Autonomous vessel docking presents significant challenges due to complex vessel dynamics, confined waterways, and critical safety requirements, demanding robust trajectory prediction models. This study proposes an interactive multiple-model (IMM) approach that seamlessly integrates physics-based and data-driven approaches to enhance predictive accuracy, interpretability, and reliability in real-world docking operations. The proposed approach adapts dynamically to changing operational conditions throughout the docking process, ensuring robust performance. The IMM approach incorporates a temporal belief decay mechanism to manage prediction uncertainties, assigning higher confidence to near-term estimates while accommodating long-term variability. This approach is complemented by a locally linear weighting mechanism, which provides interpretable insights into the influence of each model component, thereby enhancing transparency and trust in autonomous docking systems. The approach is validated in 28.9 m-length research vessel Gunnerus’ real-world docking tests, demonstrating superior trajectory prediction accuracy, confidence-aware outputs, and explainable decision-making compared to conventional single-model approaches. Results highlight that the hybrid methodology improves prediction precision and reliability, effectively bridging the gap between physics-based knowledge and data-driven adaptability. These findings suggest that the proposed IMM approach can enhance the safety, reliability, and operational efficiency of autonomous maritime systems.

This research provides a comprehensive examination of hybrid modeling approaches that integrate physics-based and data-driven methods across three transportation sectors: automotive, aviation, and maritime. Motivated by the need for improved generalization, interpretability, and robustness, hybrid models combine the expressiveness of machine learning with the reliability of physical laws. Existing hybridization techniques are categorized according to the fusion point, including input features, loss functions, model architecture, and output correction, and their respective advantages and challenges are assessed. Domain-specific trends are identified: automotive and aviation benefit from high-fidelity physical models and abundant labeled datasets, while maritime applications often contend with sparse, noisy data and low-fidelity models, thus placing greater emphasis on hybrid methods to ensure safety and reliability. We further analyze strategies such as physics-informed neural networks, residual learning, transfer learning with synthetic data, and multimodel architectures, and evaluate their suitability under different data availability and physical modeling constraints. This review highlights the critical role of domain knowledge, the impact of physical model fidelity, and the importance of adaptive integration strategies in achieving robust and trustworthy dynamic models in real-world transport systems.

Self-adaptive robots (SARs) in complex, uncertain environments must proactively detect and address abnormal behaviors, including out-of-distribution (OOD) cases. To this end, digital twins offer a valuable solution for OOD detection. Thus, we present a digital twin-based approach for OOD detection (ODiSAR) in SARs. ODiSAR uses a Transformer-based digital twin to forecast SAR states and employs reconstruction error and Monte Carlo dropout for uncertainty quantification. By combining reconstruction error with predictive variance, the digital twin effectively detects OOD behaviors, even in previously unseen conditions. The digital twin also includes an explainability layer that links potential OOD to specific SAR states, offering insights for self-adaptation. We evaluated ODiSAR by creating digital twins of two industrial robots: one navigating an office environment, and another performing maritime ship navigation. In both cases, ODiSAR forecasts SAR behaviors (i.e., robot trajectories and vessel motion) and proactively detects OOD events. Our results showed that ODiSAR achieved high detection performance—up to 98% AUROC, 96% TNR@TPR95, and 95% F1-score—while providing interpretable insights to support self-adaptation.

Synchronization monitoring atmospheric corrector (SMAC) sensor is equipped with polarization and intensity data, providing the possibility of retrieving more accurate aerosol parameters for main sensor’s atmospheric correction. In this study, a novel aerosol retrieval algorithm based on intensity and polarization data was proposed. First, we analyze and construct the ratio relationship of different intensity and polarization channels of SMAC on different surface types and analyze the correlation between these ratios and normalized difference vegetation index (NDVI) and scattering angle (SCA). Then, for the first time, high-precision surface prior knowledge for SMAC aerosol retrieval is constructed, and then, intensity and polarization data were used to retrieve high-precision aerosol products simultaneously. These results are compared and validated with Moderate Resolution Imaging Spectroradiometer (MODIS) and ground-based observations aerosol products, and aerosol optical depth (AOD) product comparison between the SMAC and MODIS showed that they had similar spatial distribution, scattered dots of them with a Pearson correlation coefficient (R) of 0.84 and a root-mean-square error (RMSE) of 0.11. Meanwhile, their differences were also analyzed. The validation between the ground-based sites and the SAMC retrieval results also showed a good performance with R of 0.88 and RMSE of 0.10. These results revealed that the new algorithm is an effective method for retrieving reliable AOD products for the main sensor’s atmosphere correction.

