This PDF file contains the front matter associated with SPIE Proceedings Volume 13515, including the Title Page, Copyright information, Table of Contents, and Conference Committee information.

At the end of the 20th century, the 3D printing technology developed from the United States introduced new blood to the field of traditional sculpture, many countries slowly began to try to combine 3D printing technology to improve the traditional model making process. Computer modeling is increasingly becoming mainstream. Based on computer modeling technology and 3D printing technology, this paper researches, sorts out and practices many new methods and possibilities of digital engraving and production of figure models.

Fused Deposition Modeling (FDM) 3D printing technology has received widespread attention for its efficiency and reliability in prototype production, small batch production, and complex structure construction. However, printing quality and efficiency largely depend on the performance of layered algorithms, which poses a serious challenge to algorithm design. This study proposes a new type of layering algorithm, which is validated by simulation experiments. The validation results show that the algorithm is more detailed than traditional adaptive layering at feature height, and retains model features to a greater extent compared to traditional adaptive layering, while balancing printing quality and efficiency. This study not only provides new design ideas for the layered algorithm of FDM 3D printing technology, but also opens up new avenues for further improving printing efficiency and quality.

Traditional DIMMs, due to their large size and unstable connection methods, are not suitable for miniaturized applications. Therefore, a DDR4 SDRAM memory module based on a flexible substrate is proposed. It is formed by folding the entire flexible substrate and then encapsulating it to create a smaller BGA package module while retaining the universality of traditional DIMMs. Regarding the selection of the module’s topology during the design process, an analysis of the structures of memory modules on the market is conducted. This paper proposes an evaluation parameter for different topologies of this module and provides a method for selecting the optimal topology for this module.

As an emerging technology, additive manufacturing still has some defects in the forming process. In order to study thermodynamics in the additive manufacturing process of parts, taking the 3D model of the flange as an example, the multi-layer multi-pass welding function of Simufact Welding simulation software is used to simulate the additive manufacturing of parts, and the stress and deformation generated in the process are derived and analyzed. Through the simulation of welding residues, thermal deformation and welding defects, the possible problems in the manufacturing process of parts are revealed, and the advantages and limitations of Simufact Welding software in additive manufacturing are summarized. It provides considerable reference and guidance for improving welding quality and reducing manufacturing costs. The results show that the software can simulate the additive manufacturing of metal materials well, analyze the deformation and other parameters during the welding process, and provide a basis for the subsequent part processing.

A virtual product creation technology was explored, focusing on the customized cam with a rise-dwell-fall-dwell (RDFD) motion program taking high and low speeds into account using artificial intelligence which is necessary to transform the related data of the product development (represented by CAD models) into the product. The digital solution which is the manufacturing process of numbers and can completely describe the working components to eventually realize the physical shape of the product was mentioned. The database carrying different interpolation functions for designing some special curvilinear paths meeting special kinematic requirements of the two types of cams was set up. The entire process starts with Computer-Aided Process Planning (CAPP) and then there is Computer-Aided Manufacturing with Numeric Control (NC). A further discussion for customized parts design and manufacturing in specialized fields has been conducted based on digital modeling technology and expert systems with the development of artificial intelligence in engineering applications.

The application of intelligent chip technology based on mobile terminal in the management of ultra-high voltage equipment can significantly improve the reliability and security of equipment operation, reduce the failure rate and maintenance costs. This technology combines advanced sensors, data processing and wireless communication technologies to provide a comprehensive device condition management solution through real-time monitoring, intelligent analysis and remote management. Abstract: The application of intelligent chip technology based on mobile terminal in the management of ultra-high voltage equipment can significantly improve the reliability and security of equipment operation, reduce the failure rate and maintenance costs. This technology combines advanced sensors, data processing and wireless communication technologies to provide a comprehensive device condition management solution through real-time monitoring, intelligent analysis and remote management.

Efficient two-dimensional material-based photodetectors are promising candidates for industrial applications due to their high sensitivity, flexible surface modification capabilities, and advantages in high-density integration. However, they still face significant challenges, such as poor inherent charge carrier mobility and weak light-matter interaction of the materials themselves. Here, we successfully prepared sub-micrometer cross arrays on monolayered molybdenum disulfide (MoS₂), achieving a remarkable enhancement in photodetection performance at specific resonant wavelengths. The cross arrays induce strong plasmonic resonance across a wide spectral range, leading to enhanced electromagnetic field (EM) and hotelectron injection effects. These effects reduce the Schottky barrier at metal-semiconductor contacts and generate a large number of photo-generated carriers, thereby enhancing the photoresponsivity of photodetector. Optoelectronic measurements reveal that the MoS₂ phototransistor exhibits a maximum photoresponse of 650A W⁻¹ and a high detectivity of 6×10¹⁴ Jones under 520nm excitation. This work demonstrates that plasmonic nanostructures play a important role in altering the device performance of low-dimensional semiconductors.

Wet grinding is an efficient and clean method. However, its processing materials are expensive and companies can only ensure the grinding effect through experience. Because developing new products is difficult and costly, research on wet grinding is conducted based on CFD-DEM coupled simulation. Taking the disc pin milling an example, the effects of rotational speed, bead filling and bead diameter on the grinding effect were analyzed. Using multiple linear regression analysis, the impact of three factors on the grinding effect and the level of superiority of each factor were studied. The experimental results showed that the combination of 0.8, 2500 and 90% of the three factors was determined to be the optimal level combination for this experiment. The primary and secondary order of factors affecting the experimental results is zirconium bead diameter, followed by mass filling, and rotational speed.

In order to realize a comprehensive and accurate evaluation of automotive steering gear performance, this paper focuses on the architecture design and simulation analysis of automotive steering gear comprehensive test bench. Comparative analysis of existing automobile steering gear, including gear rack steering gear. Through the investigation of the research status of the test bench at home and abroad, it is found that the domestic test bench has made a breakthrough in independent research and development, while the foreign test bench has the advantages of strong versatility. This paper also proposes the architecture design of automobile steering gear comprehensive test bench, including the overall structure design, input shaft steering unit design and output shaft steering unit design. Through MATLAB simulation analysis, the performance of different speed and simulated operation is studied. The results show that the speed has a significant influence on the performance of the steering gear, and the wear and fatigue under specific operating conditions also need attention. This paper provides a new idea and method for the development and testing of automotive steering gear.

Gas cylinders, serving in high-pressure environments for extended periods, are prone to significant residual stress in their thin-walled tank regions during the additive manufacturing and subsequent rolling strengthening processes. This poses a potential threat to the safety and performance of the gas cylinders. Therefore, effectively controlling the residual stress after additive manufacturing and rolling becomes a key research topic. This paper establishes a control system for residual stress in the additive manufacturing and rolling process of thin-walled tanks. By combining experimental data and finite element simulation data from the additive manufacturing and rolling process of thin-walled tanks, a database management system for the additive manufacturing and rolling process is established. Then, neural network algorithms are used to construct the relationship between additive manufacturing and rolling process parameters and target values. Based on these relationship models and the hill-climbing optimization algorithm, a residual stress control system for the additive manufacturing and rolling process is constructed, which realizes intelligent analysis, optimization, and design of process parameters in the additive manufacturing and rolling process of thin-walled tanks. This ultimately achieves the goals of controlling residual stress and enhancing the performance of thin-walled tanks.

