Real-time Implementation Research Articles

Performance Optimization of Maglev Train’s Electromagnetic Levitation System: Control Structure and Algorithm

This article investigates the performance optimization structure and algorithm of the electromagnetic levitation (EML) system. Firstly, a functionalized control structure is proposed by the incremental attachment of a controller gain system. Under this control structure, the predesigned controller is designed for nominal performance while the controller gain system handles performance optimization. This is of great help because the system trajectory of the highly dynamic and open-loop unstable EML system can be contained in the admissible region during the optimization of the controller gain system. Secondly, it is demonstrated that optimizing the incrementally attached controller gain system (under the proposed control structure) for performance optimization is equivalent to optimizing the predesigned controller. The advantage of the equivalence is that the predesigned control system needs not to be modified and the incremental attachment can be performed even when the EML system is running. Furthermore, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Q</i> -learning algorithm is presented for the real-time implementation of the proposed structure and the incrementally attached controller gain system. Finally, the effectiveness of the structure and algorithm is validated on the EML system. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —The EML system is key to maglev transportation. It is open-loop unstable, highly dynamic, and its accurate model is generally unavailable. On the other hand, the levitation performance may not be optimal given the predesigned controller. Moreover, during the long-term operation, it is desired in practice that the controller of EML system can be upgraded in a friendly fashion. This article presents a control structure that optimizes the levitation performance in an incremental style, such that the predesigned controller does not need to be modified. The incremental attachment can be realized via the onboard CAN network or UDP channel. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Q</i> -learning algorithm optimizes the incrementally attached controller gain system without distabilizing the levitation stability of the EML system under the proposed control structure. In such a way, the open-loop and highly dynamic characteristics can be handled and the friendly implementation style can be achieved.

Real-Time Monitoring of Cable Sag and Overhead Power Line Parameters Based on a Distributed Sensor Network and Implementation in a Web Server and IoT.

Based on the need for real-time sag monitoring of Overhead Power Lines (OPL) for electricity transmission, this article presents the implementation of a hardware and software system for online monitoring of OPL cables. The mathematical model based on differential equations and the methods of algorithmic calculation of OPL cable sag are presented. Considering that, based on the mathematical model presented, the calculation of cable sag can be done in different ways depending on the sensors used, and the presented application uses a variety of sensors. Therefore, a direct calculation is made using one of the different methods. Subsequently, the verification relations are highlighted directly, and in return, the calculation by the alternative method, which uses another group of sensors, generates both a verification of the calculation and the functionality of the sensors, thus obtaining a defect observer of the sensors. The hardware architecture of the OPL cable online monitoring application is presented, together with the main characteristics of the sensors and communication equipment used. The configurations required to transmit data using the ModBUS and ZigBee protocols are also presented. The main software modules of the OPL cable condition monitoring application are described, which ensure the monitoring of the main parameters of the power line and the visualisation of the results both on the electricity provider's intranet using a web server and MySQL database, and on the Internet using an Internet of Things (IoT) server. This categorisation of the data visualisation mode is done in such a way as to ensure a high level of cyber security. Also, the global accuracy of the entire OPL cable sag calculus system is estimated at 0.1%. Starting from the mathematical model of the OPL cable sag calculation, it goes through the stages of creating such a monitoring system, from the numerical simulations carried out using Matlab to the real-time implementation of this monitoring application using Laboratory Virtual Instrument Engineering Workbench (LabVIEW).

