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Abstract Inconel 718 (IN718) alloy is widely applied to fabricate high temperature resistant or corrosion resistant parts due to its excellent mechanical performance. However, the machining of IN718 alloy is difficult as it may cause serious tool wear and poor surface quality (SQ) of the workpiece. In this work, grinding experiments on IN718 alloy at different speeds were conducted by using a CBN grinding wheel. The relationship between grinding speed, SQ and subsurface damage (SSD) was well studied. With increasing grinding speed, surface roughness decreased, and SQ was greatly improved. Meanwhile, the microhardness of the grinding surface declined as the grinding speed increased. The SSD depth was almost unchanged when the grinding speed was lower than 15 m/s, then it decreased with higher grinding speeds. It was attributed to the mechanical-thermal synergistic effect in the grinding process. The results indicated that increasing grinding speed can effectively improve the SQ and reduce the SSD of IN718 alloy. The conclusion in the work may also provide insight into processing other hard-to-machining materials.

Abstract Existing mobile robots mostly use graph search algorithms for path planning, which suffer from relatively low planning efficiency owing to high redundancy and large computational complexity. Due to the limitations of the neighborhood search strategy, the robots could hardly obtain the most optimal global path. A global path planning algorithm, denoted as EDG*, is proposed by expanding nodes using a well-designed expanding disconnected graph operator (EDG) in this paper. Firstly, all obstacles are marked and their corners are located through the map pre-processing. Then, the EDG operator is designed to find points in non-obstruction areas to complete the rapid expansion of disconnected nodes. Finally, the EDG* heuristic iterative algorithm is proposed. It selects the candidate node through a specific valuation function and realizes the node expansion while avoiding collision with a minimum offset. Path planning experiments were conducted in a typical indoor environment and on the public dataset CSM. The result shows that the proposed EDG* reduced the planning time by more than 90% and total length of paths reduced by more than 4.6%. Compared to A*, Dijkstra and JPS, EDG* does not show an exponential explosion effect in map size. The EDG* showed better performance in terms of path smoothness, and collision avoidance. This shows that the EDG* algorithm proposed in this paper can improve the efficiency of path planning and enhance path quality.

Abstract The all-wheel drive (AWD) hybrid system is a research focus on high-performance new energy vehicles that can meet the demands of dynamic performance and passing ability. Simultaneous optimization of the power and economy of hybrid vehicles becomes an issue. A unique multi-mode coupling (MMC) AWD hybrid system is presented to realize the distributed and centralized driving of the front and rear axles to achieve vectored distribution and full utilization of the system power between the axles of vehicles. Based on the parameters of the benchmarking model of a hybrid vehicle, the best model-predictive control-based energy management strategy is proposed. First, the drive system model was built after the analysis of the MMC-AWD's drive modes. Next, three fundamental strategies were established to address power distribution adjustment and battery SOC maintenance when the SOC changed, which was followed by the design of a road driving force observer. Then, the energy consumption rate in the average time domain was processed before designing the minimum fuel consumption controller based on the equivalent fuel consumption coefficient. Finally, the advantage of the MMC-AWD was confirmed by comparison with the dynamic performance and economy of the BYD Song PLUS DMI-AWD. The findings indicate that, in comparison to the comparative hybrid system at road adhesion coefficients of 0.8 and 0.6, the MMC-AWD's capacity to accelerate increases by 5.26% and 7.92%, respectively. When the road adhesion coefficient is 0.8, 0.6, and 0.4, the maximum climbing ability increases by 14.22%, 12.88%, and 4.55%, respectively. As a result, the dynamic performance is greatly enhanced, and the fuel savings rate per 100 km of mileage reaches 12.06%, which is also very economical. The proposed control strategies for the new hybrid AWD vehicle can optimize the power and economy simultaneously.

Abstract The most widely adopted method for diagnosing respiratory infectious diseases is to conduct polymerase chain reaction (PCR) assays on patients’ respiratory specimens, which are collected through either nasal or oropharyngeal swabs. The manual swab sampling process poses a high risk to the examiner and may cause false-negative results owing to improper sampling. In this paper, we propose a pneumatically actuated soft end-effector specifically designed to achieve all of the tasks involved in swab sampling. The soft end-effector utilizes circumferential instability to ensure grasping stability, and exhibits several key properties, including high load-to-weight ratio, error tolerance, and variable swab-tip stiffness, leading to successful automatic robotic oropharyngeal swab sampling, from loosening and tightening the transport medium tube cap, holding the swab, and conducting sampling, to snapping off the swab tail and sterilizing itself. Using an industrial collaborative robotic arm, we integrated the soft end-effector, force sensor, camera, lights, and remote-control stick, and developed a robotic oropharyngeal swab sampling system. Using this swab sampling system, we conducted oropharyngeal swab-sampling tests on 20 volunteers. Our Digital PCR assay results (RNase P RNA gene absolute copy numbers for the samples) revealed that our system successfully collected sufficient numbers of cells from the pharyngeal wall for respiratory disease diagnosis. In summary, we have developed a pharyngeal swab-sampling system based on an “enveloping” soft actuator, studied the sampling process, and implemented whole-process robotic oropharyngeal swab-sampling.

Abstract Each joint of a hydraulic-driven legged robot adopts a highly integrated hydraulic drive unit (HDU), which features a high power–weight ratio. However, most HDUs are throttling-valve-controlled cylinder systems, which exhibit high energy losses. By contrast, pump control systems offer a high efficiency. Nevertheless, their response ability is unsatisfactory. To fully utilize the advantages of pump and valve control systems, in this study, a new type of pump–valve compound drive system (PCDS) is designed, which can not only effectively reduce the energy loss, but can also ensure the response speed and response accuracy of the HDUs in robot joints to satisfy the performance requirements of robots. Herein, considering the force control requirements of energy conservation, high precision, and fast response of the robot joint HDU, a nonlinear mathematical model of the PCDS force control system is first introduced. In addition, pressure–flow nonlinearity, friction nonlinearity, load complexity and variability, and other factors affecting the system are considered, and a novel force control method based on quantitative feedback theory (QFT) and a disturbance torque observer (DTO) is designed, which is denoted as QFT–DTOC herein. This method improves the control accuracy and robustness of the force control system, reduces the effect of the disturbance torque on the control performance of the servo motor, and improves the overall force control performance of the system. Finally, experimental verification is performed using the PCDS performance test platform. The experimental results and quantitative data show that the QFT–DTOC proposed herein can significantly improve the force control performance of the PCDS. The relevant force control method can be used as a bottom-control method for the hydraulic servo system to provide a foundation for implementing the top-level trajectory planning of the robot.

Abstract The twin-body plasma arc has the decoupling control ability of heat transfer and mass transfer, which is beneficial to shape and property control in wire arc additive manufacturing. In this paper, with the wire feeding speed as a characteristic quantity, the wire melting control ability of twin-body plasma arc was studied by adjusting the current separation ratio (under the condition of a constant total current), the wire current/main current and the position of the wire in the arc axial direction. The results showed that under the premise that the total current remains unchanged (100 A), as the current separation ratio increased, the middle and minimum melting amounts increased approximately synchronously under the effect of anode effect power, the first melting mass range remained constant; the maximum melting amount increased twice as fast as the middle melting amount under the effect of the wire feeding speed, and the second melting mass range was expanded. When the wire current increased, the anode effect power and the plasma arc power were both factors causing the increase in the wire melting amount; however, when the main current increased, the plasma arc power was the only factor causing the increase in the wire melting amount. The average wire melting increment caused by the anode effect power was approximately 2.7 times that caused by the plasma arc power. The minimum melting amount was not affected by the wire-torch distance under any current separation ratio tested. When the current separation ratio increased and reached a threshold, the middle melting amount remained constant with increasing wire-torch distance. When the current separation ratio continued to increase and reached the next threshold, the maximum melting amount remained constant with the increasing wire-torch distance. The effect of the wire-torch distance on the wire melting amount reduced with the increase in the current separation ratio. Through this study, the decoupling mechanism and ability of this innovative arc heat source is more clearly.

