An image-driven model based on spatiotemporal neural network (STNN) is proposed for prediction of crack growth in aluminum alloy. Fatigue experiments with an initial edge crack angle of 0° and a 15.0% limit load level are designed, and images of specimen deformation are captured using digital image correlation (DIC) resulting in 5 511 frames of displacement field data used as datasets of STNN after interpolation, augmentation, and dimension-raising. Two neural netwroks, convolutional long short-term memory (Conv-LSTM) and SimVP, are employed to predict the fatigue crack growth, with their prediction accuracies further compared based on the structural similarity index measure (SSIM) and the root mean square error (RMSE). The results show that the SimVP neural network performs better in the test stage predicting fatigue crack growth rate and propagation path. This method provides a reference for damage tolerance analysis and determination of inspection intervals for structures.

To support the airworthiness certification of aircraft engines regarding CCAR 33.63 provisions, a three-dimensional contact dynamics-based computational model for fan blade-flexible casing rubbing vibration was established. Modal reduction was employed to reduce the size of the computational model, while a cubic B-spline surface fitting technique was utilized to enhance the discretization accuracy of the casing inner surface. A bilinear elastic-plastic model was adopted to describe the rubbing mechanical properties of the abradable coating, and contact forces between blades and casings were calculated based on coating wear depth, blade thickness, and element shape functions. Using this model, the rubbing response characteristics between fan blades and flexible casings were investigated. The results showed that blade damping is a key factor affecting rubbing vibration response. For blades with lower damping, in addition to coupling vibrations between blade-casing single nodal diameter modes during rubbing process, there also exist blade-casing coupled vibrations caused by traveling waves associated with multi-nodal diameter modes of casings. The variations in contact strength during rubbing process leading to changes in dynamic characteristics of components cause the above interactions. The dynamic model established in this paper provides a new approach for identifying and evaluating blade-casing rubbing coupled vibrations.

The honeycomb at the inner band of a compressor stator and the labyrinth on the disk form a sealed cavity, in which the clearance leakage flow has a key impact on the aerodynamic performance of the axial flow compressor. In this paper, focusing on the first 1.5-stage of a low-speed research compressor, numerical simulations are conducted to compare the impact of honeycomb seal structure and slide wall on the aerodynamic performance of the compressor, as well as the performance changes of honeycomb seal structure under two different labyrinth seal clearance conditions. The performance calculation results show that the isentropic efficiency of the compressor equipped with the 0.2-mm-clearance honeycomb cavity model is slightly lower than that of the slide-wall cavity model, which is attributed to the elevated total temperature rise induced by the leakage flow inside the cavity. Comparative analysis of cavity leakage flow characteristics and detailed flow field investigation indicate that honeycomb structure induces a simultaneous increase in both the cavity leakage flow rate and swirl angle. A strong interaction is observed between the airflow within the cavity and the honeycomb structure, creating a complex vortex flow that significantly elevates the total temperature in both the seal cavity and the honeycomb itself. This temperature elevation ultimately leads to a reduction in compressor efficiency. In contrast, when the zero-clearance honeycomb model is compared with the 0.2-mm-clearance honeycomb model, the decreased total temperature ratio contributes to an improvement in isentropic efficiency. In the zero-clearance model, although the average total temperature of the leakage flow rises owing to the reduced clearance, the compressor efficiency is enhanced by the substantial reduction in leakage flow rate. The findings in this paper clarify the quantitative relationship and influence mechanism of leakage flow under different clearance conditions on the performance of the honeycomb and labyrinth seal structure applied to the compressor stator, which therefore provide certain references for the engineering design of honeycomb seal cavities.

This paper explores the seismic response differences between cutting slopes and embankment slopes, with a particular focus on the impact of initial water content on the seismic performance of unsaturated slopes. To achieve this, a soil-water-air three-phase coupled numerical analysis method is developed within the framework of unsaturated soil mechanics. This numerical approach is employed to simulate the deformation patterns and acceleration responses of both slope types during an earthquake. Additionally, the influence of initial water content on the dynamic behavior of the slopes is examined. The results indicate that high initial water content reduces the risk of sliding failure in cut slopes due to the stabilizing effect of the adjacent undisturbed ground. Embankment slopes exhibit a more pronounced sliding tendency, with greater displacement observed at the slope toe.

