Search Results (2,474)

With the large-scale deployment of 5G technology, the rationality of communication base station siting is crucial for network performance, construction costs, and operational efficiency. Traditional site selection methods rely heavily on manual experience, exhibiting strong subjectivity and difficulty in balancing multi-objective optimization. Existing heuristic algorithms suffer from slow convergence speeds and susceptibility to local optima. To address these challenges, this paper constructs a multi-objective base station site selection model that simultaneously minimizes costs, maximizes coverage contributions, and minimizes interference. It achieves quantitative balance among objectives through normalization and weight fusion, while introducing constraints to ensure engineering feasibility. Concurrently, the genetic algorithm underwent targeted optimization by introducing an adaptive migration strategy based on population diversity and a cosine-type parameter adjustment strategy. This approach was integrated with the particle swarm optimization algorithm to balance exploration and exploitation while mitigating premature convergence. Experimental validation demonstrates that the improved algorithm achieves faster convergence and greater stability compared to traditional genetic algorithms and particle swarm optimization, while satisfying engineering constraints such as base station quantity, coverage, and interference. This research provides an efficient and feasible solution for intelligent base station site planning. Full article

The wind environment in furnace cities has attracted considerable research attention. Investigating the impact of suburban residential building arrangements in furnace cities on inter-building wind speed fields is useful and cost-effective for scientifically optimizing layouts. This study simulated 13 wind speed fields across six symmetric and asymmetric building arrangements: linear, inclined, convex, concave, M-shaped, and V-shaped, with varying building offsets and spacing widths. We used the standard k–ε model for simulations through finite element method. Results demonstrated that larger building offsets enhanced inter-building wind speeds, with the concave arrangement most effectively enhanced the wind speed between buildings among the configurations. V-shaped arrangements slightly underperformed concave layouts in wind speed uniformity. Based on the summer wind direction data from Wuhan Tianhe Meteorological Station, we propose two corresponding layouts: concave and V-shaped arrangements, which are conductive to enhancing inter-building wind speed. In practical planning, the orientation of building clusters can be adjusted according to the local wind rose diagram. Full article

In winter, stay cable sheaths are prone to icing, which increases cable loads and poses a falling-ice hazard upon thawing. While manual and chemical de-icing are common methods, their safety and cost drawbacks make robotic de-icing a promising alternative. Robotic de-icing offers a promising alternative. However, to protect the sheath from damage, the de-icing blade is designed to minimize contact with its surface. Consequently, a thin layer of residual ice is often left behind, which reduces the surface friction coefficient and complicates the climbing process. This study evaluates the climbing performance of a self-manufactured cable-climbing mechanism through laboratory tests and dynamic simulations (ADAMS). A physical prototype was built, and dynamic simulations of the cable-climbing mechanism were conducted using Automated Dynamic Analysis of Mechanical Systems (ADAMS) software. The preliminary validation results demonstrate that the mechanism is capable of maintaining stable climbing under extreme conditions, including a friction coefficient of 0.12 to reflect thin-ice variability and indicated stable climbing even at μ = 0.12), a vertical inclination of 90°, and a load of 12 kg, confirming the design’s validity. Furthermore, we analyzed key parameters. A lower friction coefficient requires a higher clamping force and adversely affects the climbing speed due to increased slip. Similarly, an increased payload elevates the mechanism’s deflection angle, spring force, and wheel torque, which in turn reduces the climbing speed. Cable inclination has a complex effect: deflection decreases with slope, yet clamping force peaks near 70°, showing a bell-shaped trend. This peak requirement dictated the damping spring selection, which was given a safety margin. This ensures safe operation and acceleration at all other angles. Limitations: The present results constitute a feasibility validation under controlled laboratory conditions and rigid-support simulations. The long-term effects of residual ice and field performance remain to be confirmed in planned field trials. Full article