Real-time implementation of IoT-enabled cyberattack detection system in advanced metering infrastructure using machine learning technique

Real-time implementation of IoT-enabled cyberattack detection system in advanced metering infrastructure using machine learning technique

Unveiling Anomalies in Surveillance Videos

Abstract: The implementation of Convolutional Neural Network (CNN) algorithm for the detection of anomalies like fire and potholes in images or videos. Leveraging deep learning technique, the model aims to accurately identify fire, potholes, etc. within various contexts. The CNN architecture is trained on a dataset comprising diverse images to enhance its ability to generalize. This proposed project not only addresses safety concerns by promoting helmet usage and proper maintenance of roads but also contributes to the broader field of computer vision and object detection. This implementation involves training the CNN on annotated datasets, fine-tuning the model for optimal performance, and integrating it into a practical application for efficient anomaly identification. With a focus on real-time detection, the system utilizes image processing techniques and a trained CNN model to analyze visual data from cameras or video feeds. The system's versatility allows integration into surveillance systems, industrial sites, or any environment where fire detection is crucial for safety. By automating the detection process, the project contributes to minimizing human error and ensuring consistent monitoring. The proposed solution holds the potential to significantly impact safety protocols, particularly in industries where protective headgear is paramount. Potholes pose a significant threat to both drivers and pedestrians, leading to accidents and infrastructure damage. The proposed solution leverages CNN's ability to effectively process visual data, making it well-suited for image recognition tasks. Our approach involves training the CNN on a diverse dataset of road images to enable it to accurately identify potholes. The model will learn distinctive features and patterns associated with potholes, allowing for robust detection under various environmental conditions. Real-time implementation on embedded systems or cameras along roadways will enable instantaneous identification and alerting.

Fixed-time Synergetic Control for Synchronization of Fractional-order Chaotic Systems

In this paper, a fixed-time synergetic control is proposed for the synchronization of fractional-order chaotic systems. It is similar to the sliding mode approach but without chattering and achieves exact convergence of the macro-variable. Synergetic control is a robust approach that does not require linearization and is suitable for real-time implementation since it does not require a discontinuous term in its control law. The Lyapunov framework is used to ensure the convergence of the controlled system. Computer simulations are performed to illustrate the effectiveness of the proposed method.

Online optimal scheduling for battery swapping charging systems with partial delivery

Battery swapping–charging system (BSCS) is a promising operating paradigm to provide centering charging and battery swapping service for electric vehicles in transportation electrification. Facing real-time information on battery demand under the limited transporting trucks, flexible online optimization of battery delivery and transportation routing is essential for meeting practical requirements. This paper investigates the real-time scheduling problem in BSCS, considering the battery partial delivery, energy demand, delivery deadline, and vehicle routing. Considering the non-deterministic polynomia hardness of battery transportation, the offline BSCS is a time-consuming task and is unsuitable for the online setting. A Lagrangian relaxation-based Benders decomposition is proposed for parallel and real-time implementation, improving the scheduling efficiency. To tackle future information such as battery demands and delivery deadlines, by introducing the dummy copy, the offline algorithm is embedded within a rolling horizon framework to solve in real-time repeatedly. Finally, case studies using real road maps in Shanghai and Belgium have verified the validity of the proposed online framework and confirmed the necessity of considering partial delivery in enhancing the operation flexibility of BSCS. The computational efficiency of the proposed algorithm is studied under different scales of the road network, and the profit from partial delivery and online implementation are highlighted.

Digital neutron-gamma discrimination algorithm using adaptive noise filter

Pulse shape discrimination of a liquid scintillator-based detector (BC501A) has been studied using a digital approach. The objective of this work is to develop a new neutron-gamma discrimination algorithm to improve the discrimination efficiency. The new approach is a combination of two parts. The first part uses an adaptive Sallen-Key filter to reduce noise in the pulse followed by traditional approaches of either charge comparison or time over threshold. The approach of adaptive Sallen-Key combined with time over threshold was found to have a much better capability to discriminate neutron events from that of gamma. The pulse shape discrimination performance obtained for a 5 inches by 5 inches (length by diameter) liquid scintillator detector using the combination of the adaptive Sallen-Key filter with time over threshold is found to be significantly higher compared to that obtained using traditional methods. Moreover, this combination has the potential for real-time hardware implementation.