An innovative design for a tri-band dual circularly polarized microstrip antenna is presented in this study. The structure utilizes a dual-layer metallic patch design, integrating single-point feed and perturbation techniques. Tri-band circular polarization is achieved through the employment of chamfered edges and the incorporation of small stubs in the design. A compact structure and high radiation gain are boasted by the resulting antenna. The simulated -10 dB reflection bandwidths are 3.48% (1608-1665 MHz), 1.31% (1672-1694 MHz), and 0.52% (1733-1742 MHz). The frequency ranges where the axial ratio is less than 3 dB are 1614-1628 MHz, 1682-1687 MHz, and 1733-1739 MHz, all falling within the effective impedance bandwidth, where the reflection coefficient is less than -10 dB. Additionally, a peak gain of 5.84 dBi is demonstrated by the antenna within the 3 dB axial ratio bands, showcasing its effectiveness for applications requiring compact and high-performance antenna solutions.

In order to overcome the shortcomings of existing ultrasonic Doppler fetal heart rate detectors, such as using wired connection to measure fetal heart rate and analog demodulation to obtain Doppler frequency shift information, which lead to limited monitoring space of pregnant women and vulnerable interference, this article proposes to design a digital wireless ultrasonic Doppler fetal heart rate detection system. The system adopts digital technology based on orthogonal demodulation, and designs a fetal heart rate detection system and key circuits composed of wireless ultrasonic probe and host, which can effectively solve the problems of space limitation and easy interference in fetal heart rate measurement. The hardware of wireless probe includes MCU, CPLD, excitation signal power amplification module, impedance conversion circuit, ultrasonic transducer, filter amplification module, ADC module, Bluetooth module, etc. A mechanical fetal heart rate simulator is used to test the detection system. The test results show that the fetal heart rate detection system has high accuracy of fetal heart rate detection, good repeatability and stability, and its technical indicators meet the requirements. The designed detection system can effectively and accurately complete the measurement of Doppler fetal heart rate.

Based on a band gap reference source in 0.18um process, a single particle transient current model is used to simulate single particle bombardment to conduct circuit level simulation. A single particle sensitivity analysis is carried out on the whole band gap reference source circuit and the internal sensitive nodes are confirmed by simulation verification. Finally, the method based on filtering and isolation is used to strengthen the sensitive nodes against single particle transient, which can effectively suppress the abnormal SET output voltage.

The emergence of additive manufacturing technology has enabled convenient and reliable vertical interconnections in novel three-dimensional(3D) packaging solutions. This paper presents a fabrication scheme for cross-layer interconnection wires based on electrospraying, which utilizes the principles of electrohydrodynamics to directly print cross-layer interconnection structures on various substrates by precisely controlling process parameters, without the need for additional chemical treatments. Specifically, by fine-tuning parameters such as nozzle height, printing speed, printing voltage, and sintering methods, interconnection wires with adjustable line widths of 10 μm or more were achieved on flat substrates, while a cross-layer interconnection structure with a surface resistivity of 0.07 Ω/sq was realized on a stepped substrate with a layer height of 200 μm. Furthermore, the additional resistance introduced by the cross-layer spray printing and the thermal cycling stability of the cross-layer structures were evaluated, verifying the application potential of electrospraying in achieving 3D interconnections for miniature multilayer circuits.

In the automotive industry, lightweight design has become a key strategy for reducing environmental pollution and improving energy efficiency. As a core component of the suspension system, the lightweight design of steering knuckles significantly impacts the overall vehicle performance. To ensure stable operation and good dynamic performance under various working conditions while reducing mass, this study uses ANSYS software to perform topological optimization on the steering knuckle structure with the comprehensive goal of improving static stiffness and the first-order modal frequency. Through optimization, the mass of the steering knuckle has been effectively reduced to 1.075 kg, and the first-order natural frequency has been increased to 551 Hz, demonstrating excellent dynamic characteristics and significantly enhancing the overall performance level of the steering knuckle.

Human movement muscle contraction and diastole is controlled by the brain, the brain through the nerve control of the muscle will produce bioelectric signals, surface EMG signal is one of the many bioelectric signals, surface EMG signal in the field of medical rehabilitation has a significant role in this paper designed a surface EMG signal acquisition equipment, due to the surface of the EMG signal is extremely weak, a little bit of noise will produce a large interference, so this paper according to the characteristics of surface EMG signal designed amplification, filtering circuit, using the AD8221 instrumentation amplifier to collect the raw signal, combined with the amplification, filtering circuit, using the STM32 controller chip will be analog data A/D. Therefore, according to the characteristics of surface EMG signals, this paper designs an amplification and filtering circuit, using AD8221 instrumentation amplifier to collect the original signal, combined with the amplification and filtering circuit, using the STM32 master chip to convert the analog data to A/D and transfer it to the upper computer system, and then carry out the operation of real-time visualization of waveforms. Finally, through the experimental test this system can better amplify the original signal and have good anti-interference.

In response to the current issues of low efficiency and high labor intensity in the installation of aluminum profile tubes and corner codes in the frames of solar photovoltaic modules, which mainly relies on manual labor, an automatic installation device for aluminum profile tubes and corner codes has been designed . This study aims to design a reliable and stable installation device for aluminum profile tubes and corner codes. By designing an installation platform, corner code carrier assembly, carrier lift kit, corner code outfeed assembly, and profile loading assembly to work in coordination and cooperation, the assembly of aluminum profile tubes and corner codes is completed, improving assembly efficiency. Optimized design, the external excitation reaches 67.66% of the first-order frequency, which is below the 75% safety threshold, therefore, this design does not induce resonance, ensuring the stability of the installation. While also significantly reducing the risk of injury to workers, ensuring safety and reliability.

Despite the numerous advantages offered by the utilisation of ultrasonic motors in automotive valve applications, it is characterised by nonlinearity. In order to enhance the control performance of automotive valve motors, which are characterised by nonlinear behaviour, fuzzy PID control is a particularly effective approach that addresses the inherent fuzziness and uncertainty associated with the system. However, the fuzzy PID controller has a greater number of parameters and is more challenging to regulate. This paper therefore combines the fuzzy PID with the grey wolf optimisation algorithm to adjust the fuzzy PID parameters in real time, thereby enabling the controller to adapt to the nonlinear changes that exist in the automotive valve motor.

