Evaluation and fusion of SST data from MTSAT and TMI in East China Sea, Yellow Sea and Bohai Sea in 2008

Two typical satellite sea surface temperature (SST) datasets, from the Multi-functional Transport Satellite (MTSAT) and Tropical Rainfall Measuring Mission Microwave Imager (TMI), were evaluated for the East China Sea, Yellow Sea, and Bohai Sea throughout 2008. Most monthly-mean availabilities of MTSAT are higher than those of TMI, whereas the seasonal variation of the latter is less than that of the former. The analysis on the one-year data shows that the annual mean availability of MTSAT (61%) is greater than that of TMI (56%). This is mainly because MTSAT is a geostationary satellite, which achieves longer observation than the sun-synchronous TMI. The daily availability of TMI (28%-75%) is more constant than that of MTSAT (9%-93%). The signal of infrared sensors on MTSAT is easily disturbed on cloudy days. In contrast, the TMI microwave sensor can obtain information through clouds. Based on in-situ SSTs, the SST accuracy of TMI is superior to that of MTSAT. In 2008, the root mean square (RMS) error of TMI and MTSAT were 0.77 K and 0.84 K, respectively. The annual mean biases were 0.14 K (TMI) and -0.31 K (MTSAT). To attain a high availability of SSTs, we propose a fusion method to merge both SSTs. The annual mean availability of fusion SSTs increases 17% compared to MTSAT. In addition, the availabilities of the fusion SSTs become more constant. The annual mean RMS and bias of fusion SSTs (0.78 K and -0.06 K, respectively) are better than those of MTSAT (0.84 K and -0.31 K).

Noise cancellation of a multi-reference full-wave magnetic resonance sounding signal based on a modified sigmoid variable step size least mean square algorithm

Nano-volt magnetic resonance sounding(MRS) signals are sufficiently weak so that during the actual measurement, they are affected by environmental electromagnetic noise, leading to inaccuracy of the extracted characteristic parameters and hindering effective inverse interpretation. Considering the complexity and non-homogeneous spatial distribution of environmental noise and based on the theory of adaptive noise cancellation, a model system for noise cancellation using multi-reference coils was constructed to receive MRS signals. The feasibility of this system with theoretical calculation and experiments was analyzed and a modified sigmoid variable step size least mean square(SVSLMS) algorithm for noise cancellation was presented. The simulation results show that, the multi-reference coil method performs better than the single one on both signal-to-noise ratio(SNR) improvement and signal waveform optimization after filtering, under the condition of different noise correlations in the reference coils and primary detecting coils and different SNRs. In particular, when the noise correlation is poor and the SNR<0, the SNR can be improved by more than 8 dB after filtering with multi-reference coils. And the average fitting errors for initial amplitude and relaxation time are within 5%. Compared with the normalized least mean square(NLMS) algorithm and multichannel Wiener filter and processing field test data, the effectiveness of the proposed method is verified.

A new method of lung sounds filtering using modulated least mean square—Adaptive noise cancellation

Advanced processing of lung sound (LS) recording is a significant means to separate heart sounds (HS) and combined low frequency noise from instruments (NI), with saving its characteristics. This paper proposes a new method of LS filtering which separates HS and NI simultaneously. It focuses on the application of least mean squares (LMS) algorithm with adaptive noise cancelling (ANC) technique. The second step of the new method is to modulate the reference input r1(n) of LMS-ANC to acquiesce combining HS and NI signals. The obtained signal is removed from primary signal (original lung sound recording-LS). The original signal is recorded from subjects and derived HS from it and it is modified by a band pass filter. NI is simulated by generating approximately periodic white gaussian noise (WGN) signal. The LMS-ANC designed algorithm is controlled in order to determine the optimum values of the order L and the coefficient convergence μ. The output results are measured using power special density (PSD), which has shown the effectiveness of our suggested method. The result also has shown visual difference PSD (to) normal and abnormal LS recording. The results show that the method is a good technique for heart sound and noise reduction from lung sounds recordings simultaneously with saving LS characteristics.

基于K-means和Random Forest的WiFi室内定位方法

为了减小室内环境因素对室内WiFi定位的影响,降低定位成本,提高定位精度以及扩大定位区域,通过对室内定位系统和机器学习算法的讨论,提出了一种基于K-means和Random Forest融合的WiFi室内定位算法。针对室内WiFi信号强度分布的特点,该算法通过K-means聚类改进算法对数据进行初始分类,然后使用Random Forest对初始分类结果进行二次分类。实验结果表明,该定位算法的定位精度在2米以内的概率为89.1%,达到预期的定位效果,同时对缺失值数据具有较好的适应能力。

