Radar-based ground target recognition faces significant challenges, including complex terrain, diverse target types, high recognition difficulty, and low accuracy. Moreover, the non-cooperative nature of military targets limits access to comprehensive target data, leading to sample imbalances that further degrade recognition performance. Addressing these issues, this paper proposes a target recognition method based on LightGBM, which balances model complexity and recognition accuracy. This method integrates a weighted focal loss function with dual-stage ground clutter suppression and enhancement techniques. Initially, during the data preprocessing phase, spherical hypothesis clustering, coupled with the local outlier factor algorithm, is utilized to mitigate ground target clutter. Subsequently, in the training phase for target recognition, the weights of imbalanced samples are dynamically adjusted to augment the model's learning capacity and heighten its focus on challenging targets. This approach dynamically adjusts the weights of imbalanced samples, thereby enhancing the model's learning ability and increasing its attention to difficult-to-classify instances. Additionally, to better accommodate complex backgrounds and bolster the model's robustness, an adaptive weighting coefficient adjustment mechanism is incorporated. Ultimately, ground targets are identified using a LightGBM multi-classifier. Simulations based on actual radar seeker data have validated the effectiveness of this method, and the recognition performance for six distinct target types has been evaluated. Comparative analyses with other classifiers demonstrate that this method exhibits superior performance in ground target recognition under conditions of imbalanced samples.

Synthetic aperture radar (SAR) can detect moving targets on the ground/sea, and high-resolution imaging on the ground/sea has critical applications in both military and civilian fields. This paper attempts to use a spaceborne SAR system to detect and image moving targets in the air for the first time. Due to the high velocity of aerial targets, they usually appear as two-dimensional range and azimuth direction defocus in SAR images, and clutter will also have a profound impact on target detection. To solve the above problems, a method of detecting and focusing on a spaceborne SAR target based on a two-dimensional velocity search is proposed by combining the BP algorithm. According to the current environment of the aerial target and the number of system channels, the clutter suppression methods are set and combined with two-dimensional velocity search with different precision, the Shannon entropy under different search velocity groups is used to obtain the search velocity group closest to the actual velocity and realize the integrated processing of moving target detection–focused imaging parameter estimation. Combined with simulation data, the effectiveness of the proposed method is verified.

In recent years, the detection performance of SAR-GMTI (synthetic aperture radar-ground moving target indication) algorithm based on deep learning has always been limited by insufficient measured data due to the heavy operation complexity and high cost of real SAR systems. To solve this problem, this paper proposes an overall DT-based implementation framework for SAR ground moving target intelligent detection tasks. In particular, by virtue of a SAR imaging algorithm, a high-fidelity twin replica of SAR moving targets is established in digital space through parameter traversal based on the prior target characteristics of the obtained measured datasets. Then, the constructed SAR twin datasets is fed into the neural network model to train an intelligent detector by fully learning features of the moving targets and preset the SAR scene in the twin space, which can realize the robust detection of ground moving targets in related practical scenarios with no need for multiple and complex field experiments. Moreover, the effectiveness of the proposed framework is verified on the MiniSAR measured system, and a comparison with traditional CFAR detection method is given simultaneously.

Combined with the synthetic aperture radar (SAR) imaging algorithm, diverse simulation sample sets of ground moving targets are constructed to tackle the problem of insufficient measured data in the SAR ground moving target indication algorithm based on deep learning. In view of this, a overall scheme of realizing robust detection of ground moving targets under varying backgrounds is conducted on the integration of the adaptive spatial location extraction network based on deformable module and the multichannel clutter suppression technology. In particular, a spatial deformable module is incorporated into the network to enhance its modeling capacity of the input targets with different shapes. Furthermore, the multichannel clutter suppression technology of airborne SAR is adopted to significantly mitigate the interference of complex background clutter. The effectiveness of the proposed method is verified on the simulation sample sets, and comparison with other detection methods is given simultaneously.

A high precision estimation algorithm for ground moving targets in multi-channel wide-area surveillance ground moving target indication systems is proposed based on maximum likelihood method. The main concept of this novel algorithm is to estimate the azimuth angle of the detected targets using maximum likelihood method with the space steering vector formed by the estimated interferometric phase extracted from the mainlobe clutter region of the real data. Through this novel algorithm, the effect of channel errors among the multi-channels can be well reduced. Simulation experiments demonstrate the effectiveness of the proposed algorithm.

