DWT算法在乒乓球动作识别分析中的应用

针对传统乒乓球动作识别未考虑动作的时序性和动态特性的问题,研究在二维DWT算法的基础上引入奇异值分解,设计出一种改进DWT算法,以获得乒乓球动作的局部特征,从而提升其识别准确率。实验结果显示:在识别准确率计算中,改进DWT算法的平均识别准确率为97.8%,优于其他算法,表明其识别精度较高;在识别时间计算中,改进DWT算法的识别时间与其他算法相比,均下降了50%左右,表明其识别效率较高。以上结果证明了改进DWT算法的优越性能,为乒乓球的动作识别提供了有效的技术支持。

基于Depth-wise卷积和视觉Transformer的图像分类模型

图像分类作为一种常见的视觉识别任务,有着广阔的应用场景。在处理图像分类问题时,传统的方法通常使用卷积神经网络,然而,卷积网络的感受野有限,难以建模图像的全局关系表示,导致分类精度低,难以处理复杂多样的图像数据。为了对全局关系进行建模,一些研究者将Transformer应用于图像分类任务,但为了满足Transformer的序列化和并行化要求,需要将图像分割成大小相等、互不重叠的图像块,破坏了相邻图像数据块之间的局部信息。此外,由于Transformer具有较少的先验知识,模型往往需要在大规模数据集上进行预训练,因此计算复杂度较高。为了同时建模图像相邻块之间的局部信息并充分利用图像的全局信息,提出了一种基于Depth-wise卷积的视觉Transformer(Efficient Pyramid Vision Transformer,EPVT)模型。EPVT模型可以实现以较低的计算成本提取相邻图像块之间的局部和全局信息。EPVT模型主要包含3个关键组件:局部感知模块(Local Perceptron Module,LPM)、空间信息融合模块(Spatial Information Fusion,SIF)和“+卷积前馈神经网络(Convolution Feed-forward Network,CFFN)。LPM模块用于捕获图像的局部相关性;SIF模块用于融合相邻图像块之间的局部信息,并利用不同图像块之间的远距离依赖关系,提升模型的特征表达能力,使模型学习到输出特征在不同维度下的语义信息;CFFN模块用于编码位置信息和重塑张量。在图像分类数据集ImageNet-1K上,所提模型优于现有的同等规模的视觉Transformer分类模型,取得了82.6%的分类准确度,证明了该模型在大规模数据集上具有竞争力。

Investigation of Groundwater Quality with Borehole Depth in the Basin Granitoids of the Ashanti Region of Ghana

The dependence of groundwater quality on borehole depth is usually debatable in groundwater studies, especially in complex geological formations where aquifer characteristics vary spatially with depth. This study therefore seeks to investigate the relationship between borehole depth and groundwater quality across the granitoid aquifers within the Birimian Supergroup in the Ashanti Region. Physicochemical analysis records of groundwater quality data were collected from 23 boreholes of public and private institutions in the Ashanti Region of Ghana, and the parametric values of iron, fluoride, total hardness, pH, nitrate, and nitrite were used to study the groundwater quality-depth relationship. The results showed that the depth-to-groundwater quality indicated a marginal increase in water quality in the range of 30 to 50 m, which is mathematically represented by the low-value correlation coefficient (r2 = 0.026). A relatively significant increase occurs in the depth range of 50 to 80 m, which is given by a correlation coefficient of r2 = 0.298. The mean percent parameter compatibility was 74%, 82%, 89%, and 97% at 50, 60, 70, and 80 m depths, respectively. The variations in groundwater quality per depth ratio ranged from 1.48, 1.37, 1.27, and 1.21 for 50, 60, 70, and 80 m depth, respectively. The recommended minimum borehole depth for excellent groundwater quality is suggested with a compatibility per meter depth ratio of 1.37. This results in a range between 50 and 70 m as the most desirable drilling depth for excellent groundwater quality within the granitoids of the Birimian Supergroup of the Ashanti Region in Ghana.

