Abstract:[Objective]Industrial control protocol parsing is a critical component of industrial internet security.However,traditional methods suffer from poor universality and low accuracy.These issues lead to a low efficiency in protocol parsing,making it difficult to meet the demands for high precision and adaptability in real-world industrial scenarios.[Methods]A deep learning-based reverse engineering method was proposed for industrial control protocols by integrating a bidirectional encoder representations from transformers(BERT)pre-trained model,a bidirectional long short-term memory(BiLSTM)network,and conditional random fields(CRF).The goal is to enhance the universality and accuracy of protocol parsing,thereby providing technical support for security analysis and vulnerability mining in industrial control systems.First,the BERT pre-trained model was employed to dynamically encode industrial control protocol data into high-dimensional word vector representations,so as to capture the semantic information of the protocol data.Leveraging the powerful contextual understanding capabilities of BERT,the model effectively handled the complexity and diversity of protocol data.Subsequently,a BiLSTM network was utilized to model the relationships between protocol data as well as between protocol data and label data.The BiLSTM network captured long-range dependencies within the protocol data,enabling a better understanding of the structure and semantics of the protocol.Finally,CRF were introduced as constraints to optimize the prediction of protocol formats and semantics.By incorporating transition probabilities between labels,CRF further enhanced prediction accuracy and consistency.The combination of the BERT pre-trained model,BiLSTM network,and CRF enabled the format extraction and semantic analysis of industrial control protocols.Additionally,the proposed method was optimized for large-scale protocol data,which ensured efficiency and stability in complex industrial scenarios.[Results]Experiments were conducted on three typical industrial control protocols.The results demonstrate that the proposed method achieves an accuracy of over 96％in both format extraction and semantic analysis,outperforming traditional methods.The method exhibits high adaptability and accuracy across different protocols,effectively identifying field boundaries and semantic information.[Conclusion]The proposed method significantly improves the universality and accuracy of industrial control protocol parsing,providing reliable technical support for security analysis in industrial control systems.Future work will focus on further optimizing the model,expanding its application scenarios,and enhancing its practicality.

Abstract:[Objective]In the context of the rapid development of world industrialization,the indiscriminate discharge of dye wastewater poses a threat to the ecological environment and human life.Therefore,seeking an efficient,clean,and economical wastewater treatment method has become a research hotspot.In recent years,a photo-Fenton catalytic technology has shown great advantages in treating organic dye wastewater.BiOBr is a photocatalyst with a good visible light response,a strong light stability,and a layered structure.However,it has the problem of easy recombination of photogenerated electron-hole pairs.Monometallic irons such as Fe2O3,Fe3O4,FeOOH,and nanoscale zerovalent iron exhibit excellent performance in Fenton catalytic activity.Furthermore,their photo-Fenton catalytic activity has also been acknowledged.[Methods]Coupling BiOBr with iron-based oxides with a good Fenton catalytic activity or modifying BiOBr can effectively improve the treatment efficiency of organic wastewater.To further improve the efficiency of photo-Fenton catalytic technology in treating organic pollutants in water,a Fe2O3/BiOBr composite photo-Fenton catalyst with a Z-scheme heterojunction was prepared by precipitation and calcination methods.The composite catalyst was characterized by X-ray diffraction(XRD),scanning electron microscopy(SEM),and transmission electron microscopy(TEM),and the photo-Fenton catalytic activity and mechanism of the composite catalyst were studied using Rhodamine B(RhB)as an organic pollutant model.[Results]The results indicate that the Fe2O3/BiOBr composite photo-Fenton catalysts all exhibit excellent photo-Fenton catalytic activities compared to the individual catalysts,and the 1.2％Fe2O3/BiOBr has the highest photo-Fenton catalytic activity(99.59％,60 min),which is about 24.8 and 3.6 times higher than those of pure Fe2O3 and BiOBr,respectively.Electrochemical testing and the radical trapping experiment show that the 1electrons in the composite catalyst flow from the conduction band of BiOBr to the valence band of Fe2O3.This achieves effective separation of photogenerated electron-hole pairs and promotes the regeneration of Fe2+,thereby improving the photo-Fenton catalytic efficiency of the composite catalyst.[Conclusion]The composite catalyst 1.2％Fe2O3/BiOBr achieves complete degradation of the organic pollutant in all five cycles of the catalytic degradation experiments with no decrease in catalytic degradation efficiency.This demonstrates that the composite catalyst possesses excellent catalytic activity and stability,which makes it a promising candidate for application as a photo-Fenton catalyst in green,economical,and efficient industrial wastewater treatment processes.

Abstract:[Objective]Due to the influence of the limited regulated direct current(DC)power supply,amplitude control of each variable of the chaotic system,that is,variable compression,has become an essential prerequisite for chaotic circuit design and implementation.Currently,geometric control of the attractors of chaotic systems,such as amplitude control and bias control,is a hot research direction in the field of chaotic systems.Based on existing methods,a new amplitude control method was proposed in this paper in the expectation of exploring more potential applications of chaotic systems.[Methods]A five-dimensional chaotic system was developed,and its chaos was verified by using a three-dimensional phase diagram and Lyapunov exponents.After the absolute values of state variable-u in the two equations of the system were taken,two new switched chaotic systems were obtained.Compared with the phase diagram of the chaotic system,the amplitudes of these two new systems changed,and their shapes were highly similar,namely that global amplitude control was achieved.After the absolute value of-u in the second equation was taken,it became a memristive chaotic system.The existence of the memristor was verified by the pinched hysteresis loops of three frequencies.Further analysis of the memristive chaotic system was carried out.By adding the parameter k to the three nonlinear terms of the memristive chaotic system,it was found that the average amplitudes of the attractor on five dimensions changed accordingly,which indicated that the memristive chaotic system had a global amplitude control parameter.The existence of multi-stability in the memristive chaotic system was verified by the Lyapunov exponent spectrum changed with the memristive parameter a.Moreover,the absolute mean value of the signal and the phase diagram changed with a proved that when an appropriate value of the memristive parameter a was selected,global amplitude control could also be achieved.[Results]The simulation circuit equations,equivalent circuit diagram of the memristive chaotic system,and the simulated phase diagram of the chaotic system on the oscilloscope are highly similar to the computer simulation results,which indicates that the chaotic circuit design is of reliability.[Conclusion]The proposed five-dimensional chaotic system has strong chaotic property.The switching system with switching amplitude variation was proposed,providing a new direction for the research of memristive chaotic systems.In future work,it is possible to attempt to use a curved surface as the switching surface.Additionally,through computer simulation experiments,whether the phenomenon of switching amplitude variation widely exists in memristive chaotic systems will be further studied,and further work will be carried out to explore the principle of its existence.The phase diagram on the oscilloscope is highly consistent with the computer simulation experiment in five dimensions.The system has the characteristics of high dimensionality,strong chaos,and switching amplitude control,which make it have good application prospects in engineering.

