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Fault tree analysis (FTA) is a prominent reliability analysis method, which is widely used in safety-critical industries. Computing the minimal cut sets (MCSs) of a fault tree, i.e., finding all the smallest combinations of the basic events that cause system failures, is a fundamental step in FTA. Since coherent fault trees are the most common in industrial systems in practice, they are the focus of this article. Computing MCSs is a computationally hard problem. Classical methods have been proposed based on manipulation of Boolean expressions and binary decision diagrams. However, given the inherent intractability of computing MCSs in practice, there are still limitations on time and memory in these methods. Therefore, developing new methods over different paradigms remains to be an interesting research direction. In this article, motivated by recent progress on modern Boolean satisfiability problem (SAT) solvers, we present a new method for computing MCSs based on SAT, namely SATMCS . Specifically, given a fault tree, we iteratively search for a cut set based on the conflict-driven clause learning framework. By exploiting local propagation graph, which characterizes the partial failure propagation based on the cut set, we provide efficient algorithms for extracting an MCS. The new MCS is learned as a block clause for SAT solving, and the conflict clauses in iterations are incrementally recorded, which helps to prune search space and ensures completeness of the results. Moreover, we adopt a jump-chronological backtracking strategy to prepare the next iteration, which allows for reusing the same search steps in SAT solving. We compare SATMCS with state-of-the-art commercial tools on practical fault trees. Although SATMCS is only a prototype, it shows comparable performance in time consumption with one tool ( XFTA ), and in various cases, it outperforms the others ( FaultTree + and Commander ). Besides, SATMCS exhibits much better performance on memory usage than these tools. Specifically, SATMCS consumes about one order of magnitude less memory usage in most instances.

A fusion algorithm is proposed for the restoration and enhancement of underwater images. Color balance, contrast optimization and histogram stretching are carried out. To alleviate the effect of color shift in an underwater image, the scalar values of R, G, B channels are renewed so that the distributions of the three channels in histogram are similar. Instead of refining the transmittance in dark channel prior based restoration, an optimized contrast algorithm is employed by which the optimal transmittance is determined. To further improve the brightness and contrast of underwater images, a histogram stretching algorithm based on the red channel is given. To verify the effectiveness of the proposed fusion algorithm, experimental underwater images are treated. Results show that the quality of underwater images is improved significantly, both in term of subjective visual effect and objective evaluation. The proposed underwater image processing strategy is also compared with some popular techniques. Comparison results indicate the advantage of the proposed strategy over others.

The YOLOv4 neural network is employed for underwater target recognition. To improve the accuracy and speed of recognition, the structure of YOLOv4 is modified by replacing the upsampling module with a deconvolution module and by incorporating depthwise separable convolution into the network. Moreover, the training set used in the YOLO network is preprocessed by using a modified mosaic augmentation, in which the gray world algorithm is used to derive two images when performing mosaic augmentation. The recognition results and the comparison with the other target detectors demonstrate the effectiveness of the proposed YOLOv4 structure and the method of data preprocessing. According to both subjective and objective evaluation, the proposed target recognition strategy can effectively improve the accuracy and speed of underwater target recognition and reduce the requirement of hardware performance as well.

Lithium-ion batteries (LIBs) are increasingly popular for electric vehicle and grid storage applications, but degradation mechanisms remain poorly understood and are difficult to characterize accurately. Here a new method for in situ monitoring of internal degradation in LIBs using an electrochemical model (EM) is introduced. The main contributions of this work can be summarized as follows: (1) An EM is developed based on a single particle model, the parameters of which are reorganized from the original physical property parameters for convenience of identification, with each new parameter assigned a specific physical meaning. (2) Identification methods for all parameters are determined through activation-and-response analysis, and a combined load profile for parameter identification is developed. (3) Cyclic aging experiments are carried out and the aforementioned model and parameter identification methods are applied at appropriate intervals. The predicted trends of 7 (internal resistance included) of the 10 model parameters show strong correlations with the experimentally observed degradation.

In goal-oriented requirements engineering, goal conflict identification is of fundamental importance for requirements analysis. The task aims to find the feasible situations which make the goals diverge within the domain, called boundary conditions (BCs). However, the existing approaches for goal conflict identification fail to find sufficient BCs and general BCs which cover more combinations of circumstances. From the BCs found by these existing approaches, we have observed an interesting phenomenon that there are some pairs of BCs are similar in formula structure, which occurs frequently in the experimental cases. In other words, once a BC is found, a new BC may be discovered quickly by slightly changing the former. It inspires us to develop a local search algorithm named LOGION to find BCs, in which the structural similarity is captured by the neighborhood relation of formulae. Based on structural similarity, LOGION can find a lot of BCs in a short time. Moreover, due to the large number of BCs identified, it potentially selects more general BCs from them. By taking experiments on a set of cases, we show that LOG I ON effectively exploits the structural similarity of BCs. We also compare our algorithm against the two state-of-the-art approaches. The experimental results show that LOGION produces one order of magnitude more BCs than the state-of-the-art approaches and confirm that LOGION finds out more general BCs thanks to a large number of BCs.

