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SAR Regional All-Azimuth Observation Orbit Design for Target 3D Reconstruction 用于目标三维重建的合成孔径雷达区域全方位观测轨道设计
IF 2.1 3区 计算机科学 Q3 AUTOMATION & CONTROL SYSTEMS Pub Date : 2024-01-24 DOI: 10.23919/jsee.2023.000161
Yanan Wang, Chaowei Zhou, Aifang Liu, Qin Mao
Three-dimensional (3D) synthetic aperture radar (SAR) extends the conventional 2D images into 3D features by several acquisitions in different aspects. Compared with 3D techniques via multiple observations in elevation, e.g. SAR interferometry (InSAR) and SAR tomography (TomoSAR), holographic SAR can retrieve 3D structure by observations in azimuth. This paper focuses on designing a novel type of orbit to achieve SAR regional all-azimuth observation (AAO) for embedded targets detection and holographic 3D reconstruction. The ground tracks of the AAO orbit separate the earth surface into grids. Target in these grids can be accessed with an azimuth angle span of 360°, which is similar to the flight path of airborne circular SAR (CSAR). Inspired from the successive coverage orbits of optical sensors, several optimizations are made in the proposed method to ensure favorable grazing angles, the performance of 3D reconstruction, and long-term supervision for SAR sensors. Simulation experiments show the regional AAO can be completed within five hours. In addition, a second AAO of the same area can be duplicated in two days. Finally, an airborne SAR data process result is presented to illustrate the significance of AAO in 3D reconstruction.
三维(3D)合成孔径雷达(SAR)通过不同方面的多次采集,将传统的二维图像扩展为三维特征。与在仰角进行多次观测的三维技术(如合成孔径雷达干涉测量法(InSAR)和合成孔径雷达层析成像法(TomoSAR))相比,全息合成孔径雷达可以通过在方位角进行观测来获取三维结构。本文的重点是设计一种新型轨道,以实现合成孔径雷达区域全方位观测(AAO),用于嵌入式目标探测和全息三维重建。AAO 轨道的地面轨道将地球表面分成若干网格。这些网格中的目标可通过 360° 的方位角跨度获取,这与机载环形合成孔径雷达(CSAR)的飞行路径类似。受光学传感器连续覆盖轨道的启发,提出的方法进行了多项优化,以确保有利的掠射角、三维重建性能和对合成孔径雷达传感器的长期监控。模拟实验表明,区域 AAO 可在五小时内完成。此外,同一区域的第二次 AAO 可在两天内完成。最后,介绍了一个机载合成孔径雷达数据处理结果,以说明 AAO 在三维重建中的重要性。
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引用次数: 0
Fast Solution to the Free Return Orbit's Reachable Domain of the Manned Lunar Mission by Deep Neural Network 利用深度神经网络快速求解载人登月任务的自由返回轨道可达域
IF 2.1 3区 计算机科学 Q3 AUTOMATION & CONTROL SYSTEMS Pub Date : 2024-01-16 DOI: 10.23919/jsee.2023.000117
Luyi Yang, Haiyang Li, Jin Zhang, Yuehe Zhu
It is important to calculate the reachable domain (RD) of the manned lunar mission to evaluate whether a lunar landing site could be reached by the spacecraft. In this paper, the RD of free return orbits is quickly evaluated and calculated via the classification and regression neural networks. An efficient database-generation method is developed for obtaining eight types of free return orbits and then the RD is defined by the orbit's inclination and right ascension of ascending node (RAAN) at the perilune. A classify neural network and a regression network are trained respectively. The former is built for classifying the type of the RD, and the latter is built for calculating the inclination and RAAN of the RD. The simulation results show that two neural networks are well trained. The classification model has an accuracy of more than 99% and the mean square error of the regression model is less than 0.01° on the test set. Moreover, a serial strategy is proposed to combine the two surrogate models and a recognition tool is built to evaluate whether a lunar site could be reached. The proposed deep learning method shows the superiority in computation efficiency compared with the traditional double two-body model.
