World Journal of Engineering Research (WJER) is an international peer-reviewed, open access journal.
PERFORMANCE OPTIMIZATION OF TRAJECTORY DEVIATION CORRECTION IN LONG-RANGE BALLISTIC MISSILES: AN ADAPTIVE PID CONTROL APPROACH
Addressing the trajectory deviation problem of long-range ballistic missiles (5000-8000 km range) caused by multiple factors such as aerodynamic disturbance, thrust fluctuation, and gravity field variation during flight, this paper proposes an adaptive PID trajectory correction control method based on a self-tuning parameter strategy. The paper adopts a composite guidance scheme combining inertial and satellite guidance, establishes a three-degree-of-freedom missile flight dynamics model, and designs an improved PID controller incorporating fuzzy parameter tuning, and differential filtering. In simulation experiments on a parabolic-gliding composite trajectory with long-range ballistic missile characteristics, the adaptive PID controller outperforms the traditional PID controller under different flight phases and disturbance conditions, reducing steady-state error by 42.3% and shortening adjustment time by 31.7%. The proposed adaptive PID controller achieves high-precision trajectory tracking during long-range flight, providing theoretical support and simulation validation for control systems in long-range ballistic missile applications.
Copyright (c) 2026 RuoHao Zhang https://creativecommons.org/licenses/by/4.0
SIMSCAPE-EQUIVALENT MODELING AND VERIFICATION OF A GAN DEVICE SPICE BEHAVIORAL MODEL FOR PV DC–DC SYSTEM-LEVEL SIMULATION
A Simulink/Simscape-compatible device model is developed for system-level simulation of PV DC–DC power conversion, using a vendor-provided SPICE behavioral subcircuit of the EPC2302 GaN power transistor. After an automatic translation from the SPICE netlist to a Simscape description, semantic and syntactic incompatibilities are analyzed and are found to mainly originate from representing voltage-dependent capacitances as parameterized primitive components, which introduces runtime terminal voltages into parameter evaluation and leads to compilation conflicts. To preserve the device dynamic behavior while complying with Simscape semantics, the voltage-dependent capacitances are reformulated as explicit branch-current relations at the equation level, and the smooth fitting structure of the vendor model is retained to enhance numerical robustness. A datasheet-referenced verification workflow is established to assess consistency in static conduction behavior and dynamic capacitance-related characteristics. The resulting model compiles successfully and runs stably in time-domain simulations, providing a practical foundation for system-level transient analysis and loss-trend evaluation of high-frequency GaN DC–DC converters.
Copyright (c) 2026 ZiYu Qin; Tu Hong; Lei Shi https://creativecommons.org/licenses/by/4.0
MODAL ANALYSIS AND STRUCTURAL OPTIMIZATION OF GEAR MESHING NOISE IN NEW ENERGY VEHICLES
With the rapid development of new energy vehicles, the noise source of its power system has changed significantly, in which the gear mesh noise of the gearbox has a direct constraint on the NVH performance of the whole vehicle. In this paper, a new energy vehicle three-in-one motor in the 7500-9000rpm speed range of the obvious gear whistling problem to carry out research, through the analysis to determine its main noise source for the first gear pair. The study used SolidWorks software to establish a three-dimensional model of the first gear, and imported into ANSYS for finite element modal analysis, and also calculated the gear meshing frequency based on the formula f=n*z/60. The analysis results show that under 8000rpm, the gear meshing frequency is very close to the first-order intrinsic frequency of the structure, which is very easy to trigger structural resonance, resulting in a stepwise increase in the noise sound pressure level. In order to suppress this resonance phenomenon, an economical and direct structural optimization scheme is proposed in this paper: the number of weight-reducing holes in the gear body is reduced from 8 to 4 to enhance the structural stiffness of the spokes. The optimized simulation results show that the first-order modal frequency of the gear is increased to about 3000 Hz, which successfully avoids the excitation frequency under the main working speed, so that the noise source under the common working conditions is transformed into the wind noise and the tire noise, and the gear meshing noise is effectively masked. The study confirms that structural optimization by adjusting the number of weight-saving holes is an effective and economical way to suppress the resonance noise of the gears in new energy vehicles, which improves the dynamic performance while taking into account the lightweight design objective.
Copyright (c) 2026 KangZhe Si https://creativecommons.org/licenses/by/4.0
HYBRID-CONTROLLED IPOS LLC CONVERTER FOR FUEL CELL APPLICATIONS
Fuel-cell power conditioning systems require DC–DC converters to achieve high efficiency, wide-range voltage regulation, and reliable operation under significant input and load variations. To address these requirements, this paper investigates an IPOS-type secondary LLC resonant DC–DC converter and proposes a hybrid control strategy combining pulse-frequency modulation (PFM) and phase-shift modulation (PSM). In the proposed scheme, PFM is employed as the primary control method to realize wide-range voltage regulation, while a frequency-clamping with phase-shift compensation mechanism is introduced to maintain effective power control and dynamic performance when the switching frequency reaches its practical upper limit. The effectiveness of the proposed topology and control strategy is validated through MATLAB/Simulink simulations and experimental studies on a laboratory prototype. Experimental results demonstrate a peak conversion efficiency of 96.6% under rated conditions, with efficiency remaining above 96% near the rated load range. In addition, the maximum output voltage deviation between two IPOS phases is limited to 1.9% under full-load operation, and fast, stable input-current tracking is achieved under step reference changes from 20 A to 80 A without noticeable overshoot. These results confirm that the proposed converter and hybrid control strategy are well suited for high-efficiency, wide-range fuel-cell energy conversion applications.