Quantitative prediction model for the depth limit of oil accumulation in the deep carbonate rocks:A case study of Lower Ordovician in Tazhong area of Tarim Basin

With continuous hydrocarbon exploration extending to deeper basins,the deepest industrial oil accumulation was discovered below 8,200 m,revealing a new exploration field.Hence,the extent to which oil exploration can be extended,and the prediction of the depth limit of oil accumulation(DLOA),are issues that have attracted significant attention in petroleum geology.Since it is difficult to characterize the evolution of the physical properties of the marine carbonate reservoir with burial depth,and the deepest drilling still cannot reach the DLOA.Hence,the DLOA cannot be predicted by directly establishing the relationship between the ratio of drilling to the dry layer and the depth.In this study,by establishing the relationships between the porosity and the depth and dry layer ratio of the carbonate reservoir,the relationships between the depth and dry layer ratio were obtained collectively.The depth corresponding to a dry layer ratio of 100%is the DLOA.Based on this,a quantitative prediction model for the DLOA was finally built.The results indicate that the porosity of the carbonate reservoir,Lower Ordovician in Tazhong area of Tarim Basin,tends to decrease with burial depth,and manifests as an overall low porosity reservoir in deep layer.The critical porosity of the DLOA was 1.8%,which is the critical geological condition corresponding to a 100%dry layer ratio encountered in the reservoir.The depth of the DLOA was 9,000 m.This study provides a new method for DLOA prediction that is beneficial for a deeper understanding of oil accumulation,and is of great importance for scientific guidance on deep oil drilling.

What induced the trend shift of mixed-layer depths in the Antarctic Circumpolar Current region in the mid-1980s?

An obvious trend shift in the annual mean and winter mixed layer depth(MLD)in the Antarctic Circumpolar Current(ACC)region was detected during the 1960–2021 period.Shallowing trends stopped in mid-1980s,followed by a period of weak trends.The MLD deepening trend difference between the two periods were mainly distributed in the western areas in the Drake Passage,the areas north to Victoria Land and Wilkes Land,and the central parts of the South Indian sector.The newly formed ocean current shear due to the meridional shift of the ACC flow axis between the two periods is the dominant driver for the MLD trends shift distributed in the western areas in the Drake Passage and the central parts of the South Indian sector.The saltier trends in the regions north to Victoria Land and Wilkes Land could be responsible for the strengthening mixing processes in this region.

Three-dimensional constrained gravity inversion of Moho depth and crustal structural characteristics at Mozambique continental margin

Mozambique's continental margin in East Africa was formed during the break-off stage of the east and west Gondwana lands. Studying the geological structure and division of continent-ocean boundary(COB) in Mozambique's continental margin is considered of great significance to rebuild Gondwana land and understand its movement mode. Along these lines, in this work, the initial Moho was fit using the known Moho depth from reflection seismic profiles, and a 3D multi-point constrained gravity inversion was carried out. Thus, highaccuracy Moho depth and crustal thickness in the study area were acquired. According to the crustal structure distribution based on the inversion results, the continental crust at the narrowest position of the Mozambique Channel was detected. According to the analysis of the crustal thickness, the Mozambique ridge is generally oceanic crust and the COB of the whole Mozambique continental margin is divided.

DGConv: A Novel Convolutional Neural Network Approach for Weld Seam Depth Image Detection

We propose a novel image segmentation algorithm to tackle the challenge of limited recognition and segmentation performance in identifying welding seam images during robotic intelligent operations.Initially,to enhance the capability of deep neural networks in extracting geometric attributes from depth images,we developed a novel deep geometric convolution operator(DGConv).DGConv is utilized to construct a deep local geometric feature extraction module,facilitating a more comprehensive exploration of the intrinsic geometric information within depth images.Secondly,we integrate the newly proposed deep geometric feature module with the Fully Convolutional Network(FCN8)to establish a high-performance deep neural network algorithm tailored for depth image segmentation.Concurrently,we enhance the FCN8 detection head by separating the segmentation and classification processes.This enhancement significantly boosts the network’s overall detection capability.Thirdly,for a comprehensive assessment of our proposed algorithm and its applicability in real-world industrial settings,we curated a line-scan image dataset featuring weld seams.This dataset,named the Standardized Linear Depth Profile(SLDP)dataset,was collected from actual industrial sites where autonomous robots are in operation.Ultimately,we conducted experiments utilizing the SLDP dataset,achieving an average accuracy of 92.7%.Our proposed approach exhibited a remarkable performance improvement over the prior method on the identical dataset.Moreover,we have successfully deployed the proposed algorithm in genuine industrial environments,fulfilling the prerequisites of unmanned robot operations.