A low-complexity scheme based on Gurobi is proposed to address the problem of high complexity of waveform design solution algorithms in dual-functional radar-communication (DFRC) systems. Firstly, a DFRC system is constructed, and the objective function for minimizing downlink multi-user interference (MUI) energy is designed. Subsequently, the aforementioned non-convex quadratic constrained programming problem is addressed using Gurobi, a global solver for large-scale optimization. Finally, the results of the simulation experiments demonstrate that, under identical signal-to-noise ratio (SNR) conditions, the proposed scheme achieves a higher total transmission rate and significantly reduced complexity compared to the benchmark algorithms. Additionally, it effectively performs radar target detection concurrently with data communication within the DFRC system.

Fourier Ptychographic Microscopy (FPM) is a kind of micro-computational imaging technique that simultaneously realizes high resolution and a large field of view. To address the problems of artifacts and incomplete information extraction in the reconstructed area features of FPM, we propose a reconstruction method based on the Window Self-attention Network (WSAN) for Fourier Ptychographic Microscopy. Window Self-attention Network (WSAN). The application of multi-head self-attention mechanism in WSAN enables the network to focus its attention on the critical regions in the frequency spectrum of the input image, effectively capturing the local details and structural information of automotive micro-sensors.

We developed a novel theoretical model of a dual-wavelength laser amplifier based on intra-cavity pumping technology. In this model, we derived analytical expressions for the output power of the dual-wavelength laser amplifier. Simulations were conducted to explore the output characteristics of the dual-wavelength amplifier based on intra-cavity pumping technology. The effects of amplifier crystal length and beam radius on the laser's output performance were investigated, providing a solid foundation for further research.

AlGaAsSb materials have been widely studied in avalanche photodiodes (APD) because of their low noise properties. In this paper, an APD with low noise effect and high gain bandwidth product is designed by silvaco Atlas using AlGaAsSb's wide band gap and low noise characteristics. The focus of this paper is to use silvaco simulation software to simulate dark current, photocurrent and optical bandwidth, and establish a simulation model that fits the measured data. Dc characterization was performed under dark and light conditions at room temperature to measure the performance of APD. The optical bandwidth of APD is obtained by high frequency characterization of the device. The measured data are in good agreement with the simulation data, and the gain bandwidth reaches 270GHz.

In order to solve the problem of remote control of some non-IoT home appliances and the problem that various home appliances can't share one remote control, an IoT remote control device with ESP8266 as the control core is designed. The device is equipped with FreeRTOS system to ensure the stable operation of the device; the use of MQTT protocol to realize the remote control of the device; the use of infrared decoding technology to ensure that the signals of the infrared remote control can be correctly received, and the use of multiple infrared tubes to ensure that there is no dead-end control of various home appliances.

At present, the key reason that restricts the commercialization of power batteries lies in their safety issues. Among them, the uncontrollable growth of lithium dendrites will penetrate the separator during the cycle process, resulting in a short circuit or even thermal runaway in the battery, and it is difficult to monitor the internal characteristics of the battery after the occurrence of internal short circuit. In this paper, the electrochemical-thermal coupling model of ternary pouch lithium-ion battery is established in finite element software, and the internal short circuit setting caused by lithium dendrites is added to analyze the characteristics of batteries after internal short circuits at different positions, different starting SOCs, and different lithium dendrite radii. The results show that the larger the radius of lithium dendrites, the stronger the inhomogeneity of the internal temperature of the battery at the same time after the occurrence of the internal short circuit, and the greater the peak value of the battery temperature. The temperature uniformity of the internal short circuit occurring in the center position is better, and the peak temperature reaches is slightly lower due to the good heat dissipation conditions; the rate of battery temperature increase and the peak value reached are positively correlated with the initial SOC of the battery.

With the development of signal processing technology, statistical analysis, time domain analysis, frequency domain analysis and time-frequency domain analysis based on sensor signals enrich the scope of feature extraction. The development of modern manufacturing technology makes the monitoring mode transition from the traditional single sensor to the multi-sensor fusion monitoring mode. Aiming at the failure of tool breakage and tool breakage, this paper proposes an on-line monitoring method of milling tool breakage and tool breakage based on the fusion of spindle load and acoustic emission signal. The eigenvalues of acoustic emission signal and spindle load signal are fused to establish the feature vector, and the tool status decision model based on support vector machine is established. Compared with the single signal monitoring method, the tool status recognition accuracy of the signal fusion method proposed in this paper is significantly improved, which overcomes the problems of low accuracy and poor fault tolerance of tool status recognition of single signal. The kernel parameters and penalty factors of SVM are optimized by grid search, genetic algorithm and particle swarm optimization algorithm respectively. The research shows that the support vector machine decision model optimized by genetic algorithm has the best effect on the classification of milling cutter breakage and cutter breakage, and the fusion of the two sensor signals also improves the accuracy of the detection of breakage and cutter breakage. The feasibility of this method is verified by two sets of actual cutting experiments.

The fan rotor is one of the main components of the turbofan engine, and its performance not only determines the performance of the engine, but also directly relates to the reliability and service life of the engine . Traditional fan blades are made of aluminum alloy or titanium alloy, and during operation, external impact or metal fatigue may cause local fracture of the blades, or even cause the entire blade body to fly out, resulting in elastic vibration or plastic deformation of the shaft system, causing serious air crashes and resulting in aircraft destruction and death. In recent years, fiber-reinforced composites based on lay-up and braiding processes have been rapidly applied to the manufacture of engine fan blades for their advantages of high specific strength, high toughness and low density. Due to the anisotropic material parameters of composites, composites contain a variety of materials, and their failure forms are stripping failure, fiber fracture failure and fatigue failure, etc., which are vastly different from the crack extension failure, fatigue failure and corrosion failure of metal materials, and it is difficult to learn from previous research experience accumulated on metal material blades. Therefore, it is necessary to conduct rotary test studies on fan rotors made of composite materials to provide data support for the design and use of composite fan blades.

At present, SMT defects have problems such as dense distribution of small components and similar and overlapping characteristics among defects, which lead to low accuracy of defect detection. It is proposed to use CS-YOLO defect detection algorithm to improve the SMT detection accuracy. Firstly, based on the YOLOv5 model, the SPD-Conv module is used to enhance the extraction ability of pixel feature information in the pooling process; secondly, the CBAM module retains important feature information from channel attention and spatial attention to improve the perception ability of the model to represent different degrees of features; the MPDIoU is used to calculate the loss function, reduce the distance between the target box and the detection box and reduce influences of additional redundant information, and to improve the efficient positioning and classification of small target positions. Finally, the experimental data show that the mAP, recall rate and accuracy of the algorithm are somewhat improved compared with the original algorithm, and they are deployed in the actual PCB production process. This algorithm significantly enhances the performance and detection rate of SMT small targets, demonstrating its effectiveness.