Abstract Copper particles emitted from braking have become a significant source of environmental pollution. However, copper plays a crucial role in resin-based braking materials. Developing high-performance braking materials without copper has become a significant challenge. In this paper, the resin-based braking materials were filled with fly-ash cenospheres to develop copper-free braking materials. The effects of fly-ash cenospheres on the physical properties, mechanical and friction and wear properties of braking materials were studied. Furthermore, the wear mechanism of copper-free resin-based braking materials filled with fly-ash cenospheres was discussed. The results indicate that the inclusion of fly-ash cenospheres in the braking materials improved their thermal stability, hardness and impact strength, reduced their density, effectively increased the friction coefficient at medium and high temperatures, and enhanced the heat-fade resistance of the braking materials. The inclusion of fly-ash cenospheres contributed to the formation of surface friction film during the friction process of the braking materials, and facilitated the transition of form from abrasive wear to adhesive wear. At 100–350 ℃, the friction coefficient of the optimal formulation is in the range of 0.57–0.61, and the wear rate is in the range (0.29–0.65) × 10−7 cm3·N−1·m−1, demonstrating excellent resistance to heat-fade and stability in friction coefficient. This research proposes the use of fly-ash cenospheres as a substitute for environmentally harmful and expensive copper in brake materials, which not only improves the performance of braking materials but also reduces their costs.

Abstract Exoskeletons generally require accurate dynamic models to design the model-based controller conveniently under the human-robot interaction condition. However, due to unknown model parameters such as the mass, moment of inertia and mechanical size, the dynamic model of exoskeletons is difficult to construct. Hence, an enhanced whale optimization algorithm (EWOA) is proposed to identify the exoskeleton model parameters. Meanwhile, the periodic excitation trajectories are designed by finite Fourier series to input the desired position demand of exoskeletons with mechanical physical constraints. Then a backstepping controller based on the identified model is adopted to improve the human-robot wearable comfortable performance under cooperative motion. Finally, the proposed Model parameters identification and control are verified by a two-DOF exoskeletons platform. The knee joint motion achieves a steady-state response after 0.5 s. Meanwhile, the position error of hip joint response is less than 0.03 rad after 0.9 s. In addition, the steady-state human-robot interaction torque of the two joints is constrained within 15 $${\text{N}\cdot\text{m}}$$ N · m . This research proposes a whale optimization algorithm to optimize the excitation trajectory and identify model parameters. Furthermore, an enhanced mutation strategy is adopted to avoid whale evolution's unsatisfactory local optimal value.

Abstract Grinding, a critical precision machining process for difficult-to-cut alloys, has undergone continual technological advancements to improve machining efficiency. However, the sustainability of this process is gaining heightened attention due to significant challenges associated with the substantial specific grinding energy and the extensive heat generated when working with difficult-to-cut alloys, renowned for their exceptional physical and mechanical properties. In response to these challenges, the widespread application of massive coolant in manufacturing industries to dissipate grinding heat has led to complex post-cleaning and disposal processes. This, in turn, has resulted in issues such as large energy consumption, a considerable carbon footprint, and concerns related to worker health and safety, which have become the main factors that restrict the development of grinding technology. This paper provides a holistic review of sustainability in grinding difficult-to-cut alloys, encompassing current trends and future directions. The examination extends to developing grinding technologies explicitly tailored for these alloys, comprehensively evaluating their sustainability performance. Additionally, the exploration delves into innovative sustainable technologies, such as heat pipe/oscillating heat pipe grinding wheels, minimum quantity lubrication, cryogenic cooling, and others. These groundbreaking technologies aim to reduce dependence on hazardous coolants, minimizing energy and resource consumption and carbon emissions associated with coolant-related or subsequent disposal processes. The essence of these technologies lies in their potential to revolutionize traditional grinding practices, presenting environmentally friendly alternatives. Finally, future development trends and research directions are put forward to pursue the current limitation of sustainable grinding for difficult-to-cut alloys. This paper can guide future research and development efforts toward more environmentally friendly grinding operations by understanding the current state of sustainable grinding and identifying emerging trends.

Abstract Improvement of fabrication efficiency and part performance was the main challenge for the large-scale powder bed fusion (PBF) process. In this study, a dynamic monitoring and feedback system of powder bed temperature field using an infrared thermal imager has been established and integrated into a four-laser PBF equipment with a working area of 2000 mm × 2000 mm. The heat-affected zone (HAZ) temperature field has been controlled by adjusting the scanning speed dynamically. Simultaneously, the relationship among spot size, HAZ temperature, and part performance has been established. The fluctuation of the HAZ temperature in four-laser scanning areas was decreased from 30.85 ℃ to 17.41 ℃. Thus, the consistency of the sintering performance of the produced large component has been improved. Based on the controllable temperature field, a dynamically adjusting strategy for laser spot size was proposed, by which the fabrication efficiency was improved up to 65.38%. The current research results were of great significance to the further industrial applications of large-scale PBF equipment.

Abstract Minimum quantity Lubrication (MQL) is a sustainable lubrication system that is famous in many machining systems. It involve the spray of an infinitesimal amount of mist-like lubricants during machining processes. The MQL system is affirmed to exhibit an excellent machining performance, and it is highly economical. The nanofluids are understood to exhibit excellent lubricity and heat evacuation capability, compared to pure oil-based MQL system. Studies have shown that the surface quality and amount of energy expended in the grinding operations can be reduced considerably due to the positive effect of these nanofluids. This work presents an experimental study on the tribological performance of SiO2 nanofluid during grinding of Si3N4 ceramic. The effect different grinding modes and lubrication systems during the grinding operation was also analyzed. Different concentrations of the SiO2 nanofluid was manufactured using canola, corn and sunflower oils. The quantitative evaluation of the grinding process was done based on the amount of grinding forces, specific grinding energy, frictional coefficient, and surface integrity. It was found that the canola oil exhibits optimal lubrication performance compared to corn oil, sunflower oil, and traditional lubrication systems. Additionally, the introduction of ultrasonic vibrations with the SiO2 nanofluid in MQL system was found to reduce the specific grinding energy, normal grinding forces, tangential grinding forces, and surface roughness by 65%, 57%, 65%, and 18% respectively. Finally, regression analysis was used to obtain an optimum parameter combinations. The observations from this work will aid the smooth transition towards ecofriendly and sustainable machining of engineering ceramics.

Abstract The kinematic equivalent model of an existing ankle-rehabilitation robot is inconsistent with the anatomical structure of the human ankle, which influences the rehabilitation effect. Therefore, this study equates the human ankle to the UR model and proposes a novel three degrees of freedom (3-DOF) generalized spherical parallel mechanism for ankle rehabilitation. The parallel mechanism has two spherical centers corresponding to the rotation centers of tibiotalar and subtalar joints. Using screw theory, the mobility of the parallel mechanism, which meets the requirements of the human ankle, is analyzed. The inverse kinematics are presented, and singularities are identified based on the Jacobian matrix. The workspaces of the parallel mechanism are obtained through the search method and compared with the motion range of the human ankle, which shows that the parallel mechanism can meet the motion demand of ankle rehabilitation. Additionally, based on the motion-force transmissibility, the performance atlases are plotted in the parameter optimal design space, and the optimum parameter is obtained according to the demands of practical applications. The results show that the parallel mechanism can meet the motion requirements of ankle rehabilitation and has excellent kinematic performance in its rehabilitation range, which provides a theoretical basis for the prototype design and experimental verification.

