3D simultaneous seismic data reconstruction and noise suppression based on the curvelet transform

Seismic data contain random noise interference and are affected by irregular subsampling. Presently, most of the data reconstruction methods are carried out separately from noise suppression. Moreover, most data reconstruction methods are not ideal for noisy data. In this paper, we choose the multiscale and multidirectional 2D curvelet transform to perform simultaneous data reconstruction and noise suppression of 3D seismic data. We introduce the POCS algorithm, the exponentially decreasing square root threshold, and soft threshold operator to interpolate the data at each time slice. A weighing strategy was introduced to reduce the reconstructed data noise. A 3D simultaneous data reconstruction and noise suppression method based on the curvelet transform was proposed. When compared with data reconstruction followed by denoizing and the Fourier transform, the proposed method is more robust and effective. The proposed method has important implications for data acquisition in complex areas and reconstructing missing traces.

Defect Detection Model Using Time Series Data Augmentation and Transformation

Time-series data provide important information in many fields,and their processing and analysis have been the focus of much research.However,detecting anomalies is very difficult due to data imbalance,temporal dependence,and noise.Therefore,methodologies for data augmentation and conversion of time series data into images for analysis have been studied.This paper proposes a fault detection model that uses time series data augmentation and transformation to address the problems of data imbalance,temporal dependence,and robustness to noise.The method of data augmentation is set as the addition of noise.It involves adding Gaussian noise,with the noise level set to 0.002,to maximize the generalization performance of the model.In addition,we use the Markov Transition Field(MTF)method to effectively visualize the dynamic transitions of the data while converting the time series data into images.It enables the identification of patterns in time series data and assists in capturing the sequential dependencies of the data.For anomaly detection,the PatchCore model is applied to show excellent performance,and the detected anomaly areas are represented as heat maps.It allows for the detection of anomalies,and by applying an anomaly map to the original image,it is possible to capture the areas where anomalies occur.The performance evaluation shows that both F1-score and Accuracy are high when time series data is converted to images.Additionally,when processed as images rather than as time series data,there was a significant reduction in both the size of the data and the training time.The proposed method can provide an important springboard for research in the field of anomaly detection using time series data.Besides,it helps solve problems such as analyzing complex patterns in data lightweight.

AN UNSUPERVISED CLASSIFICATION FOR FULLY POLARIMETRIC SAR DATA USING SPAN/H/α IHSL TRANSFORM AND THE FCM ALGORITHM

In this paper, the IHSL transform and the Fuzzy C-Means (FCM) segmentation algorithm are combined together to perform the unsupervised classification for fully polarimetric Synthetic Ap-erture Rader (SAR) data. We apply the IHSL colour transform to H/α/SPANspace to obtain a new space (RGB colour space) which has a uniform distinguishability among inner parameters and contains the whole polarimetric information in H/α/SPAN.Then the FCM algorithm is applied to this RGB space to finish the classification procedure. The main advantages of this method are that the parameters in the color space have similar interclass distinguishability, thus it can achieve a high performance in the pixel based segmentation algorithm, and since we can treat the parameters in the same way, the segmentation procedure can be simplified. The experiments show that it can provide an improved classification result compared with the method which uses the H/α/SPANspace di-rectly during the segmentation procedure.

Digital Transformation of Enterprise Human Resource Management Enabled by Big Data

With the continuous development of big data technology,the digital transformation of enterprise human resource management has become a development trend.Human resources is one of the most important resources of enterprises,which is crucial to the competitiveness of enterprises.Enterprises need to attract,retain,and motivate excellent employees,thereby enhancing the innovation ability of enterprises and improving competitiveness and market share in the market.To maintain advantages in the fierce market competition,enterprises need to adopt more scientific and effective human resource management methods to enhance organizational efficiency and competitiveness.At the same time,this paper analyzes the dilemma faced by enterprise human resource management,points out the new characteristics of enterprise human resource management enabled by big data,and puts forward feasible suggestions for enterprise digital transformation.

High-resolution frequency-domain Radon transform and variable-depth streamer data deghosting

Receiver ghost reflections adversely affect variable-depth streamer （VDS） data acquisition. In addition, the frequency notches caused by the interference between receiver ghosts and primary waves strongly affect seismic data processing and imaging. We developed a high-resolution Radon transform algorithm and used it to predict receiver ghosts from VDS data. The receiver ghost reflections are subtracted and removed from the raw data. We propose a forward Radon transform operator of VDS data in the frequency domain and, based on the ray paths of the receiver ghosts, we propose an inverse Radon transform operator. We apply the proposed methodology to model and field data with good results. We use matching and subtracting modules of commercially available seismic data processing software to remove the receiver ghosts. The frequency notches are compensated and the effective frequency bandwidth of the seismic data broadens.

