This PDF file contains the front matter associated with SPIE Proceedings Volume 13081, including the Title Page, Copyright information, Table of Contents, and Conference Committee information.

A control strategy based on Controlled Lagrangians (CL) is designed for the Electric Multiple Unit (EMU) with a power unit’s actuator complete failure to realize fault-tolerant control in this acticle. Firstly, the multi-particle EMU model is established, which is nonlinear, coupled, and underactuated. Then, the desired controlled energy and generalized forces are used to construct an expected controlled system. By equating the original system with the controlled system, the matching condition and controller structure are confirmed. A smooth nonlinear feedback control law is obtained from simplifying and solving the partial differential equations and algebraic equations of the matching conditions. The control law ensures simultaneous global asymptotic stability of position and velocity, and precision stopping of each power unit. Finally, the controlled energy is selected as the Lyapunov function, and the LaSalle invariance principle is used to prove the asymptotic stability. Simulations demonstrate the effectiveness of the theoretical results and that the controller has a large convergence range.

To investigate the impact of laser surface texture on the dynamic performance of gears, modal analysis was conducted on both ordinary gears and laser surface-textured gears. A high-precision cylindrical gear model was established in Pro/E. MSC.Nastran was employed to analyze the modal characteristics of laser surface textured gears. Modal analysis was performed to calculate their first 10 natural frequencies and vibration modes of gears. The simulation results indicate that, in comparison to ordinary gears, laser surface-textured gears exhibited smaller maximum amplitudes for each order. Furthermore, the natural frequency of laser surface-textured gears had significantly decreased, and its trend was more stable. These findings suggest that laser surface texture can effectively enhance the dynamic characteristics of gears, thereby improving their fatigue strength and reliability.

At present, the vibration mode matching and optimization design of tracked vehicles are mainly based on the adjustment of design parameters by researchers according to experience. This method is difficult to form a standard process and has low research efficiency, especially for situations where multiple excitation sources act on multiple subsystems simultaneously, and the difficulty of optimizing design increases significantly. In order to solve the above problems, this paper proposes a vibration mode matching and optimization design method for tracked vehicles under multi-source excitation. The Dynamics evaluation results of the optimal modal matching design scheme show that this method can provide an effective vibration mode matching design scheme.

The heat dissipation system provides cooling air flow to each cooling module through a fan driven by a hydraulic motor. The original product design scheme was a direct connection between the motor and fan. During the final assembly and testing process, it reflected the problem of cumbersome disassembly and assembly of the protective net and the generation of significant additional noise. In order to facilitate the connection and installation of the motor and fan after product delivery, and to reduce additional noise, a shaft-hub transmission device has been designed. The new scheme considers the mechanical properties and lifespan of the parts, providing a solution for simplifying installation, noise reduction and elimination of safety hazards.

This paper analyzes the reasons for machining deformation from the production process, physical characteristics and other factors of slender and thin-walled waveguides. The LS-DYNA explicit analysis was used to simulate the reverse deformation of pre-stress in slender thin-walled waveguides, and the ANSYS implicit analysis was used to simulate the rebound state after unloading. The application practice has verified the effectiveness, reliability and accuracy of the new process in improving the straightness of slender and thin-walled slot waveguides.

The brake is a crucial automotive safety component, and the persistent brake squeal noise significantly impacts passengers' long-term riding experience. Utilizing a non-smooth textured surface enhances the tribological behavior of the interface, offering a novel solution to address brake friction squeal. In this study, we design a brake disc with a composite structured surface, modeled using ABAQUS and analyzed through complex eigenvalue analysis. Results indicate that the composite textured surface disc, compared to its smooth counterpart, exhibits a smaller tendency for unstable vibration and a larger negative damping ratio. This highlights its heightened efficacy in mitigating brake friction squeal behavior.