Applying Source Parameter Imaging (SPI) to Aeromagnetic Data to Estimate Depth to Magnetic Sources in the Mamfe Sedimentary Basin

Aeromagnetic data over the Mamfe Basin have been processed. A regional magnetic gridded dataset was obtained from the Total Magnetic Intensity (TMI) data grid using a 3 × 3 convolution (Hanning) filter to remove regional trends. Major similarities in magnetic field orientation and intensities were observed at identical locations on both the regional and TMI data grids. From the regional and TMI gridded datasets, the residual dataset was generated which represents the very shallow geological features of the basin. Processing this residual data grid using the Source Parameter Imaging (SPI) for magnetic depth suggests that the estimated depths to magnetic sources in the basin range from about 271 m to 3552 m. The highest depths are located in two main locations somewhere around the central portion of the study area which correspond to the area with positive magnetic susceptibilities, as well as the areas extending outwards across the eastern boundary of the study area. Shallow magnetic depths are prominent towards the NW portion of the basin and also correspond to areas of negative magnetic susceptibilities. The basin generally exhibits a variation in depth of magnetic sources with high, average and shallow depths. The presence of intrusive igneous rocks was also observed in this basin. This characteristic is a pointer to the existence of geologic resources of interest for exploration in the basin.

Spatiotemporal Changes of Snow Depth in Western Jilin,China from 1987 to 2018

Seasonal snow cover is a key global climate and hydrological system component drawing considerable attention due to glob-al warming conditions.However,the spatiotemporal snow cover patterns are challenging in western Jilin,China due to natural condi-tions and sparse observation.Hence,this study investigated the spatiotemporal patterns of snow cover using fine-resolution passive mi-crowave(PMW)snow depth(SD)data from 1987 to 2018,and revealed the potential influence of climate factors on SD variations.The results indicated that the interannual range of SD was between 2.90 cm and 9.60 cm during the snowy winter seasons and the annual mean SD showed a slightly increasing trend(P>0.05)at a rate of 0.009 cm/yr.In snowmelt periods,the snow cover contributed to an increase in volumetric soil water,and the change in SD was significantly affected by air temperature.The correlation between SD and air temperature was negative,while the correlation between SD and precipitation was positive during December and March.In March,the correlation coefficient exceeded 0.5 in Zhenlai,Da’an,Qianan,and Qianguo counties.However,the SD and precipitation were neg-atively correlated over western Jilin in October,and several subregions presented a negative correlation between SD and precipitation in November and April.

基于DWT-Informer模型的水量预测研究

为准确呈现水消耗的变化趋势以及预测未来的用水需求,提出一种基于DWT-Informer模型的用水量预测方法。与传统方法相比,该预测方法具有以下优势:(1)对历史用水量数据进行DWT分解,可以更好地捕捉用水量信号的不同频率成分和变化趋势;(2)Informer模型具有更强的时间序列建模能力和预测能力,可以更准确地预测未来日用水量;(3)采用多头注意力机制构建输入与输出的全局关系,有利于提升参数水平。通过实际日用水量数据进行算例分析,分析结果表明,相较于其他常用预测方法,该文提出的方法在MAE、RMSE、MAPE等指标上均表现优异。

Improving path planning efficiency for underwater gravity-aided navigation based on a new depth sorting fast search algorithm

This study focuses on the improvement of path planning efficiency for underwater gravity-aided navigation.Firstly,a Depth Sorting Fast Search(DSFS)algorithm was proposed to improve the planning speed of the Quick Rapidly-exploring Random Trees*(Q-RRT*)algorithm.A cost inequality relationship between an ancestor and its descendants was derived,and the ancestors were filtered accordingly.Secondly,the underwater gravity-aided navigation path planning system was designed based on the DSFS algorithm,taking into account the fitness,safety,and asymptotic optimality of the routes,according to the gravity suitability distribution of the navigation space.Finally,experimental comparisons of the computing performance of the ChooseParent procedure,the Rewire procedure,and the combination of the two procedures for Q-RRT*and DSFS were conducted under the same planning environment and parameter conditions,respectively.The results showed that the computational efficiency of the DSFS algorithm was improved by about 1.2 times compared with the Q-RRT*algorithm while ensuring correct computational results.

