Solar Radiation Estimation Based on a New Combined Approach of Artificial Neural Networks (ANN) and Genetic Algorithms (GA) in South Algeria

When designing solar systems and assessing the effectiveness of their many uses,estimating sun irradiance is a crucial first step.This study examined three approaches(ANN,GA-ANN,and ANFIS)for estimating daily global solar radiation(GSR)in the south of Algeria:Adrar,Ouargla,and Bechar.The proposed hybrid GA-ANN model,based on genetic algorithm-based optimization,was developed to improve the ANN model.The GA-ANN and ANFIS models performed better than the standalone ANN-based model,with GA-ANN being better suited for forecasting in all sites,and it performed the best with the best values in the testing phase of Coefficient of Determination(R=0.9005),Mean Absolute Percentage Error(MAPE=8.40%),and Relative Root Mean Square Error(rRMSE=12.56%).Nevertheless,the ANFIS model outperformed the GA-ANN model in forecasting daily GSR,with the best values of indicators when testing the model being R=0.9374,MAPE=7.78%,and rRMSE=10.54%.Generally,we may conclude that the initial ANN stand-alone model performance when forecasting solar radiation has been improved,and the results obtained after injecting the genetic algorithm into the ANN to optimize its weights were satisfactory.The model can be used to forecast daily GSR in dry climates and other climates and may also be helpful in selecting solar energy system installations and sizes.

Constructing low-dimensional perovskite network to assist efficient and stable perovskite solar cells

The use of low-dimensional(LD)perovskite materials is crucial for achieving high-performance perovskite solar cells(PSCs).However,LD perovskite films fabricated by conventional approaches give rise to full coverage of the underlying 3D perovskite films,which inevitably hinders the transport of charge carriers at the interface of PSCs.Here,we designed and fabricated LD perovskite structure that forms net-like morphology on top of the underlying three-dimensional(3D)perovskite bulk film.The net-like LD perovskite not only reduced the surface defects of 3D perovskite film,but also provided channels for the vertical transport of charge carriers,effectively enhancing the interfacial charge transfer at the LD/3D hetero-interface.The net-like morphological design comprising LD perovskite effectively resolves the contradiction between interfacial defect passivation and carrier extraction across the hetero-interfaces.Furthermore,the net-like LD perovskite morphology can enhance the stability of the underlying 3D perovskite film,which is attributed to the hydrophobic nature of LD perovskite.As a result,the net-like LD perovskite film morphology assists PSCs in achieving an excellent power conversion efficiency of up to 24.6%with over 1000 h long-term operational stability.

MACDCGAN的发电机轴承故障诊断方法

在实际工况中,发电机中传感器采集到的故障样本数据有限,使用基于深度学习的方法进行故障诊断存在过拟合问题导致模型泛化能力较差以及诊断精度不高。为了解决这个问题,采用样本扩充的思路,提出了一种改进的辅助分类器条件深度卷积生成对抗网络(MACDCGAN)的故障诊断方法。通过对采集的一维时序信号进行小波变换增强特征,构建简化结构参数的条件深度卷积生成对抗网络模型生成样本,并在模型中采用Wasserstein距离优化损失函数解决训练过程中存在模式崩塌和梯度消失的缺点;通过添加一个独立的分类器来改进分类模型的兼容性,并在分类器中引入学习率衰减算法增加模型稳定性。试验结果表明,该方法可以有效地提高故障诊断的精度,并且验证了所提模型具有良好的泛化性能。

基于Transformer和GAN的对抗样本生成算法

对抗攻击与防御是计算机安全领域的一个热门研究方向。针对现有基于梯度的对抗样本生成方法可视质量差、基于优化的方法生成效率低的问题,提出基于Transformer和生成对抗网络(GAN)的对抗样本生成算法Trans-GAN。首先利用Transformer强大的视觉表征能力,将其作为重构网络,用于接收干净图像并生成攻击噪声;其次将Transformer重构网络作为生成器,与基于深度卷积网络的鉴别器相结合组成GAN网络架构,提高生成图像的真实性并保证训练的稳定性,同时提出改进的注意力机制Targeted Self-Attention,在训练网络时引入目标标签作为先验知识,指导网络模型学习生成具有特定攻击目标的对抗扰动;最后利用跳转连接将对抗噪声施加在干净样本上,形成对抗样本,攻击目标分类网络。实验结果表明:Trans-GAN算法针对MNIST数据集中2种模型的攻击成功率都达到99.9%以上,针对CIFAR10数据集中2种模型的攻击成功率分别达到96.36%和98.47%,优于目前先进的基于生成式的对抗样本生成方法;相比快速梯度符号法和投影梯度下降法,Trans-GAN算法生成的对抗噪声扰动量更小,形成的对抗样本更加自然,满足人类视觉不易分辨的要求。

