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Domain adaptation techniques have effectively tackled fault diagnosis under varying operational conditions. Many existing studies presume that machine health states remain consistent between training and testing data. However, in real-world scenarios, fault modes during testing are often unpredictable, introducing unknown faults that challenge the effectiveness of domain adaptation-based fault diagnosis methods. To address these challenges, this paper proposes a Deep Open Set Domain Adaptation Network (DODAN). Firstly, a feature extraction module based on multi-scale depthwise separable convolutions is constructed for discriminative feature extraction. To improve the model’s adaptability, an adversarial training strategy is implemented to learn generalized features that are resilient to unknown domain shifts. Additionally, an outlier detection module is employed to determine the optimal decision boundaries for each class representation space, enabling the classification of known fault modes and the identification of unknown ones. Extensive diagnostic experiments on two marine machinery datasets validate the effectiveness of the proposed method. Furthermore, ablation studies verify the efficacy of the proposed modules and strategies, highlighting significant potential for practical applications.

Magnetoresistance, the change in resistance with applied magnetic fields, is crucial to the magnetic sensor technology. Linear magnetoresistance has been intensively studied in semimetals and semiconductors. However, the air-stable oxides with a large linear magnetoresistance are highly desirable but remain to be fully explored. In this paper, we report the direct observation of linear magnetoresistance in polycrystalline MoO2 without any sign of saturation up to 7 T under 50 K. Interestingly, the linear magnetoresistance reaches as large as 1500% under 7 T at 2 K. The linear field dependence is in great contrast to the parabolic behavior observed in single-crystal MoO2, probably due to phonon scattering near the grain boundaries. Our results pave the way to comprehending magneto-transport behavior in oxides and their potential applications in magnetic sensors.

The visual monitoring of ship-engine-room equipment is an essential component of ship-cabin intelligence. In response to issues such as imbalanced quantities of different categories of engine room equipment and severe occlusion, this paper presents improvements to YOLOv8-M. Firstly, the introduction of the SPPFCSPC module enhances the feature extraction capabilities of the backbone extraction network. Subsequently, improvements are implemented in the neck network to create GCFPN, facilitating further feature fusion, and introducing the Dynamic Head module, which fuses the deformable convolution, in the part of the detection head, so as to improve the performance of the network. Finally, the FOCAL EIOU LOSS is introduced, while mitigating the impact of dataset imbalance through class-wise data augmentation. In this paper, the ship cabin equipment dataset and the public dataset MS COCO2017 are evaluated. Compared with YOLOv8-M, the mAP50 of GCD-YOLOv8 is improved by 2.6% and 0.4%, respectively.

Immune thrombocytopenia (ITP) is common in the elderly. Because of the coexistence of multiple diseases, there are many reservations regarding corticosteroid use in the elderly. Thrombopoietin (TPO) and its analogs can promote platelet production, but it is often difficult to correct TP in a short period. Recombinant human TPO (rh-TPO) acting on the cell membrane and the small-molecule TPO-receptor (MPL) agonist acting on the transmembrane receptor may have synergistic effects and accelerate platelet production because of different sites of action in the signaling pathway. In this study, two elderly patients with refractory ITP were successfully treated with two TPO-MPL signaling pathway agonists: recombinant human thrombopoietin (rh-TPO) and eltrombopag. This combination is safe with rapid and lasting effects. However, in elderly patients with refractory, recurrent, and glucocorticoid contraindications, the combination of different TPO agonists' clinical efficacy and adverse reactions needs to be further evaluated.

The accurate environment potential field (EPF) modeling method and highly efficient collision avoidance (CA) approach are key technologies for maritime autonomous surface ships (MASS). A novel and accurate environment potential field (EPF) model is proposed using electronic navigation chart (ENC) face objects to describe different types of navigable and non-navigable areas, and an improved artificial potential field (APF) method is presented to realize collaborative CA and obstacle avoidance (OA). Implicit equations of complex-shaped face objects were constructed based on $R$ -function theory, and the discrete-convex hull method was introduced to realize automatic EPF modeling. Collaborative CA and OA experiments in restricted waters were conducted on a ship handling simulator. The results show that the improved APF method can obtain a robust and deterministic collision-free path under different weather conditions and in restricted waters, and the track zone width remains within 0.1 nm. The proposed face object EPF model is efficient and accurate, even with numerous vertices and complex shapes, and can drive the ship apart at a relatively safe distance in accordance with the recommended CA parameter. We present a practical CA approach and an effective EPF modeling method for APF-based ship path planning.