Time-resolved clinical dose volume metrics, calculations and predictions based on source tracking measurements and uncertainties to aid treatment verification and error detection for HDR brachytherapy—a proof-of-principle study

Objective. High-dose-rate (HDR) brachytherapy lacks routinely available treatment verification methods. Real-time tracking of the radiation source during HDR brachytherapy can enhance treatment verification capabilities. Recent developments in source tracking allow for measurement of dwell times and source positions with high accuracy. However, more clinically relevant information, such as dose discrepancies, is still needed. To address this, a real-time dose calculation implementation was developed to provide more relevant information from source tracking data. A proof-of-principle of the developed tool was shown using source tracking data obtained from a 3D-printed anthropomorphic phantom. Approach. Software was developed to calculate dose-volume-histograms (DVH) and clinical dose metrics from experimental HDR prostate treatment source tracking data, measured in a realistic pelvic phantom. Uncertainty estimation was performed using repeat measurements to assess the inherent dose measuring uncertainty of the in vivo dosimetry (IVD) system. Using a novel approach, the measurement uncertainty can be incorporated in the dose calculation, and used for evaluation of cumulative dose and clinical dose-volume metrics after every dwell position, enabling real-time treatment verification. Main results. The dose calculated from source tracking measurements aligned with the generated uncertainty bands, validating the approach. Simulated shifts of 3 mm in 5/17 needles in a single plan caused DVH deviations beyond the uncertainty bands, indicating errors occurred during treatment. Clinical dose-volume metrics could be monitored in a time-resolved approach, enabling early detection of treatment plan deviations and prediction of their impact on the final dose that will be delivered in real-time. Significance. Integrating dose calculation with source tracking enhances the clinical relevance of IVD methods. Phantom measurements show that the developed tool aids in tracking treatment progress, detecting errors in real-time and post-treatment evaluation. In addition, it could be used to define patient-specific action limits and error thresholds, while taking the uncertainty of the measurement system into consideration.

AlertTrap: A Study on Object Detection in Remote Insect Trap Monitoring System Using on the Edge Deep Learning Platform

Fruit flies are one of the most harmful insect species to fruit yields. In AlertTrap, implementation of Single-Shot Multibox Detector (SSD) architecture with different state-of-the-art backbone feature extractors such as MobileNetV1 and MobileNetV2 appears to be potential solutions for the real-time detection problem. SSD-MobileNetV1 and SSD-MobileNetV2 perform well and result in AP at 0.5 of 0.957 and 1.0, respectively. You Only Look Once (YOLO) v4-tiny outperforms the SSD family with 1.0 in AP at 0.5; however, its throughput velocity is considerably slower, which shows SSD models are better candidates for real-time implementation. We also tested the models with synthetic test sets simulating expected environmental disturbances. The YOLOv4-tiny had better tolerance to these disturbances than the SSD models. The Raspberry Pi system successfully gathered environmental data and pest counts, sending them via email over 4 G. However, running the full YOLO version in real time on Raspberry Pi is not feasible, indicating the need for a lighter object detection algorithm for future research. Among model candidates, YOLOv4-tiny generally performs best, with SSD MobileNetV2 also comparable and sometimes better, especially in scenarios with synthetic disturbances. SSD models excel in processing time, enabling real-time, high-accuracy detection. TFLITE versions of SSD models also process faster than their inference graph on TPU hardware, suggesting real-time implementation on edge devices like the Google Coral Dev Board. The results demonstrate the feasibility of real-time implementation of the fruit fly detection models on edge devices with high performance. In addition, YOLOv4-tiny is shown to be the most probable candidate because YOLOv4-tiny demonstrates a robust testing performance toward citrus fruit fly detection. Nevertheless, SSD-MobileNetV2 will be the better model, considering the inference time.