Digital twin (DT) technology, which creates virtual representations of physical systems to optimize their life-cycle, has drawn significant attention across various industries. The automotive and aviation industries have been pioneers in adopting DTs for enhanced efficiency, predictive maintenance, and real-time decision-making. However, the maritime industry, crucial to global trade and logistics, has lagged in DT implementation. This paper aims to bridge this gap by systematically surveying DT applications in the automotive and aviation industries and exploring how this knowledge can be transferred to the maritime industry. By analyzing existing literature, identifying key trends, and summarizing best practices, a comprehensive roadmap is provided for maritime industry adoption of DT technology. The surveyed papers are selected systematically following the PRISMA statement and categorized based on characteristics such as single vs. multiple systems, modeling methods (model-driven, data-driven, and hybrid), and life-cycle phases. We introduce DT models using a five-dimensional framework and analyze their characteristics in terms of research object, subsystem application, and modeling method. Additionally, DT applications from a product life-cycle perspective, covering design, manufacturing, operation, and maintenance phases are examined. Knowledge transfer from the automotive and aviation industries to the maritime industry is summarized. In the automotive industry, DTs enhance vehicle efficiency and safety, particularly for autonomous and electric vehicles. Aviation DT research focuses on predictive maintenance, pilot training, and real-time monitoring to improve operational efficiency and safety. The maritime industry faces data challenges and operational complexity but has significant potential for DTs to enhance ship performance, safety, and predictive maintenance.

The safety of navigation is critical in areas with heavy and complex traffic. Accurate prediction of the future trajectory of ships is crucial, especially in encounter situations where conventional navigational devices are prone to high uncertainty and risk due to their limitations. Unfortunately, kinematic models and data-driven methods suffer from numerous issues, such as poor performance, intricate architecture, and reduced interpretability. In response, this article introduces a novel approach, driven by automatic identification system (AIS) data, to enhance the efficacy of ship encounter trajectory prediction. It revolves around encounter classification, wherein the invaluable insights of seamanship play a pivotal role in identifying and categorizing legitimate encounters into three distinct types. This categorization forms the foundation for developing a probability-based classification model, in conjunction with a hybrid predictor that amalgamates a kinematics-based model and a neural network-based model. Historical AIS data collected in Oslofjord, Norway, are utilized in this study, and experiments have been conducted to assess the performance of the proposed method through real cases. The results substantiate the promise of the classification model and underscore the exceptional predictive capabilities of the hybrid approach. This superiority is attributed to the synergistic effects arising from the integration of its constituent models.

Toward autonomy in marine vehicles, a high-quality dynamic model is the first step before robust controls and should be properly addressed. However, vessels operating on the ocean surface are subjected to continuous disturbances, and the maneuvering dynamics exhibit a high degree of complexity and nonlinearity. In this study, a learning-based model is proposed to capture the ship maneuvering dynamics along with temporal variations. By taking the time series of ship maneuver commands as input, the model directly predicts the corresponding motions sequence, considering the presence of disturbances. The proposed neural network model is formulated as time-discretized ODEs, and model training is facilitated by posing multistep constraints. Extensive full-scale ship maneuvering experiments are conducted in the open sea to validate the effectiveness. Comparative experiments against existing learning-based models demonstrate improved accuracy in estimating ship velocities and positions over multistep intervals.

Autonomous vessels have been identified as a promising innovation in advancing marine transportation, providing an effective means to mitigate the risk of accidents, pollution incidents, and carbon dioxide emissions. Accurate sea state estimation (SSE) plays a critical role in facilitating onboard decision-making and optimizing operational efficiency for autonomous ships. Traditional SSE approaches relying on external sensors, such as wave buoys and wave radars, are limited by cost considerations. Model-based methods are highly relying on the understanding of human knowledge to ships. Data-driven models also provide promising solutions, but their generalization is low. To address this challenge, a semi-supervised transfer learning approach for SSE (SAFENESS) is proposed. The model is trained using sufficient data in the source ship and limited data from the target ship and finally applied to the target ship. A data alignment algorithm is utilized to use the limited data of the target ship. To enhance the learning capability of the framework, two attention mechanisms are proposed, and a multi-class adversarial discriminator is introduced that can align the distributions of different domains. The effectiveness of our approach is validated through comprehensive comparisons with eleven established transfer learning methods, demonstrating the superiority of our model. The competitiveness of the proposed attention modules is verified by comparing them with state-of-the-art attention modules. The significance of each component and the influence of key parameters have been thoroughly explored in the ablation and sensitivity analysis. Our method has potential applications in maritime safety, navigation, and operation optimization.