Abstract:[Objective]With the increasing demand for application of unmanned aerial vehicles(UAVs)in complex scenarios such as power grid inspection and emergency rescue,the limitations of single UAV in task execution have become increasingly prominent.Multi-UAV formation can effectively improve inspection efficiency and expand operation coverage,but significant challenges remain in formation maintenance,collaborative trajectory optimization,and environmental adaptability to complex environments during practical application.An optimal control method that integrated virtual spring forces with the hp-adaptive pseudospectral method was proposed to address the difficulties of formation maintenance and path optimization during large planar maneuvers of UAV swarms,thus enhancing the stability,flexibility,and disturbance resistance of collaborative flight of UAV formations and providing technical support for high-demand scenarios for UAVs such as power grid inspection.[Methods]First,a multi-UAV system dynamics model was built,and a virtual spring mechanism was incorporated into the formation control system to realize flexible constraints and elastic self-adjustment between UAVs.By combining the virtual spring method with the traditional leader-follower method,a formation strategy that could balance rigid support and adaptive adjustment ability of formations was designed.On this basis,the hp-adaptive pseudospectral method was then applied to solve the optimal control problem of UAV formations.By discretizing state and control variables at Legendre-Gauss nodes and constructing global interpolation polynomials,the trajectory optimization problem was transformed into a nonlinear programming(NLP)problem,with constraints such as dynamics,energy consumption,and velocity combined to conduct a high-precision numerical solution.In simulation experiments,a typical four-UAV diamond formation was set up,and the algorithm's adaptability to different terrains,wind disturbances,and mission requirements was comprehensively explored.[Results]Simulation results show that the proposed virtual spring-based hp-adaptive pseudospectral method can realize smooth formation turning and velocity control.During a 90° large maneuver,UAVs can not only satisfy multiple constraints such as path deflection and speed change,but also maintain a stable formation.Compared with traditional leader-follower and artificial potential field methods,the new method demonstrates significant advantages in position error,formation maintenance,and wind resistance.Under 10 m/s strong wind,the formation stability of the proposed method exceeds 70％,showing significant advantages over its competing algorithms.3D terrain simulations and real flight tests further validate the algorithm's adaptability and robustness,and the method still maintains lower formation deformation rates and trajectory tracking error under multiple terrains such as hills,mountains,and canyons,with the features of reasonable energy consumption control and strong engineering practicability.[Conclusion]By innovatively integrating the virtual spring elastic constraint with the hp-adaptive pseudospectral method,an optimal control technique for UAV formation trajectory planning in complex environments was proposed.The rigidity constraint limitations of traditional formation methods are overcome,flexible maintenance and adaptive adjustment of formations are realized,and the accuracy and efficiency of collaborative trajectory optimization are significantly improved by the method.The research results provide an efficient and reliable technical path for collaborative flight of UAV swarms in demanding tasks such as power grid inspection and emergency rescue.Future studies may further increase the method's application potential in multi-formation collaboration and complex obstacle environments,promoting the intelligent and practical development of UAV formations.

Abstract:[Objective]In recent years,the development of lithium-ion batteries have encountered bottlenecks such as slow energy density improvement,high cost and narrow temperature adaptation range.Potassium-ion batteries featuring low cost and high energy density have become the ideal choice for the next generation of large-scale electrochemical energy storage systems.Phosphate fluoride(KVPO4F)serves as the first-choice cathode material for potassium-ion batteries due to its solid three-dimensional framework and high operating voltage.However,the repeated embedding/removal of large potassium ions in the charge and discharge process will cause structural pulverization to KVPO4F,resulting in rapid capacity decay and poor cyclical stability.Moreover,the structure formed by the covalent bond of the coordination polyhedron restricts the electron transfer mode,greatly hindering the dynamics performance of KVPO4F cathode material,and resulting in poor magnification behavior and low actual capacity.The modification of KVPO4F material is usually studied by such strategies as element doping,carbon coating,and morphology engineering to improve the capacity,magnification and cyclical stability of KVPO4F cathode material,and thus enhance the potassium storage performance.However,due to the imbalance between lattice spacing,crystal face exposure and V3+content,the capacity,magnification,and cyclical stability are difficult to be improved simultaneously.The synthesis of KVPO4F cathode material usually consists of two successive heat treatment steps,including the preparation of the VPO4 precursor and the secondary calcination of VPO4 mixed with KF to produce KVPO4F.Therefore,the crystal structure of VPO4 is bound to affect the particle size and crystal face orientation of KVPO4F,thus affecting the potassium storage stability of KVPO4F.[Methods]A series of VPO4 materials were prepared by the sol-gel and high-temperature annealing method,and the effects of different VPO4 materials on the lattice and electrochemical properties of the final product KVPO4F were studied.[Results]The results show that VPO4 prepared at different temperatures can significantly affect the lattice exposure intensity,lattice spacing and V3+content of KVPO4F.As the temperature rises from 700 ℃to 800 ℃,the lattice exposure intensity,lattice spacing and V3+content increase first and then decrease.When VPO4 annealed at 750 ℃ is employed as the precursor,the prepared KVPO4F has the most intense lattice plane exposure,the largest lattice spacing and the highest V3+content,which ensures excellent structural stability,ion migration and ion storage quantity during the charge and discharge process.The electrochemical property test shows that after 30 cycles at 0.2 C(1 C=131 mA/g),the specific capacity of KVPO4F is 57.3 mAh/g,much higher than that of the control sample under the same conditions.Additionally,the reversible specific capacity of KVPO4F at 0.2 C,0.5 C,1 C,and 2 C is 62.1,53.8,44.6,and 30.6 mAh/g,respectively.[Conclusion]Based on VPO4 regulation,this study determines the effect of precursor VPO4 on the microstructure of the final product KVPO4F,and reveals the internal mechanism of improving electrochemical properties,laying a sound foundation for obtaining high-capacity KVPO4F cathode material.