Copyright (c) 2026 JueXiao Chen; EnYong Li; Lei Shi; ZhongYao Jiang https://creativecommons.org/licenses/by/4.0
INORGANIC COMPOSITE STONE-LIKE CAST-IN-PLACE TECHNOLOGY AND ENGINEERING APPLICATION PERFORMANCE EVALUATION FOR URBAN RENEWAL
To address the demand for green, adaptable building materials in urban renewal’s existing building renovation and public space transformation, and solve traditional stone-like materials’ defects (long construction period, poor adaptability, low solid waste utilization), this study optimizes inorganic composite stone-like cast-in-place technology and evaluates its performance. Industrial solid wastes (steel slag, recycled construction aggregates) serve as main raw materials; composite cementitious systems and functional admixtures are developed, with key process parameters determined via orthogonal experiments. Results show that at 72% solid waste replacement rate, 0.28 water-binder ratio, 0.4% retarder content and 20–25℃ pouring temperature, the material’s 28-day compressive/flexural strength reaches 105/9.8 MPa, setting time 4–6 h, meeting on-site construction requirements. It exhibits excellent durability, with 8.2% strength loss after 50 freeze-thaw cycles and grade-4+ stain resistance. Engineering verification confirms 40% higher construction efficiency, 35% lower cost and 1.2 t/100 m2 carbon emission reduction versus traditional dry-hanging. This scheme offers green, efficient technical support for urban renewal, with significant engineering and environmental value.
Copyright (c) 2025 LianDi Zhao; XianWei Wang https://creativecommons.org/licenses/by/4.0
DYNAMIC MODELING AND ANALYSIS OF IN-WHEEL SUSPENDED ELECTRIC WHEEL SYSTEM
The introduction of in-wheel motors increases the unsprung mass of electric vehicles, exacerbates vibrations when the vehicle is subjected to road excitation, and affects ride comfort and stability. The in-wheel suspended electric wheel has become an important technical means to improve the above-mentioned problems of in-wheel motors. This paper conducts a vertical dynamic characteristic analysis of two electric wheel configurations. A 1/4 vehicle model was established based on the Matlab/Simulink platform, and the vibration response characteristics of the two electric wheels under different road excitations were obtained. Experimental tests were performed on the vibration characteristics of the two electric wheels using a swept-sine road excitation. The experimental results were in good agreement with the simulation results, verifying the reliability and accuracy of the simulation model.
Copyright (c) 2025 Peng Zhang; Lei Jiang; ShangQing Liu; ShengShuo Yan; JingXin Bai; Ping Zhang https://creativecommons.org/licenses/by/4.0
INTELLIGENT IDENTIFICATION AND DECISION SUPPORT SYSTEM FOR TBM CONSTRUCTION RISK SYNERGISTICALLY DRIVEN BY KNOWLEDGE GRAPHS AND LARGE LANGUAGE MODELS
Tunnel Boring Machines (TBMs) are widely used in subway and tunnel projects due to their efficiency and safety. However, the complexity and high uncertainty of the construction environment lead to lag and limitations in traditional risk identification methods. Addressing the urgent need for intelligent management, this study combines the advantages of Knowledge Graphs (KG) in structured knowledge representation and Large Language Models (LLMs) in natural language understanding to construct a synergistically driven intelligent risk identification and decision support system for TBM construction.First, the study employs ontology modeling to build a risk knowledge graph covering risk factors, risk-causing mechanisms, and consequences, achieving entity-relation extraction through multi-source heterogeneous data mining. Second, the Large Language Model is adapted and fine-tuned by constructing an engineering corpus and injecting domain knowledge. Finally, a synergistic mechanism is designed to realize intelligent risk identification and decision-making.The innovations of this study include: (1) Proposing a "Knowledge Retrieval - Semantic Understanding - Logical Reasoning" three-layer architecture, effectively fusing explicit knowledge with reasoning capabilities; (2) Developing a multi-modal risk feature fusion identification model, improving identification accuracy and real-time performance; and (3) Building an intelligent recommendation engine for risk response plans, supporting multi-scenario simulation. Experimental and engineering application results indicate that the system significantly improves the precision and recall of risk identification, effectively enhancing the risk management level of TBM construction.
Copyright (c) 2026 YongKun Li; Yin Bo https://creativecommons.org/licenses/by/4.0
MULTI-AGENT COORDINATION AND RESOURCE ALLOCATION OPTIMIZATION STRATEGIES FOR SMOKE SCREEN DEPLOYMENT AGAINST DYNAMIC TARGETS
This paper investigates optimal defense strategies for countering single or multiple high-speed incoming missiles by deploying smoke screen decoys through coordinated operations of multiple unmanned aerial vehicles (UAVs) in complex dynamic battlefield environments. The research focuses on the challenging problem of multi-agent cooperative defense and large-scale resource allocation. First, for a three-UAV, single-decoys cooperative countermeasure strategy, a joint optimization model incorporating parameters such as UAV flight direction, velocity, deployment timing, and detonation timing is constructed. A differential evolution algorithm is employed to solve this high-dimensional combinatorial optimization problem. Through iterative differential evolution, optimal parameter configurations for the three UAVs are obtained, achieving effective shielding against missile M1. Subsequently, the scenario is expanded to the most complex five-UAV, three-missile integrated defense problem. This problem involves multiple UAVs, multiple missiles, multiple missile deployments, and resource allocation, constituting a typical large-scale combinatorial optimization problem. This paper innovatively proposes a two-layer hybrid optimization framework combining the Hungarian algorithm and genetic algorithm. The upper layer utilizes the Hungarian algorithm to determine the optimal task allocation between missiles and UAVs, minimizing the initial distance cost to ensure optimal spatial separation. The lower layer, with fixed allocations, employs a genetic algorithm to refine the deployment parameters for each UAV. The final optimized solution successfully achieves comprehensive interference against three missiles, demonstrating the dual-layer model's capability to obtain near-global-optimal multi-objective defense strategies while ensuring real-time feasibility.
