Comparison of Spatiotemporal Fusion Models for Producing High Spatiotemporal Resolution Normalized Difference Vegetation Index Time Series Data Sets

It has a great significance to combine multi-source with different spatial resolution and temporal resolution to produce high spatiotemporal resolution Normalized Difference Vegetation Index (NDVI) time series data sets. In this study, four spatiotemporal fusion models were analyzed and compared with each other. The models included the spatial and temporal adaptive reflectance model (STARFM), the enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM), the flexible spatiotemporal data fusion model (FSDAF), and a spatiotemporal vegetation index image fusion model (STVIFM). The objective of is to: 1) compare four fusion models using Landsat-MODIS NDVI image from the Banan district, Chongqing Province;2) analyze the prediction accuracy quantitatively and visually. Results indicate that STVIFM would be more suitable to produce NDVI time series data sets.

Image Fusion Real-time System Based on FPGA and Multi-DSP

In order to solve complex algorithm that is difficult to achieve real-time processing of Multiband image fusion within large amount of data, a real-time image fusion system based on FPGA and multi-DSP is designed. Five-band image acquisition, image registration, image fusion and display output can be done within the system which uses FPGA as the main processor and the other three DSP as an algorithm processor. Making full use of Flexible and high-speed characteristics of FPGA, while an image fusion algorithm based on multi-wavelet transform is optimized and applied to the system. The final experimental results show that the frame rate of 15 Hz, with a resolution of 1392 × 1040 of the five-band image can be used by the system to complete processing within 41ms.

Brain Time Stack图像融合技术在CT中的应用

目的:分析Brain Time Stack图像融合技术在CT中的应用。方法:选取2021年3月—2022年9月衡水市第四人民医院收治的50例CT检查患者作为研究对象。所有患者进行CT检查并进行Brain Time Stack后处理。比较四组不同部位CT值、标准差(SD)、信噪比(SNR)。比较四组图像主观质量评分。分析不同部位CT值、SD、SNR与图像主观质量评分的相关性。结果:B组的延髓、额叶灰质、额叶白质、小脑内侧、小脑外侧、颞肌肌肉CT值明显低于A组;C组的延髓、脑室、额叶白质、小脑内侧、小脑外侧、颞肌肌肉CT值高于A组;D组延髓、额叶灰质、颞肌肌肉CT值明显低于A组,脑室、额叶白质、小脑外侧CT值明显高于A组;C组延髓、额叶灰质、额叶白质、小脑内侧、小脑外侧、颞肌肌肉CT值明显高于B组;D组延髓、脑室、额叶白质、小脑内侧、小脑外侧、颞肌肌肉CT值明显高于B组;D组延髓、额叶灰质、额叶白质、小脑内侧、小脑外侧、颞肌肌肉CT值明显低于C组;D组脑室CT值明显高于C组,差异有统计学意义(P<0.05)。B组、C组、D组延髓、脑室、额叶灰质、额叶白质、小脑内侧、小脑外侧、颞肌肌肉SD值明显低于A组;C组延髓、脑室、额叶白质、小脑内侧、小脑外侧、颞肌肌肉SD值均明显高于B组;C组额叶灰质SD明显低于B组;D组延髓、脑室、额叶灰质、额叶白质、小脑内侧、小脑外侧、肌肉SD均明显低于B组、C组,差异有统计学意义(P<0.05)。B组、C组、D组延髓、脑室、额叶灰质、额叶白质、小脑内侧、小脑外侧、颞肌肌肉SNR均明显高于A组;C组、D组延髓、额叶灰质、额叶白质、小脑内侧、小脑外侧、颞肌肌肉SNR值明显高于B组;C组、D组脑室SNR明显低于B组;D组延髓、脑室、额叶灰质、额叶白质、小脑内侧、小脑外侧、颞肌肌肉SNR明显高于C组,差异有统计学意义(P<0.05)。D组图像主观质量评分最高,差异有统计学意义(P<0.05)。延髓、脑室、额叶灰质、额叶白质、小脑内侧、小脑外侧及颞肌肌肉SD与主观质量评分呈明显负相关,SNR与主观质量评分间呈明显正相关,差异有统计学意义(P<0.05)。结论:利用Brain Time Stack图像融合技术对头部CT扫描检查图像处理,动脉期结合前一期及后一期的图像数据在处理后具有更好的质量和更少的噪音。

