Highly efficient polarization-independent grating coupler used in silica-based hybrid photodetector integration

A highly efficient polarization-independent output grating coupler was optimized and designed based on silicon-on-insulator used for silica-based hybrid photodetector integration in an arrayed waveguide grating demodulation-integrated microsystem. The finite-difference time-domain (FDTD) method optimizes coupling efficiency by enabling the design of the grating period, duty cycle, etch depth, grating length, and polarization-dependent loss (PDL). The output coupling efficiencies of both the transverse electric (TE) and transverse magnetic (TM) modes are higher than 60% at 1517 to 1605 nm and similar to 67% at around 1550 nm. The designed grating exhibits the desired property at the 3-dB bandwidth of 200 nm from 1450 to 1650 nm and a PDL \u3c0.5 dB of 110 nm from 1513 to 1623 nm. The power absorption efficiency at 1550 nm for TE and TM modes reaches 78% and 70%, respectively. Both the power absorption efficiency of TE mode and that of TM mode are over 70% in a broad band of 1491 to 1550 nm

Preliminary investigation of an SOI-based arrayed waveguide grating demodulation integration microsystem

An arrayed waveguide grating (AWG) demodulation integration microsystem is investigated in this study. The system consists of a C-band on-chip LED, a 2 × 2 silicon nanowire-based coupler, a fiber Bragg grating (FBG) array, a 1 × 8 AWG, and a photoelectric detector array. The coupler and AWG are made from silicon-on-insulator wafers using electron beam exposure and response-coupled plasma technology. Experimental results show that the excess loss in the MMI coupler with a footprint of 6 × 100 μm(2) is 0.5423 dB. The 1 × 8 AWG with a footprint of 267 × 381 μm(2) and a waveguide width of 0.4 μm exhibits a central channel loss of −3.18 dB, insertion loss non-uniformity of −1.34 dB, and crosstalk level of −23.1 dB. The entire system is preliminarily tested. Wavelength measurement precision is observed to reach 0.001 nm. The wavelength sensitivity of each FBG is between 0.04 and 0.06 nm/dB

Fully Photonic Integrated Wearable Optical Interrogator

Wearable technology constitutes a pioneering and leading innovation and a market development platform worldwide for technologies worn close to the body. Wearable optical fiber sensors have the most value for advanced multiparameter sensing in digital health monitoring systems. We demonstrated the first example of a fully integrated optical interrogator. By integrating all the optical components on a silicon photonic chip, we realized a stable, miniaturized and low-cost optical interrogator for the continuous, dynamic, and long-term acquisition of human physiological signals. The interrogator was integrated in a wristband, enabling the detection of body temperature and heart sounds. Our study paves the way for the development of watch-sized integrated wearable optical interrogators with potential applications in health monitoring and can be directly exploited for the customized design of ultraminiaturized optical interrogator systems.H.L. acknowledges the support from the Tianjin Talent Special Support Program. J.D.P.G. acknowledges the support from the Serra Hunter Program, the ICREA Academia Program, and the Tianjin Distinguished University Professor Program. This work was supported by the National Natural Science Foundation of China (no. 61675154), the Tianjin Key Research and Development Program (no. 19YFZCSY00180), the Tianjin Major Project for Civil-Military Integration of Science and Technology (no. 18ZXJMTG00260), the Tianjin Science and Technology Program (no. 20YDTPJC01380), and the Tianjin Municipal Special Foundation for Key Cultivation of China (no. XB202007)

Research on fault detection of belt conveyor drum based on improved YOLOv5s

At present, the detection efficiency of belt conveyor drum fault detection methods is low, the recognition accuracy is not high, and the feature extraction capability is poor. In order to solve the above problems, a belt conveyor drum fault detection method based on improved YOLOv5s is proposed. A small-sized detection layer has been added to the YOLOv5s network model, making it easier to detect smaller drum faults. The method introduces the convolutional block attention module (CBAM) between the Backbone and Neck to improve the accuracy of target detection. The method introduces efficient channel attention mechanism (ECA) in Neck to enhance feature extraction capabilities for drum faults. The experimental results show the following points. ① On the premise of meeting the real-time detection requirements, the average recognition accuracy of the improved YOLOv5s network model reaches 94.46%, which is 1.65% higher than before the improvement. ② The average accuracy of the improved YOLOv5s network model for detecting drum opening, rubber coating wear, and rubber coating detachment are 95.29%, 96.43%, and 91.65%, respectively, which are 1.56%, 0.89%, and 2.50% higher than before the improvement. A belt conveyor drum fault detection system based on improved YOLOv5s is designed and validated. ① The experimental platform test results show that the average accuracy of the belt conveyor drum fault detection system based on improved YOLOv5s for drum welding, rubber coating wear, and rubber coating detachment detection reach 95.29%, 96.43%, and 91.65%, respectively. The average accuracy of the three types of faults reaches 94.46%, and the detection speed is about 14 frames/s. ② The on-site test results show that the confidence levels for rubber coating wear and rubber coating detachment are 0.92 and 0.97, respectively. The fault type and location of the drum can be accurately identified. This indicates that the improved YOLOv5s-based belt conveyor drum fault detection system is feasible