Deep Transfer Learning (DTL) signifies a novel paradigm in machine learning, merging the superiorities of deep learning in feature representation with the merits of transfer learning in knowledge transference. This synergistic integration propels DTL to the forefront of research and development within the Intelligent Fault Diagnosis (IFD) sphere. While the early DTL paradigms, reliant on fine-tuning, demonstrated effectiveness, they encountered considerable obstacles in complex domains. In response to these challenges, Adversarial Deep Transfer Learning (ADTL) emerged. This review first categorizes ADTL into non-generative and generative models. The former expands upon traditional DTL, focusing on the efficient transference of features and mapping relationships, while the latter employs technologies such as Generative Adversarial Networks (GANs) to facilitate feature transformation. A thorough examination of the recent advancements of ADTL in the IFD field follows. The review concludes by summarizing the current challenges and future directions for DTL in fault diagnosis, including issues such as data imbalance, negative transfer, and adversarial training stability. Through this cohesive analysis, this review aims to offer valuable insights and guidance for the optimization and implementation of ADTL in real-world industrial scenarios.

The development of intelligent ships has an urgent demand for intelligent fault diagnosis technology. The working conditions and fault modes of high-power marine diesel engines gradually tend to be diversified and complicated, and the problems of reliability and safety are becoming more and more prominent. There are a lot of working condition data that lack fault labels, and the fault modes are asymmetric among different working conditions, so it is urgent to study effective fault diagnosis methods. Taking a marine diesel engine as the case validation object, we set up cross-condition and partial set fault diagnosis scenarios, proposed transferring knowledge from the source condition to the target condition for the problem of the lack of fault labels in the target condition, and designed a multi-scale and multi-view domain adversarial network (MMDAN) method for experimental validation using 6S50MC-C7 marine diesel engine system operation data. According to the experimental results, the average diagnostic accuracy of this method reached 96.58%, with a short processing time. Furthermore, it exhibits superior diagnostic performance compared to other transfer learning models in the cross-condition partial set transfer task. Additionally, the method proposed in this paper also offers a new approach and reference for the intelligent diagnosis of other equipment in ships.

In the intelligent engine room, the visual perception of ship engine room equipment is the premise of defect identification and the replacement of manual operation. This paper improves YOLOv5 for the problems of mutual occlusion of cabin equipment, an unbalanced number of different categories, and a large proportion of small targets. First, a coordinate attention (CA) mechanism is introduced into the backbone-extraction network to improve the ability of the network to extract main features. Secondly, this paper improves the neck network so that the network can learn a relatively important resolution for feature-fusion and enrich the semantic information between different layers. At the same time, this paper uses the Swin transformer as the prediction head (SPH). This enables the network to establish global connections in complex environments, which can improve detection accuracy. In order to solve the problem of cabin equipment covering each other, this paper replaces the original non-maxima suppression (NMS) with Soft-NMS. Finally, this paper uses the K-means algorithm based on the genetic algorithm to cluster new anchor boxes to match the dataset better. This paper is evaluated on the laboratory’s engine room equipment dataset (EMER) and the public dataset PASCAL VOC. Compared with YOLOv5m, the mAP of CBS-YOLOv5m increased by 3.34% and 1.8%, respectively.

Ship fires are one of the main factors that endanger the safety of ships; because the ship is far away from land, the fire can be difficult to extinguish and could often cause huge losses. The engine room has many pieces of equipment and is the principal place of fire; however, due to its complex internal environment, it can bring many difficulties to the task of fire detection. The traditional detection methods have their own limitations, but fire detection using deep learning technology has the characteristics of high detection speed and accuracy. In this paper, we improve the YOLOv7-tiny model to enhance its detection performance. Firstly, partial convolution (PConv) and coordinate attention (CA) mechanisms are introduced into the model to improve its detection speed and feature extraction ability. Then, SIoU is used as a loss function to accelerate the model’s convergence and improve accuracy. Finally, the experimental results on the dataset of the ship engine room fire made by us shows that the mAP@0.5 of the improved model is increased by 2.6%, and the speed is increased by 10 fps, which can meet the needs of engine room fire detection.