Multi-rate sensor fusion-based adaptive discrete finite-time synergetic control for flexible-joint mechanical systems

This paper proposes an adaptive discrete finite-time synergetic control （ADFTSC） scheme based on a multi-rate sensor fusion estimator for flexible-joint mechanical systems in the presence of unmeasured states and dynamic uncertainties. Multi-rate sensors are employed to observe the system states which cannot be directly obtained by encoders due to the existence of joint flexibilities. By using an extended Kalman filter （EKF）, the finite-time synergetic controller is designed based on a sensor fusion estimator which estimates states and parameters of the mechanical system with multi-rate measurements. The proposed controller can guarantee the finite-time convergence of tracking errors by the theoretical derivation. Simulation and experimental studies are included to validate the effectiveness of the proposed approach.

Fusion of Activation Functions: An Alternative to Improving Prediction Accuracy in Artificial Neural Networks

The purpose of this study was to address the challenges in predicting and classifying accuracy in modeling Container Dwell Time (CDT) using Artificial Neural Networks (ANN). This objective was driven by the suboptimal outcomes reported in previous studies and sought to apply an innovative approach to improve these results. To achieve this, the study applied the Fusion of Activation Functions (FAFs) to a substantial dataset. This dataset included 307,594 container records from the Port of Tema from 2014 to 2022, encompassing both import and transit containers. The RandomizedSearchCV algorithm from Python’s Scikit-learn library was utilized in the methodological approach to yield the optimal activation function for prediction accuracy. The results indicated that “ajaLT”, a fusion of the Logistic and Hyperbolic Tangent Activation Functions, provided the best prediction accuracy, reaching a high of 82%. Despite these encouraging findings, it’s crucial to recognize the study’s limitations. While Fusion of Activation Functions is a promising method, further evaluation is necessary across different container types and port operations to ascertain the broader applicability and generalizability of these findings. The original value of this study lies in its innovative application of FAFs to CDT. Unlike previous studies, this research evaluates the method based on prediction accuracy rather than training time. It opens new avenues for machine learning engineers and researchers in applying FAFs to enhance prediction accuracy in CDT modeling, contributing to a previously underexplored area.

基于细粒度代码表示和特征融合的即时软件缺陷预测方法

即时软件缺陷预测指在软件更改初次提交之际预测该更改引入缺陷的倾向。此类预测针对单一程序变更,而非在粗粒度上进行。由于其即时性和可追溯性,该技术已在持续测试等领域得到广泛应用。目前的研究中,提取变更代码表示的方法粒度较粗,仅标出了变更行,而没有进行细粒度的标记。此外,现有的使用提交内容进行缺陷预测的方法,仅仅是把提交消息与变更代码的特征进行简单拼接,缺失了在特征空间上的深度对齐,这使得在提交消息质量参差不齐的情况下,会出现预测结果易受噪声干扰的情形,并且现有方法也未将领域专家设计的人工特征以及变更内容中的语义语法信息综合起来进行预测。为了解决上述问题,提出了一种基于细粒度代码表征和特征融合的即时软件缺陷预测方法。通过引入新的变更嵌入计算方法来在细粒度上表示变更代码。同时,引入特征对齐模块,降低提交消息中噪声对方法性能的影响。此外,使用神经网络从人工设计的特征中学习专业知识,充分利用现有特征进行预测。实验结果表明,相较于现有方法,该方法在3个性能指标上均有显著提升。

