Rate of penetration (ROP) is a key factor in drilling optimization, cost reduction and drilling cycle shortening. Due to the systematicity, complexity and uncertainty of drilling operations, however, it has always been a problem to establish a highly accurate and interpretable ROP prediction model to guide and optimize drilling operations. To solve this problem in the Tarim Basin, this study proposes four categories of hybrid physics-machine learning (ML) methods for modeling. One of which is residual modeling, in which an ML model learns to predict errors or residuals, via a physical model; the second is integrated coupling, in which the output of the physical model is used as an input to the ML model; the third is simple average, in which predictions from both the physical model and the ML model are combined; and the last is bootstrap aggregating (bagging), which follows the idea of ensemble learning to combine different physical models’ advantages. A total of 5655 real data points from the Halahatang oil field were used to test the performance of the various models. The results showed that the residual modeling model, with an R2 of 0.9936, had the best performance, followed by the simple average model and bagging with R2 values of 0.9394 and 0.5998, respectively. From the view of prediction accuracy, and model interpretability, the hybrid physics-ML model with residual modeling is the optimal method for ROP prediction.

Viewers of 360-degree videos are provided with both visual modality to characterize their surrounding views and audio modality to indicate the sound direction. Though both modalities are important for saliency prediction, little work has been done by jointly exploiting them, which is mainly due to the lack of audio-visual saliency datasets and insufficient exploitation of the multi-modality. In this article, we first construct an audio-visual saliency dataset with 57 360-degree videos watched by 63 viewers. Through a deep analysis of the constructed dataset, we find that the human gaze can be attracted by the auditory cues, resulting in a more concentrated saliency map if the sound source's location is further provided. To jointly exploit the visual and audio features and their correlation, we further design a saliency prediction network for 360-degree videos (SVGC-AVA) based on spherical vector-based graph convolution and audio-visual attention. The proposed spherical vector-based graph convolution can process visual and audio features directly in the sphere domain, thus avoiding projection distortion incurred by traditional CNN-based predictors. In addition, the audio-visual attention scheme explores self-modal and cross-modal correlation for both modalities, which are further hierarchically processed with the U-Net's multi-scale structure of SVGC-AVA. Evaluations on both our and public datasets validate that SVGC-AVA can achieve higher prediction accuracy, both qualitatively and subjectively.

The prediction of fuel consumption and Carbon Intensity Index (CII) of ships is crucial for optimizing decarbonization strategies in the maritime industry. This study proposes a ship fuel consumption prediction model based on the Long Short-Term Memory with Self-Attention Mechanism (SA-LSTM). The model is applied to a container ship of 2400 TEU to predict its hourly fuel consumption, hourly CII, and annual CII rating. Four different feature sets are selected from these data sources and are used as inputs for SA-LSTM and another ten models. The results demonstrate that the SA-LSTM model outperforms the other models in prediction accuracy. Specifically, the Mean Absolute Percentage Error (MAPE) for fuel consumption predictions using the SA-LSTM model is reduced by up to 20% compared to the XGBoost and by up to 12% compared to the LSTM model. Additionally, the SA-LSTM model achieves the highest accuracy in annual CII predictions.

The viscoelastic tissue under dual-frequency ultrasound excitation affects the acoustic cavitation of a single gas-vapor bubble. To investigate the effect of the cavitation dynamics, the Gilmore-Akulichev-Zener (GAZ) model is coupled with the Peng-Robinson equation of state (PR EOS). Results indicate that the GAZ-PR EOS model can accurately estimate the bubble dynamics by comparing with the Gilmore PR EOS and GAZ-Van der Waals (VDW) EOS model. Furthermore, the acoustic cavitation effect in different viscoelastic tissues is investigated, including the radial stress at the bubble wall, the temperature, pressure, and the number of water molecules inside the bubble. Results show that the creep recovery and the relaxation of the stress caused by viscoelasticity can affect the acoustic cavitation of the bubble, which could inhibit the bubble's expansion and reduce the internal temperature and pressure within the bubble. Moreover, the effect of dual-frequency ultrasound on the cavitation of single gas-vapor bubbles is studied. Results suggest that dual-frequency ultrasound could increase the internal temperature of bubbles, the internal pressure of bubbles, and the radial stress at the bubble wall. More importantly, there is a specific optimal combination of frequencies for particular viscoelasticity by exploring the impact of different dual-frequency ultrasound combinations and tissue viscoelasticity on the acoustic cavitation of a single gas-vapor bubble. In conclusion, this study helps to provide theoretical guidance for dual-frequency ultrasound to improve acoustic chemical and mechanical effects, and further optimize its application in acoustic sonochemistry and ultrasound therapy.

