基于GAN序列的卫星数据接收系统故障定位

现有的卫星数据接收系统故障定位方法受困于专家知识的难以获取。提出一种基于生成对抗网络(GAN)序列的故障定位方法GANseq。利用故障传播特性,将接收系统分解为M个信号处理单元(SPU),形成一个按照信号处理顺序排列的SPU序列;将故障定位问题转化为有序排列的M个异常检测子问题,其中第m个子问题为对前m个SPU的联合状态的异常检测;然后针对每个子问题采用基于GAN的状态参数检测模型,进而形成GAN序列;综合分析该序列在所有子问题上的检测结果以定位故障SPU。利用实际接收系统进行的实验证明所提出的GANseq不仅从数据驱动的角度完成了故障定位,而且在总体上降低了虚警率,并提高了定位精度。     

儒学展开的新向度：评干春松的“制度儒学”

激进主义思潮的冲击与现代新儒家之偏误,共同促进了＂制度儒学＂的形成。基于现代儒学研究之缺失,干春松主张以儒家思想与社会制度相结合的方式重新审视儒学的本来面目和未来的发展空间,并对制度化儒学的基本形态、解体因缘及近代重建作了周详深湛的剖析。＂制度儒学＂是近年来＂儒学复兴运动＂之一种,它的出现很大程度上标志着儒学研究路向的新展开。不过,总的看来,＂制度儒学＂学理上不够圆通,理论构建的意义也不大。     

基于注意力生成对抗网络的图像强光去除

图像中的强光在一定程度上会降低图像的质量,本文致力于从受到强光影响的图像中去除强光并生成清晰图像。为解决这个问题,提出一种带有注意力辅助模块的生成对抗网络。它主要由加入压缩-激励模块的卷积长短期记忆网络和注意力矩阵辅助模块组成,注意力辅助模块可以指导自动编码器生成清晰的图像。该方法可以轻松地移植处理其他类似的图像恢复问题。实验证明,改进后的网络体系结构是有效的并且有一定的意义。     

Segmentation mask and feature similarity loss guided GAN for object-oriented image-to-image translation

While image-to-image translation has been extensively studied, there are a number of limitations in existing methods designed for transformation between instances of different shapes from different domains. In this paper, a novel approach was proposed (hereafter referred to as ObjectVariedGAN) to handle geometric translation. One may encounter large and significant shape changes during image-to-image translation, especially object transfiguration. Thus, we focus on synthesizing the desired results to maintain the shape of the foreground object without requiring paired training data. Specifically, our proposed approach learns the mapping between source domains and target domains, where the shapes of objects differ significantly. Feature similarity loss is introduced to encourage generative adversarial networks (GANs) to obtain the structure attribute of objects (e.g., object segmentation masks). Additionally, to satisfy the requirement of utilizing unaligned datasets, cycle-consistency loss is combined with context-preserving loss. Our approach feeds the generator with source image(s), incorporated with the instance segmentation mask, and guides the network to generate the desired target domain output. To verify the effectiveness of proposed approach, extensive experiments are conducted on pre-processed examples from the MS-COCO datasets. A comparative summary of the findings demonstrates that ObjectVariedGAN outperforms other competing approaches, in the terms of Inception Score, Frechet Inception Distance, and human cognitive preference.     

基于GAN的大幅面MODIS浒苔检测实验方案设计

为实现大幅面的浒苔区域检测,设计了一种基于生成对抗网络(GAN)的检测实验方案.为增强浒苔信息和去除陆地干扰,首先,通过使用归一化植被指数(NDVI)和海陆分离来对MODIS浒苔图像进行预处理.其次,通过最小化f散度进行对抗训练GAN中的生成器和判别器.最后,通过对抗训练,实现生成检测图像和解译图像之间的差异最小化,建...     

基于生成对抗网络的半监督遥感图像飞机检测

遥感图像上的飞机目标检测是一件极富挑战性的工作,吸引了广大研究者的兴趣。基于人工神经网络的方法是当前遥感图像飞机目标检测的主流方法,这类方法要求人工标记大量的数据用于训练。对训练图像的人工标注工作费时费力,是制约有效利用大规模数据的主要瓶颈之一。为解决这个问题,提出一种基于生成对抗网络（generative adversarial networks,GAN）的半监督检测方法。在遥感图像飞机目标检测中,该方法不需要标记全部用于训练的图像,只需要标记其中一小部分样本,再和大量未标记数据一起进行训练便能取得优异的检测结果。该方法结合传统的检测网络和基于GAN的半监督学习网络。在对抗训练过程中,生成器学习数据分布并生成假样本,判别器判别真假样本,同时判别器还需要从标记数据中学习类别信息。最后,判别器学习到的决策分类面不仅仅区分出标记数据,而且平行于数据分布的边界。实验证明,在存在大量可供训练的图像的基础上,减少标注数据的比例,全监督学习方法性能会大幅下降;而本文提出的半监督学习方法,由于利用了未标注的数据,能保持更好的检测性能。     

Enhancing sequential recommendation with contrastive Generative Adversarial Network

Sequential recommendation models a user’s historical sequence to predict future items. Existing studies utilize deep learning methods and contrastive learning for data augmentation to alleviate data sparsity. However, these existing methods cannot learn accurate high-quality item representations while augmenting data. In addition, they usually ignore data noise and user cold-start issues. To solve the above issues, we investigate the possibility of Generative Adversarial Network (GAN) with contrastive learning for sequential recommendation to balance data sparsity and noise. Specifically, we propose a new framework, Enhanced Contrastive Learning with Generative Adversarial Network for Sequential Recommendation (ECGAN-Rec), which models the training process as a GAN and recommendation task as the main task of the discriminator. We design a sequence augmentation module and a contrastive GAN module to implement both data-level and model-level augmentations. In addition, the contrastive GAN learns more accurate high-quality item representations to alleviate data noise after data augmentation. Furthermore, we propose an enhanced Transformer recommender based on GAN to optimize the performance of the model. Experimental results on three open datasets validate the efficiency and effectiveness of the proposed model and the ability of the model to balance data noise and data sparsity. Specifically, the improvement of ECGAN-Rec in two evaluation metrics (HR@N and NDCG@N) compared to the state-of-the-art model performance on the Beauty, Sports and Yelp datasets are 34.95%, 36.68%, and 13.66%, respectively. Our implemented model is available via https://github.com/nishawn/ECGANRec-master.     

大漠深处的故事:关于甘建华的《西部之西》

“西部之西”不仅是甘建华先生提出的一个地域上的概念 ,也是他所举起的一面文学旗帜 ,这面旗帜曾在青海湖边产生过一定的影响 ,并为日渐凋敝的文学之圣坛增添了一些生气。在其结集出版的《西部之西》一书中共收录了 3部中篇小说和 4篇散文 ,从这里 ,我们可以一斑见豹地了解到他为文的激荡与为人的执著。同时 ,《西部之西》也是我们研究西部文学时不可忽视的一个文本。     

基于生成对抗网络与 FACS 的面部表情合成研究

为实现从单张图片合成连续变化的目标表情,提出一种基于生成对抗网络（GAN）和面部表情编码系统FACS（Facial Action Coding System）的表情合成方法。通过提取面部表情 AU（Action Unit）作为生成表情的一种约束,利用生成对抗算法合成目标表情,这种结合表情编码的合成更具合理性。同时在网络中引入注意力机制,使网络在特征提取阶段更关注表情变化。实验表明,该方法能够克服图像中的光照和背景影响,合成表情具备连续性和真实性。该合成表情方法可应用于数字娱乐、影视制作等领域。