Low-rank tensor completion (LRTC) has become more and more popular in the field of tensor completion. Because solving the tensor rank minimization is NP-hard, extensive surrogate norms of tensor rank have been proposed successively. Among these norms, the innovative nonconvex orthogonal transformed tensor Schatten-p norm (OTT S_p ) can better capture the low-rank property of tensor than most competitive norms. However, the OTT S_p method solely depends on the global low-rank prior and ignores the importance of the nonlocal similar structures, which play a significant role in the tensor data processing. In this paper, to address the defect of the OTT S_p method, we propose a novel LRTC model based on nonlocal self-similarity (NSS) regularization, which combines NSS regularization with the OTT S_p . As a nonlocal prior, NSS can preserve the nonlocal similar details, so the introduction of NSS regularization contributes to promoting the final inpainting performance. Therefore, our proposed model is capable of further conserving nonlocal self-similarities based on the global low-rankness. Moreover, the alternating direction method of multipliers is adopted to solve our proposed model. Experimental results on color images, grey-scale videos, and multispectral images demonstrate the superiority of our proposed method compared with other existing state-of-the-art methods.

Let X={Xn:n∈N} be a long memory linear process in which the coefficients are regularly varying and innovations are independent and identically distributed and belong to the domain of attraction of an α-stable law with α∈(0,2). Then, for any integrable and square integrable function K on R, under certain mild conditions, we establish the asymptotic behavior of the partial sum process ∑n=1[Nt][K(Xn)−EK(Xn)]:t≥0as N tends to infinity, where [Nt] is the integer part of Nt for t≥0.

Low-rank tensor completion aims to recover the missing entries of the tensor from its partially observed data by using the low-rank property of the tensor. Since rank minimization is an NP-hard problem, the convex surrogate nuclear norm is usually used to replace the rank norm and has obtained promising results. However, the nuclear norm is not a tight envelope of the rank norm and usually over-penalizes large singular values. In this paper, inspired by the effectiveness of the matrix Schatten-q norm, which is a tighter approximation of rank norm when 0 < q <  1, we generalize the matrix Schatten-q norm to tensor case and propose a Unitary Transformed Tensor Schatten-q Norm (UTT-Sq) with an arbitrary unitary transform matrix. More importantly, the factor tensor norm surrogate theorem is derived. We prove large-scale UTT-Sq norm (which is nonconvex and not tractable when 0 < q < 1) is equivalent to minimizing the weighted sum formulation of multiple small-scale UTT- $S_{q_{i}}$ (with different qi and qi ≥ 1). Based on this equivalence, we propose a low-rank tensor completion framework using Unitary Transformed Tensor Multi-Factor Norm (UTTMFN) penalty. The optimization problem is solved using the Alternating Direction Method of Multipliers (ADMM) with the proof of convergence. Experimental results on synthetic data, images and videos show that the proposed UTTMFN can achieve competitive results with the state-of-the-art methods for tensor completion.

In the video synthetic aperture radar (Video SAR) system, the moving target will leave a shadow at its actual position due to the Doppler effect. As the shadow of the moving target moves between Video SAR frames, the amplitudes of pixel points at the corresponding positions will jump between frames as well. According to this characteristic, a new method of Video SAR ground moving target detection and tracking based on the interframe amplitude temporal curves is proposed in this article. In this method, the specially designed multiple receptive field fusion neural network model based on frame variation (MRFN-FV) is used to classify the pixel points with obvious interframe amplitude jumps on the whole-time axis, and then, the false alarms are suppressed based on the temporal change characteristics of pixel points. Finally, the improved clustering algorithm is used to detect, locate, and track the moving targets in each frame of SAR images. The effectiveness of the proposed method is verified through the measured data recorded by the THz band Video SAR system.

智能工厂需要智能物流系统的支撑,尤其对立体工厂而言,物流系统往往是制约整个智能工厂生产系统能力和效率的主要瓶颈之一.通过仿真技术对智能工厂物流系统进行分析,针对智能工厂的立体物流结构特征,提出了两种优化方案,即分楼层单独优化和多楼层协同优化,并基于Anylogic仿真平台构建了立体物流仿真模型,设计了多种逻辑结构以确保货运电梯和自动引导车(Automated Guided Vehicle,AGV)顺利运行.通过模拟多场景下的物流活动,对不同情境下的AGV数量和利用率以及暂存区物料堆积量等指标进行了分析.结果表明,分楼层单独优化方案可以减少AGV数量,而多楼层协同优化方案能够提高AGV利用率.

