Performances of Clustering Methods Considering Data Transformation and Sample Size: An Evaluation with Fisheries Survey Data

Clustering is a group of unsupervised statistical techniques commonly used in many disciplines. Considering their applications to fish abundance data, many technical details need to be considered to ensure reasonable interpretation. However, the reliability and stability of the clustering methods have rarely been studied in the contexts of fisheries. This study presents an intensive evaluation of three common clustering methods, including hierarchical clustering(HC), K-means(KM), and expectation-maximization(EM) methods, based on fish community surveys in the coastal waters of Shandong, China. We evaluated the performances of these three methods considering different numbers of clusters, data size, and data transformation approaches, focusing on the consistency validation using the index of average proportion of non-overlap(APN). The results indicate that the three methods tend to be inconsistent in the optimal number of clusters. EM showed relatively better performances to avoid unbalanced classification, whereas HC and KM provided more stable clustering results. Data transformation including scaling, square-root, and log-transformation had substantial influences on the clustering results, especially for KM. Moreover, transformation also influenced clustering stability, wherein scaling tended to provide a stable solution at the same number of clusters. The APN values indicated improved stability with increasing data size, and the effect leveled off over 70 samples in general and most quickly in EM. We conclude that the best clustering method can be chosen depending on the aim of the study and the number of clusters. In general, KM is relatively robust in our tests. We also provide recommendations for future application of clustering analyses. This study is helpful to ensure the credibility of the application and interpretation of clustering methods.

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.

SIDE-LOOKING ECHO DATA TRANSFORM TO SQUINT MODE SAR IMAGING

Nowadays the side-looking SAR echo data can be obtained easily from the commercial channel, while that of other SAR imaging modes such as squint, spotlight are difficult to be acquired. This paper presents a new scheme to transform the side-looking returns to squint ones, in a direct and an indirect approach respectively. Direct transformation uses the data with a wide azimuth beam angle. The maximum of the required squint angle is limited under several degrees. Squint data under indirect transformation can be obtained by adding a platform velocity along slant range according to the required squint angles. Then the squint data is determined by the angle between the new forward velocity and line-of-sight direction. This method results in higher squint angle compared with the first one. Verification shows the feasibility of these approaches with illustration of side-looking E-SAR raw data processing. The future work will be on the precise Doppler centroid estimation and effective imaging algorithm development.

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.

Explainable data transformation recommendation for automatic visualization

Automatic visualization generates meaningful visualizations to support data analysis and pattern finding for novice or casual users who are not familiar with visualization design.Current automatic visualization approaches adopt mainly aggregation and filtering to extract patterns from the original data.However,these limited data transformations fail to capture complex patterns such as clusters and correlations.Although recent advances in feature engineering provide the potential for more kinds of automatic data transformations,the auto-generated transformations lack explainability concerning how patterns are connected with the original features.To tackle these challenges,we propose a novel explainable recommendation approach for extended kinds of data transformations in automatic visualization.We summarize the space of feasible data transformations and measures on explainability of transformation operations with a literature review and a pilot study,respectively.A recommendation algorithm is designed to compute optimal transformations,which can reveal specified types of patterns and maintain explainability.We demonstrate the effectiveness of our approach through two cases and a user study.

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。

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。

基于Transformer的城市三角网格语义分割方法

对城市三角网格(Urban Triangle Mesh)数据进行语义分割以识别不同类别的物体,是理解和分析三维城市场景的一种非常重要的方法.城市三角网格是一种具有丰富空间拓扑关系的三维空间几何数据,包含大量的几何信息,然而,现有的方法仅仅单独对每种几何信息进行特征提取,然后简单地融合再进行语义分割,难以利用几何信息之间的关联性,对个别物体的分割性能不佳.为了解决上述问题,提出一种基于自注意力机制Transformer的模型UMeT(Urban Mesh Transformer),其由多层感知机和MeshiT(Mesh in Transformer)模块构成,不仅可以利用多层感知机提取高维特征,还可以利用MeshiT模块计算各种几何信息之间的关联性,有效挖掘城市三角网格数据中隐含的关联.实验证明,UMeT能提取高维特征,同时保证城市三角网格数据的空间不变性,从而提升了语义分割的准确性.