This study investigates the effects of multi-angle along-track interferometric synthetic aperture radar (ATI-SAR) observations on the accuracy of sea surface current retrieval. Utilizing a high-fidelity, full-link SAR ocean simulator, this study systematically assesses the influence of three key factors—the angle between observation directions, the relative orientation of wind and current, and wind speed—on the precision of two-dimensional (2D) current vector retrievals. Results demonstrate that observation geometry is a dominant factor: retrieval errors are minimized when the two viewing directions are near-orthogonal (~90°), while near-parallel (0° or 180°) geometries result in significant error amplification. Furthermore, the angle between wind and current introduces complex, non-linear error characteristics, with a perpendicular alignment minimizing velocity error but maximizing direction error. Higher wind speeds are found to degrade both velocity and direction retrieval accuracy. Collectively, these findings provide crucial quantitative guidance for optimizing the mission design, observation planning, and algorithm development for future multi-angle ATI-SAR satellite constellations dedicated to ocean current monitoring. Full article

To capture bus passenger flow fluctuations and address the problems of slow convergence and high error in machine learning parameter optimization, this paper develops an improved Whale Optimization Algorithm (IWOA) integrated with a Bidirectional Gated Recurrent Unit (BiGRU). First, a Logistic–Tent chaotic mapping is introduced to generate a diverse and high-quality initial population. Second, a hybrid mechanism combining elite opposition-based learning and Cauchy mutation enhances population diversity and reduces premature convergence. Third, a cosine-based adaptive convergence factor and inertia weight strategy improve the balance between global exploration and local exploitation. Based on the correlation analysis between bus passenger flow and weather condition data in Harbin, and combined with the fluctuation characteristics of bus passenger flow, the data were divided into windows with a 7-day weekly cycle and processed by fuzzy information granulation to obtain three groups of fuzzy granulated window data, namely LOW, R, and UP, representing the fluctuation trend and spatial characteristics of bus passenger flow. The IWOA was employed to optimize and solve parameters such as the hidden layer weights and bias vectors of the BiGRU, thereby constructing a bus passenger flow fluctuation trend and spatial prediction model based on FIG-IWOA-BiGRU. Simulation experiments with 21 benchmark functions and real bus data verified its effectiveness. Results show that IWOA significantly improves optimization accuracy and convergence speed. For bus passenger flow forecasting, the average MAE, RMSE, and MAPE of LOW, R, and UP data are 2915, 3075, and 8.1%, representing improvements over existing classical models. The findings provide reliable decision support for bus scheduling and passenger travel planning. Full article

Thrust vectoring technology significantly improves the manoeuvrability and environmental adaptability of unmanned aerial vehicles by dynamically regulating the direction and magnitude of thrust. In this paper, the principles and applications of mechanical thrust vectoring technology, fluidic thrust vectoring technology and the distributed electric propulsion system are systematically reviewed. It is shown that the mechanical vector nozzle can achieve high-precision control but has structural burdens, the fluidic thrust vectoring technology improves the response speed through the design of no moving parts but is accompanied by the loss of thrust, and the distributed electric propulsion system improves the hovering efficiency compared with the traditional helicopter. Addressing multi-physics coupling and non-linear control challenges in unmanned aerial vehicles, this paper elucidates the disturbance compensation advantages of self-disturbance rejection control technology and the optimal path generation capabilities of an enhanced path planning algorithm. These two approaches offer complementary technical benefits: the former ensures stable flight attitude, while the latter optimises flight trajectory efficiency. Through case studies such as the Skate demonstrator, the practical value of these technologies in enhancing UAV manoeuvrability and adaptability is further demonstrated. However, thermal management in extreme environments, energy efficiency and lack of standards are still bottlenecks in engineering. In the future, breakthroughs in high-temperature-resistant materials and intelligent control architectures are needed to promote the development of UAVs towards ultra-autonomous operation. This paper provides a systematic reference for the theory and application of thrust vectoring technology. Full article

