Enhancing Human Action Recognition with Adaptive Hybrid Deep Attentive Networks and Archerfish Optimization

In recent years,wearable devices-based Human Activity Recognition(HAR)models have received significant attention.Previously developed HAR models use hand-crafted features to recognize human activities,leading to the extraction of basic features.The images captured by wearable sensors contain advanced features,allowing them to be analyzed by deep learning algorithms to enhance the detection and recognition of human actions.Poor lighting and limited sensor capabilities can impact data quality,making the recognition of human actions a challenging task.The unimodal-based HAR approaches are not suitable in a real-time environment.Therefore,an updated HAR model is developed using multiple types of data and an advanced deep-learning approach.Firstly,the required signals and sensor data are accumulated from the standard databases.From these signals,the wave features are retrieved.Then the extracted wave features and sensor data are given as the input to recognize the human activity.An Adaptive Hybrid Deep Attentive Network(AHDAN)is developed by incorporating a“1D Convolutional Neural Network(1DCNN)”with a“Gated Recurrent Unit(GRU)”for the human activity recognition process.Additionally,the Enhanced Archerfish Hunting Optimizer(EAHO)is suggested to fine-tune the network parameters for enhancing the recognition process.An experimental evaluation is performed on various deep learning networks and heuristic algorithms to confirm the effectiveness of the proposed HAR model.The EAHO-based HAR model outperforms traditional deep learning networks with an accuracy of 95.36,95.25 for recall,95.48 for specificity,and 95.47 for precision,respectively.The result proved that the developed model is effective in recognizing human action by taking less time.Additionally,it reduces the computation complexity and overfitting issue through using an optimization approach.

Pervasive Attentive Neural Network for Intelligent Image Classification Based on N-CDE’s

The utilization of visual attention enhances the performance of image classification tasks.Previous attentionbased models have demonstrated notable performance,but many of these models exhibit reduced accuracy when confronted with inter-class and intra-class similarities and differences.Neural-Controlled Differential Equations(N-CDE’s)and Neural Ordinary Differential Equations(NODE’s)are extensively utilized within this context.NCDE’s possesses the capacity to effectively illustrate both inter-class and intra-class similarities and differences with enhanced clarity.To this end,an attentive neural network has been proposed to generate attention maps,which uses two different types of N-CDE’s,one for adopting hidden layers and the other to generate attention values.Two distinct attention techniques are implemented including time-wise attention,also referred to as bottom N-CDE’s;and element-wise attention,called topN-CDE’s.Additionally,a trainingmethodology is proposed to guarantee that the training problem is sufficiently presented.Two classification tasks including fine-grained visual classification andmulti-label classification,are utilized to evaluate the proposedmodel.The proposedmethodology is employed on five publicly available datasets,including CUB-200-2011,ImageNet-1K,PASCAL VOC 2007,PASCAL VOC 2012,and MS COCO.The obtained visualizations have demonstrated that N-CDE’s are better appropriate for attention-based activities in comparison to conventional NODE’s.

Assessing nest attentiveness of Common Terns via video cameras and temperature loggers

