Real-time Control Capabilities Research Articles

Liquid microlens compound structure for enhanced sub-micron resolution imaging and accelerated reconfigurable deformation manipulation

The optical observation of sub-micron structures encounters significant challenges due to the inadequate spatial shape matching of optical devices and the limitations imposed by diffraction. These factors result in suboptimal imaging resolution and the introduction of defocus distortion. One approach to mitigating these limitations involves utilizing a liquid microlens (LML) assisted microscope, which offers real-time and localized control capabilities. However, the dynamic tuning of LML is subject to volatility and instability, adversely affecting sub-micron resolution imaging. To address this issue, a contour-following coating inspired by the natural cornea is applied to the droplet's surface, resulting in the formation of liquid microlens compound structures (LMLCS). Firstly, photoresist microholes are fabricated on the optical disc. Then, liquid self-assembly technology is used to fill glycerol in the microholes. At last, a conformal spreading method of UV-curable adhesive precursor film is developed to ensure the combination of the microlens bilayer structure. Through the application of optical information transmission theory, finite difference time domain (FDTD) method, and the principle of Abbe microscopic imaging, the imaging magnification process and sub-micron resolution characteristic of the LMLCS are revealed. Remarkably, the obtained focal width at half maximum (FWHM) of 1.54 μm is smaller than the theoretical value, enabling reconfigurable sub-micron resolution observation and 1.63 times imaging magnification of 226 nm grating lines under the optical microscope. Compared with unencapsulated LML, this compound structure exhibits better sub-micron resolution capability, greater imaging magnification, and faster adaptive dynamic response characteristics. Consequently, the LMLCS introduces exciting prospects for exploring optical sub-micron measurement and sensing devices with exceptional performance.

Thermoelectric-Feedback Nanocomposite Hydrogel for Temperature-Synchronized Monitoring and Regulation in Accurate Photothermal Therapy.

Photothermal therapy (PTT) is a promising approach for tumor ablation and cancer treatment. However, controlling the therapeutic temperature during treatment remains challenging, and imprecise thermal regulation can harm adjacent healthy tissues, reduce therapeutic accuracy, and promote the thermotolerance of cellular phenotypes, potentially leading to tumor invasion and recurrence. Although existing methods provide basic temperature control by adjusting irradiation power and photothermal agent dosing, they lack real-time temperature monitoring and feedback control capabilities, underscoring the urgent need for more integrated and precise PTT systems. In this context, we developed an innovative photothermoelectric (PTE) cobalt-infused chitosan (CS) nanocomposite hydrogel (PTE-Co@CS) for precise temperature-regulated PTT, exhibiting desirable mechanical properties and exceptional biocompatibility. Enhanced by embedded nanoparticles, PTE-Co@CS demonstrates superior photothermal conversion efficiency compared with existing methods, while also featuring thermoelectric responsiveness and increased sensitivity to photostimuli. Its advantageous PTE response characteristics ensure a linear correlation between temperature shifts and resistance changes (e.g., R2 = 0.99053 at 0.5W/cm2), enabling synchronized qualitative and quantitative control of PTT temperature through electrical signal monitoring. This allows for real-time monitoring and regulation during PTT, effectively addressing the issue of uncontrollable temperatures and improving therapeutic efficacy. This article is protected by copyright. All rights reserved.

Next-Gen Medical Technology a Smart Approach to Syringe Injection Pump Automation