In the field of geotechnical engineering, it is of great significance to analyze the law and mechanism of soil hydraulic fracturing, which can provide a basis for the study of the diffusion and distribution of splitting grouting veins and guide practical engineering. In this paper, a three-dimensional finite element program is developed by using block diagonal preprocessing, preconditioned symmetric quasi-minimal residual (PSQMR) iterative method, and improved sparse matrix vector multiplication parallel algorithm, which enables large-scale Biot consolidation finite element calculation, realizing the finite element solution based on CPU serial computing platform and GPU parallel computing platform, greatly improving the calculation scale, and accomplishing large scale finite element calculation on personal computer. By comparing the numerical simulation results, experimental simulation results, and theoretical analysis with the calculation results, the correctness of the calculation method is verified, which provides a numerical simulation tool for the study of hydraulic fracturing in soil.

To investigate the degradation laws and deterioration mechanisms of interlaminar shear strength (ILSS) of glass fiber reinforced polymer (GFRP) rebars with different matrices in seawater and sea-sand concrete (SWSSC) environment, an accelerated corrosion test was conducted on epoxy-based and vinyl ester-based GFRP rebar specimens in a simulated SWSSC pore solution, and then the ILSS tests and scanning electron microscope (SEM) tests were conducted. For epoxy-based GFRP rebars, two kinds of curing agents naming MHHPA and MDA were adopted. The results indicate that the uncorroded MHHPA cured epoxy-based GFRP rebars possesse the highest initial ILSS (42.44 MPa), followed by the vinyl ester-based GFRP rebars (37.10 MPa), while the MDA cured epoxy-based GFRP rebars have the lowest initial ILSS (27.20 MPa). After immersion in a 55 ℃ pore solution environment for 84 d, the ILSS retention of MHHPA cured epoxy-based GFRP rebars is 7.43% while the ILSS retention of MDA cured epoxy-based GFRP and vinyl ester-based GFRP rebars are 39.51% and 71.06% respectively. With the increase in temperature and immersion time in the SWSSC simulated pore solution, the ILSS of three kinds of GFRP rebars all show a declining trend. The reasons for the degradation of ILSS are the interfacial debonding between fibers and matrix and the hydrolytic loss of the matrix. Among the tested specimens, the vinyl ester-based GFRP rebars exhibit the strongest resistance to corrosion in the simulated SWSSC pore solution, while the MHHPA cured epoxy-based GFRP rebars show the weakest resistance with the MDA cured epoxy-based GFRP rebars being intermediate.

Anchorage and submarine cables are crucial maritime facilities safeguarding sea traffic and communication. However, anchoring and dragging anchors in anchorage areas may pose risks for damaging nearby submarine cables. This paper scientifically analyzes the factors contributing to submarine cable damage from the perspectives of vessels, environment, personnel, and cables. Starting from the causes of damage, the anchoring failure and drifting process is simulated by incorporating the internal and external factors of anchor penetration depth, vessel type, and crew decision-making during emergencies. A risk model for submarine cable damage is developed, with a case study conducted on the Luxi Dao pilot quarantine anchorage in Wenzhou Port and its surrounding submarine cables. The study reveals that the risk of cable damage depends on vessel-cable distance, vessel type, and vessel scale. The model provides a quantitative analysis method for the interaction between anchorage and submarine cable, investigates cable damage from multiple angles, and offers valuable support for safe maritime operations.