Background:While nest attentiveness plays a critical role in the reproductive success of avian species,nest attentiveness data with high temporal resolution is not available for many species.However,improvements in both video monitoring and temperature logging devices present an opportunity to increase our understanding of this aspect of avian behavior.Methods:To investigate nest attentiveness behaviors and evaluate these technologies,we monitored 13 nests across two Common Tern(Sterna hirundo)breeding colonies with a paired video camera-temperature logger approach,while monitoring 63 additional nests with temperature loggers alone.Observations occurred from May to August of 2017 on Poplar(Chesapeake Bay,Maryland,USA)and Skimmer Islands(Isle of Wight Bay,Maryland,USA).We examined data respective to four times of day:Morning(civil dawn‒11:59),Peak(12:00‒16:00),Cooling(16:01‒civil dusk),and Night(civil dusk‒civil dawn).Results:While successful nests had mostly short duration off-bouts and maintained consistent nest attentiveness throughout the day,failed nests had dramatic reductions in nest attentiveness during the Cooling and Night periods(p<0.05)with one colony experiencing repeated nocturnal abandonment due to predation pressure from a Great Horned Owl(Bubo virginianus).Incubation appeared to ameliorate ambient temperatures during Night,as nests were significantly warmer during Night when birds were on versus off the nest(p<0.05).Meanwhile,off-bouts during the Peak period occurred during higher ambient temperatures,perhaps due to adults leaving the nest during the hottest periods to perform belly soaking.Unfortunately,temperature logger data alone had limited ability to predict nest attentiveness status during shorter bouts,with results highly dependent on time of day and bout duration.While our methods did not affect hatching success(p>0.05),video-monitored nests did have significantly lower clutch sizes(p<0.05).Conclusions:The paired use of iButtons and video cameras enabled a detailed description of the incubation behavior of COTE.However,while promising for future research,the logistical and potential biological complications involved in the use of these methods suggest that careful planning is needed before these devices are utilized to ensure data is collected in a safe and successful manner.

The Secret of Attentive Listening

At the University of California at Berkeley, as with other colleges, students complain about 8: 00 a.m. classes. During one course

Attentive Neighborhood Feature Augmentation for Semi-supervised Learning

Recent state-of-the-art semi-supervised learning(SSL)methods usually use data augmentations as core components.Such methods,however,are limited to simple transformations such as the augmentations under the instance’s naive representations or the augmentations under the instance’s semantic representations.To tackle this problem,we offer a unique insight into data augmentations and propose a novel data-augmentation-based semi-supervised learning method,called Attentive Neighborhood Feature Aug-mentation(ANFA).The motivation of our method lies in the observation that the relationship between the given feature and its neighborhood may contribute to constructing more reliable transformations for the data,and further facilitating the classifier to distinguish the ambiguous features from the low-dense regions.Specially,we first project the labeled and unlabeled data points into an embedding space and then construct a neighbor graph that serves as a similarity measure based on the similar representations in the embedding space.Then,we employ an attention mechanism to transform the target features into augmented ones based on the neighbor graph.Finally,we formulate a novel semi-supervised loss by encouraging the predictions of the interpolations of augmented features to be consistent with the corresponding interpolations of the predictions of the target features.We carried out exper-iments on SVHN and CIFAR-10 benchmark datasets and the experimental results demonstrate that our method outperforms the state-of-the-art methods when the number of labeled examples is limited.

Unsupervised construction of health indicator for rotating machinery via multi-criterion feature selection and attentive variational autoencoder

Health indicator(HI)construction is a crucial task in degradation evaluation and facilitates the prognostic and health management(PHM)of rotating machinery.Excluding interference from artificial labeling,the HI construction approaches in an unsupervised manner have attracted substantial attention.Nevertheless,current unsupervised methods generally struggle with two problems:(1)ignorance of both redundancy between features and global variability of features during the feature selection process;(2)inadequate utilization of information from different sampling moments.To tackle these problems,this work develops a novel unsupervised approach for HI construction that integrates multi-criterion feature selection and the Attentive Variational Autoencoder(Attentive VAE).Explicitly,a multi-criterion feature selection(Mc FS)algorithm together with an elaborately designed metric is proposed to determine a superior feature subset,considering the relevance,the redundancy,and the global variability of features simultaneously.Then,for the adequate utilization of the information from distinct sampling moments,a deep learning model named Attentive VAE is established.The Attentive VAE is solely fed with the selected features in the health state for model training and the HI is derived through the reconstruction error to reveal the degradation degree of machinery.Two case studies based on genuine experimental datasets are involved to quantitatively evaluate the superiority of the developed approach,demonstrating its superiority over other unsupervised methods for characterizing degradation processes.The effectiveness of both the Mc FS algorithm and the Attentive VAE is verified by ablation experiments,respectively.