Adaptive Multi-View Image Mosaic Method for Conveyor Belt Surface Fault Online Detection

In order to improve the accuracy and real-time of image mosaic, realize the multi-view conveyor belt surface fault online detection, and solve the problem of longitudinal tear of conveyor belt, we in this paper propose an adaptive multi-view image mosaic (AMIM) method based on the combination of grayscale and feature. Firstly, the overlapping region of two adjacent images is preliminarily estimated by establishing the overlapping region estimation model, and then the grayscale-based method is used to register the overlapping region. Secondly, the image of interest (IOI) detection algorithm is used to divide the IOI and the non-IOI. Thirdly, only for the IOI, the feature-based partition and block registration method is used to register the images more accurately, the overlapping region is adaptively segmented, the speeded up robust features (SURF) algorithm is used to extract the feature points, and the random sample consensus (RANSAC) algorithm is used to achieve accurate registration. Finally, the improved weighted smoothing algorithm is used to fuse the two adjacent images. The experimental results showed that the registration rate reached 97.67%, and the average time of stitching was less than 500 ms. This method is accurate and fast, and is suitable for conveyor belt surface fault online detection

Research on the energy-saving control strategy of a belt conveyor with variable belt speed based on the material flow rate.

Aiming at solving the problem of high energy consumption in the rated belt speed operation of a belt conveyor system when the material flow rate is reduced, the power consumption of the frequency converter, motor, and belt conveyor is analyzed, a power consumption model of the belt conveyor system is established, the relationship between the power consumption of the belt conveyor system and belt speed is obtained, and a energy-saving control strategy of the belt conveyor with variable belt speed based on the material flow rate is put forward. The energy consumption of the belt conveyor is analyzed for a practical case. Results show that the power consumption model is accurate and the control strategy effectively reduces energy consumption. The model has high application value in coal, ports, power, mine, metallurgy, chemical, and other industries

Hazard source detection of longitudinal tearing of conveyor belt based on deep learning.

Belt tearing is the main safety accident of belt conveyor. The main cause of tearing is the doped bolt and steel in the conveying belt. In this paper, the bolt and steel are identified as the Hazard source of tear. In this paper, bolt and steel are defined as the risk sources of tearing. Effective detection of the source of danger can effectively prevent the occurrence of conveyor belt tearing accidents. Here we use deep learning to detect the hazard source image. We improved on the SSD(Single Shot MultiBox Detector) model. Replace the original backbone network with an improved Shufflenet_V2, and replace the original position loss function with the CIoU loss function. Additionally, it compares this new approach to previous methods. The proposed model has surpassed other state-of-art methods with more than 94% accuracy. In addition, when deployed without GPU acceleration, the detection speed can reach 20fps. It can meet the requirements of real-time detection. The experimental results show that the proposed model can realize the online detection of hazard sources, so as to prevent longitudinal tearing of conveyor belt

Hazard source detection of longitudinal tearing of conveyor belt based on deep learning

Belt tearing is the main safety accident of belt conveyor. The main cause of tearing is the doped bolt and steel in the conveying belt. In this paper, the bolt and steel are identified as the Hazard source of tear. In this paper, bolt and steel are defined as the risk sources of tearing. Effective detection of the source of danger can effectively prevent the occurrence of conveyor belt tearing accidents. Here we use deep learning to detect the hazard source image. We improved on the SSD(Single Shot MultiBox Detector) model. Replace the original backbone network with an improved Shufflenet_V2, and replace the original position loss function with the CIoU loss function. Additionally, it compares this new approach to previous methods. The proposed model has surpassed other state-of-art methods with more than 94% accuracy. In addition, when deployed without GPU acceleration, the detection speed can reach 20fps. It can meet the requirements of real-time detection. The experimental results show that the proposed model can realize the online detection of hazard sources, so as to prevent longitudinal tearing of conveyor belt