Thrust allocation serves as a critical means for achieving vector propulsion in unmanned surface vessels (USV) equipped with dual waterjet thrusters. However, existing thrust allocation methods employed in vessels featuring azimuth thrusters fail to address the resolution of vector forces for dual waterjet propulsion, due to characteristics such as thrust angle limitations and reverse thrust. To achieve vector motion control of a dual waterjet propelled USV, a hierarchical optimization-based thrust allocation algorithm is proposed. In the first tier, a vector synthesis approach incorporating enhanced angle constraints is utilized to acquire top-tier vector thrust satisfying constraints on the rotating range and rate characteristics of the thrusters. In the second tier, leveraging the top-tier vector thrust values as inputs and considering constraints on thruster power and power change frequency, an optimization method based on seeking minimal distance is proposed. This method facilitates the allocation of reverse thrust angles and nozzle flow velocities for waterjet thrusters, thereby resolving singular issues in dual waterjet thrust allocation. Simulation experiments and the semi-physical simulation experiments validate the effectiveness of the hierarchical optimization-based thrust allocation algorithm for dual waterjet thrusters. The results indicate that this method enables efficient thrust allocation for dual waterjet thrusters, while concurrently limiting fluctuations in thruster power frequency and amplitude during expected thrust variations, thereby reducing shafting wear while achieving target vector thrust.

To address the trajectory tracking control problem of azimuth stern drive tug, a three-degree-of-freedom motion data-driven model of the tug is developed by using gated recurrent unit (GRU) neural network, and a model predictive control (MPC) trajectory tracking controller is designed based on the GRU model to overcome the limitations of the traditional control methods that rely on the precise system mechanism model. This controller regulates the tug speed and heading by adjusting the left and right rudder angles without changing the tug propeller speed. Simulation experiments are conducted to validate the effectiveness of the proposed scheme, showing that the model achieves satisfactory accuracy even under noise interference. Furthermore, by comparing the control performance under different prediction step sizes, the influences of these parameters on the control effect and solution time are explored. Due to the increase in the complexity of the optimization solution, when the prediction horizon increases, the control accuracy improves, but the solution time also rises. This study provides new ideas for the precise trajectory tracking control of tugs, and offers valuable references for the control of similar nonlinear systems.

To study the cavitation characteristics of propellers under oblique flow conditions and quantitatively analyze the blade cavitation area, this paper takes the PC456 propeller model as the research object. Based on the OpenFOAM platform, the cavitation performance of the PC456 propellers under oblique flow is numerically calculated using the SST k-ω turbulence model and Schnerr-Sauer cavitation model. The results show that the influence of oblique flow on the hydrodynamic performance of the propeller cavitation increases with the increase of the oblique flow angle. Within the phase range of [-90°, 40° ], the cavitation area of the blade reaches its maximum at the -20° phase, and as the oblique angle increases, the amplitude of the change in bubble area also increases. In addition, the numerically predicted cavitation areas are in good agreement with the experimental results, especially under conditions of low cavitation numbers, which validates the effectiveness of the numerical method used in this study.

To explore the effect of tip skew coupled rake on the hydrodynamic performance of Kappel propellers, the fourth-order B-spline method was used to design and change the radial distribution of Kappel propellers in the skew and rake, and an 8-type propeller was constructed. The SST k-ω+γ transition turbulence model, where k denotes the turbulent kinetic energy, ω the specific dissipation rate, and γ the intermittency factor, was employed for simulation, with the Kap509 propeller serving to validate the accuracy of the simulation. The numerical results of the propeller’s open water efficiency are 1.06% to 2.50% lower than the experimental data. It is found that in the vicinity of J=0.8, except for conventional end-plate propellers, the efficiency of the Kap01 series propellers is consistently higher than that of the Kap02 series, with an average increase of 3%. This effect is most pronounced when the skew is 24°, where the efficiency rises by 4.78%. The propeller efficiency increases with the increase of skew when J=0.9-1.0. Under low rake radial distribution, the skew of the propeller can be appropriately improved, which can strengthen the end-plate effect of the Kappel propeller, thereby improving the propulsion performance of the Kappel propeller.

