Small Ships Research Articles

Assessment of ship speed, operational carbon intensity indicator penalty and charterer profit of time charter ships

The operational carbon intensity indicator (CII) proposed by the International Maritime Organization (IMO) has been officially implemented on January 1, 2023. As an important way of ship operation, applicable time charter ships are subject to the CII regulation. How to properly deal with the CII regulation is a challenge for the shipowner and charterer of time charter ships. Speed reduction is an effective measure to reduce carbon emissions and carbon intensity of ships. This study establishes a speed model including CII penalty for time charter ships. Results show that speed reductions of a time charter ship of 10 %, 20 % and 30 % reduce carbon emissions by 27.1 %, 48.8 % and 65.7 % and carbon intensity by 19 %, 36 % and 51 %, respectively. Speed reduction leads to reductions of carbon emissions, carbon intensity and CII penalty, with greater reductions for larger ships. Under the optimal charterer profit, the speed of a time charter ship increases with the rise of freight rate and reduces with the decrease of freight rate. When the fluctuation range of freight rate is the same, the larger the ship type is, the smaller the speed adjustment range is. For the same ship type, when its freight rate decreases, it is suggested that the charterer reduces speed; otherwise, it is suggested that the charterer increases speed. For different ship types, if the shipping market is booming, the charterer should charter more large ships; otherwise, the charterer can choose more small ships.

Zelena perspektiva općeg i namjenskog kontejnerskog prijevoza od Azije do sjevernog Jadrana

Intermodal transport is often touted as an environmentally sustainable mode of transport, especially when considering its share of the total supply chain. This article addresses the topical and sensitive issue of supply chain assessment in terms of the carbon footprint and energy efficiency of external transportation from the point of production to the customer. The long transportation distances overseas and the variety of operational sea container services result in significant variations in delivery times from Asian markets. In addition, disruptive events such as the Covid-19 pandemic and the Ukraine conflict have led to longer transportation times and lower reliability of various maritime services. The study examines the variety of existing direct container services from Asia to the Northern Adriatic. In addition to the two direct container services already established, another dedicated container service has been established primarily to serve a large retail chain in the European market. The “newly established” container service is characterised by its limited port coverage and the use of relatively small container ships with a maximum capacity of 5.500 TEU. The comparisons between the services highlight the differences in transport time and greenhouse gas (GHG) emissions, as well as the energy efficiency of container transport. The study empirically confirms the widespread assumption that larger and modern container ships offer environmental benefits, provided that cargo space is used efficiently and ship operators adopt slow steaming. However, the study also highlights the significant differences in GHG emissions between different services and emphasises the need for more comprehensive information and awareness among cargo owners to design sustainable supply chains.

MODAN: Multifocal Object Detection Associative Network for Maritime Horizon Surveillance

In maritime surveillance systems, object detection plays a crucial role in ensuring the security of nearby waters by tracking the movement of various objects, such as ships and aircrafts, that are found at sea, detecting illegal activities and preemptively countering or predicting potential risks. Using vision sensors such as cameras to monitor the sea can help to identify the shape, size, and color of objects, enabling the precise analysis of maritime situations. Additionally, vision sensors can monitor or track small ships that may escape radar detection. However, objects located at considerable distances from vision sensors have low resolution and are small in size, rendering their detection difficult. This paper proposes a multifocal object detection associative network (MODAN) to overcome these vulnerabilities and provide stable maritime surveillance. First, it searches for the horizon using color quantization based on K-means; then, it selects and partitions the region of interest (ROI) around the horizon using the ROI selector. The original image and ROI image, converted to high resolution through the Super-Resolution Convolutional Neural Network (SRCNN), are then passed to the near-field and far-field detectors, respectively, for object detection. The weighted box fusion removes duplicate detected objects and estimates the optimal object. The proposed network is more stable and efficient in detecting distant objects than existing single-object detection models. Through performance evaluations, the proposed network exhibited an average precision surpassing that of the existing single-object detection models by more than 7%, and the false detection rate was reduced by 59% compared to similar multifocal-based state-of-the-art detection methods.