ECGAN:Translate Real World to Cartoon Style Using Enhanced Cartoon Generative Adversarial Network

Visual illustration transformation from real-world to cartoon images is one of the famous and challenging tasks in computer vision.Image-to-image translation from real-world to cartoon domains poses issues such as a lack of paired training samples,lack of good image translation,low feature extraction from the previous domain images,and lack of high-quality image translation from the traditional generator algorithms.To solve the above-mentioned issues,paired independent model,high-quality dataset,Bayesian-based feature extractor,and an improved generator must be proposed.In this study,we propose a high-quality dataset to reduce the effect of paired training samples on the model’s performance.We use a Bayesian Very Deep Convolutional Network(VGG)-based feature extractor to improve the performance of the standard feature extractor because Bayesian inference regu-larizes weights well.The generator from the Cartoon Generative Adversarial Network(GAN)is modified by introducing a depthwise convolution layer and channel attention mechanism to improve the performance of the original generator.We have used the Fréchet inception distance(FID)score and user preference score to evaluate the performance of the model.The FID scores obtained for the generated cartoon and real-world images are 107 and 76 for the TCC style,and 137 and 57 for the Hayao style,respectively.User preference score is also calculated to evaluate the quality of generated images and our proposed model acquired a high preference score compared to other models.We achieved stunning results in producing high-quality cartoon images,demonstrating the proposed model’s effectiveness in transferring style between authentic images and cartoon images.

Using GAN Neural Networks for Super-Resolution Reconstruction of Temperature Fields

A Generative Adversarial Neural(GAN)network is designed based on deep learning for the Super-Resolution(SR)reconstruction task of temperaturefields(comparable to downscaling in the meteorologicalfield),which is limited by the small number of ground stations and the sparse distribution of observations,resulting in a lack offineness of data.To improve the network’s generalization performance,the residual structure,and batch normalization are used.Applying the nearest interpolation method to avoid over-smoothing of the climate element values instead of the conventional Bicubic interpolation in the computer visionfield.Sub-pixel convolution is used instead of transposed convolution or interpolation methods for up-sampling to speed up network inference.The experimental dataset is the European Centre for Medium-Range Weather Forecasts Reanalysis v5(ERA5)with a bidirectional resolution of 0:1°×0:1°.On the other hand,the task aims to scale up the size by a factor of 8,which is rare compared to conventional methods.The comparison methods include traditional interpolation methods and a more widely used GAN-based network such as the SRGAN.Thefinal experimental results show that the proposed scheme advances the performance of Root Mean Square Error(RMSE)by 37.25%,the Peak Signal-to-noise Ratio(PNSR)by 14.4%,and the Structural Similarity(SSIM)by 10.3%compared to the Bicubic Interpolation.For the traditional SRGAN network,a relatively obvious performance improvement is observed by experimental demonstration.Meanwhile,the GAN network can converge stably and reach the approximate Nash equilibrium for various initialization parameters to empirically illustrate the effectiveness of the method in the temperature fields.

Solar Power Plant Network Packet-Based Anomaly Detection System for Cybersecurity

As energy-related problems continue to emerge,the need for stable energy supplies and issues regarding both environmental and safety require urgent consideration.Renewable energy is becoming increasingly important,with solar power accounting for the most significant proportion of renewables.As the scale and importance of solar energy have increased,cyber threats against solar power plants have also increased.So,we need an anomaly detection system that effectively detects cyber threats to solar power plants.However,as mentioned earlier,the existing solar power plant anomaly detection system monitors only operating information such as power generation,making it difficult to detect cyberattacks.To address this issue,in this paper,we propose a network packet-based anomaly detection system for the Programmable Logic Controller(PLC)of the inverter,an essential system of photovoltaic plants,to detect cyber threats.Cyberattacks and vulnerabilities in solar power plants were analyzed to identify cyber threats in solar power plants.The analysis shows that Denial of Service(DoS)and Manin-the-Middle(MitM)attacks are primarily carried out on inverters,aiming to disrupt solar plant operations.To develop an anomaly detection system,we performed preprocessing,such as correlation analysis and normalization for PLC network packets data and trained various machine learning-based classification models on such data.The Random Forest model showed the best performance with an accuracy of 97.36%.The proposed system can detect anomalies based on network packets,identify potential cyber threats that cannot be identified by the anomaly detection system currently in use in solar power plants,and enhance the security of solar plants.