As a core component of underwater production facilities, subsea jumpers are prone to vortex-induced vibration (VIV) in the ocean currents, which can cause structural fatigue damage. However, limited experimental research on VIV of jumpers has left a gap in understanding the VIV response characteristics. To address this gap, an out-of-plane VIV experiment of a π-shaped jumper is conducted in uniform flow, aiming to mechanistically investigate its VIV behavior. Strain data under different flow velocities are analyzed using common methods such as modal analysis and wavelet transform. The results show that the π-shaped jumper exhibits dominant frequency ratios of 3 and 4 between the in-line (IL) and cross-flow (CF) directions, differing from those of a single riser in out-of-plane uniform flow. As flow velocity increases, the strain amplitudes in both IL and CF directions follow a variation pattern resembling an inverted quadratic curve under the dominant mode, with their peak values coinciding at the same flow velocity.

To accurately predict the joint distribution of short-term wave height and period, this paper proposes a new model based on the conditional probability approach. In this model, wave height follows a two-parameter Weibull distribution, while the conditional period distribution is characterized by a log-normal distribution. To incorporate the influence of wave spectral shape, the broad-width Wallops spectrum is adopted, and the corresponding model parameters are derived. Simulations are conducted using both the Wallops spectrum and measured wave spectra as target spectra to obtain the wave height-period joint distribution. Taking the simulated data as a benchmark, the proposed model is compared with five commonly used joint distribution models. Additionally, the wave height and period distributions are analyzed, with the sources of prediction errors discussed. The results indicate that the model proposed closely matches the simulated data for both Wallops and measured spectra, whereas the other five models only perform well in specific cases. Moreover, this model takes an explicit closed-form expression, making it applicable to non-Gaussian wave conditions.

The time-domain calculation method for the dynamic response of floating wind turbines under the combined action of wind, waves, and flow has been extensively researched and developed. However, due to its low computational efficiency, substantial computational resources are required when applying this method to optimize the layout of units in a floating wind farm and to optimize the preliminary design of floating wind turbines and mooring systems. Therefore, this paper focuses on the frequency-domain analysis method enabling rapid calculation of the dynamic response of floating wind turbines. It comprehensively considers various environmental load factors, including first- and second-order wave loads, pulsating wind loads on the turbine, slender member viscous loads, mooring systems, and wind turbine pitch control strategies. For different operating conditions such as below-rated operation, pitch operation, and shutdown, the results obtained from the frequency-domain method were compared and analyzed with the time-domain prediction results from OrcaFlex software. Both methods show good consistency in terms of steady-state response, dynamic response spectra, and statistical values, thus validating the applicability and accuracy of the frequency-domain analysis method in providing reliable reference value for the preliminary design and parameter sensitivity analysis of floating wind turbines. Furthermore, based on the frequency-domain method, the influence of wind-wave parameters, second-order wave forces, and the implementation of wind turbine control strategies on the dynamic response characteristics of floating wind turbines was studied.

The field of minimally invasive laparoscopic surgery is undergoing a significant paradigm shift from multi-port to single-port access, driven by the imperative to further reduce patient trauma. This transition, however, introduces critical technical bottlenecks, particularly in achieving high-payload dexterous manipulation and effective kinematic decoupling of multiple instruments within severely constrained intra-abdominal spaces. This review provides a systematic analysis of the first domestically developed single-port surgical robotic platform (SHURUI), led by Professor Xu Kai at Shanghai Jiao Tong University and approved by the National Medical Products Administration (NMPA). From a mechanism design perspective, this review examines the innovative dual-continuum mechanism that has enabled the system to overcome international technological monopolies. Particular emphasis is placed on the rigid-flexible coupling architecture, which achieves an effective balance between high payload capacity and enhanced dexterity through an ultra-compact diameter of 12 mm access port, while demonstrating versatility across multi-port, single-port, and hybrid-port surgical configurations. At the modelling and perception levels, it traces key advancements, including the transition from conventional constant-curvature kinematic assumptions to more sophisticated variable-curvature dynamic compensation strategies, as well as the progression from markerless visual tracking to intelligent assisted perception frameworks. Building upon these innovations, it critically evaluates persistent limitations and future challenges, including nonlinear distortion under complex loading conditions, the absence of high-fidelity haptic feedback, and the need for more extensive long-term clinical evidence. In conclusion, the SHURUI system and the foundational research led by Professor Xu mark a landmark milestone in single-port surgical robotics, achieving core mechanism autonomy, overcoming longstanding international technological barriers, and enabling successful clinical translation. The field currently stands at a critical inflection point, poised to transition from conventional passive master-slave teleoperation tools to active, intelligent surgical platforms. Future advancements should prioritize the deep integration of multimodal sensing and artificial intelligence-driven decision-making architectures, facilitating the evolutionary leap from remote-controlled operation to autonomous intelligence. This trajectory will ultimately propel minimally invasive endoscopic surgical robotics toward a new paradigm of intelligent collaborative systems.

