With the recent development of deep learning (DL), DL-based autoencoder techniques provide a novel paradigm for end-to-end physical layer optimization. In this paper, we address the dynamic interference in an end-to-e...
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ISBN:
(数字)9798350303582
ISBN:
(纸本)9798350303599
With the recent development of deep learning (DL), DL-based autoencoder techniques provide a novel paradigm for end-to-end physical layer optimization. In this paper, we address the dynamic interference in an end-to-end communication system with a multiuser Gaussian interference channel. In this context, the standard constellation is not optimal under high interference conditions. To address this issue, we propose an adaptive learning algorithm for learning and predicting dynamic interference. Note that existing DL-based autoencoders are unable to train end-to-end learning systems by deep learning without a known channel. Thus, we propose a generative adversarial network (GAN)-based training scheme to imitate the real channel. Simulation results show that compared with traditional PSK and QAM modulation schemes, our proposed adaptive learning-based auto encoder can achieve significantly lower block error rate (BLER) in presence of interference. Besides, the BLER performance of our proposed GAN-based training scheme is close to that of the optimal training scheme with known channel on different channel models.
Adaptive dynamic programming is used in this paper to solve the hierarchical decision problem for non-affine multi-player systems. A new affine system is formed by adding control dynamics to the original system. To so...
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This paper investigates the robust containment control problem of uncertain heterogeneous linear multi-agent systems with unbounded transmission delays under some mild assumptions. A novel distributed output feedback ...
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This paper investigates the containment control problem of heterogeneous linear multi-agent systems with nonuniform bounded distributed communication delays under an assumption that only the neighboring agents of mult...
The failure rate of aluminum electrolytic capacitor (AEC) is high in power electronic systems. Due to the complex structure of AEC, it is difficult to establish an actual model, so experiments are the common way to an...
The failure rate of aluminum electrolytic capacitor (AEC) is high in power electronic systems. Due to the complex structure of AEC, it is difficult to establish an actual model, so experiments are the common way to analyze the failure of AEC. Thus, this paper proposes a finite element method (FEM) for AEC considering electrolytic failure. Firstly, the mechanism of electrolytic failure and changes of characteristic parameters in the AEC are elucidated. Secondly, an equivalent finite element model is established to simulate and analyze the electrolytic failure of AEC. Finally, the effectiveness of the proposed AEC FEM method is verified through capacitor aging experiments.
Compared to knowledge-based diagnostic systems, data-based methods tend to perform better in terms of speed and accuracy in diagnosing reactor accidents, and have significant advantages in terms of scalability of mode...
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Compared to knowledge-based diagnostic systems, data-based methods tend to perform better in terms of speed and accuracy in diagnosing reactor accidents, and have significant advantages in terms of scalability of models. With the increasing use of high-precision system analysis programs in nuclear engineering, the number of high-fidelity computational data for accident simulation is exploding. Therefore, an algorithm that can achieve both automatic extraction of low-dimensional features from the data and guarantee the validity of the features is needed to improve the performance and confidence of the accident diagnosis system. This study proposes an autoencoder-based autonomous learning framework, namely Padded Auto-Encoder (PAE), which is able to automatically encode accident monitoring data that has been noise-added and with partially missing data into low-dimensional feature vectors via a Vision Transformer-based encoder, and to decode the feature vectors into noise-free and complete reconstructed monitoring data. Thus, the encoder part of the framework is able to automatically infer valid representations from partially missing and noisy monitoring data that reflect the complete and noise-free original data, and the representation vectors can be used for downstream tasks for accident diagnosis or else. In this paper, LOCA of HPR1000 was used as the study object, and the PAE was trained by an unsupervised method using cases with different break locations and sizes as the dataset. The encoder part of the pre-trained PAE was subsequently used as the feature extractor for the monitoring data, and several basic statistical learning algorithms for predicting the break locations and sizes. The results of the study show that the pre-trained diagnostic model with two stages has a better performance in break location and size diagnostic capability with an improvement of 41.62% and 80.86% in the metrics respectively, compared to the diagnostic model with end-to-end model st
The corrosion behavior of alumina-forming austenitic (AFA) stainless steels with different Nb additions in supercritical carbon dioxide environment at 500℃, 600℃ and 20 MPa were investigated. A novel structure with ...
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Electrolytes hold the key to realizing reliable zinc(Zn)*** organic molecules have been proven effective in stabilizing Zn anodes;however,irrational comparisons exist due to the uncontrolled molecular weights and func...
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Electrolytes hold the key to realizing reliable zinc(Zn)*** organic molecules have been proven effective in stabilizing Zn anodes;however,irrational comparisons exist due to the uncontrolled molecular weights and functional group *** this work,two“isomeric molecules”:1,2-dimethoxyethane(DME)and 1-methoxy-2-propanol(PM),with identical molecular weights but different functional groups,have been studied as co-solvents in electrolytes,which have delivered distinct electrochemical *** and simulative study indicates the dipole moment induced by the hydroxyl groups in PM(higher molecular polarity than ether groups in DME)reconstructs the space charge region,enhances the concentration of Zn^(2+)in the vicinity of Zn anodes,and in-situ derives different solid electrolyte interphase(SEI)models and electrode-electrolyte interfaces,resulting in exceptional cycling ***,the Zn||Cu cell with PM worked over 2000 cycles with high Coulombic efficiency(CE)of 99.7%.The Zn||Zn symmetric cell cycled over 2000 h at 1 mA·cm^(−2),and showed excellent stability at an ultrahigh current density of 10 mA·cm^(−2)and capacity of 20 mAh·cm^(−2)over 200 h(depth of discharge,DOD of 70%).The Zn||sodium vanadate pouch cell with a high mass loading of 6.3 mg·cm^(−2)and a high capacity of 24 mAh demonstrates superior cyclability after 570 *** work can be a good starting point to provide reliable guidance on electrolyte design for practical aqueous Zn batteries.
The magnetic jack type control rod drive mechanism (CRDM) is a critical electromagnetic actuator utilized in reactors, with its movable components subject to the combined effects of electromagnetic force, liquid resis...
The magnetic jack type control rod drive mechanism (CRDM) is a critical electromagnetic actuator utilized in reactors, with its movable components subject to the combined effects of electromagnetic force, liquid resistance, spring force, and other factors, resulting in highly nonlinear and uncertain motion characteristics. This article establishes a novel model for the dynamic process analysis of the CRDM. The dynamic electromagnetic force and inductance of the CRDM are analyzed based on the equivalent magnetic network (EMN) model, taking into account mesh reconstruction and magnetic saturation. Through kinematic analysis and parameter coupling analysis, electromagnetic and kinematic parameters during the CRDM lifting process are obtained. Finally, finite element analysis (FEA) and experimentation are conducted to validate the accuracy of the proposed model.
This paper presents a simulation method for investigating the failure of optocoupler equipment (OCEP) in multi-physical fields. The method is based on the aging of the solder layer of the photosensitive field-effect t...
This paper presents a simulation method for investigating the failure of optocoupler equipment (OCEP) in multi-physical fields. The method is based on the aging of the solder layer of the photosensitive field-effect tube (PFET) and aims to analyze the performance degradation of the PFET. Firstly, a comprehensive multi-physical field model of the OCEP is developed using the actual structure of the CT test OCEP, taking into account the conductivity of the chip. Next, an OCEP failure simulation method is proposed, which takes into account the shedding of the PFET solder layer caused by the failure mechanism of the OCEP. Finally, the simulation results demonstrate that the degree of solder layer shedding reaches 70%, resulting in the failure of the OCEP.
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