This paper proposes an air-filled substrate integrated waveguide (AFSIW) bandpass filter with a miniaturized non-resonant node (NRN). The NRN structure is introduced between the three resonators, and its size is smaller than the resonator size, which can realize the NRN structure’s miniaturization and reduce the model’s size. The NRN size of this filter is 41% of the NRN size of the existing AFSIW filter. This filter also introduces a transmission zero (TZ) above the passband. The measured results show that the filter’s center frequency is 20.73 GHz, and the bandwidth is 0.86 GHz. The insertion loss in the passband is 0.95 dB, and the return loss is better than 23 dB. Due to the TZ in the upper stopband, the AFSIW filter obtained good selectivity.

This paper presents a design methodology for a broadband high-efficiency power amplifier (PA). The large available impedance space of the extended continuous Class-GF mode is employed. A novel output matching network of the PA consisting of a rectangular double transmission line structure is proposed to meet impedance requirements. To validate the effectiveness of this structure, a high-efficiency PA operating in 0.8–3.0 GHz is designed using a CGH40010F GaN transistor. The measured results demonstrate that the drain efficiency falls within the range of 63.2%–71.9%, the output power varies from 40.2 to 42.2 dBm, and the gain ranges from 9.4 to 11.3 dB within the frequency band of 0.8–3 GHz. The realized PA exhibits an extremely competitive relative bandwidth of 115.8%.

In the contemporary maritime industry, characterised by intense competition, reduced visibility due to heavy fog is a primary cause of accidents, significantly impairing maritime operational efficiency. Consequently, investigating foggy weather navigation safety holds crucial practical significance. This paper, through an analysis and synthesis of various aspects of foggy navigation technology, including foggy navigation regulations at different ports, fog warnings, foggy vessel environmental perception and foggy auxiliary navigation systems, explores the key issues concerning vessel navigation during foggy conditions from a scientific perspective. This discussion encompasses the aspects of regulatory frameworks, standardisation, and the development of intelligent and responsive onboard equipment. Finally, the paper offers a glimpse into potential strategies for fog navigation.

This paper proposes a novel method of applying an iterative generation differential equation method to the multi-component nonlinear signal analysis of a diesel engine. The characteristics of a dynamic model of the single cylinder are analysed and discussed. The iterative generation differential decomposition method decomposes the multi-component signal and extracts multiple single-component signals. The sensitive single-component analysis technology of the complex vibration signal of a diesel engine is formed. The relationship between characteristic parameters of engine vibration dynamics and operation law is derived. A priori information about the unmeasured vibration signals of the roll-on/roll-off (Ro-Ro) passenger ships is not required. The experimental data is validly processed based on this developed method. Results show that this method is practical and feasible in analysing diesel engine vibration signals, especially under different load operating conditions.

Research findings based on the data of current automatic identification systems (AISs) can only be applied to some parts of navigation research owing to their insufficient mining depth. Previously, route planning research has been based on the waypoint and corresponding optimised algorithm without considering the actual navigation situation and sailing habits. The planned route considerably differs from the actual sailing route, and the application result is undesirable. A novel solution to support the route planning problem has been introduced owing to the large accumulation of AIS big data. In this study, the ship navigable route framework (SNRF) which is reflected by real data via mining AIS big data serves as the basic network for the planned maritime route. This study uses the concept of manifold distance based on AIS big data to build a maritime SNRF through high-density searching. It can provide basic theoretical support for actual navigation distance calculation, route planning and route accessibility inspection in the future.

This study describes an optimal method for deploying rescue ships in response to marine accidents using dynamic programming and particle swarm optimisation in an archipelago. We solved the shortest distance problem from a rescue ship to a marine accident using dynamic programming, which avoids obstacles, such as land or aquacultures. The optimal location problem is NP-hard. However, the optimal locations were found to be efficient among the various candidate combinations using particle swarm optimisation. We compared two models based on the set covering location model (SCLM) and P-median model (PMM). The PMM outperformed the SCLM approach in the test. The findings of this study may be valuable for directing judgments regarding search and rescue (SAR) vessel placements to maximise resource utilisation efficiency and service quality. Furthermore, this process can flexibly arrange multiple rescue ships.