Toward Robust and Efficient Low-Light Image Enhancement:Progressive Attentive Retinex Architecture Search

In recent years,learning-based low-light image enhancement methods have shown excellent performance,but the heuristic design adopted by most methods requires high engineering skills for developers,causing expensive inference costs that are unfriendly to the hardware platform.To handle this issue,we propose to automatically discover an efficient architecture,called progressive attentive Retinex network(PAR-Net).We define a new attentive Retinex framework by introducing the attention mechanism to strengthen structural representation.A multi-level search space containing micro-level on the operation and macro-level on the cell is established to realize meticulous construction.To endow the searched architecture with the hardware-aware property,we develop a latency-constrained progressive search strategy that successfully improves the model capability by explicitly expressing the intrinsic relationship between different models defined in the attentive Retinex framework.Extensive quantitative and qualitative experimental results fully justify the superiority of our proposed approach against other state-of-the-art methods.A series of analytical evaluations is performed to illustrate the validity of our proposed algorithm.

Attentiveness to Early Warning Drought Information:Implications for Policy Support and Climate Risk Reduction in Ghana

Successful drought planning is dependent on the generation of timely and accurate early warning information.Yet there is little evidence to explain the extent to which crop farmers pay attention to and assimilate early warning drought information that aids in the policy formulation in support of drought risk reduction.A socioecological survey,using a structured questionnaire administered to 426 crop farming households,was carried out in the Talensi District of the Upper East Region,Ghana.The data analytic techniques used were frequency tables,relative importance index,and multinomial logistics embedded in SPSS v.20 software.The results show that crop farmers predominantly rely on agricultural extension officers for early warning drought information,with an estimated 78% of them paying little to very much attention to the information.The likelihood ratio Chi-square test showed that there is a significant improvement in fit as X^(2)(20)=96.792,p<0.000.Household status,average monthly income,and age were the significant predictors for crop farmers paying no attention at all to early warning drought information,while household status was the only significant factor among those paying a little attention.The drive to build a climate-resilient society with effective early warning centers across Ghana will receive 60% lower support from crop farmers paying no to a little attention as compared to farmers paying very much attention to early warning drought information.Broader stakeholder engagements should be carried out to harness inclusive support from crop farmers to build a climate-resilient society in Ghana.

引入上下文信息和Attention Gate的GUS-YOLO遥感目标检测算法

目前基于通用YOLO系列的遥感目标检测算法存在并未充分利用图像的全局上下文信息,在特征融合金字塔部分并未充分考虑缩小融合特征之间的语义鸿沟、抑制冗余信息干扰的缺点。在结合YOLO算法优点的基础上提出GUS-YOLO算法,其拥有一个能够充分利用全局上下文信息的骨干网络Global Backbone。除此之外,该算法在融合特征金字塔自顶向下的结构中引入Attention Gate模块,可以突出必要的特征信息,抑制冗余信息。另外,为Attention Gate模块设计了最佳的网络结构,提出了网络的特征融合结构U-Net。最后,为克服ReLU函数可能导致模型梯度不再更新的问题,该算法将Attention Gate模块的激活函数升级为可学习的SMU激活函数,提高模型鲁棒性。在NWPU VHR-10遥感数据集上,该算法相较于YOLOV7算法取得宽松指标mAP^(0.50)1.64个百分点和严格指标mAP^(0.75)9.39个百分点的性能提升。相较于目前主流的七种检测算法,该算法取得较好的检测性能。

基于改进YOLOv5s的输电线路螺栓缺销检测方法

针对无人机输电线路巡检图像中螺栓缺销检测精度较低、漏检较多的问题,提出了一种基于改进YOLOv5s的输电线路螺栓缺销检测方法。在Backbone部分嵌入Coordinate Attention注意力模块;在Neck部分原有的“FPN+PAN”结构的基础上,新增一条“自顶向下”的特征信息传递路径,跨越临近的同尺度特征层,与较浅层网络以加权融合的方式进行特征融合;将Head部分设置为解耦检测头,将对螺栓检测的分类任务与定位任务分开进行。改进后的YOLOv5s算法增强了对螺栓特征信息的学习能力。使用本方法在螺栓缺销数据集上实验,精确率提升了2.3%,召回率提升了3.4%,平均精度提升了3.1%,检测速度达到了41.1帧/秒,表明改进后的方法能提升输电线路螺栓缺销的检测能力,在智能巡检中具有一定的应用价值。