Copyright (c) 2025 TianJian Zhong https://creativecommons.org/licenses/by/4.0
SOME PROPERTIES OF HYPERBOLIC TRIGONOMETRIC FUNCTIONS
This paper investigates some fundamental properties of hyperbolic trigonometric functions on the hyperbolic number plane. Hyperbolic numbers form a commutative ring with zero divisors, generated by two real numbers via the Cartesian basis. Within this algebraic framework, this paper systematically establish the system of hyperbolic trigonometric functions. By fully leveraging the decomposition properties of hyperbolic numbers and their associated trigonometric functions, this work effectively overcomes the mathematical difficulties arising from the presence of zero divisors in the hyperbolic number ring. On this basis, this paper for the first time in the context of a hyperbolic number ring containing zero divisors, systematically derived and rigorously proved the addition theorems for the hyperbolic sine, cosine, tangent, and cotangent functions. On this basis, we have successfully derived and rigorously proved the addition theorems for the hyperbolic sine, cosine, tangent, and cotangent functions, establishing a complete system of angle addition formulas and laying a solid foundation for the theory of hyperbolic functions. The addition theorems established in this research will provide an important theoretical foundation for the further development of hyperbolic analysis in function theory, while simultaneously injecting new research momentum into the further study of the properties and expansions of hyperbolic trigonometric series. These theoretical achievements are expected to play significant roles in the study of hyperbolic differential equations, geometric analysis, and related physical problems.
Copyright (c) 2025 HaoLin Fu https://creativecommons.org/licenses/by/4.0
PRECISE MEASUREMENT OF SILICON CARBIDE EPITAXIAL LAYER THICKNESS BASED ON INFRARED INTERFERENCE PRINCIPLE AND LEAST SQUARES OPTIMIZATION ALGORITHM
This paper focuses on the precise measurement of epitaxial layer thickness in silicon carbide wafers. An ideal mathematical model based on infrared interference and its solution algorithm were constructed and solved. To construct the ideal mathematical model for epitaxial layer thickness measurement, the study first analyzes the physical principles of reflection and refraction of infrared light at the epitaxial layer-substrate interface. Utilizing Snell's law and trigonometric functions, it derives formulas for calculating optical path difference and phase difference. By establishing the conditions for wave interference maxima and minima, the theoretical formula for calculating epitaxial layer thickness was ultimately derived, successfully establishing a mathematical framework under the idealized assumption of only two reflections. Building upon this ideal model, for the thickness measurement algorithm design task, the study first performed standardized preprocessing on the provided spectral data, including S-G smoothing filtering, effectively reducing noise interference. Subsequently, a peak localization and selection algorithm was applied to precisely identify and jointly utilize the extreme points of all 60 interference fringes. To resolve the integer ambiguity issue for interference order m, a traversal optimization algorithm was designed. By searching for the m value that minimizes the variance of the thickness calculation sequence, the optimal interference order was coherently determined. Finally, the least-squares fitting optimization technique yielded an average epitaxial layer thickness of approximately 11.61 μm. A four-dimensional reliability assessment—encompassing parameter sensitivity, data integrity, theoretical consistency, and computational stability—validated the algorithm's exceptional robustness and reliability within the idealized model framework.
Copyright (c) 2026 Yi Wang https://creativecommons.org/licenses/by/4.0
IDENTIFICATION OF A CRITICAL INITIAL TEMPERATURE WINDOW FOR PEMFC COLD START UNDER LINEAR 25 S LOADING BASED ON A ONE-DIMENSIONAL MULTIPHASE MODEL
The low-temperature cold-start capability of proton exchange membrane fuel cells (PEMFCs) is strongly influenced by the initial temperature and the water-thermal coupling behavior within the stack. In this study, a one-dimensional multiphase cold-start model was developed based on a lumped-parameter approach. A 5 kW stack consisting of 30 single cells was investigated under a unified linear 25 s loading strategy, in which the current density was linearly increased from 0 to 0.5 A·cm -2 and then maintained constant. The cold-start evolution characteristics were systematically analyzed over an initial temperature range from -4 °C to -20 °C. The simulation results reveal the existence of a distinct critical initial temperature interval (approximately -12 °C to -14 °C) under the prescribed loading protocol, with the upper and lower bounds corresponding to two fundamentally different evolutionary pathways. When the initial temperature exceeds the lower bound of this interval, the stack establishes a stable positive thermo-electrical coupling feedback, enabling voltage recovery and sustained operation. In contrast, when the initial temperature falls below this interval, the system progressively enters an irreversible degradation trajectory and ultimately becomes unstable. Further analysis indicates that the decisive factor governing cold-start success is not the overall heat generation level, but whether the edge cells can surpass the freezing point and complete ice melting within a critical time window. These findings provide a theoretical basis for cold-start boundary identification and strategy optimization.
