This article proposes a novel collaborative-flip synchronized switch harvesting on capacitors (CF-SSHCs) rectifier and multioutput synchronous dc-dc converters with shared capacitors. Compared to the traditional SSHC, our CF-SSHC rectifier can increase the number of flipping phases, potentially enhancing the flipping efficiency and output power under specific conditions where C FLY is close to C_P. The synchronous dc-dc converters reuse the flying capacitors to achieve a high maximum output power improving rate (MOPIR) over a limited input power range and provide multiple outputs. This work achieves an advanced number of flipping phases in capacitor-based rectifier interface technology and explores multiple-input multiple-output configurations, evaluating the system's performance under periodic and shock conditions for the first time. The system's adaptability to various piezoelectric transducer (PT) array configurations is validated, highlighting its potential for Internet of Things (IoT) networks. The design is fabricated in standard 0.18- μ m CMOS. Measurement results demonstrate that the voltage flipping efficiency of up to 83% is achieved. Compared with full-bridge rectifier (FBR), the MOPIR can be increased to 5.06 × and 4.78 × under off-resonance and on-resonance excitation, respectively. It can also achieve a 2.14 × power enhancement under shock excitation. Additionally, when the input power P INFBR is in the range of 1.42-28.4 μ W, the MOPIR of the proposed system is always greater than 4.

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Belt conveyor is considered as a momentous component of modern coal mining transportation system, and thus it is an essential task to diagnose and monitor the damage of belt in real time and accurately. Based on the deep learning algorithm, this present study proposes a method of conveyor belt damage detection based on ADCN (Adaptive Deep Convolutional Network). A deep convolution network with unique adaptability is built to extract the different scale features of visible light image of conveyor belt damage, and the target is classified and located in the form of anchor boxes. A data set with data diversity is collected according to the actual working conditions of the conveyor belt. After training and regression, the ADCN model can perfectly capture and classify the damaged target in the video of the conveyor running. Compared with the SVM based method, the method based on ADCN can better meet the real-time and reliability requirements of belt damage detection, and it has the positioning function which SVM does not have.

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Conveyor belt tear detection is a very important part of coal mine safety production. In this paper, a new method of detecting conveyor belt damage named audio-visual fusion (AVF) detection method is proposed. The AVF method uses both a visible light CCD and a microphone array to collect images and sounds of the conveyor belt in different running states. By processing and analyzing the collected images and sounds, the image and sound features of normal, tear and scratch can be extracted respectively. Then the extracted features of images and sounds are fused and classified by machine learning algorithm. The results show that the accuracy of AVF method for conveyor belt scratch is 93.66%, and the accuracy of longitudinal tear is higher than 96.23%. Compared with existing methods AVF method overcomes the limitation of visual detection condition, and is more accurate and reliable for conveyor belt tear detection.

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When sapphire crystal is prepared with Kyropoulos method, the necking-down growth process is a key stage. Sapphire growth defect is a big problem in this stage. However, diagnosing growth defects is subject to the interference of workers subjectivity and accuracy always goes down. To address the problem, a novel defect diagnosis method is proposed for necking-down growth process in this paper. Industrial CCD sensors replace eyes of skilled workers to observe in this method. A new Defect-Diagnosing Siamese network (DDSN) is used in this method. We use Siamese architecture to learn similarity through pairs of images. We use the deep separable convolution (DSC) into the DDSN to optimize running speed and model size. In experiment, dataset is acquired by industrial CCD sensors in the necking-down growth process. The accuracy of defect diagnosis can reach up to 94.5%. The method significantly improves the traditional way.@en

The seeding technique is critical to the quality of the sapphire single crystal. The core of the seeding technique is the identification of seeding state. The MIVD method is proposed in the paper to detect the optimal seeding state. Compared with the OCS method, the MIVD method detects the characteristics of spoke pattern before a single point is formed on melt surface. Based on the similarity and periodicity of the melt motion, the optimal seeding state is detected by Fréchet distance. In the experiment, the MIVD method was compared with the OCS method and the artificial seeding method in terms of seeding efficiency and crystal quality. The MIVD method improves the quality of the sapphire crystal, at same time, it ensures seeding efficiency. The MIVD method is more suitable for practical production.

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The first and second order sensitivity analysis of the eigenvalue problem of generalised, nonsymmetric matrices using perturbation theory is developed. These results are then applied to sensitivity analysis of wave propagation in structures modelled using the wave and finite element (WFE) method. Three formulations of the WFE eigenvalue problem are considered: the transfer matrix method, the projection method and Zhong’s method. The sensitivities with respect to system parameters of wavenumbers and wave mode shapes are derived. Expressions for the group velocity are presented. Numerical results for a thin beam, a foam core panel and a cross-laminated timber panel are used to demonstrate the proposed approach. It is shown that sensitivities can be calculated at negligible computational cost.@en

A novel approach based on infrared spectrum analysis for early-warning of longitudinal tearing of the conveyor belt was proposed in the paper. Unlike most existing methods, the proposed method monitors the change process of the infrared radiation field of the longitudinal tearing through the infrared thermal imaging technology, and judges whether there is the risk of the longitudinal tearing of the conveyor belt through the frequency-domain characteristic coefficient T of the infrared radiation field. Experimental results exhibit that the characteristic coefficient T can quantitatively describe the change characteristics of infrared radiation field during the tearing process of conveyor belt. When the conveyor belt is complete penetration, the T value fluctuates violently from 0.6 to 1.6. This characteristic can be used as the precursor information of the tearing process, which broadens the train of thought for identification and early warning of conveyor belt longitudinal tearing.

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During the operation of the belt conveyor, measuring speed of the belt conveyor is vital to the safe and efficient operation. In the existing measuring speed system, the measurement instrument is required contacting with the surface of the belt. The contact measurement method cannot avoid the occurrence of measuring error caused by slipping on the contact surface and wear of the measurement instrument. In order to solve the problems mentioned above, a new contactless measuring speed system is proposed in this paper. The system uses the CCD camera to capture the side image of belt. The speed of belt conveyor can be obtained by measuring the regularity of image texture. The proposed measuring system can meet the requirement of measuring speed in long running process of belt conveyor. Experimental results show that the measuring accuracy indicators can reach RMSE of 0.018 m/s and MAE of 0.010 m/s.

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