Prototype System Research Articles

ANÁLISE DE PROTÓTIPO VOLTADO A EFICIÊNCIA ENERGÉTICA APLICADA A TORRE DE RESFRIAMENTO INDUSTRIAL

Introduction: The integration of an energy control system into an industrial cooling tower, by combining this technological advance that current technology provides us, guaranteeing maximum efficiency and environmental protection. Objective: To analyze a prototype focused on energy efficiency applied to industrial cooling towers, with Arduino controlling an automation system aimed at this. Methodology: This study is a qualitative research with a case study type involving the development of a prototype of an energy control system applied to an industrial cooling tower and supporting literature with databases from engineering journals. Results: The results indicate that the system is viable for any industrial use, thus ensuring greater control and efficiency to the industry, being able to adapt to any environment and also adding to it any type of electrical machines for control. Conclusion: integration can contribute significantly to the development of innovative solutions in the field of automation and safety, ensuring greater control and efficiency in the supervision of industrial environments.

Starting electro-motor loads in battery-less off-grid photovoltaics using a super-capacitor based charge-pump

Starting Electromotor (EM) loads with off-grid photovoltaics (PV) is always challenging. Because their starting current makes the PV voltage fall, leading to converter instability. A practical solution is using batteries as a backup, but the repeating inrush current lowers the battery lifespan. This paper proposes an electrical charge pump based on a super-capacitor-bank (SCB) to fix the stability issue in off-grid battery-less photovoltaics. It contains an SCB connected to the DC-link of the inverter via a bi-directional DC/DC converter. The SCB is first pre-charged from the PV. Then at the moment of starting an electromotor load (EM), it supplies the DC-link capacitor with a pulse current. The main challenge here is to control the charging/discharging current of the SCB. In the proposed method, the system model is first developed by circuit analysis. Then a pre-defined charging pattern is obtained to charge the SCB from the zero to nominal voltage with a controlled current with no transient inrush. When injecting the pulse current as a charge pump (SCB discharging mode), constant duty-cycle switching for a pre-defined period is applied to ensure the converter stability in boost mode. To get the best results, a time shift is implemented between the pulse current injection and the load start-up, as a pre-charging phase. Experimental results from a 100 w system prototype running a 70 w universal motor show that a 2 Amp/200 ms pulse current, starting 50 ms before the load start-up, keeps the DC-link voltage deviation under 10 %, and no current transient is observed.

Renewable energy driven on-road wireless charging infrastructure for electric vehicles in smart cities: A prototype design and analysis

The adoption of electrical vehicles (EVs) has increased due to the increasing concerns about fossil fuel shortages, rising costs, and environmental impacts. This increasing adoption of EVs significantly increase power demand on utility grids. Moreover the EVs require more time for charging compared to fossil fuel vehicles. To address these challenges, this study proposes a renewable energy-based on-road wireless charging (ORWC) infrastructureto reducing charging time and utilizing renewable energy within a smart city framework. This study develops a prototype of the ORWC system and examines its techno-economic and environmental benefits. Moreover, the study also evaluates annualized energy share, energy economics, and cost-effectiveness. The results of the proposed study indicate that the ORWC system in combination with RERs, effectively reduces the cost of energy from $0.112/kWh to $0.30/kWh and lowers carbon emissions from 76.9 tons/year to 29.5 tons/year. Furthermore, the results also reveals optimal energy distribution among different energy resources PV, BESS, and the grid. These results highlight that ORWC infrastructure can reduce peak load demand on existing grids and enhance the power density of EV charging systems. This has potential implications for future-oriented fast charging stations and policy development to support the widespread adoption of EVs.