To decrease the risk of collisions and ensure safe navigation, an Automatic Identification System (AIS) was developed to broadcast real-time ship states information, such as ship position and sailing speed. Due to the real-time characteristic of AIS data, it, hence, has been widely used to construct a data-driven model for ship trajectory prediction, which can provide onboard decision support for navigators. However, data provided by AIS is sometimes partially missing or simply wrong. Poor quality of AIS data can degrade the performance of data-driven models and lead to large uncertainty in the predicted ship positions. Therefore, this paper proposes a hybrid model to reduce uncertainty by integrating the multi-output Gaussian process (MOGP) model predictions and historical trajectory information. Historical trajectories provide useful prior knowledge to calibrate the performance of MOGP model predictions. This model is built through three steps: 1) extracting historical ship trajectory information from the route that a ship is following; 2) predicting ship positions with a data-driven predictor built by an MOGP; and 3) obtaining a trajectory by knowledge fusion of historical information and predicted results. The experiment shows the proposed hybrid model outperforms other data-driven models and has small errors and uncertainty quantification of the ship position prediction. Especially for the 6-minute position prediction, its RMSE is over 100 meters smaller than other methods.

As the development of emerging technologies is applied in the maritime industry and nautical operations, an interdisciplinary supervision approach is expected to be designed in this subject to enable students the ability to handle the issues brought by the fusion of industrial conventions and technological evolutions. This paper provides the profile of the participants and the subject in which the co-supervision is to be engaged. We investigate how interdisciplinary supervision and co-supervision can be implemented in the nautical science undergraduate program. The resources of both undergraduate education and maritime-related research at the Department of Ocean Operations and Civil Engineering at NTNU are taken as the principal for the fundamental subject. As expected, not only do the undergraduates are benefitted from the co-supervision, the researchers taking co-supervision responsibilities also pro-mote their insights by absorbing the human-dominant expertise knowledge generated by the students. Through the paper, we propose a route map to explore the mechanism of co-supervision in this subject and the expected outcomes.

Ship roll is a crucial metric in assessing the vessel's safety in offshore operations. This paper investigates input selection for predicting short-term ship roll motion using the Bidirectional Long Short-Term Memory Network (Bi-LSTM) and the Sobol sensitivity analysis of ship roll based on the predicted models. Considering the complexity of the impact of forces, velocities, and positions with six degrees of freedom on ship roll, a data-driven model is established to represent the relationship adequately. Firstly, one-step prediction models with different time intervals are established based on Bi-LSTM to express the relationship between all input features and output. Afterward, the Sobol sensitivity analysis is carried out to evaluate the impact of input features on the output based on the predicted models. Finally, mathematical statistics are utilized to optimize input selection for multi-step prediction models by analyzing the sensitivity results. The experimental results demonstrate that optimizing the input feature dimensions can improve the accuracy of one-step, five-step, and ten-step prediction models.

Thrust allocation (TA) is an important component in dynamic positioning (DP) system of marine vessels. It plays a crucial role in offering power optimisation while meeting physical constraints of actuators in the vessel. Non-linear objective functions in TA formulation poses challenge to deriving the solution of TA schemes. While numerical optimisation techniques have tried to offer solution, the ability of neural network to model non-linearity offers a good technique of solving it. In this paper, design of constraints for TA scheme based on neural network is discussed. The allocator is based on a multi-layered autoencoder network. The allocator thus formulated is tested in various environmental and operational profiles against a numerical optimisation scheme to test its effectiveness in meeting the constraints.

Hyperspectral images (HSIs) have been widely used in Earth observation because they contain continuous and detailed spectral information which is beneficial for the fine-grained diagnosis of the land cover. In the past few years, convolutional neural network (CNN)-based methods show limitations in modeling spectral-wise long-range dependences. Recently, transformer-based deep learning methods are proposed and have shown superiority in modeling the continuous representation of the spectral signatures because the self-attention (SA) mechanism has a global receptive field. Due to the special tokenization of the transformer-based methods, the redundant tokens contained in spectral embeddings are always involved in SA operation. Redundant tokens do not positively contribute to classification. Specifically, the overlapped group-wise tokenization approach may aggravate the Hughes phenomenon and impose additional computations. To address this issue, a lightweight spatial–spectral pyramid transformer (SSPT) framework is proposed to efficiently extract the spatial–spectral features of HSI by progressively reducing redundant tokens in an end-to-end manner. In particular, a token reduction (TR) method is proposed to decide which tokens will be involved by computing and comparing token attentiveness between spectral embeddings and the class token. In addition, for those tokens that are defined as redundant information, a token compensation mechanism is proposed to automatically extract supplementary information for classification. Extensive experiments on three standard datasets quantitatively show the superiority of our methods, and the ablation experiments qualitatively prove our hypothesis about the feature distribution in transformer architecture.

