基于GAN的通信干扰波形生成技术

现有通信干扰方法, 通常基于通信侦察中获取的目标信号特征进行干扰决策, 选取合适的干扰波形实施干扰, 难以应对目标信号特征未知或参数动态变化的情况。为此, 提出一种基于生成对抗网络(generative adversarial networks, GAN)的通信干扰波形生成技术, 运用GAN直接提取目标信号的潜在特征, 并生成与目标信号特征相似的干扰波形。在介绍GAN原理的基础上, 首先设计网络模型, 并对学习率进行优化, 使GAN更适用于时间序列通信干扰波形的生成。然后通过对不同类型和参数的通信信号进行干扰波形生成实验, 验证了该技术的泛化性。最后进行干扰效果对比试验, 结果表明, GAN生成的干扰波形干扰效果能够逼近最佳干扰效果。     

Improving the photocatalytic activity of graphitic carbon nitride by thermal treatment in a high-pressure hydrogen atmosphere

Extending visible light absorption range and suppressing the recombination of photogenerated charge carriers are always important topics in developing efficient solar-driven photocatalysts. In this study, the thermal treatment process at 400 °C in a high-pressure hydrogen atmosphere was applied to modify graphitic carbon nitride. Compared to the normal atmospheric hydrogen treatment process, this process has the merit of producing nitrogen deficient graphitic carbon nitride in high-yield. The optimal photocatalytic activity of modified graphitic carbon nitride was demonstrated by controlling the treatment duration in the hydrogen atmosphere.The changes in the crystal structure, microstructure and optical properties of carbon nitrides were investigated by several characterizations. The relationship between the photocatalytic activity and structures of graphitic carbon nitride was preliminarily established. The results obtained in this study could provide some new ways of improving the activity of graphitic carbon nitride based photocatalyst.     

哈密翼龙及其3D胚胎化石研究

 翼龙是地球上第一类飞向天空也是唯一绝灭的飞行脊椎动物，人类对其产卵繁殖、生长发育和生活习性等方面的了解还十分有限。本文介绍了一件超过200枚哈密翼龙蛋、胚胎和骨骼化石三位一体保存的重要标本，包括16枚翼龙蛋含有三维立体的胚胎化石。针对这件全世界首次发现的3D翼龙胚胎，研究提出哈密翼龙具有相对早熟的胚胎发育模式，其后肢发育速度较前肢快，孵化之后只能走不能飞；胚胎发育期间牙齿尚未萌出，出生后还需要父母照料；从胚胎到亚成年都具有快速生长的骨骼结构；显示哈密翼龙具有群居的生活习性，白垩纪的湖泊风暴导致其集群死亡并快速埋藏。     

Bidirectional Feedback Dynamic Particle Filter with Big Data for the Particle Degeneracy Problem

Particle Filter(PF) is a data assimilation method to solve recursive state estimation problem which does not depend on the assumption of Gaussian noise, and is able to be applied for various systems even with non-linear and non-Gaussian noise. However, while applying PF in dynamic systems, PF undergoes particle degeneracy,sample impoverishment, and problems of high computational complexity. Rapidly developing sensing technologies are providing highly convenient availability of real-time big traffic data from the system under study like never before. Moreover, some sensors can even receive control commands to adjust their monitoring parameters. To address these problems, a bidirectional dynamic data-driven improvement framework for PF(B3 DPF) is proposed.The B3 DPF enhances feedback between the simulation model and the big traffic data collected by the sensors,which means the execution strategies(sensor data management, parameters used in the weight computation,resampling) of B3 DPF can be optimized based on the simulation results and the types and dimensions of traffic data injected into B3 DPF can be adjusted dynamically. The first experiment indicates that the B3 DPF overcomes particle degeneracy and sample impoverishment problems and accurately estimates the state at a faster speed than the normal PF. More importantly, the new method has higher accuracy for multidimensional random systems.In the rest of experiments, the proposed framework is applied to estimate the traffic state on a real road network and obtains satisfactory results. More experiments can be designed to validate the universal properties of B3 DPF.     