The hydrodynamic forces of KVLCC2 ship sailing near inclined banks are calculated by using CFD based on RANS equation. Corresponding CFD uncertainty analysis is conducted according to the procedure recommended by ITTC. An unstructured grid, tetrahedral grid, is employed for discretization. To control the number of grids, global element scale factor is selected as the same as refinement ratio. In numerical simulation, straightforward and oblique navigation conditions are investigated. The variation of transverse force and yaw moment with the ship-shore distance, bank angle, water depth, and drift angle are analyzed. Both hull model and hull-propeller-rudder model are considered in numerical simulation. The simulation results show the hydrodynamic characteristics of ship sailing near inclined banks.

System identification provides an effective way to predict the ship manoeuvrability. In this paper several measures are proposed to diminish the parameter drift in the parametric identification of ship manoeuvring models. The drift of linear hydrodynamic coefficients can be accounted for from the point of view of dynamic cancellation, while the drift of nonlinear hydrodynamic coefficients is explained from the point of view of regression analysis. To diminish the parameter drift, reconstruction of the samples and modification of the mathematical model of ship manoeuvring motion are carried out. Difference method and the method of additional excitation are proposed to reconstruct the samples. Using correlation analysis, the structure of a manoeuvring model is simplified. Combined with the measures proposed, support vector machines based identification is employed to determine the hydrodynamic coefficients in a modified Abkowitz model. Experimental data from the free-running model tests of a KVLCC2 ship are analyzed and the hydrodynamic coefficients are identified. Based on the regressive model, simulation of manoeuvres is conducted. Comparison between the simulation results and the experimental results demonstrates the validity of the proposed measures.

To reduce the ship roll motion, an adaptive robust fin controller based on a feedforward neural network is proposed. The dynamics of the fin actuator is considered in the plant of a roll-fin cascaded system with uncertainties which refer to as the modeling errors and the environmental disturbance induced by waves. An on-line feedforward neural network is constructed to account for the uncertainties. Lyapunov design is employed to obtain the fin stabilizer with guaranteed robustness. Simulation results demonstrate the validity of the controller designed and the superior performance over a conventional PD controller.

A heating method for lithium-ion battery is studied based on a simplified first principle electrochemical model. The criterion for avoiding lithium deposition is converted into current constraints under different temperature and state of charge. An experimental platform with closed-loop pulse current control function is built, by using of which, the effectiveness of the heating strategy is verified through heating a 18650 cylindrical LiFePO 4 battery 100 times. The results show that no capacity fading is observed.

In this paper, aging mechanism of lithium ion batteries and its impact on capacity loss is analyst in detail, based on the simplified electrochemical model. The internal aging mechanism of the battery is identified from the open circuit voltage curve. These aging behaviors which result in capacity loss are classified into four parts: capacity loss of positive and negative electrode, loss of lithium ion inventory, and total polarization potential increase. The positive and negative electrode active materials continuously decay during cycling; and the lithium ions circulating between the positive and negative electrodes are also consumed inevitably during the charging and discharging process; and the total polarization voltage of the battery increases significantly which makes the battery reach the charge and discharge cut-off points in advance; these all lead to the battery capacity drop. Find out these factors from identification can help us understand the battery aging state and ensure the safe use of the battery. The experiment shows the identification results can reflect the internal aging state of the battery very well.

Electrochemical impedance spectroscopy (EIS) is an effective method to characterize electrochemical electrodes, and is potentially a solution for estimating the state of health (SOH) of lithium-ion batteries (LIBs). A time domain EIS measurement technique followed by an equivalent circuit model interpretation is proposed in this paper. The accuracy of the measurement and the EIS-based SOH estimation are validated on Sanyo's UR14500P type LiCoO 2 cells, the results of which show that the proposed methods are effective in fast and economical assessment of LIBs.

Lithium-ion battery thermal management system is critical to improving charge and discharge safety and cycle life. At present, the thermal simulation is an important means to study the thermal problem of lithium battery, and the correct identification of the thermal parameters is the key to thermal simulation. This paper is based on COMSOL Multiphysics for the thermal simulation of a 48V/50Ah LiFePO 4 battery pack. The thermal model for the battery simulation is first established. Secondly, for the thermal parameters involved in the heat transfer model, this paper heats the battery based on different heaters in an adiabatic environment, decoupling the acquisition of thermal parameters. Then, based on the above, the temperature field of battery pack at different charge rates are simulated and analyzed. Finally, experiment results validate the accuracy of the model. The results obtained through simulation can provide reference and guidance for the thermal safety problem.