This paper systematically reviews the breakthrough achievements of Professor Wang Jinwu’s research team in digital diagnosis and treatment of limb osteoarthritis (OA). Addressing key challenges in conventional OA management, such as difficulties in early screening, the lack of biomechanically adaptive stepwise treatment strategies, and the disconnect between surgery and postoperative rehabilitation, the team has developed a novel “screening-treatment-rehabilitation” model through interdisciplinary integration of medicine and engineering supported by digital technologies. Specifically, they developed a biplanar fluoroscopy system for precise dynamic screening, proposed the theory of “joint unloading mechanical correction”, and implemented precision treatment through the integration of 3D-printed personalized orthoses, stem cell enrichment techniques, and customized prostheses. In addition, they pioneered an Internet of Things (IoT)-based remote cloud rehabilitation network. Although this framework still requires further optimization regarding micro-metabolic mechanism coupling and industrial cost reduction, deeper integration with cutting-edge technologies such as organoids and digital twins in the future is expected to drive OA diagnosis and treatment toward a new stage of multidimensional precision medicine, demonstrating the profound value of medical-engineering integration in advancing human health.

Surface texture technology is a method to improve the tribological properties of friction pairs. In this study, a cylindrical texture is designed in cage pocket, and then the volume of fluid model and the multireference frame method are used to investigate the oil volume fraction inside the bearing cavity, the pressure and oil distribution on the ball surface, and the oil distribution on the inner/outer raceway. The results show that the cylindrical texture in cage pocket is helpful to increase the oil volume fraction inside the bearing cavity, improve the pressure distribution on the ball surface, and increase the oil content on the ball surface. The cage pocket texture helps the ball to carry more lubrication oil in the high-speed rotation process, which increases the oil content of the outer raceway and improves the oil-air lubrication effect of the ball. This study proposes a new texture arrangement in cage pocket of angular contact ball bearings, and introduces the mixed mesh method to divide the fluid domain of bearing. Through comparative study, the cage pocket texture is helpful to improve the oil-air lubrication efficiency.

Deployment of integrated energy system is conducive to improving energy efficiency and achieving the transformation of the global energy system. However, recent appearance of extreme natural disasters poses a great challenge to the safe and stable operation of the integrated energy system. Therefore, the resilience of the integrated energy system, namely the ability to anticipate, withstand, respond to and recover to normal state, is to be enhanced urgently. This paper proposes a master-slave optimization model for the resilience enhancement of integrated energy system in the integrated stage of disaster response and post-disaster recovery, in view of the strong correlation between the two stages. The master model develops the optimal fault repair plan, and the sub model determines the optimal energy supply recovery scheme. Based on the master-slave model, which adopts the repair and operation state of the component as coupling variables, a coordinated optimization framework is constructed. Then, the master-slave model is merged into a two-stage robust optimization model for iterative solution in order to develop the optimal fault repair strategy and energy supply recovery scheme of the integrated energy system, enhancing its resilience in the integrated stage of disaster response and post-disaster recovery.

