Aiming at the pain point of slow traditional rescue response in water areas， this paper proposes a structural design scheme of an amphibious rescue equipment that integrates the functions of unmanned aerial vehicles （UAVs） and rescue boats. The UAV adopts a lightweight fuselage and NACA4412 airfoil aerodynamic design. Through the rotation of wings and elescopic mechanism of the counter-rotating propellers， a rapid cross-medium form switching can be achieved. Meanwhile， the dynamic performance is analyzed by establishing the mathematical model and state-space model of the drone， and a fuzzy PID controller is designed. MATLAB/Simulink is used to carry out dynamic response simulation verification for the mathematical model of the drone and the designed fuzzy PID controller. The results show that when the input is a square wave and a step signal， the fuzzy PID controller designed in this paper has a faster response speed and better stability compared with the traditional PID controller.

The dexterous multi-fingered robotic hand， serving as a key end-effector， is pivotal for enabling robots to perform fine-grained grasping and compliant manipulation. Its advancement holds significant importance for promoting automation in manufacturing， enhancing the intelligence of service robots， and expanding applications in specialized environments. Focusing on humanoid multi-fingered dexterous hand technologies， this paper systematically reviews the current state-of-the-art and future trends. It begins by elucidating the fundamental concepts， system architecture， and typical characteristics of dexterous hands. This is followed by a comprehensive of research achievements from domestic and international teams and commercially available mainstream multi-fingered dexterous hand products， covering various degrees-of-freedom designs and their respective hardware and software implementations. Key technologies， including core hardware components， multi-modal sensory fusion， and control strategies， are critically analyzed. The paper subsequently summarizes practical applications across domains such as industrial assembly， daily life assistance， and operations in extreme environments. Current challenges， particularly in reliability， multi-modal coordination， generalization capability， human-robot safety， and integration and application， are identified. Finally， future research directions are prospected from multiple perspectives， including standard establishment， novel mechanical structures， advanced multi-modal perception and fusion， bionic evolution， and embodied intelligence， aiming to provide valuable insights for in-depth research and groundbreaking applications of dexterous hands.

Facing the requirements for diverse starting and ending positions as well as velocity in the field of robotic motion control， this paper proposes a novel universal S-curve velocity planning algorithm aiming at adapting to arbitrarily specified starting and ending positions and velocity conditions. The paper first introduces the velocity-to-velocity S-curve velocity planning algorithm， then elaborates on the general 7-segment S-curve velocity planning algorithm. Based on this， a more versatile S-curve velocity planning algorithm is proposed. For different input parameters， this paper classifies the S-curve （s-t curve） into ten types and provides detailed segmented planning strategies for each type. Through simulation tests， it is verified that the algorithm not only excels in efficiency but also demonstrates outstanding performance in the smoothness and positional accuracy of the planned curve. Furthermore， tests conducted on actual robotic platforms further confirm that this algorithm can effectively reduce shocks and vibrations during robotic operations， significantly enhancing the operational performance of robots while demonstrating good practicality and broad application prospects.

The machining quality of electric spindles critically determines precision， efficiency， and stability in precision manufacturing. However， the machining process faces challenges due to diverse product types， multiple operating conditions and scarce target-condition data， making consistent quality of electric spindle difficult to guarantee. To address this， this paper proposes a transfer-learning-based method for multi-operating-condition quality prediction. The method first extracts spindle time-series signals and employs the Synthetic Minority Over-sampling Technique to balance historical and target-condition data distributions. Subsequently， constructs a two-stage regression model， TrAdaboost.R2， and leverages knowledge transfer to predict spindle quality under target conditions. Finally， the proposed method is validated with electric spindle data， demonstrating its superior prediction performance. This approach provides an effective framework for the precise quality prediction of electric spindles across varying operating conditions.

To address the problems of scattered knowledge systems in ship assembly and ineffective mining and utilization of massive process data， this paper proposes an automatic knowledge graph construction technology for the shipbuilding domain based on large language models. This method uses large language models to convert unstructured and semi-structured ship data into structured data to build a ship process corpus. It models ship ontology knowledge structure with the assistance of large language models， designs an instruction prompting framework for ship assembly domain， and achieves efficient entity-relationship extraction， to complete the automatic construction of knowledge graphs. Additionally， the method uses triple sets constructed by general large language model instruction prompts as fine-tuning training sets to further fine-tune specialized small language models， ensuring the security of specific private ship data while reducing computational resources. Experimental results show that this method outperforms traditional baseline models in key metrics such as accuracy， providing a new technical approach for knowledge management and intelligent upgrading in the shipbuilding domain.