The adhesive bonding structure is widely used in mechanical structure assembly due to its high connection reliability, low assembly stress, good vibration damping and insulation performance. In the case of large temperature coverage and longterm continuous operation, the creep of adhesive bonding structure is the core factor affecting the stability of mechanical structure. Through experimental testing, an accurate adhesive material parameter constitutive model covering the operating temperature range is constructed. On this basis, the adhesive bonding structure in a certain type of accelerometer is selected for research. Through finite element simulation analysis of the structural creep of the adhesive bonding structure, a finite element simulation model of the creep characteristics of the adhesive bonding structure under the coupling of temperature and magnetic fields is established. The influence law of adhesive process parameters on the stability of mechanical structure at different temperatures is obtained. Finally, based on the response surface method, the adhesive process parameters are optimized and the optimized specific adhesive process parameters are obtained. This article proposes a process method that can effectively improve the stability of mechanical structure, providing a theoretical basis for guiding the formulation and optimization of adhesive bonding process in precision mechanical products.

In order to solve the problem of low level of automation of spraying actuators of upland clearance plant protection machine, an automatic folding five-section spray bar based on electric actuator drive is designed, and a folding hinge is designed between the inner spray bar and outer spray bar that can automatically unfold the outer spray bar when the inner spray bar is extended, so as to realize the smooth action of horizontal unfolding and folding of the spray bar. The design of this paper has certain feasibility, which provides the basis for processing the prototype and studying its spraying characteristics.

Knotty vegetable transplanter operation requirements are high, at present China's vegetable transplanter is mainly semi - automatic transplanter, due to the need to manually feed the seedlings, the degree of automation is not high, so the development of a high degree of mechanical automation of the automatic seedling pickup device is of great significance, this paper designs a kind of automatic seedling pickup and seedling pickup mechanism as well as hole tray conveying device, the use of Solidworks to pick up the seedling casting device modeling, seedling pick up the end of the kinematic analysis of the actuator, and ultimately, through the ADAMS software kinematics simulation of the key components to verify the reasonableness of the structure.

This paper introduces a practical wide-beam tri-polarized antenna design, integrating a dual-polarized microstrip antenna with a monopole antenna. The proposed antenna facilitates simultaneous transmission and reception of incident waves with arbitrary polarization, offering a bandwidth exceeding 200 MHz and an isolation between ports better than 17.54 dB. In the x and y directions, the E-plane achieves a beamwidth greater than 122.64°, and the H-plane exceeds 86.13°. Furthermore, the antenna demonstrates omnidirectional radiation in the z-direction. This cost-effective and straightforward design holds promise for applications requiring broad-angle scanning and versatile polarization capabilities.

Microlenses are important micro-optical components with functions such as laser collimation, focusing, and homogenization. They are widely used in various fields such as laser communications, fiber optics sensing, laser radar ranging, and laser pumping. The traditional manufacturing process of microlens array has the problem of low precision, low efficiency, high cost, and can not guarantee high surface profile accuracy. In this paper, a moving mask projection lithography process for manufacturing microlens arrays is proposed, in which a 0.2x projection objective is used to improve the linewidth resolution. We use mask shift filtering technology based on projection lithography to reduce the complexity of mask preparation and obtain higher surface profile accuracy.

Mask optimization has arisen as a vital challenge in the flow of VLSI manufacturing, primarily because the critical dimension of integrated circuits stands much smaller than the light source's wavelength. Inverse lithography technology (ILT), a notable resolution enhancement technology, is known for its efficacy in enhancing mask printability. However, its extensive computational complexity has obstructed its broad-spectrum adoption. In our paper, we introduce a detail-enhanced Pix2Pix network, founded on GAN principles, to speed up the ILT process. This network has the capacity to generate quasi-optimal masks from given target layouts, thereby reducing the amount of traditional ILT steps required to produce high-quality masks. Our experiments on the ICCAD 2013 benchmarks demonstrate that, in comparison to the latest cGAN-based method, the L2 error, PVB, and runtime in our work have seen reductions of 7.2%, 5.9%, and 18.4% respectively. Thus, our approach not only expedites the ILT process but also guarantees enhanced printability.

The amount of intake resistance of the air filter has a significant impact on engine performance. This article proposes a structural optimization plan to address the issue of excessive intake resistance in an engine air filter. Based on computational fluid dynamics, simulation analysis was conducted on the pressure and velocity fields inside the air filter before and after optimization. The analysis results showed that the velocity uniformity of the air flow inside the optimized air filter was improved, and the intake resistance was significantly reduced. The physical inspection and inspection of the optimized air filter trial sample showed that the intake resistance was lower than the standard allowable value, and all performance indicators met the requirements, proving the effectiveness of the optimization plan.