Abstract The current parallel ankle rehabilitation robot (ARR) suffers from the problem of difficult real-time alignment of the human-robot joint center of rotation, which may lead to secondary injuries to the patient. This study investigates type synthesis of a parallel self-alignment ankle rehabilitation robot (PSAARR) based on the kinematic characteristics of ankle joint rotation center drift from the perspective of introducing "suitable passive degrees of freedom (DOF)" with a suitable number and form. First, the self-alignment principle of parallel ARR was proposed by deriving conditions for transforming a human-robot closed chain (HRCC) formed by an ARR and human body into a kinematic suitable constrained system and introducing conditions of "decoupled" and "less limb". Second, the relationship between the self-alignment principle and actuation wrenches (twists) of PSAARR was analyzed with the velocity Jacobian matrix as a "bridge". Subsequently, the type synthesis conditions of PSAARR were proposed. Third, a PSAARR synthesis method was proposed based on the screw theory and type of PSAARR synthesis conducted. Finally, an HRCC kinematic model was established to verify the self-alignment capability of the PSAARR. In this study, 93 types of PSAARR limb structures were synthesized and the self-alignment capability of a human-robot joint axis was verified through kinematic analysis, which provides a theoretical basis for the design of such an ARR.

Abstract Regarding the lane keeping system, path tracking accuracy and lateral stability at high speeds need to be taken into account especially for commercial vehicles due to the characteristics of larger mass, longer wheelbase and higher mass center. To improve the performance mentioned above comprehensively, the control strategy based on improved artificial potential field (APF) algorithm is proposed. In the paper, time to lane crossing (TLC) is introduced into the potential field function to enhance the accuracy of path tracking, meanwhile the vehicle dynamics parameters including yaw rate and lateral acceleration are chosen as the repulsive force field source. The lane keeping controller based on improved APF algorithm is designed and the stability of the control system is proved based on Lyapunov theory. In addition, adaptive inertial weight particle swarm optimization algorithm (AIWPSO) is applied to optimize the gain of each potential field function. The co-simulation results indicate that the comprehensive evaluation index respecting lane tracking accuracy and lateral stability is reduced remarkably. Finally, the proposed control strategy is verified by the HiL test. It provides a beneficial reference for dynamics control of commercial vehicles and enriches the theoretical development and practical application of artificial potential field method in the field of intelligent driving.

Abstract For dissimilar metal welds (DMWs) involving nickel-based weld metal (WM) and ferritic heat resistant steel base metal (BM) in power plants, there must be an interface between WM and BM, and this interface suffers mechanical and microstructure mismatches and is often the rupture location of premature failure. In this study, a new form of WM/BM interface form, namely double Y-type interface was designed for the DMWs. Creep behaviors and life of DMWs containing double Y-type interface and conventional I-type interface were compared by finite element analysis and creep tests, and creep failure mechanisms were investigated by stress-strain analysis and microstructure characterization. By applying double Y-type interface instead of conventional I-type interface, failure location of DMW could be shifted from the WM/ferritic heat-affected zone (HAZ) interface into the ferritic HAZ or even the ferritic BM, and the failure mode change improved the creep life of DMW. The interface premature failure of I-type interface DMW was related to the coupling effect of microstructure degradation, stress and strain concentrations, and oxide notch on the WM/HAZ interface. The creep failure of double Y-type interface DMW was the result of Type IV fracture due to the creep voids and micro-cracks on fine-grain boundaries in HAZ, which was a result of the matrix softening of HAZ and lack of precipitate pinning at fine-grain boundaries. The double Y-type interface form separated the stress and strain concentrations in DMW from the WM/HAZ interface, preventing the trigger effect of oxide notch on interface failure and inhibiting the interfacial microstructure cracking. It is a novel scheme to prolong creep life and enhance reliability of DMW, by means of optimizing the interface form, decoupling the damage factors from WM/HAZ interface, and then changing the failure mechanism and shifting the failure location.

Abstract This paper presents an integrated control scheme for enhancing the ride comfort and handling performance of a four-wheel-independent-drive electric vehicle through the coordination of active suspension system (ASS) and anti-lock braking system (ABS). First, a longitudinal-vertical coupled vehicle dynamics model is established by integrating a road input model. Then the coupling mechanisms between longitudinal and vertical vehicle dynamics are analyzed. An ASS-ABS integrated control system is proposed, utilizing an H ∞ controller for ASS to optimize load transfer effect and a neural network sliding mode control for ABS implementation. Finally, the effectiveness of the proposed control scheme is evaluated through comprehensive tests conducted on a hardware-in-loop (HIL) test platform. The HIL test results demonstrate that the proposed control scheme can significantly improve the braking performance and ride comfort compared to conventional ABS control methods.

This research aimed to investigate and compare the demineralization of goldstripe sardinella (GS; Sardinella gibbosa) scales, a major by-product in the canned fish industry prevalent in East Africa and Southeast Asia, particularly Thailand. The study focused on conventional and alternative demineralization methods, assessing the yield and quality of the demineralized scales. After removing minerals, fish scales have potential value as an alternative source of collagen and gelatin. For the strong acid treatment using hydrochloric acid (HCl), the effects of HCl concentrations (0.2-1.5 M) and treatment times (30-120 min) on demineralization efficiency were assessed. The results from RSM indicated that HCl concentration was the only treatment factor that significantly affected demineralization yield, HCl concentrations at or above 0.82 M gave a demineralization efficiency of e” 99%, independent of treatment time (P 0.0001). A preliminary investigation into high-pressure carbon dioxide (HPCD) treatment of GS scales (at 10 bar for 1 to 4 h) showed comparatively lower demineralization efficiency. Within the studied parameters, the highest demineralization efficiencies for both methods were 99.89±0.06 and 16.13±1.92%, respectively. SEM images and EDX analysis confirmed the complete removal of minerals (primarily Ca and P) after HCl treatment using HCl 0.85 M for 30 min. Conversely, HPCD-treated scales exhibited altered structure and physical damage.

Traditional Acoustic Doppler Current Profiler (ADCP) instruments measure marine flow velocities along diverging acoustic beams. This allows estimation of approximate mean velocity and turbulence at a point between the beams. Alternatively, a converging beam instrument can measure velocity components at a single point and hence provide a higher quality measurement. Here we show a new design of instrument with converging beams together with some preliminary flow measurement results. The instrument is constructed around a triangular frame with Doppler transceivers on the ends of cable stayed arms. It is designed to be installed and recovered from the seabed without use of a crane vessel. This is achieved by a pressurised air buoyancy system. The system can be slipway launched with a boat trailer and flat packed for transport on the same trailer. The system performed well for three test deployments; however, measurements of the seabed stability of the frame showed undesirable flexing of one arm when it was positioned perpendicular to the main flow direction. A traditional ADCP was located on the frame and the two instruments were operated in burst mode, with each instrument measuring alternately every 20 minutes. Results for velocity and turbulent kinetic energy at the same depth are reported for both instruments and compared. A 2D oceanographic model of the deployment site is used as an additional point of comparison to illuminate some differences in the mean flow velocity observations of both instruments. This instrument will add significantly to measurement capabilities at tidal stream turbine deployment locations. Improved turbulence measurements will give better understanding of turbine loading and hence improve reliability of these systems. The unit can also be used to deploy different types of sensors for oceanographic measurements.

