Shanghai Maritime University

Wang H., Jiao J.

As an important online learning resource, Massive Open Online Courses have a large amount of comments, which can be exploited by aspect-level sentiment analysis to optimize MOOC teaching from different perspectives. However, there are two essential problems. One is that there is no open-source dataset on Chinese MOOC. The other problem is semantic information confusion caused by inherent polysemy of Chinese words and ambiguous expressions relatively relying on the context. In order to further characterize the special features of Chinese MOOC reviews, we build an open-source dataset with clean 5000 MOOC reviews and propose a sentiment knowledge dependency tree based graph neural network. The proposed model firstly uses the latest term frequency–inverse document frequency algorithm to extract high-frequency words, and combines it with the Semantic Orientation Pointwise Mutual Information algorithm so that a sentiment dictionary in the field of Chinese MOOCs is constructed. Then, the grammatical information of the dependency tree is merged with the sentiment knowledge information of the sentiment dictionary. Next, this novel model uses GCN to capture the long-distance feature information of the sentiment dependency tree, and finally adopts the softmax function for sentiment classification. To further improve the model's performance, we also use Bert to enhance the text representation for higher accuracy. Meanwhile, the comparative experiments demonstrate that our proposed model takes advantages of the customized dependency tree by knowledge dictionary to achieve more accurate sentiment analysis than the state-of-the-art methods under different word embedding approaches.

Xu H., Zuo J., Wang T.

Leakage in oil and gas transportation pipelines is a critical issue that often leads to severe hazardous accidents at oil and gas chemical terminals, resulting in devastating consequences such as ocean environmental pollution, significant property damage, and personal injuries. To mitigate these risks, timely detection and precise localization of pipeline leaks are of paramount importance. This paper employs a distributed fiber optic sensing system to collect pipeline leakage signals and processes these signals using the traditional variational mode decomposition (VMD) algorithm. While traditional VMD methods require manual parameter setting, which can lead to suboptimal decomposition results if parameters are incorrectly chosen, our proposed method introduces an improved particle swarm optimization algorithm to automatically determine the optimal parameters. Furthermore, we integrate VMD with fuzzy dispersion entropy to effectively select and reconstruct intrinsic mode functions, significantly enhancing the denoising performance. Our results demonstrate that this approach can achieve a signal-to-noise ratio of up to 24.15 dB and reduce the mean square error to as low as 0.0027, showcasing its superior capability in noise reduction. Additionally, this paper proposes a novel threshold setting technique that addresses the limitations of traditional methods, which often rely on instantaneous values and are prone to false alarms. This innovative approach significantly reduces the false alarm rate in gas pipeline leakage detection, ensuring higher detection accuracy and reliability. The proposed method not only advances the technical capabilities of pipeline leakage monitoring but also offers strong practical applicability, making it a valuable tool for enhancing the safety and efficiency of oil and gas transportation systems.

Zhao M., Fang J., Chen B.

Zhou D., Yang Y., Cai R.

To address the challenges of carbon emission reduction in the global shipping industry and the requirements of the International Maritime Organization (IMO)’s Carbon Intensity Indicator (CII) rating, this paper takes China’s commuter ships as an example to study the dynamic optimization of ship routes based on CII implementation requirements. In response to the existing research gap in the collaborative optimization of routes and carbon emissions under CII constraints, this paper constructs a mixed-integer programming model that comprehensively considers CII limits, port throughput capacity, channel capacity, and the stochastic demand for spot cargo. The objective is to minimize the operating costs of shipping companies, and an adaptive genetic algorithm is designed to solve the dynamic route scheduling problem. Numerical experiments demonstrate that the model can reasonably plan routes under different sequences of spot cargo arrivals, ensuring compliance with CII ratings while reducing total costs and carbon emissions. The results indicate that the proposed method provides efficient decision-making support for dynamic ship scheduling under CII constraints, contributing to the green transformation of the shipping industry. Future work will extend the model to scenarios involving multiple ship types and complex maritime conditions, further enhancing its applicability.

Du R., He D., Song Y., Ding Z., Chan S., Li X.

Yuanlin C., BEILIN Z., Li Y., long W., Jiao J., Yu C., An B.