The visual attention of navigators is imperative to understand the logic of navigation as well as the surveillance of navigators' status and operation. Current studies are implemented with the help of wearable eye-tracker glasses; yet, the high expenditure demanded by such equipment and service and its limitations on usability have impeded related research from further development. In this letter, the authors propose a framework, which is the first attempt in the maritime domain, to provide a camera-based deep-learning (CaBDeeL) visual attention recognition solution that outperforms the intrusive eye tracker regarding its shortcomings. A wide-angle camera is configured in front of the navigator in the advanced ship-bridge simulator in a way that visual attention reflected by their facial and head movements is captured in the front view. A pair of eye-tracker glasses is used to classify the captured visual attention images, which then form the primary database. During the process of classifying camera-captured images, a convolutional neural network (CNN) is built as an automatic classifier. The CNN is applied to two scenarios, and it shows an overall 95 % accuracy.

The development of maritime autonomous surface ships (MASS) has triggered interest from both academia and industry in recent years. Nevertheless, human operators will continue to perform dominant roles onboard for the next decades. There are several critical phases where human operators are in the navigation loop (MASS-I, II, and III) before full ship autonomy (MASS-IV) is achieved. The authors conceive a cyber-physical human framework for the experiment design and implementation in the maritime domain: based on multiple experimental platforms with data exchange ports, apply monitoring on and learning from navigators’ behaviors, and adapt the ship-bridge system to provide decision support in terms of guidance, navigation, and control. The platforms include a compact simulator from Kongsberg®and an immersive simulator for preliminary research design, a group of standard maritime training simulators, and a research vessel. These platforms are utilized for data collection, scenario design, testbeds to demonstrate and verify new techniques and algorithms, etc. The authors illustrate how the framework aids the MASS research and benefits the development process.

In recent years, maritime operations have become more technologically demanding due to the more complex working condition and stricter safety requirements. The need to improve the performance of human-machine cooperation in navigation through a more intelligent system in path planning, while taking into account the human factors, is more and more urgent. In this paper, a model predictive control (MPC) optimization scheme is proposed for collision-free path planning taking into account the ship dynamics and International Regulations for Preventing Collisions at Sea (COLREGS) explicitly. It utilizes three potential fields which are designed based on COLREGS and the experience of navigators. Different tuning parameter sets are tested in a single encounter scenario including give-way, head-on and overtaking, and multiple ship encounter with 5 target ships is evaluated. The simulation shows promising results where the own ship can perform evasive action according to different encounter types following COLREGS Rule 13-15 and achieve the target positions while maintaining a safe distance during the collision avoidance period. The shortest distances between the own ship and target ships in multiple encounter scenario are all larger than 250m, which further proves the effectiveness of the algorithm.

Improving the accuracy of block adjustment with few or no ground control points (GCPs) is one of the core issues for satellite imagery high-precision mapping. The GaoFen-7 (GF-7) satellite laser altimeter system is the first Chinese formal spaceborne laser altimeter system, which is equipped with laser footprint cameras for the first time. Given the very high height accuracy and high planimetric accuracy of the instrument, this letter proposed an adjustment method of ZiYuan-3 (ZY-3) satellite imagery by integrating GF-7 laser altimetry data. The laser control points (LCPs) were automatically extracted according to the registration of the footprint images and stereo images. Furthermore, the combined adjustment was performed with the LCPs as the vertical control and horizontal constraint. The performance of the proposed method was evaluated by using 417 stereo scenes of ZY-3 images and 954 LCPs, covering an area of 158 000 km2in Shandong, China. The results demonstrated that, only by using all LCPs as the vertical control, the root mean square error (RMSE) of elevation can be significantly improved from the original 8.82 to 1.40 m. Moreover, the planimetric RMSE decreased from the original 15.31 to 3.58 m with residual randomness when 45 LCPs were used as the horizontal constraint. Our method provides an alternative solution for satellite imagery geopositioning improvement without GCPs.

Laser footprint images (LFIs) are auxiliary sensors of satellite laser altimeters, which are mainly used to determine whether the laser pulse is obscured by clouds, and establish a link between the laser and the high-resolution image, which requires an algorithm for achieving high recognition accuracy while preserving the edge contour features of clouds. This study uses the AdaBoost algorithm to achieve the fusion of multiple semantic segmentation models and combines the dark channel priori model to enhance the overall cloud detection effect, realize the removal of thin clouds, and improve the matching accuracy of footprint and altimeter images. The experimental results show that the model outperforms the basic cloud detection model by approximately 14.89% and that the contours of the extracted cloud-covered area are more consistent with subjective human perceptions. Furthermore, the matching accuracy of the optimized footprint and high-resolution altimeter images improved by approximately 17.67%.