大传统与小传统:明清时期鲁西地区的碧霞元君信仰

明清时期鲁西地区是碧霞元君信仰极为盛行的地区,呈现出庙宇数量众多、分布地域广泛等特征.地方官员和士绅对碧霞元君信仰大多持默许和认可态度,并试图借助祀神活动传达官方祭祀理念,进行社会教化.地方官员和士绅关于泰山碧霞元君信仰的不同态度,反映了他们对于地方社会道德秩序的关注,表达了他们试图用官方正统文化去改造民间文化的强烈愿望.相较地方官员和士绅,普通民众崇祀神灵的目的则较为简单,带有显著的世俗性和功利性的特征.在满足其自身信仰需求同时,神灵信仰也起到了增强社会凝聚力、强化地域认同的作用.地方家族也利用重修庙貌、祭祀神灵等手段,实现庙宇与宗族的结合,以此达到增强家族凝聚力和认同感的目的.基于各自不同的目的和需要,官员、士绅和民众共同参与到信仰活动之中,使得信仰在地方社会不断得以延续和传承.     

Most Likely Optimal Subsampled Markov Chain Monte Carlo

Journal of Systems Science and Complexity - Markov Chain Monte Carlo (MCMC) requires to evaluate the full data likelihood at different parameter values iteratively and is often computationally...     

基于mRMR与因子分解机的分类模型研究

很多学者用“全球恐怖主义研究数据库”GTD数据集，采用博弈论、K近邻法和支持向量机等分析恐怖事件的聚集性，已经取得一些成果.但在前期研究中未有很好考虑数据的稀疏性以及高维度多冗余等会导致聚集分类准确率不高的问题.本文提出一种基于最小冗余最大相关与因子分解机结合的TFM分类模型，使用增量搜索方法寻找近似最优的特征解决高维度多冗余问题和FM方法解决数据稀疏问题，并对预处理后的恐怖袭击事件数据用TFM模型做量化分类.文中使用朴素贝叶斯NB、支持向量机SVM、逻辑回归LR与TFM等4个模型的“马修斯相关系数”MCC进行比较，结果显示TFM的MCC相对于其他三个模型NB、SVM、LR分别提高了49.9%,2.5%,2.3%，可见TFM模型有一定可行性.     

五味子有效成分保护心室重构的研究进展

五味子对心血管、免疫系统及CNS具有调节作用,阐述了五味子中有效成分木脂素和多糖保护心室重构的作用.     

基于复杂网络的国际石油贸易关系定量研究

针对如何更加准确地评价国际石油贸易关系问题，开展了将该贸易关系进行网络化和结构化的特征指标量化分析研究。研究中以复杂网络理论进行网络化建模，以结构平衡理论进行定量分析，采用了计算聚类系数、平衡度等关键指标进行年度基准对比。获得了国际石油贸易关系网络随着贸易者数量的增加尤其是进口国数量增加，网络的聚类系数变大，平衡度值增加，整个贸易网络平衡性变差，贸易关系变得更加紧张这一结论。本研究采用网络化和结构化的方式进行关系评价，是将石油贸易关系的研究从定性推进至定量的有益尝试。     

A Dependent-Chance Programming Model for Proactive Scheduling

Proactive scheduling based on expected value model is an effective method to develop robust schedules in consideration of minimizing project cost caused by deviations between realized and planed activity starting times.However,these schedules may be realized with low probabilities.In this paper,a novel model based on dependent-chance programming(DCP) is proposed,considering probability as well as solution robustness.A hybrid intelligent algorithm integrating stochastic simulation and genetic algorithm(GA)is designed to solve the proposed model.Moreover,a numerical example is conducted to reveal the effectiveness of the proposed model and the algorithm.