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Review on Diagnostic Techniques for Laser Inertial Confinement Fusion Implosion
WU Yuji;YE Jiajie;ZHANG Qing;SHEN Ruiqiang;LUO Yibo;LI Yu;LIU Jiajun;The diagnosis of laser inertial confinement fusion(ICF) implosions is critical for understanding fusion physics, evaluating compression efficiency, and achieving ignition goals. Thus, it has significant importance for realizing stable high-gain ignition. Based on the implosion timeline, this study systematically reviewed the fundamental principles and research progress of four diagnostic techniques, namely shock wave, X-ray, neutron, and proton diagnosis methods. Shock wave diagnosis has evolved from point measurements to multidimensional imaging and integrated active-passive approaches. X-ray imaging has developed into a complementary system comprising self-emission, backlighting, and phase-contrast imaging. Proton diagnosis has expanded from passive time-integrated measurements to active single-frame imaging and time-resolved imaging. Neutron diagnosis has achieved comprehensive characterizations of the ion temperature, fuel areal density, and hot spot morphology through the synergistic development of energy-spectrum, yield-measurement, and imaging technologies. Consequently, this study analyzed the challenges faced by each diagnostic technique and comprehensively compared the four technologies and core sub-approaches across eight dimensions, namely the measurement core physical quantities, temporal resolution, spatial resolution, relative measurement error, applicable implosion stages, core advantages, core bottlenecks, and engineering maturity. It clarified the applicable boundaries and selection criteria for different technologies and discussed future trends toward higher precision, fuller dimensionality, higher temporal resolution, and intelligent development, aiming to provide systematic diagnostic technology references for the laser fusion ignition project of China.
Review on Cyber-Attack-Based Countermeasures for Distributed Unmanned Aerial Vehicle Swarms
ZHANG Yichi;WANG Le;GAO Jiuan;LIU Nanchi;XI Jianxiang;With the extension of modern control technologies to unmanned and clustered systems, distributed unmanned aerial vehicle(UAV) swarms have gained widespread application across multiple domains because of their flexible deployment and strong adaptability. The abuse of UAV swarms poses an increasingly severe threat to lowaltitude airspace, and distributed UAV swarms present unprecedented challenges to existing low-altitude defense systems. This paper provided a systematic review of countermeasures for distributed UAV swarms based on cyber attacks. First, an overview of non-cooperative distributed UAV swarms was provided, highlighting the severe threat they pose to existing countermeasure systems. Second, starting from the classic swarm countermeasure methods, this study elaborated on the existing swarm countermeasure technologies from the perspectives of physical and electromagnetic countermeasures. The advantages, disadvantages, and applicable scenarios of each countermeasure method were summarized, leading to the introduction of implementation methods for cyber-attack-based swarm countermeasures. Subsequently, focusing on cyber-attack methods, an overview of current theoretical and practical researches on cyber-attack methods in swarm countermeasure was conducted. Emphasis was placed on the operational pathways, mechanisms, and research progress of countermeasure methods based on denial-of-service, deception, and induced attacks, along with an analysis of the strengths and limitations of these three methods. Finally, based on the current research status of distributed UAV swarm technology and cyber-attack methods, the limitations of existing swarm countermeasures were summarized and the future development trends for distributed UAV swarm countermeasure methods based on cyber attacks were explored. Through the review of existing research and relevant technical methods, this study aims to provide a reference for research and practice in the field of non-cooperative distributed UAV swarm countermeasures, and promote the development of unmanned low-altitude defense systems in China.
Fault-Tolerant Predictive Control for Open-Winding Permanent Magnet Synchronous Motor Under Single-Phase Open-Circuit Fault
ZHANG Dongdong;CAI Boyan;HUANG Hongtao;YI Jiawei;CHU Shuaijun;GOH Hui Hwang;LIN Xiaogang;To address the limited available voltage vector space, high computational burden of an exhaustive search in traditional multi-vector model predictive current control, and increased current harmonics and torque ripple during fault-tolerant operation after a single-phase open-circuit fault in a common-DC-bus dual-inverter open-winding permanent magnet synchronous motor, a novel three-vector model predictive current control method was proposed. Taking a phase-a open-circuit fault as an example, the voltage vector distribution patterns during fault-tolerant operation with phases b and c and four healthy bridge arms were first analyzed. Nine independent voltage vectors were obtained from 16 switching states. After eliminating two large vectors that exceeded the linear modulation range, six equal-amplitude effective voltage vectors were selected as the basic control vectors. A reference voltage vector was calculated using the deadbeat prediction principle, and the first optimal vector was directly determined based on the sector that contained the reference voltage vector. Then, the second optimal vector was adaptively selected based on the predicted current error after applying the first vector. The action times of the zero, first, and second vectors were allocated with the minimum d-and q-axis current errors as constraints, thereby avoiding the sequential traversal of candidate sectors required by the traditional algorithm. Simulation results show that under 1 000 r/min and 5 N·m, the proposed algorithm reduces the total harmonic distortion rate of the B-phase current from 18.08% to 12.75%, and narrows the torque fluctuation range from approximately ±0.4 N·m to ±0.2 N·m. Experimental results demonstrate that under 400 r/min and 5 N·m, the total harmonic distortion of the B-phase current decreases from 18.57% to 13.38%. Under 1 000 r/min, when the load decreases from 5 N·m to 2 N·m and then increases from 2 N·m to 5 N·m, the system regulating times decreases from 0.332 and 0.541 s, to 0.278 and 0.495 s, respectively. These results verify that the proposed method can reduce the online computational overhead, improve the current waveform quality under single-phase open-circuit faults,suppress torque ripple,and enhance the dynamic disturbance rejection capability of the system.