The short-term forecasting of multiple loads is crucial for the optimization and scheduling of integrated energy system (IES). However, the load within the IES exhibits diversified and strongly coupled characteristics, which seriously affects the forecast accuracy. Moreover, only using deep learning forecasting methods cannot analyze the factors that affect the forecast results, which is not conducive to guiding the optimization and scheduling of comprehensive energy systems. Therefore, a multivariate load forecasting model based on knowledge-guided multi-task spatial-temporal synchronous graph convolutional network is proposed. Firstly, the user clusters are classified according to the energy-using characteristics of different buildings. Then, the domain knowledge base is built by combining the dimensionless trends of different groups and expert experience. At the same time, the input features are filtered based on the improved maximum information coefficient method to construct spatialtemporal graph data, forming a more refined and efficient input sample data. Finally, the knowledge-data fusion model for multivariate load forecasting is constructed to predict local fluctuations of the multivariate load series and reconstruct the load ratio. The IES data set of Arizona State University Tempe Campus is taken as a test case. The results show that the proposed method is interpretable, has higher forecast accuracy and has better generalization ability.

Aiming at the practical problems of high energy consumption and low energy efficiency during the exploitation of low-permeability oil wells because of insufficient traceability and poor matching performance of production parameters, this paper proposes a multi-objective approach for optimizing production parameters of low-permeability oil well to enhance its energy efficiency. First, a sub-model of daily liquid production yield and a sub-model of unit production energy consumption cost for single low-permeability oil well were established, and the Gaussian mixture model method was employed to compensate for the errors in the sub-model of unit production energy consumption cost, to solve the problem of the influence of uncertain facts during the oil well exploitation and to improve the precision of the model. Second, a multi-objective optimization model was established by taking into account the decision variables and constraints of the model, to maximize the daily liquid production yield while minimizing the unit production energy consumption cost. Subsequently, the non-dominated sorting genetic algorithm was employed to solve the multi-objective optimization model and obtain the production parameters. Finally, the solution set with obvious features was taken as the production parameters and applied to the actual production verification of low-permeability oil wells in a certain oil production plant of the ChangQing Oilfield. The results showed an increase in oil well production yield, and a significant energy-saving effect, thereby verifying the effectiveness of the proposed model and optimization algorithm in this paper.

Dewatering in multilayered aquifers is closely related to stratigraphic configuration. However, previous studies concentrate on the excavation response to dewatering in particular strata, lacking systematic study on the influence of stratum configuration. This study developed a hydro-mechanical coupled numerical model based on the practical engineering of Shanghai Tower deep excavation. The model and input parameters were validated by field measurements. Besides, a parametric study was conducted to investigate the effect of the thickness, permeability and depth of the aquitard on excavation performances. The analysis indicated that settlements in the excavation were insensitive to the thicknesses and permeability of aquitards, while ground surface settlement increased significantly with the increase of the confined aquifer thickness. Based on the numerical analysis, a fit relationship was established to predict the maximum ground settlements of deep excavation in Shanghai considering the excavation depth, groundwater drawdown and distribution of multilayered aquifers. The applicability of the fit equation was verified by field measurements.

In recent years, underwater image enhancement techniques has received a wide range of attention from related researchers with the rise of marine resource exploitation. As the existing network feature extraction is not sufficient and the enhancement results have the problems of incomplete defogging and inaccurate color bias correction, in this paper, an underwater image enhancement method based on global dense two-branch cascade network and spatial domain grayscale transformation is proposed. The global dense two-branch cascade network can amplify the global dimensional interaction features while reducing information reduction on the one hand, and extract spatial features by obtaining spatial information at different scales to achieve richer feature extraction on the other hand; the spatial domain grayscale transformation operation can improve the contrast while color correcting the image, which makes the image visual effect better. After the training is completed, an end-to-end inference can be performed on the underwater images. The experimental results show that this paper’s model works best on the EUVP dataset, and compared with the second best, this paper’s model obtains 3.371, 0.06, 0.716, 0.024, and 1.727 improvements in PSNR, SSIM, UIQM, UCIQE, and CCF, respectively. Compared with other representative methods, the proposed network achieves significant visual enhancement in dealing with severe color bias, low light, and detail loss in underwater images.

