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Durational action timed automata (daTAs) are state transition systems like timed automata (TAs) that capture information regarding the concurrent execution of actions and their durations using maximality-based semantics. As the underlying semantics of daTAs are infinite due to the modeling of time progress, conventional model checking techniques become impractical for systems specified using daTAs. Therefore, a finite abstract representation of daTA behavior is required to enable model checking for such system specifications. For that, we propose a finite abstraction of the underlying semantics of a daTA-like region abstraction of timed automata. In addition, we highlight the unique benefits of daTAs by illustrating that they enable the verification of properties concerning concurrency and action duration that cannot be verified using the traditional TA model. We demonstrate mathematically that the number of states in durational action timed automata becomes significantly smaller than the number of states in timed automata as the number of actions increases, confirming the efficiency of daTAs in modeling behavior with action durations. [ABSTRACT FROM AUTHOR]

The paper describes low-cost hardware-based analog and digital real-time circuit simulators for the development of power electronics control circuits. During the process of designing and developing digital control circuits for power electronics systems, preliminary verification of control algorithms is required. For this purpose, software simulators such as Pspice, Psim, Matlab-Simulink, and many others are commonly used. Afterward, the developed control algorithm is implemented in the digital control system. For further verification of the implemented control algorithms, a hardware-based analog or digital simulator can be utilized. The paper presents the author's proposed analog simulators. In the digital version of the simulator, TMS320F28388D microcontroller with 200 MHz clock was used. These simulators have demonstrated their usefulness in the development of power electronics systems. [ABSTRACT FROM AUTHOR]

This project introduces a metric-driven strategy for enhancing real-time task planning within a mobile distributed system. The core design revolves around a realtime task planning algorithm that exploits consensus mechanisms among multiple nodes. By incorporating innovative real-time scheduling algorithms, validated on a supercomputer, the project establishes frameworks for simulating distributed mobile environments in real-time. This enables the execution of tasks in unpredictable contexts, ensuring adherence to stringent time constraints and empowering decision-making capabilities. The primary objective is to optimize task allocation in mobile distributed systems, including aerial nodes, thereby facilitating seamless data transfer while maintaining data integrity. The project builds upon the Fan task scheduler, integrating compensatory measures for online load distribution and optimization of message routes, effectively reducing communication latency in dynamic mobile environments. The contributions made aim to enhance real-time task allocation across diverse nodes, enabling the transfer of location and search data across networks without any data loss. This approach bears significant importance for the efficient management of tasks within dynamic scenarios. [ABSTRACT FROM AUTHOR]

Ensuring the safety of water airport runways is essential for the correct operation of seaplane flights. Among other tasks, airport operators must identify and remove various objects that may have drifted into the runway area. In this paper, the authors propose a complete and embedded‐friendly waterway obstacle detection pipeline that runs on a camera‐equipped drone. This system uses a class‐agnostic version of the YOLOv7 detector, which is capable of detecting objects regardless of its class. Additionally, through the usage of the GPS data of the drone and camera parameters, the location of the objects are pinpointed with 0.58 m Distance Root Mean Square. In our own annotated dataset, the system is capable of generating alerts for detected objects with a recall of 0.833 and a precision of 1. [ABSTRACT FROM AUTHOR]

Generalized 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. [ABSTRACT FROM AUTHOR]

This paper presents a hardware implementation of a one-dimensional convolutional neural network using depthwise separable convolution (DSC) on the VC707 FPGA development board. The design processes the one-dimensional rolling bearing current signal dataset provided by Paderborn University (PU), employing minimal preprocessing to maximize the comprehensiveness of feature extraction. To address the high parameter demands commonly associated with convolutional neural networks (CNNs), the model incorporates DSC, significantly reducing computational complexity and parameter load. Additionally, the DoReFa-Net quantization method is applied to compress network parameters and activation function outputs, thereby minimizing memory usage. The quantized DSC model requires approximately 22 KB of storage and performs 1,203,128 floating-point operations in total. The implementation achieves a power consumption of 527 mW at a clock frequency of 50 MHz, while delivering a fault diagnosis accuracy of 96.12%. [ABSTRACT FROM AUTHOR]

Advancements in neural network approaches have enhanced the effectiveness of surface Electromyography (sEMG)-based hand gesture recognition when measuring muscle activity. However, current deep learning architectures struggle to achieve good generalization and robustness, often demanding significant computational resources. The goal of this paper was to develop a robust model that can quickly adapt to new users using Transfer Learning. We propose a Multi-Scale Convolutional Neural Network (MSCNN), pre-trained with various strategies to improve inter-subject generalization. These strategies include domain adaptation with a gradient-reversal layer and self-supervision using triplet margin loss. We evaluated these approaches on several benchmark datasets, specifically the NinaPro databases. This study also compared two different Transfer Learning frameworks designed for user-dependent fine-tuning. The second Transfer Learning framework achieved a 97% F1 Score across 14 classes with an average of 1.40 epochs, suggesting potential for on-site model retraining in cases of performance degradation over time. The findings highlight the effectiveness of Transfer Learning in creating adaptive, user-specific models for sEMG-based prosthetic hands. Moreover, the study examined the impacts of rectification and window length, with a focus on real-time accessible normalizing techniques, suggesting significant improvements in usability and performance. [ABSTRACT FROM AUTHOR]

This paper presents a real-time wearable system designed to assist Parkinson's disease patients experiencing freezing of gait episodes. The system utilizes advanced machine learning models, including convolutional and recurrent neural networks, enhanced with past sample data preprocessing to achieve high accuracy, efficiency, and robustness. By continuously monitoring gait patterns, the system provides timely interventions, improving mobility and reducing the impact of freezing episodes. This paper explores the implementation of a CNN+RNN+PS machine learning model on a microcontroller-based device. The device operates at a real-time processing rate of 40 Hz and is deployed in practical settings to provide 'on demand' vibratory stimulation to patients. This paper examines the system's ability to operate with minimal latency, achieving an average detection delay of just 261 milliseconds and a freezing of gait detection accuracy of 95.1%. While patients received on-demand stimulation, the system's effectiveness was assessed by decreasing the average duration of freezing of gait episodes by 45%. These preliminarily results underscore the potential of personalized, real-time feedback systems in enhancing the quality of life and rehabilitation outcomes for patients with movement disorders. [ABSTRACT FROM AUTHOR]