Abstract:[Objective]6061 aluminum alloy is widely used because of its good comprehensive properties.However,the quality of its welded joints generally has certain limitations.This study aims to effectively improve the quality of 6061 aluminum alloy welded joints by using the method of laser shock peening,deeply explore the changes of mechanical properties and microstructure of welded joints before and after laser shock peening,and analyze the internal influence mechanisms,so as to provide a solid theoretical basis and practical guidance for the optimization of aluminum alloy welding process.[Methods]A 6061 aluminum alloy welded joint was selected as the research object,and its surface was treated by laser shock peening technology.In the process of treatment,the parameters of laser frequency,shock range,pulse width,and overlap rate of laser pulses were strictly controlled.The influence of laser energy on 6061 aluminum alloy welded joints was studied.The mechanical properties of welded joints before and after laser shock peening were analyzed,such as tensile strength and hardness.At the same time,the changes of microstructure characteristics such as grain size and shock layer thickness at the weld were observed and compared by means of microstructure analysis technologies,including optical microscopy,scanning electron microscopy,and electron backscatter diffraction(EBSD).[Results]First of all,in terms of the relationship between laser energy and tensile strength of welded joints,there is a clear positive correlation.Specifically,with the gradual increase in laser energy,the tensile strength of welded joints also increases steadily.Secondly,the detection of the hardness of the weld surface shows that the hardness is significantly improved after laser shock peening,and the increase is about 23％.Finally,from the microstructure point of view,the thickness of the laser shock layer changes significantly,greatly increasing from the initial 15.83 μm to 30.77 μm,which indicates that the laser shock has a deep impact on the surface of the material.At the same time,the grain size of the weld center also changes significantly,decreasing from the original 33.68 μm to 14.5 μm.The grains obviously become finer,namely that the microstructure is optimized.[Conclusion]Based on the above research results,it can be concluded that laser shock peening technology shows excellent effect in the treatment of 6061 aluminum alloy welded joints.The high energy generated on the surface of metal materials can effectively reduce the adverse effects of plastic deformation on the surface and interior of materials and promote grain refinement,which is the key factor to improve the mechanical properties of welded joints.Through laser shock peening,the tensile strength and hardness of welded joints are effectively improved,which not only helps to improve the reliability and durability of 6061 aluminum alloy welded structures in practical applications but also provides strong technical support for further expanding the application range of aluminum alloys in high-end manufacturing.In the future,the optimal process parameter combination of laser shock peening can be further studied in order to improve the quality of 6061 aluminum alloy welded joints more accurately and efficiently and promote the continuous development and innovation of aluminum alloy welding technology.

Abstract:[Objective]With the expanding user group of social software,multi-label annotation has been increasingly adopted for text information.How to analyze the behavior and psychology of the user group through data mining of multi-label text information has become a research hotspot.A data mining algorithm for multi-label implicit knowledge based on a deep topic feature extraction model was utilized to enhance text classification accuracy and data mining efficiency.[Methods]To deeply understand the implicit knowledge in text information,the socialization,externalization,combination,and internalization(SECI)theory was employed to convert the implicit knowledge into explicit knowledge.The short-term memory capability of recurrent neural networks was utilized to improve the conversion efficiency.Considering the complexity of text information,local and global features were analyzed separately,and feature fusion was used to improve data mining efficiency.Due to the strong correlation between the context of text information,the gate mechanism of the long short-term memory(LSTM)model was applied to extract contextual dependencies,while the unsupervised latent Dirichlet allocation(LDA)topic model was selected to model the topic structure of the text to mitigate standard differences from manual labeling.Combining LDA-derived global features and LSTM-derived local features,feature stitching was performed to reduce information loss during the feature extraction.A theme controller was introduced to narrow down the inference scope,which obtained more effective text features.Simultaneously,a Gaussian decoder-based contextual topic layer was constructed to calculate the conditional probability matrix of each vocabulary under a given topic,and a Gaussian mixture decoder was used to obtain the conditional probability of the vocabulary.Topic modeling optimization and content expansion were achieved through a Gaussian mixture decoder.Finally,multi-label classification was implemented using the Softmax function to calculate label probabilities.[Results]During model training,perplexity was used as a criterion for evaluation.The proposed model exhibited better perplexity than the control groups(LDA topic model and LSTM model),demonstrating the effectiveness of feature concatenation combining the LDA topic model and LSTM model.By comparing with NVDM,LSTM,LDA,and VAETM models,with precision and recall as evaluation metrics,the proposed model improves precision and recall by 5.05％and 2.75％,respectively.[Conclusion]The comparative experimental results show that the proposed model can significantly improve the performance of text classification.Compared with the LDA topic model and the LSTM model,it outperforms in processing multi-label texts.It can efficiently mine the implicit knowledge in multi-label text data,providing an efficient and accurate solution for tasks such as text classification,semantic analysis,and information retrieval.

