一种面向声纳群目标检测应用的盲波束形成改进算法

随着水下自主航行器（UUV）及其声纳检测系统的智能化水平的提高，多UUV协同工作成为重要的研究方向，阵列信号处理中的盲波束形成理论可用于处理该类问题。Coviello提出了一种便于实时应用的盲波束形成算法，但其算法适用面窄，本文通过阵元对理论对其算法加以改进，以适应UUV间的位置误差，从而改善UUV群对目标方位的被动估计能力。仿真实验显示，改进算法在保持Coviello算法良好性能的前提下具有更广阔的适用性。     

改进的无线射频识别阅读器定位算法

针对RFID故障频率较高而导致RFID阅读器定位准确性较低的问题,提出一种改进的RFID阅读器定位算法。首先对RFID阅读器的故障类型进行分析,然后基于识别区域几何知识和线性二阶锥形规划处理长时大范围故障,并且采用质量指数指标对定位结果进行评价,最后对算法进行仿真测试。仿真结果表明,相对于当前的RFID阅读器定位算法,本文算法不仅提高了定位精度,而且可以提供定位质量信息。     

WDM光网络安全生存机制研究

针对WDM光网络安全生存性问题的故障的发现、定位和恢复三个重要环节之中的恢复问题,提出了一种WDM光层中的安全保护与恢复方案。采用了恢复路由选择算法和恢复波长分配算法,提出了一种混合的多层恢复机制,从而构建了一种从故障的发现、定位到故障恢复的覆盖各环节的连续性光网络安全生存机制,并在理论上进行了性能比较。     

基于信息增益率的WNB水下机器人故障分类

提高故障诊断能力对于确保水下机器人系统的稳定运行具有重要意义,故障分类是目前水下机器人故障诊断所面临的一个重要问题。针对水下机器人推进器系统数据特征,提出一种基于信息增益率的加权朴素贝叶斯故障分类算法。首先,计算故障训练样本的先验概率,将各属性的信息增益率作为权值;其次,构建基于增益率加权的朴素贝叶斯分类模型;然后,对检测的故障数据利用分类模型获取具有最大后验概率的故障模式,实现故障分类。与朴素贝叶斯算法和决策树算法相比,仿真实验结果表明基于信息增益率加权的朴素贝叶斯算法的分类成功率更高,能够有效地实现水下机器人的故障分类。     

无人水下航行器势场航路规划方法研究

对运动障碍威胁环境下UUV航路规划问题进行了研究。首先分别对规划过程中固定障碍、运动障碍建立了排斥势场,对目标点建立了吸引势场,将航路规划问题转变为寻找最优势场点问题;然后提出一种改进的粒子群算法(NPSO),在UUV航路规划过程中,寻找距离当前路径点固定步长范围内的最优势场点,将其作为下一路径点,最终实现UUV在运动障碍威胁环境中的航路规划;最后对所提方法进行了仿真验证,UUV可以有效躲避固定障碍与运动障碍威胁,寻找到较优航路,取得了较好的仿真效果。     

基于有色Petri网的工作流故障诊断方法

工作流系统的故障自动诊断和定位是云计算环境提供持续服务的基础。为了提高工作流系统的故障诊断准确性,本文提出了一种基于有色Petri网的故障诊断方法。首先,用开放世系模型对工作流进行建模。然后,提出了一种基于有色Petri网的故障模型。最后,将工作流的开放世系模型转化为有色Petri网故障模型,并提出了相应的多故障诊断方法。实验表明,本文提出的方法不仅故障定位的准确率和执行效率高于相关算法,还能有效的识别系统中的多个故障。     

