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Electromagnetic launch (EML) systems with energy storage units in transient operating states are gradually being applied in modern ships, satellite launches, etc., power switching devices with higher voltage and switching frequency, Additionally, innovative topologies are being developed to enhance the electromagnetic performance of the EML systems. This inevitably leads to significant EMI issues. Therefore, this paper proposes a transient conducted EMI source modeling method for EML systems based on a multi-port equivalent circuit cascade model. Initially, a multi-port transient conducted EMI source model for supercapacitors is developed, taking into account the time-varying load characteristics of supercapacitors during charging and transient high-current discharge characteristics during discharging. Subsequently, a multi-port transient conducted EMI source model of the dual three-phase linear motor has been developed utilizing experimental measurement data. Leveraging the port-to-port cascade characteristics of various devices in the EML system, a dual three-phase dc-ac inverter multi-port conducted EMI model with high-frequency parasitic effects has been developed. A system-level modeling method based on the multi-port equivalent circuit cascade transient conducted EMI source modeling method is developed by cascading the multi-port equivalent circuit models of each functional component for the EML system. To evaluate the accuracy of this system-level modeling method, an experimental setup is constructed to assess transient conducted EM disturbances in a low-power EML system. The time-domain simulation results of the transient operating state are compared and analyzed with the actual operating conditions from the voltage, current, and energy perspectives, further validating the accuracy and efficacy of the EML system conducted EMI source modeling method based on a cascade of multi-port equivalent circuit models, as well as the transient conducted EM disturbances time-domain and frequency-domain characteristics.

Reconstructing high-quality images with few measurements has always been a primary goal for single-pixel imaging (SPI). Diffusion models have shown outstanding performance in image generation and have been effectively attempted in image reconstruction for ghost imaging. However, there is still a great deal of space for improvement in the quality of image reconstruction at low sampling rates. Inspired by the proximal gradient descent algorithm (PGD), we propose Diffusion Model with Gradient Descent Module Guiding Reconstruction for Single-Pixel Imaging. The gradient descent module in PGD is utilized for preliminary image reconstruction. The preliminary reconstruction serves as prior information to iteratively constrain the diffusion model, allowing it to generate target images consistent with the training data distribution. Additionally, the strong mapping ability of the diffusion model replaces the traditional proximal operator to accelerate convergence. Full connected sampling and convolutional sampling are proposed as alternative sampling methods to the traditional Gaussian random matrix sampling. Sampling and generation are optimized jointly to capture key image information and improve reconstruction accuracy. Simulations and experiments confirm that our proposed network can significantly improve the quality of image reconstruction at low measurement rates.

Predicting the wideband radiation of the high-power interconnection cable network within the system is necessary for the electromagnetic compatibility design verification of power electronic systems, and it is urgent to accurately model and analyze its radiation characteristics. The current modeling methods, such as the equivalent dipole method, have numerous constraints, making it difficult to effectively analyze multi-core cable network with connectors, transmission lines of arbitrary arrangement, and large-scale branches. To address this problem, this paper proposes a hybrid modeling method, which integrates multi-conductor transmission line (MTL) equations, dipole radiation theory, and multi-port network theory. Firstly, by using multi-port network theory, this method creates an equivalent circuit for the connection by extracting the S-parameters of the connector. Then, a cascaded transmission model is established using MTL equations, which can analyze the radiation characteristics of various interconnecting cable networks between electromechanical devices, including both straight and bent configurations. Next, the current distribution along the cable network is solved through termination conditions. Furthermore, dipole array radiation theory is used to calculate the radiated field of the complicated cable network with connectors, large-scale branches, and transmission lines of arbitrary layout. Finally, some examples are used to validate the validity and accuracy of this method.

A civil aircraft scaled demonstrator is an unmanned aerial vehicle (UAV) obtained by reducing the geometry of a civil aircraft by an equal scale and simplifying the airframe and airborne subsystems. Due to the low development cost and flight risk of scaled demonstrators, flight tests with demonstrators are an attractive way to assess the reliability and effectiveness of new configurations and/or technologies available for civil aircraft. Nevertheless, engineers still hope to reduce the development cycles and costs of demonstrators to evaluate their multiple civil aircraft design solutions through flight tests in a short period of time. Model-based systems engineering (MBSE) is a formalized requirement and architecture modeling method which is a useful tool in aircraft design. However, no existing MBSE framework has been found suitable for the development of demonstrators due to their unique features. In this paper, an MBSE approach for civil aircraft scaled demonstrator is proposed based on the MagicGrid methodology and the existing technical process for the demonstrator. This approach formalizes the requirement analysis and architecting for demonstrators via system modeling language (SysML). Meanwhile, the stakeholders of civil aircraft scaled demonstrators are identified, and a requirement ontology and modeling standard are established according to existing demonstrator development practice. Then, a case study is performed through a hybrid power demonstrator which carries a hydrogen fuel cell as the main power supply, a set of solar cells, and two lithium cells. The requirement traceability and verifiability are examined, and a logic simulation is executed for architecture, which shows the feasibility of applying the MBSE approach for the development of civil aircraft scaled demonstrators.