Abstract:[Objective]With the rapid development of long-span suspension bridges,wind-induced vibration has gradually become a crucial factor affecting their safety and comfort.Small horizontal-axis wind turbines installed on bridges can not only effectively suppress vortex-induced vibration but also provide wind energy for powering ancillary facilities.However,the impact of small horizontal-axis wind turbines on bridges has not been comprehensively and systematically studied,especially their specific influence on bridge buffeting response.Therefore,this study aims to explore the influence of small horizontal-axis wind turbines on bridge buffeting response and assess the effects of different wind turbine layout schemes on bridge dynamic response,so as to provide a theoretical basis and practical guidance for control of wind-induced vibration of bridges by wind turbines.[Methods]This study took the typical flat box girder of the Great Belt Bridge in Denmark as the research object and employed such means as wind tunnel tests,finite element analysis,and harmonic superposition,combined with the actual wind environment and structural characteristics of the Great Belt Bridge,to simulate and analyze the influence of wind turbines on bridge buffeting response.Static three-component force coefficients of the bridge with wind turbines installed were measured in wind tunnel tests,and time-history response data of the bridge subjected to wind loads were generated depending on relevant data.Based on the quasi-steady assumption and Davenport buffeting force model,combined with the finite element model,the dynamic response of the bridge under different wind speeds was calculated and simulated.Six different wind turbine layout schemes were designed during the research process,considering variations in parameters such as the rotation axis height and layout spacing of wind turbines,to investigate the effects of different layout schemes on the lateral and vertical displacement and acceleration responses of the bridge.[Results]The results indicate that the installation of small horizontal-axis wind turbines increases the displacement and acceleration responses of the bridge to a certain extent.However,by selecting appropriate wind turbine layout schemes,it is possible to control vortex-induced vibration with a small effect on the structural safety and comfort of the bridge.The overall increase in lateral displacement of the bridge tends to decrease as the rotation axis height of the wind turbine blades decreases.For vertical response,the smallest increase in vertical displacement occurs when the wind turbine layout spacing is three times the beam height.Furthermore,by fitting the static wind loads caused by wind turbines on the bridge,this study proposed estimation formulas for drag and lift unit loads of bridges with wind turbines installed,which could effectively assess the impact of wind turbines on bridges under different layout schemes.[Conclusion]The impact of small horizontal-axis wind turbines on bridge dynamic response can be reduced through reasonable layout parameters(such as rotation axis height and layout spacing)without significantly affecting the structural safety and comfort of the bridge.The installation height and spacing of wind turbines have significant impacts on the dynamic response of the bridge,and reasonable layout schemes should be selected according to the specific conditions of the bridge to ensure its structural safety and comfort.This study proposed a mathematical model relating to the layout spacing and rotation axis height of wind turbines and wind load data of the bridge,providing theoretical support for optimizing control of wind-induced vibration of bridges by wind turbines in the future.

Abstract:[Objective]In an alternating current(AC)power system,the distributed control system(DCS)controller serves as a core component responsible for real-time acquisition and processing of various critical data,which is vital for the stable operation and fault prediction of the system.However,in practical applications,the data acquisition process of DCS controllers often encounters issues such as data loss or anomalies due to external electromagnetic interference,hardware failures,and other factors,which makes it difficult to determine data density and thus affects system reliability and accuracy.In view of this,a high-speed multi-channel synchronous sampling method for DCS controller data in AC power systems was proposed to address interference and data missing during data acquisition,thereby enhancing data quality and system performance.[Methods]The signal conditioning circuits preprocessed analog data signals from different channels to ensure that the signal quality met the analog-digital converter(ADC)conversion requirements.Field-programmable gate array(FPGA),serving as the control center,leveraged its parallel processing capabilities and programming flexibility to precisely control the ADC conversion process for each channel,achieving high-precision,low-latency synchronous sampling and effectively addressing the issues of phase deviation and data inconsistency caused by asynchronous sampling.For data missing,the Clearbout theory was adopted for data interpolation,intelligently estimating and filling missing data based on the time-frequency characteristics of the signal and the correlation of known data points and thereby ensuring data continuity and integrity.Additionally,the synchronous sampling algorithm was optimized using the ant colony algorithm,which dynamically adjusted sampling parameters by simulating the pheromone update mechanism of ants searching for food to enhance sampling efficiency and accuracy.[Results]Experimental results demonstrate that the proposed multi-channel synchronous sampling method significantly enhances the data acquisition performance of DCS controllers.The frequency spectrum diagram of the acquired DCS data is highly consistent with the actual data frequency spectrum diagram,which verifies the accuracy and reliability of the sampling method.The sampling speed is significantly increased,meeting the high real-time requirements of AC power systems.[Conclusion]In summary,the proposed method incorporates FPGA control to achieve high-precision,low-latency multi-channel synchronous sampling,solving phase deviation and data inconsistency issues.Introducing the Clearbout theory and ant colony algorithm effectively guarantees data integrity and optimizes the sampling algorithm.The designed multi-channel data upload mechanism avoids conflicts during data upload,ensuring smooth data transmission.These innovations not only improve the data acquisition capability of DCS controllers in AC power systems but also provide useful reference for the design and optimization of similar systems.Therefore,the application of the proposed method helps enhance the stability and reliability of entire AC power systems,reduces the risk of system failures caused by data anomalies,and is of great significance for ensuring the safe operation of power systems.