The design of the HPR1000 main control room safety lighting system developed and applied the lighting equipment with seismic class I requirements for the first time, compared with the existing second-generation nuclear power plant, it is necessary to consider the working conditions of providing lighting in specific areas of the main control room during and after SSE. This paper describes the test purpose, test requirements, test equipment, test content, and test results of the Seismic qualification test of the safety lighting system equipment in the HPR1000 main control room, and focuses on the selection of the lighting sample in the main control room, the support structure used in the test and the envelope of the floor response spectrum, In the test, the natural vibration frequency and damping ratio of the equipment were obtained by the method of continuous white noise wave scanning, the vibration was excited in the three orthogonal axes of the sample at the same time by the multi-frequency wave method, and the acceleration signal of the seismic platform was used as the control signal to complete the seismic identification test. The benchmark performance test of the lamp sample before and after the test met the requirements, the structure was complete, the function was intact, and the code requirements were met.

Small unmanned aerial vehicle (UAV) cluster warfare has gradually become an important driver to subvert future battlefield rules and styles, and its development and application have attracted much attention. This paper starts with the common unmanned aerial vehicle (UAV) cluster combat style, studies the intelligent cooperative control architecture of UAV cluster, and sorts out mission planning as the core technology of UAV cluster combat. This paper focuses on the two key links of mission planning, task assignment and path planning, summarizes the typical technologies and application methods at present, and analyzes some development directions that should be paid attention to in the current research of UAV cluster mission planning technology, in order to provide technical reference and support for China's cluster combat.

This paper proposes a decision tree model for the failure prediction of industrial machinery and equipment, with the aim of addressing the various failure problems caused by the inevitable wear and aging that occurs during the operation of such machinery and equipment. In order to optimize the model, tuning parameters are also included. First, the characteristics of industrial machinery and equipment are examined for potential correlations, and appropriate indicators are identified for fault prediction. Subsequently, a decision tree-based machine learning method is employed to construct a fault prediction model for industrial machinery and equipment. The confusion matrix is utilized as an evaluation index to assess the model's efficacy, and the developed model is employed for prediction. Ultimately, the primary causes of each category of failure are investigated with the objective of effectively preventing industrial machinery and equipment failures, thereby enhancing the safety of the equipment. In comparison to the logistic regression model and the decision tree model prior to optimization, the prediction accuracy of the optimized decision tree model in this paper has been significantly enhanced, reaching up to 98.28%. Furthermore, the training speed of the proposed model is more rapid, which is anticipated to be utilized for actual industrial machinery and equipment failure prediction.

When the induction motor fails, it not only affects its normal operation, but also causes personal safety problems, resulting in unpredictable and serious consequences. Therefore, it is of great significance to research the fault diagnosis method of induction motor. When the motor is in steady state operation or in the initial stage of rotor bar failure, the fault characteristic frequency in the stator current of the motor is close to the fundamental frequency, and the fault signal is weak and easy to be submerged by the fundamental frequency signal, so it is difficult to effectively extract the fault current component when the rotor bar is broken. In this regard, this paper takes the stator current of asynchronous motor as the research object, proposes to use the successive variable mode decomposition (SVMD) method to extract the fault features, and proposes to use the Grey Wolf optimization algorithm (GWO) to optimize the parameters of SVMD method and use the one-dimensional convolutional neural network (1DCNN) to classify the fault severity. Thus the fault diagnosis of rotor broken bar is realized when the motor is running in steady state.

A type of lead-free compensation mud is designed for steel materials radiography inspection. The compensation mud is mainly composed of barium sulfate, tantalum and ultralight clay in a certain proportion. The radiation attenuation coefficient of the compensation mud is approximately same as steel. The lead-free mud is convenient and harmless to inspection, which can be moulded freely according to the shape and size of the detected object. It is considered that the film density difference between the mud and steel changes non-linearly with the proportions of the components upon the theoretical analysis. The radiography experimental data analysis shows that the respective component ratio of compensation mud should satisfy the special linear equations to acquire the same attenuation coefficient as steel. Some workpieces such as aircraft turbine blade, bolt and propeller shaft with complex shape and large mutation size have been radiography inspected with the designed mud. The negative images indicate the details of structures and defects without edge erosion effect.

The study is based on the gear transmission system of a blade roll mill, considering the comprehensive meshing stiffness with gear displacement, tooth surface friction, and geometric eccentricity, as well as the time-varying tooth side clearance caused by geometric eccentricity. A dynamic model is established using Newton's second law, and numerical methods are employed to solve the system of differential equations. The study investigates the dynamic characteristics of the gears under different influencing factors. The results indicate that eccentricity alters the node position of gear meshing, increasing the friction coefficient during the meshing phase and decreasing it during the disengagement phase. Eccentricity also amplifies the dynamic meshing force, changes the vibration pattern, and offsets part of the frictional force, causing a transition from chaotic to nearly periodic motion.

The scraper conveyor, as a large-scale transportation equipment in coal mine longwall mining faces, plays a crucial role in the coal transportation process. The middle trough, being the component of the scraper conveyor that comes into contact with coal the most, experiences impacts and wear that directly affect its service life. Due to the fact that scraper conveyors often operate underground in complex and harsh environments, it is challenging to conduct effective experimental research. Experimental studies under such complex working conditions are difficult to carry out, making it hard to investigate the wear generated between the middle trough of the scraper conveyor and the coal material. To address this, a coupled model of the scraper conveyor was constructed using multibody dynamics and the discrete element method. The wear on the middle trough of the scraper conveyor caused by different types of coal bulk materials was analyzed, focusing on the transportation behavior of coal bulk materials with varying particle sizes within the middle trough. The results indicate that coal particles of different sizes exhibit a stratification phenomenon during transportation, with distinct movement patterns observed for each particle size. Based on Archard's wear theory and the discrete element method, a simulation analysis of the wear on the middle trough was conducted using the controlled variable method to study the impact of coal particles with different particle sizes on the wear depth of the middle trough. The results showed that compared to larger coal particles, smaller coal particles cause more severe wear on the middle trough. This is primarily because smaller particles are more likely to cause three-body wear during the scraper transportation process, and the wear caused by three-body interactions is significantly greater than that caused by two-body wear. Based on the above conclusions, in practical applications, it is essential to enhance the wear resistance of the regions in the middle trough that are most prone to severe wear. Simulation experiments should be conducted to verify the wear conditions in different areas of the middle trough. This approach provides theoretical guidance for improving the transportation efficiency of the scraper conveyor and extending the service life of the middle trough. Additionally, it offers new research perspectives for optimizing the model of the scraper conveyor.

Junction temperature is an important parameter affecting semiconductor lasers, and accurate and fast measurement of junction temperature is crucial for thermal design and performance inspection of semiconductor laser products. The spectra of white light semiconductor laser were measured under different driving currents and different ambient temperatures, and the effects of current and temperature on white light laser spectra were analyzed. Through the connection between junction temperature, driving current, center-of-mass wavelength, and integration width, the relationship graph of the four is constructed. The corresponding junction temperature can be obtained by combining the relationship graph with the characteristic parameters of the spectral distribution of the white light laser in the actual working state. The experimental results show that, compared with the peak wavelength method, the average error of the measurement of this method is 1.82 °C, which is a fast, accurate, low-cost and contact-free method for the measurement of the junction temperature of white light semiconductor laser.