This paper analyzes a hybrid power system containing a fuel cell (FC) and proposes an improved scheme involving the replacement of a single energy storage system with a hybrid energy storage system. In order to achieve a reasonable power distribution between fuel cells and energy storage units and stable operation of the power grid, an efficient energy management system (EMS) based on the equivalent consumption minimization strategy (ECMS) is proposed. To enhance the dynamic response capability of hybrid energy storage systems, a low-pass filter with a variable time constant based on the ultracapacitor SOC feedback is proposed. This paper describes the design of a fuzzy logic controller that adaptively improves the equivalent consumption minimization control strategy, adjusts the equivalent factor in real time, optimizes the operating points of the fuel cell system, and improves system efficiency. The simulation model of the fuel cell hybrid system was established using MATLAB/Simulink. The proposed adaptive equivalent consumption minimization strategy (A-ECMS) was simulated and compared to the state-based and fuzzy logic energy management systems under the simulation of the real operating conditions of the ship. The results show that the proposed strategy can maintain a fuel cell system efficiency above 60% under most operating conditions and can significantly suppress the fluctuation of a fuel cell’s output power. The proposed strategy outperforms the state-based and fuzzy logic-based EMS in terms of stabilizing the hybrid power system and reducing hydrogen consumption. The effectiveness of the proposed strategy has been verified.

The initial boundary conditions, combustion, and emission characteristics of methyl decanoate (MD) mixed with different proportions of n-heptane (N-Hep) for homogeneous charge compression ignition (HCCI) are studied in this work. MAN B&W 6S70MC two-stroke diesel engine was used as the engine model of the reactor. The results showed that, when the equivalence ratio is 0.48, the NO emissions in the MD and N-Hep HCCI combustion process decrease with the decrease of the initial temperature. The initial temperature is determined to be 380 K. At the same time, NO emissions decrease with the increase of initial pressure, and the initial pressure is determined to be 1.3 atm. The results also indicate that, at a certain initial temperature, the initial pressure and total mole fraction of fuel, CO2, NOx reaction rates, and emissions are reduced significantly with the increase of N-Hep percentage in MD and N-Hep mixing combustion.

This study aims to investigate the impact of the High Pressure Selective Catalytic Reduction system (SCR-HP) on a large marine two-stroke engine performance parameters by employing thermodynamic modelling. A coupled model of the zero-dimensional type is extended to incorporate the modelling of the SCR-HP components and the Control Bypass Valve (CBV) block. This model is employed to simulate several scenarios representing the engine operation at both healthy and degraded conditions considering the compressor fouling and the SCR reactor clogging. The derived results are analysed to quantify the impact of the SCR-HP on the investigated engine performance. The SCR system pressure drop and the cylinder bypass valve flow cause an increase of the engine Specific Fuel Oil Consumption (SFOC) in the range 0.3–2.77 g/kWh. The thermal inertia of the SCR-HP is mainly attributed to the SCR reactor, which causes a delayed turbocharger response. These effects are more pronounced at low engine loads. This study supports the better understanding of the operating characteristics of marine two-stroke diesel engines equipped with the SCR-HP and quantification of the impact of the components degradation on the engine performance.

An accurate model plays an important role in control strategy development of smart ships. For the control-oriented engine models, calibration by experienced personnel is key to outputting high accuracy. However, the dual-fuel engine runs in liquid fuel mode, gas fuel mode, and fuel sharing mode. It is impossible to tune a single model or a set of parameters for the dual-fuel engine under all operating modes and loads. On the basis of our experience and practice, a Wiebe-based heat release rate model is used. To make the Wiebe model available for the dual-fuel engine, the Wiebe parameters are assumed to be linear functions. The combustion beginning angle is modeled as a function of fuel quantity in liquid fuel mode and as a look-up table in gas fuel mode for all loads. The combustion duration and the combustion distribution factor are modeled as a function of fuel quantity and engine revolution both in liquid fuel mode and in gas fuel mode. In fuel sharing mode, the heat release rate is modeled as a combination of the heat release rate models in liquid fuel mode and gas fuel mode. This model is called the SL model. For a further discussion, four types of combinations in fuel sharing mode are investigated. In addition, in liquid fuel mode and gas fuel mode, the combustion duration model and the combustion distribution factor model are replaced by the Woschni/Anisits model, which was specifically used in the diesel engine. This variation of model is called the WA model. To validate our hypothesis and models, the Wiebe parameters in liquid fuel mode and gas fuel mode are given, four types of combinations and two cases of comparisons in fuel sharing model are discussed, and the engine performance is checked and analysed. Results show that for the SL model, the average RMSE is 1.45% in the liquid fuel mode, 2.22% in the gas fuel mode, and 2.53% in the fuel sharing mode. For the WA model, the RMSE of the NOx is 9.79% in liquid fuel mode and 45.20% in gas fuel mode. Its maximum error reaches −65.54%. The proposed SL model is accurate and can generate Wiebe parameters that are better than the carefully tuned parameters. The WA model is not suitable for engine models that require NOx-emission-related parameters.

