Search

A non-contact piezoelectric actuator is proposed. The non-contact power transfer between stator and rotor is realized by pneumatic transmission, characterized by fast response, long life, compact structure, and easy miniaturization and control. The structure of the non-contact piezoelectric actuator is designed and its working principle is elucidated. The equation of the relationship between the output displacements of the non-contact piezoelectric actuator’s micro-displacement amplifying mechanism and the input displacements of piezoelectric stack is deduced, and the simulation analysis method of output displacement of the micro-displacement amplifying mechanism is established. Using the equation and the simulation analysis, the output characteristics of micro-displacement amplifying mechanism for the non-contact piezoelectric actuator and their changes along with the system parameters are investigated. The detailed process of optimal design of the micro-displacement amplifying mechanism is given by means of mathematical statistics. The prototype is made and the performance test is carried out. The correctness of the theoretical calculation and simulation analysis is verified by comparing the experimental values with the theoretical and simulated values of the output displacement of the micro-displacement amplifying mechanism. The results show that the initial angle of bridge structure I has an obvious effect on the output characteristics of the micro-displacement amplifying mechanism in the range of 5°–15°. When the lever’s rod length is 13 mm–15 mm, the bridge structure II’s rod length is 6 mm–7 mm, and the power arm length of bridge structure I’s driving lever is 5 mm–7 mm, the bridge structure II’s rod horizontal projection length is 5 mm–6 mm and the output displacement of the micro-displacement amplifying mechanism is larger. Through the optimal design, it is obtained that the bridge structure I’s initial angle is 8°, the lever’s rod length is 15 mm, the bridge structure II’s rod length is 7 mm, and the power arm length of bridge structure I driving lever is 5 mm, the bridge structure II’s rod horizontal projection length is 6 mm, and the simulated output displacement of the micro-displacement amplifying mechanism is 0.1415 mm. The prototype test reveals that as the input excitation displacement decreases, the error increases, while as the input excitation displacement increases, the error decreases. Specifically, when the input excitation displacement is 0.005 mm, the measured output displacement of the micro-displacement amplifying mechanism is 0.1239 mm, resulting in a 19.8% deviation from the theoretical value and a 12.44% deviation from the simulated value. The research work in this paper enriches the research achievements of non-contact piezoelectric actuators, and also provides a reference for designing small structure and large travel micro-displacement amplifying mechanisms of this type of actuator.

Recommender systems are widely used in e-commerce, movies, music, social media, and other fields because of their personalized recommendation functions. The recommendation algorithm is used to capture user preferences, item characteristics, and the items that users are interested in are recommended to users. Matrix factorization is widely used in collaborative filtering algorithms because of its simplicity and efficiency. However, the simple dot-product method cannot establish a nonlinear relationship between user latent features and item latent features or make full use of their personalized information. The model of a neural network combined with an attention mechanism can effectively establish a nonlinear relationship between the potential features of users and items and improve the recommendation accuracy of the model. However, it is difficult for the general attention mechanism algorithm to solve the problem of attention interaction when the number of features between the users and items is not the same. To solve the above problems, this paper proposes an attention interaction matrix factorization (AIMF) model. The AIMF model adopts a symmetric structure using MLP calculation. This structure can simultaneously extract the nonlinear features of user latent features and item latent features, thus reducing the computation time of the model. In addition, an improved attention algorithm named slide-attention is included in the model. The algorithm uses the sliding query method to obtain the user’s attention to the latent features of the item and solves the interaction problem among different dimensions of the user, and the latent features of the item.

Floquet topological insulators (FTIs) have richer topological properties than static systems. In this work, we designed different domain wall (DW) structures consisting of a Floquet photonic lattice with opposite helical directions. We investigated the existence and types of edge states in three shared coupling structures and the impact of these shared coupling structures on edge states. When two opposite helical lattices share a straight waveguide array coupling, the edge states are localized on the straight waveguide. When two opposite helical lattices share a clockwise (or anticlockwise) helical waveguide array coupling, the DWs consist of zigzag and bearded edges, but the positions of the zigzag and bearded edges of the shared clockwise waveguide array are different from those of the shared anticlockwise waveguide array. The slope and transmission rate of the edge states both vary with the degree of coupling between the shared waveguides. The characteristics of these edge states, such as transmission speed and band gap width, are also affected by the incidence angle, modulation phase factor, and helical radii, and the methods for controlling the edge states in different shared coupling structures are provided. This will help deepen our understanding of how topological structures influence the electronic and photonic properties of materials. This could also lead to combining topology with metasurface-based structured light, which would highlight many novel properties with great application potential for various fields, such as imaging, metrology, communication, quantum information processing, and light–matter interaction.