To address the challenges of frequent bearing failures under complex working conditions， as well as the low real-time performance and strong dependence on manual feature extraction in traditional diagnostic methods， this paper proposes a bearing fault diagnosis method based on a deep learning model combining a Multi-Scale Convolutional Neural Network （MSCNN） and Long Short-Term Memory （LSTM）， and develops an intelligent bearing health management system. The system adopts an end-to-end diagnostic workflow， directly taking raw time-domain vibration signals as input. It extracts hierarchical local features across different frequency domains through MSCNN， and captures the temporal evolution of fault characteristics using LSTM， thereby achieving high-accuracy automated fault classification. To enhance the interpretability of diagnostic results and support intelligent maintenance decisions， the system integrates the Chinese large language model iFLYTEK Spark， which generates natural language diagnostic reports and maintenance suggestions through standardized prompts. The system is deployed on a domestically developed Phytium quad-core processor platform， ensuring full autonomy and reliability of both hardware and software components for industrial applications. Experimental results show that the proposed system achieves an average classification accuracy of 98.46% on the CWRU bearing dataset， and 96.73% on the AITHE bearing fault dataset， demonstrating strong robustness and cross-dataset generalization under complex and noisy conditions. With real-time visualization of diagnostic results and maintenance recommendations through a human-machine interface （HMI）， this system provides a reliable and intelligent solution for equipment health management and predictive maintenance.

By analyzing the existing gangue sorting manipulator claw and its use， it is concluded that the existing claw has a large weight， is susceptive to wear and tear as well as higher cost of the overall replacement. A method is hence adopted to install replaceable wear-resistant shims and to select lighter quality materials for improvement. The finger force analysis is made before and after the improvement through the Ansys Workbench， and the improved finger effect proves to be better， verifying the feasibility of the installation of replaceable wear-resistant shims， while determining the replaceable wear-resistant shims material being 20CrMnSi， and finger base material being TC4. The fatigue life analysis is made for the finger before and after the improvement using fatigue analysis tools， and the conclusion is drawn that the fatigue life of improved finger matrix is longer， and the replaceable wear-resistant shims begin to fail after being used 4.3794×105 times， and are therefore needed to be replaced after about two months of use.

The identification of the starting point of the welding is an important step in the intelligent welding process. Due to the influence of fixture positioning error and workpiece deformation， the actual position of the welding starting point often deviates greatly from the theoretical position of the workpiece. Taking the common "three-sided corner joint" weldments as the research object， a two-stage 2S-RANSAC algorithm was proposed by using a line structured light camera， which realized the fast and accurate identification of the welding starting point. In the first stage of the algorithm， the position of the weld corner point was found， while a "three-point sampling method" was proposed to carry out a pre-test of the model so as to improve the accuracy of point cloud sampling and the efficiency of the algorithm. The experiments show that the efficiency and the accuracy of the 2S-RANSAC algorithm were increased by 67% and 43% compared with the traditional RANSAC algorithm. With the recommended point cloud data acquisition scheme， about 10 groups were collected at intervals of 10 mm on both sides of the starting point， and the weight accuracy of the welding starting point fitting could be controlled within 0.34 mm， meeting the process requirements of automatic argon arc welding of general "three-sided corner joint" weldments.

In the industrial software ecosystem， Computer-Aided Design（CAD） interfaces play a pivotal role. This study outlines the composition and collaborative mechanisms of the industrial software ecosystem， reviews the evolutionary trajectory of CAD interface technologies， and summarizes their core roles within the ecosystem from the perspectives of data transmission， functional integration， and innovation-driven development.Building on this foundation， an in-depth analysis of the application bottlenecks and challenges faced by CAD interfaces is conducted， including data interface standards， the depth of system integration， and the convergence with emerging technologies. Furthermore，future development trends for CAD interfaces are explored， emphasizing key directions such as data standardization and semantic enrichment， multi-user collaborative design with real-time interaction， and the deep integration of artificial intelligence technologies. This work aims to provide theoretical insights and practical guidance for the research and application of CAD interfaces within the industrial software ecosystem.

In order to adapt to the characteristics of complex electronic equipment， such as multi-variety， variable batch， multi-level blind matching and vertical interconnection， a six-degree of freedom heterogeneous assembly robot arm has been independently developed and applied to the assembly line of complex electronic equipment. Firstly， the structure composition， configuration advantages and problems in practical application of the heterogeneous six-axis manipulator are introduced. Secondly， the forward and inverse kinematics algorithm of the heterogeneous six-axis manipulator is established by using D-H parameter method， and the kinematics model of the heterogeneous six-axis manipulator is constructed. Then the calibration algorithm， trajectory planning algorithm， collision control algorithm and other methods of the heterogeneous six-axis manipulator are studied. Finally， the field calibration experiment and MATLAB simulation analysis are used to verify the motion planning method， which proves the rationality and practicability of the relevant methods.