Nova Scotia’s marine renewable energy (MRE) sector can contribute to provincial goals for carbon neutrality. With this, tidal energy has been an interest to industry stakeholders for over two decades, yet general momentum for the industry has shifted. Previous tidal energy projects and recent studies suggest there needs to be a shift in focus towards scalable tidal energy (i.e., small-scale) development in the province. This research explores the considerations of developing tidal energy in Southwest Nova Scotia with an emphasis on small-scale tidal devices. Using a comprehensive literature review and in-depth semi-structured interviews with stakeholders from different sectors of Nova Scotia’s MRE energy industry, key issues and challenges were explored. Results underline four main themes that affect industry potential, which include cost and financing, technology, policy, and energy distribution. These results support a discussion around the value of small-scale technology, regulatory pathways, industry collaboration, and using global best practices to accelerate tidal energy industry goals in Nova Scotia.

Abstract Four-wheel independently driven electric vehicles (FWID-EV) endow a flexible and scalable control framework to improve vehicle performance. This paper integrates the torque vectoring and active suspension system (ASS) to enhance the vehicle’s longitudinal and vertical motion control performance. While the nonlinear characteristic of the tire model leads to a relatively heavier computational burden. To facilitate the controller design and ease the load, a half-vehicle dynamics system is built and simplified to the linear-time-varying (LTV) model. Then a model predictive controller is developed by formulating the objective function by comprehensively considering the safety, energy-saving and comfort requirements. The in-wheel motor efficiency and the power loss of tire slip are treated as optimization indices in this work to reduce energy consumption. Finally, the effectiveness of the proposed controller is verified through the rapid-control-prototype (RCP) test. The results demonstrate the enhancement of the energy-saving as well as comfort on the basis of vehicle stability.

Abstract An increasing number of researchers have researched fixture layout optimization for thin-walled part assembly during the past decades. However, few papers systematically review these researches. By analyzing existing literature, this paper summarizes the process of fixture layout optimization and the methods applied. The process of optimization is made up of optimization objective setting, assembly variation/deformation modeling, and fixture layout optimization. This paper makes a review of the fixture layout for thin-walled parts according to these three steps. First, two different kinds of optimization objectives are introduced. Researchers usually consider in-plane variations or out-of-plane deformations when designing objectives. Then, modeling methods for assembly variation and deformation are divided into two categories: Mechanism-based and data-based methods. Several common methods are discussed respectively. After that, optimization algorithms are reviewed systematically. There are two kinds of optimization algorithms: Traditional nonlinear programming and heuristic algorithms. Finally, discussions on the current situation are provided. The research direction of fixture layout optimization in the future is discussed from three aspects: Objective setting, improving modeling accuracy and optimization algorithms. Also, a new research point for fixture layout optimization is discussed. This paper systematically reviews the research on fixture layout optimization for thin-walled parts, and provides a reference for future research in this field.

Abstract This paper proposes a modified iterative learning control (MILC) periodical feedback-feedforward algorithm to reduce the vibration of a rotor caused by coupled unbalance and parallel misalignment. The control of the vibration of the rotor is provided by an active magnetic actuator (AMA). The iterative gain of the MILC algorithm here presented has a self-adjustment based on the magnitude of the vibration. Notch filters are adopted to extract the synchronous (1 × Ω) and twice rotational frequency (2 × Ω) components of the rotor vibration. Both the notch frequency of the filter and the size of feedforward storage used during the experiment have a real-time adaptation to the rotational speed. The method proposed in this work can provide effective suppression of the vibration of the rotor in case of sudden changes or fluctuations of the rotor speed. Simulations and experiments using the MILC algorithm proposed here are carried out and give evidence to the feasibility and robustness of the technique proposed.

Abstract Accurate vehicle dynamic information plays an important role in vehicle driving safety. However, due to the characteristics of high mobility and multiple controllable degrees of freedom of drive-by-wire chassis vehicles, the current mature application of traditional vehicle state estimation algorithms can not meet the requirements of drive-by-wire chassis vehicle state estimation. This paper proposes a state estimation method for drive-by-wire chassis vehicle based on the dual unscented particle filter algorithm, which make full use of the known advantages of the four-wheel drive torque and steer angle parameters of the drive-by-wire chassis vehicle. In the dual unscented particle filter algorithm, two unscented particle filter transfer information to each other, observe the vehicle state information and the tire force parameter information of the four wheels respectively, which reduce the influence of parameter uncertainty and model parameter changes on the estimation accuracy during driving. The performance with the dual unscented particle filter algorithm, which is analyzed in terms of the time-average square error, is superior of the unscented Kalman filter algorithm. The effectiveness of the algorithm is further verified by driving simulator test. In this paper, a vehicle state estimator based on dual unscented particle filter algorithm was proposed for the first time to improve the estimation accuracy of vehicle parameters and states.

Abstract Wheel polygonal wear can immensely worsen wheel/rail interactions and vibration performances of the train and track, and ultimately, lead to the shortening of service life of railway components. At present, wheel/rail medium- or high-frequency frictional interactions are perceived as an essential reason of the high-order polygonal wear of railway wheels, which are potentially resulted by the flexible deformations of the train/track system or other external excitations. In this work, the effect of wheel/rail flexibility on polygonal wear evolution of heavy-haul locomotive wheels is explored with aid of the long-term wheel polygonal wear evolution simulations, in which different flexible modeling of the heavy-haul wheel/rail coupled system is implemented. Further, the mitigation measures for the polygonal wear of heavy-haul locomotive wheels are discussed. The results point out that the evolution of polygonal wear of heavy-haul locomotive wheels can be veritably simulated with consideration of the flexible effect of both wheelset and rails. Execution of mixed-line operation of heavy-haul trains and application of multi-cut wheel re-profiling can effectively reduce the development of wheel polygonal wear. This research can provide a deep-going understanding of polygonal wear evolution mechanism of heavy-haul locomotive wheels and its mitigation measures.

Abstract The advancement of autonomous driving heavily relies on the ability to accurate lane lines detection. As deep learning and computer vision technologies evolve, a variety of deep learning-based methods for lane line detection have been proposed by researchers in the field. However, owing to the simple appearance of lane lines and the lack of distinctive features, it is easy for other objects with similar local appearances to interfere with the process of detecting lane lines. The precision of lane line detection is limited by the unpredictable quantity and diversity of lane lines. To address the aforementioned challenges, we propose a novel deep learning approach for lane line detection. This method leverages the Swin Transformer in conjunction with LaneNet (called ST-LaneNet). The experience results showed that the true positive detection rate can reach 97.53% for easy lanes and 96.83% for difficult lanes (such as scenes with severe occlusion and extreme lighting conditions), which can better accomplish the objective of detecting lane lines. In 1000 detection samples, the average detection accuracy can reach 97.83%, the average inference time per image can reach 17.8 ms, and the average number of frames per second can reach 64.8 Hz. The programming scripts and associated models for this project can be accessed openly at the following GitHub repository: https://github.com/Duane711/Lane-line-detection-ST-LaneNet .