基于FasterNet和YOLOv5改进的玻璃绝缘子自爆缺陷快速检测方法

为了实现对电力输电线路中绝缘子缺陷实时快速的巡检需求,提出了一种结合FasterNet-tiny和YOLOv5-s-v6.1网络模型改进的缺陷快速检测算法FasterNet-YOLOv5。首先引入参数量小推理速度更快的FasterNet网络替换原先的CSPDarkNet53主干网络,加快网络的检测速度。然后结合由GhostNetv2网络提出的解耦全连接注意力机制(decoupled fully connected,DFC),在主干特征提取网络中设计了DFC-FasterNet模块,模块中的DFC Attention机制可以在特征提取过程中增大感受野,提升网络的检测精度。最后针对玻璃绝缘子自爆缺陷目标较小和背景较复杂的情况,重新设计Neck模块,提出BiFPN-F特征融合模块,使网络更精确地定位绝缘子缺陷区域。实验结果表明:改进后的算法可以快速精准定位,其均值平均精度(mean average precision,mAP)达到93.3%,相较于改进前提升5.67%,检测速度达到45.7 Hz,较改进前提升近1倍。同时与最新的YOLOv8n和YOLOv7-tiny相比,改进后的FasterNet-YOLOv5在自爆缺陷上的检测精度和速度更具优势,该文所提算法能够更快速地对绝缘子及其自爆缺陷实时定位识别。

一种采用记忆神经网络和曲线形状修正的负荷预测方法

针对分布式电源和新型负荷容量累积造成负荷影响因素多元化和不确定性特性增强的问题,文中提出一种采用记忆神经网络和曲线形状修正的负荷预测方法。在负荷峰值预测中,采用最大信息系数计算负荷峰值与影响因素的非线性相关性,实现对输入特征的筛选;综合考虑负荷峰值序列的长短期自相关性和输入特征与负荷峰值的不同程度相关性,结合Attention机制和双向长短时记忆(bidirectional long short-term memory,BiLSTM)神经网络建立负荷峰值预测模型。在负荷标幺曲线预测中,通过误差倒数法组合相似日和相邻日,建立负荷标幺曲线预测模型;针对预测偏差的非平稳特征,利用自适应噪声的完全集成经验模态分解和BiLSTM网络建立误差预测模型,对曲线形状进行修正。应用中国北方某城市的区域电网负荷数据为算例,验证了所提模型的有效性。

改进注意力机制嵌入PR-Net模型的水稻病害识别仿真

针对现有的CNN模型在水稻叶部病害的识别中准确率较低的问题,提出了一种结合并行结构和残差结构的混合卷积神经网络模型PRC-Net(parallel residual with coordinate attention network)。引入并行结构,提高卷积的感受野;结合残差结构,使特征信息完整的连续传递;在骨干模型PR-Net中嵌入改进的空间注意力机制,增强对不同尺度病斑特征信息的凝聚程度;为进一步提升病害识别的准确率,并减少模型的训练时间和推理时间,通过改变加权方式对模型结构进行优化。仿真结果表明:与InceptionResNetV2等分类模型相比,PRC-Net具有更少的训练参数、更短的训练时间和更高的识别精度,性能优于其他作物病害识别模型。