The quality of screw tightening greatly affects the safety of mechanical products， whereas traditional diagnosis approaches are time-consuming and imprecise， and the implementation of effective fault diagnosis， therefore， bears significant engineering value. In this paper， an innovative method of fault diagnosis for screw-tightening based on LSTM and Expert knowledge is proposed. Firstly， tightening process curve under specific failure mode was studied and several expert knowledge rules were established. Secondly， a data pre-processing algorithm was established based on the characteristics of sequential data such as noise clipping， stage segmentation， fitting and sampling to improve the quality of data. After that， the feature vector obtained through LSTM was used as the input of the expert knowledge model to obtain the expert knowledge vector， and the two vectors were combined as the input of the classifier. Finally， compared with SVM and LSTM， the results show that the method has higher diagnostic accuracy in multiple failure modes.

Irregularly shaped objects often appear inside the pipelines of nuclear power systems， and soft grippers with passive deformation ability are hence needed for grabbing operations. While the load capacity of the fixed stiffness soft gripper is usually low， the variable stiffness is an important way to improve the load capacity of the soft gripper. On the other hand， the force perception is very important for grasping fragile objects， but rigid force perception is difficult to integrate with soft grippers. Therefore， it is crucial to develop a flexible force perception function module that can be integrated with the soft gripper. Based on the characteristics of the tendon-air hybrid drive structure and the coordinated working relationship of the muscles on the upper and lower sides of the bionic human fingers， this paper proposes a design idea of a soft gripper that uses antagonistic action to change the stiffness of the soft fingers and integrates a force perception function. The designed soft gripper has three pneumatic chamber fingers， each of which is prepared by the distributed casting silicone rubber process， and the deformation process of a single pneumatic chamber finger is simulated using simulation software. To achieve contact force perception during grasping， a force sensing system based on Hall chip magnetic field intensity sensing is integrated at the tip of the soft finger， and the performance of the sensor is calibrated and tested. Through experiments， the adaptive grasping ability of the soft gripper for different target objects and the load capacity of the tendon-air antagonistic drive are tested. The experiments show that the tendon-air antagonistic drive can effectively improve the load capacity of the soft finger， and the perception module can achieve real-time measurement of the contact force of the grasping target.

Heat exchangers play a crucial role in improving the energy efficiency of industrial processes， reducing fuel consumption， and decreasing greenhouse gas emissions. This paper addresses the innovative design problem of heat exchangers with numerous parameters， variable structures and complex medium flow characteristics by proposing a generative design method for spiral tube heat exchangers. Firstly， it analyzes the design principles， the structural advantages， and the performance characteristics of spiral tube heat exchangers， introduces the application process of the generative design method， the design optimization logic， and the automated parametric model generation method. Then， through computational fluid dynamics simulation， it evaluates the thermal transfer efficiency and fluid dynamics performance advantages of the spiral tube heat exchangers. Finally， through structural mechanics simulation， it assesses the risk resistance performance advantages of the spiral tube structure under various operating conditions. The proposed generative design method achieves rapid optimization iteration of design solutions and rapid generation of heat exchanger models， providing the possibility for rapid exploration and design of high-performance spiral tube heat exchangers.

To realize the effective continuous target detection and tracking of the insulator flushing robot in the occlusion environment， a detection and tracking method based on computer vision in the occlusion environment is proposed. Firstly， the attention mechanism is added to the YOLOv5 detection algorithm to enhance the recognition accuracy of the detection algorithm， the scale filter in the DSST algorithm is then combined with the KCF tracking algorithm to make the KCF scale adaptive. Thereafter， the Multi-PROSAC-ORB occlusion recognition algorithm is constructed to realize the occlusion recognition. Finally， the above three algorithms are fused and an occlusion judgment condition is proposed to ensure the continuous and stable recognition and real-time performance of the target in the case of occlusion. The experimental results show that the proposed method can effectively avoid the low accuracy of target tracking in the occlusion environment while ensuring the real-time performance， and the target tracking accuracy is increased by 10.9% and the tracking success rate is increased by 13.6% compared with the target tracking accuracy in the unocclusion recognition， which has high accuracy and real-time performance.

As an important component of intelligent manufacturing and intelligent operation and maintenance systems， industrial inspection robots are playing a key role in various complex industrial scenarios. With the continuous progress of deep learning， multi-sensor fusion， and autonomous navigation technologies， industrial inspection robots have significantly been improved in terms of accuracy， efficiency， and adaptability. This article systematically reviews the concept， key technologies， and typical applications of industrial inspection robots， and focuses on analyzing the research status of core technologies such as perception and recognition， autonomous positioning and navigation， advanced control， and intelligent decision-making. It also assesses the maturity and industrialization progress of current technologies by combining practical applications in fields such as power， workshops， and special environments. Despite significant achievements in this field， challenges still exist in perception accuracy， dynamic environment adaptability， and task execution intelligence. The development of key technologies is expected to continue in the directions of multi-source data fusion， autonomous learning， and collaborative operation. The article aims to provide a systematic reference and guidance for future research and industrial development of industrial inspection robot technology.