Real Time PCR监测慢性粒细胞白血病bcr/abl融合基因的临床意义

目的通过Real Time PCR实时监测慢性粒细胞白血病bcr/abl融合基因的表达,探讨其在诊断及预后评价价值。方法采用荧光定量Real Time PCR反应技术检测39例慢性粒细胞白血病及其它血液系统疾病20例的bcr/abl融合基因表达水平。结果骨髓原始、外周血原始与bcr/abl融合基因拷贝数有显著相关性,初诊慢性粒细胞的bcr/abl融合基因表达水平普遍明显增高,经羟基脲±干扰素治疗后表达明显下降,复发或加速期患者可提前2-4个月左右早期发现,格列卫或移植治疗后,多数bcr/abl融合基因可转阴性。结论 bcr/abl融合基因检测对诊断慢性粒细胞白血病有重要价值,同样是慢性粒细胞白血病治疗和预后观察的决定性指标。

基于语音信号时频特征融合的帕金森病检测方法

发音障碍是帕金森病的早期症状之一。近年来,基于语音信号的帕金森病检测的研究大多采用梅尔刻度下的相关语音特征与深度神经网络模型相结合的方法。然而,现有的模型无法充分关注语音信号的全局时序信息,且梅尔刻度特征在准确表征帕金森病的病理信息方面效果有限。为此,提出了一种基于语音时频特征融合的帕金森病检测方法。首先,提取语音的梅尔频率倒谱系数,并将其作为模型的输入。接着,在已有的S-vectors模型中引入Conformer编码器模块,以提取语音的时域全局特征。最后,将与帕金森病语音检测相关的频域全局特征嵌入时域特征中进行时频信息融合,以实现帕金森病语音检测。在公开帕金森病语音数据集和自采语音数据集上验证了方法的有效性。

面向微震时序波形的无监督聚类方法

为更加快速准确地从微震时序数据中提取微震事件,提高异常事件的捕捉效率,提出一种基于多尺度融合卷积和空洞卷积的自动编码器(multi-scale fusion convolution and dilated convolutions auto encoder,MDCAE)与融合波动率和限制窗口的动态时间扭曲(constraints dynamic time warping for fusing volatility,CDTW-Vol)方法。提出MDCAE的特征提取方法,将波形信号转变为低维特征信号,引入微震波形的波动率的概念,通过改进后的DTW算法对特征信号进行相似性度量,得到的相似性矩阵进行k-medoids聚类,得到聚类结果。应用某矿区501工作面和802工作面微震监测数据集进行实验,验证所提方法的准确性和泛化性,经实验得出所提聚类方法轮廓系数89%,兰德系数90%,相比普通的k-medoids聚类算法聚类精度上升57%,为捕捉微震系统的异常事件提供了一种新方法。

改进遗传算法求解含单转运系统车间调度问题

研究单转运系统分布式置换流水线调度问题,任一工厂内连续两台机器间有一台运输能力有限的转运机器人。基于此,提出一种多策略融合改进遗传算法以最小化最大完工时间。引入Logistic-tent混沌搜索、基于K-均值聚类的NEH算法和修正NEH算法以改善初始工厂加工序列群的质量,运用结合均匀多点交叉和互换变异的自适应交叉变异算子或工厂内/间交叉变异算子进行解的调整,设计一种基于主工厂的邻域搜索(key-factory-based local search,KFLS)和半初始化策略进行再次优化。仿真结果表明了该算法的有效性。

lp norm inverse spectral decomposition and its multi-sparsity fusion interpretation

Spectral decomposition has been widely used in the detection and identifi cation of underground anomalous features(such as faults,river channels,and karst caves).However,the conventional spectral decomposition method is restrained by the window function,and hence,it mostly has low time–frequency focusing and resolution,thereby hampering the fi ne interpretation of seismic targets.To solve this problem,we investigated the sparse inverse spectral decomposition constrained by the lp norm(0<p≤1).Using a numerical model,we demonstrated the higher time–frequency resolution of this method and its capability for improving the seismic interpretation for thin layers.Moreover,given the actual underground geology that can be often complex,we further propose a p-norm constrained inverse spectral attribute interpretation method based on multiresolution time–frequency feature fusion.By comprehensively analyzing the time–frequency spectrum results constrained by the diff erent p-norms,we can obtain more refined interpretation results than those obtained by the traditional strategy,which incorporates a single norm constraint.Finally,the proposed strategy was applied to the processing and interpretation of actual three-dimensional seismic data for a study area covering about 230 km^(2) in western China.The results reveal that the surface water system in this area is characterized by stepwise convergence from a higher position in the north(a buried hill)toward the south and by the development of faults.We thus demonstrated that the proposed method has huge application potential in seismic interpretation.