This presents the design, implementation, and successful validation of a cutting-edge IoT-based Syringe Injection Pump (SIP) for precision medication administration. In response to the increasing demand for enhanced healthcare automation, this system leverages technologies to address critical challenges in medication delivery. This SIP incorporates a robust Internet of Things (IoT) framework, enabling seamless communication between the device and a central healthcare management system. The automated syringe injection process is governed by a sophisticated algorithm that considers patient-specific parameters, ensuring precise dosage administration. Through extensive testing and real-world simulations, the results demonstrate a significant reduction in human error and improved medication adherence. The system’s architecture integrates secure communication protocols, ensuring data confidentiality and integrity during transmission. An innovative user interface provides healthcare professionals with real-time monitoring and control capabilities, enhancing situational awareness and responsiveness. Furthermore, the SIP’s adaptability to diverse medical environments underscores its versatility in meeting the evolving needs of healthcare institutions. To validate the system’s effectiveness, a comprehensive performance evaluations, including dosage accuracy, response time, and reliability under varying conditions was conducted. Results indicate an impressive dosage precision of 99.8%, showcasing the system’s reliability in critical healthcare scenarios. The IoT connectivity facilitates remote monitoring and control, allowing healthcare providers to intervene promptly in case of anomalies or emergencies. In addition to its technical prowess, the SIP aligns with current healthcare trends by promoting sustainability and cost-effectiveness. The automated system optimizes resource utilization, reduces medication waste, and minimizes the environmental impact associated with traditional manual injection methods. In conclusion, this IoT-based Syringe Injection Pump represents a paradigm shift in medical technology, offering a smart and results-oriented solution for precise medication administration. The successful implementation and validation of this system pave the way for improved patient outcomes, streamlined healthcare workflows, and a more sustainable approach to medication delivery. This research contributes to the ongoing discourse on advancing medical technology and underscores the potential of IoT in shaping the future of healthcare.

GAZE DIRECTION MONITORING MODEL IN COMPUTER SYSTEM FOR ACADEMIC PERFORMANCE ASSESSMENT

This paper focuses on the research and application of eye movement and gaze direction analysis in online testing systems. The practical novelty of the proposed model of monitoring the direction of gaze in a computer-based knowledge control system lies in the possibility of automated remote control over a large audience of students. The practical significance lies in creating the same conditions for computer testing for all students and increasing the correspondence of knowledge level to the received test results. The implemented and tested system is relevant and necessary in higher educational institutions, particularly in Ukraine, where remote education has emerged as the safest means of acquiring knowledge. This is especially true for fields of study where practical tasks and laboratory work do not necessitate a student's physical presence at the institution. That is why the application of the latest information technologies is extremely important. The dynamic authentication based on the sequence of eye movements proposed in the model allows error-free user's eye area detection for further analysis of the test subject's behavior. The proposed authentication method excludes the need to enter passwords or CAPTCHAs, ensures the speed of determining the user's presence. Further analysis of the user's direction of vision includes responding to the information received, such as skipping a question or the need for re-authorization. Question skipping occurs when the system decides that the user has not been looking at the screen for an extended period (looking sideways, down, up for more than 30 seconds). Re-authentication becomes necessary if the user exits the test or if there is a user replacement. Real-time gaze control capability is implemented by using massive parallel processing system (NVIDIA GeForce GTX 1650 graphics card) for calculations. The analysis of the results obtained shows that the proposed approach allows gaze detection at different illumination ranges (from 0.3 lux to 10,000 lux), as well as to detect states of the user's eyes that violating test rules for further system response (skipping a question or re-authentication request).

Fuzzy-based predictive deep reinforcement learning for robust and constrained optimal control of industrial solar thermal plants

Integrating distributed solar fields (DSFs) into conventional heat and power plants (CHPs) of industries is mostly constrained by the availability of a real-time capable control scheme. Safe and efficient operation of industrial DSF requires the supply of a fluctuating and periodically available energy at the required temperature while reducing losses and ensuring operational constraint. Reinforcement learning (RL), specifically Q-learning methods, are being recently applied for such control tasks. However, existing RL approaches cannot be directly applied to continuous domain control tasks or tasks with operation constraints. While using deep learning in model-free RL can remove these limitations, tractability and scalability boundaries on performing the deep learning are becoming significant, with scarce data and multi-objective concerns. In contrast, model-based deep RL schemes can be applied for sample efficiency and satisfying operational constraints, but their modeling framework is not general and accurate. To address these challenges, the work here develops a hybrid fuzzy convolution model (HFCM) that takes full advantages of data, models (dynamic and steady-state), and prior knowledge on industrial DSF. The HFCM is then extended for use in deep deterministic policy gradient (DDPG) algorithm to learn the DSF control task. This is done so by solving a multi-objective optimization problem, which is formulated as a constrained Markov decision process (CMDP) with continuous state and actions. Some practical and relevant findings were made with this HFCM. Firstly, it allowed the DDPG agent to learn independently a temperature state and a disturbance state, using only temperature measurement and single realizations of state vector. It also permitted the testing of an operational state violation strategy on a DDPG actions, and at the same time, the correction of the effects at a safety layer, if needed. Furthermore, the proposed control strategy simultaneously reduced mean temperature tracking error by 24%–51% and energy gain by 13.9%–17.35% when compared respectively to model-free DDPG and MPC baselines.