基于灰色预测和KID离群点检测的AUV故障诊断

提高故障诊断能力对于确保水下机器人AUV系统的稳定运行具有重要意义。针对水下机器人推进器系统,提出一种基于离群点检测的AUV故障检测方法。首先,将传感器采集的数据进行灰色预测处理;然后,提出了一种结合K-mean和DBSCAN的改进迭代聚类(Iterative K-mean DBSCAN,IKD)算法进行离群点检测;最后,与K-mean和DBSCAN算法相比,仿真实验结果表明基于灰色预测和KID离群点检测算法的故障检测准确率高,能够有效地实现水下机器人AUV的无监督故障诊断。     

改进GSO与二维OTSU融合的红外图像多阈值分割方法

针对二维最大累间方差（Nobuyuki OTSU,OTSU）图像分割算法在电气设备故障诊断与定位中,其红外图像的多阈值分割中存在的耗时多、分割精度低、误分割等不足,造成故障区域欠分割或者过分割的问题,提出一种改进的萤火虫算法（glowworm swarm optimization,GSO）与二维OTSU的融合算法来提高电气设备红外图像多阈值分割的实时性与准确度。寻优过程中,将局部寻优扩展到全局寻优,并引入非线性递减步长及新的移动策略对GSO进行优化改进。实验结果表明:该融合算法在分割结果上较二维OTSU及未改进GSO与二维OTSU融合算法更能准确分割运行电气设备图像异常区域,分割速度分别提高19倍、1.28倍,为红外图像早期故障的有效识别与定位奠定基础。     

分布式压缩感知麦克风阵列多声源方位估计*

麦克风阵列已被广泛应用于音/视频会议等人机交互领域中时,多声源应用场景对声源方位估计性能提出了更高的要求。压缩感知(CS)声源定位算法将声源定位问题转化为信号的稀疏重构问题,相比传统的定位算法如相位变换加权（SRP-PHAT）和时延累加定位（DS）能够获得较高的定位性能,但多声源的存在一定程度上降低了稀疏程度,影响了CS重构性能。考虑到传统的CS定位算法并未利用多个连续语音帧之间声源空间向量的共同稀疏性,提出采用分布式压缩感知（DCS）理论以改善多声源的稀疏恢复估计的性能。仿真和实验结果表明,相比于传统定位算法和CS-OMP算法,DCS-SOMP算法在不同信噪比和不同声源强度的环境中,对多声源的方位估计都具有更好的定位性能和定位稳健性。     

基于OTDR和光功率测试的光纤故障监测应用方法

为了降低光纤光缆故障率和缩短光缆网络故障诊断时间,提出了一种基于光功率测试和光时域反射仪的光纤故障监测方法;利用光功率测试技术对光缆性能进行实时监控并预警光缆故障;分析光纤功率衰减的原因,计算得到光缆修理增加的长度,设计了修正故障地点的算法;当光缆网络发生故障时,利用光时域反射仪测试故障位置,并利用误差检测算法对电缆衰减进行分析排除,对故障地点进行故障位置修正以便快速抢修;结果表明,该方法可以降低光缆网络故障率,在故障定位中可以提高光缆故障定位的精度,计算的故障位置与实际故障位置的误差不超过10 m,可以进行实时快速的维修,对于运营时间较长的光缆网络的效果更明显,具有较好的应用价值。     

Fuzzy control of optical PPM–CDMA with M-ary orthogonal signaling

This paper introduces an incorporated spectral-amplitude coding (SAC) optical code-division multiple-access (OCDMA) scheme. One novel class of optical signature codes based on combinatorial designs is employed with M-ary pulse-position modulation (PPM) signaling to improve the system performance beyond the interference limit. A union upper bound on the bit error rate (BER) is derived and the performance characteristics are then discussed with a variety of system parameters. Furthermore, fuzzy logic (FL) control is proposed to provide tolerance of different degrees of reliability in multirate transmission and to achieve distinct service differentiation for multimedia applications. It is shown that the proposed system can effectively suppress noise effects and offer improved adaptation capabilities for multi-quality network requirements in comparison with systems without optimization.     

