Joint Iterative Decoding for Network-Coding-Based Multisource LDPC-Coded Cooperative MIMO

A network-coding-based multisource LDPC-coded cooperative MIMO scheme is proposed,where multiple sources transmit their messages to the destination with the assistance from a single relay.The relay cooperates with multiple sources simultaneously via network-coding.It avoids the issues of imperfect frequency/timing synchronization and large transmission delay which may be introduced by frequency-division multiple access(FDMA)/code-division multiple access(CDMA)and time-division multiple access(TDMA)manners.The proposed joint″Min-Sum″iterative decoding is effectively carried out in the destination.Such a decoding algorithm agrees with the introduced equivalent joint Tanner graph which can be used to fully characterize LDPC codes employed by the sources and relay.Theoretical analysis and numerical simulation show that the proposed scheme with joint iterative decoding can achieve significant cooperation diversity gain.Furthermore,for the relay,compared with the cascade scheme,the proposed scheme has much lower complexity of LDPC-encoding and is easier to be implemented in the hardware with similar bit error rate(BER)performance.     

当前经济增长的周期性、阶段性特征及政策思路

根据经济周期谷底的特性以及中美两国经济紧密关联和经济周期越来越契合的规律,可以印证2009年就是新一轮经济周期的谷底,由此可以断定当前处于复苏阶段.然而近三年来我国经济增速持续下滑至谷底以下,这一反常现象是由于经济短周期冲高回落与经济增长阶段转换相互叠加造成的.河南省与全国经济周期高度一致,时滞半年左右.在高速经济增长阶段河南省潜在经济增长率比全国高出2个百分点,中高速阶段高出1.5个百分点.河南省近几年经济增速深度下滑,其关键原因是投资增速下滑,其根本原因是结构升级缓慢.未来河南经济要摆脱持续下行趋势,保持经济中高速增长,应坚持调结构、扩投资、促改革的政策思路,“调结构”是根本,“扩投资”是基础,“促改革”是保障.     

权力制约：经典范式、内在逻辑与语境化

实质意义上的权力制约肇始于权力分化之后，历经以道德制约权力、以权力制约权力、以社会制约权力等经典范式，体现了权力制约活动从关注权力执掌者、权力本体到关注权力归属者不断演变的内在逻辑。植根于我国国情的权力制约实践在伦理学、社会学途径方面与西方存有共性，但于政治学途径却存有个性。实现我国权力制约的语境化既需要体现权力制约的普遍性规律，更需要把握马克思主义政治观视域下权力制约的特殊性。     

合肥市会计服务业发展研究

对合肥市会计服务业及其发展进行了研究,研究2009-2013年安徽省（合肥市）会计师事务所数量和注册会计师数量,发现合肥市会计服务业发展形势较为严峻,具体表现为：会计师事务所规模较小,内部治理机制不够完善、业务种类不够全面,管理机制不够健全,人才结构不够合理,高端人才较少,国际化程度不高,并提出合肥市应当加强会计服务行业地方法规政策的完善与制定、提高会计服务业的社会透明度、加强会计服务业的多元化发展、加强业务创新,开创自身特色、加强复合型人才培养,提升会计服务业人员整体素质、推行联合重组,避免内部竞争,以促进会计服务业的进一步发展。     

基于神经网络的多卫星姿态协同控制预测模型

摘要： 针对多卫星近距离对空间目标监测中的姿态协同控制问题，研究基于神经网络的智能控制模型.首先设计了面向任意姿态控制方法的神经网络自适应控制模型，该模型不改变卫星姿态控制方法本身，而是接收由理想控制模型生成的理想控制量，理想控制量经过神经网络后直接生成适用于卫星的控制量.根据神经网络对控制系统的系统级状态预测，基于一致性控制协议，设计了多卫星姿态协同控制模型.采用等效姿态角，通过数值仿真，分别验证了预测控制方法、输出状态预测、协同控制的方法正确性和有效性.     

Adaptive neural network control of nonlinear systems with unknown dynamics

In this study, an adaptive neural network control approach is proposed to achieve accurate and robust control of nonlinear systems with unknown dynamics, wherein the neural network is innovatively used to learn the inverse problem of system dynamics with guaranteed convergence. This study focuses on the following three contributions. First, the considered system is transformed into a multi-integrator system using an input–output linearization technique, and an extended state observation technique is used to identify the transformed states. Second, an iterative control learning algorithm is proposed to achieve the neural network training, and stability analysis is given to prove that the network’s predictions converge to ideal control inputs with guaranteed convergence. Third, an adaptive neural network controller is developed by combining the trained network and a proportional-integral controller, and the long-standing challenge of model-based methods for control determination of unknown dynamics is resolved. Simulation results of a virtual control mission and an aerospace altitude tracking mission are provided to substantiate the effectiveness of the proposed techniques and illustrate the adaptability and robustness of the proposed controller.     