Sea ice leads in the Arctic Ocean and Antarctic Ocean are of great significance to polar ecology, climate change, and ship navigation. While rule-based remote-sensing classification methods, such as the threshold method, have been widely used for surveying and mapping polar sea ice leads, they have difficulty in overcoming the problems of noise and poor generalization. In this article, we presented an automated, deep learning sea ice open lead detection network in low wind speed conditions, entropy-weighted network (EW-Net). In EW-Net, a dense block was introduced into a U-Net baseline network to strengthen feature propagation, and an entropy sampling structure was designed to improve the pertinence of the network to leads and alleviate the influence of noise. Furthermore, an entropy-weighted feature fusion block was designed to better restore features. EW-Net was trained on the Sentinel-1 synthetic aperture radar (SAR) dataset in order to obtain lead maps of different polar regions from Sentinel-1 images. The results showed that the proposed EW-Net model was more effective and more generalizable for polar sea ice lead detection than the threshold method and the deep learning methods U-Net and DeepLab V3 Plus, with an overall accuracy (OA) exceeding 0.97. In addition, EW-Net showed advantages in terms of outstanding generalization from cross-sensor image monitoring of leads. The method proposed in this article could be beneficial for providing accurate maps of polar sea ice leads in open water and contribute to further research on polar science.

Understanding current environmental conditions is essential for autonomous ships, among which real-time estimation of sea conditions is a key aspect. Considering the ship as a large wave buoy, the sea state can be estimated from motion responses without extra sensors installed. This task is challenging since the relationship between the wave and the ship motion is hard to model. Existing methods include a wave buoy analogy (WBA) method, which assumes linearity between wave and ship motion, and a machine learning (ML) approach. Since the data collected from a vessel in the real world are typically limited to a small range of sea states, the ML method might fail when the encountered sea state is not in the training dataset. This article proposes a hybrid approach that combines the above two methods. The ML method is compensated by the WBA method based on the uncertainty of estimation results, and thus, the failure can be avoided. Real-world historical data from the Research Vessel Gunnerus are applied to validate the approach. Results indicate that the hybrid approach improves the estimation accuracy.

High-fidelity models capable of accurately predicting ship motion are critical for promoting innovation and efficiency in the maritime industry. However, creating an advanced model that comprehensively represents the system and its interaction with dynamic environments has always been challenging. Many models provide partial knowledge about a system. To handle the deficiency and improve model fidelity, in this artile, we propose a hybrid modeling methodology, in which prior knowledge describing the ship dynamic effects is incorporated into a data-driven calibrator, yielding a representative model with high predictive capability. Enabled by the integration of model estimated ship states into the calibrator, the informative information could be interpreted and carried forward. Simulation and full-scale experiments are conducted on the research vessel Gunnerus to exemplify the concept. A best available numerical model and a neural network are prepared to be the foundation and calibrator, respectively. Experiment results show that the cooperative model greatly improves the predictive capability of the research vessel. From the ship modeling perspective, this study provides new insights by bridging the gap between two separate domains: 1) model-based and 2) data-driven.

Sea state estimation is beneficial for marine systems to enhance on-board decision-making and improve work efficiency. In the era of ship intelligence, artificial intelligence has greatly promoted the technology of sensing environment, such as by using the deep learning. However, it is difficult to collect enough motion data from a marine system to train a deep learning model. In addition, the model for sea state estimation is trained using the data from a specific marine system; applying the model directly to another marine system may result in performance degradation. In this paper, a supervised transfer learning based framework for sea state estimation (STLSSE) is proposed. The STLSSE focuses on knowledge transfer when the collected data for the source marine system is sufficient but the collected data of the target marine system is scarce. In STLSSE, a data pairing algorithm is proposed to determine the relationship of the source and the target marine system. Based on these paired data, a Siamese convolutional neural network, including a new proposed residual fully convolutional network and two novel attention modules, is designed for the semantic alignment. Moreover, the conventional contrastive loss is improved to characterize the distributions when there are only few samples in the target marine system. The extensive comparisons between STLSSE and state-of-the-art transfer learning approaches show its superior performance. The comparisons with state-of-the-art attention modules has verified the competitiveness of the proposed attention modules. The key parameters and each component of STLSSE are emphasized in the ablation and sensitivity studies.

