Traditional Vehicles have an adverse effect on the environment. Therefore, the current technological shift is constantly seeking an alternative to replace traditional vehicles fueled by fossil fuels, and Electric vehicles are, so far, the best alternative. The adoption of Electric Vehicles (EVs) is growing rapidly due to their eco-friendly benefits and technological advancements. This growth, however, brings a significantly larger attack surface due to increased interconnectivity between electric vehicles, charging stations and the smart grid system. To prevent such types of attacks, we need a robust system to detect them beforehand and prevent the system from being compromised. Although some prior work has been conducted in this area, their approaches did not incorporate deep learning algorithms, nor did they evaluate model performance under noisy data conditions. Therefore, we proposed a novel ensemble-based intrusion detection system (IDS) to detect these attacks in Electric Vehicle Charging Stations (EVCS). We implement different Machine learning algorithms such as k-nearest neighbors (KNN), Logistic Regression (LR), Support Vector Machine (SVM) and Decision Tree (DT). Moreover, as different types of malwares often exhibit distinct structural characteristics when visualized as images, we also use Convolutional Neural Networks (CNNs) to detect such attacks and malware. We are focusing on detecting attacks in Electric vehicle charging stations by analyzing the network traffic. For this, we utilize the latest labelled dataset, the Canadian Institute of Cybersecurity EV Charger Attack Dataset 2024 (CICEVSE2024), which is a multidimensional dataset containing both benign and attack data. We then evaluate & compare the performance of these algorithm in detecting the network traffic attacks in Electric Vehicle Charging Stations (EVCS). Our proposed model employs an ensemble voting strategy to combine the predictions from different classifiers, thereby improving the system's robustness and accuracy, and achieves an accuracy of 99.5% in detecting cyberattacks. With the addition of small noise to the dataset, a few individual classifiers perform poorly; however, the ensemble model still maintains an accuracy of 99.2%.

Artificial intelligence systems increasingly rely on large volumes of sensitive data to support decision making in domains such as healthcare, finance, education, and public administration. While these systems offer substantial benefits, their growing dependence on personal information has intensified concerns about privacy, data misuse, and loss of public trust. This study examines privacy-preserving artificial intelligence as a design approach that enables meaningful data analysis while limiting exposure of sensitive information. The paper analyses key privacy-preserving techniques, including federated learning, differential privacy, homomorphic encryption, and secure multi party computation, and evaluates their relevance across critical application domains. It also identifies practical challenges related to computational cost, data heterogeneity, regulatory compliance, and explainability. The study shows that privacy-preserving methods can support responsible and trustworthy artificial intelligence when privacy, utility, and governance considerations are addressed together.

In order to improve the defects of the Sparrow Search Algorithm (SSA) in single-species search, which causes redundancy in its speed of collection and easily ignores high-quality solutions and falls into local optimality, a K-means Multigroup Sparrow Search Algorithm (KSSA) based on K-means clustering was proposed. Firstly, the multi-population mechanism is introduced into SSA to weaken the collection ability of a single population and reduce the probability of falling into the local optimum. Secondly, the K-means algorithm is used to divide the sub-populations to increase the differences between the sub-populations, and at the same time, the individuals in the sub-populations focus on searching in a small range, thereby improving the efficiency of the initial search. Then, the weighted centroid communication strategy is used to improve the quality of communication between populations, reduce the interference of the own population, and reduce the risk of all sub-populations falling into the local optimum due to a sub-population falling into the local optimum. Finally, dynamic reverse learning is introduced into the vigilant to enhance its anti-predator behavior and improve the defects of slower collection speed and insufficient collection accuracy caused by the increase in the number of sub-populations. The test function simulation experiment shows that KSSA has better optimization performance than SSA and other algorithms.

The Cyclone Global Navigation Satellite System (CYGNSS) provides high-quality Global Navigation Satellite System Reflectometry (GNSS-R) data, which can be reliably used for the inversion of Significant Wave Height (SWH). Due to the high dynamics of CYGNSS, the received signal is easily affected by environmental factors, and the complexity of the sea conditions makes it difficult for simple models to accurately invert SWH. In order to solve the above problems, this paper proposes a multivariate SWH inversion model based on machine learning. According to the formation mechanism of waves and the correlation analysis between CYGNSS parameters and SWH, relevant parameters are selected, and three training schemes of 5 parameters, 9 parameters and 17 parameters are designed. Subsequently, the inversion model was trained and validated using Random Forest (RF) and Convolutional Neural Network (CNN), and the SWH inversion results were compared with the reference values of the European Centre for Medium-range Weather Forecasts (ECMWF). The best inversion model was the 17-parameter CNN inversion model with an RMSE of = 0.1840 m.