A new kind of rubber Isolator is proposed in this paper. Rubber and metal are combined to form an hour-glass-shaped vibration control cell and the cell is arrayed form a vibration damping sheet. The finite element method is used to simulate the random vibration response of the electronic equipment and vibration damping sheet system. By using this method, the transmission curves, deformation and equivalent stress of the vibration damping sheet driven by the random vibration are obtained. It is found that this new sheet can satisfy the requirement of reducing vibration of the electronic equipment with broadband, multi-direction, lightweight, small deformation characteristics. It also has strong practicality and expansibility for other box structure with strong random vibration excitation.

Neutralization box, settling box and flocculation box (commonly known as triple box) is one of the main body of the equipment in FGD. In FGD industry now, agitator selection is depend on the experiment and practical engineering experience, this leads to the market on the joint box agitator selection is multifarious, good and evil people mixed up. The selection of agitator cannot be strictly determined by the selection of the original mixer of the same size. After all, different mixing materials have different mixing purposes, including mixing, reaction, mass transfer, heat transfer, etc. Therefore, in the case of determining the specific material parameters and size of the 3 cases, the parts in this paper, 3 cases of mixer and the internal components selection and optimization, it includes the shape of the stirring blade, the diameter of the blade, the speed, and the size of the stirring shaft, etc. Based on the analysis and comparison of the existing performance, the blender suitable for 5 types of common specifications of the triple box is determined.

For the short-life turbine blade, the low-fatigue test results do not match with the numerical simulation, and other factors are investigated and attributed to material and structural defects. The fracture mechanics life prediction method is used for life prediction, and the crack extension results of the defective turbine blade are obtained by combining the FRACN3D software and the low-week fatigue test to get the fatigue life and crack extension length-load cycle number (a-N) curves of the turbine blade. The results show that: through the fracture mechanics numerical simulation to derive the crack tip expansion path test is consistent with the turbine blade from 4mm crack expansion to 5mm with 43 load cycles, and the difference with the results of the low weekly fatigue test is small, the error is only 14%, from the 5mm crack length expansion to the turbine blade failure with 40 cycles, based on the defective turbine blade with a 4mm crack the remaining life of the turbine blade is 83 cycles, the remaining life of the defective turbine blade is 83 cycles based on a 4 mm crack, and the total number of loading cycles from crack initiation to crack failure is 133, which provides an analytical method for the design of damage tolerance and remaining life of the turbine blade.

This paper summarizes the deformation of the main beam of the crane, the method of photoelectric detection and designs the photoelectric detection system, through the idea of the deformation of the crane main beam into photoelectric signal, and the signal to the host through wireless transmission, through software calculation of the crane main beam under deformation and horizontal deformation, significantly simplify the detection process, reduce the manual workload, improve the detection accuracy, and realize the automatic detection of crane main beam deformation.

Under the working conditions of high speed and heavy load of large wind turbines, the load changes caused by wind are easy to cause bearing defects, and then affect the transmission efficiency and stability of gear system. This paper takes the angular contact ball bearing of wind turbine gear system as the research object, establishes the dynamic model of the angular contact ball bearing considering defects, and analyzes the contact force response of the defective bearing. On this basis, the influence of bearing defects on the dynamic characteristics of the gear system is studied and the dynamic response of the defective bearing to the gear system under cyclic load is analyzed. The results show that: when the bearing defects occur, the bearing capacity will be reduced or lost when the rolling element passes through the defect position, the effective stiffness of the bearing assembly will change due to load redistribution, and the bearing failure will make the system tend to be unstable, but the influence on the system motion state form is small within a certain range. Under the action of cyclic load, bearing defects can make the system tend to be unstable, but they have little influence on the motion state of the system within a certain range.

In order to investigate the underlying mechanism of how process parameters in galvanometer-scanned laser welding affect weld formation, the galvanometer-scanned laser welding technology was applied to SUS304 stainless steel under different oscillating frequency and amplitude. The finite element analysis was used to predict the effect of oscillation parameters on the temperature field of SUS304 stainless steel weld joint. The results indicate that compared to conventional laser welding, laser oscillating scanning welding significantly improved the quality of weld formation and suppressed defects such as burn-through, undercut and overlap. With the increase of scanning amplitude or scanning frequency, the weld forming quality is improved. However, with the further increase of scanning amplitude or frequency, undercut defects will occur on the front side of the weld, and spatter/overlap defects will occur on the back side of the weld. This is because laser oscillating scanning welding changes the energy deposition path. The simulated temperature field is in good agreement with the actual weld geometry. Compared with conventional laser welding, laser oscillating scanning welding reduces the peak temperature and temperature gradient in the welding process.

