基于自注意力机制和CycleGAN的高分三号ScanSAR图像的扇贝效应抑制

高分三号卫星是我国首颗分辨率达到1 m的C波段 多极化合成孔径雷达(synthetic aperture radar,SAR) 卫星,其中扫描 式合成孔径雷达(scan synthetic aperture radar,ScanSAR)模式是高分三号卫星重要的工 作模式之一,由于该模式的工作机制导致生成的图像可能发生扇贝效应,一般呈现为明暗相 间的条纹。本文针对高分三号卫星ScanSAR模式下存在的扇贝效应,提出自注意力机制与循 环一致对抗网络(cycle-consistent adversarial networks,CycleGAN)结合的模型对Scan S AR图像进行处理,从而抑制扇贝效应产生的条纹现象。本文所示方法与传统扇贝效应抑制方 法和深度学习相关算法进行比较,并通过亮度均值、平均梯度等指标进行分析。实验结果表 明,本文方法可以对高分三号ScanSAR图像存在的扇贝效应进行较好的处理,有效抑制图像 的条纹现象,使得图像质量得到提升,具有较大的实用意义。     

基于改进YOLOv5的输电线路鸟巢检测方法研究

输电线路上的鸟巢会对电力设备的安全运行构成威胁,甚至影响整个电力系统的稳定性。针对复杂场景下输电线路鸟巢检测方法适用性较差的问题,提出一种基于改进YOLOv5的输电线路鸟巢检测方法。该方法结合通道注意机制和空间注意机制设计特征平衡网络,以通道权值和空间权值作为引导,实现检测网络不同层次特征之间语义信息和空间信息的平衡。同时,为了避免因网络层数增加导致特征信息不断被弱化的问题,设计特征增强模块以捕获与鸟巢相关的通道关系和位置信息。最后,利用输电线路无人机巡检图像建立鸟巢数据集进行训练和测试。实验结果表明,所提出的输电线路鸟巢检测方法具有较强的泛化能力和适用性,同时也为电力图像缺陷检测提供技术参考。     

基于机器学习的配电网异常缺失数据动态清洗方法

针对传统配电网数据清洗过程中异常数据判断阈值需要人为设定、缺失数据填补效率不佳的局限性,提出基于机器学习的配电网异常缺失数据一体化动态清洗方法。首先,基于局部异常因子检测算法和高斯混合模型,提出一种异常数据动态检测改进算法,实现配电网异常数据阈值的准确自动选择。其次,基于随机森林算法与最小二乘回归法,提出一种配电网缺失数据动态填补算法。根据缺失数据时间长度自适应优化填补算法,在保证数据填补精度的同时降低计算时间。在此基础上,通过异常数据检测和缺失数据填补共同构建一体化动态清洗架构。采用湖南某地区配电网数据进行实例验证,结果表明所提方法可实现异常辨识阈值准确自动选择,有效检测配电网异常数据,并且实现缺失数据填补精度与速度的平衡,具有较好的工程应用价值。     

基于一致性的综合能源系统低碳经济调度

综合能源系统(integrated energy system, IES)是解决能源损耗和环境污染问题的有效途径。为提高IES的经济性和环保性,提出了一种基于一致性的IES低碳经济调度策略。首先,在IES中引入阶梯式碳交易机制,在碳交易成本函数构造中,提出一种随着碳排区间而递增的成本系数,通过成本的非线性递增来限制碳排放量。其次,综合考虑能量传输过程中的损耗问题,构建一种结合用能需求和传输损耗的供需平衡约束条件。然后,将电、热、气供能单元的增量成本作为一致性变量,利用领导一致性算法对IES进行分布式调度,进而实现各单元的供能最优输出。最后,结合不同的实验案例验证了所提低碳经济调度策略的有效性。     

参与多元耦合市场的电-气综合能源系统低碳经济调度

为进一步提升综合能源系统（IES）绿电消纳水平,减少CO2排放量及降低系统运行成本,提出一种计及绿证交易与碳交易交互机制下的IES低碳经济调度模型及其求解方法。首先,对传统阶梯式碳交易机制进行改进,并根据绿色证书碳减排原理,提出一种绿证-碳交易交互机制,即通过冗余绿色证书联动碳交易与绿证交易的耦合市场机制;然后,以系统总成本最小为目标,结合低碳技术,构建绿证-碳交易交互机制下计及碳捕集、利用与封存（CCUS）技术的电-气互联综合能源系统优化调度模型;最后,以IEEE39节点电网、比利时20节点气网构成的电-气互联综合能源系统为例进行仿真研究,结果表明所提方法在提高风电消纳的同时,可显著减少CO2排放。     