The advent of the COVID-19 pandemic disrupted global commercial activities and the tourism industry heavily. Impacts on maritime transportation were huge, as seaborne trade represents over 80% of global merchandise trade. Investigating how COVID-19 has affected ship behaviours is significant for economic condition evaluation, port management. This paper develops an analysis method to mine knowledge from raw Automation Identification System (AIS) data. First, berths are identified by improved density-based spatial clustering of applications with noise by Pythagoras distance (PD-DBSCAN). Data features, such as ship deadweight, arrival time, dwelling time, ship types, etc., can then be extracted using information matching and statistical analysis. Next, the dynamic time warping method is employed to analyse abnormal ship behaviour patterns and quantify the impacts of COVID-19. After that, a significance test is employed to determine an impact threshold through year-on-year analysis on ship flow, daily throughout and berthing time of quays. Finally, statistical analysis is used for the short-term impact analysis. This research examines a case study based on four-year AIS data in the Oslo port area. The results show that the proposed method can identify abnormal patterns caused by COVID-19 and estimate its impacts. Passenger ships are influenced heavily compared with cargo ships. The variation of passenger ships’ flow is over 90% during 2020, larger than the average variation before 2020. The discovered knowledge could be used for future decision-making and preplanning in the next health crisis.

Real-time digital twins of ships in operation find many applications such as predictive maintenance, climbing the ladders of ship autonomy, and offshore operational excellence. The literature describes a focus on digital twinning of individual equipment such as navigation, propulsion, engine and power system, or crane. Yet, digital twinning and virtual prototyping for offshore operations are in their infancy and the onboard digitisation hardware and the telecommunication infrastructure are becoming accessible and affordable. Previous work has failed to address the need for building a holistic model and thus contextualising the equipment with the state of the whole vessel. A prototype of an online digital twin of a research vessel is proposed, its architecture described and its suitability for virtual prototyping demonstrated in a remote control centre. The study shows a viable proof of concept for remote monitoring and crew assistance in nominal and contingency response for offshore crane operations.

Situation awareness is of great importance for autonomous ships. One key aspect is to estimate the sea state in a real-time manner. Considering the ship as a large wave buoy, the sea state can be estimated from motion responses without extra sensors installed. However, it is difficult to associate waves with ship motion through an explicit model since the hydrodynamic effect is hard to model. In this paper, a data-driven model is developed to estimate the sea state based on ship motion data. The ship motion response is analyzed through statistical, temporal, spectral, and wavelet analysis. Features from multi-domain are constructed and an ensemble machine learning model is established. Real-world data is collected from a research vessel operating on the west coast of Norway. Through the validation with the real-world data, the model shows promising performance in terms of significant wave height and peak period.

This paper presents a high-performance finite-set model predictive control for an efficient power electronics interface for photovoltaic (PV) system. The proposed control scheme is able to harvest the maximum available power from the PV panel without the requirement of complex modulation scheme with multi-nested loop classical controllers for the considered efficient power electronic interface. Two-stage back-to-back dc/dc and dc/ac converter architecture is a common power conversion unit for PV systems. The back-to-back stages requires multi-loop controller to harvest the maximum power. In this paper, an efficient multilevel dc/ac converter is used, which is challenging to control by classical modulation schemes. Furthermore, this adds more complexity to the back-to-back power conversion system under study. The goal of this paper is to mitigate the control complexity while maximizing the benefits of the efficient topology. Thus, realizing a high-performance power conversion for PV systems with straight forward control scheme. Several case studies are presented that validates the functionality of the proposed architecture.

Abstract In November 2019, the GaoFen‐7(GF‐7) satellite was equipped with China's first laser altimeter with full waveform recording capability, which obtains high‐precision long‐range three‐dimensional coordinates. The influence of clouds is noticeable for laser transmission, and a footprint camera is used to determine laser pointing and to image the ground. However, the cloud inevitably appears in the laser footprint image. In this study, the authors propose a cloud detection scheme for footprint images based on deep learning. First, an adaptive pooling model is proposed according to the characteristics of the cloud region. Next, model fusion was performed based on the SegNet and U‐Net training results. Finally, test time augmentation was used to enhance the data and to improve cloud detection accuracy. The experimental results show that the fusion result of the model was approximately 5% better than that of the traditional cloud detection algorithm, which improved the shortcomings of the traditional algorithm, such as poor detection effect for thin clouds and complex underlying cloud surfaces. The related conclusions have certain reference significance for GF‐7 data processing and related research on footprint images.