With the rapid advancement of minimally invasive surgical techniques, the high-precision navigational capabilities of steerable needles in complex tissue environments have garnered increasing attention. This study investigates the application of PETG (Polyethylene terephthalate glycol) in steerable needles and its mechanical behavior in tissue phantoms of varying stiffness. By designing and fabricating steerable needles made from PETG and conducting insertion experiments in bio-mimetic tissue phantoms with different Young’s moduli, we systematically analyzed the relationship between pre-bent, insertion step length, and final curvature. The results indicate that, regardless of tissue stiffness, changes in step length have a linear effect on insertion curvature. In softer tissue phantoms, an increase in step length significantly exacerbates the deviation of the needle’s path, while in stiffer tissue phantoms, the needle’s path remains more stable. Therefore, steerable needles made from PETG demonstrate significant potential for application in minimally invasive surgery, providing a solid theoretical foundation and technical support for the future optimization of steerable needle design.

This study aims to address the issue of tool system vibration in the critical field of deep hole machining, with the objective of enhancing machining accuracy, efficiency, and material utilization rate, while also reducing costs. To achieve this, a multi-field coupling mathematical model that describes the physical characteristics of the system is introduced, taking into account linear viscoelasticity and dual gyro effects. The stability of the system and the mutual cancellation mechanism of parameters are analyzed. The Galerkin truncation method was employed to discretize the dynamic equations. The coupling properties of dual gyros were investigated. Concurrently, the influence of key parameters on the natural frequency was analyzed. The research outcomes will furnish theoretical backing for minimizing vibrations in deep hole machining tool systems, contribute to achieving precise machining, and serve the progression of high and innovative technology.

The protection devices of Voltage Source Converter-based High-Voltage Direct Current Transmission (VSC-HVDC) are crucial in modern power grid transmission. The existing fatigue testing of actual DC protection system chips is resource-intensive, and the failure mechanisms, macroscopic manifestations, and simulation methods of these chips remain unclear. Against this research backdrop, this paper models and simulates weak components in the DC protection system. Steady-state thermal analysis was conducted to determine the temperature distribution of the components. Subsequently, the Arrhenius model was employed to accurately estimate the lifespan of the components, and the results were compared with the calculated data, revealing a low overall error. Furthermore, stress and deformation analyses identified areas in two vulnerable components that are prone to damage. The findings from these simulations highlight the impact of temperature on the structure and lifespan of the components, providing a foundational basis for optimizing the design of these components.

Based on variational mode and XGBoost solved by particle swarm optimization algorithm, a data-driven early fault diagnosis method for wind turbine gearbox was proposed. Through the VMD decomposition of the original signal, the fault feature of the best mode is taken as the feature vector, and the XGBoost model is used for intelligent diagnosis and classification of the fault feature vector. Through the test and experiment of the gearbox fault signal collected in practice, the test results show that the method can realize the early fault warning quickly.

An algorithm, AODV-PR, is proposed to predict link failure and reconstruct routing in mobile Ad Hoc networks. Routing reconstruct mechanism of AODV routing protocol restores routes when links are broken, and excessive number of routing failures leads frequent link reconstruction, which increases delay and overhead of routing. AODV-PR is an optimization of AODV protocol aiming at the above problems. This algorithm monitors the neighbors’ powers, uses Newton interpolation polynomial to predict the link disconnection time when danger threshold model alarms, and then advance 2-hop local routing repair algorithm will be initiated before routing fails. The simulations show that AODV-PR has good performance in routing overhead, end-to-end delay, and packet delivery rate.

Tactical networks, as a crucial component of the informationized battlefield, profoundly influence the speed of the OODA loop during warfare, playing a significant role in enhancing combat efficiency. Characterized by intermittent connectivity and the demand for lightweight protocols, tactical networks often experience suboptimal end-to-end transmission with traditional stateless routing protocols. To address this issue, this paper proposes an NDN-based adaptive interface status aware forwarding strategy. By integrating the entropy weight method with the TOPSIS approach and considering static network conditions to calculate interface status scores, the strategy effectively identifies the most suitable output interface for interest packets. Additionally, a tournament algorithm is incorporated to prevent rapid degradation of interface performance. Simulation results demonstrate that, compared to other typical forwarding strategies, the proposed strategy performs better in terms of throughput, latency, and packet loss rate.

The issue was examined for corrugated compensators experiencing fatigue failure in high-pressure pipelines with complicated operating circumstances. A finite element model was established for stress simulation of the corrugated compensator under coupled load containing internal pressure and axial force. And a method was proposed to calculate the stress concentration factor using path integral. The corner area of the corrugated compensator was predicted to be a risky location for the fatigue crack sprouting. By carefully taking into account Goodman average stress correction, stress concentration factor, surface treatment procedure, load form, etc., the fatigue life prediction model of corrugated compensator was developed with multiple factors correction, and the values of each correction factor were obtained. According to the prediction model, the corrugated compensator's fatigue life prediction had a divergence of less than 10% from the engineering measured data. This result provides theoretical guidance for fatigue life assessment and design optimization of corrugated compensators in engineering applications.

This article investigates the passive control problem of a class of nonlinear singular Hamiltonian systems. By designing appropriate feedback controllers, the Hamiltonian system can be equivalently transformed into its differential-algebraic form to study its passive control problem. A passive controller was designed. The relationship between passive control and asymptotic stability is given. Finally, the effectiveness of the designed controller was verified through actual circuit simulation.

The P-U characteristic curves of PV arrays under localized shading conditions may exhibit multiple peaks. Conventional approaches are prone to encountering the local maximum power point (LMPP) and face challenges in effectively balancing the trade-off between speed and precision inherent in maximum power point tracking (MPPT) techniques. A hybrid IGWO-INC algorithm is proposed in this paper, which combines the Improved Gray Wolf (IGWO) algorithm with the conductivity incremental method (INC) to effectively search for the vicinity of the maximum power point (MPP) through velocity and position updates, thereby achieving accurate tracking of local maximum power points. The final simulation results demonstrate that the IGWO-INC algorithm enhances tracking speed and accuracy, reduces power oscillations, and ensures normal operation. The validity of the algorithm has been verified.

In this paper, the problem of adaptive robust control for saturated constrained hydro generating units with unknown parameter disturbances is studied, that is, the integrated control of water gate excitation for hydro generating units. In order to facilitate the research, firstly, the differential algebraic equations of hydro generating units are transformed into the nonlinear singular systems. Secondly, according to the sufficient condition of pulse controllability, the static output feedback adaptive robust controller is designed. Finally, the effectiveness of the designed controller is verified by theoretical derivation and data simulation for saturated constrained hydro generating units.

This article describes the adaptive control problem of input-output finite-time stabilization of nonlinear descriptor Hamiltonian systems. With the considered L₂ disturbance input, the paper proposes a state feedback adaptive controller to investigate the stabilization problem of the systems in accordance with the impulse controllable condition. At last, the efficacy of the devised controller was validated through the simulation of a nonlinear descriptor circuit system.