As real-time systems are safety critical, guaranteeing a high reliability threshold is as important as meeting all deadlines. Periodic tasks are replicated to mitigate the negative impact of transient faults, which leads to redundancy and high energy consumption. On the other hand, energy saving is widely identified as increasingly relevant issues in real-time systems. In this paper, we formalize this challenging tri-criteria optimization problem, i.e., minimizing the expected energy consumption while enforcing the reliability threshold and meeting all task deadlines, and propose several mapping and scheduling heuristics to solve it. Specifically, a novel approach is designed to (i) map an arbitrary number of replicas onto processors, (ii) schedule each replica of each task instance on its assigned processor with less temporal overlap. The platform is composed of processing units with different characteristics, including speed profile, energy cost and fault rate. The heterogeneity of the computing platform makes the problem more complicated, because different mappings achieve different levels of reliability and consume different amounts of energy. Moreover, scheduling plays an important role in energy saving, as the expected energy consumption is the average over all failure scenarios. Once a task replica is successful, the other replicas of that task instance can be canceled, which calls for minimizing the overlap between any replica pair. Finally, to quantitatively analyze our methods, we derive a theoretical lower-bound for the expected energy consumption. Comprehensive experiments are conducted on a large set of execution scenarios and parameters. The comparison results reveal that our strategies perform better than the random baseline under almost all settings, with an average gain in energy consumption of more than 40%, and our best heuristic achieves an excellent performance: its energy saving is only 2% less than the lower-bound on average. [ABSTRACT FROM AUTHOR]

An online non‐model‐based procedure is presented for estimating the synchronous generator (SG) dynamic parameters using practical phasor measurement unit (PMU) signals in the presence of uncertainty and noisy data. For this purpose, considering 8th‐order approximation, the SGs model is estimated in which, based on evaluating voltage and current phasors achieved from PMU data, dynamic parameters are estimated online. The proposed approach is a generalised concept of the Heffron–Philips model in which the variables and the gain factors are adaptable according to operating conditions. The proposed scheme is an online and non‐model‐based method in which the SG magnetic saturation behaviours are modelled through multivariable non‐linear definition to extend the accurate controlling structure. In this case, two different studies are carried out. In the first study, considering a single SG is connected to the infinite bus, the ability of the proposed method through simulation studies is evaluated. In the second study, the proposed scheme is developed practically in the laboratory whereby performing the experimental structure on different types through real‐time working mode, validation of the proposed estimated model through different operating points is evaluated. Experimental results show the effectiveness of the proposed practical scheme for estimating the generator's detailed model and non‐linear dynamic parameters through real‐time evaluations. [ABSTRACT FROM AUTHOR]

For flight test engineering, the flight test task schedule is of great importance to the delivery node and the development cost of an aircraft, while in the real flight test process, dynamic events may frequently occur, which affect the schedule implementation and flight test progress. To adaptively adjust the real-world flight test schedule, this paper proposes a deep reinforcement learning (DRL) approach to solve the dynamic task scheduling problem for flight tests, with the objectives of flight test duration and task tardiness. Firstly, the task scheduling characteristics for flight tests are introduced, and a mixed-integer programming (MIP) model is constructed. Then, the addressed problem is formulated as a Markov decision process (MDP), including the well-designed state features, reward functions, and action space based on the heuristic rules for selecting the uncompleted flight test task and allocating the selected task to an appropriate aircraft. Proximal policy optimization (PPO) is adopted to train and learn the optimal policy. Finally, extensive experiments are carried out to verify the proposed method's effectiveness and efficiency in constructing a high-quality flight test task schedule in a dynamic flight test environment. [ABSTRACT FROM AUTHOR]

This paper presents an energy-efficient and high-accuracy sampling synchronization approach for real-time synchronous data acquisition in wireless sensor networks (saWSNs). A proprietary protocol based on time-division multiple access (TDMA) and deep energy-efficient coding in sensor firmware is proposed. A real saWSN model based on 2.4 GHz nRF52832 system-on-chip (SoC) sensors was designed and experimentally tested. The obtained results confirmed significant improvements in data synchronization accuracy (even by several times) and power consumption (even by a hundred times) compared to other recently reported studies. The results demonstrated a sampling synchronization accuracy of 0.8 μs and ultra-low power consumption of 15 μW per 1 kb/s throughput for data. The protocol was well designed, stable, and importantly, lightweight. The complexity and computational performance of the proposed scheme were small. The CPU load for the proposed solution was <2% for a sampling event handler below 200 Hz. Furthermore, the transmission reliability was high with a packet error rate (PER) not exceeding 0.18% for TXPWR ≥ −4 dBm and 0.03% for TXPWR ≥ 3 dBm. The efficiency of the proposed protocol was compared with other solutions presented in the manuscript. While the number of new proposals is large, the technical advantage of our solution is significant. [ABSTRACT FROM AUTHOR]

The development vector of modern central processing units, which increasingly involves using a more significant number of cores and prioritizing parallelism over the high power of a single computational unit, presents new challenges for the existing software design. This work investigates and addresses the problem of access to shared memory in multithreaded environments, such as operating systems, interactive distributed computing systems, and high-performance simulation systems. Thus, the object of study is a non-blocking approach to organizing access to memory and performing basic operations with it through non-blocking synchronization. The research methods include developing an approach to organizing access to shared memory using the doubleword compare-and-swap algorithm, followed by a theoretical and practical comparison of the resulting outcome with the standard blocking access algorithm to shared memory for different configurations of the number of threads and the number of simultaneous memory access attempts. Additionally, testing was conducted within the framework of an unnamed closed-source project by integrating the solution into it, followed by A/B testing. The results showed that using non-blocking approaches is advisable, especially in comparison with locking approaches, which demonstrated a performance degradation relative to the standard allocation algorithm by more than 300 %, while non-blocking approaches provided an improvement of 40–90 %. It was also found that using hybrid approaches to the organization of shared memory systems at the software level can lead to more stable results and mitigate application performance degradation compared to classical approaches such as buddy algorithms or free lists. Despite the results obtained, the author remains cautious about the idea of memory management and pool organization at the software level and does not recommend using specialized allocation algorithms without an urgent need to speed up memory allocation itself. The purpose of these structures is still not to improve software performance directly but to enhance and speed up access to the data stored in them. [ABSTRACT FROM AUTHOR]