With the development of construction robot technology, the problem of path planning has received extensive attention. Aiming at the problem that the traditional path planning algorithm cannot plan the orientation, we propose an improved A* algorithm to improve the flexibility of robot movement. Firstly, the mathematical model of the robot system is established in the two-dimensional plane, and the environment is preprocessed. Secondly, the position planning method of A* algorithm is improved to improve the search efficiency of the algorithm. Finally, the optimization method of robot position and orientation is proposed, and the optimization function is added to the A* algorithm to improve the flexibility of the whole robot system. The results show that the improved A* algorithm can shorten the path length by 40.67 % and the turning angle by 10.29 %, which verifies the correctness of the proposed method. The research results of this paper have reference and guiding significance for solving the path planning problem of curtain wall installation robot.

Feature extraction is of significant importance for machine condition monitoring, which extracts the potential machine condition information from monitored data. Autoencoder can extract the inherent structural feature in data, thus widely used for feature extraction from vibration signals. However, the training of autoencoder is a time-consuming process. Especially when facing a practical condition monitoring application in which numerous abnormal conditions emerge successively, an autoencoder needs to be retrained to adapt to the new faulty data, thus the training time multiplies. This paper develops the incremental perceptual vibration hashing autoencoder (IPVHAE), dedicated to mapping the inherent machine condition information in vibration signals into a compact, low-dimensional machine condition hash (MCH). An incremental learning approach based on parameter constraints is employed to facilitate the retraining of the IPVHAE on new data. The parameters of an autoencoder only need to be updated with the new data instead of all the data under available machine conditions. The effectiveness of the developed algorithm is verified using the Case Western Reserve University bearing dataset. Results show that the machine condition hash generated by the IPVHAE can balance low dimensionality with high discriminability, meanwhile minimizing the time and computing cost during the incremental learning process.

To enhance the overall performance of the phase-locked loop frequency synthesizer, this paper presents a low-phase noise dual-core voltage-controlled oscillator (VCO) designed with a Class-F_2,3 architecture. Utilizing the 55-nm CMOS, the wide tuning range is achieved through variable capacitance and switch capacitor arrays. The use of adjacent-layer coupled transformer structures, with the adjustment of capacitance ratios in two resonant cavities, facilitates low phase noise. Postsimulation results indicate that the frequency tuning range of the VCO is 11.74-13.68 GHz. The VCO achieves phase noise of -123.66 dBc/Hz@1MHz at 11.74 GHz with figure of merit (FoM) of 185.7dBc/Hz and FoMT of 189.9dBc/Hz. The oscillator core consumes 67mW at 0.9V supply.

In this paper, working medium diversion elbow of underwater engineering equipment is taken as the research object. Based on the Johnson-cook failure model, the erosion failure of Ni electroplating on the inner wall of the elbow caused by Al2O3 particles is analyzed by ABAQUS software. Combined with the erosion failure model of the elbow, the corrosion process of the inner wall of the elbow is simulated in the environment of seawater immersion. The simulation results of particle erosion show that The outer side of the elbow's inner wall 45°-50° from the entrance of the elbow is the area where Ni electroplating layer failure is easy to occur, mainly because the particle erosion inclination angle is large, resulting in the maximum stress and strain. The corrosion simulation results show that the maximum corrosion rate on the four monitoring lines in the elbow is 0.234 mm·a-1. The most serious corrosion area of the elbow is the inner wall of 921 steel at the junction of the erosion wear area and the unwear area. The open-circuit potential difference between Ni electroplating and 921 steel matrix is large, which lead to galvanic corrosion.