With the rapid development of electric vehicle technology, the vehicle controller, as the core of the vehicle system, plays a crucial role in achieving efficient, safe, and comfortable driving experience through its drive mode control function. Firstly, the requirements for driving mode control functions were clarified, including multiple modes such as normal driving, braking energy recovery, and fault protection. The driving modes of pure electric vehicles can be divided into neutral mode, driving mode, parking mode, limp driving mode, and anti-skid mode. Among them, the driving mode is subdivided into forward driving mode and reverse driving mode. Each driving mode is divided into Drike mode, Break mode, and energy recovery mode. In the sensor and signal acquisition stage, key data such as accelerator pedal position, brake pedal force, vehicle speed, motor speed, battery status, etc. are obtained in real time through sensors arranged in various key parts of the vehicle, providing a basis for the formulation of subsequent control strategies. In the control strategy design section, based on knowledge from multiple fields such as vehicle dynamics, battery management, and motor control, reasonable control algorithms have been developed to ensure that the vehicle can maintain optimal driving performance under different operating conditions.

Taking the EML340 continuous miner as the research object, the fatigue life and damage values of the output planetary carrier were obtained using Ncode Design life. The single factor method was used to discuss the effects of drum helix angle ranging from 15° to 23°, coal rock strength coefficient ranging from 1.5 to 3.5, and cutting arm swing speed ranging from 3m/min to 7m/min on the fatigue life of key components. The experimental results reveal the specific effects of these factors on the fatigue life of the key parts: the increase of the spiral rising Angle of the drum will lead to the decrease of the fatigue life of the key parts in the initial stage, but with the further increase of the rising Angle, the fatigue life will rise. The increase of coal coefficient will continuously reduce the fatigue life of key parts. The increase in the swing speed of the cutting arm has no significant negative impact on the fatigue life of the key parts.

With the continuous development of science and technology, the performance requirements of power electronic devices are getting higher and higher, so the wide band gap devices based on SiC devices are gradually widely used in many fields. This paper introduces the basic working principle of SiC MOSFET and the parameter characteristics under the influence of temperature, studies the influence mechanism of threshold voltage, transconductance, on-resistance and temperature control source of SiC MOSFET model under different temperatures, and establishes the equivalent circuit model for simulation verification. The static characteristics of SiC MOSFET is significantly temperature-sensitive, with transconductance and gate-source resistance increasing with temperature, and the change in on-resistance is regulated by gate voltage and temperature.

To address the issue of significant noise and interference in satellite communication channels, this study introduces a noise reduction approach that employs Variational Mode Decomposition (VMD) enhanced by adaptive parameter optimization. Given the substantial impact of the number of decompositions and the penalty factors on the outcomes of VMD, this research develops a strategy for parameter optimization. Initially, it utilizes the count of effective spectral peaks in the singular value difference spectrum to ascertain the signal count in scenarios of multiple signal overlays, subsequently optimizing the modal layer count for VMD decomposition. Furthermore, it refines the penalty factor by comparing the difference in central frequency between adjacent modes against a set threshold. The testing of actual signals demonstrates that the parameter optimization strategy not only addresses the challenge of determining decomposition parameters in the traditional VMD algorithm but also mitigates the potential for information loss or over-decomposition. Moreover, this method demonstrates effective noise reduction in channel environments characterized by intense noise and interference, offering valuable insights for channel noise mitigation.

Path planning is a critical issue in enhancing the level of automated material handling in production workshops, as it significantly impacts the efficiency of automated guided vehicles (AGVs). To address the efficiency problems of AGVs in most production workshops, this paper proposes a path planning algorithm based on the artificial potential field (APF) method. The algorithm dynamically adjusts the generation of repulsive and attractive potential fields in the APF method according to the distance between the AGV and obstacles or the target point, which resolves the issue of non-smooth path planning when there are obstacles near the starting point and the end point in the traditional APF method. By introducing an additional external force at the points where the resultant force is zero along the path, the problem of the traditional APF method being prone to local minima is also addressed. The performance of the improved APF method for path planning is validated through Matlab simulation and actual testing in a production workshop, and is compared to the traditional APF approach. The results demonstrate that the new algorithm outperforms the conventional APF method across various metrics, and effectively improves the efficiency of AGV material handling.

Current research on multi-AGV task scheduling mainly focuses on minimizing the total running time of multiple AGVs, with less consideration for issues such as AGV wear and cost balancing. This paper proposes a multi-AGV task scheduling optimization method that considers not only the total running time, but also the maximum running time of individual AGVs. The method aims to ensure high overall operational efficiency while improving AGV wear and cost imbalance issues. The method is based on an improved double-chromosome genetic algorithm (IDCGA), adopting a double-layer encoding structure with task chromosomes and AGV chromosomes, and designing an optimized greedy search strategy and population destruction-reconstruction strategy to avoid being trapped in local optima. To improve the computation speed, parallel computing is also employed to accelerate the IDCGA algorithm. Simulation experiments show that when the weight coefficient w is set to 0.9, the method not only has good robustness, but also the fastest computation speed. The simulation experiments also verify that the IDCGA algorithm can obtain better results, with faster running speed and better robustness, compared to the traditional genetic algorithm (GA).

Bus is the most popular means of mass transportation in the city. With the development of urbanization and motorization, the population of the city and region growing, people demand for public transportation corresponding rapid growth, traveler to transit much higher requirements are put forward. Bus arrival time is one of the important parameters of urban intelligent public transport system service. It is also one of the basic information that public transport travelers are most concerned about. In order to meet the needs of the bus travelers can query the bus arrival time in time, the paper introduces the design of real-time arrival time query system of city bus. On the basis of requirement analysis, the data model of the query system is established through conceptual structure and logical structure design. A prediction algorithm for arrival time is presented. The algorithm divides the route into the station section, on the base of the data collected in real time, and the running time of each section is obtained from the historical record. The predicted running time is confirmed by clustering analysis and discriminant analysis. Finally, the prediction of arrival time that users’ need is obtained by simple calculation. And the arrival time is displayed to the system user through the network.

For imported equipment with strictly confidential design data and for old transmission devices with lost design documents, gear mapping is an important technical means for replication and upgrade transformation. For the measurement and drafting of angularly modified spur gears, existing contact-based measurement methods have the disadvantages of cumbersome steps, unstable measurement accuracy, and low efficiency. To enhance the efficiency and precision of detection and measurement, this paper utilizes a machine vision approach to design a method for mapping angularly modified spur gears, which is efficient in detection and widely applicable. It enables the reverse engineering of design parameters such as gear modulus, pressure angle, displacement coefficient, and addendum reduction coefficient. First, this method combines image processing and point list segmentation methods to quickly identify the number of gear teeth and obtain geometric parameters of the gear; on this basis, a novel fitting tooth profile smooth search method is proposed, which abandons the traditional auxiliary positioning of the base circle, directly measures and calculates the gear common normal length; combined with reverse engineering technology, the gear parameter solving problem is transformed into an optimization problem, and the target function is constructed to achieve accurate solving of gear parameters. The actual gear mapping experiments were carried out and compared with the design parameters. Among them, the measurement results of modulus and pressure angle are highly consistent with the design values, and the maximum error of the displacement coefficient is only 0.0037. The average detection time for each gear is 8.6 seconds, proving that this method has advantages in improving detection efficiency and measurement accuracy, which can provide data support for gear replication and optimization. In addition, this method is compatible with the mapping of high displacement and standard spur gears, and has a wide range of applications. It has practical value for improving the application development of machine vision in the field of gear mapping.