Abstract The human digital twin (HDT) emerges as a promising human-centric technology in Industry 5.0, but challenges remain in human modeling and simulation. Digital human modeling (DHM) provides solutions for modeling and simulating human physical and cognitive aspects to support ergonomic analysis. However, it has limitations in real-time data usage, personalized services, and timely interaction. The emerging HDT concept offers new possibilities by integrating multi-source data and artificial intelligence for continuous monitoring and assessment. Hence, this paper reviews the evolution from DHM to HDT and proposes a unified HDT framework from a human factors perspective. The framework comprises the physical twin, the virtual twin, and the linkage between these two. The virtual twin integrates human modeling and AI engines to enable model-data-hybrid-enabled simulation. HDT can potentially upgrade traditional ergonomic methods to intelligent services through real-time analysis, timely feedback, and bidirectional interactions. Finally, the future perspectives of HDT for industrial applications as well as technical and social challenges are discussed. In general, this study outlines a human factors perspective on HDT for the first time, which is useful for cross-disciplinary research and human factors innovation to enhance the development of HDT in industry.

Abstract Titanium alloys are excellent structural materials in engineering fields, but their poor tribological properties limit their further applications. Electroless plating is an effective method to enhance the tribological performance of alloys, but it is difficult to efficiently apply to titanium alloys, due to titanium alloy’s strong chemical activity. In this work, the electroless Nickel-Boron (Ni-B) coating was successfully deposited on the surface of titanium alloy (Ti-6AL-4V) via a new pre-treatment process. Then, linearly reciprocating sliding wear tests were performed to evaluate the tribological behaviors of titanium alloy and its electroless Ni-B coatings. It was found that the Ni-B coatings can decrease the wear rate of the titanium alloy from 19.89×10−3 mm3 to 0.41×10−3 mm3, which attributes to the much higher hardness of Ni-B coatings. After heat treatment, the hardness of Ni-B coating further increases corresponding to coating crystallization and hard phase formation. However, heat treatment does not improve the tribological performance of Ni-B coating, due to the fact that higher brittleness and more severe oxidative wear exacerbate the damage of heat-treated coatings. Furthermore, the Ni-B coatings heat-treated both in air and nitrogen almost present the same tribological performance. The finding of this work on electroless coating would further extend the practical applications of titanium alloys in the engineering fields.

Abstract The current gait planning for legged robots is mostly based on human presets, which cannot match the flexible characteristics of natural mammals. This paper proposes a gait optimization framework for hexapod robots called Smart Gait. Smart Gait contains three modules: swing leg trajectory optimization, gait period & duty optimization, and gait sequence optimization. The full dynamics of a single leg, and the centroid dynamics of the overall robot are considered in the respective modules. The Smart Gait not only helps the robot to decrease the energy consumption when in locomotion, mostly, it enables the hexapod robot to determine its gait pattern transitions based on its current state, instead of repeating the formalistic clock-set step cycles. Our Smart Gait framework allows the hexapod robot to behave nimbly as a living animal when in 3D movements for the first time. The Smart Gait framework combines offline and online optimizations without any fussy data-driven training procedures, and it can run efficiently on board in real-time after deployment. Various experiments are carried out on the hexapod robot LittleStrong. The results show that the energy consumption is reduced by 15.9% when in locomotion. Adaptive gait patterns can be generated spontaneously both in regular and challenge environments, and when facing external interferences.

The vertical center of gravity (VCG) has a significant impact on the roll motion response of a surface ship, particularly oil tankers based on the oil level in the tanker after discharging oil at several stations or positional changes, such as changes in the superstructure and deck structure. This study examined the motion response of the Korea very large crude carrier 2 (KVLCC2) at various VCGs, especially roll motion when the VCG changed. The potential theory in the Ansys AQWA program was used as a numerical simulation method to calculate the motion response. On the other hand, the calculations obtained through potential theory overestimated the roll amplitudes during resonance and lacked precision. Therefore, roll damping is a necessary parameter that accounts for the viscosity effect by performing an experimental roll decay. The roll decay test estimated the roll damping coefficients for various VCGs using Froude’s method. The motion response of the ship in regular waves was evaluated for various VCGs using the estimated roll-damping coefficients. In addition, the reliability of the numerical simulation in motion response was verified with those of the experiment method reported elsewhere. The simulation results showed that the responses of the surge, sway, heave, pitch, and yaw motion were not affected by changing the VCG, but the natural frequency and magnitude of the peak value of the roll motion response varied with the VCG.

This study assessed the potential for crabbing motion in waterjet ships by exclusively employing stern thrusters. The theoretical considerations were validated through practical sea trials on the naval vessel PKG (Patrol Killer Guided missile) equipped with three stern thrusters. The control forces were calculated using the force equilibrium equation. The results showed that the hull exhibited rotations and lateral movements under wind influence. The port tail exhibited a leftward turning tendency due to the wind. This phenomenon arises from the dominance of the rotational force generated by the stern thruster over the lateral force exerted by the hull, making it challenging to maintain force equilibrium. In the sea trial, the hull rotated by 10° and moved 10.8 m laterally, with a longitudinal movement of 0.26 m. Remarkably, the lateral movement surpassed the longitudinal displacement, indicating the success of the trial. The substantial lateral travel distance provided tangible evidence that the crabbing motion of the ship is achievable using only stern thrusters. This study contributes valuable insights into enhancing the maneuverability of waterjet ships, offering practical applications for naval operations and maritime activities.

Waves are a complex phenomenon in marine and coastal areas, and accurate wave prediction is essential for the safety and resource management of ships at sea. In this study, three types of machine learning techniques specialized in nonlinear data processing were used to predict the waves of Korea waters. An optimized algorithm for each area is presented for performance evaluation and comparison. The optimal parameters were determined by varying the window size, and the performance was evaluated by comparing the mean absolute error (MAE). All the models showed good results when the window size was 4 or 7 d, with the gated recurrent unit (GRU) performing well in all waters. The MAE results were within 0.161 m to 0.051 m for significant wave heights and 0.491 s to 0.272 s for periods. In addition, the GRU showed higher prediction accuracy for certain data with waves greater than 3 m or 8 s, which is likely due to the number of training parameters. When conducting marine and offshore research at new locations, the results presented in this study can help ensure safety and improve work efficiency. If additional wave-related data are obtained, more accurate wave predictions will be possible.

This study examined the jack-up spudcan penetration for a new type of offshore wind substructure newly proposed using the jack-up concept to reduce construction costs. The jack-up spudcan for offshore wind turbines should be designed to penetrate a stable soil layer capable of supporting operational loads. This study evaluated multi-layered soil conditions using centrifuge tests: loose sand over clay and loose sand–clay–dense sand. The penetration resistance profiles of spudcan recorded at the centrifuge tests were compared with the ISO and InSafeJIP methods. In the tests, a spudcan punch-through effect slightly emerged under the sand-over-clay condition, and a spudcan squeezing effect occurred in the clay-over-sand layer. On the other hand, these two effects were not critically predicted using the ISO method, and the InSafeJIP result predicted only punch-through failure. Nevertheless, ISO and InSafeJIP methods were well-matched under the conditions of the clay layer beneath the sand and the penetration resistance profiles at the clay layer of centrifuge tests. Therefore, the ISO and InSafeJIP methods well predict the punch-through effect at the clay layer but have limitations for penetration resistance predictions at shallow depths and strong stratum soil below a weak layer.