If the operating voltage of anode materials is below 1.0 V versus Li+/Li, the side reaction between electrolyte and anode materials will occur extensively. Thus, high-voltage anode materials have aroused interest recently. In this work, we report the preparation of PNb9O25 nanofiber via a facile electrospinning method. The PNb9O25 nanofiber shows the high rate performance and excellent cycling performance when it is used as anode in lithium ions batteries. For instance, the PNb9O25 nanofiber can deliver a capacity of 233, 212.1, 193.8, and 181.4 mA h g−1 at 0.2, 1, 3, and 6C, respectively. After 1000 cycles, it can reach at 134.3 mA h g−1 with capacity retention of 70.9%. Meanwhile, the ex situ X-ray photoelectron spectroscopy technique has been adopted to investigate the evolution in valence state of each element for PNb9O25 nanofiber. In addition, the PNb9O25 nanofiber is chosen as the anode material in lithium ion full cell in this work, demonstrating the potential for practical application.

With the growing demand for emission reductions and fuel efficiency improvements, alternative energy sources and energy storage technologies are becoming popular in a ship microgrid. In order to balance the two non-compatible objectives, a new differential evolution variant, which is named as SaCIDE-r, was proposed to solve the optimization problem. In this algorithm, a Collective Intelligence (CI) based mutation operator was proposed by mixing some promising donor vectors in the current population. Besides, a self-adaptive mechanism which was developed to avoid introducing extra control parameters. Further, to avoid being trapped in local optima, a re-initialization mechanism was developed. Then, we have evaluated the performances of the proposed SaCIDE-r approach by studying some numerical optimization problems of Congress on Evolutionary Computation (CEC) 2013 with D = 30, compared with seven stateof-the-art DE algorithms. Moreover, the proposed SaCIDE-r method was applied for economic scheduling of a shipboard microgrid under different cases compared with other multi-objective optimizing methods, resulting in very competitive performances. The comprehensive experimental results have demonstrated that the presented SaCIDE-r method might be a feasible solution for such a kind of optimization problem.

In this paper, a Self-learning Collective Intelligence Differential Evolution (SLCIDE) algorithm was proposed to optimize both the architecture and parameters of a Feedforward Neural Network (FNN). In order to improve the exploration-exploitation capability, a new Collective Intelligence (CI) based mutation operator was proposed by mixing some promising donor vectors in the current population. Besides, a self-learning mechanism which was designed to adaptively select m top ranked donor vectors was developed by using a widely used unsupervised learning method, k-means. As a result, the proposed approach can be more adaptive and statistically powerful on versatile problems. Then, we evaluated the performances of the proposed SLCIDE approach by studying some numerical optimization problems of CEC 2013 with D = 30 and D = 50. Further, the proposed SLCIDE method was applied to train a FNN on four most popular datasets, resulting in very competitive performances. The comprehensive experimental results have demonstrated that the presented SLCIDE method obtain better results compared with other state-of-the-art algorithms.

Most rolling element bearing (REB) fault diagnosis algorithms are evaluated on the Case Western Reserve University (CWRU) bearing dataset for its popularity and simplicity. However, the diagnosis accuracy on CWRU bearing dataset is overly saturated; it is nearly up to 100%. In this study, an input feature mappings (IFMs)-based deep residual network (ResNet) is proposed to conduct detailed and comprehensive fault diagnosis on REB with complicated bearing dataset. Firstly, a new data preprocessing method named as a signal-to-IFMs method is proposed to automatically extract features from raw signals without predefined parameters. Then, a deep ResNet is used as the fault classifier to learn the discriminative features from IFMs and identify the faults of REB. Finally, the proposed model is evaluated on the artificial, real, and mixed damages of the Paderborn university bearing dataset. The proposed method yields the average testing accuracies of 99.7%, 99.7%, and 99.81% in artificial, real, and mixed bearing damages, which outperforms other methods.