动态时间序列建模的多模态情感识别方法

现有的情感识别研究未充分考虑语音信号中的局部-全局信息和长期时间依赖关系,文本特征提取也存在特征稀疏和信息丢失的问题。为解决上述问题,提出动态时间序列建模的多模态情感识别方法。设计动态时间窗口模块分割语音信号从而捕捉局部-全局信息,并通过双向序列建模捕获信号中的空间信息。考虑到文本信息对情感分析的重要性,采用基于Transformer模型的卷积神经网络捕捉文本中不同位置间的依赖关系建模较长的上下文信息,最后将两种模态进行融合得到最终的情感分类。模型在IEMOCAP数据集上的实验结果表明,相比其他主流模型具有更好的多模态情感识别效果。

Research on Windowing Image Fusion

The speed and quality of the image fusion always restrain each other.The real-time image fusion is one of the problems which needs to be studied and solved urgently.The windowing processing technology for the image fusion proposed in this paper can solve this problem in a certain extent.The windowing rules were put forward and the applicable scope for the windowing fusion and the calculation method for the maximum windowing area were determined.And,the results of the windowing fusion were analyzed,verified and compared to confirm the feasibility of this technology.

小尺度靶丸冲击波调控的冲击波测量技术优化及应用

激光聚变研究中,冲击波调控技术是实现靶丸压缩过程的熵增调谐,保证高性能内爆的关键实验技术.本文在十万焦耳激光装置上首次实现了0.375 mm半径小尺度内爆靶丸下双台阶辐射驱动的高精度冲击波调控实验测量.针对小靶丸下任意反射面速度干涉仪(VISAR)诊断有效反射区域不足的问题,通过建立的球形反射面VISAR图像光强的理论计算方法,提出了利用锁孔(keyhole)锥反射效应提升VISAR诊断空间区域的实验技术路线,使得小靶丸尺度下有效VISAR数据区域提升了近3倍.在实验中首次获得了整形内爆实验条件下低温液氘靶的冲击波测量实验数据,实现了高精度冲击波调控实验测量.实验发现,小时空尺度内爆设计条件下,由于反射冲击波的作用,激光参数的较小偏差都会对冲击波追赶后的传输行为产生显著影响,揭示了我国当前小靶丸尺度下高性能整形内爆物理过程中冲击波传输的多因素敏感性,以及冲击波调控实验对于内爆设计验证的重要性.本文提出的小靶丸冲击波调控实验技术,不仅为我国十万焦耳激光装置上整形脉冲下熵增调谐实验的开展提供了技术基础,也对基于球汇聚压缩的超高压物理研究具有重要意义.

基于数据驱动的分布式光伏发电功率预测方法研究进展

从综述的角度,以分布式光伏系统为对象,分析了功率预测技术的发展情况、存在的难点以及主要影响因素,梳理了应用数据驱动方法实现功率准确预测的技术路线。再以空间相关性、历史出力功率以及气象等影响因素为切入点,梳理了光伏系统数据驱动的功率预测研究现状,分析其相应的数据增强、时空图信息以及特征融合的手段,讨论了技术的优缺点。最后给出了功率预测数据驱动方法研究方向和发展建议。

四川地区精细化降水预报融合订正试验及检验

精细化定量降水网格预报是天气预报业务的重点和难点,基于西南区域智能数值网格模式预报系统(Southwest China WRF-based Intelligent Numeric Grid forecast System,SWC-WINGS)1 km×1 km分辨率的小时降水预报,利用时间滞后和概率匹配方法开展融合订正试验,再利用中国气象局多源融合降水系统(CMA Multi-source Precipitation Analysis System,CMPAS)三源融合降水实况格点数据,对2022年7—8月四川地区的小时降水预报融合订正结果进行检验,并在四川盆地西部一次短时强降水天气过程中进行应用,结果表明:(1)时间滞后集合降水预报相较于模式降水预报,存在小量级预报过度,大量级预报过于保守的问题;(2)时间滞后结合概率匹配的降水预报融合订正方法有效提升了各量级降水预报的TS评分,尤其1~2 h预报时效提升显著,小时雨量超过0.1 mm、5 mm、10 mm和20 mm量级的TS评分平均提升率分别为7.2%、17.2%、28.3%和36.3%;(3)一次短时强降水天气过程的应用结果表明,时间滞后结合概率匹配的融合订正方法对模式小时降水预报有较好的改进效果,尤其对大量级降水预报有较强的订正能力。