融合门控变换机制和GAN的低光照图像增强方法

针对低光照图像增强过程中存在的配对图像数据依赖、细节损失严重和噪声放大问题,提出结合门控通道变换机制和生成对抗网络(GAN)的低光照图像增强方法AGR-GAN,该方法可以在没有低/正常光图像对的情况下进行训练。首先,设计特征提取网络,该网络由多个基于门控通道变换单元的多尺度卷积残差模块构成,以提取输入图像的全局上下文特征和多尺度局部特征信息;然后,在特征融合网络中,采用卷积残差结构将提取的深浅层特征进行充分融合,再引入横向跳跃连接结构,最大程度保留细节特征信息,获得最终的增强图像;最后,引入联合损失函数指导网络训练过程,抑制图像噪声,使增强图像色彩更自然匀称。实验结果表明,该方法在主观视觉分析和客观指标评价方面相较其他算法均具有显著优势,其能有效提高低光照图像的亮度和对比度,减弱图像噪声,增强后的图像更清晰且色彩更真实,峰值信噪比、结构相似度和无参考图像质量评价指标平均可达16.48 dB、0.93和3.37。

面向舰船目标检测的SAR图像数据PCGAN生成方法

针对现有合成孔径雷达(SAR)图像数据生成方法大多无法同时生成舰船图像及其检测标签的问题,面向SAR舰船图像生成及目标检测任务,构建基于位置信息的条件生成对抗网络(PCGAN).首先,提出将舰船位置信息作为约束条件用于限制生成图像中舰船的位置,并将其作为舰船图像的检测标签;随后,引入Wasserstein距离稳定PCGAN的训练过程;最后,利用生成的SAR舰船图像及对应检测标签完成YOLOv3网络的端到端训练,实现舰船数据增强与目标检测的协同学习,进而获得更耦合目标检测实际应用的多样性数据.在HRSID(high resolution SAR image dataset)数据集上的实验结果表明,PCGAN方法能生成清晰、鲁棒的SAR舰船数据,舰船检测准确度最高提升1.01%,验证了所提出方法的有效性.

基于GAN改进的红外光与可见光图像融合算法研究

针对夜晚户外场景下,传统的单一鉴别器生成对抗网络(GAN)容易忽略红外光的亮度信息和边缘信息的问题,提出一种基于注意力机制与双鉴别器的红外光与可见光图像融合算法。首先,为了有针对性地获得红外光图像的目标信息和可见光图像的背景纹理信息,在生成器网络中引入通道注意力机制;其次,使用双鉴别器的生成对抗网络,并设计一种新的鉴别器输入,在提高训练稳定性的同时更好地保留源图像信息;最后,损失函数设置为对抗损失、结构相似性损失和梯度损失,以约束鉴别器使其生成细节信息丰富的融合图像。在TNO数据集上的实验结果表明,所提算法得到的融合图像梯度变化更明显、边缘更加清晰,更符合人眼视觉效果。

融合情感分析和GAN-TrellisNet的股价预测方法

将时序深度神经网络应用于股票价格预测,已成为量化金融领域的重要研究方向。时序神经网络具有很好的序列数据捕捉能力和学习记忆能力,在股票预测上有一定适用性。但是现有的模型大多存在预测准确度不高、模型结构复杂导致训练时间较长等问题.为了解决以上问题,提出了一种基于情感分析和GAN-TrellisNet的股价预测方法。提出了一个基于LSTM-CNN的情感分析模型,用于分析爬虫获取的主流金融论坛股票评论,并获得股票情感指数。为了提高预测准确度,将情感指数和百度搜索指数加入股票交易数据中作为训练集,提出了一个基于TrellisNet和CNN的改进型GAN股价预测模型,利用TrellisNet生成器的卷积特性来捕捉数据的局部特征,选取特征提取能力较强的CNN作为判别器来区别预测结果和真实股价。通过选取10只代表性股票和三种大盘指数的不同时段数据进行算法验证,结果表明,与ConvLSTM和GAN-LSTM预测模型相比,GAN-TrellisNet模型能有效缩短训练时间,提高预测准确率。

