A wide wavelength range tunable guided-mode resonance filter (GMRF) with high peak efficiencies, narrow linewidths, and low sidebands is experimentally demonstrated. The resonance wavelength can be tuned under TM polarized light illumination by rotating the angle of incidence. The GMRF composed of a one-dimensional grating layer and two waveguide layers on a glass substrate is designed with rigorous coupled-wave analysis. The grating structure of the GMRF is patterned by interference lithography with two ultraviolet laser beams. The reflection colours of fabricated GMRF can be shifted from blue to red by rotating the angle of incidence. The measured full width at half-maximums of the reflection peaks located at 450.6, 500.9, 551.0, 601.1, and 651.3?nm are 3.1, 3.9, 4.3, 3.8, and 4.1?nm, respectively. The corresponding sidebands of measured spectra are below 0.2. Compared with previous experimental studies on tunable structures, the narrow linewidths and low sidebands of the proposed GMRF are remarkably improved. 相似文献
In the process of manufacturing, a large amount of manufacturing data is produced by different departments and different domain. In order to realise data sharing and linkage among supply chains, master data management method has been used. Through master data management, the key data can be shared and distributed uniformly. However, since these cross-domain data form a data network through the association of master data, how to evaluate the effectiveness and rationality of this network becomes the major issue in the proposed method. In this paper, a model of the master data network is built based on the theory of set pair analysis. In order to verify the master data, an evaluation method for the network is proposed. Finally, a case was presented to validate this network model and evaluation method.
本文提出了一种对分布式光纤声传感器的入侵事件分类方法.该方式采用小波包去噪方式对原始信号进行去噪;将去噪后的原始信号进行小波变换,得到原始信号的小波时频图;构建双输入型的卷积神经网络,将滤波后的原始一维信号直接输入到一个三层的1-D CNN中、滤波后得到的二维小波时频图直接输入到一个两层的2-D CNN中;将两种CNN输出的特征输入到支持向量机(SVM),使用SVM对事件进行分类.本文中主要识别3种振动事件:汽车通过、挖掘机挖掘和破路机工作.实验结果表明,所提方式对实际环境中3种振动事件的识别准确率平均可以达到96％,并且识别时间仅为0.61 s. 相似文献
Wireless multimedia sensor networks (WMSN), with self-organizing and high fault tolerant characteristics, have achieved great advantages in target tracking region. However, the capabilities of these tiny devices are limited by their battery power, storage capacity, computational ability and communication bandwidth. In this paper, hybrid wireless multimedia sensors networks composed of acoustic and image sensors are proposed for target tracking. When the target appears in the detection area, it may change the environment parameters nearby, so acoustic sensors are used to gather target signal firstly. Then, a target location method is executed based on the strength of the received acoustic signal. Furthermore, to achieve energy-efficient target tracking with high reliability and robust, image sensors are used as supplements to the acoustic sensors. This approach also reduces the power consumption communication burden of the whole networks. In order to decrease the number of active nodes, Gauss Markov mobility model is also adopted to predict the target trajectory and minimize the tracking region with considering of vehicular kinematics. Simulation results verify that, compared with other algorithms, our scheme can reduce the energy consumption and improve tracking accuracy. 相似文献
Stagnant water on roads has always been a major cause of traffic jams and accidents. Traditional urban waterlogging monitoring and warning system is mainly based on a large amount of historical data and predictive network, which has low accuracy and weak generalization ability. Considering the deep neural network algorithms have demonstrated strong capabilities in computer vision tasks such as object detection, we aim to apply them to road stagnant water detection. In this paper, a novel automatic stagnant water localization method under weak supervision based on visual image is proposed. First, the template matching method is applied to extract road information from the traffic image. Then, due to the complexity of data annotation, we locate stagnant water in image based on Class Activation Maps (CAM) mechanism, which is a weakly supervised method. The detection model consists of the ResNet-18 and the Grad-CAM++ mechanism. Finally, based on the heat map and template, we set a suitable threshold to segment stagnant water area in image. In the experiments, the precision and recall for road stagnant water classification by the proposed model are 99.39% and 99.60%, while the Intersection over Union (IoU) for stagnant water area segmentation is up to 63%. These show that our method is effective for road stagnant water localization.