Carbon emissions from ship outfitting pallet distribution account for a significant proportion of shipbuilding logistics. However, the complexity of the problem and the lack of carbon emission considerations make it difficult to achieve efficient and low-carbon distribution scheduling. To improve distribution efficiency while reducing carbon emissions, this paper formulates it as a heterogeneous fleet green multi-pickup and delivery problem with time windows. To effectively solve the problem, we develop a powerful hybrid meta-heuristic and propose a request insertion pruning strategy to accelerate the procedure. Computational results on multiple instances demonstrate the significant advantages of the proposed hybrid approach. The differences in cost components and transportation strategies between the economic and emission cost objective models are analyzed to provide meaningful managerial insights. This paper also quantifies the trade-offs between two costs and the benefits of a heterogeneous fleet over a homogeneous one. The method proposed can effectively reduce carbon emissions while improving distribution efficiency to help improve the sustainability of shipbuilding.

To achieve the control effect of high precision, fast response speed, and good stability for a class of underactuated systems, a control strategy based on the improved active disturbance rejection controller is designed. First, a new sliding mode tracking differentiator is designed on the basis of a new sliding mode reaching rate. Given that traditional active disturbance rejection control is only suitable for single-input and single-output systems, two sliding mode tracking differentiators are used in the proposed model to obtain the given displacement and velocity as well as the actual displacement and velocity, respectively. Then, a new nonlinear function with enhanced smoothness and convergence is designed. Using the nonlinear function, an improved extended state observer for the underactuated system is designed to optimize its following error ability. Given that the parameter values of the control rate part are difficult to adjust, the particle swarm optimization algorithm is used to optimize the four parameter values of the control rate part. Finally, the simulation results show that the proposed control strategy can realize a fast and stable control of such underactuated systems.

Clutch slipping torque varies complicatedly and has a strong nonlinearity during the mode transition of hybrid electric vehicles. In order to estimate the clutch slipping torque, an online estimator based on the nonlinear disturbance observer (NDO) is proposed. First, an estimation-oriented model of the clutch slipping torque is established based on the LuGre friction model. Next, the NDO is designed to estimate the unknown part of clutch slipping torque based on the dynamics of the output shaft, while the output shaft torque required by NDO is estimated by a Luenberger state observer. To verify the effectiveness of the proposed estimator, experiments are conducted under different initial slipping speeds and inlet oil temperatures. The results show that the proposed estimator gains high accuracy.

Most of the published work related to the influence of ship hulls on hydrodynamic characteristics of underwater robots takes ship hulls as nearly infinite planes, paying less attention to the effect of hull shape. Thus using an unsteady Reynolds-averaged Navier-Stokes solver, this work investigates hydrodynamic interactions between an open-frame underwater cleaning robot (OFUCR), which is put into commercial use, and the parallel middle body of a real full-scale floating production storage and offloading (FPSO) with round bilge. Calculated results are validated by some published results. In simulation, the OFUCR moves at a speed of 1 kn, and keeps 0.1m away from the hull. Drag and lateral forces of OFUCR, and repulsive force due to the interference of FPSO are calculated. Further, dynamic pressure, velocity and vorticity in the gap between OFUCR and FPSO are drawn. The influence of longitudinal and lateral currents is concerned. As a result, drag obviously increases with the presence of FPSO owing to the wall shear stress and drop of dynamic pressure. Lateral force is found to be a repulsion force as OFUCR moves along ship bottom, and an attraction force as OFUCR moves along ship bilge and ship side.

We consider the well-studied sequential posted pricing scenarios. In these scenarios, an auctioneer typically learns the value distributions of all agents as prior information and then offers a take-it-or-leave-it price to each sequentially coming agent. If the value distributions are correctly learned, the dominant strategy of each agent is telling the truth. However, an agent could manipulate her value distribution to exploit the auctioneer. We study the behavior of sophisticated agents predicted by two prominent bounded rationality models: the level-k and the cognitive hierarchy models. We begin with analyzing the structure of the optimal reported distributions and then provide algorithms to compute the optimal distributions for each model. In the continuous scenarios, we show that both models are ill-defined by some examples. Moreover, we evaluate both models in discrete scenarios with different numbers of agents, different minimum units of the values, and different risk tolerances. The empirical results and a brief discussion about the Bayesian Nash equilibrium of the experimental scenarios show that both the level-k model and the equilibrium suggest the highest possible prices. In contrast, the cognitive hierarchy model suggests low prices. The level-k model and the equilibrium somehow explain the “winner’s curse” in online markets. The models and the equilibrium fail to explain that the same item could have different prices in different shops. To explain the different-price phenomenon, we suggest trying other bounded rationality models for agents and/or considering the auctioneers with bounded rationality.