Aiming at the error estimation problem of a radar detection system when the variation law of system error is unknown, an improved Gaussian mean-shift radar dynamic error registration algorithm (IGMSR) is proposed. The algorithm can effectively adapt to the variation of system error when the variation law of system error is unknown. The IGMSR algorithm uses the mean-shift method to contribute different characteristics to the estimation results of different sample points, and constructs weight coefficients according to the deviation of sample points from the mean and sampling time. The simulation results show that more than 90% of the constant system errors can be eliminated; for the systematic error with slow change, more than 80% of the bias can be eliminated in real time, while a previous method of Zhu and Wang (2018) can only eliminate 60% of the systematic error and require the change law to be known. This method overcomes the influence of random error and abnormal point, and the estimation results are more robust.

Identifying the absence of situation awareness (SA) in air traffic controllers is critical since it directly affects their hazard perception. This study aims to introduce and validate a multimodal methodology employing electroencephalogram (EEG) and eye-tracking to investigate SA variation within specific air traffic control contexts. Data from 28 participants executing the experiment involving three different SA-probe tests illustrated the conceptual relationship between EEG and eye-tracking indicators and SA variations, using behavioural data as a proxy. The results indicated that both EEG and eye-tracking metrics correlated positively with the SA levels required, that is, the frequency spectrum in the β (13–30 Hz) and γ (30–50 Hz) bands, alongside the fixation/saccade-based indicators and pupil dilation increased in response to higher SA levels. This research has substantial implications for investigating SA using a human-centric approach via psychophysiological indicators, revealing the intrinsic interactions between the human capability envelope and SA, contributing to the development of a real-time monitoring system of SA variations for air transportation safety research.

The International Regulations for the Prevention of Collisions at Sea (IRPCS) provide a comprehensive set of instructions for watchkeeping officers to follow and prevent collisions at sea. This study compares how six newly qualified deck officers and six Master Mariners, who were all trained at the same college, applied the IRPCS. Individual, semi-structured interviews were used to uncover how the 12 participants applied and interpreted the rules for three authentic scenarios. Phenomenography was used to capture the qualitatively different means by which participants interpreted the IRPCS. For basic collision avoidance situations, the results indicated little difference between the cohorts' ability to interpret and apply the IRPCS. However, when the scenarios became more complicated, Master Mariners outperformed newly qualified deck officers. In these cases, Master Mariners displayed a greater capacity to assess the overall situation, whereas newly qualified deck officers tended to simplify by focusing on a single rule. These findings indicate that training needs to focus on developing situational awareness; and training scenarios need to incorporate multiple vessels in authentic scenarios to enhance newly qualified deck officers' capacities to interpret the IRPCS.

Wall-climbing robots work on large steel components with magnets, which limits the use of wireless sensors and magnetometers. This study aims to propose a novel autonomous localisation method (RGBD-IMU-AL) with an inertial measurement unit and a fixed RGB-D camera to improve the localisation performance of wall-climbing robots. The method contains five modules: calibration, tracking, three-dimensional (3D) reconstruction, location and attitude estimation. The calibration module is used to obtain the initial attitude angle. The tracking and 3D reconstruction module are used jointly to obtain the rough position and normal vector of the robot chassis. For the location module, a normal vector projection method is established to screen out the top point on the robot shell. An extended Kalman filter (EKF) is used to estimate the heading angle in the attitude estimation module. Experimental results show that the positioning error is within 0⋅02 m, and the positioning performance is better than that of the MS3D method. The heading angle error remains within 3⋅1°. The obtained results prove its applicability for the autonomous localisation in low-texture and magnetically disturbed environments.