With the implementation of the new international conventions, higher evaluation requirements for a marine engine room simulator have been put forward. Based on the cloud theory, an improved fuzzy comprehensive evaluation method was studied: First, the Delphi method was adopted to get the original cloud drops of the judgments, and the original judgment weight clouds were generated by the backward cloud generator. Second, the judgment cloud matrix was built using the comparison results of the original judgment weight clouds, and the cloud weights of the evaluation factors were further calculated. Third, the appraisal grade clouds were generated, and the cloud appraisal vector taking into account the importance of each factor for each appraisal grade was calculated. Finally, the evaluation cloud result $E$ was aggregated, and the similarity vector between $E$ and the grade clouds was calculated. The calculation process reflects an effective uncertainty conversion between a qualitative concept and quantitative characteristics. The effectiveness was verified using three interrelated examples. The results show the following: the expectation of $E$ is mainly determined by the operating process and, second, by the expectation of the cloud weights; the uncertainty index and the randomness index of $E$ are determined by the parameter values of the cloud weights and the impact of the closest grade cloud; and the similarity vector is directly affected by $E$ and the distribution of the grade clouds. The introduction of cloud model theory into fuzzy comprehensive evaluation is an effective evaluation method.

In the intelligent ship field, with the upgrading of ship maintenance mode, the human-centered system maintenance will be gradually replaced by the artificial intelligence decision methods. To improve the training speed and testing accuracy of the state estimation model, an optimized Support Vector Machine (SVM) driven approach by Improved Artificial Bee Colony (IABC) was proposed to solve the global parameters optimization problem. First, the IABC method was achieved from three aspects: nectar source initializing, employed bee global neighborhood searching, and scouts mutation neighborhood searching. Second, the multi-class SVM with one-against-one classifiers was selected, and the best global parameters were achieved by the IABC. Third, the optimized SVM model was adopted in the testing to verify the effectiveness of state estimation. Finally, the elaborated methodology was applied to two actual ship systems to get the analysis results. The effectiveness was verified by using two examples. The results show the following: the IABC optimized SVM can obtain the global optimal parameters at a faster speed than the traditional ABC optimized method; the IABC optimized method can help the training start with better initial parameters, and get a higher classification accuracy rate than the traditional ABC optimized method. Based on the comparative analysis results, the IABC optimized SVM shows an obvious advantage of parameter optimization in the training process, and it can also significantly improve the model training efficiency and achieve a higher state estimation accuracy. The optimized SVM by IABC is an effective state estimation method in ship systems.

This study aims to determine hub genes related to the incidence and prognosis of EGFR-mutant (MT) lung adenocarcinoma (LUAD) with weighted gene coexpression network analysis (WGCNA). From The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases, we used 253 EGFR-MT LUAD samples and 38 normal lung tissue samples. At the same time, GSE19188 was additionally included to verify the accuracy of the predicted gene. To discover differentially expressed genes (DEGs), the R package “limma” was used. The R packages “WGCNA” and “survival” were used to perform WGCNA and survival analyses, respectively. The functional analysis was carried out with the R package “clusterProfiler.” In total, 1450 EGFR-MT–specific DEGs were found, and 7 tumor-related modules were marked with WGCNA. We found 6 hub genes in DEGs that overlapped with the tumor-related modules, and the overexpression level of B3GNT3 was significantly associated with the worse OS (overall survival) of the EGFR-MT LUAD patients (p < 0.05). Functional analysis of the hub genes showed the metabolism and protein synthesis–related terms added value. In conclusion, we used WGCNA to identify hub genes in the development of EGFR-MT LUAD. The established prognostic factors could be used as clinical biomarkers. To confirm the mechanism of those genes in EGFR-MT LUAD, further molecular research is required.

Compared to alternating current (AC) grids, direct current (DC) grids are becoming more and more popular. A power distribution approach is suggested in order to solve the issue of uneven power distribution of distributed generation (DG) in a ship DC microgrid. Power control is carried out using a tracking differentiator (TD), while the output power change rate is not greater than the maximum power ramp rate permitted by the battery, and state-of-charge balance is attained quickly. The proposed strategy also reduces the communication pressure on the power grid. A distributed hierarchical control model of a DC microgrid based on a consensus algorithm is created in order to validate the suggested methodology. The simulation results demonstrate that the established model is capable of simulating the DC microgrid accurately, that the states of charge values of the five batteries gradually converge under the adjustment of the secondary strategy, and that the suggested strategy is reasonable and efficient.