The remote control ship is considered to be the most likely implementation of maritime autonomous surface ships (MASS) in the near-term future. With collaborative control from onboard controllers and operators ashore, ships may operate in three navigation control modes (NCMs), manual, autonomous, and remote control, based on different levels of control authority. The scientific selection of the appropriate NCM for MASS under multiple driving modes is crucial for ensuring ship navigation safety and holds significant importance for operators and regulatory authorities overseeing maritime traffic within specific areas. To aid in selecting the proper NCM, this study introduces a risk-based comparison method for determining optimal control modes in specific scenarios. Firstly, safety control paths and processes for MASS under different NCMs are constructed and analyzed using system-theoretic process analysis (STPA). By analyzing unsafe system control actions, key Risk Influencing Factors (RIFs) and their interrelationships are identified. Secondly, a Hidden Markov Model (HMM) process risk assessment model is developed to infer risk performance (hidden state) through measuring RIF states. Cloud modeling with expert judgments is utilized to parameterize the HMM while addressing inherent uncertainty. Lastly, the applicability of the proposed framework was verified through simulation case studies. Typical navigation scenarios of conventional ships in coastal waters were chosen, and real-time data collected by relevant sensors during navigation were used as simulation inputs. Results suggest that in the same scenario, process risks differ among the analyzed NCMs. Traffic complexity, traffic density, and current become the primary factors influencing navigation risks, and it is necessary to select the appropriate NCM based on their real-time changes.

Many-objective optimization problem is one of the most important and widely faced optimization problems in the real world. To solve many-objective optimization problems (MaOPs), numerous multi-objective evolutionary algorithms (MOEAs) have been developed to find a good convergence and well-distributed Pareto front. However, with the increase of dimensions, the distribution of solutions obtained by MOEAs becomes more complex and tends to be orthogonal, which significantly reduces the effectiveness of the algorithms. In this paper, we propose an improved many-objective evolutionary algorithm (MaOEA-MSAR), which incorporates a multi-strategy selection mechanism into an existing MOEA, and develops an adaptive reproduction operation to produce promising offspring individuals. Firstly, the selection strategy based on the angle-penalized distance is used to improve the coverage of the solutions in the objective space. Then, the selection strategy based on convergence rate is employed to strengthen the balance between diversity and convergence. Finally, an adaptive reproduction operation is used to select different reproduction strategies for the gene-level global exploration or local exploitation. A series of experiments are carried out against seven state-of-the-art many-objective optimization algorithms. Experimental results on commonly used 31 benchmark test problems with up to 15 objectives and a multi-objective vehicle routing problem have demonstrated that MaOEA-MSAR is competitive in handling various kinds of MaOPs.

Marine traffic safety for Maritime Autonomous Surface Ship (MASS)is affected by the maritime environment and complex human-machine technical systems, and it is necessary to reveal its risk evolution characteristics involving new technology. The novel risk evolution model based on the system-theoretic process analysis (STPA)method for specific scenarios is established to determine the risk mechanism in random processes. First, by combining Markov chain (MC) with cloud model based on the information transmission path, the Markov process hypothesis is proposed to devise a coupling effect model of system components and external interference environment. Second, operation and control modes for MASS under different scenarios based on STPA are constructed and analyzed, which are based on clarifications regarding two models for the parallel control of MASS. Third, a systemic risk model using safety control methodology is constructed considering the operation scenario of MASS. Last and not least, the risk performance in the MASS navigation process is simulated to reveal the emergence characteristics of ship navigation process risk after applying specific scenarios under three modes. Application examples show that the process risk emergence of MASS navigation is characterized by the dependence on safety information transmission path. Different transmission paths lead to inconsistent risk evolution results. The randomness and complexity of external environmental disturbances are the main factors involved in the formation of navigation risks in MASS.

