and PCB both contain matching through-holes designed to hold metallic contact posts coated with silver epoxy, allowing for mechanical and electrical integration. This assembly scheme allows us to interchange subsystem components, such as various PCB designs and reservoir solutions. We present three PCB designs: a wired version and two battery-powered versions with and without onboard memory. The wired design uses an external voltage controller for device actuation. The battery-powered PCB design uses a microcontroller unit to enable pre-programmed applied voltages and deep sleep mode to prolong battery run time. Finally, the battery-powered PCB with onboard memory is developed to record delivered currents, which enables us to verify treatment dose delivered. To demonstrate the functionality of the platform, the devices are used to deliver H^+ in vivo using mouse models and fluoxetine ex vivo using a simulated wound environment. Immunohistochemistry staining shows an improvement of 35.86% in the M1/M2 ratio of H^+—treated wounds compared with control wounds, indicating the potential of the platform to improve wound healing.

Model-based closed-loop control strategies for flex-fuel capability

Meeting future limits for greenhouse gas (GHG) and pollutant emissions represents major challenges for the further development of internal combustion engines for light and heavy-duty applications. To comply with increasingly higher standards, the use of renewable fuels is an important option due to their great potential in terms of energy density, sustainability and low-pollution combustion. However, the increased fuel diversity associated with their use increases the complexity of the powertrain development and calibration process, resulting in an increase in development time and cost. To address these challenges, advanced model-based closed-loop control strategies are applied to optimize the fuel- and air-path settings and make best use of the properties of the different fuels. One such approach is Combustion Rate Shaping (CRS). This ensures a closed-loop control of combustion parameters such as the indicated mean effective pressure, the center of heat release, and the combustion noise excitation. In addition, a model-based closed-loop NOx control could simplify the control on the air-path side. Such a closed-loop approach can further be combined with an onboard fuel detection algorithm and learning functionalities that eventually allow switching to optimal setpoints based on the fuel properties. This publication presents an evaluation of CRS as control concept with flex-fuel operation. The control strategy is implemented on a demonstrator vehicle built up with a Rapid Control Prototyping (RCP) system. The results of emission test cycles with conventional diesel fuel and later with a blend with 1- Octanol, OME3–5 and a paraffinic renewable diesel fuel are presented to illustrate the potential of the novel control concept. The developed CRS concept shows a reduced combustion parameters and emissions dispersion. In particular, the results with OME3–5 blend shows up to 40% reduction in NOx emissions and up to 1 dB(A) lower average combustion noise in comparison to conventional open-loop control. Moreover, an onboard fuel detection and an adaptive control functionality are proposed and experimental validations are carried out to demonstrate the potential to optimize engine emissions.

Adaptive control concepts using radial basis functions and a Kalman filter for embedded control applications

Look-up tables or semi-physical models are used in engine control application to enhance the transient control performance. However, the calibration of these look-up tables and/or semi-physical models is highly data dependent, requires significant calibration effort, and does not account for effects such as hardware aging, drifts, fuel variations, etc. To address these challenges, this paper presents a generic adaptive control concept for real-time engine control applications. The boundary conditions for the operation on a state-of-the-art engine control unit (ECU) are considered and Radial Basis Function (RBF)-based neural networks are identified for the approximation of non-linear models. To ensure optimal convergence and un-susceptibility to signal noise, a Kalman filter (KF) is exploited for training the RBF network and a modified approximation is derived in this work. The derived KF approximation shows ∼30% better training performance in comparison to the approximation without variable noise consideration. The developed control concept is validated on an engine test bench and a demonstrator vehicle using a Rapid Control Prototyping (RCP) system. The potential of the developed control concept to ensure optimal control response and robustness has been demonstrated.