Rice, a staple food crop worldwide, is pivotal in agricultural productivity and public health. Automatic classification of typical rice pests and diseases is crucial for optimizing rice yield and quality in practical production. However, infrequent occurrences of specific pests and diseases lead to uneven dataset samples and similar early-stage symptoms, posing challenges for effective identification methods. In this study, we employ four image enhancement techniques—flipping, modifying saturation, modifying contrast, and adding blur—to balance dataset samples throughout the classification process. Simultaneously, we enhance the basic RepVGG model by incorporating the ECA attention mechanism within the Block and after the Head, resulting in the proposal of a new classification model, RepVGG_ECA. The model successfully classifies six categories: five types of typical pests and diseases, along with healthy rice plants, achieving a classification accuracy of 97.06%, outperforming ResNet34, ResNeXt50, Shufflenet V2, and the basic RepVGG by 1.85%, 1.18%, 3.39%, and 1.09%, respectively. Furthermore, the ablation study demonstrates that optimal classification results are attained by integrating the ECA attention mechanism after the Head and within the Block of RepVGG. As a result, the classification method presented in this study provides a valuable reference for identifying typical rice pests and diseases.

Micro burner is the fundamental element of a micro energy power system. The performance, output power, and efficiency of the system are directly involved by the combustion stability, efficiency, and temperature distribution of the exterior wall. Owing to the small combustion space of the micro burner and the resident short time of the premixed fuel/air, the fuel is difficult to burn completely, resulting in poor burning efficiency and flame stability. Therefore, the study of micro burner technology is the focus of current research to improve combustion performance. This article introduces the micro power system, micro combustion technology, and combustion status and characteristics, focusing on four kinds of micro combustion technology. The purpose is tantamount to fully understand the current status of micro combustion technology and compare the characteristics of different combustion technologies. For improving output power and efficiency of the power system, the combustion stability and performance are enhanced, which provides theoretical support for the effective realization of micro scale combustion and application.

Online detection of yarn roll’s margin is one of the key issues in textile automation, which is related to the speed and scheduling of bobbin (empty yarn roll) replacement. The actual industrial site is characterized by uneven lighting, restricted shooting angles, diverse yarn colors and cylinder yarn types, and complex backgrounds. Due to the above characteristics, the neural network detection error is large, and the contour detection extraction edge accuracy is low. In this paper, an improved neural network algorithm is proposed, and the improved Yolo algorithm and the contour detection algorithm are integrated. First, the image is entered in the Yolo model to detect each yarn roll and its dimensions; second, the contour and dimensions of each yarn roll are accurately detected based on Yolo; third, the diameter of the yarn rolls detected by Yolo and the contour detection algorithm are fused, and then the length of the yarn rolls and the edges of the yarn rolls are calculated as measurements; finally, in order to completely eliminate the error detection, the yarn consumption speed is used to estimate the residual yarn volume and the measured and estimated values are fused using a Kalman filter. This method overcomes the effects of complex backgrounds and illumination while being applicable to different types of yarn rolls. It is experimentally verified that the average measurement error of the cylinder yarn diameter is less than 8.6 mm, and the measurement error of the cylinder yarn length does not exceed 3 cm.

The structured light projection (SLP) method occupies a crucial position in three-dimensional (3D) imaging technology. Different working wavelengths of structured light can be employed depending on the situation. However, there are few structured lights that can be modulated based on wavelength at present. Therefore, we have comprehensively investigated and designed a Dammann grating (DG) based on metasurface, which can be controlled through multi-beam interference (MBI) to achieve a change of the working wavelength. In this work, we can convert the straight waveguide to the helical waveguide by fine-tuning the related parameters of the incident lights and generate 5 × 5 diffraction spot arrays in the wavelength range of 480–510 nm and 950–1020 nm, respectively. Furthermore, the metasurfaces exhibit good performance. For example, their spread angles can be up to 44° × 44° and they can reach a conversion efficiency of over ≥50% while maintaining a contrast ratio of roughly 40%. Compared with traditional structured light, it can be used in different working wavelengths and has a broader application range in 3D sensing systems.