Robust Speed Control of Permanent Magnet Synchronous Motor Drive System Using Sliding-Mode Disturbance Observer-Based Variable-Gain Fractional-Order Super-Twisting Sliding-Mode Control

This paper proposes a novel nonlinear speed control method for permanent magnet synchronous motors that enhances their robustness and tracking performance. This technique integrates a sliding-mode disturbance observer and variable-gain fractional-order super-twisting sliding-mode control within a vector-control framework. The proposed control scheme employs a sliding-mode control method to mitigate chattering and improve dynamics by implementing fractional-order theory with a variable-gain super-twisting sliding manifold design while regulating the speed of the considered motor system. The aforementioned observer is suggested to enhance the control accuracy by estimating and compensating for the lumped disturbances. The proposed methodology demonstrates its superiority over other control schemes such as traditional sliding-mode control, super-twisting sliding-mode control, and the proposed technique. MATLAB/Simulink simulations and real-time implementation validate its performance, showing its potential as a reliable and efficient control approach for the system under study in practical applications.

A hybrid optimization method based on extreme learning machine aided factor graph for INS/GPS information fusion during GPS outages

To enhance the navigation capability of the microelectromechanical system (MEMS)-based inertial navigation system (INS)/global positioning system (GPS) integrated system under satellite denied, a hybrid optimization navigation method using minimal learning parameter (MLP) to improve extreme learning machine (ELM) aided adaptive factor graph (AFG) is proposed. The proposed method mainly contains two innovative optimizations: (1) Fully considering the key parameters of each sensor in the INS/GPS integrated navigation system, the factor graph technology is introduced to develop an information fusion model for the system. Based on the maximum correlation entropy criterion and adaptive technology, the existing factor graph method for information fusion is optimized to solve the problem that the abnormal output of each sensor in the INS/GPS integrated system caused by complex environments affects the accuracy of information fusion, thus enhancing the robustness and navigation performance of the INS/GPS integrated system; (2) Moreover, ELM is optimized by MLP to predict the velocity and position observation information of the INS/GPS integrated system and tackle the performance deterioration of the system during GPS outages. The structural parameters of ELM are optimized with the MLP method, aiming to abate the computational burden of the traditional neural network (NN) to avoid “dimension explosion”, as well as enhance the generalization ability and robustness of the network to make it learn from the uncertainty of the system smoothly and quickly. Relevant ground vehicle experiments are designed to evaluate the proposed method. The experimental results demonstrate that the proposed strategy has superior performance and is more feasible for real-time implementation than other comparison approaches.

Improved bridge modal identification from vibration measurements using a hybrid empirical Fourier decomposition

Bridge health monitoring has been a prominent focus within the global engineering community. Bridge owners, stakeholders, and engineers face the formidable tasks of ensuring efficient monitoring, conducting reliable data analysis, interpreting data logically, and making timely decisions. With the increasing global infrastructure deficit, there is an ever-increasing need to develop reliable and economical bridge monitoring solutions. In this paper, a bridge condition assessment technique is proposed that can utilize the vibration data collected from the instrumented sensors and provide reliable system identification results. The proposed method develops a hybrid approach by integrating the Natural Excitation Technique (NExT) and Empirical Fourier Decomposition (EFD) to analyze ambient bridge vibration data and determine the modal parameters of the bridge. First, NExT is formulated to determine the cross-correlation functions of the bridge measurements, and then EFD is explored to decompose the signals into their monocomponents to identify the bridge modal parameters. The proposed methodology can overcome mode mixing and perform modal identification of a system with closely spaced frequencies and low energy modes. The estimated modal parameters such as bridge frequencies, mode shapes, and damping ratio are used for condition assessment of numerical, experimental and full-scale structures, including a short-span steel bridge located in Ontario, Canada. The results demonstrate that the proposed methodology can provide accurate and robust estimates of bridge modal parameters. Future research is reserved for real-time implementation of the proposed methodology for a wide range of civil structures.