Abstract:[Objective]304 stainless steel is a chromium-nickel stainless steel with austenite as the main crystal structure.It is widely used in the aerospace,marine,and chemical industries for its excellent heat and corrosion resistance.However,its hardness is low,and its cavitation erosion resistance is poor.When it is used as a material for turbine blades,exposure to complex environmental conditions leads to surface pitting and spalling,which severely shortens the service life of the blades.[Methods]To enhance the service life of 304 stainless steel,a novel iron-based alloy cladding layer was fabricated on its surface by using laser cladding.The obtained iron-based alloy cladding layer was subjected to phase analysis,microstructural observation,EBSD analysis,hardness testing,and cavitation erosion testing to analyze its phase composition,crystallographic characteristics,microhardness,and cavitation erosion resistance.[Results]The results show that the iron-based alloy cladding layer is mainly composed of α-Fe phase and Cr23C6 phase.The cladding layer has good forming quality without microcracks and with only a few pores.The microstructure of the cladding layer shows typical non-equilibrium solidification structure characteristics,which is composed of dendrites and interdendritic network structures,showing the morphologies of planar crystals,cellular crystals,columnar crystals,and equiaxed crystals from the bottom region to the top region.The EBSD results show that high-density grain boundaries were formed in the cladding layer and no obvious texture was formed.The cross-sectional microhardness of the cladding layer fluctuates between 640 HV0.2 and 750 HV0.2,which is considerably higher than the microhardness of the 304 substrate(187.6 HV0.2).The higher microhardness of the cladding layer is attributed to solid solution strengthening,the second phase strengthening by Cr23C6 and Cr7C3 hard phases distributed among cellular dendrites,and grain boundary strengthening brought by high-density grain boundaries.The cumulative mass losses of the 304 substrate and the iron-based alloy cladding layer after cavitation erosion test for 300 min are 24.8 mg and 7.8 mg,respectively.The mass loss of the iron-based alloy cladding layer is about 31.5％of that of the 304 substrate.During the whole cavitation erosion test,the cumulative mass loss of the iron-based alloy cladding layer is less than that of the 304 substrate.The surface analysis results after the cavitation erosion test show that the shear waves generated by the collapse of bubbles can cause stress accumulation on the surface of the material,thereby promoting the formation of slip bands.Cracks are prone to generation and expansion on the slip bands,eventually leading to material spalling and forming cavitation pits.Small grain sizes,a high grain boundary density,and high microhardness are the key reasons for the excellent cavitation erosion resistance of the cladding layer.[Conclusion]The higher microhardness of iron-based alloy cladding layer significantly improves the cavitation erosion resistance of the 304 stainless steel substrate.In this study,a high-microhardness iron-based alloy cladding layer for surface modification of 304 stainless steel was designed and prepared to promote the application of laser cladding technology in the reinforced coatings for turbine blade surfaces to a certain extent.

Abstract:[Objective]With the rapid development of power generation from renewable energy,photovoltaic power generation is widely adopted due to its merits of safety,reliability,flexible adjustment,and clean production.Due to the real demand for large-scale photovoltaic power generation,multiple inverters connected in parallel and grid-connected inverters are often adopted in photovoltaic power stations to enhance the power generation efficiency.However,with the expansion of the grid-connected scale,the inductive impedance under the weak grid poses a threat to the stability and reliability of the grid,leading to poor global resonance suppression as well as a high risk of uncontrollable system stability.The aim of this study is to propose a global resonance suppression strategy for photovoltaic(PV)multi-inverter parallel system to guarantee the stable operation of the system and improve its power quality.[Methods]Firstly,a Norton equivalent model of the PV multi-inverter parallel system was constructed.Based on this model,this paper analyzed in depth the resonance characteristics of the multi-inverter parallel system under a weak grid,and it was found that the coupling resonance frequency was negatively correlated with the number of inverters.Secondly,based on the control theory,the optimal control strategy combining capacitor current feedback and grid voltage feed-forward was applied to solve the global coupling resonance problem in the multi-inverter system.At the same time,the global resonance suppression strategy of paralleling virtual admittance at the point of common coupling(PCC)was designed to realize the effective suppression of global resonance from the system level.Finally,comparative simulation experiments before and after adopting the strategy proposed in this paper were conducted under two-inverter parallel system and four-inverter parallel system.In addition,simulation experiments were also carried out to compare the suppression effect under the same system by using other methods reported previously and the strategy proposed in this paper.The correctness and effectiveness of the proposed strategy were verified through simulation.[Results]Theoretical analysis and simulation results show that the proposed global resonance suppression strategy can significantly improve the stability of the system.The rationality of the control strategy and its parameters are validated and optimized by the Nyquist criterion.Simulation test results show that after the application of the proposed strategy,the harmonic content in the system is reduced from 17.32％to 1.71％.This indicates that the proposed strategy can effectively suppress the global resonance of the system and enhance the stability of the system operation.[Conclusion]In this paper,a Norton equivalent model of PV multi-inverter parallel system was constructed.Innovatively,the resonance characteristics of the multi-inverter parallel system under a weak grid were analyzed,and a global resonance suppression strategy of paralleling virtual admittance at the PCC was proposed on the basis of the optimal control of capacitor current feedback and grid voltage feed-forward.The strategy effectively improves the stability of the system operation in the presence of a large number of parallel inverters and high inductive reactance of the grid.The comparative simulation verification further demonstrates that the proposed strategy can suppress the global resonance of the system effectively,providing important reference for the efficient operation of PV power generation grid-connected system.

Abstract:[Objective]With the rapid development of artificial intelligence,object detection technology based on visible light images has become increasingly advanced and has been widely applied in fields such as autonomous driving,security monitoring,and intelligent transportation.However,in low-light scenes(such as nighttime or dimly lit environments),the performance of object detection algorithms based on visible light images decreases significantly.This is primarily due to severe information loss in visible light images under low-light conditions,making it difficult to extract target features.To solve this problem,multi-modal object detection technology combining visible light and infrared images was proposed,which could effectively enhance object detection performance in low-light scenes.However,the multi-modal method is costly and requires precise registration of images from different modalities,which increases system complexity and processing burden.In response,this study proposed an object detection network with infrared sensing(InSCnet),aimed at using a visible light camera to predict infrared thermal radiation characteristics,thus improving the network's object detection capability in low-light scenes without increasing modality.[Methods]The InSCnet network used visible light images as input and generated infrared images through an infrared prediction branch(IPB),which predicted thermal radiation characteristics to enhance the network's perception of low-light scenes.A complementary fusion filter(COFF)module was designed to effectively integrate multi-scale visual and thermal radiation features.By complementing these two features,the COFF module enhanced their mutual complementarity and avoided the network's over-reliance on a single modality.In addition,a hybrid feature pyramid(HyFP)module was employed to further improve the fusion and extraction of multi-scale global and local features through feature pyramids and attention mechanisms,ensuring that the network maintained high detection accuracy under varying low-light conditions.[Results]Experimental results show that InSCnet performs excellently on the LLVIP pedestrian detection dataset,with SmAP50 reaching 0.830 and SmAP50-95 reaching 0.426.Moreover,experiments conducted on the DroneVehicle dataset show a SmAP50 of 0.702,confirming its ability to handle multi-class low-light detection.[Conclusion]InSCnet improves object detection performance in low-light scenes by introducing infrared thermal radiation characteristics and a feature fusion mechanism.The network can effectively detect objects that are difficult to identify in visible light images under low-light conditions,providing an effective solution for object detection in such environments.Future research will further explore ways to optimize the network structure.