In the field of modern logistics warehousing， carton identification is crucial for inventory management and logistics automation. Aiming at the shortcomings of traditional methods and some existing automation schemes in carton detection tasks， a new carton detection method based on improved YOLOv8 network is proposed. Firstly， an Adaptive Batch Normalization（ADBN） mechanism is proposed and introduced to the YOLOv8 backbone network， enhancing the feature extraction ability. The C2f-Faster-CGLU mechanism combining FasterBlock and Convolutional Gated Linear Unit （CGLU） is introduced into the YOLOv8 detection header， which improves the computational efficiency. In addition， a new boundary frame similarity comparison index based on the minimum point distance （MPDIoU） is introduced， which can evaluate the similarity between prediction and real frame more accurately. Finally， the improved network model is applied to Rectagular Stacked Carton Dataset（RSCD）， Online Stacked Carton Dataset（OSCD） and Live Stacked Carton Dataset（LSCD）. Compared with that of the original model， the mAP of the improved model is increased by 1.6%， and the recall rate is increased by 1.3%. The improved model has also improved performance compared with other mainstream detection algorithms，providing more accurate and efficient technical support for the object detection of modern logistics and warehousing industry.

To address the inefficiency of traditional manual visual inventory counting and the high deployment costs of existing automated solutions in Automated Storage and Retrieval Systems （AS/RS）， this paper proposes an intra-warehouse visual inventory system based on modular visual devices. A retrofit-free stacker-accessible modular visual inventory device is designed. On the basis of a YOLOv8-powered visual inventory algorithm for multi-surface information fusion from a single view， the accurate counting of complex stack patterns （e.g.， non-full stacks and staggered stacks） is effectively solved by combining front pallet layer identification with top pallet layer counting. The system also features a non-intrusive integration architecture between the Warehouse Visual Stock System （WVSS） and the existing Warehouse Control System （WCS） via a database， enabling dynamic task scheduling and data closed-loop. Experimental results on four palletized cargo datasets demonstrate a stack quantity recognition accuracy of 96.3% with a processing time of 0.11 seconds per storage location. This solution provides a new engineering path for automated warehousing， characterized by high precision， low deployment cost， and minimal operational disruption.

Accurate force/position control in the presence of modeling uncertainty in robots is a challenging task. This article is based on improved integral sliding mode control for hybrid visual & force control. To perform visual servoing， an optimized visual feature is adopted to avoid the ill-condition visual Jacobian matrix. An improved super-twisting algorithm is proposed based on time delay estimation for integral sliding mode control， and an improved sliding surface and convergence law is used for the positive definite part of integral sliding mode control. Correspondingly， visual control method and force control method are proposed to apply to the hybrid visual & force control framework. To avoid the impact of noise from the force control part on the control output， a visual admittance framework is used for hybrid control. To optimize the solidification impedance characteristics caused by fixed admittance parameters， a fuzzy adaptive admittance framework is proposed to inherit the advantages of admittance while adaptively adjusting parameters in real-time. Finally， a 6-degree-of-freedom deviation model is used for simulation to verify that the proposed scheme can accurately track visual trajectories and expected forces under relatively small chatting. The performance of the three with different focuses is compared based on direct visual perception hybrid control， visual admittance control framework， and fuzzy adaptive admittance framework. Under the deviation model， the proposed i-CISMC algorithm is validated by surface force tracking to have better tracking accuracy while ensuring smaller control chattering； the proposed algorithm is combined with the fuzzy adaptive admittance framework， and the results of tracking under noise have proved that the framework inherites the suppleness of the admittance framework to the disturbance of the force loop， and at the same time， can adaptively change the parameters of the admittance to obtain better tracking speed and tracking accuracy.

Detection on the surface defect of Pouch Cells is a key procedure of the production process. Aiming at the problems of low detection accuracy and difficult imaging of large-sized batteries in the existing detection methods， a detection method based on photometric stereo imaging and deep learning was proposed. Firstly， a Multi-Source Time-Sharing Exposure Imaging System （MSTIS） was established by combining photometric stereo and line scan camera imaging technology. After obtaining the surface images of batteries under different light sources through time-sharing exposure， photometric stereo calculation was conducted to obtain the curvature map with 3D information. Then， to solve the problem of missed detection of minor target and multi-scale defects， the YOLOv8 algorithm was improved. An edge information enhancement module （EIEM） was developed using a dual-channel convolution structure， which incorporated Sobel convolution and conventional convolution to improve feature edge extraction capabilities. The semantic and detail information fusion method （SDI） was integrated with the bidirectional feature pyramid module to boost the recognition accuracy of tiny defects. A lightweight shared convolution detection head was also implemented to reduce the algorithm's computational load.The experimental results show that the average detection accuracy of this method reaches 94.2% and the detection speed reaches 116 FPS， which can effectively detect the surface defects of pouch cells.