False Data Injection Attacks (FDIA) on ship Direct Current (DC) microgrids may result in the priority trip of a large load, a black-out, and serious accidents of ship collisions when maneuvering in the port. The key of the prevention of FDIA is the detection of and countermeasures to false data. In this paper, a defense strategy is developed to detect and mitigate against FDIA on ship DC microgrids. First, a DC bus voltage estimator is trained with an Artificial Neural Network (ANN) model. The error between the estimate value and the measure value is compared with a threshold generated from history data to detect the occurrence of FDIA. Combined with the correlation of artificial neural network inputs, bad data are identified and recovered. The method is tested under six cases with different network and physical disturbances in Matlab/Simulink. The results show that the method can identify and mitigate the FDIA effectively; the error of identifying FDIA by ANN is less than 0.5 V. Therefore, the deviation between the actual bus voltage and the reference voltage is less than 0.5 V.

With the development of ship intelligence, the frequency of upgrading the marine engine room simulator, which is essential for crew training, has increased. Traditionally, the data processing unit (DPU) of the marine engine room simulator is upgraded by manually downloading the firmware. This makes the hardware maintenance high-cost. In this paper, we first propose a WAN-based firmware upgrade system to enable secure over-the-air upgrades of DPUs and reduce operation and maintenance costs. A distributed hardware structure is given to manage DPU in the simulator via the Internet. We have designed two methods of firmware upgrades, automatic upgrades and remote upgrades. In automatic upgrades, the DPU can download new firmware upgrades from the web server through the router. By designing a series of mechanisms including code rollback, code backup and code confirmation, the In-Application Programming (IAP) technique is realized through the Internet. Firmware upgrades have good fault tolerance mechanisms to ensure that the emulator can still work in the event of an upgrade error. In remote upgrades, we upgrade the DPU firmware through the remote control center. We assessed the performance of the system by measuring the success rate of DPU upgrades, upgrade time and performance after the upgrade. The results show that the DPU upgrade success rate is close to 100% and performance is as good as expected. The results show that the remote firmware upgrade system proposed in this paper is reliable and practical.

In the engine room of intelligent ships, visual recognition is an essential technical precondition for automatic inspection. At present, the problems of visual recognition in marine engine rooms include missing detection, low accuracy, slow speed, and imperfect datasets. For these problems, this paper proposes a marine engine room equipment recognition model based on the improved You Only Look Once v5 (YOLOv5) algorithm. The channel pruning method based on batch normalization (BN) layer weight value is used to improve the recognition speed. The complete intersection over union (CIoU) loss function and hard-swish activation function are used to enhance detection accuracy. Meanwhile, soft-NMS is used as the non-maximum suppression (NMS) method to reduce the false rate and missed detection rate. Then, the main equipment in the marine engine room (MEMER) dataset is built. Finally, comparative experiments and ablation experiments are carried out on the MEMER dataset to verify the strategy’s efficacy on the model performance boost. Specifically, this model can accurately detect 100.00% of diesel engines, 95.91% of pumps, 94.29% of coolers, 98.54% of oil separators, 64.21% of meters, 60.23% of reservoirs, and 75.32% of valves in the actual marine engine room.

Cyber security has drawn more attention in the research of intelligent and unmanned ships. The remote control command transmitted in time sequence has a high similarity. This makes the ships more vulnerable to cyber-attacks, especially when they are controlled remotely. Aiming at the defense of Differential Fault Attack (DFA), this paper improved the SM4 algorithm in the phase of the S-box generation and circular encryption. The Wasserstein GAN Gradient Penalty (WGAN-GP) is used to generate S-boxes dynamically to confuse differential distribution tables. After the round encryption, the combination transformation is further applied to prevent from DFA. The corresponding symmetric decryption algorithm is also developed. Simulation result shows that the generated S-box meets the cryptography criteria and the combined transformation effectively hides the sensitive information in output ciphertext and guards against the DFA.