计算载人登月任务的可达域(RD)对于评估航天器能否到达月球着陆点非常重要。本文通过分类和回归神经网络对自由返回轨道的 RD 进行了快速评估和计算。本文开发了一种高效的数据库生成方法,用于获取八种类型的自由返回轨道,然后根据轨道的倾角和近月点(RAAN)的升交点右升角定义 RD。分别训练分类神经网络和回归网络。前者用于对 RD 的类型进行分类,后者用于计算 RD 的倾角和 RAAN。仿真结果表明,两个神经网络训练有素。在测试集上,分类模型的准确率超过 99%,回归模型的均方误差小于 0.01°。此外,还提出了将两个代用模型结合起来的串行策略,并构建了一个识别工具来评估是否能到达月球站点。与传统的双二体模型相比,所提出的深度学习方法在计算效率上更具优势。
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引用次数: 0
Design Methodology of a Mini-Missile Considering Flight Performance and Guidance Precision 考虑飞行性能和制导精度的微型导弹设计方法
IF 2.1 3区 计算机科学 Q3 AUTOMATION & CONTROL SYSTEMS Pub Date : 2024-01-16 DOI: 10.23919/jsee.2024.000007
Licong Zhang, Chunlin Gong, Hua Su, Da Ronch Andrea
The design of mini-missiles (MMs) presents several novel challenges. The stringent mission requirement to reach a target with a certain precision imposes a high guidance precision. The miniaturization of the size of MMs makes the design of the guidance, navigation, and control (GNC) have a larger-than-before impact on the main-body design (shape, motor, and layout design) and its design objective, i.e., flight performance. Pursuing a trade-off between flight performance and guidance precision, all the relevant interactions have to be accounted for in the design of the main body and the GNC system. Herein, a multi-objective and multidisciplinary design optimization (MOO) is proposed. Disciplines pertinent to motor, aerodynamics, layout, trajectory, flight dynamics, control, and guidance are included in the proposed MOO framework. The optimization problem seeks to maximize the range and minimize the guidance error. The problem is solved by using the nondominated sorting genetic algorithm II. An optimum design that balances a longer range with a smaller guidance error is obtained. Finally, lessons learned about the design of the MM and insights into the trade-off between flight performance and guidance precision are given by comparing the optimum design to a design provided by the traditional approach.
微型导弹(MMs)的设计面临着一些新的挑战。任务要求以一定的精度到达目标,这就对制导精度提出了严格的要求。微型导弹的小型化使得制导、导航和控制(GNC)设计对主体设计(外形、电机和布局设计)及其设计目标(即飞行性能)的影响比以往更大。为了在飞行性能和制导精度之间进行权衡,在设计主体和 GNC 系统时必须考虑到所有相关的相互作用。在此,提出了一种多目标、多学科优化设计(MOO)方法。与电机、空气动力学、布局、轨迹、飞行动力学、控制和制导相关的学科都被纳入了所提出的 MOO 框架。优化问题旨在实现航程最大化和制导误差最小化。该问题采用非支配排序遗传算法 II 解决。最终获得了兼顾较远航程和较小制导误差的最优设计。最后,通过将最佳设计与传统方法提供的设计进行比较,总结了 MM 设计的经验教训,并深入探讨了飞行性能与制导精度之间的权衡。
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引用次数: 0
Time-Varying Parameters Estimation with Adaptive Neural Network EKF for Missile-Dual Control System 利用自适应神经网络 EKF 为导弹双控制系统进行时变参数估计
IF 2.1 3区 计算机科学 Q3 AUTOMATION & CONTROL SYSTEMS Pub Date : 2024-01-16 DOI: 10.23919/jsee.2024.000008
Yuqi Yuan, Di Zhou, Junlong Li, Chaofei Lou
In this paper, a filtering method is presented to estimate time-varying parameters of a missile dual control system with tail fins and reaction jets as control variables. In this method, the long-short-term memory (LSTM) neural network is nested into the extended Kalman filter (EKF) to modify the Kalman gain such that the filtering performance is improved in the presence of large model uncertainties. To avoid the unstable network output caused by the abrupt changes of system states, an adaptive correction factor is introduced to correct the network output online. In the process of training the network, a multi-gradient descent learning mode is proposed to better fit the internal state of the system, and a rolling training is used to implement an online prediction logic. Based on the Lyapunov second method, we discuss the stability of the system, the result shows that when the training error of neural network is sufficiently small, the system is asymptotically stable. With its application to the estimation of time-varying parameters of a missile dual control system, the LSTM-EKF shows better filtering performance than the EKF and adaptive EKF (AEKF) when there exist large uncertainties in the system model.