Copyright (c) 2026 YueWen Zeng; Lei Shi https://creativecommons.org/licenses/by/4.0
CURRENT STATUS OF NEAR-SURFACE OZONE POLLUTION RESEARCH IN CHINA
In recent years, with the effective control of fine particulate matter (PM2.5) pollution in China, the problem of near-surface ozone (O3) pollution has become increasingly prominent, becoming one of the important factors restricting the continuous improvement and attainment of ambient air quality standards. This article systematically reviews the latest progress in research on near-surface ozone pollution in China. Studies show that since the 13th Five-Year Plan, the overall O3 concentration in China has shown a trend of first rising and then plateauing, exhibiting significant regional differences and seasonal high incidence in key urban clusters such as the Beijing-Tianjin-Hebei region and the Yangtze River Delta. In terms of formation mechanism, the nonlinear relationship between ozone formation and its precursors, nitrogen oxides (NOx) and volatile organic compounds (VOCs), has always been the core of research: most urban central areas are in VOCs control zones, while regional background areas and some suburban areas are mostly NOx control zones or transition zones, and their control sensitivity dynamically evolves with the progress of pollution reduction . Meteorological conditions, especially extreme high temperatures and strong solar radiation, have a significant promoting effect on ozone formation and accumulation, and the "heat wave-ozone" compound pollution is gradually becoming a new prominent problem. In terms of prevention and control strategies, the synergistic control of ozone and PM2.5 has risen to an important strategic direction. It is urgent to implement differentiated NOx and VOCs emission reduction measures based on precise sensitivity analysis, and to strengthen regional joint prevention and control mechanisms. Future research should focus on the synergistic governance of compound pollution, refined source spectrum analysis and efficient control of VOCs, the microscopic processes of ozone formation, and the systematic upgrading of forecasting and early warning technologies.
Copyright (c) 2025 Xin Lin https://creativecommons.org/licenses/by/4.0
FEAST: FEATURE ENGINEERING AND STACKING FRAMEWORK FOR COMPLEX SYSTEM PREDICTION
The challenge of capturing high-dimensional interactions and ensuring stable predictions in complex engineering systems, characterized by rapidly growing heterogeneous data, limits traditional models. To address this, we introduce FEAST (Feature Engineering and Advanced Stacking Framework), whose core innovation lies in its integrated approach: (1) Hierarchical feature optimization using K-Means++ clustering and adaptive correlation to significantly reduce redundancy and enhance feature discrimination; (2) An advanced stacked ensemble leveraging diverse base learners (MLR, XGBoost, LightGBM, CatBoost, GBDT) fused via Shapley-weighted combination and a regularized (Elastic Net) meta-learner to robustly capture complex patterns; (3) An efficient Monte Carlo Dropout-based uncertainty quantification module providing reliable confidence intervals for risk-aware decisions. Comprehensive experiments demonstrate FEAST's superiority: it achieves 12.7% higher prediction accuracy (p<0.01), 41% lower cross-validation error variability, and a 92.3% confidence interval coverage rate, significantly outperforming baseline models in complex engineering prediction tasks.
Copyright (c) 2026 WanJun Cai; JiangYi Le; Rui Gu; Hang Zhao; SiYu Tang https://creativecommons.org/licenses/by/4.0
PRE-SYNCHRONIZATION CONTROL BASED ON VSG PARAMETER COOPERATIVE ADAPTATION
Virtual synchronous generator (VSG) emulate synchronous generators to provide inertia and damping support for distributed power integration. However, conventional VSG control employs fixed parameters that hinder dynamic adaptation to system changes, often resulting in grid connection impact currents and voltage harmonics. To overcome these limitations, this study proposes a pre-synchronization strategy based on coordinated adaptive control of VSG parameters. A VSG mathematical model is established to analyze parameter influences on system output characteristics. A coordinated adaptive control method incorporating rotational inertia and damping coefficients is then developed, which demonstrates faster suppression of excessive changes and deviations in output response compared to conventional strategies. Furthermore, a pre-synchronization mechanism is integrated to achieve smooth grid synchronization while significantly reducing inrush currents and voltage harmonics before and after connection. Simulation results via PSCAD validate the feasibility and effectiveness of the proposed control strategy.
Copyright (c) 2026 YuLin Yuan https://creativecommons.org/licenses/by/4.0
RESEARCH ON DUAL PLC SIMULATION TECHNOLOGY BASED ON MODBUS TCP COMMUNICATION
With the rapid development of industrial automation technology, efficient communication between PLCs has become the key to realizing complex control systems. Based on the MODBUS TCP protocol, this paper studies the simulation method of dual PLC communication, aiming to improve the reliability and real-time performance of industrial control networks. By constructing a dual PLC communication simulation platform based on MODBUS TCP, the data frame structure and communication mechanism of the protocol are analyzed. The logic design and communication configuration of the PLC program are implemented using TIA Portal software, and the simulation platform adopts S7-PLCSIM Advanced. The experimental results show that the simulation system can stably realize data exchange between dual PLCs, providing a reference for multi-PLC collaborative control in industrial fields. This research provides theoretical support and practical basis for the application of MODBUS TCP in industrial automation.