In nuclear power plants, due to the special working environment, the status of the fuel rods will be changed by the influence of radiation. And these changes, such as diameter expansion, will cause damage to the fuel rods and even affect the safe operation of nuclear power plants. Therefore, it is necessary to accurate diameter measurement for nuclear fuel rods. This paper aims to solve the measurement problem of fuel rods, and proposes a measurement technique based on image processing. In the proposed scheme, based on Zhang's method, tangential distortion is considered to be an affecting factor at first to improve the accuracy of camera calibration. Then, a particle swarm algorithm with all-parameter adaptive mutation method is proposed for the optimization of camera parameters. Finally, a simple and convenient method to calculate pixel equivalent is designed by considering the feature extraction of corner coordinates of subpixel to accurately obtain pixel equivalents. The effectiveness of this measurement scheme is highlighted on a measuring platform. The relative error of the proposed method reaches 0.041%, and RMSE reaches 4.16 μm. Experimental results demonstrate that this approach is efficient for high-accuracy diameter measurement of fuel rods.

For Internet of Vehicles (IoV) systems with multiple users, network coding can be introduced to provide efficient error control and throughput improvement services. However, if the heterogeneity characteristics and requirements of the end users (vehicles) are neglected, it will be difficult for an IoV system to provide each end user with fair system services, without which the advantages of network coding cannot be fully achieved and the performance of the multi-user diversity system will be degraded. In this paper, we propose a Dynamic Resource Scheduling Optimization (DRSO) algorithm, a dynamic fair scheduling algorithm combined with network coding for system resource allocation in a multi-user IoV system. We construct a general solution framework for service scheduling: first, we estimate the fairness index for each end user (vehicle) with the key information on Quality of Service (QoS). Second, we construct a service scheduling control model based on the service capability of control entities (multi-access edge computing servers), and propose a new utility evaluation function. Third, based on the fairness index, we select end users into multiple network coding sets. Network coding sets are the basic units of service scheduling. The optimization objective of the scheduling service is to maximize the total utility of all the network coding sets (the utility of the control entity). Finally, we establish a coding cache queue in the control entity based on the scheduling decision. To obtain the global optimal solution for active queue control, we combine a Quantum Particle Swarm Optimization (QPSO) algorithm with a Proportional Integral (PI) model. Then, the optimal scheduling decision can be made. Extensive simulation results show that DRSO outperforms related scheduling algorithms in varying traffic loads, demonstrating that DRSO can effectively guide service resource allocation.

Distracted driving causes a large number of traffic accident fatalities and is becoming an increasingly important issue in recent research on traffic safety. Gesture patterns are less distinguishable in vehicles due to in-vehicle physical constraints and body occlusions from the drivers. However, by capitalizing on modern camera technology, convolutional neural network (CNN) can be used for visual analysis. In this paper, we present a hybrid CNN framework (HCF) to detect the behaviors of distracted drivers by using deep learning to process image features. To improve the accuracy of the driving activity detection system, we first apply a cooperative pretrained model that combines ResNet50, Inception V3 and Xception to extract driver behavior features based on transfer learning. Second, because the features extracted by pretrained models are independent, we concatenate the extracted features to obtain comprehensive information. Finally, we train the fully connected layers of the HCF to filter out anomalies and hand movements associated with non-distracted driving. We apply an improved dropout algorithm to prevent the proposed HCF from overfitting to the training data. During the evaluation, we apply the class activation mapping (CAM) technique to highlight the feature area involving ten tested classes of typical distracted driving behaviors. The experimental results show that the proposed HCF achieves the classification accuracy of 96.74% when detecting distracted driving behaviors, demonstrating that it can potentially help drivers maintain safe driving habits.

This paper proposes a quick and accurate method based on an improved quantum-behaved particle swarm optimization (QPSO) algorithm for route planning of fixed-wing unmanned aerial vehicle (UAV). To overcome the deficiencies of local optima and slow global convergence speed, a novel strategy of particle dimension search is proposed in the QPSO algorithm. It is implemented by transforming original evaluation function into evaluation function of waypoint to more easily escape from local optima and accelerate global convergence speed. In addition, an efficient pretreatment technology for the initial trajectory is set to shorten the calculation time of route planning. Compared with other representative route planners, the comparison results indicate that the proposed route planner is more effective and feasible, which can take on faster convergence speed and better global search ability. The proposed route planner can provide a valuable reference for the route planning of fixed-wing UAVs in different environments.

Electrooptic modulators (EOMs) are key devices in performing the conversion between the electrical and optical domains in data communication links. With respect to a road map for photonic computing, future EOMs are required to be highly scalable, should feature strong modulation performance, and must not consume much power during operation. In light of these requirements, here, we investigate indium-tin-oxide (ITO) as an electrooptic switching material. The results show that ITO is capable of changing its extinction coefficient by a factor of 136. Utilizing these findings, we analyze an ultracompact (i.e., sub- λ long λ = 1310 nm) electroabsorption modulator based on a plasmonic MOS-mode design. In our analysis, we investigate the performance, i.e., the extinction ratio and insertion loss of the device as a function of various geometric parameters of the device. The optimized device is 0.78 λ long and features an extinction ratio and on-chip insertion loss of about 6 dB/μm and 0.7 dB, respectively. Furthermore, we suggest a metric to benchmark electroabsorption modulators and show that silicon plasmonics has potential for high-end switching nodes in future integrated photonic circuits.