本文提出了一种滤波方法,用于估算以尾翼和反作用喷流为控制变量的导弹双控制系统的时变参数。在该方法中,长短期记忆(LSTM)神经网络被嵌套到扩展卡尔曼滤波器(EKF)中,以修改卡尔曼增益,从而在存在较大模型不确定性的情况下提高滤波性能。为避免系统状态突变导致的网络输出不稳定,引入了自适应校正因子对网络输出进行在线校正。在网络训练过程中,为了更好地拟合系统内部状态,提出了多梯度下降学习模式,并采用滚动训练的方式实现在线预测逻辑。基于李雅普诺夫第二方法,我们讨论了系统的稳定性,结果表明当神经网络的训练误差足够小时,系统是渐近稳定的。将 LSTM-EKF 应用于导弹双控制系统的时变参数估计,当系统模型存在较大不确定性时,LSTM-EKF 比 EKF 和自适应 EKF(AEKF)具有更好的滤波性能。
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引用次数: 0
Scale Effect Removal and Range Migration Correction for Hypersonic Target Coherent Detection 高超声速目标相干探测的尺度效应消除和范围迁移校正
IF 2.1 3区 计算机科学 Q3 AUTOMATION & CONTROL SYSTEMS Pub Date : 2024-01-16 DOI: 10.23919/jsee.2023.000151
Shang Wu, Zhi Sun, Xingtao Jiang, Haonan Zhang, Jiangyun Deng, Xiaolong Li, Guolong Cui
The detection of hypersonic targets usually confronts range migration (RM) issue before coherent integration (Cl). The traditional methods aiming at correcting RM to obtain Cl mainly considers the narrow-band radar condition. However, with the increasing requirement of far-range detection, the time band-width product, which is corresponding to radar's mean power, should be promoted in actual application. Thus, the echo signal generates the scale effect (SE) at large time bandwidth product situation, influencing the intra and inter pulse integration performance. To eliminate SE and correct RM, this paper proposes an effective algorithm, i.e., scaled location rotation transform (ScLRT). The ScLRT can remove SE to obtain the matching pulse compression (PC) as well as correct RM to complete Cl via the location rotation transform, being implemented by seeking the actual rotation angle. Compared to the traditional coherent detection algorithms, ScLRT can address the SE problem to achieve better detection/estimation capabilities. At last, this paper gives several simulations to assess the viability of ScLRT.
对高超声速目标的探测通常会在相干积分(Cl)之前遇到测距迁移(RM)问题。传统的方法主要考虑窄带雷达条件,旨在修正RM以获得Cl。然而,随着远距离探测要求的不断提高,在实际应用中应推广与雷达平均功率相对应的时间带宽乘积。因此,在大时间带宽积情况下,回波信号会产生尺度效应(SE),影响脉冲内和脉冲间的积分性能。为了消除 SE 并修正 RM,本文提出了一种有效的算法,即缩放位置旋转变换(ScLRT)。ScLRT 可通过位置旋转变换消除 SE 以获得匹配脉冲压缩(PC),并通过寻求实际旋转角度来修正 RM 以完成 Cl。与传统的相干检测算法相比,ScLRT 可以解决 SE 问题,从而获得更好的检测/估计能力。最后,本文给出了几个仿真来评估 ScLRT 的可行性。
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引用次数: 0
Ship Recognition Based on HRRP via Multi-Scale Sparse Preserving Method 基于多尺度稀疏保全法的 HRRP 船舶识别技术
IF 2.1 3区 计算机科学 Q3 AUTOMATION & CONTROL SYSTEMS Pub Date : 2023-12-26 DOI: 10.23919/jsee.2023.000136
Xueling Yang, Gong Zhang, Hu Song
In order to extract the richer feature information of ship targets from sea clutter, and address the high dimensional data problem, a method termed as multi-scale fusion kernel sparse preserving projection (MSFKSPP) based on the maximum margin criterion (MMC) is proposed for recognizing the class of ship targets utilizing the high-resolution range profile (HRRP). Multi-scale fusion is introduced to capture the local and detailed information in small-scale features, and the global and contour information in large-scale features, offering help to extract the edge information from sea clutter and further improving the target recognition accuracy. The proposed method can maximally preserve the multi-scale fusion sparse of data and maximize the class separability in the reduced dimensionality by reproducing kernel Hilbert space. Experimental results on the measured radar data show that the proposed method can effectively extract the features of ship target from sea clutter, further reduce the feature dimensionality, and improve target recognition performance.