Aiming at the aviation complex rotary parts manufacturing workshop in the material distribution often faced by the material supply is not timely， difficult path planning and other difficult problems， analysis of the workshop material distribution characteristics and constraints， with a time window of the vehicle path optimization problem as the basic model， to minimize the integrated trolley call number， the total distance travelled， the delivery time penalty cost of the distribution cost as the optimization objective， to build the material distribution path planning model， design a hybrid adaptive large neighborhood search genetic algorithm to solve the problem. A hybrid adaptive large neighborhood search genetic algorithm is designed to solve the problem， and the actual shortest feasible path between two workstations is obtained in advance considering the complexity of the logistics channel in the workshop， and then the distribution path planning between workstations is carried out. The optimization effect and performance of the proposed algorithm on different scale distribution problems are evaluated through the validation and comparative analysis of the actual cases in the workshop and the classical standard cases.

This paper addresses the challenge of inadequate timeliness and accuracy in processing multi-source heterogeneous data within special equipment assembly workshops， which tends to hinder real-time transparent management and control during the assembly manufacturing process of specialized equipment. To tackle this， a method for fusing multi-source heterogeneous data in these workshops is proposed with the approach based on an analysis of the composition and characteristics of operational data in these workshops， and the benefits of Multi-Agent technology is applied to construct a framework for data fusion. The research on the methods involved in data layer fusion and feature layer fusion is also conducted. The feasibility and effectiveness of the proposed method are confirmed through simulation examples， thereby providing reliable， timely， and accurate data support for the intelligent operation and control of special equipment assembly workshops.

Most amphibious biomimetic robots encounter problems such as insufficient motion ability， poor environmental adaptability and low simulation rate. This article adopts a novel central pattern generator （CPG） with dual neuron mutual inhibition as its main controller based on the basic rhythmic gait of salamanders， and ensures the phase coupling relationship between adjacent CPG units by adjusting the excitation suppression parameters between each neuron. Based on this， a salamander robot spinal cord like control neural network is established. The neural network consists of two layers：Interneuron and Motor neuron. The Interneuron layer generates rhythmic signals， which are then integrated by the Motor neuron layer before outputing to the joint muscle model to drive the robotic movement The performance of spinal cord control network was simulated and analyzed by combining Simulink and Webots. The simulation results show that the amphibious salamander biomimetic robot can effectively achieve rhythmic gait such as swimming and land crawling. The neural network for motion control of the salamander robot designed in this paper is feasible and effective.

Aiming at the problem of poor hole quality caused by excessive axial force in drilling aluminum alloy， the simulation models of drilling 6061 aluminum alloy with step drill and traditional twist drill were established， and drilling experiments were carried out to analyze the drilling quality in the two drilling cases and to verify the simulation model. The tool structure was closely related to the quality of the hole. The step drilling simulation with different diameter ratios \lambda （the ratio of the first diameter to the second diameter） was conducted to compare the axial force and average burr height during the drilling process. On this basis， the response surface method was used to optimize the geometric parameters of the step drill tool. Taking the first apex angle θ ₁， the second apex angle θ ₂ and the spiral angle β as variables， the influence of different factors on the axial force was analyzed and the optimal parameter combination was obtained. The results show that the modified step drill has smaller axial force and better processing performance than the traditional twist drill. When the diameter ratio of the step drill is 0.6， the optimal geometric parameter combination of the tool is θ ₁=90°， θ ₂=106° and β=28°.

This study addresses the issue of low efficiency in manually operated ship void compartment inspection robots by proposing a position-based visual servo control method for an eight wheel-legged ship void compartment inspection robot. The method utilizes a global threshold segmentation technique to extract real-time geometric features of the ship void compartment doors from depth images， enabling the selection of sampling points for calculating the robot's relative pose to the void compartment. This approach provides the control system with highly responsive visual feedback. A hierarchical controller is designed for the robot's eight steering wheels， with an upper layer comprising decoupled controllers for lateral， longitudinal， and rotational motions， and a lower layer consisting of target velocity tracking controllers for the steering wheels， enabling the omni-directional movement control. The method is validated through experiments in a simulated environment. The results indicate that in the simulated experiment， the robot maintains lateral errors below 2 cm and angular errors below 0.02 radians， accurately executing obstacle avoidance actions upon reaching the predefined poses. In the physical experiments， the method， compared to manual operations， reduces inspection time by 69.74%， and increases the door passage rate by 59.24%， validating the effectiveness of the proposed method.