To address the low accuracy of existing nighttime airport runway foreign object detection algorithms, this paper proposes the Low-Light YOLOv5s FOD algorithm by integrating low-light enhancement and YOLOv5 detection concepts. During the training of the low-light enhancement network, the brightness layer channel, normalized for brightness, is incorporated into the attention mechanism, allowing the model to focus more on dark areas. Subsequently, dark-light FOD images undergo low-light enhancement. On the YOLOv5s detection network, multiscale feature fusion, a small object detection layer, and the NWD loss function are employed to enhance small object detection and address position sensitivity issues. ODConv, a full-dimensional dynamic convolution, replaces the standard convolution, further improving accuracy with multidimensional complementary attention mechanisms. Finally, foreign object detection is performed on enhanced images. Experimental results indicate a 12.6% improvement in NIQE scores for the improved EnlightenGAN restored images and a 4.3% increase in mAP for the improved YOLOv5s compared to the original model. In nighttime environments, the proposed algorithm achieves an average detection accuracy of 99.39%, a 67.99% improvement over the original algorithm without low-light enhancement, at a detection speed of 50.30 frames/s. Balancing accuracy and real-time performance, this algorithm effectively addresses false positives and misses in FOD detection during nighttime conditions.

The mine road is unstructured, the line shape is changeable, and the color of the road surface is similar to that of the mountains on both sides. In order to make up for the deficiency of the segmentation effect of the road detection model based on shape and color, a driving area segmentation method based on rut texture features is proposed. Initially, the collected images are preprocessed in the region of interest. Secondly, the gray level co-occurrence matrix (GLCM) is used to obtain the characteristic parameters of the rutting area, and four texture feature indexes are used as the input feature vectors of the genetic algorithm (GA) to obtain the optimal segmentation threshold. Finally, by filling the hole noise in the segmented image, a complete drivable area is obtained. The comparison test shows that the proposed segmentation method based on rut texture features can effectively overcome the problem that the road is similar to the background color and is difficult to segment. Compared with other similar segmentation methods, the segmentation effect of the road has better accuracy and robustness.

Gear systems are extensively implemented in industrial applications such as shipping, wind turbine and aerospace. The performance of the transmission equipment will be directly affected by the dynamic characteristics of the gear system. As the demand for high-power and heavy-load gear transmissions continues to rise, gear systems are inclined to exhibit high volume and weight. It is essential to take gravity effects into consideration during the analysis of gear dynamics, because they will make components of gear systems deform, change the tooth contact status and consequently affect the dynamic response of gear systems. In the past few decades, research on the gear systems dynamics with gravity effects has increased dramatically. This paper provides a comprehensive review of published literatures related to the gear dynamics considering gravity effects. The studies focus on three primary themes: the impact of gravity upon the time-domain dynamic response, the impact of gravity upon the frequency-domain dynamic response, and the impact of gravity upon the gap nonlinear response of gear systems. Finally, some conclusions are summarized.

Addressing the shortcomings of low accuracy and time-consuming nature in manual weld defect detection, this study applies machine vision to weld defect detection. Based on the SSD target detection network, it firstly introduces the model structure and detection process of the SSD target detection network. Secondly, it describes the relevant evaluation indicators and the preparation of the dataset. Finally, the SSD network is used to train and verify the dataset. The result shows that the average accuracy of the model for these four types of recognition is 66.49%, indicating that the SSD target detection model has good detection ability for weld defects, providing a way for the application of machine vision in recognizing weld defects.

Gear fault features are submerged in strong background noise with the influence of background noise and vibration signal transmission paths. The adaptive auto-regressive (AR) model with the largest spectral kurtosis (SK) can effectively eliminate the linear stationary part of the signal, which is selected for signal preprocessing. Multipoint Optimal Minimum Entropy Deconvolution Adjusted (MOMEDA) is suitable for the extraction of periodic fault signal fault features in rotating machinery. Therefore, the adaptive AR and MOMEDA are combined to extract gear fault features, which is verified by experimental results.

This paper introduces a novel approach called ultra-high-frequency (UHF) induction-assisted laser wire deposition. This method utilizes induction heat as an auxiliary source to create a high-temperature zone on the substrate’s surface, just ahead of where the metal is deposited. This ensures a strong metallurgical bond between the deposited track and substrate, while the metal wire is also preheated by the induction heat, which helps distribute heat more evenly in the part generated by the high-power laser. To gain insights into the deposition process, we establish a numerical model that combines temperature and electromagnetic fields. Thus, the penetration depth of the deposited track can be predicted through numerical simulation to control dilution rates effectively.