Abstract:[Objective]Substations,as the core hubs of power transmission and distribution,play a crucial role in ensuring the safe and stable operation of power systems,which is essential for efficient and reliable power supply.However,traditional substation monitoring methods face challenges such as limited automatic monitoring capabilities and inadequate target detection accuracy,making it difficult to meet the increasing safety demands of modern power systems.This study aimed to develop substation target recognition and safety monitoring technology based on the regional fully convolutional network(R-FCN)to overcome the shortcomings of traditional monitoring methods,significantly enhance substation safety assurance,and establish a solid foundation for the stable operation of the power system.[Methods]This method combined the unique advantages of region extraction and fully convolutional networks to construct an efficient and intelligent monitoring system.High-definition video surveillance cameras were deployed in the data collection phase to continuously capture real-time image data of the substation from multiple angles,providing massive and precise raw data for subsequent in-depth analysis.The advanced R-FCN model was applied for object detection based on the collected images.Due to its fully convolutional nature,R-FCN could effectively maintain high-resolution feature maps when processing images of different sizes,avoiding the information loss commonly encountered during downsampling with traditional methods,thus significantly improving object detection accuracy.A specially designed area extraction module,resembling an intelligent navigation system,accurately located key facilities such as transformers,switchgear,and insulators within the complex substation environment,ensuring real-time and precise monitoring of equipment status.Moreover,abnormal behaviors,such as unauthorized personnel entering hazardous areas or equipment suddenly emitting smoke or catching fire,were detected promptly,allowing for valuable time to be saved for emergency responses.[Results]Extensive simulation experiments and practical testing in real substation monitoring scenarios demonstrate the system's excellent performance.In comparison with traditional object detection methods,the system significantly improves detection accuracy,enhances monitoring reliability,and reduces unnecessary manpower and resource expenditure.[Conclusion]The R-FCN-based substation target recognition and safety monitoring technology combines efficient real-time processing and precise target positioning capabilities.When handling massive monitoring data,it can quickly and accurately identify various targets and abnormal situations,providing robust technical support for the safe and stable operation of the power system.This technology has profound implications for enhancing substation monitoring levels and ensuring the reliable power supply of the power system.

Abstract:[Objective]With the continuous expansion of network scale and the evolving complexity of attack techniques,network traffic anomaly detection has become a critical link in ensuring network security and maintaining the stable operation of key information infrastructure.However,traditional machine learning methods generally face bottlenecks such as slow convergence and insufficient feature representation accuracy when handling complex network traffic feature extraction,which limits their effectiveness in practical anomaly detection scenarios.To address these challenges,an innovative spatiotemporal fusion deep learning model,C2-GRU,was proposed in this paper,which was based on a convolutional neural network(CNN)-enhanced learner with a gated recurrent unit(GRU).The proposed model aims to enhance the multi-dimensional detection performance for abnormal traffic.[Methods]A dual-fusion deep learning framework was designed,leveraging the strength of CNN in spatial feature extraction and the capability of GRU in temporal feature modeling.A C-GRU model was constructed to achieve preliminary spatiotemporal feature fusion.It was then cascaded with CNN to form the C2-GRU model,which extracted spatiotemporal features through dual parallel convolution operations.This approach effectively captured the multidimensional features of abnormal traffic in complex network environments.[Results]The experimental results demonstrate that the proposed model achieves optimal overall performance on the KDD99 dataset.Specifically,the fused model attains an accuracy of 99.89％and an area under curve(AUC)of 0.990 2,significantly outperforming individual CNN and GRU models.Furthermore,compared to traditional anomaly detection models,the proposed model not only achieves high recognition performance but also exhibits a relatively short model runtime,which highlights its superior engineering applicability.[Conclusion]The proposed C2-GRU hybrid model,employing a dual-convolution fusion strategy,effectively enhances spatiotemporal feature learning,suitable for abnormal traffic detection in complex network environments.It has dual advantages in anomaly recognition accuracy and computational efficiency,capable of offering technical support for securing key information infrastructure and mitigating the economic losses caused by network attacks.The model is of significant practical reference value for ensuring network information security.

Abstract:[Objective]Due to the long-term exposure of overhead transmission lines to the natural environment and the significant impact of environmental factors,timely monitoring of their operating status plays a key role in the safe operation of the power grid.With the development of UAV flight control technology and the widespread use of detection technologies such as infrared,ultraviolet,and LiDAR,these methods are increasingly used in the inspection of power transmission lines.However,traditional methods currently only show optimal results in single-scenario line inspection.In more complex environments,such as mixed transmission line inspections,it is challenging to quickly and accurately analyze transmission line inspection data.Therefore,this study proposed an optimization method for point cloud data processing in hybrid transmission line inspection.[Methods]First,a transmission line inspection point cloud data processing platform was constructed.LiDAR mounted on the UAV platform collected the mixed point cloud data of the transmission line and processed it in four stages:data management,preprocessing,classification,and intelligent inspection.The mixed point cloud data were thinned using the octree method to reduce redundant data and ensure the accuracy and quality of the data.Finally,a neural network model was designed to optimize the sparse data,consisting of three main parts:the feature learning layer,the convolutional layer,and the classification layer.The feature learning layer avoided the impact of the disorder in 3D point cloud data on feature extraction through multiple projections and maximum pooling.The convolutional layer extracted common features from voxel grids and surrounding entities while incorporating traditional transmission line feature extraction algorithms to extract voxel grid features.The classification layer included a fully connected layer with a ReLU activation function,using the Softmax model as the classification function to obtain the classification results of the mixed point cloud data.[Results]In the experiment,the LDLRS3100 LiDAR was selected to collect point cloud data of a transmission line channel in a certain area.The UAV LiDAR system has a range of 360 m,a flight speed of 20 km/h,and a flight altitude of 150 m.The proposed method was analyzed based on the Pytorch platform,and the results show that it can effectively identify the differences between transmission lines and ground objects,and obtain clear information on the tower and its surrounding environment.The overall accuracy reaches 92.71％,which is significantly better than other comparative methods.To balance the highest sparsity rate and the best visual effect of the point cloud data,the sparsity density is set to 0.02 m.[Conclusion]By optimizing the point cloud data of mixed transmission line inspections using the octree sparsity method and a neural network model,various types of point cloud data can be quickly and accurately classified,thus improving the reliability of intelligent transmission line inspections.