Unsupervised Ship Detection in SAR Imagery Based on Energy Density-Induced Clustering

Article Unsupervised Ship Detection in SAR Imagery Based on Energy Density-Induced Clustering Zifeng Yuan 1, Yu Li 1,*, Yu Liu 1, Jiale Liang 1, and Yuanzhi Zhang 2,3 1 Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China 2 School of Astronomy and Space Science, University of Chinese Academy of Sciences, Beijing 100049, China 3 Key Laboratory of Lunar and Deep Space Exploration, National Astronomical Observatories, Chinese Academy of Sciences, Beijing 100101, China * Correspondence: yuli@bjut.edu.cn Received: 6 March 2023 Accepted: 24 April 2023 Published: 26 September 2023 Abstract: Intelligent recognition of maritime ship targets from synthetic aperture radar (SAR) imagery is a hot research issue. However, interferences such as the strong sea clutter, sidelobe, small ship size and weak backscattered signal continually affect the detection results. To address this problem, a novel unsupervised machine learning-based ship detection algorithm, named energy density-induced clustering (EDIC), is proposed in this paper. It is discovered that the singular values between ship targets and interference signals are significantly different in a local region because of their various concentration degrees of signal energy intensity. Accordingly, in this study, two novel energy density features are proposed based on the singular value decomposition in order to effectively highlight the ship targets and suppress the interference. The proposed novel energy density features have the advantage of clearly distinguishing ship targets from sea surfaces regardless of the effects of interferences. To test the performance of the proposed features, unsupervised K-means clustering is conducted for obtaining ship detection results. Compared with the classical and state-of-the-art SAR ship detectors, the proposed EDIC method generally yields the best performance in almost all tested sea sample areas with different kinds of interferences, in terms of both detection accuracy and processing efficiency. The proposed energy density-based feature extraction method also has great potential for supervised classification using neural networks, random forests, etc.

IWIN: the Isfjorden Weather Information Network

Abstract. In an effort led by the University Centre in Svalbard (UNIS), with support from the Norwegian Meteorological Institute (MET Norway), the Isfjorden Weather Information Network (IWIN) is under development in the Isfjorden region in central Svalbard. The network substantially expands upon the relatively sparse existing operational network of weather stations and consists of compact and cost-efficient all-in-one weather stations permanently installed at lighthouses around Isfjorden and on board small tourist cruise ships trafficking the fjord from spring to autumn. All data from the network until June 2023, as presented in this paper, can be found at https://doi.org/10.5281/zenodo.8137588 (Frank et al., 2023b). New data become freely available in near-real time via MET Norway's data portals (https://doi.org/10.21343/ebrw-w846, Frank et al., 2023a). The IWIN data are highly valuable for scientific purposes such as atmospheric boundary layer research, the validation and development of numerical weather prediction models, and assimilation in these, as well as the planning and safe conduction of outdoor activities in the region.

DSF-Net: A Dual Feature Shuffle Guided Multi-Field Fusion Network for SAR Small Ship Target Detection

SAR images play a crucial role in ship detection across diverse scenarios due to their all-day, all-weather characteristics. However, detecting SAR ship targets poses inherent challenges due to their small sizes, complex backgrounds, and dense ship scenes. Consequently, instances of missed detection and false detection are common issues. To address these challenges, we propose the DSF-Net, a novel framework specifically designed to enhance small SAR ship detection performance. Within this framework, we introduce the Pixel-wise Shuffle Attention module (PWSA) as a pivotal step to strengthen the feature extraction capability. To enhance long-range dependencies and facilitate information communication between channels, we propose a Non-Local Shuffle Attention (NLSA) module. Moreover, NLSA ensures the stability of the feature transfer structure and effectively addresses the issue of missed detection for small-sized targets. Secondly, we introduce a novel Triple Receptive Field-Spatial Pyramid Pooling (TRF-SPP) module designed to mitigate the issue of false detection in complex scenes stemming from inadequate contextual information. Lastly, we propose the R-tradeoff loss to augment the detection capability for small targets, expedite training convergence, and fortify resistance against false detection. Quantitative validation and qualitative visualization experiments are conducted to substantiate the proposed assumption of structural stability and evaluate the effectiveness of the proposed modules. On the LS-SSDDv1.0 dataset, the mAP50−95 demonstrates a remarkable improvement of 8.5% compared to the baseline model. The F1 score exhibits a notable enhancement of 6.9%, surpassing the performance of advanced target detection methods such as YOLO V8.