基于生成对抗Transformer的电力负荷数据异常检测

电力负荷异常数据将给电力系统规划、负荷预测以及用能分析等带来较大的负面影响,因此亟须对负荷数据异常进行检测与识别。首先,针对电力负荷数据异常分类、原因及其特征开展分析。其次,改进传统Transformer编码器结构,采用多头注意力层代替掩码多头注意力层,同时移除前馈网络,以提高模型对负荷时序序列的全局注意力。基于生成对抗网络(generative adversarial networks,GAN)生成器与判别器的博弈结构,提出一种改进的GAN-Transformer模型,以更好地捕捉趋势性特征并加速模型收敛。然后,引入多阶段映射与训练方法,综合焦点分数打分机制,通过分阶段负荷序列重构帮助模型更好地提取负荷数据异常特征。最后,算例分析结果表明,GAN-Transformer模型在负荷数据异常检测精确率、召回率、F_(1)值以及训练时间方面均具有更优的性能,验证了所提方法的有效性和优越性。文中研究工作为基于深度学习进一步实现电力负荷数据异常分类与数据修复提供了有益参考。

基于Transformer的纯电动汽车充电时间预测

纯电动汽车充电时间的安排是车主日常生活中至关重要的环节,直接影响车主出行的便利度和舒适体验。然而,目前仍然面临充电桩资源不足、充电须提前规划等挑战,为解决车主因车辆电量不足而无法立即用车的问题,提出一种基于Transformer模型的充电时间预测解决方案,帮助车主更好地规划日常行程。为了更好地了解电池性能衰减程度和容量损失情况,采用容量法评估电池健康状态,并分析驾驶人的充电行为,对电池充电行为特征进行构建。使用Savitzky-Golay滤波器对表征电池衰减的特征进行平滑处理,并进行累积变换,使特征能更全面地表征电池信息;再耦合皮尔逊相关系数和LASSO(least absolute shrinkage and selection operator)回归算法二次筛选得到最优特征集。最后,利用Transformer模型的超强注意力机制,对充电时间进行预测。通过实验数据验证,此方案可以准确且快速地预测纯电动汽车的充电时间,决定系数达到0.999,运行时间为156 ms。

CNN-Transformer结合对比学习的高光谱与LiDAR数据协同分类

针对高光谱图像(hyperspectral images,HSI)与LiDAR数据多模态分类任务中的跨模态信息表达和特征对齐等问题,提出一种基于对比学习CNN-Transformer高光谱和LiDAR数据协同分类网络(Contrastive Learning based CNNTransformer Network,CLCT-Net)。CLCT-Net通过由ConvNeXt V2 Block构成的共有特征提取模块,获得不同模态间的共性特征,解决异构传感器数据之间语义对齐的问题。构建了包含空间-通道分支和光谱上下文分支的双分支HSI编码器,以及结合频域自注意力机制的LiDAR编码器,以获取更丰富的特征表示。利用集成对比学习进行分类,进一步提升多模态数据协同分类的精度。在Houston 2013和Trento数据集上的实验结果表明,相较于其他高光谱图像和Li‐DAR数据分类模型,本文所提模型获得了更高的地物分类精度,分别达到了92.01%和98.90%,实现了跨模态数据特征的深度挖掘和协同提取。

融合Transformer和卷积LSTM的轨迹分类网络

为了减少原始轨迹数据的噪声,充分提取轨迹的时空特征,提高基于轨迹数据的交通模式分类精度,提出一种融合堆叠降噪自编码器、Transformer和卷积长短期记忆网络的轨迹分类网络(networks fusing stacked denoising auto-encoder, Transformer and ConvLSTM,SDAETC)。通过堆叠降噪自编码器减少原始轨迹数据中的噪声;利用结合了Transformer的递归图自编码器,提取到更为丰富的时间特征,同时利用特征图自编码器提取空间特征;改进卷积长短期记忆网络,充分提取轨迹中的时空特征,并与提取到的时间特征和空间特征相融合,从而实现交通模式分类。实验结果表明,提出的SDAETC与基线模型相比,在GeoLife和SHL数据集上的准确率分别提升了1.8%和2%。此外,消融实验结果和模型训练时间分析表明,引入堆叠降噪自编码器、Transfomer和ConvLSTM虽然增加了时间消耗,但是对分类精度有积极贡献。