Convolutional Neural Network-Bidirectional Long Short-Term Memory-Based Multi-State Joint Prediction Method for Lithium-Ion Batteries
LIU Hanru;ZHI Pengpeng;RONG Hao;JIAO Sheng;YANG Changlin;To address the challenge of joint prediction of lithium-ion battery state of health(SOH), state of charge(SOC), and state of energy(SOE), a joint prediction method based on convolutional neural network-bidirectional long short-term memory(CNN-Bi LSTM) was proposed. First, to overcome the redundancy and strong correlation of multidimensional health features in charge/discharge operational data, multi-dimensional feature parameters reflecting battery states were analyzed, and kernel principal component analysis method was employed for nonlinear dimensionality reduction. Thus, a low-dimensional feature space was constructed to characterize batterydegradation properties. Consequently, a convolutional neural network was utilized to extract local temporal features of data, and a bidirectional long short-term memory network was incorporated to mine forward and backward correlation information during degradation. A degradation-aware self-attention mechanism and weighted mean squared error loss function were introduced to enhance the ability of the model to represent critical degradation features in the later stage of battery aging and increase sample attention. Finally, the predicted SOH was incorporated as a dynamic boundary constraint into the SOC and SOE estimation processes, establishing a closed-loop multi-state joint prediction framework. Experimental results on the NASA lithium-ion battery dataset demonstrate that the proposed method achieves a root mean square error of 0.013 8 for SOH prediction, which is a reduction of 27.7% compared to the suboptimal method DAE-CNN-Bi LSTM-Attention. In the multi-state joint estimation, the prediction root mean squared error values for both the SOC and SOE reach 0.003 1, representing reductions of 26.19% and 39.22%, respectively, compared to the suboptimal method. The proposed method can effectively track the battery degradation trend while enabling accurate multi-state joint predictions.
Mechanical Performance of Concrete-Filled Steel Tube Column Bases with a Load-Transfer Core
XUE Hongjing;WANG Xiaochen;ZHANG Guowei;SHU Weinong;CAI Qing;LIU Jingyang;Beijing Institute of Architectural Design Co.Ltd.;China Academy of Building Research Co.Ltd.;To address the construction complexity and poor economic efficiency of traditional embedded column bases, as well as the insufficient seismic performance of exposed column bases, a novel concrete-filled steel tube(CFST) column base with a load-transfer core was proposed. This configuration incorporated a reinforced concrete core column inside a steel tube, which collaborated with inner-wall studs to establish an efficient composite load-transfer mechanism. To systematically evaluate the mechanical performance of CFST column bases, 1:4 scale quasi-static tests and refined finite element simulations were conducted to study the failure mode, hysteretic behavior, and seismic performance. The results were compared with those for exposed and embedded column bases with identical specifications. The force mechanism of the CFST column bases resembled that of reinforced concrete structures, with pronounced plastic development during the failure process. The final failure mode involved concrete crushing in the column base region and yielding of the internal longitudinal reinforcement, demonstrating typical ductile failure characteristics. Compared with exposed column bases, CFST column bases showed a significantly enhanced bearing capacity and ductility, with a gently declining skeleton curve, an increase in their cumulative energy dissipation capacity exceeding 130%, and slower stiffness degradation, representing a substantial improvement in comprehensive seismic performance. Compared with embedded column bases, CFST column bases achieved a comparable ultimate bearing capacity but maintained 92% of the capacity during large deformation stages, effectively avoiding the brittle failure caused by local buckling, exhibiting superior ductility and collapse resistance. Furthermore, the CFST column bases had an exposed configuration, which eliminated deep embedding requirements, significantly simplifying construction processes and reducing project costs, while ensuring robust seismic resistance. The result of this study provides a novel solution for steel column bases that combines technical advancement with economic feasibility.