Dielectric barrier discharge (DBD) has been one of the most promising techniques for flow control, but the practical application needs a large plasma surface. A three-electrode sliding discharge plasma actuator (SDPA) can generate plasma filling the inter-electrode distance. In this study, the performance of an SDPA for practical flight at low pressures is reported. When the pressure decreases, plasma discharge becomes more intensive. Current peaks and power consumption of electrode 1 become higher at low pressure. Sliding discharge is fully developed at 54 kPa, and the plasma morphology resembles plumes. PIV results show vortex structures and an induced wall jet above the actuator surface. A ‘potential-arc-discharge’ is observed at 38 kPa, leading to an inhomogeneous surface temperature distribution and a velocity decrease of the jet.

The automatic detection of the thread roll’s margin is one of the kernel problems in the textile field. As the traditional detection method based on the thread’s tension has the disadvantages of high cost and low reliability, this paper proposes a technology that installs a camera on a mobile robot and uses computer vision to detect the thread roll‘s margin. Before starting, we define a thread roll‘s margin as follows: The difference between the thread roll‘s radius and the bobbin’s radius. Firstly, we capture images of the thread roll‘s end surface. Secondly, we obtain the bobbin’s image coordinates by calculating the image’s convolutions with a Circle Gradient Operator. Thirdly, we fit the thread roll and bobbin’s contours into ellipses, and then delete false detections according to the bobbin’s image coordinates. Finally, we restore every sub-image of the thread roll by a perspective transformation method, and establish the conversion relationship between the actual size and pixel size. The difference value of the two concentric circles’ radii is the thread roll’s margin. However, there are false detections and these errors may be more than 19.4 mm when the margin is small. In order to improve the precision and delete false detections, we use deep learning to detect thread roll and bobbin’s radii and then can calculate the thread roll’s margin. After that, we fuse the two results. However, the deep learning method also has some false detections. As such, in order to eliminate the false detections completely, we estimate the thread roll‘s margin according to thread consumption speed. Lastly, we use a Kalman Filter to fuse the measured value and estimated value; the average error is less than 5.7 mm.

N-vinylpyrrolidone (NVP) has a large molecular structure, so it is difficult to diffuse during holographic recording, especially at low spatial frequencies. We used glycerol to promote the diffusion of NVP, and successfully improved the holographic performance of the photopolymer at low spatial frequencies. As the concentration of glycerol increases, the holographic performance first increases and then remains stable. The optimal concentration of glycerol is 0.21 mol/L. At this concentration, the maximum diffraction efficiency of the photopolymer is 84%, the refractive index modulation is 1.95 × 10−3, and the photosensitive sensitivity is 7.91 × 10−4 cm2/mJ. Compared with the control group, the maximum diffraction efficiency, maximum refractive index modulation and photosensitivity at low spatial frequencies (800 lp/mm) have increased by 11.19 times, 4.69 times and 1.71 times, respectively. Using the optimized photopolymer for transmission holographic recording and reproduction, we have obtained a clear and bright transmission hologram. The photopolymer modified with glycerol is expected to be applied to the fields of holography, diffractive optics, and so on.

With the prosperity of the Internet of Things (IoT) industry environment, the variety and quantity of IoT devices have grown rapidly. IoT devices have been widely used in smart homes, smart wear, smart manufacturing, smart cars, smart medical care, and many other life-related fields. With it, security vulnerabilities of IoT devices are emerging endlessly. The proliferation of security vulnerabilities will bring severe risks to users’ privacy and property. This paper first describes the research background, including IoT architecture, device components, and attack surfaces. We review state-of-the-art research on IoT device vulnerability discovery, detection, mitigation, and other related works. Then, we point out the current challenges and opportunities by evaluation. Finally, we forecast and discuss the research directions on vulnerability analysis techniques of IoT devices.