A proton exchange membrane fuel cell (PEMFC) has great application prospects due to its low emission and high efficiency. An accurate model to predict the dynamic output voltage is essential for the optimal control of the PEMFC for the applications on vehicles and power stations. In this article, a novel deep learning framework with the long short-term memory (LSTM) and artificial neural network (ANN) fusion is proposed to develop the PEMFC dynamic model by extracting both the historical and current information. The LSTM extracts the temporal information from the past PEMFC states with its order determined with autocorrelation and partial autocorrelation functions, while the influence of the system current inputs is learnt by the ANN. Then, the outputs of LSTM and ANN are concatenated with the multiple information fused to predict the PEMFC dynamic output voltage. After validated by the operating data from a laboratory-scale PEMFC system, the LSTM and ANN fusion model is compared with the existing models, such as support vector regression, ANN, and LSTM methods. The comparison results show that the proposed LSTM and ANN fusion model can provide the best prediction performance with the lowest mean square error of 1.303. The proposed LSTM and ANN fusion model can be helpful to develop the optimal control strategy of the PEMFC.

As a critical structure of aerospace equipment, aluminum alloy stiffened plate will influence the stability of spacecraft in orbit and the normal operation of the system. In this study, a GWO-ELM algorithm-based impact damage identification method is proposed for aluminum alloy stiffened panels to monitor and evaluate the damage condition of such stiffened panels of spacecraft. Firstly, together with numerical simulation, the experimental simulation to obtain the damage acoustic emission signals of aluminum alloy reinforced panels is performed, to establish the damage data. Subsequently, the amplitude-frequency characteristics of impact damage signals are extracted and put into an extreme learning machine (ELM) model to identify the impact location and damage degree, and the Gray Wolf Optimization (GWO) algorithm is employed to update the weight parameters of the model. Finally, experiments are conducted on the irregular aluminum alloy stiffened plate with the size of 2200 mm × 500 mm × 10 mm, the identification accuracy of impact position and damage degree is 98.90% and 99.55% in 68 test areas, respectively. Comparative experiments with ELM and backpropagation neural networks (BPNN) demonstrate that the impact damage identification of aluminum alloy stiffened plate based on GWO-ELM algorithm can serve as an effective way to monitor spacecraft structural damage.

With the growing concern for environmental sustainability and the need to mitigate climate change, accurately tracking carbon footprints has become crucial. This paper explores the use of NILM technology for carbon footprint tracking at the household level. A carbon footprint tracking model is proposed based on NILM technology. On this basis, the relationship between carbon footprint and NILM technology is explored. An example is designed to verify the validity of NILM technology in calculating carbon footprint. The simulation results show that the NILM technology can be used to track the carbon footprint at the household. Finally, the paper is summarized.

The impairment of antibody-mediated immunity is a major factor associated with fatal cases of severe fever with thrombocytopenia syndrome (SFTS). By collating the clinical diagnosis reports of 30 SFTS cases, we discovered the overprolifera-tion of monoclonal plasma cells (MCP cells, CD38+cLambda+cKappa-) in bone marrow, which has only been reported previously in multiple myeloma. The ratio of CD38+cLambda+ versus CD38+cKappa+ in SFTS cases with MCP cells was significantly higher than that in normal cases. MCP cells presented transient expression in the bone marrow, which was distinctly different from multiple myeloma. Moreover, the SFTS patients with MCP cells had higher clinical severity. Further, the overproliferation of MCP cells was also observed in SFTS virus (SFTSV)-infected mice with lethal infectious doses. Together, SFTSV infec-tion induces transient overproliferation of monoclonal lambda-type plasma cells, which have important implications for the study of SFTSV pathogenesis, prog-nosis, and the rational development of therapeutics.