To solve the problems of small molding size and low printing efficiency of traditional desktop-level single-nozzle FDM 3D printer， a medium-sized FDM multi-nozzles collaborative 3D printer is designed and built. The printer adopts a Cartesian （XYZ） structure and is equipped with three side-by-side composite printing nozzles， and the materials can be selectively extruded according to the demand. The control system is divided into three parts according to the functions： main motion control module，embedded auxiliary measurement and control module and upper computer software module，while the software and hardware of these three parts are developed.Two printing modes of multi-nozzles synchronous forming and multi-nozzles stackable co-filling are designed and the corresponding path planning algorithms are proposed.After printing verification， compared with single-nozzle printing， the synchronous forming efficiency of the composite multi-nozzles printer is increased by 3 times， whereas the stackable co-filling printing time is reduced by 41%. The printing efficiency is significantly improved under the premise of ensuring the printing quality.

Electronic air suspension can improve the ride comfort， the handling stability and the fuel economy of vehicles in actual driving by actively adjusting the height and stiffness of the vehicle body. In the collaborative control of the entire vehicle air suspension， four-wheel deviations occur during the height adjustment process of the air suspension due to differences in system structural parameters at each wheel and uneven distribution of the vehicle load， thereby making the vehicle's posture unstable and affecting the actual performance of suspension control. To address this issue， this article optimizes traditional control strategies and proposes a control strategy based on the integration of PI control optimized for four-wheel deviation and duty cycle correction， effectively achieving collaborative control of the entire vehicle height. This approach effectively achieves coordinated control of the vehicle's height by fully accounting for the inconsistencies in the four-wheel suspension system， resolving dynamic deviations during the collaborative control process， and improving the accuracy and stability of the air suspension height control.

Unmanned aerial vehicles （UAVs） can be used as signal relays， and outdoor UAVs can provide signal coverage for indoor users in emergency situations. The existing research on UAV signal coverage mostly focuses on indoor users in specific locations or outdoor users， and the proposed methods available are not suitable for the signal coverage scenarios of indoor users. In the case of the random distribution of indoor user locations， a single-hovering-plane and multi-hovering-plane particle swarm optimization （PSO） algorithm for UAV deployment is proposed to determine the optimal hovering position and transmission power of a single UAV， thereby optimizing the deployment strategy of a single UAV. Furthermore， to address the issue of limited transmission power of a single UAV， a method combining K-Means and PSO is proposed to achieve multi-UAV signal coverage for indoor users. The simulation experiments show that the proposed algorithm reduces the power required for a single UAV to cover indoor users in single-UAV scenarios and optimizes the transmission power and number of UAVs in multi-UAV scenarios.

In recent years， indigenously developed， aerospace-specific 3D structural design systems in China have undergone robust development， with notable achievements in the R&D of core components. However， with the widespread adoption of Large Language Models （LLMs）， establishing an effective interface between 3D structural design and AI-driven methodologies remains a central challenge. Furthermore， existing LLMs lack the capacity for precise reasoning over 3D geometry and complex physical fields， such as aerodynamics， which precludes their direct application in the intelligent design of aircraft structures. Among aerospace structural components， the aircraft wing is critical for generating lift. Its design process is highly complex， heavily reliant on expert experience， and tightly coupled with aerodynamic performance. Consequently， traditional design paradigms are characterized by lengthy iteration cycles and substantial costs. To address this challenge， this paper presents Airfoil-LLM， an intelligent design interface for the 3D modeling of aircraft wings， using the wing as a representative case study. Based on the Transformer architecture， this interface integrates natural language encoding with the decoding of CAD modeling sequences to enable intelligent and automated 3D wing generation. To support model training and validation， we have constructed a large-scale 3D wing design dataset. This comprehensive dataset comprises parameterized 3D CAD models， a wide spectrum of flight conditions from subsonic to supersonic regimes， key aerodynamic performance metrics， and multi-level textual descriptions. Experimental results demonstrate that Airfoil-LLM is capable of deeply comprehending textual descriptions ranging from simple geometric attributes to complex， coupled "geometry-performance" requirements. The system generates 3D models that align closely with the design targets in both geometric shape， achieving a maximum Intersection over Union （IoU） of 0.831， and aerodynamic performance.

The assembly workshop for the nose section features pulsating assembly processes and synchronized unit production， with on-time delivery being a key condition for ensuring the smooth operation of the assembly line. The assembly of civil aircraft nose components is currently a semi-automatic assembly system and human resource allocation still relies heavily on experience， which can lead to mismatches between resources and production tasks. With the increasing production requirements for nose sections， the task quantity and complexity of personnel allocation are also continuously increasing， necessitating an effective allocation method. Addressing the human resource allocation problem for component assembly units under pulsating rhythm constraints， this study considers constraints such as assembly line numbers， start times of each line， and pulsating rhythm on the timely completion of component assembly and delivery. Additionally， spatial constraints and technological constraints of the component assembly unit are considered in constructing a mathematical model with the objective of minimizing human resource costs. Based on dynamic work hour estimation， a hybrid algorithm using simulated annealing and particle swarm optimization is designed to address integrated problems of team scheduling and task assignment， and the feasibility of the model and the superiority of the algorithm are verified through simulation.