基于Transformer的司法文书命名实体识别方法

命名实体识别是自然语言处理领域的关键任务之一,是实现下游任务的基础。目前针对司法领域的相关研究相对较少,司法系统的信息化和智能化转型仍有许多问题亟需解决。相比其他领域的文本,司法文书存在专业性强、语料资源少等局限,导致现有的司法文书识别结果较低。因此,从以下3方面开展研究:首先,提出了一种多标签层级迭代的文本标注方式,可以对原始司法文书文本进行自动化标注,同时有效地提升司法文书命名实体识别任务的实体识别效果;其次,提出了一种交融式的Transformer神经网络模型,对汉字固有属性的深层特征进行了充分利用,用于对司法文书进行命名实体识别;最后,对所提出的标注方法和模型与其他神经网络模型进行了对比实验。所提出的文本标注方式可以较为准确地实现司法文书的标注任务;同时,所提出的模型在通用数据集中相对于对照模型有较大的提高,并在司法领域数据集中取得了良好的效果。

Data Transformation for Super-totalstation Positioning System Integrated by GPS and Totalstation

This paper focuses on the integration and data transformation between GPS and totalstation.It emphasizes on the way to transfer the WGS84 Cartesian coordinates to the local two_dimensional plane coordinates and the orthometric height GPS receiver,totalstation,radio,notebook computer and the corresponding software work together to form a new surveying system,the super_totalstation positioning system(SPS) and a new surveying model for terrestrial surveying.With the help of this system,the positions of detail points can be measured.

DEM Compression Based on Integer Wavelet Transform

DEM data is an important component of spatial database in GIS. The data volume is so huge that compression is necessary. Wavelet transform has many advantages and has become a trend in data compression. Considering the simplicity and high efficiency of the compression system, integer wavelet transform is applied to DEM and a simple coding algorithm with high efficiency is introduced. Experiments on a variety of DEM are carried out and some useful rules are presented at the end of this paper.

Reconstruction method of irregular seismic data with adaptive thresholds based on different sparse transform bases

Oil and gas seismic exploration have to adopt irregular seismic acquisition due to the increasingly complex exploration conditions to adapt to complex geological conditions and environments.However,the irregular seismic acquisition is accompanied by the lack of acquisition data,which requires high-precision regularization.The sparse signal feature in the transform domain in compressed sensing theory is used in this paper to recover the missing signal,involving sparse transform base optimization and threshold modeling.First,this paper analyzes and compares the effects of six sparse transformation bases on the reconstruction accuracy and efficiency of irregular seismic data and establishes the quantitative relationship between sparse transformation and reconstruction accuracy and efficiency.Second,an adaptive threshold modeling method based on sparse coefficient is provided to improve the reconstruction accuracy.Test results show that the method has good adaptability to different seismic data and sparse transform bases.The f-x domain reconstruction method of effective frequency samples is studied to address the problem of low computational efficiency.The parallel computing strategy of curvelet transform combined with OpenMP is further proposed,which substantially improves the computational efficiency under the premise of ensuring the reconstruction accuracy.Finally,the actual acquisition data are used to verify the proposed method.The results indicate that the proposed method strategy can solve the regularization problem of irregular seismic data in production and improve the imaging quality of the target layer economically and efficiently.

Research on data assimilation strategy of turbulent separated flow over airfoil

In order to increase the accuracy of turbulence field reconstruction,this paper combines experimental observation and numerical simulation to develop and establish a data assimilation framework,and apply it to the study of S809 low-speed and high-angle airfoil flow.The method is based on the ensemble transform Kalman filter(ETKF)algorithm,which improves the disturbance strategy of the ensemble members and enhances the richness of the initial members by screening high flow field sensitivity constants,increasing the constant disturbance dimensions and designing a fine disturbance interval.The results show that the pressure distribution on the airfoil surface after assimilation is closer to the experimental value than that of the standard Spalart-Allmaras(S-A)model.The separated vortex estimated by filtering is fuller,and the eddy viscosity field information is more abundant,which is physically consistent with the observation information.Therefore,the data assimilation method based on the improved ensemble strategy can more accurately and effectively describe complex turbulence phenomena.

High-frequency compensation for seismic data based on adaptive generalized S transform

The low-pass fi ltering eff ect of the Earth results in the absorption and attenuation of the high-frequency components of seismic signals by the stratum during propagation.Hence,seismic data have low resolution.Considering the limitations of traditional high-frequency compensation methods,this paper presents a new method based on adaptive generalized S transform.This method is based on the study of frequency spectrum attenuation law of seismic signals,and the Gauss window function of adaptive generalized S transform is used to fi t the attenuation trend of seismic signals to seek the optimal Gauss window function.The amplitude spectrum compensation function constructed using the optimal Gauss window function is used to modify the time-frequency spectrum of the adaptive generalized S transform of seismic signals and reconstruct seismic signals to compensate for high-frequency attenuation.Practical data processing results show that the method can compensate for the high-frequency components that are absorbed and attenuated by the stratum,thereby eff ectively improving the resolution and quality of seismic data.