With the continuous expansion of power grid scale and the improvement of power grid informatization level, China has accumulated a certain amount of wire wind vibration data in years of transmission line operation, including structured and unstructured data such as text, statistical charts, images, videos, etc., providing strong data support for data-driven wire wind vibration disaster mechanism mining. Therefore, it is necessary to integrate multi-disciplinary knowledge such as computer, mathematics, information processing, mechanics, and biology based on data mining and digital machine learning techniques, effectively mine and deeply analyze the historical records of wind-induced vibration disasters on transmission lines, meteorological and geographical environmental parameter information, multi-source and wide-area wind-induced vibration monitoring information, and line structural parameters, in order to clarify the main influencing factors and disaster mechanisms of wind-induced vibration disasters on transmission lines, improve the prevention and control technology of wind-induced vibration disasters on transmission lines, enhance the identification level of wind-induced vibration disasters in power grids, and provide decision-making support for the safe operation of power grids.

This conference presentation was prepared for the Third International Conference on Advanced Manufacturing Technology and Electronic Information, 2023.

This article proposes a penetration testing payload generation tool based on the Qt graphical interface and msfvenom. The aim is to provide a user-friendly graphical interface that enables penetration testing professionals to efficiently generate customized penetration testing payloads. By integrating the msfvenom payload generation script, users can customize different payload types and parameter settings through the visual interface. While improving the efficiency of payload generation, the tool emphasizes enhancing the user experience to adapt and meet the evolving needs of penetration testing.

Edge-cloud collaboration provides a better solution for condition monitoring, which can reduce response time while maintaining computational efficiency. In practical condition monitoring scenarios, the individual differences among equipment often decrease the accuracy of diagnostic models. To tackle this problem, a transfer learning method based on stacked sparse autoencoder is proposed, which employs a data regularization strategy to improve feature extraction ability. The fault diagnosis model trained in the cloud transfers its model parameters and structure to the edge side. By a finetuning process with a small amount of data, and the model is further updated for condition monitoring of the individual machine. The experimental results show that the proposed KT-SAE method has improved transfer accuracy compared to other related transfer learning methods.

Aiming at the problem that the large weight of the construction robot cannot be ignored in the process of stiffness modeling, this paper proposes a stiffness modeling method of non-rotational drive joint redundant degree of freedom manipulator considering gravity. Firstly, the structural characteristics of the redundant degree of freedom manipulator are introduced, and the inverse kinematics is solved by using the self-motion characteristics of the redundant degree of freedom manipulator. Then, the force and deformation analysis of the parallelogram mechanism is carried out by using the equivalent principle of the force system and the translation theorem of the force, and the stiffness modeling is carried out based on the principle of virtual work. Finally, considering the gravity factor, the overall force and deformation analysis of the manipulator is completed, and the comprehensive stiffness model of the manipulator is established. The stiffness is analyzed according to the self-motion characteristics of the manipulator. The results show that under the influence of gravity and external force, the stiffness difference of the manipulator with different redundant angles at the same position can reach 240 %, and the proportion of gravity factor in deformation can reach 72 %. The research in this paper reveals the significant differences in the stiffness of the manipulator under different redundant angles. The established stiffness model is more in line with the actual situation and lays a foundation for further optimization research.

The main problem with the current radar target radial length extraction algorithm is its susceptibility to interference signals, which makes it difficult to optimize the boundaries of the target support area, especially the impact of noise at positions that tend to be farther away from the target support. Therefore, based on deep learning networks, this article trains and analyzes different pixel points, completes the segmentation of the target support area and background area through image semantic segmentation algorithms, obtains the target support area, and estimates the radial length of the target based on the boundary of the target support area. Finally, validate the effectiveness of the algorithm using simulation data.

In this paper, based on D-H method, the kinematics modeling and simulation calculation of an upper limb exoskeleton robot prototype are carried out by using SimMechanics module of MATLAB, and the simulation results of the upper limb exoskeleton motion space of one arm and both arms are analyzed, and the motion characteristics of one arm and both arms of the upper limb exoskeleton are obtained. By comparing the simulated motion characteristics with the actual motion characteristics of human arm, the rationality of the simulation method is proved. The simulation analysis method in this paper can provide reference for the design and kinematics analysis of the same type of robot.