The automotive industry has recently adopted multicore processors and microcontrollers to meet the requirements of new features, such as autonomous driving, and comply with the latest safety standards. However, inter-core communication poses a challenge in ensuring real-time requirements such as time determinism and low latencies. Concurrent access to shared buffers makes predicting the flow of data difficult, leading to decreased algorithm performance. This study explores the integration of Logical Execution Time (LET) and Time-Division Multiple Access (TDMA) models in multicore embedded systems to address the challenges in inter-core communication by synchronizing read/write operations across different cores, significantly reducing latency variability and improving system predictability and consistency. Experimental results demonstrate that this integrated approach eliminates data loss and maintains fixed operation rates, achieving a consistent latency of 11 ms. The LET-TDMA method reduces latency variability to approximately 1 ms, maintaining a maximum delay of 1.002 ms and a minimum delay of 1.001 ms, compared to the variability in the LET-only method, which ranged from 3.2846 ms to 8.9257 ms for different configurations. [ABSTRACT FROM AUTHOR]

To validate safety-related automotive software systems, experimental tests are conducted at different stages of the V-model, which are referred as "X-in-the-loop (XIL) methods". However, these methods have significant drawbacks in terms of cost, time, effort and effectiveness. In this study, based on hardware-in-the-loop (HIL) simulation and real-time fault injection (FI), a novel testing framework has been developed to validate system performance under critical abnormal situations during the development process. The developed framework provides an approach for the real-time analysis of system behavior under single and simultaneous sensor/actuator-related faults during virtual test drives without modeling effort for fault mode simulations. Unlike traditional methods, the faults are injected programmatically and the system architecture is ensured without modification to meet the real-time constraints. Moreover, a virtual environment is modeled with various environmental conditions, such as weather, traffic and roads. The validation results demonstrate the effectiveness of the proposed framework in a variety of driving scenarios. The evaluation results demonstrate that the system behavior via HIL simulation has a high accuracy compared to the non-real-time simulation method with an average relative error of 2.52. The comparative study with the state-of-the-art methods indicates that the proposed approach exhibits superior accuracy and capability. This, in turn, provides a safe, reliable and realistic environment for the real-time validation of complex automotive systems at a low cost, with minimal time and effort. [ABSTRACT FROM AUTHOR]

A 1 millisecond (1‐ms) vision system that guarantees high efficiency and timely response for tomato defect detection is essential for factory automation. Because of various defect appearances, recently many existing researches focus on CNN based defect detection, but few of them attempt to reach high processing speed to adapt to the factorial assembly line. This paper proposes a global to multi‐scale local based parallel architecture with hardwired CNN for tomato defect detection. This architecture breaks down image‐wise detection into pixel‐wise localization and block‐wise classification. The pixel‐wise localization utilizes tomato‐aware information as constraints for localization performance. The block‐wise classification uses a fully pipelined network structure to obtain the classification result for each block as the pixel stream moves through the network. The classification network has a six‐layer lightweight network structure with quantization for hardwired type implementation on FPGA. The experiment results show that the proposed architecture processes 1000 FPS images with 0.9476 ms/frame delay. And for detection performance, this architecture keeps f1−score$f_{1}-score$ at 80.18%, only 1.31% lower than ResNet50 based detection system. [ABSTRACT FROM AUTHOR]

The operational performance of grid‐connected active front‐end converters (AFEs) faces challenges arising from the intricate interplay among phase‐locked loop (PLL) non‐linearities, grid impedance, and conventional control strategies, resulting in compromised stability. This study introduces a refined approach to dynamic model predictive control (MPC) by integrating recursive least squares (RLS) for the precise estimation of physical model parameters, thereby addressing stability concerns. Unlike conventional methodologies, the proposed enhanced RLS‐based MPC approach, equipped with an auto‐tuning feature, allows for the design of controllers without a prerequisite understanding of exact external dynamics. Notably, this technique exhibits exceptional disturbance rejection capabilities. The evaluation of the cost function at each sampling interval facilitates the determination of optimal switching states based on predicted variables. Gate pulses for the switches of the AFEs are generated accordingly. Employing a simulation platform, the proposed control structure's performance across varied conditions is comprehensively assessed, encompassing alterations in grid impedance and system non‐linearities. The method adeptly integrates inherent non‐linearities within the system, showcasing exceptional robustness in diverse dynamic scenarios. To further substantiate the efficacy of the proposed control system over conventional approaches, simulation results are validated using a laboratory hardware platform equipped with Typhoon HIL and dSPACE real‐time emulators, providing tangible evidence of the proposed control system's effectiveness in real‐world hardware setups. The multifaceted approach, encompassing precise parameter estimation, predictive control, auto‐tuning, disturbance rejection, robust design, and real‐time evaluation, collectively establishes a resilient foundation for enhancing and maintaining the overall stability of the system across diverse operating scenarios. [ABSTRACT FROM AUTHOR]

The advancement of unmanned aerial vehicles (UAVs) enables early detection of numerous disasters. Efforts have been made to automate the monitoring of data from UAVs, with machine learning methods recently attracting significant interest. These solutions often face challenges with high computational costs and energy usage. Conventionally, data from UAVs are processed using cloud computing, where they are sent to the cloud for analysis. However, this method might not meet the real-time needs of disaster relief scenarios. In contrast, edge computing provides real-time processing at the site but still struggles with computational and energy efficiency issues. To overcome these obstacles and enhance resource utilization, this paper presents a convolutional neural network (CNN) model with an early exit mechanism designed for fire detection in UAVs. This model is implemented using TSMC 40 nm CMOS technology, which aids in hardware acceleration. Notably, the neural network has a modest parameter count of 11.2 k. In the hardware computation part, the CNN circuit completes fire detection in approximately 230,000 cycles. Power-gating techniques are also used to turn off inactive memory, contributing to reduced power consumption. The experimental results show that this neural network reaches a maximum accuracy of 81.49% in the hardware implementation stage. After automatic layout and routing, the CNN hardware accelerator can operate at 300 MHz, consuming 117 mW of power. [ABSTRACT FROM AUTHOR]

Efficient animal detection is essential for biodiversity protection. Unmanned aerial vehicles (UAVs) have been widely used because of their low costs and minimal environmental intrusion. However, using UAVs for practical animal detection poses two challenges: (a) the UAV's fly highly to avoid disturbing animals, resulting in small object detection problems; (b) the limited processing power of UAVs makes large state‐of‐the‐art (SOTA) methods (e.g., You Only Look Once V7, YOLOv7) difficult to deploy. This work proposes the WILD‐YOLO based on YOLOv7 to deal with the two problems. To detect small objects, WILD‐YOLO improves upon YOLOv7 by adding a small object detection head in the head part. To enable real‐time animal detection in field environments with UAVs, the lighten FasterNet and GhostNet have been used to significantly reduce the model size. Compared to YOLOv7, WILD‐YOLO significantly reduces the number of parameters, making it suitable for lightweight deployment on UAVs. Additionally, comparisons with other lightweight models such as YOLOv7‐tiny, YOLOv5‐s, YOLOv4‐s and MobilenetV2 on the datasets are conducted. The experimental results demonstrate that this proposed WILD‐YOLO method outperforms other approaches and has great potential for effective detection of wildlife in complex environments encountered by UAVs. [ABSTRACT FROM AUTHOR]