Sentiment analysis in SemEval: a review of sentiment identification approaches

&lt;div class="page" title="Page 1"&gt;&lt;div class="layoutArea"&gt;&lt;div class="column"&gt;&lt;div class="page" title="Page 1"&gt;&lt;div class="layoutArea"&gt;&lt;div class="column"&gt;&lt;p&gt;ocial media platforms are becoming the foundations of social interactions including messaging and opinion expression. In this regard, sentiment analysis techniques focus on providing solutions to ensure the retrieval and analysis of generated data including sentiments, emotions, and discussed topics. International competitions such as the International Workshop on Semantic Evaluation (SemEval) have attracted many researchers and practitioners with a special research interest in building sentiment analysis systems. In our work, we study top-ranking systems for each SemEval edition during the 2013-2021 period, a total of 658 teams participated in these editions with increasing interest over years. We analyze the proposed systems marking the evolution of research trends with a focus on the main components of sentiment analysis systems including data acquisition, preprocessing, and classification. Our study shows an active use of preprocessing techniques, an evolution of features engineering and word representation from lexicon-based approaches to word embeddings, and the dominance of neural networks and transformers over the classification phasefostering the use of ready-to-use models. Moreover, we provide researchers with insights based on experimented systems which will allow rapid prototyping of new systems and help practitioners build for future SemEval editions.&lt;/p&gt;&lt;/div&gt;&lt;/div&gt;&lt;/div&gt;&lt;/div&gt;&lt;/div&gt;&lt;/div&gt;

Advanced model-based closed-loop combustion control strategies with combustion rate shaping

Combustion rate shaping (CRS) is an advanced closed-loop concept which controls the crank-angle resolved combustion trace by adapting the fuel injection profile. The thermodynamic potential of this concept to enable real-time combustion control independent of fuel property variations, injector drift, cylinder tolerances, or changing air-path boundary conditions has already been demonstrated. The highest benefit of this control concept is achieved when it is directly applied on the engine control unit (ECU) in combination with an in-cylinder pressure measurement. This enables online optimization and adaption of the fuel- and air-path settings to the fuel used during vehicle operation. Therefore, the development of a CRS concept with real-time capability has been the main focus so far. To ensure a robust online real-time combustion control, a key development step was the identification of control variables that describe the entire combustion process and only minimally influence each other. For this purpose, the combustion phasing, the combustion gradient and the indicated mean effective pressure have been determined as parameters for the feedback control. However, the stringent requirements for higher efficiencies, emission robustness and combustion noise necessitate the extension of the CRS control concept. Therefore, in this work, the CRS control concept is extended with the aim of realizing a combustion with constant volume pressure increase to the peak firing pressure limit to achieve highest efficiency. In addition, intelligent control of the fuel injection profile is to be used to achieve a combustion temperature for lowest possible NOx raw emissions and a robust combustion noise excitation limitation. The optimized control strategy is implemented on a heavy-duty single cylinder engine test bench using a Rapid Control Prototyping (RCP) system, and the control performance is demonstrated.

Extremum Seeking as a Tool for Active Damping of Active Front-End Converters

Active damping control is necessary to mitigate the resonance issues associated with the grid-connected active front end converters equipped with LCL harmonic filters. Nonetheless, the performance and stability of active damping are highly susceptible to uncertainties due to the grid impedance variations and aging-related drift in LCL filter parameters that can lead to a mismatch between actual and modelled resonance point thereby causing deterioration in performance or even instability. This paper addresses this important practical issue by proposing a novel real-time optimization-based self-tuning algorithm to adaptively change the resonant frequency of active damping control as per variations in grid and filter conditions. The approach benefits from the concept of extremum seeking a framework to estimate actual resonant frequency. The approach is experimentally validated on a 2-level three-phase converter interfaced with a dSPACE rapid control prototyping system. The results demonstrate that the proposed approach is a straightforward and effective way of obtaining the performance and stability certainty of active damped AFE systems regardless of the grids they are connected to.