Planning and Evaluation of Water-Dropping Strategy for Fixed-Wing Fire Extinguisher Based on Multi-Resolution Modeling

The deployment of fixed-wing aircraft in fire-extinguishing operations represents a significant advancement in the domain of aviation emergency rescue. Addressing the challenge of enhancing firefighting efficacy, this study delves into the water-dropping strategies of fixed-wing extinguishers and provides a methodological framework for the strategic planning and assessment of water-dropping tactics, employing multi-resolution modeling. The formulation of the planning algorithm and the structure of the effectiveness evaluation index system are explained accordingly. The corresponding prototype system was designed, comprising four subsystems that utilized distinct resolution models: fire environment simulation, water-dropping point scheme planning, approaching path planning, and mission evaluation simulation. Case studies validate the system’s capability to forecast fire and smoke propagation, plan a water-dropping trajectory based on the fire line, optimize flight paths based on the trajectory, and simulate as well as evaluate the whole firefighting mission process. The above research comprehensively constructs the model, finishes the iterative optimization, and evaluates the water-dropping strategy by simulation. The technical path and methodological framework of studying water-dropping strategies are established. The outcomes of this study provide invaluable support for the parameter inversion design of the fixed-wing extinguisher, offering decision-making assistance to commanders and supplying training scenarios for new aviation crews.

EduLive: Re-Creating Cues for Instructor-Learners Interaction in Educational Live Streams with Learners' Transcript-Based Annotations

Educational live streaming has become a complement to in-person teaching. While synchronous instructor-learner communication is useful, the technology-mediated nature of live streaming can obscure many interaction cues (e.g., learners' facial expressions and body language), which dampens the instructors' ability to respond to remote learners' needs. We explore the opportunity of leveraging real-time transcripts generated from instructors' audio as a basis for re-creating interaction cues. Transcripts can be leveraged to reveal the content of live streams in a form that learners can trace back and annotate, and such annotations can be further aggregated and presented to instructors as signals to assist them in tracking learners' engagement. By designing and evaluating our proof-of-concept prototype system, EduLive, we show that instructors benefited from the summative information extracted from learners' annotations, and the context provided by the transcript enhanced their ability to answer learners' questions. Our system contributes to the design space of social annotations in CSCW by employing social annotations in educational live streaming scenarios.

From Function to Relation: Exploring the Dual Influences of Warmth and Competence on Generative Artificial Intelligence Services in the Hospitality Industry

Generative artificial intelligence (GenAI) has emerged as a transformative force in the rapidly-evolving hospitality industry. This study examined the correlation of acceptance of GenAI services with perceived warmth and competence, and the consequent correlations with customer outcomes in the hospitality industry. Employing a Hospitality Service Dialogue System prototype, this study collected responses from 1,306 hotel guests. Perceptions of warmth and competence correlated with increased customer intention to use GenAI services and satisfaction. Satisfaction correlated significantly with customer–brand identification and attachment. The Technology Readiness Index moderated the relationships of warmth and competence with the acceptance of GenAI services. This investigation extends the stereotype content model and the behaviors from intergroup affect and stereotypes map framework by integrating them with the technology acceptance literature, unveiling service innovations enabled by technology in the hospitality industry. This study provides hotel managers, technology developers, and marketers with strategic directions for implementing GenAI.

An automated AI-powered IoT algorithm with data processing and noise elimination for plant monitoring and actuating

This article aims to develop a novel Artificial Intelligence-powered Internet of Things (AI-powered IoT) system that can automatically monitor the conditions of the plant (crop) and apply the necessary action without human interaction. The system can remotely send a report on the plant conditions to the farmers through IoT, enabling them for tracking the healthiness of plants. Chili plant has been selected to test the proposed AI-powered IoT monitoring and actuating system as it is so sensitive to the soil moisture, weather changes and can be attacked by several types of diseases. The structure of the proposed system is passed through five main stages, namely, AI-powered IoT system design, prototype fabrication, signal and image processing, noise elimination and proposed system testing. The prototype for monitoring is equipped with multiple sensors, namely, soil moisture, carbon dioxide (CO2) detector, temperature, and camera sensors, which are utilized to continuously monitor the conditions of the plant. Several signal and image processing operations have been applied on the acquired sensors data to prepare them for further post-processing stage. In the post processing step, a new AI based noise elimination algorithm has been introduced to eliminate the noise in the images and take the right actions which are performed using actuators such as pumps, fans to make the necessary actions. The experimental results show that the prototype is functioning well with the proposed AI-powered IoT algorithm, where the water pump, exhausted fan and pesticide pump are actuated when the sensors detect a low moisture level, high CO2 concentration level, and video processing-based pests’ detection, respectively. The results also show that the algorithm is capable to detect the pests on the leaves with 75% successful rate.