多尺度迁移学习的轴承故障诊断

针对实际采煤机轴承故障诊断中存在变工况特征提取困难,故障训练样本不足等问题,结合当今流行的迁移学习的方法,提出了一种多尺度迁移学习的轴承诊断方法。首先通过经验模式分解(Empirical Mode Decomposition,EMD)从振动信号中分解成不同频率的本征模态函数(Intrinsic Mode Function,IMF);其次将得到的不同频率的IMF与卷积神经网络中不同尺寸卷积核提取到的丰富特征互补构建多尺度特征融合;采用联合最大平均差异(Joint Maximum Mean Discrep⁃ancy,JMMD)特征迁移的方法使源域与目标域联合分布差异最小化,然后通过多尺度融合模型进行分类识别;最后在凯斯西储大学轴承数据集和江南大学数据集对该方法进行了验证。实验结果证明该模型在两种不同工况和型号的轴承数据集中均取得较高的准确率,表现出模型良好的泛化能力。

基于CEEMDAN与自相关函数的心音去噪

为有效去除心音信号中的噪声,提出基于CEEMDAN(Complete Ensemble Empirical Mode Decomposition with Adaptive Noise)与自相关函数的心音去噪算法。首先,通过CEEMDAN将含噪的心音信号分解为具有不同尺度特征的IMF(Intrinsic Mode Function)分量;其次,根据噪声与心音的自相关函数性质不同,界定IMF分量的信噪分界点;最后,对以噪声为主的IMF分量进行均值滤波,并将其与以心音为主的IMF分量重构得到去噪后信号。实验表明,在不同的噪声水平下,与小波软阈值去噪算法、小波硬阈值去噪算法、CEEMDAN去噪算法相比,所提算法的信噪比最高,均方根误差最小,在去除噪声的同时,可以较好地保留心音信号中的有效信息。

Mean shift algorithm based on fusion model for head tracking

To solve the mismatch between the candidate model and the reference model caused by the time change of the tracked head, a novel mean shift algorithm based on a fusion model is provided. A fusion model is employed to describe the tracked head by sampling the models of the fore-head and the back-head under different situations. Thus the fusion head reference model is represented by the color distribution estimated from both the fore- head and the back-head. The proposed tracking system is efficient and it is easy to realize the goal of continual tracking of the head by using the fusion model. The results show that the new tracker is robust up to a 360°rotation of the head on a cluttered background and the tracking precision is improved.

基于Zernike矩与BoF-SURF特征融合的花粉图像分类识别

针对传统单一花粉图像鉴别特征普遍存在抗噪声干扰能力弱、几何不变性低等问题,提出一种融合Zernike矩全局特征和加速鲁棒性特征包BoF-SURF局部斑点特征的花粉图像分类识别算法。提取花粉图像的Zernike矩描述子以及基于尺度空间梯度信息的SURF特征描述子,使用K-means聚类算法对SURF特征描述子进行特征聚类,构建花粉图像的SURF视觉特征包,并对2种特征进行融合用于花粉图像分类识别。实验结果表明,与传统的花粉图像特征提取算法相比,该算法对花粉尺度和旋转变化具有较好的鲁棒性,在Confocal和Pollenmonitor图像数据集上均获得了较高的识别率。

Fuzzy least brain storm optimization and entropy-based Euclidean distance for multimodal vein-based recognition system

Nowadays, the vein based recognition system becomes an emerging and facilitating biometric technology in the recognition system. Vein recognition exploits the different modalities such as finger, palm and hand image for the person identification. In this work, the fuzzy least brain storm optimization and Euclidean distance(EED) are proposed for the vein based recognition system. Initially, the input image is fed into the region of interest(ROI) extraction which obtains the appropriate image for the subsequent step. Then, features or vein pattern is extracted by the image enlightening, circular averaging filter and holoentropy based thresholding. After the features are obtained, the entropy based Euclidean distance is proposed to fuse the features by the score level fusion with the weight score value. Finally, the optimal matching score is computed iteratively by the newly developed fuzzy least brain storm optimization(FLBSO) algorithm. The novel algorithm is developed by the least mean square(LMS) algorithm and fuzzy brain storm optimization(FBSO). Thus, the experimental results are evaluated and the performance is compared with the existing systems using false acceptance rate(FAR), false rejection rate(FRR) and accuracy. The performance outcome of the proposed algorithm attains the higher accuracy of 89.9% which ensures the better recognition rate.

A New Multi-sensor Data Fusion Algorithm Based on EMD-MMSE

A new multi-sensor data fusion algorithm based on EMD-MMSE was proposed.Empirical mode decomposition(EMD)is used to extract the noise of every time series for estimating the variance of the noise.Then minimum mean square error(MMSE)estimator is used to calculate the weights of the corresponding series.Finally,the fused signal is the weighted addition of all these series.The experiments in lab testified the efficiency of this method.In addition,the comparison in fusion time and fusion results with existing fusion method based on wavelet and average technique shows the advantage of this method greatly.