Developing a digital twin for a laboratory ball mill operation – a step towards mining metaverse

Digital twins (DTs) are transforming business operations across industries through accurate replication of physical entities using the Internet of Things and big data analytics. Despite booming progress in the manufacturing, aerospace and buildings sectors, the adoption of DTs in the minerals industry has been slow, and integration with efficient visualisation and user interactions has not been fully optimised to achieve maximum fidelity and usability. One promising avenue for enhancing DT capabilities is the utilisation of extended reality (XR) technologies, which also hold great potential for realising an industrial metaverse where real-world business activities can be conducted in a virtual space. This article proposes a cost-effective and scalable approach to developing a DT with real-time monitoring and control capabilities for a ball mill operation, a widely used processing equipment in the minerals industry. The case study showcases two approaches with different levels of system integration by leveraging serious game development platforms, toolkits and workflows.

Cloud Computing Based Data Processing and Automated Management of Power Dispatch

Abstract As smart grids increase, the scope of power dispatch systems has broadened, encompassing not just traditional dispatch services but also multifaceted information services, encompassing data fusion, heterogeneous system integration, and big data analytics. However, managing intricate information businesses like big data analysis poses numerous challenges for the current power dispatch system, hindering its ability to keep up with escalating business demands. We introduce a cloud computing and big data analytics-based design scheme for a power dispatch data processing and automation management system to address this. Leveraging the distributed computing prowess of cloud computing platforms, the system ensures swift processing and analysis of vast power dispatch data. Additionally, by harnessing big data analysis techniques, the system delves into historical data, uncovers potential operational patterns and risk areas, and offers insights for optimizing the Electric Power System’s (EPS) operations. Experimental outcomes demonstrate the system’s remarkable proficiency in enhancing data processing, real-time control, and automation management capabilities, thereby bolstering the growth of intelligent power dispatch systems with robust technical backing.

Smart Building Control: An Android Application for Enhanced Monitoring and Management in the Internet of Things Era

In the dynamic landscape of technology, the intersection of Smart Buildings and Internet of Things (IoT) devices continues to hold substantial promise. The integration of billions of smart devices with computing systems empowers the identification, analysis, and influence of various actions in our daily lives. A prevalent approach in IoT implementation involves leveraging Android systems, where Android applications play a pivotal role in front-end management. This paper introduces an innovative Android application designed for the comprehensive management of smart buildings, providing users with real-time data monitoring and control capabilities. The application serves as a bridge, bringing users closer to the operational intricacies of smart building systems. Notably, the application is equipped to send timely alerts when predefined limits for humidity, temperature, or light consumption are surpassed, as well as during specific time periods with detected motion. These alerts prompt users to make informed adjustments and decisions, enhancing overall user engagement and responsiveness. The proposed system architecture comprises five distinct layers: Python scripts for sensor data generation, JSON files for data storage, a Flask-based Application Programming Interface (API) for data analysis and conversion, SQLite for efficient data storage, and the Android application for user interaction. This multi-layered framework ensures seamless integration and robust functionality throughout the smart building management process.

