基于流声耦合法的超低比转数离心泵空化特性研究

为研究离心泵不同空化状态下噪声特性的变化规律,以及空化的发展对水动力噪声的影响,首先以一台超低比转数离心泵为研究对象,搭建闭式试验台,基于泵产品测试系统及数据采集系统建立了离心泵空化噪声的试验测试系统,实现了泵性能参数和内场噪声信号的同步采集。其次,分别应用不同空化模型对模型泵空化性能曲线进行预测,并与试验值进行对比,选择合适的空化模型。在此基础上将整个空化过程划分为未空化阶段、空化初生阶段、特征空化阶段及严重空化阶段,结合声学边界元法将流场信息转化为声场信息,并通过比较各空化阶段噪声预测值与试验值相对误差,发现模拟信号与实际信号吻合度较高,充分验证了预测方法的可行性。最后,基于流声耦合法研究空化对内部声场的影响。研究发现:针对超低比转数离心泵空化,Zwart模型比Kunz模型具有更好的适用性。内场噪声信号随空化的发展变化规律比较复杂。在中低频段,由于空化对动静干涉的抑制作用,使得叶频及其倍频特征值离散分量声压级随空化的发展呈现逐渐下降趋势,而轴频分量呈现增大趋势;而高频宽频噪声随空化系数的降低呈现先缓慢减小、然后急剧上升的规律,逐渐将高频特征值分量淹没在宽频带中,高频段声压级的增高造成总声压级的升高。     

基于尺度噪声水平估计的双树复数小波变换图像去噪方法

利用噪声与图像双树复数小波变换各层系数的关系,提出基于尺度噪声水平估计的双树复数小波变换图像去噪方法并进行了试验.结果表明,该估计方法与图像噪声水平近似线性相关,能很好地反映各层系数的噪声水平.双变量收缩函数阈值去噪法采用该估计比采用全局噪声水平估计去噪效果更好,平均结构相似度明显提高.     

基于移动互联的农产品二维码溯源系统设计

【目的】提出一种基于移动互联的农产品二维码(QR码)溯源系统。【方法】研究该系统的逻辑和物理结构,分析里德-索洛蒙(RS码)纠错编码原理及二维码编码算法。采用压缩感知(Compressed sensing,CS)算法预处理受污图像,对比传统的Gaussian、Disk和Log去噪方法,研究二维码数据容量与纠错的关系,研究扫描像素、受污位置和可识别图像的联系,确定手机摄像头参数。【结果】手机扫描最低像素为200万。RS编码信噪比为10.7 d Bm时,CS误码率为0.040 1,低于Log法的0.042 5;RS编码信噪比为11.7 d Bm时,CS误码率为0.011 3,低于Gaussian法的0.014 7。CS在多种噪声处理中的最大编码信噪比均大于10 d Bm。噪声掩盖区域对位置区影响最大,噪声在位置区和编码区的解码平均正确率分别为87.68%和91.24%。【结论】该系统实现了对象信息的完整性、可追溯性,解决了农产品种植、加工、流通、销售各个环节信息的滞后问题。     

基于高光谱成像技术识别水稻纹枯病

【目的】利用高光谱成像技术对水稻纹枯病进行早期的快速无损识别,结合判别分析方法建立相应的鉴别模型。【方法】以健康和感染纹枯病的水稻幼苗为研究对象,采集叶片和冠层各180个样本的380~1 030 nm波段的360条高光谱图像,剔除明显噪声部分后,以440~943 nm波段作为水稻样本的光谱范围,分别用不同的方法预处理获得水稻叶片的光谱曲线。采用偏最小二乘–判别分析(PLS-DA)对不同预处理的光谱建模。采用MNF算法对冠层的原始光谱数据进行特征信息提取,并基于特征信息建立线性判别分析(LDA)模型和误差反向传播神经网络(BPNN)判别模型。【结果】标准正态变量变换(SNV)预处理后建立的PLS-DA模型的预测集判别正确率最高,为92.1%。基于特征信息的LAD和BPNN模型的判别结果优于基于全波段的PLS-DA判别模型。基于最小噪声分离变换特征信息提取的BPNN模型取得了最优效果,建模集和预测集正确率分别达99.1%和98.4%。【结论】采用高光谱成像技术对水稻纹枯病生理特征进行无损鉴别是可行的,本研究为水稻纹枯病的识别提供了一种新方法。     

输气管道站场调压阀噪声的产生机理

针对输气管道分输站场存在的噪声超标问题,以忠武线武汉东站调压阀噪声为研究对象,利用气体动力学原理对噪声产生的过程进行分析,采用流体动力学模拟技术对噪声源内部流场进行模拟.通过理论分析和流场模拟,得到调压阀噪声产生的机理为机械振动噪声和流体动力学噪声,其中前者为调压阀的主要噪声.研究得到噪声与调压阀开度的关系：在一定的开度范围内,噪声值随着开度的增大而增大.研究结果可为输气管道站场的降噪技术研究提供指导.     