Unmanned aerial vehicle-based photon-counting ocean bathymetric light detection and ranging (LiDAR) systems rapidly acquire topographic data from islands, reefs, and shallow waters. However, the bathymetric ability of seabed topography is affected by high backscattering from the sea surface owing to its proximity, and no suitable imaging models are available. Herein, we designed a novel ray approach for bathymetry based on a dual-polarization photon-counting LiDAR. Based on the transmission characteristics of light, a dual-polarization channel strategy was proposed, and the data from two channels in vegetation, sand, and shallow and medium-depth waters were compared. Based on the ray tracing method, imaging models of the water surface and depth for light and small photon-counting LiDAR were established. Shallow-water experiments were conducted near Jiajing Island, Hainan, China, to verify the accuracy of the LiDAR bathymetry data by shipborne single-beam sounding data. The results indicate that the vertical polarization channel data had a high signal-to-noise ratio in the terrestrial part, while the horizontal polarization channel had a better water surface backscatter suppression effect and strong bathymetry ability in the water part. The detectable depth was approximately 8 m in the experimental area. The MAEs of the depth values of the LiDAR point cloud before and after refraction correction relative to the single beam depth measurement values were 1.08 and 0.09 m, respectively. And the RMSEs before and after correction were 1.12 and 0.11 m, respectively.

The interferometric synthetic aperture radar (InSAR) is able to reconstruct the Earth's surface topography with a meter-level accuracy when two-dimensional phase unwrapping (PU) is properly implemented. The two-stage programming approach (TSPA) can convert the ill-posed PU problem into a well-posed problem by integrating perpendicular baseline diversity in multiple ($\ge$2) interferograms, and is currently among the most commonly used multibaseline (MB) PU algorithms. Nevertheless, TSPA still faces two challenges in real-world applications: first, TSPA cannot ensure exceptional performance for any complex topographic scenarios, and second, the PU error of short-baseline interferometric pair tends to propagate into the PU solution of long-baseline interferometric pair, degrading height accuracy. To overcome these issues, a refined TSPA (R-TSPA) MB PU algorithm is proposed in this article. R-TSPA contains two PU procedures under the framework of TSPA, where procedure 1 unwraps the flattened interferograms with TSPA, and procedure 2 re-estimates and reunwraps the erroneous ambiguity number gradients with TSPA. It is demonstrated that R-TSPA outperforms the conventional single-baseline PU algorithms and TSPA with actual InSAR datasets in western Sichuan Province and Tibet Autonomous Region of China, revealing its potentials in accurately mapping topography and broadening application scopes of InSAR.

Remote sensing image captioning (RSIC) is a crucial task in interpreting remote sensing images (RSIs), as it involves describing their content using clear and precise natural language. However, the RSIC encounters difficulties due to the intricate structure and distinctive features of the images, such as the issue of rotational ambiguity. The existence of visually alike objects or areas can result in misidentification. In addition, prioritizing groups of objects with strong relational ties during the captioning process poses a significant challenge. To address these challenges, we propose the visual rotated position encoding transformer for RSIC. First of all, rotation-invariant features and global features are extracted using a multilevel feature extraction (MFE) module. To focus on closely related rotated objects, we design a visual rotated position encoding module, which is incorporated into the transformer encoder to model directional relationships between objects. To distinguish similar features and guide caption generation, we propose a feature enhancement fusion module consisting of feature enhancement and feature fusion. The feature enhancement component adopts a self-attention mechanism to construct fully connected graphs for object features. The feature fusion component integrates global features and word vectors to guide the caption generation process. In addition, we construct an RSI rotated object detection dataset RSIC-ROD and pretrain a rotated object detector. The proposed method demonstrates significant performance improvements on four datasets, showcasing enhanced capabilities in preserving descriptive details, distinguishing similar objects, and accurately capturing object relationships.

This article proposes an unsupervised domain adaptation (UDA) method by transferring knowledge from rich labeled optical domain to unlabeled synthetic aperture radar (SAR) domain, tackling the issue that current deep-learning-based SAR target detection methods rely on abundant labeled SAR images. Specifically, we gradually encode the dependencies across different granularity perspectives including domain invariant representations (DIR) learning based on feature decomposition and domain discriminative representations (DDR) learning based on uncertainty-guided self-training. First, existing methods usually learn the DIR by directly minimizing domain discrepancy between two domains, which is difficult to achieve in practice. Due to the huge difference between the optical and SAR images, rich domain-specific characteristics bring great challenges to learn the DIR. To alleviate the above difficulty, we explicitly model the domain-invariant and domain-specific features in the representations by constructing a network with feature decomposition to better extract the DIR across domains, where only the DIR extracted from optical images and their labels are used to train the domain-shared detector in this stage. Second, even DIR can be extracted, the domain-shared detector will lose some discriminative and valuable features of the SAR domain while minimizing the distribution discrepancy between the SAR and labeled optical domain. In order to achieve the better detection performance for SAR images, a self-training method based on pseudolabels is proposed to learn DDR and train the SAR-dedicated detector. Furthermore, for ensuring the reliability of pseudolabels, we present a novel uncertainty-guided pseudolabel selection strategy, which contains two phases: one is instance uncertainty guided selection, the other is image uncertainty guided selection. Finally, based on measured optical and SAR datasets, we conduct extensive empirical evaluation to verify the effectuality of our proposed method.

Extreme precipitation events greatly threaten people's daily lives and safety, making accurate and timely precipitation estimation especially critical. However, common methods like radar and satellite remote sensing have limitations due to coverage and environmental factors. Existing deep learning models struggle with complex scenarios and multisource data correlations. These make the precipitation estimation tasks challenging. This article proposes a Multiscale Dual Cross-Attention UNet (MS-DCA-UNet) model for near-real-time precipitation estimation. It integrates Doppler weather radar and FY-4A satellite data to overcome single-source data limitations. To narrow the semantic gap among the encoder feature maps, the MS-DCA-UNet model introduces a dual-cross attention (DCA) module at the skip connections of the backbone network U-Net. The DCA module mainly employs a channel cross-attention and a spatial cross-attention to capture remote dependencies and enable multiscale feature fusion. A multiscale convolution module is designed to reduce the risk of the model falling into local optima. It is a multibranch upsampling strategy that runs parallel to the decoder. Experimental results show that the Critical Success Index (CSI), Root Mean Square Error (RMSE), and Pearson's Correlation Coefficient (CC) of MS-DCA-UNet are 0.6033, 0.5949 mm/h, and 0.8460, respectively, with the hourly CMPAS precipitation as the benchmark. These outperform the other comparisons, such as FY-4A QPE, GPM IMERG, U-Net, Attention-UNet, and DCA-UNet on the CSI, RMSE, and CC metrics. MS-DCA-UNet reduces the RMSE of Attention-UNet, UNet, and DCA-UNet by a margin of 34.68% (0.5949 mm/h versus 0.9107 mm/h), 10.24% (0.5949 mm/h versus 0.6628 mm/h), 6.96% (0.5949 mm/h versus 0.6394 mm/h), respectively.

Remote sensing semantic segmentation tasks aim to automatically extract land cover types by accurately classifying each pixel. However, large-scale hyperspectral remote sensing images possess rich spectral information, complex and diverse spatial distributions, significant scale variations, and a wide variety of land cover types with detailed features, which pose significant challenges for segmentation tasks. To overcome these challenges, this study introduces a U-shaped semantic segmentation network that combines global spectral attention and deformable Transformer for segmenting large-scale hyperspectral remote sensing images. First, convolution and global spectral attention are utilized to emphasize features with the richest spectral information, effectively extracting spectral characteristics. Second, deformable self-attention is employed to capture global-local information, addressing the complex scale and distribution of objects. Finally, deformable cross-attention is used to aggregate deep and shallow features, enabling comprehensive semantic information mining. Experiments conducted on a large-scale hyperspectral remote sensing dataset (WHU-OHS) demonstrate that: first, in different cities including Changchun, Shanghai, Guangzhou, and Karamay, DTSU-Net achieved the highest performance in terms of mIoU compared to the baseline methods, reaching 56.19%, 37.89%, 52.90%, and 63.54%, with an average improvement of 7.57% to 34.13%, respectively; second, module ablation experiments confirm the effectiveness of our proposed modules, and deformable Transformer significantly reduces training costs compared to conventional Transformers; third, our approach achieves the highest mIoU of 57.22% across the entire dataset, with a balanced trade-off between accuracy and parameter efficiency, demonstrating an improvement of 1.65% to 56.58% compared to the baseline methods.

