This paper presents a vehicle detection and classification system using distributed fiber-optic acoustic sensing (DAS) technology and describes a comprehensive classification method including signal processing and feature extraction. This sensing device is based on Rayleigh scattering light and is used for real-time vehicle detection, classification, and speed estimation. Distributed acoustic signals from an arbitrary point can be detected and located through DAS technology which can provide fully distributed acoustic information along the entire fiber link. This technology utilizes sensing fiber in the form of distributed sensors to collect traffic vibration signals and then extracts several features from the signals to estimate the vehicle count and identify vehicle categories. According to the vehicle vibration signal characteristics, the wavelet-denoising algorithm and dual-threshold algorithm are improved. The improved algorithm is used to reconstruct the signal for feature extraction, and the vehicle count and speed are obtained. When all features have been extracted, the classification of vehicle types is implemented by a support vector machine classifier. The validation data (using a distributed fiber-optic acoustic sensor) demonstrate that the vehicle detection accuracy is higher than 80%, the speed estimation error is less than 5%, and the vehicle classification accuracy is higher than 70%.

Vehicular sensor networks have been widely applied in intelligent traffic systems in recent years. Because of the specificity of vehicular sensor networks, they require an enhanced, secure and efficient authentication scheme. Existing authentication protocols are vulnerable to some problems, such as a high computational overhead with certificate distribution and revocation, strong reliance on tamper-proof devices, limited scalability when building many secure channels, and an inability to detect hardware tampering attacks. In this paper, an improved authentication scheme using certificateless public key cryptography is proposed to address these problems. A security analysis of our scheme shows that our protocol provides an enhanced secure anonymous authentication, which is resilient against major security threats. Furthermore, the proposed scheme reduces the incidence of node compromise and replication attacks. The scheme also provides a malicious-node detection and warning mechanism, which can quickly identify compromised static nodes and immediately alert the administrative department. With performance evaluations, the scheme can obtain better trade-offs between security and efficiency than the well-known available schemes.

As one of main challenge for carriers, empty container repositioning is subject to various uncertain factors in practice, which causes more operation costs. At the same time, the movements of empty containers can result in air pollution because of the CO₂ emission, which has a negative impact on sustainable development. To incorporate environmental and stochastic characteristics of container shipping, in this paper, an empty container repositioning problem, taking into account CO₂ emission, stochastic demand, and supply, is introduced in a sea–rail intermodal transportation system. This problem is formulated as a chance-constrained nonlinear integer programming model minimising the expected value of total weighted cost. A sample average approximation method is applied to convert this model into its deterministic equivalents, which is then solved by the proposed two-phase tabu search algorithm. A numerical example is studied to conclude that the stochastic demand and supply lead to more repositioning and CO₂ emission-related cost. Total cost, inventory cost, and leasing cost increase with the variabilities of uncertain parameters. We also found that the total cost and other component costs are strongly dependent on the weights of repositioning cost and CO₂ emission-related cost. Additionally, the sensitivity analysis is conducted on unit leasing cost.

Autonomous vehicular clouds, as the combination of cloud computing and conventional vehicular ad hoc networks, will provide abundant resources and services by sharing under-utilized resources of future high-end vehicles such as computing power, storage and internet connectivity. Autonomous vehicular clouds will have significant impact if widely implemented in the intelligent transportation system. However, security and privacy issues are still big challenges in autonomous vehicular clouds. In this paper, after analyzing the particularity of autonomous vehicular clouds, we implement a two-layered architecture, in which vehicles are self-organized without the help of roadside units. Then based on the architecture, we put forward an effective key management protocol to distribute a group key efficiently and also provide the authentication and confidentiality that lots of current secure schemes ignore. In addition, according to the different scenarios and security levels we categorize the way of message transmitting into three kinds. At last, with performance evaluations, the proposed protocol can perform more efficiently than other well-known available schemes.

Railway intrusion detection is a serious task in intelligent rail transportation system, ensuring the safe operation of trains and the safety of people's lives. The existing railway intrusion detection methods mainly adopt the methods based on deep learning with large-scale datasets, which is undoubtedly costly. The emerging few-shot object detection method (FSOD) provides a potential solution to solve the suddenness and contingency of railway intrusion. However, there is an inherent contradiction between the performance of the novel class and base class in most FSOD, i.e., the model bias problem. To address the above-mentioned issues of railway intrusion detection, few-shot railway intrusion detection (FSRD), consisting of Balanced region proposal network (RPN), Double Detector and an efficient fine-tuning framework, is proposed to detect novel class by few-shot samples without forgetting base class. Balanced RPN is developed to generate high-quality proposals that are not biased towards any classes for subsequent training and inference. Double Detector is further proposed to handle the region of interest (RoI) features of the novel and base class respectively, and generalize the novel class without forgetting the base class. Upon these baselines, we also tailored an efficient fine-tuning framework to further improve overall performance. The proposed method is extensively experimented on our constructed few-shot railway intrusion dataset (FSRT2023). Experimental results show that the overall performance of FSRD significantly outperforms some state-of-the-art (SOTA) methods in all settings. FSRD can be used as a never-forgetting few-shot detector that learns rare novel classes in railway intrusion detection.

Contra-rotating fans (CRFs) have garnered significant attention due to their higher power-to-weight ratio compared to traditional fans; however, limited focus has been given to the localization and development of local aerodynamic losses. Furthermore, there is a need for further research on the impact of load distribution along the radius on local entropy production. Therefore, this study aims to investigate a contra-rotating fan as the research subject. An optimal design for load distribution along the radius is achieved by constructing a surrogate model in combination with a genetic algorithm. The effectiveness of this design has been verified through experimentation using a specific test device. In this study, a local entropy production rate (EPR) model adapted to the shear stress transport-detached eddy simulation (SST-DES) technique is constructed to evaluate the loss distribution of the contra-rotating fan. This paper primarily focuses on comparing and analyzing the blade profile and overall performance of the CRFs before and after optimization. The EPR contribution of each interval along the radius is compared to the corresponding blade channel to identify the approximate range of high-EPR regions. Furthermore, an investigation is conducted to examine the distribution of EPR along the streamwise direction in these high-EPR regions. After that, by comparing the development of the flow structure near a stall before and after optimization, combined with the analysis of the EPR contours, the EPR mechanism of this CRF is revealed.