基于SAW-YOLO v8n的葡萄幼果轻量化检测方法

葡萄簇幼果果实受背景色、遮挡和光照变化的影响,检测难度大。为了实现对背景色、遮挡和光照变化具有鲁棒性的葡萄簇幼果检测,提出了一种融合随机注意力机制(Shuffle attention,SA)的改进YOLO v8n模型(SAW-YOLO v8n)。通过在YOLO v8n模型的Neck结构中融入SA机制,增强网络多尺度特征融合能力,提升检测目标的特征信息表示,并抑制其他无关信息,提高检测网络检测精度,在不明显增加网络深度和内存开销的情况下,实现了葡萄簇幼果的高效准确检测;采用基于动态非单调聚焦机制的损失(Wise intersection over union loss,Wise-IoU Loss)作为边界框回归损失函数,加速网络收敛并进一步提高模型的准确率。构建了葡萄簇幼果的数据集GGrape,该数据集由3780幅复杂场景下的葡萄簇幼果图像及对应标注文件组成。通过该数据集对SAW-YOLO v8n模型进行训练和测试。测试结果表明,基于SAW-YOLO v8n的葡萄簇幼果检测算法的精度(Precision,P)、召回率(Recall,R)、平均精度均值(Mean average precision,mAP)和F1值分别为92.80%、91.30%、96.10%和92.04%,检测速度为140.85 f/s,模型内存占用量为6.20 MB。与SSD、YOLO v5s、YOLO v6n、YOLO v7-tiny、YOLO v8n等5个轻量化模型相比,其mAP值分别提高16.06%、1.05%、1.48%、0.84%、0.73%,F1值分别提高24.85%、1.43%、1.43%、1.09%、1.60%,模型内存占用量分别降低93.16%、56.94%、37.63%、47.00%、0,是所有模型中最小的,具有明显的轻量化、高精度优势。讨论了不同遮挡程度和光照条件的葡萄幼果检测,结果表明,基于SAW-YOLO v8n的葡萄幼果检测方法能适应不同遮挡和光照变化,具有良好的鲁棒性。结果表明,SAW-YOLO v8n不仅能满足对葡萄簇幼果检测的高精度、高速度、轻量化的要求,且具有较强的鲁棒性和实时性。

基于深度学习提取时空信息的流域内库水位预测模型研究

为了解决流域连通水库增多,库水位影响因素复杂且具有非平稳性,难以直接通过水文计算预测的问题,对流域水文站点日降雨序列进行分析,首先将时间序列经小波变换去噪,在此基础上采用最大信息系数(MIC)相关性分析筛选与日水位序列相关性,增加了输入时序降雨与预测水位相关的信息密度,并提出将强相关性序列输入引入Attention机制的长短期记忆(LSTM)预测模型,提高LSTM神经网络选择和提取序列特征的能力。以福建某流域站点实测日降雨序列为例进行试验,结果表明该方法的均方预测误差仅为0.1908,相比LSTM模型有更高的预测精度,为水库水情调度及防洪减灾管理提供了决策依据。

基于Word2vec与注意力机制的情感分析研究

针对传统情感分析模型对关键词特征抓取不准确、局部情感特征提取不全面造成分类效果差的问题,提出一种基于TW-BiLSTM-ATT情感分析模型。通过对TF-IDF改进,并与Word2vec结合,使权重特征融入词向量提升对关键信息的抓取能力;将词向量的位置特征融入到注意力机制中,使模型可以关注到目标词汇附近的词,进而更加全面地将情感特征提取出来。对比实验结果表明TW-BiLSTM-ATT模型在处理情感分析任务中分类效果好于同类模型。