Locating and maintaining multiple Pareto optimal sets (PSs) in the decision space simultaneously is a challenging issue in solving multimodal multiobjective optimization problems (MMOPs). To deal with this challenge, this paper proposed a multipopulation particle swarm optimization based on divergent guidance and knowledge transfer (MPPSO-DGKT). First, a divergent guidance strategy is proposed to utilize the information of superior and inferior particles in the subpopulation. This strategy can alleviate the premature convergence due to the excessive influence of the global Pareto optimal solutions found so far. Second, a knowledge transfer strategy is developed to promote the knowledge transfer between different subpopulations, which can enhance the exploitation ability of the population. Finally, the update and selection strategy is used to keep more promising nondominated solutions, which can help the algorithm to obtain global and local PSs. To verify the effectiveness of the proposed algorithm, MPPSO-DGKT is compared with seven state-of-the-art multimodal multiobjective optimization algorithms on CEC2020 competition. Experimental results indicate that the proposed algorithm is more competitive than its competitors when solving MMOPs with both global and local PSs.

In the actual production and life process, there are a plenty of time-varying linear systems. In this paper, ILC is extended to linear time-varying systems, and the proposed ILC update law is adopted to update the time-varying model, and the convergence performance of the algorithm is proved. Simpson integral method has an advantage in calculating the variable parameters to solve time-varying systems. Combining the extreme learning machine(ELM) in machine learning with ILC, the regression technology is employed to train the model to update the time-varying model parameters over time. Finally, the feasibility of the proposed algorithm is demonstrated by simulation.

Collision risk in ship pilotage process has complex characteristics that are dynamic, uncertain, and emergent. To reveal collision risk resonance during ship pilotage process, a hybrid probabilistic risk analysis approach is proposed, which integrates the Functional Resonance Analysis Method (FRAM), Dempster–Shafer (D–S) evidence theory, and Monte Carlo (MC) simulation. First, FRAM is used to qualitatively describe the coupling relationship and operation mechanism among the functions of the pilotage operation system. Then, the D–S evidence theory is used to determine the probability distribution of the function output in the specified pilotage scenario after quantitatively expressing the function variability, coupling effect, and the influence of operation conditions through rating scales. Finally, MC simulation is used to calculate the aggregated coupling variability between functions, and the critical couplings and risk resonance paths under different scenarios are identified by setting the threshold and confidence level. The results show that ship collision risk transmission is caused by function resonance in the pilotage system, and the function resonance paths vary with pilotage scenarios. The critical coupling ‘F2-F7(I)’ emerges as a consistent factor in both scenarios, emphasizing the significance of maintaining a proper lookout. The hybrid probabilistic risk analytical approach to ship pilotage risk resonance with FRAM can be a useful method for analysing the causative mechanism of ship operational risk.

To solve the problem that the parameter setting of the NDT point cloud registration algorithm requires considerable experience and it easily falls into local extremum when the initial poses between the point cloud to be registered with the target point cloud differ greatly, the point cloud registration algorithm fusing PCA and NDT is proposed. Firstly, PCA method is used to calculate the principal axis directions of two groups of point clouds, and the initial rigid body transformation matrix is acquired. Then, the problem of principal axis inversion is corrected by using the minimum condition of Euclidean distance mean square error when the principal axis of two groups of point clouds are in the same direction. The correct initial transformation matrix after the principal axis correction is calculated, and the coarse registration of point clouds is completed. To reduce the number of processing point, the two groups of point clouds are sampled under the grid. The rigid body transformation matrix acquired by rough registration is used as the initial position and orientation of the NDT registration algorithm for the point cloud precise registration to obtain the final rigid body transformation matrix. The registration experiment was carried out using the Stanford public database point cloud. The result of this experiment shows that the rotation angle error of the point cloud registration algorithm fusing PCA and NDT reaches 10 -3 rad, and the translation component error is better than 0.01 mm. The proposed point cloud registration algorithm can effectively solve the initial position and attitude greater difference between point clouds, and it also can solve the problem of falling into local extremum during NDT registration. The registration accuracy basically meets the point cloud registration requirements, which can achieve fast and stable registration result with two groups of point clouds under any position and attitude.