TS-InSAR analysis for monitoring ground deformation in Lanzhou New District,the loess Plateau of China,from 2017 to 2019

Large-scale land creation projects involving the cutting of mountains to infill gullies for construction have been carried out in Lanzhou New District (LZND). However, there is an urgent need for comprehensive and detailed research on the spatiotemporal evolution of ground deformation in LZND. Based on Sentinel-1A SAR data, combined with the urban geological background, the ground deformation in LZND from 2017 to 2019 was analysed. Two independent, multi-temporal techniques, persistent scatterers interferometry (PS-InSAR) and the small baseline subset (SBAS-InSAR), were used to calculate the deformation time series, and the results were cross-verified. The time series-monitoring results of the SBAS and PS calculations exhibited strong consistency in LZND and verified the high reliability of the experimental results. The results showed the whole surface of the LZND from March 2017 to October 2019 maintained stability, and the deformation rate was primarily in the range of ?10 to 10 mm/year. However, ground deformation in the Xicha area was evident. The maximum annual deformation rates monitored by SBAS-InSAR and PS-InSAR were ?52.48 mm/year and ?56.35 mm/year, respectively. The most typical deformation areas include the built-up area and the land creation area. The surface subsidence area was concentrated in the filling area. The ground deformation range of LZND kept expanding and accelerating from 2017 to 2019. Land creation, urban construction, geology and precipitation were the primary factors contributing to local severe ground deformation. The results of this study provide reference for the regional urban planning in LZND.     

小积液情况下卧式转子的稳定性实验研究

盘腔积液现象在舰用燃气轮机工作过程中时有发生，汽化的滑油和水蒸气凝结形成积液。转子带动腔内的积液旋转，引发振动失稳，严重时会对发动机结构造成致命性破坏。为此，本文开展燃气轮机压气机盘腔积液的模拟实验研究，以双盘双支承的卧式柔性转子系统作为研究对象，重点关注小积液量的特点，对积液转子的动力学稳定性进行了研究。观测到转子的失稳现象，开展了不同积液体积、不同积液类型（水、植物油和滑油）等因素的影响规律研究，揭示了小积液的卧式转子动力学特征。研究结果表明：①对于积液失稳振动，存在失稳边界积液量，当积液量大于该边界值时，失稳将会发生。②失稳边界转速及恢复转速均高于临界转速。当转速高于失稳边界转速时，失稳将会发生。转速进一步增加，高于恢复转速时，失稳现象可能消失。③失稳时的振动特征为：出现幅值突增，次谐波成分也随之增加；基频和次谐波发生调制，表现出拍振特征。④随着积液体积的增加，失稳边界转速先减小后增加。⑤随着积液粘度系数增大，失稳边界转速和边界积液量均增大。     

适应拓扑变化的拥塞最小化网络更新策略

软件定义网络的出现推动了航空集群机载网络的发展，同时也为机载网络的一致性更新带来了挑战。针对拓扑变化导致的网络更新期间可用路径失效问题，提出适应拓扑变化的拥塞最小化网络更新策略。首先，为减少可用路径失效带来的数据包丢失，在更新之前采用重路由机制进行处理；然后，针对重路由机制可能引起的网络拥塞，设计了贪婪流迁移算法缓解拥塞；最后，通过瞬时拥塞最小化更新算法完成整个网络更新。仿真结果表明：与传统的拥塞一致性更新算法相比，所提更新策略用规则开销、瞬时网络拥塞方面的少量提高换取了网络更新期间数据包丢失的显著降低。     

Spatial modeling of flood probability using geo-environmental variables and machine learning models,case study: Tajan watershed,Iran

The main objective of this study was to produce flood susceptibility maps for Tajan watershed, Sari, Iran using three machine learning (ML) models including Self-Organization Map (SOM), Radial Basis Function Neural Network (RBFNN), and Multi-layers Perceptron (MLP). To reach such a goal, different physical-geographical factors (criteria) were integrated and mapped. 212 flood inventory map was randomly divided into training and testing datasets, where 148 flood locations (70%) were used for training and the remaining 64 locations (30%) were employed for testing. Model validation was performed using several statistical indices and the area under the curve (AUC). The results of the correlation matrix showed, three factors slope (0.277), distance from river (0.263), and altitude (0.223) were the most important factors affecting flood. The accuracy evaluation of the flood susceptibility maps through the AUC method and K-index shows that in the validation phase RBFNN (AUC = 0.90) outperform the MLP (AUC = 0.839) and SOM (AUC = 0.882) models. The highest percentage flood susceptibility of the area in MLP, SOM and RBFNN models is related to moderate (28.7%), very low (40%) and low (37%), respectively. Also, the validation results of the models using the Relative Flood Density (RFD) approach showed that very high class had the highest RFD value.