Wireless Network Optimization for Federated Learning with Model Compression in Hybrid VLC/RF Systems

In this paper, the optimization of network performance to support the deployment of federated learning (FL) is investigated. In particular, in the considered model, each user owns a machine learning (ML) model by training through its own dataset, and then transmits its ML parameters to a base station (BS) which aggregates the ML parameters to obtain a global ML model and transmits it to each user. Due to limited radio frequency (RF) resources, the number of users that participate in FL is restricted. Meanwhile, each user uploading and downloading the FL parameters may increase communication costs thus reducing the number of participating users. To this end, we propose to introduce visible light communication (VLC) as a supplement to RF and use compression methods to reduce the resources needed to transmit FL parameters over wireless links so as to further improve the communication efficiency and simultaneously optimize wireless network through user selection and resource allocation. This user selection and bandwidth allocation problem is formulated as an optimization problem whose goal is to minimize the training loss of FL. We first use a model compression method to reduce the size of FL model parameters that are transmitted over wireless links. Then, the optimization problem is separated into two subproblems. The first subproblem is a user selection problem with a given bandwidth allocation, which is solved by a traversal algorithm. The second subproblem is a bandwidth allocation problem with a given user selection, which is solved by a numerical method. The ultimate user selection and bandwidth allocation are obtained by iteratively compressing the model and solving these two subproblems. Simulation results show that the proposed FL algorithm can improve the accuracy of object recognition by up to 16.7% and improve the number of selected users by up to 68.7%, compared to a conventional FL algorithm using only RF.     

Space electric propulsion plasmas

Electric thrusters offer the promise of a substantial improvement in performance over that of conventional chemical rockets currently used in space propulsion applications. There are three basically different ways in which electrical power and propellant inputs might be combined to produce thrust: (1) propellant can be heated electrically and then expanded through a nozzle; (2) electromagnetic body forces can be applied to accelerate a plasma to the desired exhaust velocity; or (3) electrostatic body forces can be applied to accelerate charged particles. Electric thrusters are classified in accordance with the mechanism by which they induce thrust as electrothermal, electromagnetic, and electrostatic. The characteristics of plasmas in electric thrusters along these lines are considered     

基于PVDF传感器的单脉冲激光推力加载过程研究

 利用高频响应材料PVDF作为敏感元件，建立了一套单脉冲瞬态推力测试系统，并用37 mm分离式Hopkinson压杆装置对该测试系统进行动态标定。标定结果显示，在0~150 MPa范围内，PVDF传感器（5 mm×5 mm）的压电特性呈线性，动态压电系数经线性拟合后为20 pC/N。用所建推力测试系统获得了单脉冲激光作用下，旋转抛物形激光推力器的推力加载曲线，并对该曲线形成多个峰值的原因进行了分析。实验结果表明，该系统可以捕获激光推力器脉冲推力加载特征。     

Similarity scaling-application and limits for high-efficiency-multistage-plasma-thruster particle-in-cell modelling

To suit a wide variety of space mission profiles, different designs of ion thrusters were developed, such as the High-Efficiency-Multistage-Plasma thrusters (HEMP-T). In the past, the optimization of ion thrusters was a difficult and time-consuming process and evolved experimentally. Because the construction of new designs is expensive, cheaper methods for optimization were sought-after. Computer-based simulations are a cheap and useful method towards predictive modelling. The physics in HEMP-T requires a kinetic model. The Particle-in-Cell (PIC) method delivers self-consistent solutions for the plasmas of ion thrusters, but it is limited by the high amount of computing time required to study a specific system. Therefore, it is not suited to explore a wide operational and design space. An approach to decrease computing time is self-similarity scaling schemes, which can be derived from the kinetic equations. One specific self-similarity scheme is investigated quantitatively in this work for selected HEMP-Ts, using PIC simulations. The possible application of the scaling is explained and the limits of this approach are derived.     