Reinforcement learning to achieve real-time control of triple inverted pendulum

This work uses reinforcement learning (RL) to achieve the first-ever data-driven real-time control of an actual, not simulated, triple inverted pendulum (TIP) in a model-free way. A swing-up control task for the TIP is formulated as a Markov decision process with a dense reward function, then conducted in real time by using a model-free RL approach. To increase the sample efficiency of learning, a structure-aware virtual experience replay (VER) method is proposed; it works together with an off-policy actor-critic algorithm. The VER exploits the geometrically-symmetric property of TIPs to create virtual sample trajectories from measured ones, then uses the resulting multifold augmented dataset to effectively train actor and critic networks during the learning process. These structure-infused training data serve to obtain additional information and hence increase the convergence speed of network learning. We combine the proposed VER with a state-of-the-art actor-critic algorithm, and then validate its effectiveness through numerical simulations. Notably, the inclusion of VER amplifies computational efficiency, slashing the requisite trials, training steps, and overall duration by approximately 66.67%. Finally experiments demonstrate the real-time control capability of the proposed approach on an actual TIP system.

LSTM-based energy management algorithm for a vehicle power-split hybrid powertrain

Recurrent neural networks (RNNs) have been used for vehicle speed prediction, trajectory prediction and state diagnosis. As a variant of RNNs, long short-term memory (LSTM) has better state memory ability to deal with problems that have sequential characteristics. In this study, a neural network with two substructures was developed and LSTM was used as the core part for a power-split hybrid powertrain energy management problem, and the optimal control data obtained from the dynamic programming (DP) algorithm was used as the training set. Then on a vehicle model established on coasting-down test data, LSTM conducted real-time control. This paper analyzes the battery status, fuel consumption, carbon dioxide emissions and operating condition of the engine and motor. The results showed that LSTM has good generalization ability and real-time control capability. In the test driving cycles of CHTC-LT and C-WTVC, the theoretical optimal scheme given by the DP algorithm is only 12.15% and 17.96% lower than the LSTM scheme in terms of fuel consumption. In addition, the influence of network size, training epochs and training set on energy-saving effect is compared in detail, it was found that the relatively optimal model required 128 LSTM layer units and 500 training epochs.

Research on power supply recovery control technology of distribution network embedding with DC links

The flexible interconnection device (FID) enables multiple distribution feeders in closed-loop mode to support a higher proportion of distributed generation (DG) and large-scale new loads connected to the distribution network (DN). Moreover, the closed-loop operation mode based on FIDs provides an effective technology roadmap for high-quality and highly reliable power users, especially the quick restoration of power supply after the failure of DN. This paper proposes a new supply shape in which the multi-terminal DC links are flexibly embedded in the AC DN (EDC-DN) to realize closed-loop operation, achieving lower investment costs than the existing mode that replaces tie switches with soft open points (SOPs). Meanwhile, a two-stage power supply quick recovery control technology is proposed for the EDC-DN, including the fast power supply recovery stage based on EDC and the optimizing restoration stage based on network reconfiguration. Firstly, the role of EDC in the process of power supply restoration of three different faults on AC feeder is analyzed. Secondly, a detailed power supply recovery process is proposed, which is based on a distribution automation system (DAS) and the real-time control capability of EDC. Thirdly, the power supply restoration second stage is implemented, which is based on network reconfiguration with switch combination and EDC continuous variable optimization. Finally, a 58-node DN with an EDC system was constructed using PSCAD/MTDC simulation software to verify the effectiveness of the two-stage strategy for EDC-DN power restoration.© 2017 Elsevier Inc. All rights reserved.

Tailorable Switching of Transmission Modes between Waveguide and Spoof Surface Plasmon Polariton for Flexible and Dynamic Control of Phase Shift.

Programmable metamaterials are suitable for their dynamic and real-time control capabilities of electromagnetic (EM) functions in radars and antenna communications, but it remains a challenge to achieve dynamic modulation of arbitrary transmission phase with high transmission efficiency. Here, we propose a paradigm to tailor transmission phase shift in real time by switching modes between waveguide and SSPP based on the voltage-driven PIN diodes. Step-like phase shift is achieved by the "ON" and "OFF" states of PIN diodes, while continuous phase regulation is by the characteristic of the nonlinear region between those two states. As validations, three systems with programmable functionalities are implemented, including the multibeam generator, the dual-beam scanner, and the active phased-array antenna. The experimental results are consistent with simulation, which verify the feasibility of the proposed approach. Our work offers an alternative route for transmission full-phase modulation and provides unprecedented potential for high-gain, real-time, and multidimensional EM capabilities in applications such as active phased array radars, self-adaption radomes, smart beam shaping.