Aiming at the problem of low node coverage during node deployment in wireless sensor network (WSN), an improved artificial rabbit optimization algorithm incorporating particle swarm optimization (ARO-PSO) is proposed for network coverage optimization. ARO-PSO successfully combines the stochastic characteristics of ARO and the global characteristics of PSO. Firstly, to optimize the quality of the initial population, Sine chaos mapping is introduced to initialize the population; secondly, to better balance the exploration and exploitation, adaptive settings are made; finally, combined with the characteristics of the ARO energy factor, a population decreasing strategy is introduced to further accelerate the convergence speed of the algorithm. Experimental and analytical comparisons are made with ARO and PSO and 6 other excellent optimizers on 13 benchmark functions. The results show that ARO-PSO largely outperforms the original algorithm. Finally, ARO-PSO is applied to WSN coverage optimization experiments in 2D and 3D environments, and the proposed algorithm exhibits higher network coverage and improves the monitoring quality of the network compared to standard ARO and PSO and other state-of-the-art algorithms. The experimental results fully demonstrate the superiority of the ARO-PSO-based WSN node deployment optimization method.

Urban drainage pipe system is an important part of city management. Automated detection of the status of storm drain in street-level images through current technologies in computer vision and AI is an important aspect of smart city construction. In this paper, a framework based on YOLOv5s for storm drain detection (YOLOSDD) in street view is proposed. By analyzing the characteristics of small-scale targets, YOLO-SDD focuses on optimizing the Backbone network and its loss function. Series of experiments demonstrated that in the task of detecting different states of storm drain under various environmental conditions, the mean average precision (mAP@.5) of the YOLO-SDD can reach 89.6%, increasing by 2% compared with the baseline model YOLOv5s. In the presence and absence of occlusion, the average precision of storm drain detection increased by 0.9% and 3.1%, respectively. In addition, the effectiveness and generalization ability of YOLO-SDD were further validated using the storm drain dataset of Urbana-Champaign (SDUC) from Illinois, USA, and the dataset for object detection in aerial images (DOTA). Finally, this work has deployed the YOLO-SDD on the Android system, which verifies its ability of real-time detecting storm drain in different states in street scenes.

In order to address the challenges associated with poor semantic segmentation results of classical semantic segmentation networks in high-resolution remote sensing images, limited performance in complex scenes, a large number of network parameters, and high training costs, this study proposes an efficient segmentation method for high-resolution remote sensing images based on an improved DeepLabv3+ approach. The method focuses on three key aspects: reducing the number of network parameters, minimizing computation volume, and enhancing performance. First, the proposed method replaces the original DeepLabv3+ backbone network Xception, which is computationally heavy, with the lighter MobileNetV2 network for feature extraction. This substitution helps reduce the number of network parameters while maintaining effective feature extraction. Second, a lightweight convolutional block attention module (CBAM) is added after the feature extraction module to enhance the network’s feature extraction capability. The inclusion of CBAM further reduces the number of network parameters. Last, coordinate attention is introduced after the shallow features obtained from the feature extraction module. This addition allows the network to focus more on relevant features in the image, while disregarding irrelevant background information. Experimental results demonstrate the effectiveness of the proposed method. In the segmentation task of the high-resolution image dataset, the method achieves a mean intersection over union (mIoU) of 75.33%. This result surpasses mainstream semantic segmentation networks such as SegNet, PSPNet, and U-Net by 12.49%, 3.16%, and 1.62% respectively. Furthermore, the proposed model has a relatively low number of network parameters, with only 6.02 × 10⁶ parameters, and a computation volume of 26.45 GFLOPs. This balance between computational efficiency and segmentation accuracy makes the model highly valuable for edge computing applications.