一种新型MMC并联双端口子模块及其三阶段故障电流阻断机理

为使模块化多电平换流器(modular multilevel converter,?MMC)具备直流故障自清除能力和电容电压均衡能力,提出了一种新型并联双端口子模块：钳位双全桥子模块(clamp double full bridge submodule,?CD-FBSM)。该子模块器件成本和运行损耗较低,正常工作时相邻子模块之间具有多种协同运行模式,通过特有的并联模式可提高电容电压均衡度。故障闭锁时,模块内部电容并联、模块之间电容串联且反向接入电路,能够可靠阻断故障电流并均衡电容电压,有利于系统快速重启。此外,提出了三阶段故障电流阻断机理分析方法,对CD-FBSM的故障电流阻断过程进行了研究。通过Matlab/Simulink的仿真结果表明,所提子模块电容电压均衡度较高,可快速阻断故障电流,且故障电流阻断过程与理论分析一致。     

考虑供需双侧响应和碳交易的氢能综合能源系统鲁棒调度

双碳”背景下,为促进可再生能源消纳,约束园区碳排放和应对可再生能源的随机性,构建了含可逆固体氧化物电池(reversible solid oxide cell, RSOC)的园区氢能综合能源系统,并提出考虑供需双侧响应、阶梯碳交易机制和可再生能源出力不确定的鲁棒调度模型。首先,采用RSOC和储氢罐消纳可再生能源富余出力。其次,引入有机朗肯循环(organic Rankine cycle, ORC)余热发电和综合需求响应构成供需双侧响应优化热电运行。然后,通过鲁棒优化理论处理可再生能源的不确定性。接着以系统购能成本、碳交易成本、弃风光惩罚成本和需求响应成本之和最小为目标构建鲁棒调度模型,并采用CPLEX求解。最后通过算例仿真结果验证了模型的有效性。     

Few-shot object segmentation with a new feature aggregation module

The success of convolutional neural network for object segmentation depends on a large amount of training data and high-quality samples. But annotating such high-quality training data for pixel-wise segmentation is labor-intensive. To reduce the massive labor work, few-shot learning has been introduced to segment objects, which uses a few samples for training without compromising the performance. However, the current few-shot models are biased towards the seen classes rather than being class-irrelevant due to lack of global context prior attention. Therefore, this study aims at proposing a few-shot object segmentation model with a new feature aggregation module. Specifically, the proposed work develops a detail-aware module to enhance the discrimination of details with diversified attributes. To enhance the semantics of each pixel, we propose a global attention module to aggregate detailed features containing semantic information. Furthermore, to improve the performance of the proposed model, the model uses support samples that represents class-specific prototype obtained by respective category prototype block. Next, the proposed model predicts label of each pixel of query sample by estimating the distance between the pixel and prototypes. Experiments on standard datasets demonstrate significance of the proposed model over SOTA in terms of segmentation with a few training samples.     

Ship detection based on multi-scale weighted fusion

Ship surveillance plays an important role in ensuring the safety of maritime transportation and navigation. Due to the influence of factors such as waves and special weather, the existing detection methods still cannot balance the accuracy, speed and the parameters of the model in the changeable and complex marine environment. To solve this problem, this paper proposes an improved real-time method based on YOLOv5, which has few parameters and achieves high detection accuracy with little memory and computation cost. Collaborative Attention (CA) mechanism is added to the network structure, which enables the model to more accurately locate and identify target regions. We also design a Spatial Pyramid Pooling module (SPP) and a weighted pyramid network called Bidirectional Feature Pyramid Network (BiFPN) based on the characteristics of the ships to better fuse feature information. Transformer encoder is introduced to capture long-distance dependencies and preserve global and local features to the greatest extent. Furthermore, the ability of our proposed structure to localize objects at each stage is improved through integrating the output of multiple modules. The experimental results show that, the comprehensive performance of this method is better than the existing technology in ship detection on different evaluation criteria.     

Ultraefficient Non-Doped Deep Blue Fluorescent OLED: Achieving a High EQE of 10.17% at 1000 cd m−2 with CIEy<0.08

The pursuit for efficient deep blue material is an ever-increasing issue in organic optoelectronics field. It is a long-standing challenge to achieve high external quantum efficiency (EQE) exceed 10% at brightness of 1000 cd m⁻² with a Commission International de L'Eclairage (CIE_y) <0.08 in non-doped organic light-emitting diodes (OLEDs). Herein, this study reports a deep blue luminogen, PPITPh, by bonding phenanthro[9,10-d]imidazole moiety with m-terphenyl group via benzene bridge. The non-doped OLED based on PPITPh exhibits an exceptionally high EQE of 11.83% with a CIE coordinate of (0.15, 0.07). The EQE still maintains 10.17% at the brightness of 1000 cd m⁻², and even at a brightness as high as 10000 cd m⁻², an EQE of 7.5% is still remained, representing the record-high result among non-doped deep-blue OLEDs at 1000 cd m⁻². The unprecedented device performance is attributed to the reversed intersystem crossing process through hot exciton mechanism. Besides, the maximum EQE of orange phosphorescent OLED with PPITPh as host is 32.02%, and remains 31.17% at the brightness of 1000 cd m⁻². Such minimal efficiency roll-off demonstrates that PPITPh is also an excellent phosphorescent host material. The result offers a new design strategy for the enrichment of high-efficiency deep blue luminogen.