A parallel multidisciplinary optimization design is proposed for the lines design of an underwater vehicle. Resistance and energy consumption are concerned about in obtaining the optimized lines. Collaborative optimization strategy is adopted to construct the optimization framework. The simulated annealing algorithm in combination of the modified method of feasible directions are taken as the optimization algorithms. CFD based simulation is conducted to analyze the resistance of the underwater vehicle. To improve the optimization efficiency, a surrogate model is incorporated into the optimization framework to approximate the CFD calculation. Unlike the conventional static surrogate models used in the design of underwater vehicles, a dynamic surrogate based on trust region and lower confidence bound is proposed. An adaptive balance parameter is taken in the lower confidence bound approach to guarantee the tradeoff between the exploitation and exploration ability of the dynamic surrogate model. Optimal Latin hypercube algorithm is employed as the method of design of experiments. Radial basis function is employed to construct the surrogate model. SUBOFF model is used to demonstrate the proposed optimization scheme. Lines of the underwater vehicle are determined and optimization results of the resistance and energy consumption show the effectiveness of optimization scheme.

To obtain a stabilizer for an underactuated surface vessel with disturbances, an L2-gain design is proposed. Surge, sway, and yaw motions are considered in the dynamics of a surface ship. To ob-tain a robust adaptive controller, a diffeomorphism transformation and the Lyapunov function are employed in controller design. Two auxiliary controllers are introduced for an equivalent sys-tem after the diffeomorphism transformation. Different from the commonly used disturbance ob-server-based approach, the L2-gain design is used to suppress random uncertain disturbances in ship dynamics. To evaluate the controller performance in suppressing disturbances, two error sig-nals are defined in which the variables to be stabilized are incorporated. Both time-invariant dis-continuous and continuous feedback laws are proposed to obtain the control system. Stability analysis and simulation results demonstrate the validity of the controllers proposed. A comparison with a sliding mode controller is performed, and the results prove the advantage of the proposed controller in terms of faster convergence rate and chattering avoidance.

In this paper, Neural Networks (NNs) are used in the modeling of ship maneuvering motion. A nonlinear response model and a linear hydrodynamic model of ship maneuvering motion are also investigated. The maneuverability indices and linear non-dimensional hydrodynamic derivatives in the models are identified by using two-layer feed forward NNs. The stability of parametric estimation is confirmed. Then, the ship maneuvering motion is predicted based on the obtained models. A comparison between the predicted results and the model test results demonstrates the validity of the proposed modeling method.

Lithium-ion batteries have become critical components to industries as automotive, military, space, medical and consumer electronics. Electrochemical impedance spectroscopy (EIS) is a well-known technique employed on various electrochemical cells including lithium-ion cells, which is practical in estimating battery state of health, as ohmic resistance, formation of solid electrolyte interphase (SEI) film, activation on solid and liquid interface and diffusion process in the battery can be reflected in the Nyquist curve of EIS. However, the long acquisition time of standard EIS makes it unsuitable for rapid on-line impedance measurements. This paper presents a time domain measuring technique based on Fast Fourier Transformation (FFT). Time domain excitation, such as a multi-sine excitation, is applied to the measured system. The excitation and response of the system are acquired simultaneously. Through FFT, acquired signals are transformed from time domain to frequency domain, thus the impedance in frequency domain is calculated. The technique is verified on both lithium-ion battery equivalent circuit and real lithium-ion battery. The resulting impedance data show that proposed technique has good concordance with standard EIS and greatly reduceds the acquisition time compared with standard EIS.

Software product line (SPL) engineering is an effective method to improve the software development process in terms of development costs and time-to-market by using comprehensive software reuse technology. The feature model is a demand model that describes the common and variability of software product family and the relationship between features in SPL engineering. The difficulty of product configuration based on the feature model is how to choose the optimal combination of features from the complex feature model to satisfy the constraints. In order to achieve the problem of constrained feature selection optimization, we propose a method based on atomic set and a genetic algorithm to optimize feature selection. Firstly, the feature model is optimized by using the atomic set algorithm. Then, the whole constraints of the model are modeled as the evaluation function of the effective and invalid configuration in the genetic algorithm. Finally, by the genetic operations of combining the effective configuration and the invalid configuration, such as crossover, selection and mutation, it selects the best effective configuration for output.