Abstract:[Objective]Conducting a state evaluation of secondary equipment in power systems is crucial for mitigating the operational risks of the power grid and improving grid reliability.To address the logical issues in the analytic hierarchy process(AHP)and the limitations of subjective judgment in the entropy weight method,this study proposed an improved entropy weight method for assessing the health status of secondary equipment.[Methods]Based on the fundamental characteristics of secondary equipment in the power system,both technical and management indicators for the state evaluation were developed.The study utilized forward,reverse,and trapezoidal mapping relationships to standardize the indicator parameters.In addition,a membership function based on normal distribution was adopted.This function retained valid information from high membership intervals,incorporating information from low membership ranges,and avoided misjudgment caused by an overemphasis on the low membership range.The weight principle of AHP was used to establish the judgment matrix of different evaluation indicators,and the coefficient of variation in the entropy weight method was introduced based on the arithmetic mean and standard deviation of each indicator,enabling an objective representation of the evaluation indicator weight.Subsequently,a comprehensive model for the state evaluation of secondary equipment was established.The model was validated using 36 protection devices in a substation.[Results]The verification results demonstrate that the evaluation results aligns with the actual operation status of the protection device.The membership value for"good"is 0.890 1,and for"fair"it is 0.097 9,which allows for the determination that the 220 kV main transformer protection device operates normally and consistents with the actual operational state of the protection device.The range of the membership function for evaluation indices obtained using AHP is 0.321 0,while the entropy weight method yields a range of 0.341 4,which may lead to misjudgments.Longitudinal comparisons of the AHP-entropy weight method and the entropy method's weighting algorithms show that the membership degree ranges are 0.125 0 and 0.184 9,with minimum values of 0.806 5 and 0.708 8,respectively,with no misjudgments.In this method,the difference in the range values of the membership degree is 0.048 1,with maximum and minimum values of 0.900 0 and 0.851 9,resulting in a narrower fluctuation range and higher judgment reliability.[Conclusion]The innovation of this study lies in the combination of AHP and the entropy weight method,which effectively avoids the interference of human factors in subjective weighting.Moreover,by incorporating objective factors into equipment evaluation and introducing an entropy weight calculation method based on the coefficient of variation,the weight calculation accurately reflects the equipment's actual operation status.The actual calculation results show that the proposed method more effectively reflects the actual state of equipment operation and provides crucial support for equipment operation and maintenance.

Abstract:[Objective]In mobile edge computing(MEC)systems in dynamic environments,traditional task offloading strategies generally have problems such as inflexible scheduling,weak adaptability to environmental changes,and limited delay control capabilities,making it difficult to meet the processing requirements of delay-sensitive tasks.To this end,this paper proposed a MEC offloading optimization method that integrated unmanned aerial vehicle(UAV)-assisted mechanisms to improve the system's service quality and task response efficiency.[Methods]Considering the dynamic user distribution and frequent link state fluctuations in UAV-MEC scenarios,this paper jointly modeled task offloading,user scheduling,and UAV trajectory control as a Markov decision process(MDP),and used the deep Q-network(DQN)framework to learn approximate optimal strategies.In state modeling,factors such as UAV energy consumption constraints,user task attributes,and timeliness requirements were fully considered,with action space discretization implemented to adapt to the DQN architecture.The reward function introduced delay loss and timeout penalty mechanisms to guide the agent in adaptively learning effective offloading strategies.[Results]The simulation results show that the proposed method is superior to the benchmark strategies such as full local computing and full edge offloading in terms of cumulative rewards,average task processing delay,and the number of task timeout penalties,showing good strategy convergence and environmental adaptability,especially when the communication link fluctuates or computing resources are limited.[Conclusions]The proposed DQN-based UAV-assisted edge computing joint optimization strategy can significantly improve the system's processing efficiency and scheduling performance for time-sensitive tasks in a dynamic and complex environment,providing a feasible method path and theoretical support for the design and optimization of high-mobility mobile edge computing systems.

Abstract:[Objective]With the rapid development of power engineering,construction site safety has become increasingly critical.Traditional manual inspection methods are time-consuming and prone to errors.In recent years,advancements in computer vision,deep learning,and knowledge graph technologies have made it possible to automatically recognize unsafe operation behaviors.However,existing computer vision methods have limitations in detecting small objects and lack high-quality databases for unsafe operation inference.To address these issues,knowledge graphs,ontology models,graph databases,and computer vision techniques were integrated to detect unsafe operations through entity detection,scene analysis,and spatial relation reasoning.An improved self-attention mechanism was also introduced to enhance small object detection capabilities.[Methods]The proposed method mainly involved ontology model construction,knowledge extraction,and knowledge reasoning.First,an ontology model of construction safety was built based on engineering documents,historical accident reports,and safety hazard reports,with information categorized into six types:entities,attributes,time,space,events,and attribute values,which were represented by normative knowledge.Second,computer vision techniques were employed to detect entities and their attributes and extract spatial relationships between entities.A Mask region-based convolutional neural network(Mask R-CNN)was used for object detection,with an improved self-attention mechanism incorporated to improve small object detection accuracy.As a result,model performance was optimized,and computational complexity was reduced.Finally,a Neo 4j graph database was utilized to store entities and their relationships,enabling automatic recognition of unsafe operations through database queries.In this way,structured reasoning for construction safety knowledge was achieved,and the intelligent level of recognizing unsafe operations was enhanced.[Results]In the experiments,a power engineering construction site was used as the test environment,and six kinds of unsafe operations that could lead to high-altitude falling were selected for simulation experiments.The simulation results indicate that the proposed method outperforms existing approaches in both detection accuracy and training efficiency.Particularly,the improved model demonstrates superior accuracy in small object detection.Additionally,scene segmentation was conducted using a feature pyramid network(FPN)and a unified perceptual parsing(UPP)method,which significantly improved the scene understanding capability of the model.Furthermore,the knowledge reasoning approach based on the Neo 4j graph database effectively integrates entity attributes and spatial relationships,enhancing the automation of unsafe operation recognition.[Conclusion]The proposed method can accurately detect unsafe operations in complex construction environments,thereby improving the intelligence level of construction site safety management.The key innovations of this research are as follows:integrating computer vision with an ontology model to enhance automation in construction safety management;improving the self-attention mechanism by modifying convolutional kernels and introducing a global max-pooling layer,which enhances the small object detection capability of the Mask R-CNN;incorporating the Neo 4j graph database for structured storage and reasoning of construction safety knowledge.This study provides an efficient and scalable solution for the automatic recognition of unsafe operations on construction sites.