CViTF-Net: A Convolutional and Visual Transformer Fusion Network for Small Ship Target Detection in Synthetic Aperture Radar Images

Detecting small ship targets in large-scale synthetic aperture radar (SAR) images with complex backgrounds is challenging. This difficulty arises due to indistinct visual features and noise interference. To address these issues, we propose a novel two-stage detector, namely a convolutional and visual transformer fusion network (CViTF-Net), and enhance its detection performance through three innovative modules. Firstly, we designed a pyramid structured CViT backbone. This design leverages convolutional blocks to extract low-level and local features, while utilizing transformer blocks to capture inter-object dependencies over larger image regions. As a result, the CViT backbone adeptly integrates local and global information to bolster the feature representation capacity of targets. Subsequently, we proposed the Gaussian prior discrepancy (GPD) assigner. This assigner employs the discrepancy of Gaussian distributions in two dimensions to assess the degree of matching between priors and ground truth values, thus refining the discriminative criteria for positive and negative samples. Lastly, we designed the level synchronized attention mechanism (LSAM). This mechanism simultaneously considers information from multiple layers in region of interest (RoI) feature maps, and adaptively adjusts the weights of diverse regions within the final RoI. As a result, it enhances the capability to capture both target details and contextual information. We achieved the highest comprehensive evaluation results for the public LS-SSDD-v1.0 dataset, with an mAP of 79.7% and an F1 of 80.8%. In addition, the robustness of the CViTF-Net was validated using the public SSDD dataset. Visualization of the experimental results indicated that CViTF-Net can effectively enhance the detection performance for small ship targets in complex scenes.

LMSD-Net: A Lightweight and High-Performance Ship Detection Network for Optical Remote Sensing Images

Ship detection technology has achieved significant progress recently. However, for practical applications, lightweight ship detection still remains a very challenging problem since small ships have small relative scales in wide images and are easily missed in the background. To promote the research and application of small-ship detection, we propose a new remote sensing image dataset (VRS-SD v2) and provide a fog simulation method that reflects the actual background in remote sensing ship detection. The experiment results show that the proposed fog simulation is beneficial in improving the robustness of the model for extreme weather. Further, we propose a lightweight detector (LMSD-Net) for ship detection. Ablation experiments indicate the improved ELA-C3 module can efficiently extract features and improve the detection accuracy, and the proposed WGC-PANet can reduce the model parameters and computation complexity to ensure a lightweight nature. In addition, we add a Contextual Transformer (CoT) block to improve the localization accuracy and propose an improved localization loss specialized for tiny-ship prediction. Finally, the overall performance experiments demonstrate that LMSD-Net is competitive in lightweight ship detection among the SOTA models. The overall performance achieves 81.3% in AP@50 and could meet the lightweight and real-time detection requirements.

Emission factors of oxygenated polycyclic aromatic hydrocarbons from ships in China

The rapid growth of maritime traffic, transportation, and fishery activities has increased shipping emissions and degraded the air quality in coastal areas. As a result, controlling ocean-based pollution sources have become increasingly important. This study investigated the real-world emission characteristics of oxygenated polycyclic aromatic hydrocarbons (OPAHs, a group of highly toxic semi-volatile organic compounds) from five types of offshore ships using diesel oil: small and medium fishing ships, tug boats, ferry, and engineering ships, under various driving mode. Both gaseous and particle emission factors (EF) of four specific OPAHs were determined in our study. Among the OPAHs species emitted from ships, 9-fluorenone (9FO; 72%) and anthrathrace-9,10-quinone (ATQ; 25%) were the most abundant. The arithmetic mean of the sum of gaseous OPAHs EFs for all ships in this study was 2.5 ± 4.4 mg/kg fuel burned, and the mean particulate OPAHs EF was 4.7 ± 7.9 mg/kg. Small fishing ships had the highest total OPAHs EFs (31.0 ± 17.0 mg/kg). Apart from small fishing ships, there was no significant difference in the total EF of OPAHs for the other four types of ships. The emissions of the four OPAHs are predominantly in the particulate phase. There were no significant differences in the emissions of the four OPAHs under different driving mode. According to estimates, the annual OPAH emissions from the four types of ships in Hainan in 2017 were approximately 4.2 (range: 2.7–7.0) tons, dwarfing the OPAH emissions from diesel-powered on-road vehicles in China (23.5 kg).

Sar Ship Detection Based on Convnext with Multi-Pooling Channel Attention and Feature Intensification Pyramid Network.