In the last decade researchers have increasingly considered eye tracking of the operators of cars and airplanes as a means to address human error and evaluate operational effectiveness. This article presents a systematic survey of recently published papers about this approach in service to the question as to whether eye tracking can be used to address operational safety in marine operations. The surveyed papers are selected systematically and were categorized according to several defined characteristics. Eye tracking depends on defining operators' areas of interest (AOIs) and measuring operators focus on them over time. We identified the method of defining AOIs as a key distinction between studies; the papers fell into four categories, depending on whether researchers relied on an expert, based it on the stimulus itself, or used an attention map or a clustering algorithm to define the AOIs they used. The article also summarizes and analyzes the design and procedure of the eye-tracking experiments in the papers. Based on the features of marine operation, instruction on AOI definition in different scenarios is extracted; guidelines on experimental design and procedure selection are provided. In the article's conclusion we apply the results to a case study of a heavy-lifting operation to demonstrate the effectiveness of eye-tracking in marine operations.

Sea State is significant to the operations on the sea. The traditional model-based approaches need lots of knowledge of vessels, which limit the real-world use. This paper proposes a spectrogram-based deep learning model for sea state estimation (SpectralNet). In this model, the ship motion data is converted to spectrogram using short time Fourier transform (STFT). Unlike other methods, the spectrogram of each sensor will be combined to a new image. And then, a 2D convolutional neural network (CNN) is built as the classifier and the sea state can be identified. The experimental results show the proposed approach can achieve higher classification accuracy compared these methods applied directly in raw time series data. Through the comparison results of the proposed approach and the combination of spectrogram of different number of sensors, the proposed approach can achieve highest classification accuracy, and the classification accuracy is growing with the number of combined sensors. The sensitivity analysis finds the classification accuracy is easily influenced by the scale factor of images.

Multi-ship collision avoidance is challenging in busy waters like the Dover Strait. Usually, ships follow the rules for avoiding collisions which are given by the Convention on the International Regulations for Preventing Collisions at Sea (COLREGS), by the International Maritime Organization (IMO). However, COLREGS can be ambiguous to follow in the close-quarters situations due to the complex crossing scenarios. In that situations, multiple ship avoid collisions with each other is highly dependent upon seamanship and crew's experience. In this study, we propose a COLREGS-compliant decision making strategy that integrates the human expertise with the artificial intelligent method. A simulation scenario was utilized to validate the effectiveness and feasibility of the proposed method.

Improving the training programs for maritime operations is beneficial to enhance the maritime safety in practice. In this article, we propose a novel approach to the assessment of visual attention in a maritime operation so as to support an expert-in-the-loop training program. Experts' knowledge of maritime operation and experiences in the simulator are incorporated into the training program in three ways. First, through a questionnaire, information about task division, identification of critical operation, and definitions of areas of interest (AOIs) are incorporated as prior knowledge for modeling visual attention. Second, a weight scale factor that emphasizes the high importance of visual focus in critical operations is utilized to generate an operations-dependent attention map. Third, based on an expert's attention map and visual switch between AOIs, a similarity metric is designed as a comprehensive evaluation between saliency and visual transition. A case study of heavy lifting operation is performed by two groups of trainees who have received different briefings about “critical operation.” The assessment result shows that the group with more detailed briefing obtains a 6% similarity score higher than the other group, which is consistent with the debriefing result of a superior performance of that group. The proposed approach is thus verified effective in assessing visual attention for the expert-in-the-loop training program.

Developing a reliable model to identify the sea state is significant for the autonomous ship. This paper introduces a novel deep neural network model (SeaStateNet) to estimate the sea state based on the ship motion data from dynamically positioned vessels. The SeaStateNet mainly consists of three components: an Long-Short-Term Memory (LSTM) recurrent neural network to capture the long dependency in the ship motion data; a convolutional neural network (CNN) to extract time-invariant features; and a Fast Fourier Transform (FFT) block to extract frequency features. A feature fusion layer is designed to learn the degree affected by each component. The proposed model is applied directly to the raw time series data, without needing of any hand-engineered features. A sensitivity analysis (SA) method is applied to assess the influence of data preprocessing. Through benchmark test and experiment on ship motion dataset, SeaStateNet is verified effective for sea state estimation. The investigation on real-time test further shows the practicality of the proposed model.