Based on the PIE cloud platform and the monthly synthesized data of nighttime lights, a dynamic visualization system has been developed for the global long-time series of nighttime lights by using three major categories of methods: the design of the top layer of the system, the determination of the display area and the display of nighttime lights. The study shows that users can select the nighttime lights in the dynamic visualization system, enter the country name they are interested in, determine the start time and end time of the nighttime lights, and then click on Display, the system will take the year as the timeline, and then dynamically display the nighttime lights of each year in turn. During the dynamic display of nighttime lights, users can click “Remove” button, “Pause” button and “Continue” button to remove, pause and resume the dynamic display of nighttime lights.

The big data processing technology plays a key role in investigating the integrated energy system and developing a detailed information database, which has potential to enhance the efficiency of energy utilization. In this paper, a novel data structure, the Frequent Items Matrix (FM), is introduced, which efficiently compresses high-dimensional sparse databases and reduces the computational load for support counting. Leveraging the FM structure, the FM-growth algorithm for frequent itemset mining is developed. The FM-growth algorithm requires only two scans of the transaction database and computes all frequent item sets using straightforward matrix operations, thereby minimizing the generation of intermediate results. In the case study, the optimized target values determined using the proposed method exhibit a consistent overall trend with the design values, which verifies the efficacy and potential of the proposed method.

Addressing the difficulty of radar signal sorting and tracking in complex electromagnetic environments, this paper proposes a radar signal tracking method that combines channelization techniques and Pulse Repetition Interval (PRI) tracker techniques. The paper details the implementation principle of channelization technology based on polyphase-filter structures, its efficient FPGA implementation, methods for extracting detection signals after channelization, and the implementation of PRI tracking circuits. This method can simultaneously sort and track multiple known radar signals in both time and frequency domains, enhancing the signal processing capabilities of electronic countermeasure equipment.

Traditional K-Nearest Neighbors (KNN) and Weighted K-Nearest Neighbors (WKNN) algorithms face challenges due to the fixed selection of the K value. To address this issue, we propose a new algorithm that combines Genetic Algorithm (GA) with WKNN, referred to as GA-WKNN. The GA algorithm optimizes and adjusts the K value, thereby reducing errors in the visible light positioning system. Experimental results show that the GA-optimized WKNN significantly improves localization accuracy compared to traditional methods with a fixed K value. This optimization strategy allows the positioning system to more accurately determine the actual position.

Diffractive Deep Neural Network refers to a neural network model constructed based on the principle of optical diffraction. It consists of multiple layers of diffractive surfaces and can perform various functions of neural networks. Due to its flexible manipulation of optical fields, the diffractive neural network can also be used for image processing. However, the image encryption and decryption functions based on this network model have not been thoroughly studied. Therefore, this project proposes an innovative image bidirectional encryption and decryption mechanism based on the diffractive neural network model, leveraging the non-reciprocal properties of multi-layer diffractive neural networks. Trained on subsets of MNIST and EMNIST datasets, the resulting image encryptor and decryptor can achieve highly flexible encryption and accurate decryption of images, validating the reliability of this non-reciprocal image codec design scheme. On one hand, this research explores the non-reciprocal properties of diffractive neural networks, achieving both image encryption and decryption functions using only one set of metasurface. This provides new ideas for the multifunctional reuse and integration design of optical devices. On the other hand, the non-reciprocal device design concept of this research is expected to provide new solutions for the design of non-reciprocal microwave devices such as duplexers and electromagnetic imaging devices. Therefore, our research has significant scientific value and application potential.

A new method for capturing signals from multiple GPS satellites has been proposed to improve the speed of GPS receiver signal acquisition. We analyzed the correlation of multiple satellite signals and proved that it is possible to simultaneously process the computation of correlation to multiple satellite signals in one correlation calculation process, thus enabling the simultaneous capture of multiple satellite signals in one capture process.

The development of machine learning is driving revolutionary changes and unprecedented opportunities across various industries. This paper introduces a new temperature monitoring method that combines the efficient deep learning model EfficientNet with Optical Frequency Domain Reflectometry (OFDR). We utilize distributed fiber-optic sensing technology based on OFDR to collect one-dimensional temperature signals under different temperature conditions, which we then transform into a two-dimensional image format suitable for machine learning processing. The dataset is subsequently trained using the EfficientNet deep learning model. The results show that the trained model achieves a high accuracy rate of 97.5% on the test dataset. This research not only demonstrates the feasibility of efficiently and accurately monitoring temperature changes but also provides new solutions and technological pathways for areas such as environmental monitoring, industrial automation, and safety systems.

Aiming at the problem of disconnection that may occur during the communication between the submersible and the host ship due to the complex marine environment, this paper investigates the position prediction model of the submersible after disconnection for T time. The triaxial acceleration is obtained by the acceleration sampler on the submersible at the moment of loss of communication, and the model is constructed to predict the pitch angle of the submersible based on the long short-term memory network (LSTM) and the feed-forward neural network. By combining the three-dimensional coordinate method and the projection method, the three-axis acceleration is projected onto the coordinate system, and the position prediction model at the moment of loss is obtained using the heading projection method. This study provides search and rescue personnel with an advanced strategy for predicting the trajectory of the submersible, which significantly improves the accuracy and reliability of the prediction, and is of great significance for improving the safety and success of submersible operations in complex underwater environments.

Asymmetrically clipped orthogonal frequency division multiplexing (ACO-OFDM) system that is based on visible light communication (VLC) system face this issue of high peak-to-average power ratio (PAPR). To address this issue, a hybrid scheme combining the partial transmission sequences technique optimized by particle swarm optimization algorithm (OPTS) and time-delay neural network (TDNN) is proposed. Compared to schemes that apply a single PAPR reduction technique, the hybrid scheme that has been proposed is more effective at reducing the PAPR. Moreover, the proposed scheme has a lower computational complexity than the conventional hybrid scheme.

High-orbit satellites, because of their wide-area coverage, are often used in data relay back applications of various types of remote sensing, surveying and mapping satellites in low and medium orbits. At present, the point-to-point method of beam alignment is commonly used to realize the space-based data relay back, which usually needs to adopt the reservation method for service planning, with low timeliness and system capacity. The introduction of multi-access technology allows high orbit satellites to form a wide-area, static, full-coverage multi-beam on a global scale, realizing full-time visible and on-demand access to users within the irradiation range, which greatly improves the timely response capability of the system. Based on the high-orbit multi-beam satellite system, this paper simulates and analyzes the user capacity of the system under the full consideration of the impact of conflict, multiple-access interference and spatial coverage overlap, so as to provide a reference basis for further exerting the effectiveness of the use of the high-orbit satellite system.