The advancements in ship detection technology using convolutional neural networks (CNNs) regarding synthetic aperture radar (SAR) images have been significant. Yet, there are still some limitations in the existing detection algorithms. First, the backbones cannot generate high-quality multiscale feature maps. Second, there is a lack of suitable attention mechanisms to suppress false alarms. Third, the current feature intensification algorithms are unable to effectively enhance the shallow feature's semantic information, which hinders the detection of small ships. Fourth, top-level feature maps have rich semantic information; however, as a result of the reduction of channels, the semantic information is weakened. These four problems lead to poor performance in SAR ship detection and recognition. To address the mentioned issues, we put forward a new approach that has the following characteristics. First, we use Convnext as the backbone to generate high-quality multiscale feature maps. Second, to suppress false alarms, the multi-pooling channel attention (MPCA) is designed to generate a corresponding weight for each channel, suppressing redundant feature maps, and further optimizing the feature maps generated by Convnext. Third, a feature intensification pyramid network (FIPN) is specifically designed to intensify the feature maps, especially the shallow feature maps. Fourth, a top-level feature intensification (TLFI) is also proposed to compensate for semantic information loss within the top-level feature maps by utilizing semantic information from different spaces. The experimental dataset employed is the SAR Ship Detection Dataset (SSDD), and the experimental findings display that our approach exhibits superiority compared to other advanced approaches. The overall Average Precision (AP) reaches up to 95.6% on the SSDD, which improves the accuracy by at least 1.7% compared to the current excellent methods.

Grounding risk quantification in channel using the empirical ship domain

Ship groundings in channels pose substantial risks to human safety and economic stability. Existing quantitative models inadequately factor in risks related to ship deviations from the channel and variations in ship grounding susceptibility. We propose a grounding risk quantification framework utilizing Automatic Identification System (AIS) data and empirical ship domain, designed to assess risks from channel deviations or shallow water approaches. We build an empirical ship domain model, then develop a spatial grounding risk function considering ship sensitivity variations across directions. By exploring spatiotemporal relationships between ships and unnavigable waters, we quantify and normalize grounding risk. We apply this framework to the Yangtze River Estuary channel, finding higher grounding risk in the main channel than in the small ship channel due to smaller widths, shallow water, and the small ship channel entrance’s proximity to the anchorage. This provides valuable support for channel management and shipping safety.

Optimization of ship routing and allocation in a container transport network considering port congestion: A variational inequality model

In recent years, the service capacity of coastal ports has failed to keep pace with the increasing demand for ocean transportation, resulting in frequent congestion that adversely affects the fleet deployment of numerous shipping companies. This research introduces a variational inequality model aimed at optimizing ship routing and allocation schemes to address port congestion. The model enhances ship allocation between ports by considering the congestion status of ports and alternative port options. The objective functions incorporate time and transportation costs, with the aim of achieving a balanced shipping cost across different routes. A diagonal algorithm is proposed for solving the model. The case study focuses on several coastal port routes in China and presents modeling and calculation results. The numerical examples demonstrate that as port congestion worsens: (1) the waiting time and shipping schedule losses incurred by shipping companies increase; (2) shipping companies tend to allocate ships to backup ports; (3) small container ships are preferred for shipping companies when choosing ship types. Therefore, in the event of port congestion, shipping companies must devise ship routing and allocation plans based on the severity of congestion and make informed decisions accordingly.

A Novel Decoupled Feature Pyramid Networks for Multi-Target Ship Detection.

The efficiency and accuracy of ship detection is of great significance to ship safety, harbor management, and ocean surveillance in coastal harbors. The main limitations of current ship detection methods lie in the complexity of application scenarios, the difficulty in diverse scales object detection, and the low efficiency of network training. In order to solve these problems, a novel multi-target ship detection method based on a decoupled feature pyramid algorithm (DFPN) is proposed in this paper. First, a feature decoupling module is introduced to separate ship contour features and position features from the multi-scale fused features, to overcome the problem of similar features in multi-target ships. Second, a feature pyramid structure combined with a gating attention module is constructed to improve the feature resolution of small ships by enhancing contour features and spatial semantic information. Finally, a feature pyramid-based multi-feature fusion algorithm is proposed to improve the adaptability of the network to changes in ship scale according to the contextual relationship of ship features. Experiments on the multi-target ship detection dataset showed that the proposed method increased by 6.3% mAP and 20 FPS higher than YOLOv4, 7.6% mAP and 36 FPS higher than Faster-R-CNN, 5% mAP and 36 FPS higher than Mask-R-CNN, and 4.1% mAP and 35 FPS higher than DetectoRS. The results demonstrate that the DFPN can detect multi-target ships in different scenes with high accuracy and a fast detection speed.