Latency optimization for Federated Learning over Wireless Power Transfer

Federal Learning (FL) is an emerging technology in the field of machine learning (ML). Compared with traditional ML, FL is an attractive method to deal with data security issues of the user-side. So that FL can realizes its full potential in terms of low latency and high energy efficiency (EE), this paper introduces a new framework: In the wireless communication network scenario, we propose an FL architecture based on Wireless Power Transfer (WPT). By combining WPT technology and FL, we can realize green wireless communication under the premise of ensuring user privacy. We formulate a joint calculation and communication optimization problem to optimize the latency of local calculation, uplink and downlink transmission without consuming user-side energy. The problem formulas listed according to the optimization problem are non-convex. They are first transformed into convex form, and then a low-complexity iterative algorithm is used to solve them optimally. Simulations show that our proposed FL method design has achieved a significant performance improvement over other benchmark tests.     

Scaling Design and Experimental Study on Hall Thrusters with Curved Magnetic Field

The existing scaling theories of Hall thrusters are based on the hypothesis of a one‐dimensional straight magnetic field, which is not suitable for the design of modern thrusters with a two‐dimensional curved magnetic field. In this paper, using the equation analysis method, we derive new similarity criterions in a curved magnetic field by analyzing the momentum equations of charged particles; consequently, we propose a new modeling design method for Hall thrusters with a constant discharge voltage. This method is further validated by experiments. A designed model with a power of 1.5 kW is made based on our proposed method from a prototype model with a power of 1 kW. The experimental results demonstrate that these two thrusters have little differences in performance and physical processes as expected from the scaling. Therefore, our method is well suited for designing a Hall thruster with a curved magnetic field (© 2011 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)     

改进的插值法用于IIM影像条带噪声去除

摘 要：针对嫦娥一号(CE-1)干涉成像光谱仪(IIM)影像中的条带噪声,应用常用的频域滤波方法和线性插值法进行了IIM条带去除试验,并提出结合了三阶插值的改进线性插值方法。对噪声点定位后,根据图像中噪声点上下行相应点的数值差别是否超过预设阈值,确定是采用线性插值还是三阶插值算法,修正噪声点像素值。实验结果的均值和标准偏差以及图像质量因子(IQ因子)的比较表明：改进的方法IQ值达到13.9081,而线性插值算法为9.8428,改进的线性插值方法在去除IIM影像条带噪声方面明显优于线性插值方法。     

灰度人脸识别形态学相关的一般理论研究

提出一般形态学相关概念，并提出一种小型联合变换相关器的硬件设计以实现一般形态学相关.提出两种改进的一般形态学相关算法，灰度图像按某种分解方法分解成一系列二值图像片.在第一种算法中，每片二值联合图像片的边缘被检测，其功率谱求和.在第二种算法中，一种情况是每片的联合变换功率谱被二值化或细化再求和；另一种情况是这些片的联合变换功率谱的总和被二值化或细化.计算机模拟结果表明，改进后的算法能改善高相似度灰度人脸图像识别的鉴别率.     

RAT selection for IoT devices in HetNets: Reinforcement learning with hybrid SMDP algorithm

Due to the increasing deployment of heterogeneous networks (HetNets), the selection of which radio access technologies (RATs) for Internet of Things (IoT) devices such as user equipments (UEs) has recently received extensive attention in mobility management research. Most of existing RAT selection methods only optimize the selection strategies from the UE side or network side, which results in heavy network congestion, poor user experience and system utility degradation. In this paper the UE side and the network side are considered comprehensively, based on the game theory (GT) model we propose a reinforcement learning with assisted network information algorithm to overcome the crucial points. The assisted information is formulated as a semi-Markov decision process (SMDP) provided for UEs to make accurate decisions, and we adopt the iteration approach to reach the optimal policy. Moreover, we investigate the impacts of different parameters on the system utility and handover performance. Numerical results validate that our proposed algorithm can mitigate unnecessary handovers and improve system throughputs.