基于STFF-ShuffleNet的掘进机截割头故障诊断

目的为了解决掘进机截割头故障振动信号信息量大且难以充分提取和现有的掘进机截割头故障诊断模型参数量多且计算量大,训练需要耗费大量时间,部署和使用对硬件设备的性能要求严苛的问题。方法提出一种小体积和运行速度快的轻量化网络模型时空特征融合(STFF)-ShuffleNet用于掘进机截割头故障诊断研究。ShuffleNet在模型进行时空特征提取时引入深度可分离卷积提高模型的运算效率,再对融合后的时空特征做通道混洗和分组卷积,既解决了通道分组的问题又提高了诊断精度。通过悬臂式掘进机截割头实验台模拟不同故障条件,将采集到的振动信号输入模型中进行测试。结果实验表明,STFF-ShuffleNet模型对掘进机截割头的诊断与其他传统模型相比参数量少,收敛速度快,运行效率更高,更容易满足掘进机截割头故障诊断高实时性要求,准确率达到99.3%。结论STFF-ShuffleNet模型对提高掘进机截割头故障诊断效率和降低硬件要求提供了一种新的解决思路。

A new information fusion white noise deconvolution estimator

The white noise deconvolution or input white noise estimation problem has important applications in oil seismic exploration, communication and signal processing. By the modern time series analysis method, based on the autoregressive moving average （ARMA） innovation model, a new information fusion white noise deconvolution estimator is presented for the general multisensor systems with different local dynamic models and correlated noises. It can handle the input white noise fused filtering, prediction and smoothing problems, and it is applicable to systems with colored measurement noises. It is locally optimal, and is globally suboptimal. The accuracy of the fuser is higher than that of each local white noise estimator. In order to compute the optimal weights, the formula computing the local estimation error cross-covariances is given. A Monte Carlo simulation example for the system with Bernoulli-Gaussian input white noise shows the effectiveness and performances.

外测实时数据融合方法分析

针对目前任务模式复杂、测量装备种类多且跟踪能力不同的问题,充分利用汇集于中心的大量装备测量数据,改变原有的接力分段处理的方式,研究测元级和弹道级的实时融合处理方法,给出切实可行的数学模型和方法步骤,并模拟异常数据分别计算,分析两种方法的处理精度和适用情况。该方法已经在实时处理软件中使用,保障了多项任务的实时数据处理,较传统处理方式有明显提高。

基于多尺度特征信息融合的时间序列异常检测

目前大多数的时间序列都缺少相应的异常标签,且现有基于重构的异常检测算法不能很好地捕获到多维数据间复杂的潜在相关性和时间依赖性,为了构建特征丰富的时间序列,提出一种多尺度特征信息融合的异常检测模型。该模型首先通过卷积神经网络对滑动窗口内的不同序列进行特征卷积来获取不同尺度下的局部上下文信息。然后,利用Transformer中的位置编码对卷积后的时间序列窗口进行位置嵌入,增强滑动窗口中每一个时间序列和邻近序列之间的位置联系,并引入时间注意力获取数据在时间维度上的自相关性,并进一步通过多头自注意力自适应地为窗口内不同时间序列分配不同的权重。最后,对反卷积过程中上采样得到的窗口数据与不同尺度下得到的局部特征和时间上下文信息进行逐步融合,从而准确重构原始时间序列,并将重构误差作为最终的异常得分进行异常判定。实验结果表明,所构建模型在SWaT和SMD数据集上与基线模型相比F1分数均有所提升。在数据维度高且均衡性较差的WADI数据集上与GDN模型相比F1分数降低了1.66%。