In embedded systems, the execution semantics of the real-time operating system (RTOS), which is responsible for scheduling and timely execution of concurrent processes, is crucial for the correctness of the overall system. However, existing approaches for the formal verification of embedded systems typically abstract from the RTOS completely, or provide a detailed and synthesizable formal model of the RTOS. While the former may lead to unsafe systems, the latter is not compatible with industrial design processes. In this paper, we present an approach for reusable abstract formal models that can be configured for custom RTOS. Our key idea is to formally capture common execution mechanisms of RTOS like preemptive scheduling, event synchronization, and communication abstractly in configurable timed automata models. These abstract formal models can be configured for a concrete custom RTOS, and they can be combined into a formal system model together with a concrete application. Our reusable models significantly reduce the manual effort of defining a formal model that captures concurrency and real-time behavior, together with the functionality of an application. The resulting formal model enables analysis, verification, and graphical simulation. We validate our approach by formalizing and analyzing a rescue robot application running the custom open source RTOS EV3RT. [ABSTRACT FROM AUTHOR]

The multi-objective optimization (MOO) problem in wireless sensor networks (WSNs) is concerned with optimizing the operation of the WSN across three dimensions: coverage, connectivity, and lifetime. Most works in the literature address only one or two dimensions of this problem at a time, except for the randomized coverage-based scheduling (RCS) algorithm and the clique-based scheduling algorithm. More recently, a Hidden Markov Model (HMM)-based algorithm was proposed that improves on the latter two; however, the question remains open if further improvement is possible as previous algorithms explore solutions in terms of local minima and local maxima, not in terms of the full search space globally. Therefore, the main contribution of this paper is to propose a new scheduling algorithm based on bio-inspired computation (the bat algorithm) to address this limitation. First, the algorithm defines a fitness and objective function over a search space, which returns all possible sleep and wake-up schedules for each node in the WSN. This yields a (scheduling) solution space that is then organized by the Pareto sorting algorithm, whose output coordinates are the distance of each node to the base station and the residual energy of the node. We evaluated our results by comparing the bat and HMM node scheduling algorithms implemented in MATLAB. Our results show that network lifetime has improved by 30%, coverage by 40%, and connectivity by 26.7%. In principle, the obtained solution will be the best scheduling that guarantees the best network lifetime performance as well as the best coverage and connectedness for ensuring the dependability of safety-critical WSNs. [ABSTRACT FROM AUTHOR]

Current real-time systems applications are mostly large-scale, where tasks are intrinsically time-constrained and meaningfully dependent. Nevertheless, while the initial partitioning solution has a huge impact on scheduling results, most tasks-allocation problems are NP-complete and lack binding semantics. The scheduling analysis strategies are inevitably required to validate the system scheduling before its actual deployment. However, once the proposed scheduling is invalid, no possible corrections are provided, and a costing NP-complete partitioning regeneration is provoked as the only possible correction action. Whereas, sometimes nearby task re-allocations guarantee the scheduling correction. This paper proposes an approach to efficiently correct initial dependent task partitioning solutions while considering their allocation semantics. The ultimate objective is to conduct a correct schedule for multiprocessor real-time systems by means of a driven task re clustering and/or re-allocation without the need for extra complex partitioning regeneration. Simulation results show that our proposed approach is faster than classic partitioning regeneration, ameliorates the partitioning semantic results, and gives efficient scheduling results. [ABSTRACT FROM AUTHOR]

We present a near real-time solution for 3D reconstruction from aerial images captured by consumer UAVs. Our core idea is to simplify the multi-view stereo problem into a series of two-view stereo matching problems. Our method applies to UAVs equipped with only one camera and does not require special stereo-capturing setups. We found that the neighboring two video frames taken by UAVs flying at a mid-to-high cruising altitude can be approximated as left and right views from a virtual stereo camera. By leveraging GPU-accelerated real-time stereo estimation, efficient PnP correspondence solving algorithms, and an extended Kalman filter, our system simultaneously predicts scene geometry and camera position/orientation from the virtual stereo cameras. Also, this method allows for the user selection of varying baseline lengths, which provides more flexibility given the trade-off between camera resolution, effective measuring distance, flight altitude, and mapping accuracy. Our method outputs dense point clouds at a constant speed of 25 frames per second and is validated on a variety of real-world datasets with satisfactory results. [ABSTRACT FROM AUTHOR]

To improve the performance of real‐time systems, heterogeneous asymmetric multiprocessors are proposed. Heterogeneous asymmetric multiprocessor architecture allows the system to better match computational resources to meet the needs and dynamic workload of each application. The benefits of system performance and reduced power consumption cannot be fully exploited unless proper task scheduling and task allocation methods are implemented at the operating system level.The EDZL scheduling algorithm has been shown to be a better scheduling algorithm in heterogeneous multiprocessor platforms. In this paper, the BSF‐EDZL (best speed fit for earliest deadline first until zero‐laxity) performance asymmetric multiprocessor scheduling algorithm is proposed, and an effective schedulability analysis is performed. The algorithm selects an appropriate processor for non‐zero relaxation highest priority task rather than the fastest processor when assigning tasks. Experimental results show that the proposed BSF‐EDZL scheduling algorithm can schedule a larger set of tasks than the ordinary EDZL scheduling algorithm. [ABSTRACT FROM AUTHOR]

Verification of real-time system properties using formal models can improve system design and quality. The Timed Petri net is a formal model for modelling and designing real-time systems with time constraints. Furthermore, model checking is a formal verification method used to verify system properties using model checkers. This article proposes an automated transformation system for mapping a Timed Petri net into one of the common model checkers, the SPIN PROMELA model, to verify real-time system properties. This approach enables the combination of two modelling paradigms and supports system verification using system design models. In this study, the system properties were verified using both the simulation method and linear temporal logic formulas supported by the SPIN model checker. Timed Petri net models of two different case studies, a central server computer system and a manufacturing Kanban system were considered to verify the boundedness, liveness and system behavioral properties using the proposed transformation system. [ABSTRACT FROM AUTHOR]