Sectoral vulnerability to distinct crisis types in small, open, and geopolitically exposed markets—such as Jordan—remains insufficiently quantified, constraining targeted policy design and portfolio allocation. This study’s primary purpose is to establish a transparent, comparable metric of sector-level market resilience that reveals how crisis typology reorders vulnerabilities and shapes recovery speed. Applying this framework, we assess Jordan’s equity market across three archetypal episodes—the Global Financial Crisis, the Arab Spring, and COVID-19—to clarify how shock channels reconfigure sectoral risk. Using daily Amman Stock Exchange sector indices (2001–2025), we estimate $GARCH\left(\mathrm{1,1}\right)$ models for each sector–crisis window and summarize volatility dynamics by persistence $(\alpha +\beta )$, interpreted as an inverse proxy for resilience; complementary diagnostics include maximum drawdown and days-to-recovery, with nonparametric (Kruskal–Wallis) and rank-based (Spearman, Friedman) tests to evaluate within-crisis differences and cross-crisis reordering. Results show pronounced heterogeneity in every crisis and shifting sectoral rankings: financials—especially banking—display the highest persistence during the GFC; tourism and transportation dominate during COVID-19; and tourism/electric-related industries are most persistent around the Arab Spring. Meanwhile, food & beverages, pharmaceuticals/medical, and education recurrently exhibit lower persistence. Higher persistence aligns with slower post-shock normalization. We conclude that resilience is sector-specific and contingent on crisis characteristics, implying targeted policy and portfolio responses; regulators should prioritize liquidity backstops, timely disclosure, and contingency planning for fragile sectors, while investors can mitigate crisis risk via dynamic sector allocation and volatility-aware risk management in emerging markets. Full article

Faced with complex, ever-changing battlefield environments and diverse attacks, enabling warship combat systems to recover rapidly and effectively after damage is key to enhancing resilience and sustained combat capability. We construct a representative naval battle scenario and propose an integrated Attack-Defense-Recovery Strategy (ADRS) grounded in warship system models for different attack types. To address high parameter sensitivity, weak initial pheromone feedback, suboptimal solution quality, and premature convergence in traditional ant colony optimization (ACO), we introduce three improvements: (i) grid-search calibration of key ACO parameters to enhance global exploration, (ii) a non-uniform initial pheromone mechanism based on the wartime importance of equipment to guide early solutions, and (iii) an ADRS-consistent state-transition rule with group-based starting points to prioritize high-value equipment during the search. Simulation results show that the improved ACO (IACO) outperforms classical ACO in convergence speed and solution optimality. Across torpedo, aircraft/missile, and UAV scenarios, ADRS-ACO improves over GRS-ACO by 7.2%, 0.3%, and 5.5%, while ADRS-IACO achieves gains of 34.9%, 17.1%, and 16.7% over GRS-ACO and 25.9%, 16.7%, and 10.6% over ADRS-ACO. Overall, ADRS-IACO consistently delivers the best solutions. In high-intensity, high-damage torpedo conditions, ADRS-IACO demonstrates superior path planning and repair scheduling, more effectively identifying critical equipment and allocating resources. Moreover, under multi-wave combat, coupling with ADRS effectively reduces cumulative damage and substantially improves overall warship-system resilience. Full article

Higher requirements have been placed on fault detection for continuously operating machines in modern factories. Manual inspection faces challenges related to timeliness, leading to the emergence of autonomous diagnostic robots. To overcome the safety limitations of existing diagnostic robots in factory environments, a hierarchical fuzzy logic-based navigation and obstacle avoidance algorithm is proposed in this study. The algorithm is constructed based on zero-order Takagi–Sugeno type fuzzy control, comprising subfunctions for navigation, static obstacle avoidance, and dynamic obstacle avoidance. Coordinated navigation and equipment protection are achieved by jointly considering the information of the fault point and surrounding equipment. The concept of a dynamic safety boundary is introduced, wherein the normalized breached level is used to replace the traditional distance-based input. In the inference process for dynamic obstacle avoidance, the relative speed direction is additionally considered. A Mamdani-type fuzzy inference system is employed to infer the necessity of obstacle avoidance and determine the priority target for avoidance, thereby enabling multi-objective planning. Simulation results demonstrate that the proposed algorithm can guide the diagnostic robot to within 30 cm of the fault point while ensuring collision avoidance with both equipment and obstacles, enhancing the completeness and safety of the fault point searching process. Full article