Frequency Loop-Shaping and IMC-Based Integer-Order Robust PID Controller Design for Fractional-Order Processes with Time-Delay

It is well accepted that fractional-order calculus yields a more accurate description of complex physical processes compared to integer-order one. So, fractional-order modelling is getting a good attention in the process industry. On the other hand, to meet design goals accurately, control designers prefer fractional-order controllers over integer-order ones due to the availability of more parameters. However, the design of such controllers are complex. More often, the design faces the challenge in real-time implementations, and the problem is circumvented by higher order approximations of fractional-order operators. In the presented work, three methods for integer-order Proportional–Integral–Derivative (PID) controller design are proposed based on Frequency Loop-Shaping (FLS) and Internal Model Control (IMC). To prove the efficacy of the presented control law, simulations are conducted and included along with a performance measure based on ISE (Integral of Square Error), ITAE (Integral of Time Absolute Error) and IAE (Integral of Absolute Error).

Design and performance validation of CWT-MCA based interference mitigation for automotive radars

Interference mitigation has been a vital area of focus within the realm of automotive radar research, holding paramount importance for augmenting advanced driver-assistance capabilities, particularly in vehicle-dense scenarios. This paper presents a novel interference mitigation scheme based on continuous wavelet transform and morphological component analysis (CWT-MCA). This method, in essence, transforms the time-domain signal into the time-frequency domain, subsequently enabling the direct separation of target and interference components, thereby circumventing the necessity for interference detection and identification. Additionally, this paper introduces a series of MCA algorithms grounded in various transform domains, which are subsequently compared with the traditional linear prediction based approach. We show through comprehensive numerical simulations and real-data experiments that CWT-MCA algorithm exhibits markedly superior interference mitigation performance while offering lower computational complexity, which makes it well-suited for real-time implementation in automotive radar systems.

Online chasing action recruits both mirror neuron and mentalizing systems: A pilot fNIRS study

Engaging in chasing, where an actor actively pursues a target, is considered a crucial activity for the development of social skills. Previous studies have focused predominantly on understanding the neural correlates of chasing from an observer's perspective, but the neural mechanisms underlying the real-time implementation of chasing action remain poorly understood. To gain deeper insights into this phenomenon, the current study employed functional near-infrared spectroscopy (fNIRS) techniques and a novel interactive game. In this interactive game, participants (N = 29) were tasked to engage in chasing behavior by controlling an on-screen character using a gamepad, with the goal of catching a virtual partner. To specifically examine the brain activations associated with the interactive nature of chasing, we included two additional interactive actions: following action of following the path of a virtual partner and free action of moving without a specific pursuit goal. The results revealed that chasing and following actions elicited activation in a broad and overlapping network of brain regions, including the temporoparietal junction (TPJ), medial prefrontal cortex (mPFC), premotor cortex (PMC), primary somatosensory cortex (SI), and primary motor cortex (M1). Crucially, these regions were found to be modulated by the type of interaction, with greater activation and functional connectivity during the chasing interaction than during the following and free interactions. These findings suggested that both the MNS, encompassing regions such as the PMC, M1 and SI, and the mentalizing system (MS), involving the TPJ and mPFC, contribute to the execution of online chasing actions. Thus, the present study represents an initial step toward future investigations into the roles of MNS and MS in real-time chasing interactions.

Optimal operation of an electricity-hydrogen DC microgrid with integrated demand response

Uncertainties introduced by the high penetration of renewable sources, plug-in-hybrid-electric vehicle load demand, and limited capacity of firm generation available in a DC microgrid make the energy scheduling task rather challenging. This paper proposes a decentralized energy management scheme with a real-time pricing-based demand response implementation for a DC microgrid, considering the sectoral coupling between electricity and hydrogen energy. The objectives of the scheduling strategy are to maximize the profit of the DC microgrid operator and reduce the cost of energy use by the consumers, considering the interaction and interdependence of the electrical and hydrogen systems with a detailed DC microgrid network model and associated network constraints. The DC microgrid operator schedules flexible resources under its control (power procurement from the upstream grid, microturbines, battery energy storage, hydrogen storage, electrolyzer and fuel cell) and sets real-time prices. The consumers set their consumption patterns according to the real-time price. The DC microgrid operator side flexibilities are coordinated with the consumer side flexibilities (thermostatically controlled load like air-conditioner and plug-in-hybrid electric vehicle) using the decentralized “Alternating Direction Method of Multipliers” approach. The probabilistic Copula theory models correlated input uncertainties. Simulation results on a six-bus DC microgrid test system reveal that the operating cost of the DC microgrid operator reduces by ∼11.06% while the energy use cost of consumers reduces by ∼4.80% using the proposed approach for the system under study.