基于SA/WGAN的新能源场景生成方法

随着新能源渗透率逐年提高,其出力的随机性与波动特性难以准确预测,给电力系统的运行、规划和调度提出了严峻的挑战,因此新能源的不确定性建模受到越来越多的关注。为了更有效地获得新能源出力场景中的时序特征,提出了一种基于数据驱动的新能源场景生成方法,通过采用SA/WGAN模型,把自注意力机制和带有梯度惩罚的生成对抗网络判别器结合,构建基于两种模型结合的深度学习模型,有效突显新能源出力场景中时序特性,增强场景生成中非线性拟合能力。算例结果表明,所提模型的新能源生成场景相较于原始WGAN和WGAN-LSTM的场景生成结果,可以有效提高精准度,同时具备了WGAN-GP训练结果稳定和SA计算速度快的优势,更高效地生成与真实新能源场景分布接近的场景。

The Influence of Air Pollution Concentrations on Solar Irradiance Forecasting Using CNN-LSTM-mRMR Feature Extraction

Maintaining a steady power supply requires accurate forecasting of solar irradiance,since clean energy resources do not provide steady power.The existing forecasting studies have examined the limited effects of weather conditions on solar radiation such as temperature and precipitation utilizing convolutional neural network(CNN),but no comprehensive study has been conducted on concentrations of air pollutants along with weather conditions.This paper proposes a hybrid approach based on deep learning,expanding the feature set by adding new air pollution concentrations,and ranking these features to select and reduce their size to improve efficiency.In order to improve the accuracy of feature selection,a maximum-dependency and minimum-redundancy(mRMR)criterion is applied to the constructed feature space to identify and rank the features.The combination of air pollution data with weather conditions data has enabled the prediction of solar irradiance with a higher accuracy.An evaluation of the proposed approach is conducted in Istanbul over 12 months for 43791 discrete times,with the main purpose of analyzing air data,including particular matter(PM10 and PM25),carbon monoxide(CO),nitric oxide(NOX),nitrogen dioxide(NO_(2)),ozone(O₃),sulfur dioxide(SO_(2))using a CNN,a long short-term memory network(LSTM),and MRMR feature extraction.Compared with the benchmark models with root mean square error(RMSE)results of 76.2,60.3,41.3,32.4,there is a significant improvement with the RMSE result of 5.536.This hybrid model presented here offers high prediction accuracy,a wider feature set,and a novel approach based on air concentrations combined with weather conditions for solar irradiance prediction.

无线传感网络中一种基于RF-GAN模型的入侵检测算法

针对现有无线传感网络入侵检测算法存在的效率低、精度差等问题,提出基于RFGAN模型的无线传感网络入侵检测算法。首先,采集无线传感网络运行数据,通过去噪、缺失补偿等步骤,完成对原始数据的预处理;然后,利用RF-GAN模型提取无线传感网络运行特征;最后,通过提取特征与检测标准的匹配,得出网络入侵检测结果。理论分析及实验结果表明:优化设计方法的入侵类型误检率明显更低,入侵数据量检测误差为0.015GB,相较于现有检测算法具有一定优势。

结合多注意力及IE-GAN的面部属性编辑方法

面部属性编辑主要有两个目的:①将图像从源域转换到目标域,对指定的目标属性(如性别、年龄、发色等)进行修改;②只改变与目标属性相关的人脸区域,保留目标属性之外其他属性的细节。而现有的人脸属性编辑方法不可避免地会对与目标属性无关区域进行更改。因此,提出一种基于IE-GAN和多注意力机制的面部属性编辑方法MAIE-GAN,引入补充注意连接(Complementary attention connection,CAC)的概念连接编解码器,解决了由直接跳跃连接引起的信息冗余问题,将自注意力机制作为生成器中卷积层的补充,使其能更好定位目标属性,限定属性变换区域,此外,还利用互补注意特征的概念,实现目标属性无关区域的更好保留。通过与现有方法的对比分析表明,该方法在属性定位和图像质量方面优于现有的方法。

基于GAN与轴向区块注意力的心脏磁共振图像分割

心脏磁共振图像分割对心功能分析和心脏疾病诊断具有十分重要的意义。针对传统心脏分割方法对心脏MR图像特征提取不全面,以及基于注意力机制的深度学习方法参数量过大的问题,设计一种基于GAN与轴向区块注意力的心脏磁共振图像分割模型,对图像特征进行多尺度、全方面地提取,结合GAN策略提升模型性能。实验结果表明,模型实现了图像的有效分割并提高了分割结果与真实标签之间的一致性。