Real-Time FPGA Simulation of High-Voltage Silicon Carbide MOSFETs

This article presents a real-time field-programmable gate array (FPGA)-based dynamic model of high-voltage and high-current silicon carbide (SiC) metal-oxide-semiconductor field-effect transistor ( <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">mosfet</small> ) half-bridge power modules. The dynamic switching model utilizes the Shichman and Hodges equations using voltage-dependent nonlinear device capacitances and module electrical parameters to obtain an accurate dynamic model of the device switching transients. The key device states gate-source voltage, drain current, and drain-source voltage are modeled and discretized using forward Euler discrete integration method. Analysis of synthesizing the discrete-time model into real-time FPGA-based system with real-time data output from the on-board digital-to-analog converter is presented in detail. The model is utilized in modeling a 320-kW, medium-voltage dc/dc dual-active bridge converter and verified using dynamic experimental results from a 3.3-kV SiC <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">mosfet</small> half-bridge power module. It has been shown that the presented discrete-time dynamic switching model accurately describes the turn- <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">on</small> and turn- <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">off</small> switching transients of the SiC power module at various voltage and current levels. Such models are useful for rapid and cost effective design and prototyping of SiC-based power electronic systems by defining key design and operating parameters, such as deadtime, switching frequency, and switching losses.

DEID-Based Control of Networked Rapid Control Prototyping System: Design and Applications

This article presents a novel networked rapid control prototyping (NRCP) system, which can significantly facilitate the theoretical analysis and product design of networked control systems (NCSs). The NRCP system consists of a PC controller equipped with MATLAB/Simulink software and an embedded target designed with open-source hardware, e.g., Raspberry Pi and STM32. Therefore, it benefits from the following advantages, such as simple structure, low cost, convenient deployment, and robust scalability. In order to verify the effectiveness and superiority of the proposed NRCP, a networked magnetic levitation control system (NMLCS) is developed on the basis of this NRCP system. Furthermore, a discrete-time equivalent-input-disturbance (DEID)-based model predictive control approach is proposed for NMLCS to deal with the common practical issues in NCSs, such as time delay and disturbance. Specifically, the time delay is modeled as delay-induced disturbance (DID), whose influence is effectively estimated and eliminated by the designed controller. Several experimental case studies are performed to verify the effectiveness of the NRCP, the DEID-based control algorithm, and the analytical method of DID.

HILS-based Demonstrator for Real-time Validation of Robust Control Strategies: An Electric Vehicle Application Case

This paper presents a demonstrator featuring closed-loop coupling of a robust-control-designed Power Management System (PMS) with a simulator – in the form of a mathematical model – of a multiple-storage-based microgrid, playing the role of the plant. The PMS ensures a reliability-aware coordinated control of the different energy storages, of various technologies, supplying a typically irregular load. Some usual applications are in stationary or mobile microgrids, like power supply systems on board of electric vehicles, the latter being the use case exemplified here. Being designed upon the hardware-in-the loop simulation (HILS) principle, the demonstrator allows calibration and real-time validation of the robust PMS and its pertinent closed-loop real-time tests in laboratory conditions. The rapid prototyping system dSPACETM MicroAutoBox II is employed for embedding the microgrid model, whereas the robust PMS is embedded on a TI C2000 microcontroller unit. General design requirements and architecture of the demonstrator are detailed, then its customization to the considered application case is presented. Finally, a set of illustrative real-time tests – obtained by using a standard driving cycle – are presented and discussed.

Applying MATLAB to Radar Systems Modeling

The paper demonstrates the important role of using radar systems in many spheres of human activity to solve complex and important problems of society. The paper examines and analyzes the capabilities of the MATLAB program package applying to the design, modernization and research of radar systems. The analysis performed shows that the MATLAB software package is a powerful tool for modeling, researching and designing radar systems for various purposes. Also, attention is paid to the MATLAB Radar Toolbox, which includes a wide range of available models that can be modified. The Radar Toolbox provides rapid modeling, upgrading and prototyping of standard and upgraded radar systems. Using the MATLAB to research radar systems is impossible without understanding the principles of construction and operation of radar systems. It is also necessary to know the features of using the MATLAB function to describe, present and model structural elements and processes in radar systems. The paper presents the main functionality and possible options for implementing models in the MATLAB for modeling and studying radar systems. The given list of model options is not exhaustive and final and can be expanded and supplemented depending on specific tasks and requirements for implementation.