为了从海面杂波中提取更丰富的舰船目标特征信息,并解决高维数据问题,提出了一种基于最大余量准则(MMC)的多尺度融合核稀疏保留投影(MSFKSPP)方法,用于利用高分辨率测距剖面(HRRP)识别舰船目标类别。引入多尺度融合,捕捉小尺度特征中的局部和细节信息以及大尺度特征中的全局和轮廓信息,有助于从海面杂波中提取边缘信息,进一步提高目标识别精度。所提出的方法可以最大限度地保留数据的多尺度融合稀疏性,并通过重现核希尔伯特空间,在降维的情况下最大限度地提高类的可分离性。在实测雷达数据上的实验结果表明,所提出的方法能有效地从海面杂波中提取船舶目标的特征,进一步降低特征维度,提高目标识别性能。
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引用次数: 0
Low Rank Optimization for Efficient Deep Learning: Making a Balance Between Compact Architecture And Fast Training 高效深度学习的低等级优化:在紧凑架构和快速训练之间取得平衡
IF 2.1 3区 计算机科学 Q3 AUTOMATION & CONTROL SYSTEMS Pub Date : 2023-12-12 DOI: 10.23919/jsee.2023.000159
Xinwei Ou, Zhangxin Chen, Ce Zhu, Yipeng Liu
Deep neural networks (DNNs) have achieved great success in many data processing applications. However, high computational complexity and storage cost make deep learning difficult to be used on resource-constrained devices, and it is not environmental-friendly with much power cost. In this paper, we focus on low-rank optimization for efficient deep learning techniques. In the space domain, DNNs are compressed by low rank approximation of the network parameters, which directly reduces the storage requirement with a smaller number of network parameters. In the time domain, the network parameters can be trained in a few subspaces, which enables efficient training for fast convergence. The model compression in the spatial domain is summarized into three categories as pre-train, pre-set, and compression-aware methods, respectively. With a series of integrable techniques discussed, such as sparse pruning, quantization, and entropy coding, we can ensemble them in an integration framework with lower computational complexity and storage. In addition to summary of recent technical advances, we have two findings for motivating future works. One is that the effective rank, derived from the Shannon entropy of the normalized singular values, outperforms other conventional sparse measures such as the $ell_{1}$ norm for network compression. The other is a spatial and temporal balance for tensorized neural networks. For accelerating the training of tensorized neural networks, it is crucial to leverage redundancy for both model compression and subspace training.