应用XCT断层扫描技术和GAN深度学习模型的多孔介质微观结构定量研究

物质在土壤中的迁移转化行为是研究地下水动力学的核心问题。定量化表征多孔介质拓扑结构并计算分析相关表面形态学参数,为研究复杂介质内物质运移微观机理提供了重要的数据基础和参数。应用微米级X射线计算机断层扫描(XCT)和图像处理技术,结合前沿的机器学习算法重建和定量分析多孔介质微观结构,可快速、批量创建高分辨率的复杂多孔介质研究样本。首先采用XCT技术,提取石英砂和散装土壤两类典型多孔介质的微观孔隙结构;而后基于生成对抗神经网络(GAN)模型重构复杂多孔介质的微观空间结构,与XCT扫描图像进行交叉验证;最后,计算获取Minkowski形态学参数,并基于多孔介质微观结构开展计算流体力学(CFD)数值模拟,计算石英砂和土壤多孔介质内的流动特征和渗透率。结果表明:1)GAN生成的合成数据与原始数据符合KS同分布,说明GAN能够成功合成与原始图像结构空间分布模式一致的图像;2)Minkowski宏观参数评价误差的较小,KS同分布结果表明,多孔介质样本的结构异质性会在一定程度上影响GAN模型的计算精度和效率;3)Open FOAM模拟计算得到的渗透率结果表明,GAN模型生成的多孔介质图像与原始图像具有一致的统计特征和物理性质。综上,综合运用前沿的XCT扫描、图像处理技术和机器学习算法,构建了土壤微观结构重建和定量分析模型,并结合多孔介质形态学和计算流体力学方法对模型进行了验证和分析。该研究为多孔介质微观结构研究提供了新技术和新方法,为进一步研究复杂多孔介质内溶质运移提供了科技支撑。

注意力融合双流特征的局部GAN生成人脸检测算法

为解决现有局部生成式对抗网络(GAN)生成人脸检测算法在检测经过后处理的图像时性能严重下降的问题,提出一种注意力融合双流特征的局部GAN生成人脸检测算法.该算法利用双流网络分别从RGB颜色空间和YCbCr颜色空间中提取鲁棒特征,并引入注意力特征融合模块在不同网络层上融合双流特征以获得更鲁棒的特征.同时采用多层次特征融合决策提高网络对局部生成区域特征的提取和辨别能力.实验结果表明,所提算法的鲁棒性优于现有算法,尤其是针对JPEG压缩和双边滤波后处理.在FFHQ+规则子集上与次优算法相比,该算法在3种强度的JPEG压缩和双边滤波上的平均准确率分别提高了1.88%和2.64%;在FFHQ+不规则子集上与次优算法相比,该算法在3种强度的JPEG压缩和双边滤波上的平均准确率分别提高了2.85%和1.60%.

基于GAN的直扩信号生成算法

将深度学习模型应用至电子干扰技术来生成干扰信号具有重要的现实意义。将生成对抗网络(generative adversarial network,GAN)应用于信号生成领域,对电磁扩频信号频谱数据的分布进行深度学习,并生成与其相干的干扰信号。在实验中GAN的生成器和判别器互相博弈训练,通过自适应矩估计(adaptive moment estimation,Adam)进行优化,最终训练出良好的模型,可以生成所需信号。实验结果表明,基于GAN的信号生成算法生成的数据分布已基本具备真实数据分布普遍具有的特点,对同一信噪比的电磁频谱数据进行深度学习后,生成数据能够较为准确地学习到不同信噪比电磁频谱数据的不同特点。

DR-GAN:一种无监督学习的探地雷达杂波抑制方法

探地雷达(GPR)是一种基于电磁波的地下无损探测技术,广泛应用于市政工程、交通、军事等领域。在数据采集过程中,由于发射天线和接收天线之间的耦合、起伏地面的散射以及地下随机媒质的复杂性等原因,采集得到的GPR B-scan回波中通常存在杂波,杂波严重影响了地下目标的检测和特征提取。该文提出一种用于GPR B-scan图像杂波抑制的解纠缠表示生成对抗网络(DR-GAN),设计了目标特征编码器和杂波特征编码器用来提取GPR B-scan图像中的目标特征和杂波特征,设计了杂波抑制生成器用来获取杂波抑制后的GPR B-scan图像。与现有的基于监督学习的GPR杂波抑制方法相比,该方法在网络训练时不需要成对的匹配数据,可以更好地应用于实测GPR图像的杂波抑制。在仿真和实测GPR数据上的实验结果表明,DR-GAN这一无监督学习网络具有更好的杂波抑制性能。对石英砂中埋设的钢筋进行数据采集,运用DR-GAN对含杂波的实测数据进行处理,处理结果的改善系数(IF)指标较现有的鲁棒非负矩阵分解(RNMF)方法提高了17.85 dB。