基于DWT-SARIMA-LSTM的流感预测模型研究

为提高流感预测模型的准确率,针对流感数据的季节性与波动性特点,提出利用离散小波分解(DWT)、季节性自回归综合移动平均模型(SARIMA)和长短期记忆神经网络(LSTM)综合建模,构建DWT-SARIMA-LSTM混合预测模型。首先,将流感数据分解为高频成分与低频成分,对低频成分使用SARIMA模型、高频成分使用LSTM模型分别进行预测;其次,将预测值融合得到最终的预测结果;最后,构建流行控制图预警模型。使用从中国香港卫生署官网获得的中国香港地区2010—2019年的流感数据对模型进行预测和验证,其MAE为0.3427,MAPE为8.0973%,RMSE为0.4632,预警模型的准确率为100%,该模型较于如ARIMA-LSTM等其他混合模型有更高的预测精度。

基于Shuffle-ZoeDepth单目深度估计的苗期玉米株高测量方法

株高是鉴别玉米种质性状及作物活力的重要表型指标,苗期玉米遗传特性表现明显,准确测量苗期玉米植株高度对玉米遗传特性鉴别与田间管理具有重要意义。针对传统植株高度获取方法依赖人工测量,费时费力且存在主观误差的问题,提出了一种融合混合注意力信息的改进ZoeDepth单目深度估计模型。改进后的模型将Shuffle Attention模块加入Decoder模块的4个阶段,使Decoder模块在对低分辨率特征图信息提取过程中能更关注特征图中的有效信息,提升了模型关键信息的提取能力,可生成更精确的深度图。为验证本研究方法的有效性,在NYU-V2深度数据集上进行了验证。结果表明,改进的Shuffle-ZoeDepth模型在NYU-V2深度数据集上绝对相对差、均方根误差、对数均方根误差为0.083、0.301 mm、0.036,不同阈值下准确率分别为93.9%、99.1%、99.8%,均优于ZoeDepth模型。同时,利用Shuffle-ZoeDepth单目深度估计模型结合玉米植株高度测量模型实现了苗期玉米植株高度的测量,采集不同距离下苗期玉米图像进行植株高度测量试验。当玉米高度在15~25 cm、25~35 cm、35~45 cm 3个区间时,平均测量绝对误差分别为1.41、2.21、2.08 cm,平均测量百分比误差分别为8.41%、7.54%、4.98%。试验结果表明该方法可仅使用单个RGB相机完成复杂室外环境下苗期玉米植株高度的精确测量。

82300 DWT多用途船混合电力系统设计应用

针对配置有高压岸电系统、储能系统、轴带发电机以及储能系统等多种能源运行模式复杂实船设计及应用情况,以82300多用途船作为研究对象,研究储能系统、高压岸电系统组成、设备配置、系统运行模式要求等方面的内容,同时结合本船甲板的四台吊机的操作负荷情况,通过合理的设置,使其达到最优负荷分配,结果表明,通过合理系统设计、配置,可以满足多种供电设备协调工作,使船舶处于最优供电情况,以减少船舶的排放和能耗损失。

基于DWT与SVM的风门开闭阶段识别方法

为解决因风门开闭导致的风速传感器数据异常波动与误报警问题,提出一种基于离散小波变换(DWT)与支持向量机(SVM)的风门开闭阶段识别方法。使用多尺度滑动窗口将传感器风速监测数据离散化为若干段不同尺度的子时间序列数据,利用统计方法与DWT,提取各尺度子时间序列数据中的统计特征与隐含的波动特征,建立SVM风门开闭阶段识别分类模型。为进一步优化识别结果,基于重叠度(IoU)规则合并、修正、组合、取优分类识别结果,再根据相似准则建立长度方向取变率为2、整体相似比为1∶16的相似试验模型,开展风门开闭扰动试验,验证方法的可行性。结果表明:在测试集上的识别准确率较高,对于风门开闭时间的识别准确率可达到90.08%,风门开闭阶段的划分准确率可达到71.05%,优化滑动窗口尺度数量,可继续增加方法识别的准确率。