Effective short text classification via the fusion of hybrid features for IoT social data

Nowadays short texts can be widely found in various social data in relation to the 5G-enabled Internet of Things (IoT). Short text classification is a challenging task due to its sparsity and the lack of context. Previous studies mainly tackle these problems by enhancing the semantic information or the statistical information individually. However, the improvement achieved by a single type of information is limited, while fusing various information may help to improve the classification accuracy more effectively. To fuse various information for short text classification, this article proposes a feature fusion method that integrates the statistical feature and the comprehensive semantic feature together by using the weighting mechanism and deep learning models. In the proposed method, we apply Bidirectional Encoder Representations from Transformers (BERT) to generate word vectors on the sentence level automatically, and then obtain the statistical feature, the local semantic feature and the overall semantic feature using Term Frequency-Inverse Document Frequency (TF-IDF) weighting approach, Convolutional Neural Network (CNN) and Bidirectional Gate Recurrent Unit (BiGRU). Then, the fusion feature is accordingly obtained for classification. Experiments are conducted on five popular short text classification datasets and a 5G-enabled IoT social dataset and the results show that our proposed method effectively improves the classification performance.

Background Error Covariance Statistics of Hydrometeor Control Variables Based on Gaussian Transform

Use of data assimilation to initialize hydrometeors plays a vital role in numerical weather prediction(NWP).To directly analyze hydrometeors in data assimilation systems from cloud-sensitive observations,hydrometeor control variables are necessary.Common data assimilation systems theoretically require that the probability density functions(PDFs)of analysis,background,and observation errors should satisfy the Gaussian unbiased assumptions.In this study,a Gaussian transform method is proposed to transform hydrometeors to more Gaussian variables,which is modified from the Softmax function and renamed as Quasi-Softmax transform.The Quasi-Softmax transform method then is compared to the original hydrometeor mixing ratios and their logarithmic transform and Softmax transform.The spatial distribution,the non-Gaussian nature of the background errors,and the characteristics of the background errors of hydrometeors in each method are studied.Compared to the logarithmic and Softmax transform,the Quasi-Softmax method keeps the vertical distribution of the original hydrometeor mixing ratios to the greatest extent.The results of the D′Agostino test show that the hydrometeors transformed by the Quasi-Softmax method are more Gaussian when compared to the other methods.The Gaussian transform has been added to the control variable transform to estimate the background error covariances.Results show that the characteristics of the hydrometeor background errors are reasonable for the Quasi-Softmax method.The transformed hydrometeors using the Quasi-Softmax transform meet the Gaussian unbiased assumptions of the data assimilation system,and are promising control variables for data assimilation systems.

Application of S-transform threshold filtering in Anhui experiment airgun sounding data de-noising

As a relatively new method of processing non-stationary signal with high time-frequency resolution, S transform can be used to analyze the time-frequency characteristics of seismic signals. It has the following characteristics: its time-frequency resolution corresponding to the signal frequency, reversible inverse transform, basic wavelet that does not have to meet the permit conditions. We combined the threshold method, proposed the S-transform threshold filtering on the basis of S transform timefrequency filtering, and processed airgun seismic records from temporary stations in "Yangtze Program"(the Anhui experiment). Compared with the results of the bandpass filtering, the S transform threshold filtering can improve the signal to noise ratio(SNR) of seismic waves and provide effective help for first arrival pickup and accurate travel time. The first arrival wave seismic phase can be traced farther continuously, and the Pm seismic phase in the subsequent zone is also highlighted.

Wavelet domain adaptive filtering algorithm for removing the seamless pipe noise contained in the magnetic flux leakage data

With the widespread application and fast development of gas and oil pipeline network in China, the pipeline inspection technology has been used more extensively. The magnetic flux leakage （MFL） method has established itself as the most widely used in-line inspection technique for the evaluation of gas and oil pipelines. The MFL data obtained from seamless pipeline inspection is usually contaminated by the seamless pipe noise （SPN）. SPN can in some cases completely mask MFL signals from certain type of defects, and therefore considerably reduces the detectability of the defect signals. In this paper, a new de-noising algorithm called wavelet domain adaptive filtering is proposed for removing the SPN contained in the MFL data. The new algorithm results from combining the wavelet transform with the adaptive filtering technique. Results from application of the proposed algorithm to the MFL data from field tests show that the proposed algorithm has good performance and considerably improves the detectability of the defect signals in the MFL data.

Technology of OptimalData Compression

The method of data compression, using orthogonal transform, is introduced so as to insure the minimal distortion of signal restoration. It, featured with transformation, can compress the data according to the needed precision. The ratio of compressed data is closely related to precision. The results show it to be favorable to different kinds of data compression.