The existing research on time‐triggered network reconfiguration neglects redundant traffic states, posing a challenge in effectively balancing traffic performance and reconfiguration efficiency in practical deployments. In response to these limitations, a novel joint optimization approach is introduced for reconfiguration and traffic planning. Initially, the efficiency of reconfiguration solutions is enhanced by sacrificing the delay performance of redundant flows and prioritizing compressed yet tailored constraints. Subsequently, a tabu‐based optimization algorithm is employed to provide more flexible enhancements to the delay performance of network‐wide traffic. The outcomes demonstrate that the proposed method significantly outperforms incremental reconfiguration, improving the solution efficiency of the reconfiguration recovery phase by over 75.38%. Furthermore, through multiple rounds of reconfiguration, the approach achieves an 8.38% improvement in the average delay performance of all flows, even with redundant flows sacrificed. [ABSTRACT FROM AUTHOR]

In various industrial domains, machinery plays a pivotal role, with bearing failure standing out as the most prevalent cause of malfunction, contributing to approximately 41% to 44% of all operational breakdowns. To address this issue, this research employs a lightweight neural network, boasting a mere 8.69 K parameters, tailored for implementation on an FPGA (field-programmable gate array). By integrating an incremental network quantization approach and fixed-point operation techniques, substantial memory savings amounting to 63.49% are realized compared to conventional 32-bit floating-point operations. Moreover, when executed on an FPGA, this work facilitates real-time bearing condition detection at an impressive rate of 48,000 samples per second while operating on a minimal power budget of just 342 mW. Remarkably, this system achieves an accuracy level of 95.12%, showcasing its effectiveness in predictive maintenance and the prevention of costly machinery failures. [ABSTRACT FROM AUTHOR]

Vessel detection and tracking is of utmost importance to river traffic. Efficient detection and tracking technology offer an effective solution to address challenges related to river traffic safety and congestion. Traditional image-based object detection and tracking algorithms encounter issues such as target ID switching, difficulties in feature extraction, reduced robustness due to occlusion, target overlap, and changes in brightness and contrast. To detect and track vessels more accurately, a vessel detection and tracking algorithm based on the LiDAR point cloud was proposed. For vessel detection, statistical filtering algorithms were integrated into the Euclidean clustering algorithm to mitigate the effect of ripples on vessel detection. Our detection accuracy of vessels improved by 3.3% to 8.3% compared to three conventional algorithms. For vessel tracking, L-shape fitting of detected vessels can improve the efficiency of tracking, and a simple and efficient tracking algorithm is presented. By comparing three traditional tracking algorithms, an improvement in multiple object tracking accuracy (MOTA) and a reduction in ID switch times and number of missed detections were achieved. The results demonstrate that LiDAR point cloud-based vessel detection can significantly enhance the accuracy of vessel detection and tracking. [ABSTRACT FROM AUTHOR]

A decomposed dynamic dispatching and preventive maintenance (D-DDPM) framework is presented to overcome the computational challenges of solving large DDPM problems. Stochastic deterioration of machine health is explicitly considered. By efficient allocation of individual machine’s capacity under different machine health, the D-DDPM framework decomposes the global DDPM problems into single-machine DDPM problems while ensuring throughput optimality. Intelligent machine agents are then designed to solve the single-machine DDPM problems efficiently and to perform real-time decisions for individual machines. After the decomposition, the overall computational complexity grows only linearly with the number of machines and the proposed framework could be adopted in large systems. In the numerical study, the D-DDPM framework is near-optimal in small systems that have optimal DDPM solutions. In which, the computation time is reduced by 90.72% and the average cycle time is within 6% of optimal. For large systems without optimal DDPM solutions, numerical results show that the D-DDPM maintains throughput optimality and reduces average cycle time by up to 90% against other control policies. [ABSTRACT FROM AUTHOR]

Motor imagery-based brain–computer interfaces (MI-BCIs) are a promise to revolutionize the way humans interact with machinery or software, performing actions by just thinking about them. Patients suffering from critical movement disabilities, such as amyotrophic lateral sclerosis (ALS) or tetraplegia, could use this technology to interact more independently with their surroundings. This paper aims to aid communities affected by these disorders with the development of a method that is capable of detecting the intention to execute movements in the upper extremities of the body. This will be done through signals acquired with an electroencephalogram (EEG), their conditioning and processing, and their subsequent classification with artificial intelligence models. In addition, a digital signal filter will be designed to keep the most characteristic frequency bands of each individual and increase accuracy significantly. After extracting discriminative statistical, frequential, and spatial features, it was possible to obtain an 88% accuracy on validation data with a random forest (RF) model when it came to detecting whether a participant was imagining a left-hand or a right-hand movement. Furthermore, a convolutional neural network (CNN) was used to distinguish if the participant was imagining a movement or not, which achieved 78% accuracy and 90% precision. These results will be verified by implementing a real-time simulation with the usage of a robotic arm. [ABSTRACT FROM AUTHOR]

Nowadays, security and scheduling have become more critical issues of rapidly increasing applications running in real-time systems. In this work, we tried to solve these intricate issues and develop a security-driven task scheduling algorithm for real-time applications using Fog computing, which is abbreviated as SDTS. It works for the enhancement of both the security and scheduling services of real-time applications. SDTS is the integration of various proposed algorithms that are modified quadratic search algorithm, Min_Heap algorithm, and modified Earliest Deadline First (m-EDF). In which, modified quadratic search, Min Heap algorithm, and m-EDF algorithms are used for evaluating the average security value of tasks, optimal nodes using Fog computing, and task selection according to their absolute deadlines, respectively. The quantitative results analysis shows that the proposed model outperforms the existing models in terms of guarantee ratio (rate of success), average security value, and overall performance, i.e. 7.4% for success ratio, 7.69% for average security value, and 1.02% for overall performance in comparison to traditional methods. [ABSTRACT FROM AUTHOR]

In avionics system networking, mixed‐critical networks are commonly used to integrate system applications with different levels of guarantees for various traffic classes on a shared resource platform, effectively addressing issues such as cabling, cost, weight, volume, power consumption, etc. However, current fault recovery and reconfiguration techniques mainly focus on high‐priority time‐triggered (TT) traffic, often neglecting the impact of TT traffic reconfiguration on low‐priority traffic. This paper proposes an avionics mixed‐critical networking approach based on the IEEE 802.1CB protocol, which is shown to be more effective for the post‐failure recovery of Rate‐Constrained (RC) traffic (a typical type of low‐priority traffic in TT networks). Furthermore, an elastic reconfiguration algorithm is presented to ensure quality of service for RC traffic during and after reconfiguration, preventing arbitrary application and traffic shutdown triggered by TT traffic reconfiguration. Simulations demonstrate that the proposed approach and algorithm can significantly improve the post‐failure recovery ratio of RC traffic, as well as guarantee quality of service and network throughput of RC traffic during and after reconfiguration to a certain extent. [ABSTRACT FROM AUTHOR]