Extracting doubly excited state lifetimes in helium directly in the time domain with attosecond noncollinear four-wave-mixing spectroscopy

The helium atom, with one nucleus and two electrons, is a prototypical system to study quantum many-body dynamics. Doubly excited states, or quantum states in which both electrons are excited by one photon, showcase electronic-correlation mediated effects. In this paper, the natural lifetimes of the doubly excited 1Po2snp Rydberg series and the 1Se2p2 dark state in helium in the 60–65 eV region are measured directly in the time domain with extreme-ultraviolet/near-infrared noncollinear attosecond four-wave-mixing (FWM) spectroscopy. The measured lifetimes agree with lifetimes deduced from spectral linewidths and theoretical predictions, and the roles of specific decay mechanisms are considered. While complex spectral line shapes in the form of Fano resonances are common in absorption spectroscopy of autoionizing states, the background-free and thus homodyned character of noncollinear FWM results exclusively in Lorentzian spectral features in the absence of strong-field effects. The onset of strong-field effects that would affect the extraction of accurate natural lifetimes in helium by FWM is determined to be approximately 0.3 Rabi cycles. This study provides a systematic understanding of the FWM parameters necessary to enable accurate lifetime extractions, which can be utilized in more complex quantum systems in the future. Published by the American Physical Society 2024

Overview and recent applications of the miniaturized HiPoLas ignition system

The requirements of the space industry for reusable, low cost or clustered engines have triggered high interest in a laser ignition system capable of multi-point ignition. The miniaturized SAL HiPoLas laser ignition system developed for single-point direct-plasma ignition has recently been enhanced towards a fiber coupling, multiplexing, and distribution system. A brief history of the HiPoLas development with an overview of the latest, highly miniaturized generation V ignition system is shown. Applications of the miniaturized ignition system, summarizing more than 10 years of development in the field of rocket engine laser ignition, as well as the first results of a recently developed prototype of a fiber distribution system, based on diffractive optical elements, are presented.

Adaptive Management of Multi-Scenario Projects in Cybersecurity: Models and Algorithms for Decision-Making

In recent years, cybersecurity management has increasingly required advanced methodologies capable of handling complex, evolving threat landscapes. Scenario network-based approaches have emerged as effective strategies for managing uncertainty and adaptability in cybersecurity projects. This article introduces a scenario network-based approach for managing cybersecurity projects, utilizing fuzzy linguistic models and a Takagi–Sugeno–Kanga fuzzy neural network. Drawing upon L. Zadeh’s theory of linguistic variables, the methodology integrates expert analysis, linguistic variables, and a continuous genetic algorithm to predict membership function parameters. Fuzzy production rules are employed for decision-making, while the Mamdani fuzzy inference algorithm enhances interpretability. This approach enables multi-scenario planning and adaptability across multi-stage cybersecurity projects. Preliminary results from a research prototype of an intelligent expert system—designed to analyze project stages and adaptively construct project trajectories—suggest the proposed approach is effective. In computational experiments, the use of fuzzy procedures resulted in an over 25% reduction in errors compared to traditional methods, particularly in adjusting project scenarios from pessimistic to baseline projections. While promising, this approach requires further testing across diverse cybersecurity contexts. Future studies will aim to refine scenario adaptation and optimize system response in high-risk project environments.