This work aims towards the automatic detection of faults in industrial air-cooling equipment used in a production line for staple fibers and ultimately provides maintenance scheduling recommendations to ensure seamless operation. In this context, various deep learning models are tested to ultimately define the most effective one for the intended scope. In the examined system, four vibration and temperature sensors are used, each positioned radially on the motor body near the rolling bearing of the motor shaft—a typical setup in many industrial environments. Thus, by collecting and using data from the latter sources, this work exhaustively investigates the feasibility of accurately diagnosing faults in staple fiber cooling fans. The dataset is acquired and constructed under real production conditions, including variations in rotational speed, motor load, and three fault priorities, depending on the model detection accuracy, product specification, and maintenance requirements. Fault identification for training purposes involves analyzing and evaluating daily maintenance logs for this equipment. Experimental evaluation on real production data demonstrated that the proposed ResNet50-1D model achieved the highest overall classification accuracy of 97.77%, while effectively resolving the persistent misclassification of the faulty impeller observed in all the other models. Complementary evaluation confirmed its robustness, cross-machine generalization, and suitability for practical deployment, while the integration of predictions with maintenance logs enables a severity-based prioritization strategy that supports actionable maintenance planning. Full article

We propose a speed-adaptive robot accurate path-tracking framework based on the soft actor–critic (SAC) and Stanley methods (STANLY_ASAC). First, the Lidar–Inertial Odometry Simultaneous Localization and Mapping (LIO-SLAM) method is used to map the environment and the LIO-localization framework is adopted to achieve real-time positioning and output the robot pose at 100 Hz. Next, the Rapidly exploring Random Tree (RRT) algorithm is employed for global path planning. On this basis, we integrate an improved A* algorithm for local obstacle avoidance and apply a gradient descent smoothing algorithm to generate a reference path that satisfies the robot’s kinematic constraints. Secondly, a network classification model based on U-Net is used to classify common road surfaces and generate classification results that significantly compensate for tracking accuracy errors caused by incorrect road surface coefficients. Next, we leverage the powerful learning capability of adaptive SAC (ASAC) to adaptively adjust the vehicle’s acceleration and lateral deviation gain according to the road and vehicle states. Vehicle acceleration is used to generate the real-time tracking speed, and the lateral deviation gain is used to calculate the front wheel angle via the Stanley tracking algorithm. Finally, we deploy the algorithm on a mobile robot and test its path-tracking performance in different scenarios. The results show that the proposed path-tracking algorithm can accurately follow the generated path. Full article

The increasing adoption of electric delivery fleets introduces significant challenges related to uneven energy utilization and suboptimal scheduling efficiency. Vehicle-to-Vehicle (V2V) energy sharing presents a promising solution, but its effectiveness critically depends on precise matching and co-optimization within dynamic urban traffic environments. This paper proposes a hierarchical optimization framework to minimize total fleet operational costs, incorporating a comprehensive analysis that includes battery degradation. The core innovation of the framework lies in coupling high-level path planning with low-level real-time speed control. First, a high-fidelity energy consumption surrogate model is constructed through model predictive control simulations, incorporating vehicle dynamics and signal phase and timing information. Second, the spatiotemporal longest common subsequence algorithm is employed to match the spatio-temporal trajectories of energy-provider and energy-consumer vehicles. A battery aging model is integrated to quantify the long-term costs associated with different operational strategies. Finally, a multi-objective particle swarm optimization algorithm, integrated with MPC, co-optimizes the rendezvous paths and speed profiles. In a case study based on a logistics network, simulation results demonstrate that, compared to the conventional station-based charging mode, the proposed V2V framework reduces total fleet operational costs by a net 12.5% and total energy consumption by 17.4% while increasing the energy utilization efficiency of EV-Ps by 21.4%. This net saving is achieved even though the V2V strategy incurs a marginal increase in battery aging costs, which is overwhelmingly offset by substantial savings in logistical efficiency. This study provides an efficient and economical solution for the dynamic energy management of electric fleets under realistic traffic conditions, contributing to a more sustainable and resilient urban logistics ecosystem. Full article