Deep learning methods have achieved great success in the field of remote sensing image segmentation in recent years, but building a lightweight segmentation model with comprehensive local and global feature extraction capabilities remains a challenging task. In this article, we propose a convolutional neural network (CNN) and cross-shaped transformer hybrid network (CCTNet) for semantic segmentation of high-resolution remote sensing images. This model follows an encoder–decoder structure. It employs ResNet18 as an encoder to extract hierarchical feature information, and constructs a transformer decoder based on efficient cross-shaped self-attention to fully model local and global feature information and achieve lightweighting of the network. Moreover, the transformer block introduces a mixed-scale convolutional feedforward network to further enhance multiscale information extraction. Furthermore, a simplified and efficient feature aggregation module is leveraged to gradually aggregate local and global information at different stages. Extensive comparison experiments on the ISPRS Vaihingen and Potsdam datasets reveal that our method obtains superior performance compared with state-of-the-art lightweight methods.

Fine-grained remote sensing ship detection is crucial in a variety of fields, such as ship safety, marine environmental protection, and maritime traffic management. Despite recent progress, current research suffers from the following three major challenges: insufficient features representation, conflicts in shared features, and inappropriate anchor labeling strategy, which significantly impede accurate fine-grained ship detection. To address these issues, we propose FineShipNet as a solution. Specifically, we first propose a novel blend synchronization module, which aims to facilitate the coutilization of semantic information from top-level and bottom-level features and minimize information redundancy. Subsequently, the blend feature maps are fed into a novel polarized feature focusing module, which decouples the features used in classification and regression to create task-specific discriminating features maps. Meanwhile, we adopt the adaptive harmony anchor labeling and propose a novel metric, harmony score, to choose high-quality anchors that can effectively capture the discriminating features of the target. Extensive experiments on four fine-grained remote sensing ship datasets (HRSC2016, DOSR, FGSD2021, and ShipRSImageNet) demonstrate that our FineShipNet outperforms current state-of-the-art object detection methods, achieving superior performance with mean average precision scores of 81.3%, 68.5%, 85.7%, and 63.9%, respectively.

Ship detection needs to identify ship locations from remote sensing scenes. Due to different imaging payloads, various appearances of ships, and complicated background interference from the bird's eye view, it is difficult to setup a unified paradigm for achieving multisource ship detection. To address this challenge, in this article, leveraging the large language models powerful generalization ability, a unified visual-language model called Popeye is proposed for multisource ship detection from RS imagery. Specifically, to bridge the interpretation gap across the multisource images for ship detection, a novel unified labeling paradigm is designed to integrate different visual modalities and the various ship detection ways, i.e., horizontal bounding box and oriented bounding box. Subsequently, the hybrid experts encoder is designed to refine multiscale visual features, thereby enhancing visual perception. Then, a visual-language alignment method is developed for Popeye to enhance interactive comprehension ability between visual and language content. Furthermore, an instruction adaption mechanism is proposed for transferring the pretrained visual-language knowledge from the nature scene into the RS domain for multisource ship detection. In addition, the segment anything model is also seamlessly integrated into the proposed Popeye to achieve pixel-level ship segmentation without additional training costs. Finally, extensive experiments are conducted on the newly constructed ship instruction dataset named MMShip, and the results indicate that the proposed Popeye outperforms current specialist, open-vocabulary, and other visual-language models in zero-shot multisource various ship detection tasks.

The Environmental Trace Gases Monitoring Instrument 2 (EMI-02) payload, embarked on a hyperspectral observation satellite, was launched on September 7, 2021. The satellite's transit time is 10:30 local time. This article, drawing on data from EMI-02, introduces a methodology for measuring the tropical tropospheric ozone column (TOC) using the convective-cloud-differential method. We successfully applied this method to derive TOC in tropical regions from the EMI-02 data. The results indicate a general consistency between the EMI-02 TOC and TROPOspheric Monitoring Instrument (TROPOMI) TOC. However, owing to the difference in transit times, the TOC observed by TROPOMI at 13:30 is higher than that observed by EMI-02 at 10:30. Comparisons with ozonesonde data revealed discrepancies attributed to varying observation times, but nonetheless presented a high degree of consistency. These findings affirm the efficacy of our algorithm in providing reliable and precise TOC data. This capability is instrumental in understanding the diurnal variations of tropical TOC, where concentrations are higher in the afternoon than in the morning. The characteristics of tropical TOC varied by month. From June to October, the tropical TOC predominantly accumulated over the South Atlantic region, whereas, it tended to disperse and distribute near the 20°N latitude from November to May.

Water hyacinth, an invasive species originating from South America, has become a significant concern since its introduction in Lake Victoria (Kenya), particularly in the Winam Gulf, where large annual blooms are observed. Monitoring the occurrence and location using in situ methods is expensive and challenging due to the lake's vastness. Remote sensing monitoring methods offer an alternate option due to the ability to cover vast areas. This study explores the potential of polarimetric synthetic aperture radar (PolSAR), specifically utilising Sentinel-1 VV-VH data to map and monitor water hyacinth cover. The change detection method based on optimization of power difference and minimum eigenvalue selection achieves a remarkable accuracy of 98.89% in separating clear and water hyacinth-infested water. Using polarimetric data offered better separability, enabling spatial and temporal monitoring. The analysis reveals that in 2018 water hyacinth cover peaked, spanning over 200 km$^{2}$. Temporal variability showcases a seasonal rise and peak from September to December. This research demonstrates the capability of using PolSAR data to accurately map and monitor water hyacinth's spatial and temporal dynamics, offering valuable insights for effective management strategies.

Lake surface water temperature (LSWT) is a crucial ecological indicator, impacting water quality, and aquatic life. Understanding its spatiotemporal trends and driving mechanisms is fundamental for lake water environment protection and management. Previous research has been limited by low-resolution satellite data and numerical simulations, hindering in-depth understanding of LSWT. This article fills the research gap by reconstructing a high-resolution LSWT dataset spanning 2000 to 2020. Employing data fusion techniques, we combined moderate resolution imaging spectroradiometer (MODIS) and Landsat observations, achieving a spatial resolution of 30 m and a revisit cycle of eight days. Seven major lakes in Yunnan Province, China, varying in urbanization intensity, were selected to investigate the impacts and mechanisms of urbanization and climate change on LSWT. The results showed that: First, the high spatiotemporal LSWT dataset reconstructed on the ubESTARFM data fusion model outperformed the existing product datasets in terms of accuracy evaluation and spatial details. Over the past 20 years, all LSWT in the study area exhibited a warming trend in both temporal and spatial dimensions; lakes in basins with higher urbanization intensity had significantly higher warming rates than the warming rates of near-surface air temperature, and the lakes showed a global warming trend. Second, the warming trend of LSWT is not only related to lake morphology and climate change, but also closely associated with urbanization; higher spatiotemporal resolution LSWT data revealed better spatiotemporal correlations between urbanization and LSWT. Third, active ecological management and enhanced watershed vegetation coverage could effectively mitigate the rate of lake warming.