Canopy height models (CHMs) were utilized to detect treetops and estimate individual-tree parameters. The treetop detection based on CHMs was affected by surface topography and crown characteristics. However, their effects have not been well studied. Therefore, this paper aimed at assessing the impacts of aforementioned factors to facilitate treetop identification from LiDAR-derived CHMs. To fulfill this objective, we first extended and improved the previous models for cases with various terrains. Then, a new theoretical model was developed to quantify treetop displacements for ellipsoidal tree crowns. Finally, we further analyzed the treetop displacements due to terrain slope, crown radius, crown shape, and offset distance to the slope surface. Our analysis indicates that the vertical displacement increases exponentially with terrain slope; thus, the effect of terrain slope must be considered over extremely steep areas; larger errors are observed for trees with a large crown radius; the treetop displacements are highly correlated with crown shape, the effect of topographic normalization can be neglected for conical crowns with a large crown angle, and the elliptical crown shape can reduce the treetop detection errors; and treetop displacements increases with offset distance to the slope surface inCase 2, while opposite results are observed inCase 5. In addition, the results also demonstrate that the effect of slope-distorted CHMs may be quite different for different types of tree crowns and terrains. Overall, this paper makes a significant contribution to the development of theoretical models for quantifying treetop displacements. Furthermore, our findings provide a theoretical basis and guidance for better identifying treetops from LiDAR-derived CHMs.

In Public‐Key Cryptography (PKC) 2015, Bergsma et al . introduced an interesting one‐round key exchange protocol (which will be referred to as BJS scheme) with strong security in particular for perfect forward secrecy (PFS). In this study, the authors unveil a PFS attack against the BJS scheme. This would simply invalidate its security proof. An improvement is proposed to fix the problem of the BJS scheme with minimum changes.

The accurate estimation of within-footprint slope is very important for measuring earth’s surface characteristics using satellite light detection and ranging (LiDAR) data. Several models have previously been proposed for slope estimation; however, these models have limitations in either accuracy or applicability. Therefore, the main purpose of this paper is to explore the influence of various factors (e.g., ground vertical extent, footprint size, footprint shape, and footprint orientation) on slope estimation to better estimate the within-footprint slope using large-footprint waveform LiDAR data. The results indicated that the absolute slope error due to the coupling effect of ground vertical extent and footprint size increased with an increase in the ratio of ground vertical extent and footprint size, while the relative slope error had an opposite trend. The slope error caused by footprint shape was relatively low when the footprint eccentricity was small. However, the slope error due to footprint shape grew rapidly when the footprint eccentricity became larger; thus, it is essential to fully take into account the influence of footprint shape on within-footprint slope estimation. In addition, the results suggest that the slope error changed regularly based on the intersection angle between footprint orientation and terrain aspect. This paper also provided guidance for the determination of an easy and practical model for within-footprint slope estimation. The determination of best model is dependent on the value of intersection angle. Once the intersection value is given, the best model can easily be determined. Using the best model, the within-footprint terrain slope can be estimated with high accuracy.

This paper presents a bio-inspired caterpillar-like climbing method. Natural caterpillars climb relatively slow, but their multisegmented body trunk strongly enhances climbing versatility and stability, which thus motivates us to design an imitative robot to study the caterpillar-like climbing locomotion. Based on observations from natural caterpillars, we propose a three-stage climbing method to imitate the caterpillar-like straight line climbing on a planar wall. Due to the redundancy property of caterpillars' multisegmented body trunk, we formulate the caterpillar-like climbing problem as an end-effector tracking problem using the redundant robotics terminology. A dual-mode optimal controller, which effectively resolves the end-effector tracking problem even when the robot configuration is ill-conditioned, is incorporated for realizing the caterpillar-like climbing locomotion. Both simulation and experiment results are presented to demonstrate the effectiveness of the proposed caterpillar-like climbing method.

In this paper a novel GUI for a modular robots simulation environment is introduced. The GUI is intended to be used by unexperienced users that take part in an educational workshop as well as by experienced researchers who want to work on the topic of control algorithms of modular robots with the help of a framework. It offers two modes for the two kinds of users. Each mode makes it possible to configure everything needed with a graphical interface and stores configurations in XML files. Furthermore, the GUI not only supports importing the user’s control algorithms, but also provides online modulation for these algorithms. Some learning techniques such as genetic algorithms and reinforcement learning are also integrated into the GUI for locomotion optimization. Thus, its easy to use, and its scalability makes it suitable for research and education.

This paper presents an efficient kinematical solution to a multi-robot system with serial and parallel mechanisms. JL-I is a reconfigurable robot featuring active spherical joints formed by serial and parallel mechanisms endowing the robotic system with the ability of changing shapes in three dimensions. The active joint here can combine the advantages of the high rigidity of a parallel mechanism and the extended workspace of a serial mechanism. However, the kinematic analysis of the serial and parallel mechanism is always the bottleneck in designing a robot and control realization. In order to deal with this problem, the whole kinematical analysis is organized in the sequence from the direct mechanical analysis related to the serial and parallel mechanism over the numerical solutions to the simplified kinematics expression. The latest results obtained demonstrate that the deduced closure-form solution is time efficient and easy to implement while offering a satisfactory motion performance in on-site experiments.