Copyright (c) 2026 Hong Chen https://creativecommons.org/licenses/by/4.0
OPTIMIZATION OF URBAN SOLID WASTE COLLECTION ROUTES AND MULTI-VEHICLE COLLABORATIVE DISPATCH DECISION-MAKING
Addressing the core demands of enhancing transportation efficiency and controlling costs in urban solid waste management, this study constructs a multi-objective optimization model for waste collection routes and multi-vehicle coordination scheduling based on multi-dimensional constraints. During the fundamental route planning phase, a mathematical model minimizing total travel distance is established for single-type waste collection points, alongside an improved genetic algorithm incorporating a Markov chain adaptive mechanism. This algorithm employs a hybrid coding scheme for primary and auxiliary chromosomes, dynamically adjusts crossover and mutation operators, and incorporates local optimization strategies like embedded neighborhood search. It successfully achieves efficient task allocation, with experimental results yielding a total travel distance of 1,144.5 kilometers. During the complex multi-vehicle coordination phase, the model was extended to a joint transportation scenario involving four specialized waste collection vehicle types, comprehensively considering constraints such as vehicle payload, volume, unit transportation cost, and maximum daily driving time. An improved multi-objective genetic algorithm was employed for solution, achieving Pareto-optimal scheduling of multi-source waste streams through segmented sequence encoding and feasibility correction mechanisms. Results demonstrate the model's ability to clearly characterize operational efficiency and cost structures across vehicle categories, with food waste collection and transportation accounting for the highest cost proportion at 38.3%.
Multi-vehicle collaborative optimization
Copyright (c) 2026 ZhiYang Chen; ChangYuan Chen; BiRui Yang https://creativecommons.org/licenses/by/4.0
AUTONOMOUS TRAJECTORY CORRECTION CONTROL STRATEGY FOR TBM IN COMPLEX GEOLOGY: A DEEP REINFORCEMENT LEARNING APPROACH
In complex geological conditions, such as variable rock hardness, Tunnel Boring Machines (TBMs) frequently suffer from trajectory deviations. Traditional control strategies, based on operator experience or simplified mechanical models, often lack the necessary adaptability to handle the non-linearity and randomness of surrounding rock, making precise and efficient trajectory correction difficult. This study introduces Deep Reinforcement Learning (DRL) to address the challenges of robustness and self-adaptation in TBM posture control. We first establish a high-fidelity TBM-geology interaction simulation environment, defining a multi-dimensional state space and action space that includes critical information such as posture deviation, thrust distribution, and geological parameters. To balance excavation accuracy and efficiency, we design a multi-objective composite reward function that incorporates penalties for posture deviation, rewards for advance rate, and constraints for control input smoothness. For policy learning, we improve DRL algorithms suitable for continuous action spaces and introduce a Prioritized Experience Replay mechanism to enhance the policy's stability under abrupt environmental changes. Simulation results demonstrate that, compared to conventional PID control, the DRL-based autonomous correction strategy achieves an improvement of over 30% in posture control accuracy and a reduction of over 20% in response time to sudden disturbances. This research validates the significant advantages of DRL in handling the high-dimensional, highly delayed, and non-linear control challenges inherent in TBM excavation, providing an innovative theoretical framework and technical support for the autonomous and intelligent development of TBM operations.
Copyright (c) 2025 MingFu Zheng; Yao Mo; Ying Zhang; Yin Bo; Rongwen Chen https://creativecommons.org/licenses/by/4.0
DESIGN AND OPTIMIZATION OF TBM ADAPTIVE CUTTERHEAD SYSTEM FOR EXTREMELY HARD ROCK AND UNEVEN SOFT-HARD STRATA
To address the challenges encountered during Tunnel Boring Machine (TBM) construction in extremely hard rock and uneven soft-hard strata, this study designs and optimizes an adaptive cutterhead system. Existing research indicates that traditional cutterhead systems possess limitations when dealing with complex strata, primarily manifested in restricted rock-breaking efficiency and excessive cutter wear. This study first analyzes the mechanical model of rock fragmentation in extremely hard rock and the interface effects within uneven soft-hard strata. A multi-degree-of-freedom (MDOF) coupled vibration model for cutterhead-rock mass dynamics is proposed, followed by a parameter sensitivity analysis. Based on these findings, and adhering to specific design principles and performance indicators, the overall architecture of the adaptive cutterhead system is constructed, comprising mechanical, sensing, and control subsystems. The comparative selection of key module schemes involves a cutter layout adjustment mechanism and a rotational speed-thrust synergistic control unit. Regarding structural optimization, this study employs high-stiffness, lightweight topology optimization alongside wear-resistant material selection and surface strengthening to enhance disc cutter performance. Additionally, a modular system for replaceable cutters is designed. In terms of adaptive control strategies, real-time recognition of rock mass conditions, adaptive matching of rotational speed and thrust, and dynamic optimization of cutter layout are realized. The implementation of control algorithms integrates reinforcement learning-based decision models with real-time optimization algorithms. Through simulation analysis, the proposed adaptive system demonstrates significant advantages in rock-breaking efficiency and cutter wear prediction. Physical model tests further validate the adaptive adjustment performance, showing strong correlation with simulation results. Field application confirms that the system effectively improves boring efficiency, extends cutter lifespan, and yields significant economic benefits. The theoretical innovations and technical breakthroughs presented offer new insights for the design and optimization of TBM cutterhead systems, while engineering verification proves their feasibility in practical applications. Future research may further explore intelligent development directions and multi-machine collaborative tunneling technologies to address increasingly complex geological conditions and enhance the intelligence level and efficiency of TBM construction.