Information metasurfaces and intelligent metasurfaces

Metamaterials and metasurfaces have inspired worldwide interest in the recent two decades due to their extraordinary performance in controlling material parameters and electromagnetic properties. However, most studies on metamaterials and metasurfaces are focused on manipulations of electromagnetic fields and waves, because of their analog natures. The concepts of digital coding and programmable metasurfaces proposed in 2014 have opened a new perspective to characterize and design metasurfaces in a digital way, and made it possible to control electromagnetic fields/waves and process digital information simultaneously, yielding the birth of a new direction of information metasurfaces. On the other hand, artificial intelligence (AI) has become more important in automatic designs of metasurfaces. In this review paper, we first show the intrinsic natures and advantages of information metasurfaces, including information operations, programmable and real-time control capabilities, and space–time-coding strategies. Then we introduce the recent advances in designing metasurfaces using AI technologies, and particularly discuss the close combinations of information metasurfaces and AI to generate intelligent metasurfaces. We present self-adaptively smart metasurfaces, AI-based intelligent imagers, microwave cameras, and programmable AI machines based on optical neural networks. Finally, we indicate the challenges, applications, and future directions of information and intelligent metasurfaces.

Hierarchical Sizing and Power Distribution Strategy for Hybrid Energy Storage System

This paper proposes a hierarchical sizing method and a power distribution strategy of a hybrid energy storage system for plug-in hybrid electric vehicles (PHEVs), aiming to reduce both the energy consumption and battery degradation cost. As the optimal size matching is significant to multi-energy systems like PHEV with both battery and supercapacitor (SC), this hybrid system is adopted herein. First, the hierarchical optimization is conducted, when the optimal power of the internal combustion engine is calculated based on dynamic programming, and a wavelet transformer is introduced to distribute the power between the battery and the SC. Then, the fuel economy and battery degradation are evaluated to return feedback value to each sizing point within the hybrid energy storage system sizing space, obtaining the optimal sizes for the battery and the SC by comparing all the values in the whole sizing space. Finally, an all-hardware test platform is established with a fully active power conversion topology, on which the real-time control capability of the wavelet transformer method and the size matching between the battery and the SC are verified in both short and long time spans.

StormReactor: An open-source Python package for the integrated modeling of urban water quality and water balance

Retrofitting watersheds with sensing and control technologies promises to enable autonomous water systems, which control themselves in real-time to improve water quality. To realize this vision, there is a need to improve the degree of fidelity in the underlying representation of pollutant processes. This paper presents an open-source Python package, StormReactor, which integrates the Stormwater Management Model's water balance engine with a new water quality module. StormReactor includes a variety of predefined pollutant generation and treatment processes, while allowing users to implement additional processes on their own. To demonstrate the range of possible water quality methodologies that can be modeled, we simulated suspended solids and nitrates in a real and anonymized stormwater network. To illustrate StormReactor's real-time control capabilities, a control strategy was implemented to maximize denitrification. Case study results indicate a controlled asset can achieve the same pollutant improvements as an uncontrolled asset in a quarter of the spatial footprint.

Electrically tunable terahertz switch based on superconducting subwavelength hole arrays.

We experimentally demonstrate an electrically tunable superconducting device capable of switching the extraordinary terahertz (THz) transmission. The planar device consists of subwavelength hole arrays with real-time control capability. The maximum transmission coefficient at 0.33 THz is 0.98 and decreases to 0.17 when the applied voltage only increases to 1.3 V. A relative intensity modulation of 82.7% is observed, making this device an efficient THz switch. Additionally, this device exhibits good narrow-bandpass characteristics within 2 THz, which can be used as a frequency-selective component. This study offers an ideal tuning method and delivers a promising approach for designing active and miniaturized devices in THz cryogenic systems.