Human operation errors and process defects in the production of large tow carbon fiber will lead to defects such as long and short filaments， hairballs， joints and stuck pulp on the surface of large tow， thereby affecting product quality and even causing security risks in the production process. The on-site vision system， however， is merely capable of detecting defects and saving pictures without realizing the classification and positioning of defects. Therefore， based on the actual application scenario of surface defect detection of large tow carbon fibers， this paper proposed a lightweight tow surface defect detection algorithm based on YOLOv5s. Firstly， MobileNetV2 was introduced into the backbone network of the original model for lightweight improvement， and ODConv dynamic convolution module was inserted to improve the model performance. Secondly， the Dyhead dynamic detection head structure was used to replace the original detection head， which could improve the model performance without excessive increase in computation. Next， CARAFE upsampling operator was introduced to replace the original nearest upsampling operation to improve the aggregation effect of model feature information. Finally， the ablation experiment and comparison experiment were carried out on the dataset of the self-made large tow carbon fiber surface defect. The experimental results show that the improved lightweight algorithm proposed in this paper has higher running speed and detection accuracy than that of the other three classical models， providing a new method and idea for solving the surface defect detection problem of large tow carbon fibers.

The realization of industrialized intelligent construction for steel structures is contingent upon two prerequisites： first， the development of a fully assembled steel structure system that is inherently efficient for repeated disassembly； second， the development of automated assembly robots to address the issues of low efficiency， low precision， and poor quality associated with on-site manual installation. This paper proposes a solution involving an automated assembly robot for the installation of torsion-shear high-strength bolts in Core-tube type steel column joint. Focusing on the assembly process of M16 torsion-shear high-strength bolts， this study emphasizes the mechanism design and structural reliability analysis of the robot end-effector. A hierarchical control system for bolt-hole assembly， based on machine vision， is designed. Simulation experiments demonstrate that the proposed robotic mechanism satisfies the assembly process requirements and significantly enhances the efficiency， precision， quality， and safety of installing torsion-shear high-strength bolts in core-tube column connections.

To enhance drone path planning in unknown three-dimensional environments and address the extended optimization search times associated with the traditional Theta^* algorithm， an improved Theta^* algorithm has been developed. By simulating real scenarios of drone flight， urban environment models with varying obstacle densities were constructed. The traditional cost function was then refined， taking into consideration the real-world flight requirements for obstacle avoidance and the smoothness of the generated paths. A layered planner was employed to segment the three-dimensional space， reducing search times in unknown flight environments. Furthermore， local optimization techniques were used to enhance the smoothness of key nodes， ensuring continuous flight in complex settings. During the planning of drone path， the improved algorithm demonstrated significantly enhanced efficiency and accuracy in finding optimal paths through complex obstacle environments compared to the traditional Theta^* algorithm.

To address the need for both independent control of Continuous Damping Control （CDC） dampers and coordinated control of the entire vehicle semi-active suspension system， while also improving upon the issues present in traditional semi-active suspension controller software design such as challenges in meeting real-time requirements and low CPU utilization in bare-metal development environments， this study proposes an innovative approach. Initially， the study establishes separate models for the seven-degree-of-freedom semi-active suspension system and the forward-inverse models of CDC dampers. Building upon the skyhook control strategy， the study integrates a vehicle-coordinated parallel fuzzy controller based on the Mamdani fuzzy control method. Subsequently， by transplanting the FreeRTOS-SMP multicore real-time operating system and utilizing the Infineon AURIX series 32-bit triple-core microcontroller TC275 as the main control chip， the study designs the software and hardware system for the CDC damper control unit. Furthermore， the study conducts task scheduling verification of the multicore real-time operating system and validates the effectiveness of the designed control unit and proposed strategy through hardware-in-the-loop testing using typical random road surfaces to demonstrate the improvement in overall ride comfort of the vehicle.

The rapid advancement of Unmanned Aerial Vehicle （UAV） technology has spurred its widespread application across military reconnaissance， civil monitoring， and logistics delivery. However， as mission requirements grow in diversity and complexity， traditional avionics system architectures struggle to meet the demands for rapid functional expansion and dynamic reconfiguration. In response， a service-oriented avionics architecture has emerged， which enhances the mission flexibility， system maintainability， and functional scalability of UAV systems by decomposing complex functions into independent， minimal， and reusable atomic service units. This paper systematically reviews the evolution of UAV avionics system architectures， provides an in-depth analysis of the theoretical foundations， modeling approaches， and core principles of service decompositions. Building on this foundation， the paper discusses the challenges of the service-oriented transformation for UAVs and explores future development trends in service model standardization， intelligent decomposition， and real-time governance， aiming to offer theoretical references and technical guidance for this field.