Equivalent Calculation Method for Dynamic Response of Shaft Structures in Layered Geotechnical Media under Lateral Explosion
SUN Shanzheng;LIU Yuan;LI Zeng;YAO Yifan;Army Engineering University of PLA;An equivalent calculation method based on elastic restraint boundaries and equivalent uniformly distributed loads was proposed to address the complexity when calculating the dynamic response of shaft structures in layered geotechnical media under a lateral explosion. First, a finite element model validated by scale-model tests was constructed to analyze the load distribution and deformation characteristics of the shaft structure in a layered medium under a lateral explosion. Consequently, based on the deformation characteristics, the shaft was simplified as a cylindrical cantilever beam model under uniformly distributed loads with an elastically embedded boundary. Through theoretical analysis and parametric modeling, the influences of the structural parameters, geotechnical properties, and charge conditions on the restraint stiffness and load equivalent coefficient were obtained. Finally, a quantitative calculation method for the restraint stiffness and load equivalent coefficient was obtained, forming a complete equivalent calculation process. The results indicate that when establishing an equivalent calculation model for shaft structures in layered geotechnical media, the soft-hard rock interface can be described by a fixed boundary, and the restraint effect of the soft rock on the structure can be characterized by an elastic restraint boundary that includes translational and rotational stiffness values.The constrained stiffness increases exponentially with the increase of the stiffness coefficient of the short beam and increases with the increase of the elastic modulus of soft rock. The load equivalent coefficient increases with the increase of the ratio of the comprehensive stiffness of the structure to the modulus of the sand, and decreases with the increase of the distance of the charge ratio.
Research on Reliable Region-Guided Learning for Event Semantic Segmentation
KONG Xinyan;YAO Junping;LI Xiaojun;CHENG Peng;Event cameras feature a high temporal resolution, wide dynamic range, and low latency, providing effective edge and temporal information for semantic segmentation in complex illumination and high-speed motion scenarios. Focusing on reliable region-guided learning in event semantic segmentation, this study defined relevant concepts and task characteristics, and systematically reviewed typical methods based on event-active, edge-salient, and high-confidence prediction regions from the perspectives of spatiotemporal encoding representations of event data and sources of reliability evidence. Consequently, the performances and technical characteristics of related models were comparatively analyzed using common datasets and evaluation metrics, and the progress of the research on sparse event representation, reliable region selection, boundary preservation, and prediction consistency modeling was summarized. This review further identified several issues in existing studies, such as single-source reliability evidence, the inadequate exploitation of regional complementary relationships, and the limited adaptability to dynamic scenarios. Finally, future research directions were discussed in terms of sparse spatiotemporal modeling, boundary measurement and prior construction, multi-region collaborative guidance, and dynamic reliable region modeling, with the goal of providing a reference for research on reliable region-guided learning in event semantic segmentation.
Temporal Convolutional Network-Gated Recurrent Unit Single Channel Based on Adaptive Feature Enhancement Blind Source Separation Network
PENG Ruiyan;GUO Wenpu;GAO Shaoyuan;MU Chenchen;To address the issues of insufficient long-term dependency modeling and weak feature-capture capability in single-channel blind source separation for communication signals under extreme co-frequency aliasing and highorder modulation scenarios, a single-channel blind separation network based on an adaptive feature-enhanced temporal convolutional network-gated recurrent unit(TCN-GRU) was proposed. The architecture comprised four core modules: a multi-kernel depthwise convolutional encoder, an adaptive multi-scale feature attention module, a stacked adaptive TCN combined with a bidirectional GRU, and a multi-scale decoder. First, the multi-kernel depthwise convolutional encoder ensured reliable scenario adaptability. Second, the adaptive attention module dynamically adjusted channel weights to focus on critical differential features. Subsequently, the TCN-GRU fusion method captured the multi-scale dependencies of the signals. Finally, the multi-scale decoder guaranteed the integrity of signal reconstruction. Simulation experiments conducted on four typical aliasing scenarios involving homogeneous/heterogeneous modulation and symbol rates demonstrate that the proposed method achieves a stable performance across a signal-to-noise ratio(SNR) range of-10 d Bto 25 d B, with the separated signal waveforms closely matching the original signals and Pearson correlation coefficients approaching one above 10 d B. Robustness tests with respect to the degree of co-frequency aliasing reveal that when the carrier frequency interval is as low as 250 Hz, the scale-invariant signal-to-noise ratios(SI-SNRs) for all the scenarios remain above 22 d B, and the bit error rate(BER) stays within a usable range, with the effective operating boundary extending to 250 Hz. Compared with advanced models such as Conv-Tas Net and DPRNN, the proposed method achieves average SI-SNR improvements of 3.01 d Band 9.42 d Bunder a high SNR environment, respectively, reaching 34.55 d Bin the optimal scenario. Furthermore, ablation experiments indicate that the GRU module significantly reduces the BER and the adaptive multi-scale feature attention module brings SI-SNR gain of approximately 1-2 d Bin the mid-to-high SNR range, verifying the substantial contribution of each module to the communication demodulation performance.