The optimization of trajectories for multiple robotic arms in a shared workspace is critical for industrial automation but presents significant challenges, including data sharing, communication overhead, and adaptability in dynamic environments. Traditional centralized control methods require sharing raw sensor data, raising concerns and creating computational bottlenecks. This paper proposes a novel Federated Learning (FL) framework for distributed multi-robotic arm trajectory optimization. Our method enables collaborative learning where robots train a shared model locally and only exchange gradient updates, preserving data privacy. The framework integrates an adaptive Rapidly exploring Random Tree (RRT) algorithm enhanced with a dynamic pruning strategy to reduce computational overhead and ensure collision-free paths. Real-time synchronization is achieved via EtherCAT, ensuring precise coordination. Experimental results demonstrate that our approach achieves a 17% reduction in average path length, a 22% decrease in collision rate, and a 31% improvement in planning speed compared to a centralized RRT baseline, while reducing inter-robot communication overhead by 45%. This work provides a scalable and efficient solution for collaborative manipulation in applications ranging from assembly lines to warehouse automation. Full article

Proximity-oriented planning aims to deliver everyday services within a short walk, yet closeness does not guarantee usable access for all residents. This study quantifies the gap between spatial proximity and functional accessibility in Timișoara, România, focusing on people with mobility and visual impairments. A three-stage analysis was conducted to evaluate accessibility to public amenities. First, (1) a survey was conducted with 605 respondents to identify distinct accessibility priorities based on 15-Minute City core dimensions defined by Carlos Moreno and adapted afterwards to the city context and needs. In the second stage (2), GIS mapping (radial buffers and isochrones) revealed major disparities among non-disabled residents and residents with mobility and visual impairments. Coverage decreased substantially across amenities under reduced-speed scenarios and after excluding wheelchair-inaccessible destinations. In the third stage (3), field-observed pedestrian routes in three areas of Timișoara were examined against the top-ranked criteria for each group, using the items sourced from the previous survey and grounded in the 15-Minute City concept. The route scoring is exploratory and specific to this context. The findings confirmed recurrent functional barriers, especially for vulnerable groups. These results expose a proximity-accessibility gap, where apparent nearness masks physical or sensory barriers. A shift toward experience-based accessibility planning is needed to ensure that proximity is not only spatial, but also usable by all and inclusive. Full article

Effective station–city integration is crucial for sustainable development around high-speed rail stations. However, research assessing public preferences regarding the aspects of this integration remains limited. We constructed a performance evaluation model for station–city integration in high-speed rail station areas. By considering the high-speed rail station area in the Yangtze River Delta region as a research object, which is located in the metropolitan cities centered on Shanghai, China, we dissected the five dimensions of population, industry, land use, function, and environment into 15 indicators that flow into the three value objectives of attraction–retention–integration (NPI). Subsequently, we systematically analyzed the performance differentiation characteristics of station–city integration in the Yangtze River Delta region’s high-speed rail station areas by employing a multiple regression model to delve into the influence mechanisms affecting the performance differentiation patterns of station–city integration. Our findings indicate the following. (1) Regarding station–city integration performance grade differentiation, a few high-speed rail station areas in the Yangtze River Delta region exhibit a high-efficiency integration level, whereas more areas fall within the higher and general integration levels. (2) Spatially, the station–city integration performance in high-speed rail station areas within the Yangtze River Delta region exhibits a distinct distribution characterized by “high-grade point-block dependence and low-grade concentrated contiguous patches.” (3) The spatial distribution of the five dimensions of station–city integration performance exhibits significant disparities. (4) Regarding the development types of station–city integration performance advantages, efficient integration of stations and cities represents a multidimensional advantageous development type and higher integration falls into the same category. (5) Station–city integration performance results from the comprehensive effects of four factors: government policy inducement, station energy level attraction, station–city relationship adhesion, and urban energy level promotion. This study advances a systematic framework—encompassing performance measurement, mechanistic inquiry, and strategy formulation—for examining station–city integration in HSR station areas. By integrating the perspective of cyclical cumulative development into the node–place model from urban planning and geographical viewpoints, we articulate a new performance model that clarifies critical influencing factors and mechanisms, thus broadening the theoretical scope of HSR station area research. We believe that the NPI evaluation model can provide valuable insights for guiding the integrated development of high-speed rail station areas and enhancing the quality of urban development. Full article