Development of a Data-driven Decision Support System for the Efficient Operation of the Research Reactor Secondary System

The High-flux Advanced Neutron Application Reactor (HANARO) is a research reactor in the Republic of Korea. The secondary cooling system of HANARO releases heat into the atmosphere via cooling towers and fans. Because a part of the system is exposed to the external atmosphere, the behavior of the secondary cooling system is coupled with atmospheric conditions. However, no procedure exists to reflect such coupling, and the cooling fans are empirically controlled by the operators. This empirical decision-making may increase the workload and reduce operational efficiency; therefore, the cooling fan operational schemes must be improved. In this study, a decision support system is developed based on two artificial intelligence (AI) models to aid operators’ decision-making in controlling the cooling fans. During model development, data augmentation was applied to alleviate data imbalances. Additionally, a graphical user interface-based decision support system prototype was developed based on the models to provide operators with more comprehensive summaries of the information related to cooling-fan operations and outputs from the AI models. The models demonstrated acceptable performance on the testing data. The prototype system was installed in the main control room of HANARO and evaluated for its usability improvements and feasibility in practical applications.

HF Trimer: A New Full-Dimensional Potential Energy Surface and Rigorous 12D Quantum Calculations of Vibrational States.

HF trimer, as the smallest and the lightest cyclic hydrogen-bonded (HB) cluster, has long been a favorite prototype system for spectroscopic and theoretical investigations of the structure, energetics, spectroscopy, and dynamics of hydrogen-bond networks. Recently, rigorous quantum 12D calculations of the coupled intra- and intermolecular vibrations of this fundamental HB trimer (J. Chem. Phys. 2023, 158, 234109) were performed, employing an older ab initio-based many-body potential energy surface (PES). While the theoretical results were found to be in reasonably good agreement with the available spectroscopic data, it was also evident that it is highly desirable to develop a more accurate 12D PES of HF trimer. Motivated by this, here we report a new, and the first fully ab initio 12D PES of this paradigmatic system. Approximately 42,540 geometries were sampled and calculated at the level of CCSD(T)-F12a/AVTZ. The permutationally invariant polynomial-neural network based Δ-machine learning approach (J. Phys. Chem. Lett. 2022, 13, 4729) was employed to perform cost-efficient calculations of the basis-set-superposition error (BSSE) correction. By strategically selecting data points, this approach facilitated the construction of a high-precision PES with BSSE correction, while requiring only a minimal number of BSSE value computations. The fitting error of the final PES is only 0.035 kcal/mol. To assess its performance, the 12D fully coupled quantum calculations of excited intra- and intermolecular vibrational states of HF trimer are carried out using the rigorous methodology developed by us earlier. The results are found to be in a significantly better agreement with the available spectroscopic data than those obtained with the previously existing semiempirical 12D PES.

The apparatus for atmospheric water harvesting in an arid climate - Prototype design and testing in laboratory conditions

The paper describes the development of a prototype system for water extraction from the air. The aim was to develop a device that allows one to autonomously obtain, without the need for external energy, an annual average of 100 L of water per day during extreme desert conditions. In this paper, a mathematical model simulating the operation of the unit for extracting water from the air in any climatic conditions is presented. Psychrometric calculations for different climatic conditions were carried out for two basic principles: condensation and sorption. The analyses confirmed that devices based on the condensation of water vapour from the air can only be used to a limited extent in extreme desert conditions. The average water production of a condensation-based system is only 20 l/day in Riyadh, with an air flow rate of 2000 m3/h. A unit with a desiccant wheel and an integrated heat pump was designed for water harvesting from the air. The prototype of the unit was tested in a climate chamber with the possibility of adjusting the climatic conditions and the presented mathematical model was experimentally verified. The final prototype designed for a nominal outdoor air flow rate of 2000 m3/h will produce 168 l of water per day under dry desert conditions (Riyadh) with continuous operation. The verification of the computational model allows one to determine the real water production and the required unit performance. An analyses of energy requirements and evaluation of levelized cost of water (LCOW) have been performed. Sorption unit has lower LCOW in target arid desert climate and electricity prices under 0.1 EUR/kWh compared to direct condensation technology.