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。

基于随机增强Swin-Tiny Transformer的玉米病害识别及应用

针对图像识别中获取全局特征的局限性及难以提升识别准确性的问题,提出一种基于随机增强Swin-Tiny Transformer轻量级模型的图像识别方法.该方法在预处理阶段结合基于随机数据增强(random data augmentation based enhancement,RDABE)算法对图像特征进行增强,并采用Transformer的自注意力机制,以获得更全面的高层视觉语义信息.通过在玉米病害数据集上优化Swin-Tiny Transformer模型并进行参数微调,在农业领域的玉米病害上验证了该算法的适用性,实现了更精确的病害检测.实验结果表明,基于随机增强的轻量级Swin-Tiny+RDABE模型对玉米病害图像识别准确率达93.5867%.在参数权重一致,与性能优秀的轻量级Transformer、卷积神经网络(CNN)系列模型对比的实验结果表明,改进的模型准确率比Swin-Tiny Transformer,Deit3_Small,Vit_Small,Mobilenet_V3_Small,ShufflenetV2和Efficientnet_B1_Pruned模型提高了1.1877%~4.9881%,且能迅速收敛.

基于Vision Transformer的小麦病害图像识别算法

小麦白粉病、赤霉病和锈病是危害小麦产量的三大病害。为提高小麦病害图像的识别准确率,构建一种基于Vision Transformer的小麦病害图像识别算法。首先,通过田间拍摄的方式收集包含小麦白粉病、赤霉病和锈病3种病害在内的小麦病害图像,并对原始图像进行预处理,建立小麦病害图像识别数据集;然后,基于改进的Vision Transformer构建小麦病害图像识别算法,分析不同迁移学习方式和数据增强对模型识别效果的影响。试验可知,全参数迁移学习和数据增强能明显提高Vision Transformer模型的收敛速度和识别精度。最后,在相同时间条件下,对比Vision Transformer、AlexNet和VGG16算法在相同数据集上的表现。试验结果表明,Vision Transformer模型对3种小麦病害图像的平均识别准确率为96.81%,相较于AlexNet和VGG16模型识别准确率分别提高6.68%和4.94%。

基于Transformer与局部特征融合的轨道紧固件缺陷检测方法

为解决传统人工巡检轨道交通线路存在的效率低和有安全隐患等问题,提出一种基于Transformer与局部特征融合的轨道紧固件缺陷检测方法。构建轨道紧固件缺陷检测模型,将Transformer与局部特征模块融合,整合局部信息,进而提取轨道紧固件缺陷特征;同时,采用数据增强的方法对轨道紧固件缺陷样本进行数据扩增,扩充数据集,验证所建模型的检测效果。实验结果表明,相较于传统方法,文章提出的方法在识别轨道紧固件缺失和损坏两类缺陷方面的精度和平均准确率均有所提升,在不同的轨道线路实验环境下也表现出良好的检测效果。

基于Transformer的空调能耗预测模型构建与参数优化

针对空调系统能耗预测建模过程中的数据质量、模型输入参数筛选等问题,研究基于Transformer神经网络的空调系统能耗预测模型构建和参数优化方法,结果表明:可以通过广义极端学生化偏差方法对数据中的离群值进行检测修正,从而提升数据质量;通过余弦相似度对输入参数进行两两相关性检验来消除各参数间的多重共线性,实现对输入参数的初步筛选;采用随机森林算法计算初选参数对空调能耗预测结果的影响来判断冗余参数,进而完成对输入特征参数的最终筛选;建立的空调能耗预测模型对数据测试集的预测结果均方根误差RMSE为38.831 kW,相关系数R^(2)为0.952,表现出了良好的预测性能。

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。

An Efficient Schema Transformation Technique for Data Migration from Relational to Column-Oriented Databases

Data transformation is the core process in migrating database from relational database to NoSQL database such as column-oriented database. However,there is no standard guideline for data transformation from relational database toNoSQL database. A number of schema transformation techniques have been proposed to improve data transformation process and resulted better query processingtime when compared to the relational database query processing time. However,these approaches produced redundant tables in the resulted schema that in turnconsume large unnecessary storage size and produce high query processing timedue to the generated schema with redundant column families in the transformedcolumn-oriented database. In this paper, an efficient data transformation techniquefrom relational database to column-oriented database is proposed. The proposedschema transformation technique is based on the combination of denormalizationapproach, data access pattern and multiple-nested schema. In order to validate theproposed work, the proposed technique is implemented by transforming data fromMySQL database to MongoDB database. A benchmark transformation techniqueis also performed in which the query processing time and the storage size arecompared. Based on the experimental results, the proposed transformation technique showed significant improvement in terms query processing time and storagespace usage due to the reduced number of column families in the column-orienteddatabase.