FPGA real-time implementation of welch transform for diagnosis of broken rotor bars in induction motors

FPGA real-time implementation of welch transform for diagnosis of broken rotor bars in induction motors

Real-Time Method and Implementation of Head-Wave Extraction for Ultrasonic Imaging While Drilling

Extracting head waves and subsequently uploading their results from the downhole to the surface system in real time could improve the real-time guidance of ultrasonic imaging logging while drilling (UILWD) for drilling operations. To realize the downhole real-time extraction of head waves in this logging, three aspects were explored in this study. First, an improved energy ratio head-wave arrival extraction algorithm based on the weighting coefficients and characteristic functions, along with an amplitude detection method relying on peak-to-peak values, was proposed. Second, an echo reception pre-processing analog circuit and a digital signal processing circuit based on FPGA were designed. A pipeline algorithm was developed in FPGA to extract the arrival time and amplitude of the head wave. Finally, software simulations, laboratory tests, and field experiments related to this method were conducted. Our results showed that the real-time head-wave extraction method demonstrated a strong anti-noise ability in real time. The maximum relative error of the arrival time was less than 5%. The relative error of the amplitude was acceptable, and 90% of this value was within 5%. Through the measurement, the time of processing a single-channel waveform by a downhole algorithm was less than 15 ms, thus meeting the requirements for the real-time processing of downholes.

Outbreak and integration of social media in public health surveillance systems: A policy review through BERT embedding technique

The COVID-19 pandemic posed significant challenges for statesmen and policymakers, especially in healthcare crisis management. During the spread of emerging infectious diseases, proactive governments’ responses and real-time implementation of precautionary measures may help countries to better manage the emergency and gradually return to normality, reducing an excessive resource waste as well as safeguarding citizens’ well-being. In this context, social media platforms represents a useful tool in predicting and early detecting outbreaks, with the potential of counteracting the rapid sequence of pre-pandemic events. This paper aims to present a bibliometric study based on a BERT embedding technique to overview the literature themes on the relation between social media alerts and infectious disease spread. Our findings underline the key role of social media platforms and the urgency of setting up an integrated digital surveillance system in which social media data can help to geo-localize chains of contagion and, in general, improve the effectiveness of public health interventions.

A novel Laplacian vector directional filter designed for real-time removal of impulsive noise from color images

Eliminating noise from a color image represents an essential task in image processing, given that noise can markedly diminish visual quality and impact the precision of subsequent analyses or applications (such as visual quality inspection, medical image analysis, etc.). In this study, we introduce an innovative approach designed to remove noise while preserving the edges and fine details of a color image. This method is based on Laplacian operators, threshold values for identifying potentially corrupted pixels due to noise, and the Vector Directional Filter (VDF) for substituting noisy pixels. Consequently, this technique is termed Laplacian VDF (LVDF). Additionally, a Hardware/Software (HW/SW) design is formulated for the real-time implementation of the LVDF filter. The hardware architecture is designed through the High-Level Synthesis (HLS) flow, while the software component is executed using the ARM Cortex-A53 hardcore processor. Furthermore, five Direct Memory Access (DMAs) are employed to enhance data throughput between the hardware coprocessor and Double Data Rate (DDR) memory. Performance evaluations conducted on the ZCU102 kit reveal that the LVDF HW/SW design facilitates the restoration of 74 images per second, resulting in a 94 % reduction in execution time compared to the software implementation, all while maintaining consistent objective and subjective image quality.