Copyright (c) 2025 Yao Mo; Ying Zhang; XueWen Li; Peng Zhang; Yin Bo; RongWen Chen https://creativecommons.org/licenses/by/4.0
GAME-THEORETIC APPROACH TO DYNAMIC CONTROL SUBAREA ADJUSTMENT IN OVERSATURATED URBAN ROAD NETWORKS: EVIDENCE FROM GUANGZHOU
Driven by rapid urbanization and motorization, peak-hour oversaturation has become a chronic issue in Guangzhou’s urban road network. Core districts such as Tianhe and Haizhu frequently experience congestion indices exceeding 7.8 during morning and evening peaks, with average vehicle speeds dropping below 19 km/h. Queue spillover triggers cascading “domino effects,” severely degrading system-wide traffic efficiency. While control subareas serve as fundamental units for regional signal coordination, conventional static partitioning fails to adapt to dynamic traffic fluctuations, and existing dynamic methods often neglect strategic interactions and interest conflicts among subareas, leading to suboptimal coordination. This study proposes a game-theoretic framework for dynamic subarea adjustment under oversaturated conditions, using Guangzhou as a case study. We develop two decision-making paradigms: a centralized cooperative game model that minimizes total network control cost, and decentralized non-cooperative models—including Stackelberg and Nash equilibria—that account for autonomous subarea behavior. Through theoretical derivation, numerical simulation, and validation via the VISSIM microsimulation platform, we analyze equilibrium properties and system performance across different game structures. Results demonstrate that the centralized model achieves the lowest total control cost—31.5% lower than the Nash equilibrium—but suffers from poor real-time responsiveness due to computational complexity. The Stackelberg model, leveraging a “central guidance-subarea response” mechanism, strikes an optimal trade-off: it reduces total delay by 18.2% compared to Nash, increases adjustment frequency by 112.5% relative to centralized control, and maintains robust performance under uncertainty. Meanwhile, the Nash model exhibits superior robustness (delay fluctuation ≤4.8% under ±20% data noise) but incurs significant efficiency losses. Parametric analysis reveals that when the collaboration weight λ > 0.6, subarea merging probability increases by 42% and queue lengths decrease by 27%. Furthermore, constraining subarea size to 2-5 intersections optimally balances management overhead and coordination benefits—a finding validated across Guangzhou’s heterogeneous urban fabric. This research provides both theoretical grounding and a practical implementation pathway for intelligent, adaptive traffic control in oversaturated megacities, directly supporting Guangzhou’s “14th Five-Year Plan” for Intelligent Transportation Systems (ITS).
Copyright (c) 2025 WeiBin Zhao; XinHai Xia https://creativecommons.org/licenses/by/4.0
DYNAMIC RESPONSE AND COUPLING MECHANISM FOR COMPOSITE FAULTS IN BEARING-BEARING SEAT SYSTEM BASED ON LS-DYNA
This study establishes a finite element model of a 6205 rolling bearing system, incorporating the housing and central shaft, to analyze dynamic characteristics under healthy and composite fault conditions. The model's reliability is validated through close agreement between simulated and theoretical kinematic values and fault characteristic frequencies. Analysis reveals that stress concentrates at the leading edge of defects due to rolling element impact. The ball-inner ring composite fault induces the highest shear stress levels and severe fluctuations, resulting from intense interaction between moving and core load-bearing surface defects. In contrast, fixed outer ring defects cause periodic, moderate stress responses. Acceleration response analysis shows that ball-outer ring faults produce higher-amplitude, sustained vibrations due to near-rigid impacts, while ball-inner ring faults generate intermittent spikes buffered by the inner ring's inertia. These distinct signatures provide a theoretical basis for differentiating fault types in vibration signals. The findings offer accurate fault characteristics and simulation data support for vibration-based condition monitoring and early diagnosis of rolling bearings.
Copyright (c) 2025 XingChen Wang https://creativecommons.org/licenses/by/4.0
CONTROL METHOD OF A MACHINE VISION-BASED ROBOT FOR PUMP PIPE INSPECTION
The pump pipe detection robot is used to detect the blockage of the concrete pump pipe, and it is an important equipment to ensure the safe and efficient transportation of the pump pipe. Currently, traditional inspection relies heavily on manual experience, which is not only time-consuming and inaccurate but also severely impacts construction progress and may even cause economic losses. To achieve precise and efficient blockage detection, this paper designs a machine vision-based pipeline detection robot control system. The core of this system utilizes optical fiber as the communication medium, fully leveraging its high bandwidth, low latency, and electromagnetic interference resistance to enable efficient and reliable transmission of robot control commands and high-definition image data.
Copyright (c) 2025 Dong Liu; ChangCheng Wan; Long Xie; Peng Liu https://creativecommons.org/licenses/by/4.0
ZERO VALUE DETECTION TECHNOLOGY FOR CERAMIC INSULATORS BASED ON CURRENT RESPONSE CHARACTERISTICS UNDER HIGH VOLTAGE IMPACT
Ceramic insulators may experience a decrease in internal insulation performance and become zero value insulators due to environmental factors after being put into operation. Regular zero value testing is required to identify zero value insulators. The impulse high voltage method is one of the effective methods for zero value detection, but existing impulse high voltage methods only focus on the voltage response characteristics of insulators under impulse high voltage, without paying attention to the current response characteristics. This study proposes a zero value detection method based on the current response characteristics under impulse high voltage. The response current is converted into an induced voltage signal through a magnetic ring inductor, and the difference between zero value and the peak induced voltage of a normal insulator is used to achieve reliable identification of zero value. In the case of insulator surface contamination, this method is more robust and has a lower risk of misjudgment compared to detection methods based on voltage response characteristics. The research provides new ideas for insulation testing of ceramic insulators, which helps to improve the reliability of the power grid. Further exploration is needed in the future to determine the reasons for the reliability differences between the two methods.