Electrical and thermal performance of multidimensional semi-transparent CdTe PV window on offshore passenger ships in moored and sailing condition

Utilizing solar energy is an effective method to decrease fossil energy consumption in marine field. Horizontally installed deck photovoltaic (PV) systems have been widely investigated to reduce navigation energy consumption on ocean-going ships. However, energy saving rate is unsatisfactory because of the low energy density of solar energy and the shadow caused by ship body. As for large amount of offshore small ships, it's even harder to utilize the existing technology since deck space is limited. In this paper, semi-transparent CdTe PV window system is proposed for offshore passenger ships without enough deck spaces but with large window area around ship cabin. Multidimensional PV windows can increase PV area and reduce heat gain inside the cabin, shadow effect caused by ship body is also avoided. Energy saving can be both achieved by electrical generation actively and cooling load reduction passively. Dynamic mathematical models of multidimensional PV window under moored and sailing conditions are established. Simulation results indicate that the daily electrical generation and average electrical efficiency are 0.74 kWh & 14.65% in moored condition and 0.83 kWh & 16.24% in sailing condition. Navigation leads to higher received solar radiation (18.2%), higher electrical generation (12.1%) and higher electrical efficiency (10.85%). Electricity created by PV window can support 6 h' continuous operation for auxiliary system on board. With PV windows, the average air temperature in cabin is decreased by 6.5 °C compared with ordinary windows. The average heat fluxes through four directions of PV windows are decreased by 75.44%, 73.64%, 47.52% and 75.33% compared with ordinary windows.

Formulation of Anchor and Chain Specifications for Establishing Subsea Cable Burial Depth

The penetration of ship anchor, which is a very important factor considered in the design stage of subsea cable burial, is highly dependent on the geometrical specifications and shapes of the anchor and chain. In order to facilitate predicting the anchor penetration depth by an arbitrary ship, formulations of the correlation between the ship mass and the existing anchor and chain specifications were attempted. In particular, for small ships with an equipment number of 40 (corresponding to DWT 90) or less, which were not covered by the existing classification data, a method for estimating the anchor mass, chain diameter, and mass per unit length was proposed.

YOLO-Lite: An Efficient Lightweight Network for SAR Ship Detection

Automatic ship detection in SAR images plays an essential role in both military and civilian fields. However, most of the existing deep learning detection methods introduce complex models and huge calculations while improving the detection accuracy, which is not conducive to the application of real-time ship detection. To solve this problem, an efficient lightweight network YOLO-Lite is proposed for SAR ship detection in this paper. First, a lightweight feature enhancement backbone (LFEBNet) is designed to reduce the amount of calculation. Additionally, a channel and position enhancement attention (CPEA) module is constructed and embedded into the backbone network to more accurately locate the target location by capturing the positional information. Second, an enhanced spatial pyramid pooling (EnSPP) module is customized to enhance the expression ability of features and address the position information loss of small SAR ships in high-level features. Third, we construct an effective multi-scale feature fusion network (MFFNet) with two feature fusion channels to obtain feature maps with more position and semantic information. Furthermore, a novel confidence loss function is proposed to effectively improve the SAR ship target detection accuracy. Extensive experiments on SSDD and SAR ship datasets verify the effectiveness of our YOLO-Lite, which can not only accurately detect SAR ships in different backgrounds but can also realize a lightweight architecture with low computation cost.

Life Cycle Assessment of LPG Engines for Small Fishing Vessels and the Applications of Bio LPG Fuel in Korea

