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In the process of modifying titanium alloy oral implants using cavitation water jet, the collapse of bubbles releases significant energy. This phenomenon is accompanied by micro-jets and shock waves, which induce changes in the three-dimensional microscopic morphology of the implant surface. The loose and porous surface of the implant will increase the adhesion area of the cells, which is more conducive to the combination of the oral implant with the surrounding bone tissue. In order to explore the coupling mechanism between the instantaneous energy of bubble collapse and the surface deformation of titanium metal, based on different flow field and solid field model parameters, the numerical analysis software Ansys and the fluid-structure coupling simulation method are used to establish the numerical simulation model of single bubble collapse on the near curved wall. In order to explore the coupling mechanism between the instantaneous energy of bubble collapse and the surface deformation of titanium metal, the bubble growth process is ignored. Based on different flow field and solid field model parameters, this paper adopts the numerical analysis software Ansys and the fluid-structure coupling simulation method to establish the numerical simulation model of single bubble collapse on the near curved wall. The effects of flow field parameters and wall morphology on the transient flow field of bubble collapse and the effect of metal surface modification are revealed. The results show that when the initial bubble diameter is 180 μm, the instantaneous collapse high pressure reaches 7.24 GPa, and the maximum stress on the titanium surface is 689 MPa, which is 1.57 times higher than that under the bubble diameter of 60 μm. When the bubble collapses away from the wall, due to the weakened constraint of the wall, more intense energy is released, but the energy decays rapidly in the propagation process, and the energy loss when it reaches the wall is more serious. In this paper, the surface micromorphology is simplified into a near-curved shape. After the modification, the flow obstruction on the near-curved concave wall inhibits bubble collapse, resulting in an increase in bubble collapse time. The stress and deformation caused by a single bubble collapse are concentrated within a radius of 1mm and a depth of 5 μm.

With the advancement of urbanization, two-lay trusses bridges are widely used because of their good traffic capacity and structural performance. However, the aerodynamic behavior of this beam type is still in the exploratory stage. The local microclimate characteristics at the bridge site in mountainous cities are obvious, and it is easy to form a large wind angle of attack, which has a significant impact on the vortex-induced vibration (VIV) performance of the bridge. Therefore, this study takes a long-span two-lay steel trusses bridge in a mountainous city as the engineering background, and uses wind tunnel test and numerical calculation methods to study the changes of the static three-component force coefficient and VIV response of the main beam in the construction and completion state under the action of high wind angle of attack. The results show that the three-component force coefficient curves under different wind speeds are close to each other, and the Reynolds number effect is not obvious. The vibration test shows that the vertical bending VIV first occurs at +3° and +5°, and then two torsional VIV with different amplitudes occur. Both vertical bending and torsional VIV are simple harmonic vibrations with a single frequency, and the vertical bending VIV frequency is locked at 2.227 Hz, and the torsional VIV frequency is locked at 4.289 Hz, which are close to the natural frequency of the test model. Compared with +3°, the maximum amplitude of vertical bending VIV under +5° increases by 30.0 %, while the maximum amplitude of torsional VIV under high and low wind speed increases by 16.6 % and 12.7 % respectively, and the locking range is longer. It can be seen that the wind angle of attack has a significant effect on the VIV response of the main beam in the completion state. Especially, the trusses beam at a large angle is more sensitive to VIV, and it is more prone to large-scale and large-amplitude VIV. The research results can provide a theoretical basis for the aerodynamic shape optimization and provide a reference for the design of related bridges.

Fixed-wing drones generate lift using a wing similar to a conventional airplane, in contrast to rotary helicopters. As a result, these machines use energy solely for propulsion rather than to maintain altitude, making them significantly more efficient. These devices can traverse greater distances and cover larger areas, making them capable of mapping and monitoring specific points over extended periods. This article uses an analytical nonlinear approach to look at how discrete H∞ can be used as a robust controller to manage the path of a quadrotor. The main goal is to create a discrete H∞ nonlinear output feedback algorithm that can accurately track the position of the quadrotor while staying stable, even when there are unknowns, disturbances, or noise. The discrete formulation of this algorithm makes it especially suitable for multi-engine aircraft. By designing the controller in a discrete space first, transitioning to a continuous phase, and then reverting to discrete space for real-world application, more desirable and design-aligned results can be achieved. However, transitioning from continuous to discrete controllers may sometimes cause deviations from the design specifications. Designing the controller directly in the discrete space simplifies the overall process and enhances robustness.