MRF-Based Multispectral Image Fusion Using an Adaptive Approach Based on Edge-Guided Interpolation

In interpretation of remote sensing images, it is possible that some images which are supplied by different sensors become incomprehensible. For better visual perception of these images, it is essential to operate series of pre-processing and elementary corrections and then operate a series of main processing steps for more precise analysis on the images. There are several approaches for processing which are depended on the type of remote sensing images. The discussed approach in this article, i.e. image fusion, is the use of natural colors of an optical image for adding color to a grayscale satellite image which gives us the ability for better observation of the HR image of OLI sensor of Landsat-8. This process with emphasis on details of fusion technique has previously been performed;however, we are going to apply the concept of the interpolation process. In fact, we see many important software tools such as ENVI and ERDAS as the most famous remote sensing image processing tools have only classical interpolation techniques (such as bi-linear (BL) and bi-cubic/cubic convolution (CC)). Therefore, ENVI- and ERDAS-based researches in image fusion area and even other fusion researches often don’t use new and better interpolators and are mainly concentrated on the fusion algorithm’s details for achieving a better quality, so we only focus on the interpolation impact on fusion quality in Landsat-8 multispectral images. The important feature of this approach is to use a statistical, adaptive, and edge-guided interpolation method for improving the color quality in the images in practice. Numerical simulations show selecting the suitable interpolation techniques in MRF-based images creates better quality than the classical interpolators.

Region-based multisensor image fusion method

Image fusion should consider the priori knowledge of the source images to be fused, such as the characteristics of the images and the goal of image fusion, that is to say, the knowledge about the input data and the application plays a crucial role. This paper is concerned on multiresolution （MR） image fusion. Considering the characteristics of the multisensor （SAR and FLIR etc） and the goal of fusion, which is to achieve one image in possession of the contours feature and the target region feature. It seems more meaningful to combine features rather than pixels. A multisensor image fusion scheme based on K-means duster and steerable pyramid is presented. K-means cluster is used to segment out objects in FLIR images. The steerable pyramid is a multiresolution analysis method, which has a good property to extract contours information at different scales, Comparisons are made with the relevant existing techniques in the literature. The paper concludes with some examples to illustrate the efficiency of the proposed scheme.

健听成年人全方向水平声源定位能力初步研究

目的利用360°全方向24和36声源测试设备,初步探讨健听中青年和健听老年前期-老年人水平声源定位特点。方法选取2021年4月至2021年9月中国人民解放军总医院耳鼻喉科收治的43例健听成年受试者为研究对象,其中男性22例,女性21例;根据年龄分为中青年组(21~49岁)20例和老年前期-老年组(50~72岁)23例。两组分别给予纯音听阈测试、全方向24声源(间隔15°)和36声源(间隔10°)水平声源定位(sound localization,SL)能力评估。给声强度60 dB HL,给声刺激为1 kHz啭音,通过计算均方根误差(root mean square,RMS)、平均绝对误差(mean absolutely error,MAE)等评估受试者的声源定位能力。结果24声源老年前期-老年组MAE、RMS均值高于中青年组的MAE、RMS均值,差异有统计学意义(P<0.05);36声源老年前期-老年组MAE、RMS高于中青年组的MAE、RMS,差异无统计学意义(P>0.05)。24声源和36声源前场MAE和RMS均高于后场的MAE和RMS,前后场的MAE和RMS比较,差异有统计学意义(P<0.01);左右场的MAE、RMS比较,差异无统计学意义(P>0.05)。24声源前后混淆比例为7.73%,36声源前后混淆比例为15.42%;24声源和36声源均为正前方的声源定位准确度最差;老年前期-老年组前后混淆的比例高于中青年组,差异无统计学意义(P>0.05)。结论健听老年前期-老年人全方向24声源和36声源水平定位能力,相比健听中青年组有所下降。左右场的定位准确度高,前后场的定位准确度低,正前方定位准确度最低。全方向水平声源定位能力的测试结果与扬声器数量有关,且反应趋势具有一致性。

基于改进Faster R-CNN的热轧带钢表面缺陷检测

热轧带钢是钢铁行业的重要产品,其表面缺陷是影响产品质量的重要因素。针对传统缺陷检测算法存在的过程繁琐、精度不足和效率低下等问题,提出一种基于改进更快速区域卷积神经网络(faster region-based convolutional neural network,Faster R-CNN)的检测算法,实现对热轧带钢表面缺陷的高效、高精度检测。首先,采用特征相加的方法对底层细节特征和高层语义特征进行融合;然后,采用精准的感兴趣区域池化(precise region of interest pooling,Precise ROI Pooling)获取固定大小的特征向量,避免特征出现位置偏差;最后,利用均值偏移聚类算法对带钢数据集进行聚类,获得适用于热轧带钢表面缺陷检测的先验框尺寸。实验结果表明,所提算法在热轧带钢表面缺陷检测数据集上的平均精度均值达到了85.34%,检测速度为23.5帧/s,且鲁棒性良好,满足实际的工业检测需求。