Augmented Reality Control based Energy Management System for Residence

Introduction: The roots of virtual reality (VR) go back to the work of Ivan Sutherland in the 1960s. At the time, virtual reality showed promise in universities and labs, peaking in the early 1990s at a stable level. The latest form of IoT is merging with the Internet of Everything (IOE). Using this technology, users can access any device, device, and machine in the environment through any middleware application or system. In recent years, smart speakers have become a common commercial method for controlling household appliances, solving some of the problems of remote control. For example, speech recognition is difficult in noisy environments and is minimized using noise cancellation. It also takes a long time to determine the current state of the device as the user need to request or issue a command to the smart speaker and need to wait for a response. The evaluation of prototype system found, using a device with gestures and interacting with a virtual 3D device instead of a real device was acceptable in terms of usability. In this article, the Unity and Vuforia are used to create an AR-based IoT virtual switch that can control any device connected to a LAN controller via HTTP requests. Methods: The system is made experimental with the Augmented Reality (AR), the virtual communication system is designed instead of physical mode. It provide the real time based image for active control of Energy management. It consist of virtual switches, it supports the AR based power control system. Results: The proposed system are made evaluated and tested under various load conditions. The virtual image consist of virtual switches to make real time control of power equipment. The proposed system achieve the latency of about 0.06 sec. Conclusion: The advancement in day today activities required with AR based energy management system with proper control measures. The designed system is supportive for such mechanism. The further system is proposed to make more energy saving concept for enhancement in proposed work.

Identification of characteristics of conceptual prototype of microprocessor resource-saving relay protection system

The object of the study is the conceptual prototype of a microprocessor resource-saving relay protection system. Currently, relay protection ensures electrical networks reliable and efficient work, however, the traditional architecture is proprietary, not allowing to fix and replace damaged parts without a company specialist. Therefore, the open-architecture relay protection is a very pressing issue, but the problem lies in meeting the relay protection requirements. The data transmission protocols nRF and ESP-NOW, Hall sensors evaluation for AC current measurement determination and sensor accuracy improvement was implemented. Experimental validation demonstrated that nRF and ESP-NOW protocols meet the delay and reliability requirements, however, the nRF protocol is more suitable due to its flexibility and obstacle penetration. The data demonstrated that the most effective conditions are without obstacles at 15 meters from the modem and with obstacles 5 meters from the modem. The experiment of Hall sensors characteristics determination demonstrated the accuracy of current measurement with the set values of the opening and closing currents. Nevertheless, it is not accurate (12.45 %) for the relay protection application. Therefore, the application of the changing values of the opening and closing currents is more effective and accuracy reaches 6.92 %. As a result, the service life of the Hall sensor was determined, and even after 10 million openings, the open state time remained unchanged. Therefore, the approximation function for current amplitude determination depending on open state time was found. On the other hand, Hall sensors may suffer from temperature drift and require further optimization to be fully reliable. The study limitation is the current range from 0 to 800 A

Optimization and synthesis on the dynamics performance of the tensioning and relaxing wearable system in a novel knee exoskeleton using co-simulation

Abstract In this paper, a novel tensioning and relaxing wearable system is introduced to improve the wearing comfort and load-bearing capabilities of knee exoskeletons. The research prototype of the novel system, which features a distinctive overrunning clutch drive, is presented. Through co-simulation with ANSYS, MATLAB, and SOLIDWORKS software, a comprehensive multi-objective optimization is performed to enhance the dynamics performance of the prototype. Firstly, the wearing contact stiffness of the prototype and the mechanical parameters of the relevant materials are simulated and fitted based on the principle of functional equivalence. And then, its equivalent nonlinear circumferential stiffness model is obtained. Secondly, to enhance the wearing comfort of the exoskeleton, a novel comprehensive performance evaluation index, termed wearing comfort, is introduced. The index considers multiple factors such as the duration of vibration transition, the acceleration encountered during wear, and the average pressure applied. Finally, through the utilization of this indicator, the system’s dynamics performance is optimized via multi-platform co-simulation, and the simulation results validate the effectiveness of the research method and the proposed wearable comfort index. The theoretical basis for the subsequent research on the effectiveness of prototype weight-bearing is provided.

The Operando Nature of Isobutene Adsorbed in Zeolite H-SSZ-13 Unraveled by Machine Learning Potentials Beyond DFT Accuracy.