The signal-to-noise ratio of optical emission spectroscopy (OES) data has decreased as the plasma etching process has advanced. As a result, not only the advanced endpoint detection method was required, but also the selection of more informative wavelengths. This paper proposes an improved endpoint detection algorithm by combining data-driven wavelength selection and a Gaussian mixture model (GMM). The data-driven wavelength selection algorithm finds the correlation between training data and a sigmoid function of time. Then, using the fitted GMM of the training data in latent space, the endpoint of the test data is determined in real-time. The proposed algorithm’s performance was evaluated using real OES data, comprised of seven operations. The correlation-based wavelength selection algorithm significantly reduced detection error by 70.2% when compared to the conventional method, which selects a few wavelengths manually based on prior knowledge. Additionally, the GMM detection method clustered OES data from low open area wafers much more clearly than the recently proposed method using GMM. This demonstrates that combining correlation-based wavelength selection with GMM is an effective method for detecting endpoints during plasma etching of small open area wafers. [ABSTRACT FROM AUTHOR]

Context modeling or multi-level feature fusion methods have been proved to be effective in improving semantic segmentation performance. However, they are not specialized to deal with the problems of pixel-context mismatch and spatial feature misalignment, and the high computational complexity hinders their widespread application in real-time scenarios. In this work, we propose a lightweight Context and Spatial Feature Calibration Network (CSFCN) to address the above issues with pooling-based and sampling-based attention mechanisms. CSFCN contains two core modules: Context Feature Calibration (CFC) module and Spatial Feature Calibration (SFC) module. CFC adopts a cascaded pyramid pooling module to efficiently capture nested contexts, and then aggregates private contexts for each pixel based on pixel-context similarity to realize context feature calibration. SFC splits features into multiple groups of sub-features along the channel dimension and propagates sub-features therein by the learnable sampling to achieve spatial feature calibration. Extensive experiments on the Cityscapes and CamVid datasets illustrate that our method achieves a state-of-the-art trade-off between speed and accuracy. Concretely, our method achieves 78.7% mIoU with 70.0 FPS and 77.8% mIoU with 179.2 FPS on the Cityscapes and CamVid test sets, respectively. The code is available at https://nave.vr3i.com/ and https://github.com/kaigelee/CSFCN. [ABSTRACT FROM AUTHOR]

It is challenging to characterize the intrinsic geometry of high-degree algebraic curves with lower-degree algebraic curves. The reduction in the curve’s degree implies lower computation costs, which is crucial for various practical computer vision systems. In this paper, we develop a characteristic mapping (CM) to recursively degenerate $\mathbf {3n}$ points on a planar curve of $n$ th order to $\mathbf {3(n-1)}$ points on a curve of $\mathbf {(n-1)}$ th order. The proposed characteristic mapping enables curve grouping on a line, a curve of the lowest order, that preserves the intrinsic geometric properties of a higher-order curve (ellipse). We prove a necessary condition and derive an efficient arc grouping module that finds valid elliptical arc segments by determining whether the mapped three points are colinear, invoking minimal computation. We embed the module into two latest arc-based ellipse detection methods, which reduces their running time by 25% and 50% on average over five widely used data sets. This yields faster detection than the state-of-the-art algorithms while keeping their precision comparable or even higher. Two CM embedded methods also significantly surpass a deep learning method on all evaluation metrics. [ABSTRACT FROM AUTHOR]

Recently, clustering-based methods have been the dominant solution for unsupervised person re-identification (ReID). Memory-based contrastive learning is widely used for its effectiveness in unsupervised representation learning. However, we find that the inaccurate cluster proxies and the momentum updating strategy do harm to the contrastive learning system. In this paper, we propose a real-time memory updating strategy (RTMem) to update the cluster centroid with a randomly sampled instance feature in the current mini-batch without momentum. Compared to the method that calculates the mean feature vectors as the cluster centroid and updating it with momentum, RTMem enables the features to be up-to-date for each cluster. Based on RTMem, we propose two contrastive losses, i.e., sample-to-instance and sample-to-cluster, to align the relationships between samples to each cluster and to all outliers not belonging to any other clusters. On the one hand, sample-to-instance loss explores the sample relationships of the whole dataset to enhance the capability of density-based clustering algorithm, which relies on similarity measurement for the instance-level images. On the other hand, with pseudo-labels generated by the density-based clustering algorithm, sample-to-cluster loss enforces the sample to be close to its cluster proxy while being far from other proxies. With the simple RTMem contrastive learning strategy, the performance of the corresponding baseline is improved by 9.3% on Market-1501 dataset. Our method consistently outperforms state-of-the-art unsupervised learning person ReID methods on three benchmark datasets. Code is made available at: https://github.com/PRIS-CV/RTMem. [ABSTRACT FROM AUTHOR]

Faster computation of a weighted median (WM) filter is impeded by the construction of a weighted histogram for every local window of data. Since the calculated weights vary for each local window, it is difficult, using a sliding window approach, to construct the weighted histogram efficiently. In this paper, we propose a novel WM filter that overcomes the difficulty of histogram construction. Our proposed method achieves real-time processing for higher resolution images and can be applied to multidimensional, multichannel, and high precision data. The weight kernel used in our WM filter is the pointwise guided filter, which is derived from the guided filter. The use of kernels based on the guided filter avoids gradient reversal artifacts and shows a higher denoising performance than the Gaussian kernel based on the color/intensity distance. The core idea of the proposed method is a formulation that allows the use of histogram updates with a sliding window approach to find the weighted median. For high precision data we propose an algorithm based on a linked list that can reduce the memory requirements of storing histograms and the computational cost of updating them. We present implementations of the proposed method that are suitable for both CPU and GPU. Experimental results show that the proposed method indeed realizes faster computation than conventional WM filters and is capable of filtering multidimensional, multichannel, and high precision data. This is an approach which is difficult to achieve with conventional methods. [ABSTRACT FROM AUTHOR]