深度神经网络(DNN)在许多数据处理应用中取得了巨大成功。然而,高计算复杂度和存储成本使得深度学习难以在资源受限的设备上使用,而且它不环保,耗电量大。在本文中,我们将重点研究低秩优化的高效深度学习技术。在空间域,DNN 通过网络参数的低秩逼近进行压缩,从而以较少的网络参数数量直接降低存储需求。在时域中,网络参数可以在几个子空间中进行训练,从而实现高效训练,快速收敛。空间域的模型压缩可归纳为三类,分别是预训练法、预设法和压缩感知法。通过所讨论的一系列可积分技术,如稀疏剪枝、量化和熵编码,我们可以将它们集合在一个积分框架中,从而降低计算复杂度和存储量。除了对近期技术进展的总结,我们还有两个发现可以激励未来的工作。一个是由归一化奇异值的香农熵推导出的有效秩优于其他传统的稀疏度量,如用于网络压缩的 $ell_{1}$ norm。另一个是张量神经网络的时空平衡。为了加速张量神经网络的训练,利用冗余进行模型压缩和子空间训练至关重要。
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引用次数: 0
Classification of Knowledge Graph Completeness Measurement Techniques 知识图谱完备性测量技术分类
IF 2.1 3区 计算机科学 Q3 AUTOMATION & CONTROL SYSTEMS Pub Date : 2023-12-06 DOI: 10.23919/jsee.2023.000150
Ying Zhang, Gang Xiao
At present, although knowledge graphs have been widely used in various fields such as recommendation systems, question and answer systems, and intelligent search, there are always quality problems such as knowledge omissions and errors. Quality assessment and control, as an important means to ensure the quality of knowledge, can make the applications based on knowledge graphs more complete and more accurate by reasonably assessing the knowledge graphs and fixing and improving the quality problems at the same time. Therefore, as an indispensable part of the knowledge graph construction process, the results of quality assessment and control determine the usefulness of the knowledge graph. Among them, the assessment and enhancement of completeness, as an important part of the assessment and control phase, determine whether the knowledge graph can fully reflect objective phenomena and reveal potential connections among entities. In this paper, we review specific techniques of completeness assessment and classify completeness assessment techniques in terms of closed world assumptions, open world assumptions, and partial completeness assumptions. The purpose of this paper is to further promote the development of knowledge graph quality control and to lay the foundation for subsequent research on the completeness assessment of knowledge graphs by reviewing and classifying completeness assessment techniques.
目前,知识图谱虽然已经广泛应用于推荐系统、问答系统、智能搜索等多个领域,但始终存在知识遗漏、知识错误等质量问题。质量评估与控制作为保证知识质量的重要手段,通过对知识图谱进行合理评估,同时修正和改进质量问题,可以使基于知识图谱的应用更加完善、更加准确。因此,作为知识图谱构建过程中不可或缺的一部分,质量评估与控制的结果决定了知识图谱的实用性。其中,完整性的评估与提升作为评估与控制阶段的重要环节,决定了知识图谱能否全面反映客观现象,揭示实体间的潜在联系。本文回顾了完备性评估的具体技术,并从封闭世界假设、开放世界假设和部分完备性假设三个方面对完备性评估技术进行了分类。本文旨在通过对完备性评估技术的回顾和分类,进一步推动知识图谱质量控制的发展,并为知识图谱完备性评估的后续研究奠定基础。
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引用次数: 0
A Survey on Joint-Operation Application for Unmanned Swarm Formations Under a Complex Confrontation Environment 复杂对抗环境下无人机蜂群编队的联合行动应用调查
3区 计算机科学 Q3 AUTOMATION & CONTROL SYSTEMS Pub Date : 2023-12-01 DOI: 10.23919/JSEE.2023.000162
Jialong Zhang;Kun Han;Pu Zhang;Zhongxi Hou;Lei Ye
With the rapid development of informatization, autonomy and intelligence, unmanned swarm formation intelligent operations will become the main combat mode of future wars. Typical unmanned swarm formations such as ground-based directed energy weapon formations, space-based kinetic energy weapon formations, and sea-based carrier-based formations have become the trump card for winning future wars. In a complex confrontation environment, these sophisticated weapon formation systems can precisely strike mobile threat group targets, making them extreme deterrents in joint combat applications. Based on this, first, this paper provides a comprehensive summary of the outstanding advantages, strategic position and combat style of unmanned clusters in joint warfare to highlight their important position in future warfare. Second, a detailed analysis of the technological breakthroughs in four key areas, situational awareness, heterogeneous coordination, mixed combat, and intelligent assessment of typical unmanned aerial vehicle (UAV) swarms in joint warfare, is presented. An in-depth analysis of the UAV swarm communication networking operating mechanism during joint warfare is provided to lay the theoretical foundation for subsequent cooperative tracking and control. Then, an in-depth analysis of the shut-in technology requirements of UAV clusters in joint warfare is provided to lay a theoretical foundation for subsequent cooperative tracking control. Finally, the technical requirements of UAV clusters in joint warfare are analysed in depth so the key technologies can form a closed-loop kill chain system and provide theoretical references for the study of intelligent command operations.