柔性浅埋物的声-振智能探测

提出一种基于目标检测算法的柔性浅埋物的声-振智能探测方法,将声波激励、激光散斑干涉测振和目标检测算法有机结合,用以柔性浅埋物的大范围快速探测。在论述YOLO系列目标检测算法原理的基础上,选择并优化柔性浅埋物的智能探测网络模型;然后,搭建声-光融合智能探测系统,构建不同柔性浅埋物的激光散斑干涉条纹图数据集;最后,对数据集进行训练和测试,验证该算法用于干涉条纹图识别的可行性。实验结果表明:在给定实验条件下,柔性浅埋物智能探测网络模型的精确率为98.39%,召回率为84.72%,平均识别精度为99.66%。该声-振智能探测方法可以在给定实验环境下对多种柔性浅埋物的激光散斑干涉条纹图进行智能识别,适用于浅层地下柔性掩埋物的大面积快速探测。

基于电弧声信号的窄间隙脉冲熔化极气体保护焊侧壁熔合状态在线识别

为在焊接过程中实时了解焊缝内部的焊接状况,构建了电弧声信号实时采集系统。在焊枪摆动中心处于不同位置的情况下,进行了电弧声信号特征与侧壁熔合状态的相关性分析。分别从时域与频域中提取了与侧壁熔合状态相关性较强的电弧声特征。为进一步提高熔合状态预测的有效性,采用电弧声特征参量构建了支持向量回归的侧壁熔合状态识别模型。为减小不良特征对识别模型的影响,显著提高模型的识别精度,采用遗传算法进行了参数寻优。参数寻优后模型的总体识别率达93.33%,实现了窄间隙侧壁熔合状态的有效识别。

改进YOLOv5s的桥梁表观病害检测方法

针对已有目标检测方法在混凝土桥梁表观病害检测的应用中识别精度低且伴随较多误检和漏检的问题,提出了一种改进的YOLOv5s桥梁表观病害检测方法。针对目前桥梁表观病害特征成分较复杂的问题,为了更有效地利用不同尺度的缺陷特征,在主干网中添加修改后的空间金字塔池化模块,提高了整体网络对缺陷特征信息的获取能力,同时减少了运算工作量;针对由病害图像中不同缺陷特征交叉分布导致的误检率、漏检率高的问题,在YOLOv5s网络中加入轻量化注意力模块;针对桥梁缺陷尺寸差异大、分类困难和数据集小而导致的边界回归不匹配的问题,采用考虑了向量角度的损失函数。实验证明,改进后的YOLOv5s检测器在桥梁表观病害目标检测识别任务中能够有效提高精度、降低误检率和漏检率。

基于高斯滤波与均值聚类的异质多源传感器数据加权融合

异质多源传感器之间工作频率存在差异,导致数据之间的一致性较差,加权融合后的观测误差较大,因此提出基于高斯滤波与均值聚类的异质多源传感器数据加权融合方法。采用高斯滤波对异质多源传感器数据空间单元格进行划分,建立基于单元格的最佳连通域,保留传感器内部数据,完成传感器数据的高斯滤波平滑处理。引入均值聚类对异质多源传感器数据进行一致性处理。通过免疫粒子群搜索最优权重和参数,利用最优权重和参数完成异质多源传感器数据加权融合。仿真结果表明,所提方法能够降低融合后传感器数据的观测误差与均方误差,观测误差与均方误差最小值均为0.002。因此,说明所提方法提高了融合后异质多源传感器数据的可利用性。

基于谱-时信息融合的机械设备异常声音检测

针对机械设备声音信号的特点提出了一种基于谱-时信息融合的机械设备异常声音检测方法(STvec-MFN),结合1DCNN网络时序特征和对数梅尔频谱的频谱特征,利用频-时联合注意力模块ST-JAM聚合频谱和时序特征,建立MobileFaceNet(MFN)模型完成机械设备故障检测,并在工业数据集MIMII和ToyADMOS上进行验证。实验表明,STvec-MFN的检测性能AUC和mAUC分别达到88.80%和81.33%,比目前性能优异的Glow算法提升4.2%和15.65%。

数字媒体中三维动画与环绕声技术的融合研究

环绕声技术的引入显著增强了三维动画的视听同步性和空间感,深化了情感表达,并为创新叙事手法提供了可能。这种融合不仅提升了动画作品的艺术表现力和观众的沉浸感,还为数字媒体的发展开辟了新路径。基于此,首先介绍三维动画技术和环绕声技术的概念,其次分析三维动画与环绕声技术的融合实践,最后分析融合过程中的问题与挑战。