基于2D DWT与MobileNetV3融合的轻量级茶叶病害识别

针对现有茶叶病害识别方法病害信息挖掘不足导致识别准确率低的问题,该研究提出了一种基于二维离散小波变换(discrete wavelet transform, DWT)和MobileNetV3融合的茶叶病害识别模型CBAM-TealeafNet。为增强网络对病害频域特征的检测能力,将2D DWT获取的频域特征与bneck结构提取的深度特征融合,形成频域与深度特征融合的识别网络。为提高特征提取能力,在bneck结构中,嵌入卷积块注意模块(convolutional block attention module, CBAM),为特征通道分配相应权重。为解决样本类别不平衡对识别模型性能的影响,利用焦点损失函数取代交叉熵损失函数以提高识别精度。经验证,CBAM-TealeafNet在5种不同茶叶病害上整体识别准确率达到98.70%,参数量为3.16×10^(6),相对MobileNetV3,准确率提升2.15个百分点,参数量降低25.12%。该方法可为茶树叶部等作物病害轻量级识别研究提供模型参考。

基于Boosting-Monodepth的管道病害深度估计与三维重建

城市地下管道是城市的血脉经络,但随着排水管道的大量投入运营和使用年限增加,引发了一系列的管道病害安全隐患,如管道整体结构变形、内表面破裂和管中异物插入等问题,传统的病害图像视频采集、检测和后期病害分类甄选都是从二维视角出发,欠缺对三维空间信息(深度)的考虑。针对上述3种病害从生成深度图、由二维深度图重建三维管道病害这两方面进行研究,提出了一种基于boosting-monodepth的双重深度估计方法以提升深度图效果,最终生成画面连续一致、轮廓清晰的深度图。性能评估方面采用Abs-Rel、RMSE、SqRel、ORD和D3R等通用指标,与传统算法对比,结果显示boosting-monodepth的RMSE值降低了30%,精确度指标δ<1.25时,模型深度信息预测精确度提高了18%,此后以得到的深度图为基础重建管道病害三维点云,并在CloudCompare软件上三维可视化,最后采用随机采样一致算法测算病害深度并和实测数据对比证明其有效性和准确性。

基于DWT的PCA+SVM优化算法在人脸识别应用中的研究

文章提出一种PCA+SVM算法优化方法,以小波变换(DWT)为基础,旨在提升人脸识别的精度。先用DWT将原本的人面影像分解成多个子带,再将每个子带进行PCA降维运算,选出最重要的特征子集作为输入资料,最后用SVM分类器来识别人面。实验结果显示,其在ORL人脸数据库中提出的方法,应用前景更好,人脸识别准确率明显提高。

DWT和AKD自动编码器的DDoS攻击检测方法研究

针对DDoS网络流量攻击检测效率低及误报率高的问题,本文提出一种基于离散小波变换(Discrete Wavelet Transform, DWT)和自适应知识蒸馏(Adaptive Knowledge Distillation, AKD)自动编码器神经网络的DDoS攻击检测方法.该方法利用离散小波变换提取频率特征,由自动编码器神经网络进行特征编码并实现分类,通过自适应知识蒸馏压缩模型,以实现高效检测DDoS攻击流量.研究结果表明,该方法对代理服务器攻击、数据库漏洞和TCP洪水攻击、UDP洪水攻击具有较高的检测效率,并且具有较低的误报率.

Determination of minimum overburden depth for underwater shield tunnel in sands:Comparison between circular and rectangular tunnels

With the development of global urbanization,the utilization of underground space is more critical and attractive for civil purposes.Various shapes of shield tunnels have been gradually proposed to cope with different geological conditions and service purposes of underground structures.Generally,reducing the burial depth of shield tunnel is conducive to construction and cost saving.However,extremely small overburden depth cannot provide sufficient uplift resistance to maintain the stability and serviceability of the tunnel.To this end,this paper firstly reviewed the status of deriving the minimum sand over-burden depth of circular shield tunnel using mechanical equilibrium(ME)method.It revealed that the estimated depth is rather conservative.Then,the uplift resistance mechanism of both circular and rectangular tunnels was deduced theoretically and verified with the model tests.The theoretical uplift resistance is consistent with the experimental values,indicating the feasibility of the proposed equations.Furthermore,the determination of the minimum soil overburden depth of rectangular shield tunnel under various working conditions was presented through integrated ME method,which can provide more reasonable estimations of suggested tunnel burial depth for practical construction.Additionally,optimizations were made for calculating the uplift resistance,and the soil thickness providing uplift resistance is suggested to be adjusted according to the testing results.The results can provide reference for the design and construction of various shapes of shield tunnels in urban underground space exploitation.