Copyright (c) 2025 Min Xie; ChenLong Zhao; XiaoGang Li; ZhuHong Liu https://creativecommons.org/licenses/by/4.0
LLM-GUIDED ADAPTIVE STEP-SIZE ZEROING NEURAL NETWORK FOR ROBOTIC MANIPULATOR TRAJECTORY TRACKING
Zeroing Neural Network (ZNN) is widely used for robotic trajectory tracking due to their rapid convergence and strong error attenuation properties. In discrete implementations, however, the choice of step-size critically influences tracking accuracy, numerical stability, and computational cost. Conventional variable-step strategies typically rely on fixed heuristics or manually tuned rules, limiting their adaptability in dynamic task conditions. To address this limitation, this paper introduces a Large Language Model (LLM)-based adaptive step-size mechanism for discrete ZNN in manipulator trajectory tracking. The LLM receives natural-language instructions together with essential system-state descriptions and outputs step-size adjustments that guide the ZNN update. This enables intuitive human-robot interaction and allows the controller to flexibly shift between high-precision tracking and low-computation execution without modifying the underlying ZNN formulation. Results show that the proposed method improves tracking accuracy when finer steps are selected, reduces computational load when coarser steps suffice, and maintains high semantic consistency in interpreting step-size-related instructions. These findings demonstrate the potential of integrating LLM reasoning into step-size regulation to enhance the flexibility and interpretability of discrete ZNN-based robotic tracking.
Copyright (c) 2025 GuangYu Long; ZhuoSong Fu https://creativecommons.org/licenses/by/4.0
CROP PLANTING STRATEGIES BASED ON PARTICLE SWARM OPTIMIZATION ALGORITHM
In order to solve the problems of efficient utilization of limited cultivated land resources and sustainable agricultural development in the North China Plain, this study aims to maximize total profits from 2024-2030, and combines field inspection data to build a planting strategy optimization model. First, the 2023 crop yield per mu and planting area data are processed through the VLOOKUP function to determine the expected future sales volume; the lowest sales price is extracted and profits are calculated based on planting costs. After Python data cleaning and standardization, SPSSPRO is used to visually analyze the profit and income ratio of different plots (flat dry land, terraced fields, etc.) to screen high-quality crops. For the two scenarios of "total waste of excess sales" and "price reduction of excess sales", corresponding objective functions are constructed, constraints such as plot planting cycle, continuous cropping taboos, and bean crop rotation are introduced, and particle swarm optimization (PSO) is used to solve the optimal solution through Python. The results show that the model can effectively balance resource utilization, cost control and ecological constraints, and provide quantitative support for scientific planting decisions in the North China Plain.
Copyright (c) 2025 YanZhuo Wu; GuangWu Ao https://creativecommons.org/licenses/by/4.0
SMOKE JAMMER DEPLOYMENT STRATEGIES FOR DRONES UNDER MULTIVARIATE OPTIMIZATION
This study investigates smoke-screen interference tactics deployed by unmanned aerial vehicles (UAVs) against incoming M1 missiles. An intuitive geometric-physical model is established for missile trajectories, UAV motion, and smoke-screen projectile descent/detonation. The effective shielding effect of smoke clouds is quantified by combining geometric cone inclusion criteria with linear distance assessment methods. To address the calculation of effective shielding duration for a single smoke grenade, a variable stride search algorithm is employed, yielding an effective shielding duration of 1.391975 seconds under initial conditions. To formulate an optimal jamming strategy, four decision parameters are introduced to explore the optimal deployment strategy for a single drone with a single smoke grenade. A hybrid strategy combining genetic algorithms and particle swarm optimization is adopted, increasing the maximum effective shielding time to 4.585 seconds. Building upon this foundation, this paper analyzes the optimization scenario of coordinated shielding by a single drone deploying multiple flares. A multivariate optimization model is established to account for the composite shielding effect of multiple smoke clouds. Through the basin-jumping particle swarm optimization algorithm, the maximum composite effective shielding time reaches 6.3020 seconds. These research findings provide optimization strategies and actionable solutions for maximizing smoke flare interference effectiveness and coordinated multi-flares interference.
Copyright (c) 2025 JinSong Zhang; JunRui Mu https://creativecommons.org/licenses/by/4.0
RELATIONSHIP BETWEEN TRAFFIC FLOW AND TIME BASED ON REGRESSION MODELS
Accurate estimation of traffic flow is crucial for urban traffic management and control, particularly when only main road monitoring data is available and feeder road data is missing. This study addresses the challenge of inferring feeder road traffic flow from main road data by developing a series of regression models tailored to different road structures and traffic conditions. For a Y-shaped basic road network, both linear and piecewise linear regression models were established, achieving perfect fitting of the main road traffic flow. In multi-branch scenarios that account for delays and cyclical fluctuations, an integrated model comprising constant, piecewise linear, and periodic functions was proposed, achieving a goodness of fit of 0.9722. Under traffic signal control conditions, a composite model including piecewise functions and periodic components was developed, effectively addressing traffic interruptions caused by signals, with a goodness of fit of 0.9642. In noisy data environments, a robust regression framework with adaptive weighting was introduced, maintaining high accuracy despite noise interference. The results indicate that the proposed models can effectively reconstruct feeder road traffic patterns, offering excellent interpretability and robustness. This provides a reliable data foundation for signal timing optimization and congestion management, offering practical solutions for traffic flow estimation in certain observed road networks.