To realize the fault diagnosis of complex systems, a fault diagnosis method for complex systems based on the explanatory structural model is proposed. According to the fault mechanism analysis, the fault association matrix of system elements is established. The explanatory structural model is applied to transform the fault association relationship of complex systems into an intuitive hierarchical structural model through matrix transformation, so as to realize the structuring and hierarchical propagation of system faults. The PageRank algorithm is introduced to evaluate the influence and impact of system element faults. According to the influence and impact of the same-layer components and the fault transmission logic, the main cause of system fault transmission is clarified, which provides a basis for fault diagnosis. Finally, a specific application is carried out with a certain device as an example to verify the effectiveness of the method.

Due to the low density and pressure of the Martian atmosphere, the aerodynamic performance of the airfoil of the Mars UAV needs to be further improved. Plasma excitation active flow control technology is used to improve the lift of the airfoil and reduce the drag of the airfoil under Martian conditions. The effects of the action position, excitation power and incoming flow angle of attack on the lift and drag of the airfoil are studied under the low Reynolds number conditions on Mars. The results show that plasma excitation increases lift in the trailing edge area of the lower surface, with a maximum lift increase rate 37%; reduces drag in the leading edge area of the lower surface, with a maximum drag reduction rate 8%; the greater the excitation power and the smaller the incoming flow angle of attack, the more obvious the improvement of the airfoil lift-to-drag ratio. Plasma excitation induces pressure waves, forming a pressurization zone and a decompression zone upstream and downstream of the excitation, respectively, resulting in the formation of a pressurization surface and a decompression surface on the airfoil surface. When the excitation position is close to the trailing edge, the pressurization surface expands, and the pressure difference between the upper and lower surfaces of the airfoil increases, thereby achieving lift increase; when the excitation position is close to the leading edge, the decompression surface expands, and the pressure difference drag of the airfoil decreases, thereby achieving drag reduction.

In order to provide a theoretical basis for the degradation of the air path performance of civil aviation engines at the unit level, the CFM56-3 engine was used as the research object. First, on the basis of using the characteristic map scaling method to obtain the component characteristic equation, the selection process of the scaling reference point of the fan general characteristic map was optimized, and a surface fitting method of the characteristic map was proposed to construct an engine component-level benchmark performance model that meets specific speed conditions under steady-state conditions. Then, by introducing the fault factor to generate the fault coefficient matrix, the deviation of the engine monitoring parameters as the component efficiency decreases was calculated, and compared with the fingerprint map data of the GE training manual, it was verified that the engine steady-state performance model that integrates the fan characteristic map scaling reference point optimization and the characteristic map surface fitting method has good accuracy and use in the analysis of air path performance degradation.

In recent years, data-driven approaches have shown promise in fault diagnosis. However, in practical industrial applications, challenges such as discrepancies between source domain and target domain data distributions, coupled with limited labeled fault data, have hindered the performance of traditional domain adaptation algorithms in bearing fault diagnosis. To address this issue, this study introduces a novel method based on MDANN (Multi Domain Adaptation Neural Network) for diagnosing rolling bearing faults using unlabeled data. Initially, the raw vibration signals are preprocessed using wavelet packet decomposition and reconstruction (WPT), which helps minimize signal redundancy while preserving critical features. Following this, the multi-kernel maximum mean discrepancy (MK-MMD) algorithm is employed to compute the differences in input feature values, and the MDANN network parameters are adjusted via backpropagation, allowing the network to extract domain-invariant features. To ensure the unlabeled target domain data can be effectively used in training, a pseudo-labeling strategy is applied, where the label with the highest probability is treated as the true label, thereby enabling the model to learn from the target domain data and improve the acquisition of reliable diagnostic knowledge. The method is validated using two publicly available datasets, CWRU and PU, with experimental results demonstrating that it outperforms conventional domain adaptation techniques in terms of diagnostic accuracy. This indicates the proposed approach is highly effective in capturing transferable features and addressing the distributional differences between datasets.

The prescribed finite-time trajectory tracking control problem with input buffer is investigated for a linkage manipulator system. The backstepping method is used to design the prescribed finite-time trajectory tracking controller. A new prescribed finite-time performance function is proposed, which can more easily regulate control performance. With the help of this performance function, a constraint control law is designed for the tracking error, and a better transient performance than the existing prescribed finite-time performance function can be obtained under the same parameter conditions for the trajectory tracking process of the manipulator system. At the same time, a control input buffer function is designed, which solves both the overvoltage problem of control input caused by the overlarge position error of the manipulator and the problem that the manipulator system is difficult to start under heavy load. The simulation experiments verify the effectiveness and superiority of the proposed controller.

Accurate prediction of airport express passenger flow is the basis for realizing intelligent, refined and efficient management and control of the airport rail transit system, and is of great significance to improving airport service levels and operational efficiency. Due to the numerous influencing factors that overlap with each other, and the complex mechanism of factors affecting passenger flow timing, accurate prediction of airport express passenger flow is extremely challenging. This paper proposes an airport express rail passenger flow prediction model based on the "time-feature" collaborative attention mechanism, which achieves accurate capture of the impact of multi-dimensional factors on airport express rail passenger flow in different time series. Experiments were conducted based on actual passenger flow data of the Beijing Capital International Airport Express Rail Link, and the results showed the effectiveness of the proposed method.