环境噪音对养生的影响

在广泛文献检索基础上,对噪音的分类、生理效应及危害(工业噪音的危害、建筑噪音的危害、社会生活噪音的危害、交通噪音的危害等)、降噪技术(改良技术设备、消音降噪、增强对噪音的抵抗力和适应能力等)进行了综述,为全民养生保健提供科学资料。     

Use of clomipramine in treatment of obsessive-compulsive disorder, separation anxiety and noise phobia in dogs: a preliminary, clinical study

OBJECTIVE: To evaluate the efficacy and tolerance of a treatment protocol for obsessive-compulsive disorder, separation anxiety and noise phobia in dogs. DESIGN: A study was undertaken to assess clinical responses in 24 dogs diagnosed with one or more of three behavioural disorders stated above to a treatment regimen that included clomipramine and behaviour modification. PROCEDURE: A detailed behavioural and clinical history was obtained for each dog. Obsessive-compulsive disorder was diagnosed in nine cases: primary presenting complaints were tail-chasing, shadow-chasing, circling and chewing; one case was diagnosed with concurrent separation anxiety. Separation anxiety was diagnosed in 14 cases: presenting complaints included destruction, vocalisation and escaping in the absence of the owner; four cases also exhibited noise phobia. The study also included one dog diagnosed with noise phobia only and another with inappropriate fear responses. Clomipramine was administered orally twice daily. The starting dose was 1 to 2 mg/kg bodyweight. The dose was increased incrementally to a maximum of 4 mg/kg if needed. A behaviour modification program was designed and the owner instructed on its implementation. Dogs continued medication for at least 1 month after clinical signs disappeared or were acceptably reduced, then withdrawal of medication was attempted by decreasing drug dosage at weekly intervals while behaviour modification continued. RESULTS: The presenting clinical sign was largely improved or disappeared in 16 dogs, 5 demonstrated slight to moderate improvement and the behaviour was unchanged in 3. Clomipramine withdrawal was attempted in nine cases: this was successful in five. CONCLUSION: Clomipramine was effective and well-tolerated in controlling signs of obsessive-compulsive disorder and/or separation anxiety and/or noise phobia in 16 of 24 assessable cases, when used in combination with behaviour modification, and improvement in clinical signs was noted in 5 others.     

Prediction of watermelon quality based on vibration spectrum

Judging watermelon quality based on its apparent properties such as size or skin color is difficult. A non-destructive method is employed here, based on vibrational response spectrum, to determine the quality indices of watermelon (Charleston gray). The responses of samples to vibration excitation were recorded by laser Doppler vibrometry (LDV). The phase shift between input and output signals were extracted over a wide frequency range. The total soluble solids (TSS), titratable acidity (TA) and TSS/TA ratio also measured as watermelon quality characters. Stepwise multiple linear regression (SMLR) as well as partial least square regression (PLS) was applied to extracted vibration spectrums to construct prediction models of watermelon quality. The results showed that performance of SMLR models were better than PLS. The determination coefficients (R²) of SMLR validation models were 0.9976, 0.9985 and 0.9542 for TSS, TA and TSS/TA respectively. It is likely that reduction of cell wall materials to soluble solids during ripening process changes viscoelastic properties of watermelon reflected by vibrational response. This study demonstrated the feasibility of mentioned method for predicting the quality of watermelons in an industrial grading system.     

噪声环境下木材纹理分类的研究

为了实现木材分类识别的自动化,应用灰度共生矩阵建立了木材纹理的参数体系,并进行了分类研究。首先在无噪声的环境下提取了木材的共生矩阵纹理原始特征参数,并对其进行特征选择,进而建立了木材纹理参数体系。对该参数体系进行噪声适应性测试的实验结果表明,无噪声情况下样本识别率为87.50%;0.2%￣1.0%椒盐噪声环境下样本识别率范围为87.00%￣88.00%。表明该参数体系具有良好的抗击噪声能力和一定的工程实用价值。