Although the exhaust gas recirculation (EGR) technology has been proven effective to decrease the marine engine’s nitrogen oxides (NOx) emissions, it is associated with a considerable fuel consumption increase and challenges to the engine–turbocharger matching. This study aims to parametrically optimise the EGR and turbocharging system settings of a large marine two-stroke engine with the objective of obtaining the highest engine efficiency whilst ensuring compliance with the prevailing NOx emissions limits. Two typical configurations of the investigated engine (baseline and alternative) are modelled in the GT-SUITE software. Parametric simulations are performed with EGR rates up to 40% along with cylinder bypass rates up to 50%, and the simulation results are analysed to quantify the impact of the engine operation with EGR on the performance and NOx emissions parameters. For the baseline engine configuration, the EGR rate increase considerably deteriorates the brake specific fuel consumption (BSFC), which is attenuated by opening the cylinder bypass valve. The optimal combinations of the EGR and cylinder bypass rates for each operating point are identified for both configurations. Following the comparative assessment between the two engine configurations, recommendations for the engine operating modes are proposed, leading to BSFC improvement in the region of 0.7 to 2.9 g/kWh. This study provides insights for the operational settings optimisation of two-stroke engines equipped with EGR systems, contributing towards the reduction of the associated environmental carbon footprint.

The combustion and emission characteristics of surrogate diesel fuel homogeneous charge compression ignition (HCCI) with different combustion boundary conditions and n-butanol (NB) mixing ratios are studied in this work. Engine data of a two-stroke low-speed direct-injection marine diesel engine were selected for the reactor. HCCI combustion was achieved by compressing a completely homogeneous mixture of fuel and air. The results show that NO emissions decrease slightly with the increase of initial boundary pressure at a constant equivalence ratio and initial temperature. In addition, the different initial boundary temperature has little effect on NO emission. The results also indicate that the ignition delay time of the mixed fuel rises with the increase of n-butanol mixing ratio. The emissions and reaction rate of NOx reduce significantly with the increase of n-butanol percentage in surrogate diesel fuel and n-butanol mixing combustion at a constant equivalence ratio and total mole fraction. Meanwhile, CO2 emissions also decrease significantly with the increase of n-butanol mixing ratio.

A novel collision avoidance (CA) algorithm was proposed based on the modified artificial potential field (APF) method, to construct a practical ship automatic CA system. Considering the constraints of both the International Regulations for Preventing Collisions at Sea (COLREGS) and the motion characteristics of the ship, the multi-ship CA algorithm was realized by modifying the repulsive force model in the APF method. Furthermore, the distance from the closest point of approach-time to the closest point of approach (DCPA-TCPA) criterion was selected as the unique adjustable parameter from the perspective of navigation practice. Collaborative CA experiments were designed and conducted to validate the proposed algorithm. The results of the experiments revealed that the actual DCPA and TCPA agree well with the parameter setup that keeps the ship at a safe distance from other ships in complex encountering situations. Consequently, the algorithm proposed in this study can achieve efficient automatic CA with minimal parameter settings. Moreover, the navigators can easily accept and comprehend the adjustable parameters, enabling the algorithm to satisfy the demand of the engineering applications.

In the intelligent perception of the marine engine room, visual identification of auxiliary equipment is the prerequisite for defect recognition and anomaly detection. To improve the detection accuracy, this study presents an auxiliary equipment detector in the cabin based on a deep learning model. Owing to the compact layout of pipeline networks and the large disparity in the equipment scales, we initially adopted RetinaNet as the basic framework, and introduced the single channel plain architecture RepVGG as the feature extraction network to simplify the complexity and improve realtime detection. Secondly, the Neighbor Erasing and Transferring Mechanism (NETM) was applied in the feature pyramid to deal with more complicated scale variations. Then, the complete IoU (CIoU) regression loss function was used instead of smooth L1, and the DIoU Soft-NMS mechanism was proposed to alleviate the misdetection in congested cabins. Further, comparison experiments and ablation experiments were performed on the auxiliary equipment in a marine engine room (AEMER) dataset to validate the efficacy of these strategies on the model performance boost. Specifically, our model can correctly detect 93.44% of coolers, 100.00% of diesel engines, 60.26% of meters, 95.30% of pumps, 55.01% of reservoirs, 97.68% of oil separators, and 74.37% of valves in a practical cabin.