Abstract:[Objective]Agricultural parks,characterized by abundant renewable energy resources,play a pivotal role in advancing green and low-carbon transformation under the carbon peaking and carbon neutrality goals.However,current agricultural parks face challenges such as low energy utilization efficiency,imbalanced multi-energy distribution,and insufficient local renewable energy accommodation capacity,which hinder agricultural productivity and sustainable development.To address these issues,this study proposed a deep learning-based optimization method for constructing a more economical and low-carbon integrated energy system(IES)in agricultural parks.[Methods]First,a multi-objective optimal scheduling model for agricultural park IES was established,integrating economic objectives such as fuel costs of gas turbines,grid interaction costs,and equipment maintenance costs,while formulating mathematical constraints for multi-energy coupling systems.Second,an improved long short-term memory(LSTM)neural network was employed to predict photovoltaic/wind power outputs and load demands.The hyperparameters of the LSTM model,including hidden layer units and learning rates,were dynamically optimized using quantum particle swarm optimization(QPSO)to enhance prediction accuracy.Finally,to mitigate premature convergence in the traditional golden sine algorithm(GSA),an enhanced GSA algorithm was proposed by incorporating Lévy flight strategies to expand the search space and designing dynamic weight mechanisms to balance global exploration and local exploitation capabilities.[Results]Case studies demonstrate that the errors of improved QPSO-LSTM prediction model are controlled within 5％,outperforming traditional optimization algorithms in accuracy and robustness against local optima.For scheduling optimization,the enhanced GSA algorithm achieves a 69.7％reduction in daily operational costs and a 27.9％improvement in local renewable energy accommodation rates compared to unscheduled scenarios,significantly surpassing conventional GSA and other methods.These results validate the algorithm's effectiveness in balancing economic efficiency and low-carbon requirements for multi-energy coordination.[Conclusion]The proposed deep learning-based optimization framework enables high-precision power prediction and cost-effective scheduling for agricultural park IES.It significantly reduces operational costs while enhancing renewable energy utilization,demonstrating superior performance in synergizing economic and low-carbon objectives.This study provides a reliable technical pathway for the efficient and sustainable operation of agricultural park IES.

Abstract:[Objective]With the increasing demands for flight safety of unmanned aerial vehicles(UAVs),the dynamic characteristics of landing gear systems have become a critical research focus in UAV design.This study focuses on the landing contact mechanical behavior of rubber footpads in six-link landing gears and investigates the problems of nonlinear mechanical characteristics in modeling.By constructing a precise dynamic contact model,this research aims to elucidate the mechanical response mechanisms of rubber buffers under impact loads and provide theoretical support for optimizing the structural design of cushioning systems at landing gear foot ends.[Methods]A nonlinear contact mechanics model for rubber materials was developed based on the theoretical framework of the continuous contact force method.Innovatively integrating Hertzian contact theory with the Mooney-Rivlin strain energy function,the model accurately characterized the hyperelastic characteristic of rubber materials and the dynamic coupling effects at contact interfaces through non-ideal elastic collision relationships.On the ABAQUS platform,a finite element model adopting the Mooney-Rivlin hyperelastic constitutive model was established,and the landing collision process was numerically simulated using an implicit dynamic solver.A drop impact test bench equipped with force sensors was constructed to obtain experimental data for model validation.This integrated methodology,combining theoretical modeling,numerical simulation,and experimental validation,effectively overcomes the limitations of traditional empirical formulas.[Results]Systematic analysis reveals the influence of multiple physical parameters on contact mechanical characteristics.When the drop height increases within the range of 50 mm to 200 mm,the peak contact force exhibits proportional growth,with an increment of 1.78 kN.Within the load mass range of 5 kg to 20 kg,the peak contact force demonstrates an approximately linear relationship with load mass,showing an increase of 1.02 kN.Notably,increasing footpad thickness has an insignificant effect on reduction in impact force,while optimizing the footpad shape can effectively mitigate impact-induced vibrations.Comparative studies on structural shapes demonstrate that conical footpads,compared to traditional cylindrical designs,exhibit more even force distribution and effectively mitigate impact-induced vibrations.Experimental validation confirms the effectiveness of the model,with the peak contact force error being merely 6％and a phase shift of key parameters controlled within 3 ms under the condition of 100 mm drop height.[Conclusion]The contact force demonstrates approximately directly proportional relationships with both drop height and footpad thickness,though the effect of thickness is relatively weak.Footpad shape optimization significantly reduces impact-induced vibrations,with conical footpads exhibiting superior cushioning performance.This study achieves theoretical breakthroughs in two aspects.A dynamic contact prediction method for rubber buffers was proposed by combining the continuous contact force method with the hyperelastic constitutive model,resolving the technical bottleneck of traditional approaches in addressing nonlinear coupling effects.A quantitative evaluation framework for cushioning performance was established by investigating the influence of multiple physical parameters on contact force at landing gear foot ends,providing a reliable theoretical basis for foot-end parameter optimization.