This study aimed to evaluate the environmental impact of using liquefied petroleum gas (LPG) in small fishing vessels by conducting a life cycle assessment (LCA) in Korea. For the first time in the country, LPG engines designed for small fishing ships were utilized in this study. In addition, this research examined the potential benefits of employing Bio LPG, a renewable LPG produced from two distinct raw materials (crude palm oil (CPO) and refined, bleached, and deodorized (RBD) palm oil), instead of conventional LPG. The LCA findings reveal that utilizing LPG fuel in small fishing vessels can reduce greenhouse gas (GHG) emissions by more than 30% over conventional gasoline and diesel fuels. During the life cycle of vessels that use LPG fuel instead of gasoline and diesel fuels, there is a reduction of 2.2 and 1.2 million tons of GHG emissions, respectively. Moreover, substituting conventional fossil fuels with Bio LPG can result in over 65% reduction in GHG emissions. For the life cycle of boats that use Bio LPG fuel in place of gasoline and diesel fuels, the reduction of GHG emissions was 4.9 million tons and 2.5 million tons for CPO and 5.2 million tons and 2.7 million tons for RBD, respectively. This study not only underscores the substantial advantages of using Bio LPG over conventional fossil fuels but also presents conventional LPG as a way to reduce GHG emissions and promote sustainable practices in the fishing industry.

PROPOSAL THE SUITABLE STRUCTURE ON SMALL VESSEL AND BARGE IN THICK SOFT SOIL AREA IN THE GEOLOGICAL AREA OF TRA VINH PROVINCE, VIETNAM

Tra Vinh Province with its interlaced river system is convenient for navigation, especially for barges and small vessels under 1000 DWT. Geology in Tra Vinh primarily has a thick soft soil surface layer, averaging from 20 m – 40 m, even over 50 m in some places, structural solutions for docks in most regions choose piers. Thus, the high platform on the pile foundation. However, in practice, we found that most of the proposed wharf structure solutions for barges and small ships are not reasonable, the piles in the berth structure have not brought into full play their bearing capacity, leading to excessive and inefficient investment costs for large-scale projects. Within the scope of the paper, we propose a more reasonable wharf structure solution based on the integrated analysis of the geological features of the structure, the load due to the ship, and the operating loads on the berth.

A New Shipping Canal Through the Vistula Spit as a Political and Transportation Project

In September 2022, a new shipping canal was opened connecting the Polish part of the Vistula Lagoon to the Baltic Sea. Largely political, the project links the lagoon and the port in Elbląg to the southern part of the Baltic, independent of the Russian Federation. In addition, its economic dimension enables the handling of small ships, as well as supporting tourism and yachting without the need to pass through the Russian-controlled Piława Strait. The scale of the new canal is relatively small—one and a half kilometre long and 25 metres wide. Nonetheless, it is sufficient for the navigation of small marine vessels of up to five-metre draft. The shipping canal through the Vistula Split is certainly not as important as the Corinth or North Sea Canals, still, it frees maritime and tourist traffic from Russian jurisdiction. The planned key port in the Vistula Lagoon is the port in Elbląg, a historic city that was once a member of the Hanseatic League, which brought together all the major cities of the Baltic Sea basin in the 14th and 15th centuries. The purpose of this article is to present the project’s historical context, its urban, technical, and shipping solutions, as well as the correlations between the new transport development and its anticipated impact on the environment (including the natural environment). The findings are complemented by a PESTEL analysis which shows the leading trends that are relevant to the implementation of the project in the region. The analysis identified areas that have a significant effect on the social, political, and economic settings of the new canal.

Optimal Control Strategy for Ship Cabin’s Active Chilled Beam System Using Improved Multi-Objective Salp Swarm Algorithm

Heating, ventilation, and air conditioning (HVAC) systems are the second largest energy consumers on cruise ships after the propulsion system. As a kind of HVAC system, active chilled beam (ACB) systems have been widely used for cabin service due to their performance of energy efficiency and good thermal comfort. However, conventional control strategies for ships’ ACB systems are not intelligent enough and cannot balance energy consumption and cabin comfort during the voyages of ships. This study developed an optimal control strategy for cabins’ ACB systems. First, a simulation environment considering dynamic conditions is established in TRNSYS. Second, an artificial neural network model is utilized to predict the energy consumption of the ACB system, while the predicted percentage dissatisfied is adopted to represent cabins’ thermal discomfort. Third, an improved multi-objective salp swarm algorithm is proposed to dynamically minimize both energy consumption and thermal discomfort. A TRNSYS–MATLAB co-simulation testbed is established to simulate the cabins served by an ACB system on a small cruise ship navigating from Hong Kong to Shanghai for validation tests and a comparison study. Compared to the conventional strategies, the proposed strategy can achieve a maximum energy savings of 12% while maintaining a predicted mean vote index less than 0.5, meeting the comfort requirements set by ASHRAE.