A multispectral-compatible camouflage device with effective thermal management is proposed, which consists of seven thin films with five materials (Si, Ge, SiO2, GST and Al). Within the visible spectrum (380-780 nm), the structural color of the device can be controlled by altering the thickness of the top Si layer, allowing it to adapt to diverse environmental backgrounds. Within the infrared spectrum, the average emissivity in the atmospheric window of 3-5 μm and 8-14 μm is as low as 17.7% and 23.0% respectively. However, it is as high as 65.7% in the non-atmospheric window 5-8 μm, which can realize effective thermal management with a net radiative cooling power of 500 W/m2 at an ambient temperature of 300 K and a working temperature of 350 K. For laser radar camouflage, it has a narrowband high emissivity of 81.3%, 84.8% and 81.4% at 1.06 μm, 1.55 μm and 10.6 μm respectively. Additionally, the device has excellent angular and polarization insensitivity. Using a simple film structure to realize multispectral camouflage and efficient thermal management guides coordinated control of electromagnetic waves and heat, which has wide implications in industrial manufacturing and military camouflage fields.

Chao Z., Zhou J., Shi D., Zheng J.

Particle size distribution (PSD) of coral sand is a critical factor that influences the mechanical properties at the coral sand-geogrid (CS-GG) interface, affected by both particle breakage and various temperatures. However, relevant research is currently scarce. This study conducts a series of large-scale interface shear tests on coral sand with three PSD ranges (0.25 ∼ 1 mm, 1 ∼ 2 mm, and 2 ∼ 4 mm) at varying temperatures (5°C ∼ 80°C). Experimental results demonstrate that the IB value at the CS-GG interface ascends and then descends with the increase of PSD from 20°C to 40°C. The IB value at the interface descends and then ascends with the increase of PSD from 60°C to 80°C. The PSD curves at the interface indicate that the particle breakage degree of coral sand increases with rising temperature (5°C ∼ 40°C). The larger PSD of coral sand, the smaller fractal dimensions (D) of the interface. A mathematical formulation of the relationship between the relative breakage rate (Br) and the D value at interfaces is presented, which considers temperature effects. The relationship between the total input energy (E) and the Br value has been expressed by empirical formulations with different PSD ranges, where the fitting curve for 2 ∼ 4 mm coral sand exhibits a hyperbolic pattern.

Guo Y., Yang R., Zhang Z., Han B.

In the domain of course control, traditional methods such as proportional–integral–derivative (PID) control often exhibit limitations when addressing complex nonlinear systems and uncertain disturbances. To mitigate these challenges, the adaptive neuro-fuzzy inference system (ANFIS) has been integrated into course control strategies. The primary objective of this study is to investigate the course control characteristics of vessels governed by the ANFIS controller under both normal and severe sea conditions. A three-degree-of-freedom (3-DOF) maneuvering model set (MMG) was employed and validated through sea turning tests. The design of the ANFIS controller involved a combination of the backpropagation algorithm with the least square method. Training data for the ANFIS control system were derived from a linear control framework, followed by simulation tests conducted under normal and severe sea conditions to assess control performance. The simulation results indicate that in normal sea conditions, ANFIS has more stable heading control (smaller Aψ), but at the cost of more energy consumption (larger Iδ). Notably, response time is reduced by approximately 36.7% compared to that of the linear controller. Conversely, during severe sea conditions, ANFIS exhibits an increase in response time by about 33.3% relative to the linear controller while maintaining a smaller Iδ. In the whole course control stage, the stability is better than the linear controller, and it has better energy-saving characteristics. Under scenarios involving small and large course alterations, Aψ values for ANFIS are approximately 11.28% and 13.97% higher than those observed with the best-performing linear controller (λψ = 60), respectively. As the propeller speed increases, the Aψ value of the ANFIS controller decreases significantly, to about 62.71%, indicating that the energy efficiency is improved and the course stability is also enhanced. In conclusion, it can be asserted that the implementation of an ANFIS controller yields commendable performance in terms of controlling vessel courses effectively.

Wei L., Xie F., Chen J., Chu F., Zhang Z., Yi M., Zhang J., Chen F.

Ji F., Wu J., Chiclana F., Sun Q., Herrera-Viedma E.

Yao Z., Xu Q.

Zhou W., Wang Y., Wu Z., Yan H.

Xiao C., Wu W., Guo Q., Orabi M., Koutroulis E., Chung H.S., Blaabjerg F.

Wang H., Wu W., Hou X., Cui L., Blaabjerg F.

Zhang Y., Wu H., Xian J., Mei X., Zhang K., Zhang Q., Wang F.