A novel electro-mechanical technique for efficient dispersion of carbon nanotubes in liquid media

In this paper, we introduce a method to disperse carbon nanotubes (CNTs) within liquid media, established based on the applied hydrodynamic stress in the alignment process of the CNTs using an electric field. The main novelty of this technique is the capability of CNTs length protection and real-time control of the dispersion degree. Herein, the agglomerates and bundles are frequently induced by switching the field in different directions, and subsequently, the CNTs are separated from the main agglomerates. UV-Vis and dynamic light scattering spectroscopies and optical microscope analysis determined the dispersibility of the presented technique is very satisfying. The CNT's length distribution was assessed by the field emission scanning electron microscope. The results revealed that the shortening of CNTs is approximately 230% less than the well-known ultrasonic and melt mixing process. This is due to the unknown fatigue mechanism in which the required energy for dispersing CNTs is much less than CNT-CNT interaction energy. In addition to the capability of CNTs length protection, this method reduces the post-dispersed CNTs’ structural defects strikingly, certified by Raman spectroscopy. For real-time monitoring of the dispersion, the time-dependent voltage is measured. It was found that more reduction in the voltage corresponds to more dispersion.

Electrochemically driven dynamic plasmonics

Dynamic plasmonics with the real-time active control capability of plasmonic resonances attracts much interest in the communities of physics, chemistry, and material science. Among versatile reconfigurable strategies for dynamic plasmonics, electrochemically driven strategies have garnered most of the attention. We summarize three primary strategies to enable electrochemically dynamic plasmonics, including structural transformation, carrier-density modulation, and electrochemically active surrounding-media manipulation. The reconfigurable microstructures, optical properties, and underlying physical mechanisms are discussed in detail. We also summarize the most promising applications of dynamic plasmonics, including smart windows, structural color displays, and chemical sensors. We suggest more research efforts toward the widespread applications of dynamic plasmonics.

Agile design of cross-section enhancement of a conducting plate radar through active metasurface

The design concept of employing the dynamically reconfigurable scattering pattern to enhance radar cross-section is proposed and examined. In combination with varactor loading, a physical unit cell geometry with an embedded bias network for the varactors is utilized to build the metasurface. Biased by an applied direct-current source with voltage gradient, the proposed active metasurface can exhibit electrically tunable reflection phase distributions for either normal or oblique incidence plane wave, so as to achieve flexible redirection of the angle of reflection. As a result, the agile effect for monostatic or bistatic cross-section enhancement is then facilitated. Taking a conducting plate as an example, three different incidence and reflection scenarios are considered for calculation and full-wave simulation. Reconfigurable scattering patterns produced at the operating frequency of 10 GHz by the presented design are observed, which indicates its capability of real-time control of the angle of reflection. In conjunction with an experimental measurement, the effective enhancement of monostatic and bistatic cross-sections are validated.

Emulation of grid-forming inverters using real-time PC and 4-quadrant voltage amplifier

A test bed for the evaluation of novel control methods of inverters for renewable power generation is presented. The behavior of grid-following and grid-forming control in a test scenario is studied and compared.Using a real-time capable control platform with a cycle time of 50 µs, control methods developed with Matlab/Simulink can be implemented. For simplicity, a three-phase 4‑quadrant voltage amplifier is used instead of an inverter. Thus, the use of modulation and switched power semiconductors can be avoided. In order to show a realistic behavior of a grid-side filter, passive components can be automatically connected as L‑, LC- or LCL-filter. The test bed has a nominal active power of 43.6 kW and a nominal voltage of 400 V.As state-of-the-art grid-following control method, a current control in the d/q-system is implemented in the test bed. A virtual synchronous machine, the Synchronverter, is used as grid-forming control method. In combination with a frequency-variable grid emulation, the behavior of both control methods is studied in the event of a load connection in an island grid environment.