To ensure the successful execution of on-orbit space missions, the operational data of space payloads must be continuously monitored. Due to the characteristics of interval-scaled variable parameters that change and fluctuate randomly with telemetry parameters, the traditional interval range anomaly detection method has low accuracy, and the deep-learning-based approach is limited in the study of parameter changes with commands. In this paper, an anomaly detection method based on LSTM network model is proposed. First, encode command (CMD) into CMD Time Series and construct the corresponding dataset. Then, generate the parameter expectation values using LSTM and design the anomaly detection discriminative parameters and rules. By training the network model and setting the thresholds of the discriminative parameters according to the performance, we demonstrate the feasibility of the method by taking the actual parameters of an experimental payload as an example.

In recent years, the field of image enhancement has gained significant attention, especially for images taken in dimly lit settings. These images frequently exhibit issues such as diminished contrast, reduced brightness, and disruptive noise, all of which can detrimentally affect their quality. Given the swift progress of deep learning technology, a multitude of methods for enhancing low-light images have emerged, leveraging deep convolutional neural networks (CNNs). Through an understanding of the relationship between low-light images and standard images, deep convolutional neural networks can adeptly extract image characteristics and improve overall image quality. In this paper, we present a deep convolutional neural network-based method that uses a deep unsupervised dehazing network model as a foundation. Our method stands out from existing ones by considering both algorithmic decomposition and image decomposition. It achieves low-light image enhancement goals by breaking down the enhancement problem into three sub-problems through laboratory space decomposition of the image. Experiments show that our proposed method can lead to significant enhancement of images with low light intensity.

In the UAV path planning problem, traditional algorithms have shortcomings such as computational complexity and convergence. WOA algorithm is concise and easy to implement, and has loose requirements on objective function conditions and less parameter control, etc. However, its lack of adaptability and difference limits its performance in complex optimization problems. In this study, a nonlinear convergence factor and an adaptive weight strategy were introduced. In the simulation verification, multiple constraints were considered and added into the objective function as a penalty term to build a route planning model under the UAV flight mission scenario, and the improved WOA algorithm was used to solve the optimal path. Through simulation verification, the results proved that the improved WOA algorithm was efficient and practical. The final simulation verifies the efficiency and practicability of improved WOA algorithm.

The verification of secondary cable core is an important task in the construction and renovation of substations, which is related to the normal operation of various relay protection devices in substations. In view of the many drawbacks of traditional secondary cable core verification methods, such as cumbersome steps, low efficiency, low accuracy, and susceptibility to working environment, this paper proposes a secondary cable core verification device based on the principle of variable frequency resonance. This paper will introduce the overview and current status of secondary cable core verification technology, the design of the secondary cable core verification device based on frequency conversion resonance, and the core verification principle of this device.

Utilizing machine vision to acquire road environment information is a crucial factor influencing the performance of advanced driver assistance systems, but under low-light conditions, obtaining high-quality road information directly through vision becomes challenging, necessitating image enhancement. Due to uneven illumination in low-light conditions, existing enhancement methods often lead to issues such as glare and blurred details in bright areas. In this paper, we propose an optimized Multi-Scale Retinex (MSR) image enhancement algorithm. Firstly, RGB images are converted to the YUV format, and the MSR algorithm is applied to enhance the Y channel. The local background brightness is then incorporated into the Just Noticeable Difference (JND) model to establish a non-linear relationship model between background brightness and adjustment factors. This enables adaptive adjustment of image enhancement intensity. Subsequently, a non-linear bilateral filtering function is applied to smooth the adjusted image, followed by Contrast Limited Adaptive Histogram Equalization (CLAHE) to enhance its contrast. Finally, the resulting image is fused with the original image in a 1:1 ratio. Experimental results indicate that, compared to the combination of MSR and histogram equalization, the proposed method achieves a 4.5% increase in standard deviation, a 7% increase in information entropy, and a 25% improvement in peak signal-to-noise ratio.

High-quality machining of micro-holes has always been one of the challenges in the manufacturing industry, and Electrical Discharge and Electrochemical Hybrid machining (EDM-ECM), a non-traditional machining process for machining difficult-to-cut materials, is prominent in the field of micro-holes manufacturing. This paper investigates the technology and analyses the machining parameters, which are the key factors affecting its performance. The working fluid is at the heart of the proper functioning of hybrid EDM, as its presence makes it possible for EDM and ECM to co-occur. In order to enhance the machining efficiency of EDM-ECM, a triple hybrid machining method based on EDM-ECM is introduced. Finally, the scope of the technique for future research is briefly discussed.