Different motion states of maneuvering targets are often influenced by various constraints. The study introduces the Interactive Model Algorithm based on Ellipsoidal and Linear Constraints (IMM-ECLC) as a solution to the difficulties in monitoring manoeuvring objects with numerous motion modes. The proposed method introduces constraints related to elliptical and linear motion into the Unscented Kalman Filter (UKF) and integrates these constraints within the IMM framework. This enhances the accuracy of model selection and target tracking in various motion states. Simulation results demonstrate that, compared to traditional unconstrained IMM algorithms, the proposed approach significantly improves both the accuracy of model selection and the precision of target tracking.

Semantic communication technology is an innovative means of information communication that improves the bandwidth effect by transmitting semantic information about the data. In this paper, a unified image semantic transfer mechanism based on deep learning technique capable of performing multiple downstream tasks is proposed. It is also possible to use the common semantic information of each target to achieve the task of combining the target with different objects. Furthermore, a novel semantic feature extractor is proposed, the results of the simulation demonstrate that the suggested approach outperforms the focused on tasks picture semantic communication system in terms of efficiency and cost.

In recent years, Mobile Ad Hoc Networks (MANETs) have been widely applied in various fields such as military missions, ecological surveillance, crisis management, smart transportation schemes, and drone operations. However, in emergency scenarios, physical communication channel congestion and unexpected technical failures of infrastructure can severely restrict or even interrupt communication, while some important data have higher transmission priority requirements. In replies to this issue, this paper proposes a priority-driven mobile ad hoc network routing protocol. This protocol, based on the Dynamic On-Demand Distance Vector (AODV) routing protocol, incorporates a priority parameter, enhancing the data packet transmission rate for emergency nodes within the network without compromising the overall network performance. Simulation results on the ns-3 simulation platform show that compared with the DSR, AODV, P-AODV, and BR-AODV, PD-AODV has increased the data packet transmission efficiency of emergency nodes by 15.9%, 10.8%, 6.2%, and 4.3% respectively. Future work will focus on optimizing and adapting to specific emergency scenarios to achieve greater breakthroughs in applications such as aerial vehicle ad hoc networking and vehicular ad hoc networking.

"Status and Prospects of Mechanical Precision Machining Technologies: an Econometric Analysis of Literature Based on 2019–2024" is a key research topic^[1]. In this study, bibliometric methods were used to systematically analyse the research developments and trends in the field of mechanical precision machining between 2019 and 2024^[2]. Relying on the Web of Science (WoS) database, this study quantitatively analyses and visually presents the relevant literature with the help of visualization tools such as CiteSpace and VOSviewer, and then deeply analyzes the current status of mechanical precision machining technology and its future development path. The study reveals the technological progress, research focuses and potential directions for future research in this field^[3], and summarises the main conclusions: mechanical precision machining technology is steadily developing in the direction of intelligence, high precision and high efficiency, which provides a solid technological support for the development of related industries^[4].

In this paper, a multi-input multi-output underwater acoustic communication method based on OTSM-IM (MIMO-OTSM-IM) is proposed. The method introduces the index modulation technique and the multi-input multi-output technique into the OTSM system in order to achieve a balance between spectral and energy efficiency, as well as to significantly improve data transmission reliability. In particular, the method employs constellation and position to transmit information bits simultaneously in the delay-sequence domain with both single-input-single-output and multi-input-multi-output transmission modes, while the receiver side is detected using maximum likelihood detection method. The final simulation results show that MIMO-OTSM-IM exhibits significant advantages in enhancing the performance of the error rate for the system.

Aiming at intersecting feature recognition, this paper presents a classification method of intersecting feature. Intersecting feature can be classified as multi-loop intersecting feature and hybrid loop intersecting feature according to topological constitution. On this basis, this paper presents a hybrid feature recognition method based on Loop Attributed Adjacency Graph and hint. The theory of this method is decomposing Attribute Adjacent Graph using base surface decomposition and sub-graph matching. Using this method, feature recognition can be implemented: First, after the base-face was obtained, the Loop Attributed Adjacency Graph for each loop of the base-face was established to decompose the Attribute Adjacent Graph. Second, every Loop Attributed Adjacency Graph was judged for hybrid loop intersecting feature: If the sub-graph doesn’t contain hybrid loop intersecting feature, it will be recognized by sub-graph matching. If not, the sub-graph will be recognized by the method based on hint. Boundary test and feature test were introduced to confirm the correctness of feature recognition. Intersecting feature can be recognized by iterating the method.

The implementation of track deception jamming by multiple UAVs on enemy network radar has high requirements for cooperation between the UAVs. The complex and changeable air combat environment will cause UAVs to deviate from the preset track, and then affect the homology test effect of false track points. In order to plan the UAV flight track with high interference rate, this paper first constructs the track deception jamming model, and then establishes an evaluation function model based on the analysis of the UAV energy consumption, probability of being detected by enemy radar and ineffective interference rate. Finally, the standard particle swarm optimization algorithm is improved based on Logistic chaos mapping, Levy flight and greedy strategy and then the improved simplified particle swarm optimization algorithm is obtained. The simulation results show that the improved simplified particle swarm optimization algorithm has faster search speed and higher search accuracy, and the UAV flight track planning based on the evaluation function in this paper can obtain greater effective interference rate.

In traditional optical reservoir computing, the least squares method are commonly used to train the output weights for regression tasks. Although these algorithms are highly versatile, their training efficiency and accuracy are somewhat inferior compared to current algorithms. Bagging trees, an ensemble learning method, works by resampling the dataset to train multiple models and then combining the predictions of these models, thus improving the stability and accuracy of the final prediction. By combining optical reservoir computing with the bagging trees, both the prediction accuracy and training efficiency are greatly improved, with the highest R-squared prediction reaching 99.56%.

The effects of smoke spread, decrease in visibility and uneven temperature distribution on evacuation safety during a fire are thoroughly investigated with respect to the characteristics of public building fires and the theory of safe evacuation. The fire model is established through BIM technology and its application and optimization methods in building models. Pyrosim software was used and data simulation analysis was performed based on the BIM model. Emphasis was placed on the effects of factors such as smoke spread, visibility and temperature distribution on the fire spread and evacuation process. In addition, the evacuation process was simulated using Pathfinder software in conjunction with the BIM model. The parameters affecting the evacuation efficiency were investigated with the aim of finding the best evacuation strategy. Finally, a fire evacuation path planning optimization scheme based on improved ant colony algorithm was proposed. By combining the ant colony algorithm with evacuation path planning, the optimal evacuation path can be found in complex building environments and the evacuation efficiency can be improved.