A novel fusion paradigm for multi-channel image denoising

Multi-channel and single-channel image denoising are on two important development fronts. Integrating multi-channel and single-channel image denoisers for further improvement is a valuable research direction. A natural assumption is that using more useful information is helpful to the output results. In this paper, a novel multi-channel and single-channel fusion paradigm (MSF) is proposed. The proposed MSF works by fusing the estimates of a multi-channel image denoiser and a single-channel image denoiser. The performance of recent multi-channel image denoising methods involved in the proposed MSF can be further improved at low additional time-consuming cost. Specifically, the validity principle of the proposed MSF is that the fused single-channel image denoiser can produce auxiliary estimate for the involved multi-channel image denoiser in a designed underdetermined transform domain. Based on the underdetermined transformation, we create a corresponding orthogonal transformation for fusion and better restore the multi-channel images. The quantitative and visual comparison results demonstrate that the proposed MSF can be effectively applied to several state-of-the-art multi-channel image denoising methods.     

数据驱动的机床等待过程节能方法研究

为了降低机床等待过程中的能耗，提出了一种实时数据驱动的机床等待时间预测与节能控制方法。首先，建立了射频识别驱动的生产进度评估方法，并以生产进度数据作为输入，构建了基于堆栈降噪自编码的机床等待时间预测模型；其次，依据预测的机床等待时间，提出了机床状态切换方法，以降低机床能耗；最后，通过一个电梯零部件制造车间的案例分析，表明该方法的预测误差仅为4.1%，同时将机床等待过程能耗降低了57%，实现了制造车间的节能减排。     

Classification and regression models of audio and vibration signals for machine state monitoring in precision machining systems

We present a data-driven method for monitoring machine status in manufacturing processes. Audio and vibration data from precision machining are used for inference in two operating scenarios: (a) variable machine health states (anomaly detection); and (b) settings of machine operation (state estimation). Audio and vibration signals are first processed through Fast Fourier Transform and Principal Component Analysis to extract transformed and informative features. These features are then used in the training of classification and regression models for machine state monitoring. Specifically, three classifiers (K-nearest neighbors, convolutional neural networks and support vector machines) and two regressors (support vector regression and neural network regression) were explored, in terms of their accuracy in machine state prediction. It is shown that the audio and vibration signals are sufficiently rich in information about the machine that 100% state classification accuracy could be accomplished. Data fusion was also explored, showing overall superior accuracy of data-driven regression models.     

基于子波变换的涡街流量传感器信号分析

长期以来 ,如何提取潜在噪声下的涡街流量信号一直是个问题。流体流速脉动 ,局部阻力 ,随机振动———所有这些因素都给解决这一问题带来难度。文章应用子波变换消噪理论 ,从软件滤波的角度分析了强噪声干扰下的涡街流量信号 ,并提出了单支重构计数方法。分析结果表明 ,这种方法对低流速流量计量效果很好 ,能够有效地扩展量程下限     

线性调频相干激光雷达的信号处理

分析了相干激光雷达线性调频脉冲的产生及其信号处理过程，讨论了该雷达信号体制的处理方法和结果。计算了采用匹配滤波和加权处理结果，对于1MHz／μs的调制速率信号的结果表明，激光相干雷达的测速和测距精度分别可达0.04m／s和0．57m.     

脉冲序列的快速搜索法及可信度分析

对于周期脉冲序列的信号分选识别，可采用序列搜索的时域方法[1，2]。本文将大周期内完全重复的脉冲序列分解为多个“单周期信号”．并把它们均定义为周期性信号．同时，提出了序列搜索的一种软件实现方法及密集视频脉冲信号分选的变步长、信步长快速搜索法．在此基础上，本文针对序列搜索分选法的可信度计算公式进行了推导和分析，证实了这种方法的可行性和有效性．     

超低频信号的记录与回放

介绍一种经济实用的用普通磁带记录与回放超低频信号的方法，该方法在地质，建筑工程和医疗等行业中有一定的实用价值。     

Driver detection and recognition of lineside signals and signs at different approach speeds

A study was carried out using simulation to investigate driver responses to lineside signals and signs at various approach speeds. The objectives of the study were: (1) to find out whether train speed would significantly affect signal/sign reading; (2) to examine at which point certain types of signs or signals could be detected or recognised, and (3) to determine a speed cut-off level above which certain types of signs or signals are no longer recognisable or detectable. Fifty-seven train drivers from 12 Train Operating Companies in the UK participated in the trials. Twenty different types of lineside signs and ten types of signals were tested under six different approach speeds ranging from 100 to 350 km/h (62–218 mph). Driver performance measures were ‘time remaining to the signal/sign’ at the point of detection or recognition, and reading error rate. The results showed a significant influence of train speed on driver responses to lineside signals/signs and demonstrated a non-linear relationship between driver responses to signals/signs and approach speed. This has been used to estimate a maximum approach speed limit within which a specific signal or sign can be correctly detected or recognised. The findings and implications of the study are discussed in the paper.     

带限信号高频重建的一种新的正则外推算法

本文提出生种新的带限信号的外推方法，该方法能够很好地抑制噪声干扰，且计算复杂性较低，本文给出算法，证明了收敛性，最后给出了误差计算机实验结果。     

A damage identification technique for CF/EP composite laminates using distributed piezoelectric transducers

In this study, a damage identification approach was developed for carbon fibre/epoxy composite laminates with localized internal delamination. Propagation of the Lamb wave in laminates and its interaction with the delamination were examined. The fundamental symmetric Lamb wave mode, S₀, and the lowest order shear wave mode, S₀^′, were chosen to predict damage location. A real-time active diagnosis system was therefore established. This technique uses distributed piezoelectric transducers to generate and monitor the ultrasonic Lamb wave with narrowband frequency. The two-way switches were employed to minimize the number of transducers. A signal-processing scheme based on the time–frequency spectrographic analysis was utilised to extract useful diagnostic information. Also, an optimal identification method was applied on damage searching procedure to reduce errors and obtain the diagnostic results promptly. Experiments were conducted on [0/−45/45/90]_s CF/EP laminates to verify this diagnosis system. The results obtained show that satisfactory detection accuracy could be achieved.