The adhesive bonding structure is widely used in mechanical structure assembly due to its high connection reliability, low assembly stress, good vibration damping and insulation performance. In the case of large temperature coverage and longterm continuous operation, the creep of adhesive bonding structure is the core factor affecting the stability of mechanical structure. Through experimental testing, an accurate adhesive material parameter constitutive model covering the operating temperature range is constructed. On this basis, the adhesive bonding structure in a certain type of accelerometer is selected for research. Through finite element simulation analysis of the structural creep of the adhesive bonding structure, a finite element simulation model of the creep characteristics of the adhesive bonding structure under the coupling of temperature and magnetic fields is established. The influence law of adhesive process parameters on the stability of mechanical structure at different temperatures is obtained. Finally, based on the response surface method, the adhesive process parameters are optimized and the optimized specific adhesive process parameters are obtained. This article proposes a process method that can effectively improve the stability of mechanical structure, providing a theoretical basis for guiding the formulation and optimization of adhesive bonding process in precision mechanical products.

Text mining is a hot research field that combines data mining with natural language processing. Using text mining technology, researchers can effectively integrate information to accurately retrieve and locate information, allowing users to find useful data efficiently. The job competency model is the cornerstone and pillar of human resources work. It contains a series of job competency elements. It costs a lot to build a competency model in the traditional way. In order to solve this problem, this article studies the use of text mining technology to build a competency model. For text clustering, this paper proposes a method based on an improved ant colony clustering model. This method makes full use of the self-organization of the ant colony clustering algorithm and its insensitivity to the order of previous data input, and improves the algorithm on its shortcomings. Experimental results show that the clustering precision rate, recall rate and 1F evaluation value of this algorithm can achieve the expected results.

In order to improve driving safety in blind-spot dangerous scenarios of which pedestrians traverse in front of stationary obstacle vehicle (OV) and collide with the host vehicle (HV), a pedestrian collision avoidance system based on vehicle-to-vehicle (V2V) communication is proposed. Position and speed information of the pedestrian is detected by the OV sensors and transmitted to the HV by V2V communication, and the relative position and speed information between HV and pedestrian is derived by coordinate transformation. Longitudinal distance model including warning safety distance, braking-trigger safety distance, and minimum safety distance, along with lateral model for categorizing danger levels are established to determine the decision-making actions (normal driving, braking or steering) of HV. A multi-constraint optimization-based obstacle avoidance trajectory planning method based on a quintic polynomial is presented for steering scenario. The effectiveness of V2V-based collision avoidance strategy in the blind spot dangerous scenario is simulated and verified by the co-simulation of PreScan, CarSim, and MATLAB. The results reveal that through the utilization of V2V communication, HV can proactively acquire real-time pedestrian motion data to determine collision avoidance strategies appropriately under different emergency scenarios, and thereby ensuring the safety of both AV and pedestrian.

The routine use of shore power and the coordination between ships and shore will become key links in improving the atmospheric environment of ports. Ships approaching the port will be connected to shore power more frequently, which will inevitably increase the frequency of electrical equipment usage and the workload of operation and maintenance of shore power equipment in the shore based power supply system. This article conducts research on the technical requirements for the operation and maintenance of the main electrical equipment in the dock shore power supply system, analyzes typical electrical faults, and standardized maintenance management is an important measure to ensure efficient and routine use of shore power. It provides technical support for the stable and reliable use of shore power by ships, and improves the utilization rate of port shore power.

Precise geometrical extrinsic calibration is a prerequisite for fusing LiDAR and Camera information. Most existing calibration techniques necessitate a significant amount of calibration target data and human effort, resulting in a tedious and laborious procedure. Moreover, the relative poses between the LiDAR and the camera may drift cumulatively as the application time increases, and even the extrinsic parameters can change drastically following an accidental impact. This paper builds an extrinsic calibration end-to-end differentiable iterative refinement network. We combine the neural network with the geometric constraints of extrinsic calibration. Our model is trained entirely on the synthetic dataset and performs well when directly applied to the real-world dataset without fine-tuning, demonstrating our method's strong generalization capability.