This paper introduces a one-dimensional convolutional neural network (CNN) hardware accelerator. It is crafted to conduct real-time assessments of bearing conditions using economical hardware components, implemented on a field-programmable gate array evaluation platform, negating the necessity to transfer data to a cloud-based server. The adoption of the down-sampling technique augments the visible time span of the signal in an image, thereby enhancing the accuracy of the bearing condition diagnosis. Furthermore, the proposed method of quaternary quantization enhances precision and shrinks the memory demand for the neural network model by an impressive 89%. Provided that the current signal data sampling rate stands at 64 K samples/s, the proposed design can accomplish real-time fault diagnosis at a clock frequency of 100 MHz. Impressively, the response duration of the proposed CNN hardware system is a mere 0.28 s, with the fault diagnosis precision reaching a remarkable 96.37%. [ABSTRACT FROM AUTHOR]

This article presents the development of a geo-visualization tool, which provides police officers or any other type of law enforcement officer with the ability to conduct the spatiotemporal predictive geo-visualization of criminal activities in short and continuous time horizons, according to the real events that are happening: that is, for those geographical areas, time slots, and dates that are of interest to users, with the ability to consider individual events or groups of events. This work used real data collected by the Colombian National Police (PONAL); it constitutes a tool that is especially effective when applied to Real-Time Systems for crime deterrence, prevention, and control. For its creation, the spatial and temporal correlation of the events is carried out and the following deep learning techniques are employed: CNN-1D (Convolutional Neural Network-1D), MLP (multilayer perceptron), LSTM (long short-term memory), and the classical technique of VAR (vector autoregression), due to its appropriate performance in the multi-step and multi-parallel forecasting of multivariate time series with sparse data. This tool was developed with Open-Source Software (OSS) as it is implemented in the Python programming language with the corresponding machine learning libraries. It can be implemented with any geographic information system (GIS) and used in relation to other types of activities, such as natural disasters or terrorist activities. [ABSTRACT FROM AUTHOR]

Drowning is a significant public health concern. A video drowning detection algorithm is a helpful tool for finding drowning victims. However, there are three challenges that drowning detection research typically encounters: a lack of actual drowning video data, subtle early drowning traits, and a lack of real time. In this paper, the authors propose an underwater computer vision based drowning detection device composed of embedded AI devices, camera, and waterproof case to solve the above problems. The detection device utilizes the high‐performance computing of Jetson Nano to realize real‐time detection of drowning events through the proposed drowning detection algorithm on the acquired underwater video stream. The proposed drowning detection algorithm primarily consists of two stages: in the first step, to successfully solve the interference of the surroundings and to give a trustworthy basis for video drowning detection, the YOLOv5n network is used to detect the near‐vertical human body based on the characteristics of the drowning person. In the second stage, the authors propose a lightweight drowning detection network (DDN) based on a deep Gaussian model for fast feature vector detection. The lightweight DDN is combined with the Gaussian model to detect anomaly in the high‐level semantic features, which has higher robustness and solves the lack of drowning videos. The experimental results show that the proposed drowning detection algorithm has good comprehensive performance and practical application value. [ABSTRACT FROM AUTHOR]

Radar warning receivers are real-time systems used to detect emitted signals by the enemy targets. The conventional method of detecting the signal is to determine the noise floor and differentiate the signals above the noise floor by setting a threshold value. The common methodology for detecting signals in noisy environment is Constant False Alarm Rate (CFAR) detection. In CFAR methodology, threshold level is determined for a specified probability of false alarm. CFAR dictates the signal power to be detected is higher than the noise floor, i.e. signal-to-noise ratio (SNR) should be positive. To detect radar signals for negative SNR values machine learning techniques can be used. It is possible to detect radar signals for negative SNR values by Long Short-Term Memory (LSTM) Artificial Neural Network (ANN). In this study, we evaluated whether LSTM ANN can replace the CFAR algorithm for signal detection in real-time radar receiver systems. We implemented a Field Programmable Gate Array (FPGA) based LSTM ANN architecture, where pulse signal detection could be performed with 94% success rate at -5 dB SNR level. To the best of our knowledge our study is the first where LSTM ANN is implemented on FPGA for radar warning receiver signal detection. [ABSTRACT FROM AUTHOR]

A passive current sharing method for paralleled LLC resonant converters is proposed in this article, and the current sharing effect of paralleled common capacitor LLC resonant converter is analyzed. The passive current sharing method is achieved by adding coupled impedance between the LLC resonant converters, and the coupled impedance can be inductive, capacitive, infinitely small, and infinitely large. Among them, it is traditional paralleled LLC resonant converter without current sharing ability when the coupled impedance is infinitely large. First, the mathematical model of paralleled common capacitor LLC resonant converter with coupled impedance is built based on fundamental harmonic analysis. Second, according to the mathematic model, the current sharing effect of the paralleled common capacitor LLC resonant converter of different coupled impedance under different parameter difference is analyzed, it is easy to see the current sharing effect is improved after the couple impedance is added, and in general, inductive coupled impedance has a better current sharing effect for paralleled common capacitor LLC resonant converter. Last, the theoretical analysis results are verified by simulation and experimental results. [ABSTRACT FROM AUTHOR]

Unexpected faults that occur in the sensors of the single-phase grid-connected converter (SGC) may deteriorate the control performance ofthe grid current and dc-link voltage and pose risks to the connected distributed generation system. This article proposes an unknown input sliding mode observer (UI-SMO) based sensor fault identification and remediation strategy. Therein, an UI-SMO is established that integrates the observations of the grid current and dc-link voltage with the reconstruction of the unknown input (i.e., load condition of the SGC). Such an observer-based analytical redundancy generates the residuals that indicate the abnormalities of the sensors for fault identification. An adaptive fault-tolerant control (AFTC) strategy for the SGC is developed, where an automated control system reconfiguration with unknown input adaptation is seamlessly implemented by substituting the data reported by the faulty sensors with their observed values. Extensive simulation and experimental studies validate the effectiveness of the proposed UI-SMO-based sensor fault identification and AFTC strategy of the SGC. [ABSTRACT FROM AUTHOR]

The electromagnetic levitation (EML) system serves as a key subsystem in maglev trains for the purpose of levitation. It is highly dynamic, open-loop unstable, and safety-critical. The expense of establishing an accurate model out of the Maglev train, in addition to the varying operating conditions, results in an imperfectly known model in engineering practice. Thus high-performance levitation control, w.r.t. an imperfectly known model, is of considerable practical interest. Motivated by such an observation, this article investigates real-time levitation performance optimization of the EML system, with an imperfectly known model. The EML system is first modeled and an equivalent demonstration benchmark is developed. Then, the structure for levitation performance optimization is presented on top of the coprime factorization technique. Furthermore, the real-time levitation performance optimization algorithm is developed, utilizing only the input and output data. In the end, the proposed methods are validated on the benchmark. [ABSTRACT FROM AUTHOR]