High-precision and high-frequency positioning technologies are crucial for ensuring the efficiency and safety of aerial construction robots. In response to the feature-sparse aerial construction environments and the requirement to maintain operations at night， a novel localization algorithm for construction robots' exterior walls is proposed. This algorithm integrates the Extended Kalman Filter （EKF） with an Inertial Measurement Unit （IMU） and a single-line laser radar. The IMU facilitates state prediction for the filter， and a single-line laser radar positioning method adapted to building wall features is proposed for the observation update of filters. The experimental results show that the integrated positioning algorithm can improve the frequency of position and attitude estimation with the positioning accuracy being relatively high. Among them， the average absolute error is less than 4 mm when the maximum swaying of X-axis is 800 mm and the maximum swaying of Y-axis is 350 mm， while the average absolute error is less than 0.1 ° when the maximum twisting of yaw angle is 11 °， meeting the needs of actual engineering.

To address the problem of high volatility of the process quality indicators data and many complicating influencing factors in the manufacturing industry and the difficulty in mining the hidden laws of the traditional prediction model to achieve high-precision prediction， a prediction model of deep Xi quality index based on VMD-Informer was proposed. Firstly， the process parameters related to the quality indicators were screened. Then， Variational Modal Decomposition （VMD） was used to decompose the quality index dataset into modal components and error terms. Thereafter， the process indicators that were related to each component were selected as the input matrix. Finally， the Informer model was used to predict and superimpose the final predicted value of each component and error term. The production data of a domestic manufacturing enterprise were selected to predict different quality indicators， and the prediction effect was compared with that of the LSTM model and the improved Informer model. The results show that the proposed VMD-Informer model has smaller predicted error， larger decision coefficient and more accurate prediction， which can serve as an effective method for manufacturing enterprises to achieve quality prediction and provide ideas as well for enterprises in terms of adjusting their production plans in time.

A new structure of U-shaped permanent magnet synchronous linear motor was proposed to address the issue of low thrust in U-shaped coreless permanent magnet synchronous linear motors. The motor increased the electromagnetic thrust by 74.36% and controlled the thrust fluctuation within a small range by adding magnetic blocks internally in the primary phase and changing the main magnetic circuit structure. In the article， the permanent magnetic field in the motor air gap was first solved using analytical methods， and the influence of harmonic components on the motor thrust and thrust fluctuation was discussed through harmonic analysis of the magnetic flux density in the air gap. Then， the influence of several parameters on motor performance was analyzed， while the Taguchi method was used to screen the optimization variables. A mathematical model reflecting the functional relationship between optimization objectives and parameters was obtained through the response surface method. Finally， the multi-objective optimization algorithm of egret swarm was used to optimize the design of the motor and obtain the Pareto frontier. The effectiveness of the theoretical analysis was verified through finite element simulation.

To solve the dynamic job shop scheduling problem in scenarios with variable job and machine quantities， a solution approach called Dense-D3QN， combining DenseNet， a densely connected convolutional network， with Dueling Double Deep Q-Learning （D3QN） is proposed. The disjunctive graph model is utilized to construct a single-objective job shop scheduling model aiming to minimize the maximum processing time， representing the scheduling state in the form of multi-dimensional matrices while designing a dense-sparse reward function. To validate the effectiveness of the proposed algorithm， both public benchmarks and real data are used to construct common and actual scheduling environments. The Dense-D3QN model is trained and tested in the common environment. In the actual environment， the Dense-D3QN model is trained and tested in both static and dynamic settings. The experimental results demonstrate that the Dense-D3QN model is more capable of handling dynamic job shop scheduling problems with variable scales.

The rapid development of the service robotics industry has introduced a diversity of modules and products， while simultaneously making it increasingly challenging to precisely match user requirements with appropriate modules. Generally， the existing service robot module retrieval and matching methods tend to suffer from strong manual dependencies， low matching precision and long response times. To address these challenges， this research presents KAMR， a knowledge-enhanced adaptive multi-source retrieval framework for service robot module matching. KAMR integrates technical specification matching， semantic functionality matching and domain knowledge graphs to achieve structured representation and association of module functions with application scenarios， dynamically adjusting processing strategies based on query complexity. The experimental results demonstrate that the KAMR framework outperforms the existing methods across queries ranging from simple to complex， with performance improvements of up to 15.3% for complex queries while maintaining low response times. Additionally， the research constructs and open-sources a service robot module semantic description dataset containing 2051 module and product entries， providing a benchmark resource for research in this field.

The application scenarios of large-scale equipment data communication in industrial sites requires a data communication solution with low latency， large capacity and high speed. This paper compares mainstream cellular IoT technologies， proposes an industrial site data collection technology solution based on the 5G lite technology RedCap， desigs a data communication terminal based on 5G RedCap technology， and verifies its feasibility through testing. The data communication terminal based on 5G RedCap technology can well meet the needs of industrial site data communication， is in a valuble position to be promoted and applied in the field of IIoT， and will push forward the development and evolution of cellular IoT towards end-network collaboration.