Image recognition, a subset of artificial intelligence, has been applied to various industries, including steel recycling, to enhance efficiency and accuracy in quality control processes. This review paper provides an overview of the current state of image recognition technology in steel recycling, including the types of images used, the algorithms employed, and the benefits and limitations of the technology. The paper also discusses potential future directions for image recognition in steel recycling, such as integrating machine learning and deep learning frameworks to improve accuracy and developing mobile applications for on-site quality control. Overall, image recognition technology has shown great potential in the steel recycling industry, and further research and development in this field could lead to significant improvements in efficiency and quality control. Experimental results show that the precision of steel scrap classification is 0.92, and the precision of steel scrap quality judgment is 0.87. The empirical findings indicate that this technique can swiftly and precisely detect the type of steel scrap and evaluate its quality, it can also identify the existence of dangerous goods. The model can directly help enterprises reduce costs and increase efficiency, is conducive to the full recovery of scrap steel, and positively affects environmental protection and enterprise profits.

Lanes changing is one of the basic behaviors of vehicle driving, which has a significant impact on road traffic safety and stability. Aiming at the problem of slow convergence rate in solving the optimal control problem of vehicle lane changing, an optimal lane changing control method based on hp adaptive pseudospectral method is proposed. By establishing a vehicle kinematic model, boundary constraints, and path constraints, combining with the physical process of vehicle lane changing the proposed method discretizes the control and state variables to transform the multi constraint optimal control problem into a nonlinear programming problem and the minimum vehicle lane changing time is set as the performance objective function. And also, the proposed method is compared with traditional solving methods. The simulation results show that the proposed method can effectively solve the optimal feasible lane changing trajectory and complete the lane changing maneuver process in the shortest possible time.

The accurate assessment of stresses, strains and loads in components under working conditions is an essential requirement of successful engineering design. In particular, the location of peak stress values and stress concentrations, and subsequently their reduction or removal by suitable design, has applications in every field of engineering. The current work presents a technique for experimental strain measurement, where a data acquisition system have been composed of strain gauge sensors and an Arduino microcontroller. The measured signal conditioning is performed by means of strain bending sensor and then discretized by an analog-digital converter external to the Arduino. To realize the full-field measurement, the current measuring approach can be employed to determine the induced strain in multi points simultaneously. The significant features of the proposed measuring system are: sensitive, precise, economical, and compact size. For the purpose of results verification of the designed measuring device, experimental tests have performed on a cantilever beam and on a simply supported thin plate loaded at the center. An average percentage error was 5.7 % between analytical and experimental recorded strains in beam test. Also, in rectangular plate loading, an average percentage errors were 5.8 % and 4.1 % for the measured strains numerically and experimentally in X-direction and Y-direction respectively. The conducted results indicated a good agreement and demonstrate the accuracy of the proposed measuring system.

Accurately analysing features in infrared images of equipment is one of the current directions in the field of power equipment detection and identification. Because infrared images of power equipment have poor resolution, low contrast, and visual blurring problems, this work proposes the use of the squirrel search algorithm to optimize the detection strategy of YOLOv5. First, due to the shortcomings of the squirrel search algorithm, which easily falls into local optima and has a slow convergence speed, in this work, the Henon Consine Seagull search algorithm (HCSSA) is proposed; this algorithm uses Henon chaotic mapping for population initialization and optimizes the predator probability based on the cosine function to improve the algorithm's performance. Second, in the YOLOv5 model, CSP_Faster is used for feature information recognition and to reduce the computational burden, the SKNet mechanism is introduced to ensure the integrity of the image feature information, the SIoU loss function in target classification is used to obtain a better classification effect, and finally, the HCSSA algorithm is optimized for the two hyperparameters of the YOLOv5 model, which are the learning rate and the weight decay. In the simulation experiments, the recognition effect of the proposed algorithm is improved by 8.87 %, 7.67 % and 5.11 % compared with those of YOLOv3, YOLOv4, and YOLOv5, respectively, which shows that the model has a better target detection effect.

Trajectory tracking and Object Localization in robots are developing rapidly, but the tasks are becoming increasingly complex and significantly increasing the range of tasks for robotic systems. Cognitive tasks in domestic, industrial or traffic conditions require not only the recognition of objects but also their evaluation by classifying them without direct recognition. One of such spheres are tunnels that are physically difficult for humans to reach and require diagnostics. In such an environment, it is difficult to globally define the direction and goal, so it is necessary to interpret the locally obtained information. To solve such a problem, sensor fusion is widely applied, but sensors of different physical natures do not allow to obtain the necessary information directly, so there is a great need to use AI to interpret and control the received information and generate the robot's trajectory [1]. Local navigation systems require a wide range of sensors [4]. Various cameras and time-of-flight LiDAR lasers are widely used. For the aforementioned reasons, an economical local trajectory generation and tracking system is being developed, one of the most important components for object recognition is the laser triangulation method. The essence of this method is that the camera reacts to the projection of the laser light in front of it and interprets the obstacle depending on its distortion. In this way, the camera's resources are more concentrated, and at the same time, a simple RGB camera is enough for this method. Also, this method is perfect in the dark, when the laser light is more pronounced. In this paper, the laser triangulation method will be reviewed in detail, evaluating its advantages and disadvantages.