Unraveling the nature of adsorbed olefins in zeolites is crucial to understand numerous zeolite-catalyzed processes. A well-grounded theoretical description critically depends on both an accurate determination of the potential energy surface (PES) and a reliable account of entropic effects at operating conditions. Herein, we propose a transfer learning approach to perform random phase approximation (RPA) quality enhanced sampling molecular dynamics simulations, thereby approaching chemical accuracy on both the determination and exploration of the PES. The proposed methodology is used to investigate isobutene adsorption in H-SSZ-13 as prototypical system to estimate the relative stability of physisorbed olefins, carbenium ions and surface alkoxide species (SAS) in Brönsted-acidic zeolites. We show that the tert-butyl carbenium ion formation is highly endothermic and no entropic stabilization is observed compared to the physisorbed complex within H-SSZ-13. Hence, its predicted concentration and lifetime are negligible, making a direct experimental observation unlikely. Yet, it remains a shallow minimum on the free energy surface over the whole considered temperature range (273--873 K), being therefore a short-lived reaction intermediate rather than a transition state species.

The Operando Nature of Isobutene Adsorbed in Zeolite H‐SSZ‐13 Unraveled by Machine Learning Potentials Beyond DFT Accuracy

Unraveling the nature of adsorbed olefins in zeolites is crucial to understand numerous zeolite‐catalyzed processes. A well‐grounded theoretical description critically depends on both an accurate determination of the potential energy surface (PES) and a reliable account of entropic effects at operating conditions. Herein, we propose a transfer learning approach to perform random phase approximation (RPA) quality enhanced sampling molecular dynamics simulations, thereby approaching chemical accuracy on both the determination and exploration of the PES. The proposed methodology is used to investigate isobutene adsorption in H‐SSZ‐13 as prototypical system to estimate the relative stability of physisorbed olefins, carbenium ions and surface alkoxide species (SAS) in Brönsted‐acidic zeolites. We show that the tert‐butyl carbenium ion formation is highly endothermic and no entropic stabilization is observed compared to the physisorbed complex within H‐SSZ‐13. Hence, its predicted concentration and lifetime are negligible, making a direct experimental observation unlikely. Yet, it remains a shallow minimum on the free energy surface over the whole considered temperature range (273‐‐873 K), being therefore a short‐lived reaction intermediate rather than a transition state species.

Framework for Integrating Large Language Models with a Robotic Health Attendant for Adaptive Task Execution in Patient Care

The development of intelligent medical service robots for patient care presents significant challenges, particularly in integrating diverse knowledge sources and enabling robots to autonomously perform tasks in dynamic and unpredictable healthcare environments. This study introduces a novel framework that combines large language models with healthcare-specific knowledge and robotic operations to enhance autonomous task execution for a Robotic Health Attendant. Utilizing OpenAI’s ChatGPT, the system processes structured information about patient care protocols and unstructured human inputs to generate context-aware robot actions. A prototype system was tested in a simulated patient room where the robot successfully performed both simple individual actions and complex tasks involving the execution of multiple actions, based on real-time dialogues with the language model and predefined task specifications. The results demonstrate the potential of language models to reduce the reliance on hardcoded logic and provide healthcare professionals with the ability to interact with robotic systems through natural language.

Implementation of preamble based GFDM prototype for robust 5G systems

AbstractGeneralized frequency division multiplexing (GFDM) is a flexible block‐structured multi‐carrier scheme recently proposed for next‐generation wireless communication systems. There are various approaches suggested for its analysis and implementation via simulations but testing in real‐time environments is not heavily investigated. This paper carries out the real‐time implementation of the GFDM system utilizing software‐defined radio (SDR) by emphasizing mainly channel estimation and synchronization. Symbol timing, frequency offset, and channel estimate algorithms are applied using a windowed preamble with two identical halves to satisfy low egress noise requirements. Time and frequency estimation is evaluated in terms of residual offsets along with symbol error rate over frequency selective channels. This algorithm is extended to a preamble composed of multiple identical parts. This facilitates a large frequency estimation range at the cost of complexity. For practical validation of the above concepts, the National Instruments (NI) universal software radio peripheral (USRP) 2953R is employed as hardware and it is interfaced with LabVIEW.