嵌入局部聚类描述符的视频问答Transformer模型

视频问答(Video Question Answering)是典型的跨模态理解任务,其目的是根据提问的文本对视频内容进行理解并推理正确的答案,如何有效地对多模态输入进行特征表示并建立跨模态间复杂的语义关联是解决这一任务的关键难点.为了正确地推理结果,模型首先必须捕获视频序列和复杂文本中包含的关键语义信息.本文提出了一种嵌入局部聚类描述符的视频问答Transformer模型,称为TVLAD-Net(Transformer Residual-less VLAD Network).TVLAD-Net主要包含一个端到端可训练的无残差局部聚合描述符模块(RVLAD,Residual-less Vec⁃tor of Local Aggregated Descriptor),以及一个统一的语义转换模块(Transformer).具体来说,RVLAD通过设置多个不同的聚类中心将视频和文本特征分别聚合为少量紧凑的局部聚类描述符;每个聚类描述符从全局角度分配及汇总了序列上权重不一的语义信息,相比于聚合前的视频帧特征或文本词特征具有更丰富的表征能力.Trans⁃former模块能够利用模态间的相互语义引导,实现多模态聚类描述符的语义交互,即采用多头注意力机制同时求解模态内和模态间的语义关联,进而避免了与所求解问题无关或者冗余的描述符语义单元的聚合.实验评估在TGIF-QA、MSVD-QA和MSRVTT-QA三个基准数据集上进行;实验结果表明本文方法能够实现先进的问答推理,在整体的评价指标上与现有方法相比有2%~5%的性能提升.

Performance of Continuous Wavelet Transform over Fourier Transform in Features Resolutions

This study presents a comparative analysis of two image enhancement techniques, Continuous Wavelet Transform (CWT) and Fast Fourier Transform (FFT), in the context of improving the clarity of high-quality 3D seismic data obtained from the Tano Basin in West Africa, Ghana. The research focuses on a comparative analysis of image clarity in seismic attribute analysis to facilitate the identification of reservoir features within the subsurface structures. The findings of the study indicate that CWT has a significant advantage over FFT in terms of image quality and identifying subsurface structures. The results demonstrate the superior performance of CWT in providing a better representation, making it more effective for seismic attribute analysis. The study highlights the importance of choosing the appropriate image enhancement technique based on the specific application needs and the broader context of the study. While CWT provides high-quality images and superior performance in identifying subsurface structures, the selection between these methods should be made judiciously, taking into account the objectives of the study and the characteristics of the signals being analyzed. The research provides valuable insights into the decision-making process for selecting image enhancement techniques in seismic data analysis, helping researchers and practitioners make informed choices that cater to the unique requirements of their studies. Ultimately, this study contributes to the advancement of the field of subsurface imaging and geological feature identification.

基于Vision Transformer的电缆终端局部放电模式识别

电缆终端缺陷类型一般与局部放电信号特征密切相关,因此可以通过对局部放电信号进行模式识别来实现缺陷分类。对15 kV XLPE电缆终端4种典型缺陷的放电脉冲波形和时频谱图特征进行分析处理,得到可用于识别的数据样本,然后分别采用Vision Transformer模型、LeNet5、AlexNet和支持向量机对数据进行训练,对比不同算法的识别准确率。结果显示在数据充足的情况下,Vision Transformer模型的识别精度高于其他识别算法。所提方法及结论可为电缆附件的绝缘评估提供可靠依据,具有一定的指导意义。

逐层Transformer在类别不均衡数据的应用

为解决临床医学量表数据类别不均衡容易对模型产生影响,以及在处理量表数据任务时深度学习框架性能难以媲美传统机器学习方法问题,提出了一种基于级联欠采样的Transformer网络模型(layer by layer Transformer,LLT)。LLT通过级联欠采样方法对多数类数据逐层删减,实现数据类别平衡,降低数据类别不均衡对分类器的影响,并利用注意力机制对输入数据的特征进行相关性评估实现特征选择,细化特征提取能力,改善模型性能。采用类风湿关节炎(RA)数据作为测试样本,实验证明,在不改变样本分布的情况下,提出的级联欠采样方法对少数类别的识别率增加了6.1%,与常用的NEARMISS和ADASYN相比,分别高出1.4%和10.4%;LLT在RA量表数据的准确率和F 1-score指标上达到了72.6%和71.5%,AUC值为0.89,mAP值为0.79,性能超过目前RF、XGBoost和GBDT等主流量表数据分类模型。最后对模型过程进行可视化,分析了影响RA的特征,对RA临床诊断具有较好的指导意义。

基于Transformer的英文粘连词还原方法

神经机器翻译(Neural Machine Translation,NMT)性能依赖于语料库的数据量和数据质量,经研究分析发现英文数据中存在多词粘连的现象,以下统称为粘连词,出现粘连词影响数据质量。为了进一步提高数据质量,需将粘连词还原成独立词,即词与词之间由空格作为分隔符的形式。针对该问题提出使用Transformer模型对粘连词进行还原。在数据预处理阶段,对数据采取三种不同的策略。实验证明,对数据进行分词、BPE切分的策略最佳,在真实数据集上准确率达到95.5%,在Transformer模型的基础上添加后处理操作后的正确率达到98.5%。该文方法具备可迁移性,对于任一种单词间用空格分割的语言都是可用的。