In this paper, third-harmonic phase velocity matching nonlinear Lamb waves are used for the early fatigue damage detection of aluminum alloys. An analysis is conducted regarding the relationship of third harmonic and third-order nonlinear parameters with fatigue accumulation. According to the analytical results, the amplitude of the third harmonic increases when the fatigue life falls below about 80%. However, when the fatigue life exceeds 80%, the third harmonic starts to decrease, and the third-order nonlinear parameters increase progressively before showing a downward trend. It can be found out that micro-cracks emerge with the accumulation of fa-tigue, thus reducing the amplitude of the third harmonic and causing the third-order nonlinear parameters to increase gradually and then decline. Furthermore, it is proposed to take the third harmonic amplitude and the third order nonlinear parameters as the indicators of early fatigue damage.

State-of-the-art learning-based stability control methods for nonlinear robotic systems suffer from the issue of reality gap, which stems from discrepancy of the system dynamics between training and target (test) environments. To mitigate this gap, we propose an adversarially robust neural Lyapunov control (ARNLC) method to improve the robustness and generalization capabilities for Lyapunov theory-based stability control. Specifically, inspired by adversarial learning, we introduce an adversary to simulate the dynamics discrepancy, which is learned through deep reinforcement learning to generate the worst-case perturbations during the controller’s training. By alternatively updating the controller to minimize the perturbed Lyapunov risk and the adversary to deviate the controller from its objective, the learned control policy enjoys a theoretical guarantee of stability. Empirical evaluations on five stability control tasks with the uniform and worst-case perturbations demonstrate that ARNLC not only accelerates the convergence to asymptotic stability, but can generalize better in the entire perturbation space.

In nonlinear ultrasonic damage detection, the appropriate Lamb wave mode to accurately characterise the nonlinearity attributed to the material damage should be selected important. However, an effective mode is more difficult to select practically as impacted by the dispersion and multi-mode characteristic of the Lamb wave. In this study, based on the dispersion curve of Lamb waves, the modes meeting the second harmonics and third harmonics phase velocity matching were given, and the nonlinear cumulative effects exerted by different modes on fatigue cracks were compared. In addition, an investigation was conducted on the amplitude change of the second and third harmonics signal of the identical fatigue damage. The results show that the harmonic amplitude decreases with the increase of the fatigue crack length. The third harmonics under the two modes are more sensitive to fatigue damage than the second harmonics. Moreover, the amplitude of the third harmonic excited by S1 mode exceeds that of the other mode. As the fatigue crack length increases, the relative second- and third-order nonlinear coefficients generated by S1 mode have approximately the same trend, first increasing and then decreasing. The results reveal that the third harmonic under S1 mode could more effectively characterise fatigue damage.

With the rapid development of electric vehicles, electric vehicle battery health diagnosis has become a hot issue. In order to realize online battery health diagnosis, an online battery health diagnosis platform based on DTW-XGBoost was proposed. The feature extraction method of multi-source data fusion based on clustering was adopted. DTW clustering was used to perform data aggregation and feature extraction for real-time battery data during charging process, and XGBoost algorithm was used to establish SOH prediction model. Build an online battery health diagnosis platform including acquisition and control module, modeling and analysis module and application service module by using cloud platform to improve charging operation and maintenance management level.

During the flight of the aircraft, it is necessary to monitor the maximum principal strain of the aircraft's own cabin or tank. For the acquisition of the maximum principal strain, the fiber Bragg grating strain sensor is often pasted on the surface of the object to be measured in the mode of three-directional strain rosettes. The relevant measurement results are brought into the calculation formula to obtain the maximum principal strain. Aiming at the current situation that when a large number of fiber Bragg grating strain sensors are pasted on a large structure, it cannot be calibrated and can only be calibrated with empirical values. According to the large error source introduced by the sensor in the actual mounting process, a method for obtaining high-consistency strain of the fiber grating strain sensor is presented, which greatly improves the strain measurement accuracy of the fiber Bragg grating strain sensor on large structures.