Semantic segmentation is a fundamental task in remote sensing image analysis. The accurate delineation of objects within such imagery serves as the cornerstone for a wide range of applications. To address this issue, edge detection, cross-modal data, large intraclass variability, and limited interclass variance must be considered. Traditional convolutional-neural-network-based models are notably constrained by their local receptive fields, Nowadays, transformer-based methods show great potential to learn features globally, while they ignore positional cues easily and are still unable to cope with multimodal data. Therefore, this work proposes an adjacent-scale multimodal fusion network (ASMFNet) for semantic segmentation of remote sensing data. ASMFNet stands out not only for its innovative interaction mechanism across adjacent-scale features, effectively capturing contextual cues while maintaining low computational complexity but also for its remarkable cross-modal capability. It seamlessly integrates different modalities, enriching feature representation. Its hierarchical scale attention (HSA) module bolsters the association between ground objects and their surrounding scenes through learning discriminative features at higher level abstractions, thereby linking the broad structural information. Adaptive modality fusion module is equipped by HSA with valuable insights into the interrelationships between cross-model data, and it assigns spatial weights at the pixel level and seamlessly integrates them into channel features to enhance fusion representation through an evaluation of modality importance via feature concatenation and filtering. Extensive experiments on representative remote sensing semantic segmentation datasets, including the ISPRS Vaihingen and Potsdam datasets, confirm the impressive performance of the proposed ASMFNet.

Soil particle size is an important indicator in soil systems, it can provide important assistance for the agricultural work. In order to address the weakness of traditional soil particle size measuring work, which are time-consuming, labor-intensive, and have limited applicability. This study utilizes the Mie theory and the Kubelka–Munk theory as the precondition, establish an empirical formula between the scattering power and the soil particle size. The study collected surface soil samples from Nong'an, Changchun City, Jilin Province, including black soil, brown soil, sandy soil, and each saline sample, based on visible and near-infrared spectroscopy. Prepare soil samples with a particle size range of 2.5–0.15 mm through drying, grinding, and sieving operations, combining scattering power parameters in the K-M theory to construct an empirical formula for it and soil particle. After verified by comparing different empirical formulas are suitable for the measured data, assume the inverse proportion formula added correction term is the most appropriate. The conclusion is there is a strong linear relationship between the scattering power and the reciprocal of particle size. The average fitting accuracy of the 400–2400 nm wavelength band reaches 94.45%, root mean square error ($\text{RMSE}$) reaches 0.0354 mm. After removing outliers, the fitting accuracy can reach up to 95.77%, $\text{RMSE}$up to 0.0337 mm. Proved there is a very high analytical relationship between soil particle size and scattering power parameters in K-M theory. The empirical formula also can find supported by Mie theory and S-shape R(D) function, and has a high transferability from the laboratory to Landsat8 satellite board, the accuracy can reach to about 90% on SWIR band, showed good generalization ability.

Hyperspectral image (HSI) is pivotal in ground object classification, owing to its rich spatial and spectral information. Recently, convolutional neural networks and graph neural networks have become hotspots in HSI classification. Although various methods have been developed, the problem of detail loss may still exist when extracting complex features within homogenous regions. To solve this issue, in this article, we proposed a double-hop graph attention multiview fusion network. This model is adept at pinpointing precise attention features by integrating a double-hop graph with the graph attention network, thereby enhancing the aggregation of multilevel node information and surmounting the limitations of a restricted receptive field. Furthermore, the spectral-coordinate attention module (SCAM) is presented to seize more nuanced spectral and spatial attention features. SCAM harnesses the coordinate attention mechanism for in-depth pixel-level global spectral–spatial view. Coupled with the multiscale Gabor texture view, we forge a multiview fusion network that meticulously highlights edge details across varying scales and captures beneficial features. Our experimental validation across four renowned benchmark HSI datasets showcases our model's superiority, outstripping comparative methods in classification accuracy with limited labeled samples.

The rapid pace of urbanization underscores the importance of waste monitoring and management in urban planning and environmental conservation. Remote sensing technology enables the aerial observation of terrestrial and marine features, with high-resolution images revealing diverse objects. Deep learning techniques have gained prominence for enhancing waste monitoring precision and efficiency. This article surveys deep learning approaches for waste monitoring in remote sensing images, focusing on relevant datasets. It reviews existing remote sensing datasets, including those from uncrewed aerial vehicles and satellites, for monitoring solid waste and marine debris. Nine publicly available datasets are described in detail, highlighting their origins and applications. The monitoring methods include two kinds of methods: 1) semantic segmentation; and 2) object detection. Semantic segmentation focuses on pixel-level classification and boundary delineation, while object detection targets object-level localization and shape. Representative methods within these categories are explored, and benchmark results from recent studies are summarized to evaluate the performance of various techniques. The discussion addresses current limitations and suggests future research directions, aiming to assist researchers and professionals in environmental monitoring.

High resolution remote sensing imagery plays a crucial role in monitoring coastal wetlands. Coastal wetland landscapes exhibit diverse features, ranging from fragmented patches to expansive areas. Mainstream convolutional neural networks cannot effectively analyze spatial relationships among consecutive image elements. This limitation impedes their performance in accurately classifying coastal wetlands. In order to tackle the above issues, we propose a Vision Transformer based UNet (ViT-UNet) model. This model extracts wetland features from high resolution remote sensing images by sensing and optimizing multiscale features. To establish global dependencies, the Vision Transformer (ViT) is introduced to replace the convolutional layer in the UNet encoder. Simultaneously, the model incorporates a convolutional block attention module and a multiple hierarchies attention module to restore attentional features and reduce feature loss. In addition, a skip connection is added to the single-skip structure of the original UNet model. This connection simultaneously links the output of the entire transformer and internal attention features to the corresponding decoder level. This enhancement aims to furnish the decoder with comprehensive global information guidance. Finally, all the extracted feature information is fused using Bilinear Polymerization Pooling (BPP). The BPP assists the network in obtaining a more comprehensive and detailed feature representation. Experimental results on the Gaofen-1 dataset demonstrate that the proposed ViT-UNet method achieves a Precision score of 93.50$\%$, outperforming the original UNet model by 4.10$\%$. Compared with other state-of-the-art networks, ViT-UNet performs more accurately and finer in the extraction of wetland information in the Yellow River Delta.

Ground-penetrating radar (GPR) data are often contaminated by hardware and environmental clutter, which significantly affects the accuracy and reliability of target response identification. Existing supervised deep learning techniques for removing clutter in GPR data require generating a large set of clutter-free B-scans as labels for training, which are computationally expensive in simulation and unfeasible in real-world experiments. To tackle this issue, we propose a two-stage unsupervised learning-based clutter removal scheme, called ULCR-Net, to obtain clutter-free GPR B-scans. In the first stage of the proposed scheme, a diffusion model tailored for GPR data augmentation is employed to generate a diverse set of raw B-scans from the input random noise. With the augmented dataset, the second stage of the proposed scheme uses a contrastive learning-based generative adversarial network to learn and estimate clutter patterns in the raw B-scan. The clutter-free B-scan is then obtained by subtracting the clutter pattern from the raw B-scan. The training of the two-stage network only requires a small set of raw B-scans and clutter-only B-scans that are readily available in real-world applications. Extensive experiments have been conducted to validate the effectiveness of the proposed method. Results on simulation and measurement data demonstrate that the proposed method has superior clutter removal accuracy and generalizability and outperforms existing algebraic techniques and supervised learning-based methods with limited training data by a large margin. With its high clutter suppression capability and low training data requirements, the proposed method is well-suited to remove clutter and restore target responses in real-world GPR applications.