Due to the existence of missing entries in real-world tensor data, low-rank tensor completion (LRTC) problem has received increasing attention. In this paper, we propose a new transformed tensor Schatten- norm to replace the rank norm and develop a transformed multi-tensor-Schatten- norm surrogate theorem to convert the non-convex transformed tensor Schatten- norm with 0<<1 into the sum of multiple convex functions. However, tensor completion constrained by low-rank prior alone cannot protect local smoothness along the spatial and tubal dimensions. To address this drawback, we combine anisotropic total variation (TV) regularization with non-convex transformed tensor Schatten- norm with 0<<1 for LRTC. The combination of global low-rank prior and local TV prior is beneficial to improving the final completion effect. Our experimental results on grey-scale video inpainting demonstrate that our proposed method outperforms other existing state-of-the-art methods.

课程思政作为高校教学改革的重点,不仅可以实现综合性素质人才的培养,也是对学生进行价值引领的重要渠道.本文以《管理学》课程为例,从教学目标、教学设计、教学组织及教学方法等方面对其课程思政的教学路径和实施进行了分析,同时也为其他专业课程的思政改革提供了借鉴.

燃气轮机研制是一个复杂的系统工程,需要众多不同类型、不同性质和不同研制合作方式的供应商共同完成,对研制项目管理而言,如何对异质供应商绩效进行评价是一个亟待解决的问题.针对此问题,首先将供应商按照特性分为技术供应商和物料供应商,然后构建了包括质量、成本、交付、技术和合作服务在内的指标体系,并提出了基于网络分析法(Analytic Network Process,ANP)和逼近理想解排序法(Technique for Order Preference by Similarity to an Ideal Solution,TOPSIS)的异质供应商绩效评价方法,通过ANP确定各级指标权重,借助TOPSIS消除指标量纲差异,最后应用示例分析,验证了该方法对复杂装备研制中异质供应商绩效评价的可行性和有效性.

立德树人是我国民高校高素质人才培养的重要目标,是"双师型"教师队伍形成的关键性因素.培养一支"课程思政"教师队伍需要建立教师对"课程思政"理念的高度认同感,形成一种科学有效的培育模式,建立一套可行的"双师型"教师培育培养机制,最终形成职业道德高尚、专业技能精湛、教学水平优质的"双师型"师资队伍,为高等教育办学和人才培养提供有力支撑和重要保障.

电力电子技术课程设计作为电气类专业的重点实践课程,需要提高实践环节的教学比重.本文通过设计并制作经典BUCK电路的示教板,给出了完成一个电力电子课程设计案例的流程.学生可以通过该案例完成电路理论计算、仿真分析、PCB绘制、元件选型以及焊接与调试的全过程,提高解决实际工程问题的能力.

现如今,进行特色专业建设与改革已成为高校核心竞争力的强有力武器,对独立学院而言亦是如此,要想使专业保持活力、获得更好的发展,必须实施特色教学.而特色课程是实施特色教学的重要载体,是培养特色人才的关键环节.本文以配载与平衡课程为例,对民航特色工商管理专业的教学方法进行分析,并提出了特色工商管理专业课程改革的建议.

在离散观测下,考虑平稳Ornstein-Uhlenbeck过程漂移项中未知参数估计量的渐近性质.利用多重Wiener-It?积分的偏差性质与渐近分析的技巧,得到了估计量的Cramér-型中偏差.同时,对于一类假设检验问题,构造了适当的检验统计量以及拒绝域.利用本文结果,可以证明第二类错误以指数速度衰减到零,最后数值模拟验证了理论的正确性.

在传统实验教学的基础上,应充分发挥相关软件的作用.尝试利用LabVIEW软件对电子负载进行程控.根据教学要求对程控的程序进行设计和优化,在此基础上设计了直观可操作性较强的虚拟面板.实例表明,通过虚拟面板讲解和演示可行性,可以加深对电子负载的认识,对其操作和使用特点更加了解.此外,程控设备的可扩展性较强,尤其适合多台设备联调.

在传统交互设计教学的基础上,应充分发挥相关软硬件的作用.尝试利用Arduino开发板作为交互设计课程的电控单元,对简单电路进行功能设计并实现.学生在完成外观设计的基础上,辅以Arduino小型电控系统,完成完整的交互设计过程.实践表明,该教学方案可以极大地提高学生对交互设计的理解,真实反映交互设计全过程,提高学生实践能力.

在传统电机学课程教学的基础上,应充分发挥相关仿真软件的作用.本文利用电磁场有限元分析软件An-soft/Maxwell,对电机学课程中涉及的电机结构进行建模仿真,通过直观的仿真波形与动画,帮助学生理解电机运行原理,提高分析能力.