The classical formulation of the transmission switching problem as a mixed‐integer problem is intractable for large systems in real‐time control settings. Several heuristics have been proposed in the past to speed up the computation time, which only limits the number of switchable lines. In this paper, a real‐time switching heuristic based on neural networks that provides almost instantaneous switching actions, are presented. The findings are shown on case studies of the IEEE 118‐bus test system, and the results show that the proposed heuristic is robust to out of distribution data. Additionally, the proposed heuristic has significant computational savings while all other performance metrics like accuracy are similar to state‐of‐the‐art machine learning methods proposed for transmission switching. [ABSTRACT FROM AUTHOR]

Real-time plasma control systems are at the heart of operation of modern tokamaks. The control system of the TCV tokamak has recently seen a major upgrade in terms of its hardware and software components. Control algorithms are entirely programmed and tested in MATLAB/Simulink(R), executable code is automatically generated and interfaced to run-time parameters and signals through introspection using the MARTe2 software framework. The primary control system (used for coil currents, plasma current, position, shape and density control) has been upgraded by installing new analog signal inputs/output hardware, connected to two real-time computers that can operate in parallel. In addition, an EtherCAT-based real-time industrial network has been deployed to operate distributed low Input/Output count subsystems, greatly boosting the system flexibility and reach. The majority of TCV's real-time codes have been ported to this new approach, such as the real-time equilibrium reconstruction (LIUQE), real-time Magneto-Hydro-Dynamics mode analysis algorithms, and a real-time plasma supervision, actuator management and plasma event monitor, including real-time plasma analysis using neural networks, and plasma disruption avoidance control schemes. • Latest upgrades and advances of the real-time control system of the TCV tokamak. • Integration of commercial products (Simulink) and open source solutions (MARTe2). • Flexible and extensible plasma control system. • Wide machine diagnostics and actuators coverage. • Updated list of plasma control experimental applications. [ABSTRACT FROM AUTHOR]

Cyber-Physical Systems (CPSs) require the interfacing of two different time domains: (1) the physical time-continuous domain and (2) the computational time-discrete domain. The Nyquist-Shannon sampling theorem allows transitions from one domain to the other. To preserve the informational signal between both domains, strictly periodic sampling, also known as uniform spaced sampling, is required. CPSs utilise real-time systems to meet temporal constraints. However, real-time systems introduce jitter when several tasks are executed; consequently, the uniform spaced sampling is jeopardized. The Paley-Wiener-Levinson theorem should be used for non-uniform spaced sampling, but its complexity makes it unsuitable for runtime execution in CPSs. Jitter in real-time systems may cause perturbations in CPS applications. In this paper, frequency domain methodologies are proposed to analyse the perturbations introduced by real-time systems in physical applications. It is shown that jitter may introduce frequency components with the potential to jeopardize the dynamics of the physical application. In this paper, it is proposed that jitter should be related to the period of the fundamental frequency to sample, instead of to the period of the real-time task. The frequency domain methodology proposed should be used to analyse the real-time system behaviour from a CPS application point of view. [ABSTRACT FROM AUTHOR]

Cybercriminals use domain generation algorithms (DGAs) to prevent their servers from being potentially blacklisted or shut down. Existing reverse engineering techniques for DGA detection is labor intensive, extremely time-consuming, prone to human errors, and have significant limitations. Hence, an automated real-time technique with a high detection rate is warranted in such applications. In this article, we present a novel technique to detect randomly generated domain names and domain name system (DNS) homograph attacks without the need for any reverse engineering or using nonexistent domain (NXDomain) inspection using deep learning. We provide an extensive evaluation of our model over four large, real-world, publicly available datasets. We further investigate the robustness of our model against three different adversarial attacks: DeepDGA, CharBot, and MaskDGA. Our evaluation demonstrates that our method is effectively able to identify DNS homograph attacks and DGAs and also is resilient to common evading cyberattacks. Promising results show that our approach provides a more effective detection rate with an accuracy of 0.99. Additionally, the performance of our model is compared against the most popular deep learning architectures. Our findings highlight the essential need for more robust detection models to counter adversarial learning. [ABSTRACT FROM AUTHOR]

Improving the efficacy of emergency responses with digital means is receiving increasing attention. Currently, several innovative information technologies and systems are being developed to raise the situation awareness of first responders like firefighters. Among them, emergency response information systems (ERIS) appear to provide a particularly promising platform, which helps to gather, analyze, and share relevant information during emergencies. However, the conditions under which firefighters accept or reject such systems remain unclear. Existing theories explain the acceptance of information technologies only on a general level that does not consider the specific usage constraints existing in the firefighter domain. To fill this literature gap, we propose a detailed, domain-specific acceptance model with factors that explain the acceptance of ERIS by firefighters. It combines findings of the user satisfaction and the technology acceptance literature and was developed based on the input of 82 domain experts. An evaluation of the acceptance model in a survey with 212 firefighters from Germany indicates that it is effective in predicting a firefighter's intention to use an ERIS. The identified acceptance factors provide guidance for the design and evaluation of ERIS, enabling the so far mostly theoretical benefits of ERIS to be transferred into practical applications more effectively. [ABSTRACT FROM AUTHOR]

In recent studies, the power scheduling in more electric aircraft (MEA) has been formulated as a mixed-integer quadratic programming problem. Many model-driven methods, such as branch-and-bound algorithms, are advocated to solve it. However, these methods are often prone to considerably high complexities, which makes real-time processing a problem. In this article, a two-stage hybrid learning-based optimization approach is proposed to address this challenging issue. In the first stage, the task of optimizing integer variables is considered as a multilabel classification problem, which is solved by a data-driven method, i.e., ensemble deep neural networks (EDNNs). In the second stage, with the obtained integer solutions, the problem is transformed into a quadratic programming problem that can be quickly solved by model-driven numerical optimization methods. Compared to the state-of-the-art power scheduling algorithms, the model-driven and data-driven methods can compensate each other so that this two-stage hybrid learning-based approach can achieve orders of magnitude speedup in computational time, while guaranteeing the optimal scheduling performance. The structures of EDNNs are well designed so that the proposed approach can adapt to varying operating conditions in MEA. An offline simulation test and an online hardware-in-the-loop test validate the above advantages of the proposed approach. [ABSTRACT FROM AUTHOR]