Incremental Learning of Radio Modulation Classification Based on Sample Recall

Radio modulation classification has always been an important technology in the field of communications.The difficulty of incremental learning in radio modulation classification is that learning new tasks will lead to catastrophic forgetting of old tasks.In this paper,we propose a sample memory and recall framework for incremental learning of radio modulation classification.For data with different signal-to-noise ratios,we use a partial memory strategy by selecting appropriate samples for memorizing.We compare the performance of our proposed method with three baselines through a large number of simulation experiments.Results show that our method achieves far higher classification accuracy than finetuning method and feature extraction method.Furthermore,it performs closely to joint training method which uses all old data in terms of classification accuracy which validates the effectiveness of our method against catastrophic forgetting.

混凝土材料组成设计的体积分析方法

混凝土的材料设计通常必须同时满足以坍落度为指标的工作性和以强度为指标的服役性能,混凝土材料组成即配合比设计方法的科学性对提高混凝土性能节省实验劳动有重要的价值。混凝土的性能由各组分的体积比决定,提出一种混凝土材料设计的体积分析方法,先采用最大粒级的粗集料与次级的粗集料进行配合,以集料堆积最小空隙率优选其比例,将这种比例的混合集料与再次一级的集料混合,也采用最小空隙率优选其比例,依次类推得到粗集料的最佳配合比。在本方法中,集料级配的优化只进行到粗集料层次,细集料用量则根据其细度模数和所配制的混凝土类型而定。经文献研究和实验研究,该方法适应从低强到超高强混凝土的材料组成设计。

钢渣分相熟料研究与应用进展

受钢渣安定性不良、易磨性差、活性低的制约,钢渣在我国的综合利用率不足30%。针对钢渣在水泥领域中的应用瓶颈,提出了一种钢渣分相熟料烧成技术,将未经粉磨的原状钢渣直接投放到回转窑中,形成一种以热处理钢渣为内核、高铁熟料为过渡区、普通熟料为外壳的钢渣分相熟料。模拟实验表明:钢渣分相熟料技术烧制的熟料中C3S和C2S晶体发育更为完整,熟料易磨性与普通熟料相当且强度高。该技术提高了窑的台时产量,降低煤耗和CO2排放。钢渣分相熟料烧成技术可以大规模、高附加值地消纳钢渣,具有较高的经济效益和生态环保效益。

Generative Adversarial Network-Based Electromagnetic Signal Classification: A Semi- Supervised Learning Framework

Generative adversarial network(GAN)has achieved great success in many fields such as computer vision,speech processing,and natural language processing,because of its powerful capabilities for generating realistic samples.In this paper,we introduce GAN into the field of electromagnetic signal classification(ESC).ESC plays an important role in both military and civilian domains.However,in many specific scenarios,we can’t obtain enough labeled data,which cause failure of deep learning methods because they are easy to fall into over-fitting.Fortunately,semi-supervised learning(SSL)can leverage the large amount of unlabeled data to enhance the classification performance of classifiers,especially in scenarios with limited amount of labeled data.We present an SSL framework by incorporating GAN,which can directly process the raw in-phase and quadrature(IQ)signal data.According to the characteristics of the electromagnetic signal,we propose a weighted loss function,leading to an effective classifier to realize the end-to-end classification of the electromagnetic signal.We validate the proposed method on both public RML2016.04c dataset and real-world Aircraft Communications Addressing and Reporting System(ACARS)signal dataset.Extensive experimental results show that the proposed framework obtains a significant increase in classification accuracy compared with the state-of-the-art studies.

双键侧含苯基硒醚的马来酰亚胺聚合物的光响应和光降解

马来酰亚胺聚合物是潜在的优异的光学材料,但如果这些聚合物不能降解,势必存在环境污染等问题。文中在笔者之前报道的双键侧含苯基硒醚的马来酰亚胺聚合物的基础上,在常规的结构表征后,利用凝胶渗透色谱(GPC)、紫外可见(UV-vis)光谱和电子自旋共振谱(ESR)进一步研究了聚合物的光响应和光降解。结果表明,所制备的聚合物用紫外光照射1 h,相对分子质量减小1倍,且随着光照时间的延长,相对分子质量继续下降,但外加相应的硒醚化合物到侧链不含苯基硒醚的马来酰亚胺聚合物中,紫外光照后并不能引起降解行为。另外,还研究了硒醚换成硫醚后马来酰亚胺聚合物的降解行为,发现硫醚的引入并不能使聚合物降解。

Development of a charge transfer space loop to improve adsorption performance in aerial electrostatic spray

In order to solve the problem of insufficient adsorption rate of droplets on the target back via aerial electrostatic spray,this study proposed a high-voltage electrostatic generator to charge the liquids in two isolated water tanks with positive and negative charges respectively.A charge transfer loop was developed in space between the aerial electrostatic spray system and the ground.This method greatly enhanced the adsorption performance under outdoor conditions that 16.7%droplets density increased on the target front,a nearly fourfold destiny increased on the target back compared with the conventional UAV spray system.The target back-to-front ratio of droplet density was improved from 6.1%to 25.7%,which validated the satisfactory performance of the developed system.

Few-shot electromagnetic signal classification:A data union augmentation method

Deep learning has been fully verified and accepted in the field of electromagnetic signal classification. However, in many specific scenarios, such as radio resource management for aircraft communications, labeled data are difficult to obtain, which makes the best deep learning methods at present seem almost powerless, because these methods need a large amount of labeled data for training. When the training dataset is small, it is highly possible to fall into overfitting, which causes performance degradation of the deep neural network. For few-shot electromagnetic signal classification, data augmentation is one of the most intuitive countermeasures. In this work, a generative adversarial network based on the data augmentation method is proposed to achieve better classification performance for electromagnetic signals. Based on the similarity principle, a screening mechanism is established to obtain high-quality generated signals. Then, a data union augmentation algorithm is designed by introducing spatiotemporally flipped shapes of the signal. To verify the effectiveness of the proposed data augmentation algorithm, experiments are conducted on the RADIOML 2016.04C dataset and real-world ACARS dataset. The experimental results show that the proposed method significantly improves the performance of few-shot electromagnetic signal classification.