In this article, to meet the requirements of marine engine room simulator on both the simulation speed and simulation accuracy, a mean value engine model (MVEM) for the 7S80ME-C9.2 marine two-stroke diesel engine was developed and validated in the MATLAB/Simulink environment. In consideration of the significant influence of turbocharger compressor on both the engine steady state performance and transient response, a novel compressor model (mass flow rate and isentropic efficiency model) based on a previous study carried out by the first author was proposed with the aim of achieving satisfactory simulation accuracy within the whole engine operating envelope. The predictive and extrapolative capability of the proposed compressor model was validated by carrying out simulation experiments and analyzing the simulation results under steady state condition and during transient process. To make the traditional MVEM capable of predicting in-cylinder pressure trace, the cylinder pressure analytic model proposed by Eriksson and Andersson for the four-stroke SI (spark ignition) engine was adapted to the 7S80ME-C9.2 marine two-stroke diesel engine based on the characteristic of in-cylinder pressure trace of this type of engine and then coupled to the MVEM developed in this paper. Since there is no need to solve any differential equation as it is done in the 0-D model, the advantage of MVEM in running speed is not impaired. For achieving satisfactory simulation accuracy by using the analytic model, the model parameters were calibrated elaborately by using engine measured data and a 0-D model and the relevant tuning procedure was discussed in detail.

This article addresses the problem of three-dimensional path following control for underactuated autonomous underwater vehicles in the presence of ocean current. Firstly, three-dimensional path following error model was established based on virtual guidance method. The control law is developed by building virtual velocity errors and backstepping method, which can simplify the virtual control input and avoid the singular problem induced by initial state constraints. Considering the curvature and torsion characteristics of the three-dimensional desired path, the approaching angle is introduced to guarantee fast convergence of error. Nonlinear damping term is introduced to offset the effects of dynamic uncertainties and external disturbances. The controller stability was proved by Lyapunov stable theory. Finally, simulations were conducted and the results indicate the effectiveness and robustness to parameter uncertainties and external disturbances of the proposed approach.

To improve the reliability of the motion control system for an autonomous underwater vehicle (AUV), sensor fault tolerant control based on replace control was talked about. This paper analyzed the decoupling controller and the sensor system firstly, and proposed a fault tolerant control strategy based on the ideas of replacing the fault sensor outputs by the sliding-mode observer outputs (also called replace control). For the irrecoverable sensor faults, the strategy minimized the damage to the AUV. For the jumping faults of sensors, replace control is used during jumping. The simulation results proved the validity of the strategy.

The objective of this study was to investigate the effects of intermittent lighting (IL) on broiler growth and slaughter performance as well as serum biochemical and tibia parameters. Two hundred and eighty 7-d-old Ross 308 broilers were selected and reared from 8 to 42 d under IL photoperiods of either 2 hours of light:2 hours of dark (2L:2D) or 4 hours of light:4 hours of dark (4L:4D), or under continuous lighting (CL). On day 42, body weight of broilers raised in the 4L:4D photoperiod was significantly increased compared to those reared in the 2L:2D photoperiod. The average daily feed intake (ADFI) of broilers in IL was significantly less than broilers in CL. The feed conversion of broilers raised in the 4L:4D photoperiod was significantly more efficient than broilers in CL. The intestinal tract weight was greater in birds raised in the 4L:4D photoperiod compared to birds in CL. Similarly, the eviscerated yield with giblet and eviscerated carcass weights in birds reared in the 4L:4D photoperiod were significantly higher than corresponding weights observed in broilers raised in CL. Serum total protein concentration was distinctly higher in birds raised in the 4L:4D photoperiod than that in the 2L:2D photoperiod. In addition, the bone elastic modulus of birds reared in the 4L:4D photoperiod was greater than measurements observed in the 2L:2D photoperiod. Accordingly, we concluded that the 4L:4D photoperiodic lighting schedule was superior to both the 2L:2D photoperiod and CL in broiler production.

Though label propagation algorithm (LPA) is one of the fastest algorithms for community detection in complex networks, the problem of trivial solutions frequently occurring in the algorithm affects its performance. We propose a label propagation algorithm with prediction of percolation transition (LPAp). After analyzing the reason for multiple solutions of LPA, by transforming the process of community detection into network construction process, a trivial solution in label propagation is considered as a giant component in the percolation transition. We add a prediction process of percolation transition in label propagation to delay the occurrence of trivial solutions, which makes small communities easier to be found. We also give an incomplete update condition which considers both neighbor purity and the contribution of small degree vertices to community detection to reduce the computation time of LPAp. Numerical tests are conducted. Experimental results on synthetic networks and real-world networks show that the LPAp is more accurate, more sensitive to small community, and has the ability to identify a single community structure. Moreover, LPAp with the incomplete update process can use less computation time than LPA, nearly without modularity loss.