Fault tree analysis (FTA) is a prominent reliability analysis method widely used in safety-critical industries. Computing minimal cut sets (MCSs), i.e., finding all the smallest combination of basic events that result in the top level event, plays a fundamental role in FTA. Classical methods have been proposed based on manipulation of boolean expressions of fault trees and Binary Decision Diagrams. However, given the inherent intractability of computing MCSs, developing new methods over different paradigms remains to be an interesting research direction. In this paper, motivated by recent progress on modern SAT solver, we present a new method for computing MCSs based on SAT solving. Specifically, given a fault tree, we iteratively search for a cut set based on the DPLL framework. By exploiting local failure propagation paths in the fault tree, we provide efficient algorithms for extracting an MCS from the cut set. The information of a new MCS is learned as a blocking clause for SAT solving, which helps to prune search space and ensures completeness of the results. We compare our method with a popular commercial FTA tool on practical fault trees. Preliminary results show that our method exhibits better performance on time and memory usage.

To reduce the ship wave-making resistance, the lines of the bulbous bow of a hull are optimized by an automatic optimization platform at the ship design stage. Parametric modeling was applied to the hull by using non-uniform rational basis spline (NURBS). The Rankine-source panel method was used to calculate the wave-making resistance. A hybrid optimization strategy was applied to achieve the optimization goal. A Ro-Ro ship was taken as an example to illustrate the optimization method adopted, with the objective to minimize the wave-making resistance. The optimization results show that wave-making resistance obviously reduces and the wave-shape of the near bow becomes gentle after the lines of the bulbous bow of the hull are optimized, which demonstrates the validity of the proposed optimization design strategy.

Neural networks (NN) are applied to the tracking control of a three-link manipulator attached to an autonomous underwater vehicle (AUV). Lyapunov design is employed to obtain the NN based robust controller. The interaction between the AUV and the manipulator is considered. Nonlinearity in the plant is compensated by NN based identification. To illustrate the validity of the proposed controller, numerical simulation is performed and the comparison between the NN based controller and a conventional proportional-derivative (PD) controller is conducted.

Support vector machines (SVM) are proposed in order to obtain a robust controller for ship course-keeping. A cascaded system is constructed by combining the dynamics of the rudder actuator with the dynamics of ship motion. Modeling errors and disturbances are taken into account in the plant. A controller with a simple structure is produced by applying an SVM and L2-gain design. The SVM is used to identify the complicated nonlinear functions and the modeling errors in the plant. The Lagrangian factors in the SVM are obtained using on-line tuning algorithms. L2-gain design is applied to suppress the disturbances. To obtain the optimal parameters in the SVM, then particle swarm optimization (PSO) method is incorporated. The stability and robustness of the close-loop system are confirmed by Lyapunov stability analysis. Numerical simulation is performed to demonstrate the validity of the proposed hybrid controller and its superior performance over a conventional PD controller.

Combined with the free-running model tests of KVLCC ship, the system identification (SI) based on support vector machines (SVM) is proposed for the prediction of ship maneuvering motion. The hydrodynamic derivatives in an Abkowitz model are determined by the Lagrangian factors and the support vectors in the SVM regression model. To obtain the optimized structural factors in SVM, particle swarm optimization (PSO) is incorporated into SVM. To diminish the drift of hydrodynamic derivatives after regression, a difference method is adopted to reconstruct the training samples before identification. The validity of the difference method is verified by correlation analysis. Based on the Abkowitz mathematical model, the simulation of ship maneuvering motion is conducted. Comparison between the predicted results and the test results demonstrates the validity of the proposed methods in this paper.

In this paper, robust adaptive tracking control is proposed for the autonomous underwater vehicle (AUV) in the presence of external disturbance. Backstepping control of the system dynamics is introduced to develop full state feedback tracking control. Using backstepping control, minimal learning parameter (MLP) and variable structure control (VSC) based techniques, the robust adaptive tracking control is presented for AUV to handle the uncertainties and improve the robustness. The proposed controller guarantees that all the close-loop signals are semi-global uniform boundedness and that the tracking errors converge to a small neighborhood of the desired trajectory. Finally, simulation studies are given to illustrate the effectiveness of the proposed algorithm.

To improve the feasibility of system identification in the prediction of ship manoeuvrability, several measures are presented to deal with the parameter identifiability in the parametric modeling of ship manoeuvring motion based on system identification. Drift of nonlinear hydrodynamic coefficients is explained from the point of view of regression analysis. To diminish the multicollinearity in a complicated manoeuvring model, difference method and additional signal method are employed to reconstruct the samples. Moreover, the structure of manoeuvring model is simplified based on correlation analysis. Manoeuvring simulation is performed to demonstrate the validity of the measures proposed.