随着信息化、自主化、智能化的快速发展,无人蜂群编队智能作战将成为未来战争的主要作战方式。地基定向能武器编队、天基动能武器编队、海基航母编队等典型的无人蜂群编队已成为未来战争制胜的王牌。在复杂的对抗环境中,这些先进的武器编队系统可以精确打击移动的威胁群目标,在联合作战中具有极强的威慑力。基于此,本文首先全面总结了无人集群在联合作战中的突出优势、战略地位和作战样式,以凸显其在未来战争中的重要地位。其次,详细分析了典型无人机群在联合作战中的态势感知、异构协同、混合作战、智能评估四个关键领域的技术突破。深入分析了联合作战中无人机群通信组网运行机制,为后续的协同跟踪与控制奠定了理论基础。然后,深入分析了联合作战中无人机集群的关机技术要求,为后续的协同跟踪控制奠定理论基础。最后,深入分析联合作战中无人机集群的技术要求,使关键技术形成闭环杀伤链体系,为智能指挥作战研究提供理论参考。
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引用次数: 0
Two-Layer Formation-Containment Fault-Tolerant Control of Fixed-Wing UAV Swarm for Dynamic Target Tracking 用于动态目标跟踪的固定翼无人机群的双层编队-遏制容错控制
3区 计算机科学 Q3 AUTOMATION & CONTROL SYSTEMS Pub Date : 2023-12-01 DOI: 10.23919/JSEE.2023.000153
Boyu Qin;Dong Zhang;Shuo Tang;Yang Xu
This paper tackles the formation-containment control problem of fixed-wing unmanned aerial vehicle (UAV) swarm with model uncertainties for dynamic target tracking in three-dimensional space in the faulty case of UAVs' actuator and sensor. The fixed-wing UAV swarm under consideration is organized as a “multi-leader-multi-follower” structure, in which only several leaders can obtain the dynamic target information while others only receive the neighbors' information through the communication network. To simultaneously realize the formation, containment, and dynamic target tracking, a two-layer control framework is adopted to decouple the problem into two subproblems: reference trajectory generation and trajectory tracking. In the upper layer, a distributed finite-time estimator (DFTE) is proposed to generate each UAV's reference trajectory in accordance with the control objective. Subsequently, a distributed composite robust fault-tolerant trajectory tracking controller is developed in the lower layer, where a novel adaptive extended super-twisting (AESTW) algorithm with a finite-time extended state observer (FTESO) is involved in solving the robust trajectory tracking control problem under model uncertainties, actuator, and sensor faults. The proposed controller simultaneously guarantees rapidness and enhances the system's robustness with fewer chattering effects. Finally, corresponding simulations are carried out to demonstrate the effectiveness and competitiveness of the proposed two-layer fault-tolerant cooperative control scheme.
本文探讨了固定翼无人机群(UAV)的编队控制问题,在UAV的致动器和传感器出现故障的情况下,利用模型的不确定性实现三维空间的动态目标跟踪。所考虑的固定翼无人机蜂群是一种 "多领导-多跟随者 "结构,其中只有几个领导者可以获得动态目标信息,而其他领导者只能通过通信网络接收邻居的信息。为了同时实现编队、围堵和动态目标跟踪,采用了双层控制框架,将问题解耦为两个子问题:参考轨迹生成和轨迹跟踪。在上层,提出了分布式有限时间估计器(DFTE),以根据控制目标生成每个无人机的参考轨迹。随后,在下层开发了分布式复合鲁棒容错轨迹跟踪控制器,其中涉及一种带有有限时间扩展状态观测器(FTESO)的新型自适应扩展超扭曲(AESTW)算法,用于解决模型不确定性、致动器和传感器故障下的鲁棒轨迹跟踪控制问题。所提出的控制器既保证了快速性,又增强了系统的鲁棒性,减少了颤振效应。最后,进行了相应的仿真,证明了所提出的双层容错协同控制方案的有效性和竞争力。
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引用次数: 0
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