基于改进YOLOv8n的煤矿井下钻杆计数方法

为提高煤矿井下钻杆计数的效率和精度,提出了一种基于改进YOLOv8n模型的煤矿井下钻杆计数方法。建立了YOLOv8n−TBiD模型,该模型可准确检测矿井钻机工作视频中的钻杆并进行有效分割:为有效捕获钻杆的边界信息,提高模型对钻杆形状识别的精度,使用加权双向特征金字塔网络(BiFPN)替换路径聚合网络(PANet);针对钻杆易与昏暗的矿井环境混淆的问题,在Backbone网络的SPPF模块后添加三分支注意力(Triplet Attention),以增强模型抑制背景干扰的能力;针对钻杆在图像中占比小、背景信息繁杂的问题,采用Dice损失函数替换CIoU损失函数来优化模型对目标钻杆的分割处理。利用YOLOv8n−TBiD模型分割出的钻杆及其掩码信息,根据打钻过程中钻杆掩码面积变小而装新钻杆时钻杆掩码面积突然增大的规律,设计了一种钻杆计数算法。选取综采工作面实际采集的钻机工作视频对基于YOLOv8n−TBiD模型的钻杆计数方法进行了实验验证,结果表明:①YOLOv8n−TBiD模型检测钻杆的平均精度均值达94.9%,与对比模型GCI−YOLOv4,ECO−HC,P−MobileNetV2,YOLOv5,YOLOX相比,检测准确率分别提升了4.3%,7.5%,2.1%,6.3%,5.8%,检测速度较原始YOLOv8n模型提升了17.8%。②所提钻杆计数算法在不同煤矿井下环境的视频数据集上实现了99.3%的钻杆计数精度。

基于SABO-GRU-Attention的锂电池SOC估计

提出一种基于SABO-GRU-Attention(subtraction average based optimizer-gate recurrent unitattention)的锂电池SOC(state of charge)估计方法。采用基于平均减法优化算法自适应更新GRU神经网络的超参数,融合SE(squeeze and excitation)注意力机制自适应分配各通道权重,提高学习效率。对马里兰大学电池数据集进行预处理,输入电压、电流参数,进行锂电池充放电仿真实验,并搭建锂电池荷电状态实验平台进行储能锂电池充放电实验。结果表明,提出的SOC神经网络估计模型明显优于LSTM、GRU以及PSO-GRU等模型,具有较高的估计精度与应用价值。

基于改进残差网络的油气柱高度预测

针对目前油气柱高度预测技术局限于传统的地质方法且预测效果不太理想的现状,展开一种基于改进残差神经网络的油气柱高度预测的研究.该模型从断层解释和油藏解剖提取的圈闭结构化特征数据中提取特征信息,以估计油气柱高度.模型将原始残差块中的串行连接网络变成多个并行连接的网络,可以在多个尺度上同时进行卷积再聚合,能提取到不同尺度的特征,使其变成一个稀疏性、高计算性能的网络结构;同时保留了网络中跳跃连接的结构,缓解了在深度神经网络中增加深度带来了梯度消失和网络退化的问题,通过直接将输入信息绕道传到输出,保护信息的完整性;并在模型的首层和尾层增加注意力模块,来捕获集中于某个局部信息,使模型其能更快地收敛.此外对机器学习中常用的RF和BP神经网络以及深度学习中CNN、GoogleNet、ResNet和ResNet+Atten在圈闭数据上的应用进行了比较和分析.实验结果表明,改进的ResNet对油气柱高度预测有更加准确的结果 .

基于XGBoost-WOA-BiLSTM-Attention的公共建筑暖通空调能耗预测研究

为在双碳目标下实现节能减排,降低能源成本,提出一种基于BiLSTM的公共建筑暖通空调能耗预测模型。在BiLSTM模型基础上,使用XGBoost算法对输入特征进行选择,剔除冗余特征,得到最佳模型输入特征;然后利用WOA优化算法对添加了Attention机制的BiLSTM模型中的6个超参数进行优化,将得到的最优参数代入BiLSTM-Attention神经网络中进行预测,并与BiLSTM模型、BiLSTM-Attention模型和WOA-BiLSTM-Attention模型进行对比。结果表明,所提出的XGBoost-WOA-BiLSTM-Attention模型的RMSE、MAE、R2分别为0.0106、0.006、0.9991,优于其他模型,且相对于持续模型在均方根误差RMSE上提升了98%,为降低公共建筑暖通空调能耗研究提供了参考。