Copyright (c) 2025 TianYi Chen; Cheng Cui https://creativecommons.org/licenses/by/4.0
DYNAMIC CHARACTERISTICS OF GEARBOX HOUSING VIBRATION ON THE BEIJING-SHANGHAI LINE
Gearbox housing vibration critically impacts the operational safety and reliability of high-speed trains. However, existing research mainly relies on the track spectrum of Wuhan-Guangzhou Line, lacking analysis tailored to the unique characteristics and curve negotiation conditions of Beijing-Shanghai Line. To address this gap, this study establishes an experimentally validated vehicle-track coupled dynamics model, conducting multi-condition simulations to explore gearbox vibration correlations with operating parameters, track conditions, and wheel polygon excitation under Beijing-Shanghai Line scenarios. Key findings show that as speed increases, vertical (Z) and lateral (Y) vibration responses of three measurement points converge. Low speeds are dominated by 11th-order wheel polygon effects, while high speeds are influenced more by the 23rd-order polygon. Upper gearbox points exhibit prominent Y-direction vibration, and lower points show significant Z-direction vibration. Beyond 300 km/h, speed becomes the dominant factor, with polygon order’s influence on vibration acceleration RMS values diminishing. Gearbox vibration intensifies at 275 km/h under 23rd-order polygon excitation, and vibration severity increases with wheel polygon amplitude. Curve negotiation analysis reveals that smaller radii induce more severe vibration: Z-direction vibration decreases monotonically with increasing radius. Y-direction vibration behaves differently by speed—increasing with radius at 250 km/h but decreasing at other speeds. Local peaks occur at 9,000 m radius (275/325 km/h) and 8,000 m radius (350 km/h) due to enhanced wheel-rail excitation.
Copyright (c) 2025 Qi Wang; JiMin Zhang; Kun He https://creativecommons.org/licenses/by/4.0
A DOUBLE LIQUID STORAGE LOOP HEAT PIPE BASED ON SLIDING SLEEVE REGULATION
To address the limitations of traditional loop heat pipe evaporators-such as uncertain operational performance under different orientation configurations, unreasonable working fluid volume in the reservoir, and thermal leakage issues—this study proposes a novel dual-reservoir loop heat pipe. Featuring an annular nozzle-coupled sleeve design, this innovative configuration enhances heat transfer efficiency during complex gravitational variations. The research investigates how varying gravitational orientations and liquid filling rates affect the heat transfer characteristics of the dual-reservoir loop. Simulation software was employed to evaluate thermal performance across different gravitational conditions and filling rates. Results demonstrate that under 65% working fluid filling rate and 40W thermal load, the phase-change liquid medium is replenished promptly. Continuous evaporation occurs on the capillary core surface without drying out, ensuring stable operation of the loop heat pipe. This design effectively meets the mission's requirements for stable thermal dissipation in complex aerospace environments.
Copyright (c) 2025 XuanNing Liang; Jing Zhang; ZhengJie Liu; Feng Qu; WenXian Kuang; ShengNan Yan; Heng Xiao https://creativecommons.org/licenses/by/4.0
FAULT DIAGNOSIS OF ELEVATOR UNBALANCED LOAD BASED ON PARAMETER-OPTIMIZED SVM
To address the fault diagnosis of elevator unbalanced loads, this study proposes a fault diagnosis method based on a parameter-optimized support vector machine (SVM). By establishing a dynamic model of the elevator system, fault features are extracted, and an improved particle swarm optimization algorithm is applied to optimize the key parameters of the SVM, thereby constructing an efficient fault diagnosis model. Experimental results indicate that the proposed method significantly outperforms traditional diagnostic approaches in terms of fault classification accuracy, and can effectively identify the unbalanced load state of elevators. The research outcome offers a new technical solution for elevator fault diagnosis and holds significant engineering application value for ensuring the safe operation of elevators.
Copyright (c) 2025 SaiNan Wang; Xian Zhou; YunTao Yang https://creativecommons.org/licenses/by/4.0
TEACHING OF ENGINEERING COST PRACTICAL TRAINING COURSES IN APPLIED UNDERGRADUATE INSTITUTIONS
Addressing issues such as content disconnect from industry standards, disjointed process training, and unrealistic evaluations in engineering cost practical training at applied undergraduate institutions, this study proposes a feasible, verifiable, and sustainable curriculum reform plan. Methodologically, it adopts a competency-based approach to reconstruct five core competency pathways: blueprint interpretation and modeling, quantity takeoff and rule verification, bill of quantities and cloud-based pricing, exam paper design and bid clarification, and version control and archiving. It organizes continuous, end-to-end training through course clusters and project weeks. High-frequency practice and timely error correction are supported by integrating computer labs with online platforms, virtual simulations, and gamified feedback. A chain-of-evidence assessment system—primarily process-based with supplementary outcome evaluations—and random sampling mechanisms ensure teaching quality. Data dashboards drive monthly refinements and semester-long reviews, creating a closed-loop cycle of problem identification, countermeasure development, retrospective analysis, and iterative optimization. Expected outcomes manifest in three dimensions: - Student level: Enhanced initial job readiness and employment alignment, with verifiable and traceable training outcomes. - Faculty and curriculum level: Standardized maintenance of rule and pricing databases, enabling verifiable and scalable course quality. - Industry-academia collaboration level: Mutual validation of course outcomes against professional certifications and job standards, establishing stable external evaluation channels. This study provides a replicable implementation pathway and quantitative management tools for transforming construction cost training from fragmented skill drills to process-oriented outcome delivery.
Copyright (c) 2025 HaoRan Zhou; MingYuan Yang; MiRen Rong; DongYang Geng; JiaWei Li; YiZe An; JiaLe Wang; ZhuoXi Hu; JianMing Guo https://creativecommons.org/licenses/by/4.0