IET Cyber-Physical Systems: Theory and Applications最新文献

Wind power forecasting considering spatio-temporal correlations can effectively improve the forecasting accuracy. However, this will lead to a complicated structure in the forecasting model, making it difficult to solve due to dimensional catastrophe. To this end, a neural network framework called Deep Lasso is applied, which achieves feature selection by adding the regularisation of Lasso to the input gradients. Primarily, a forecasting model based on Deep Lasso, considering the features of all wind farms (i.e., global variables), is constructed. Subsequently, the coefficients of Deep Lasso can directly represent the contribution of input features to wind power forecasts. Therefore, to construct a more efficient forecasting model, secondary modelling is performed by filtering the features with small coefficients. Experiments including 20 wind farms demonstrate that Deep Lasso exhibits remarkable suitability and effectiveness in ultra-short-term wind power forecasting compared with six feature selection methods. Moreover, to test the effectiveness of feature dimensionality reduction, the secondary modelling forecasting model is verified by comparing it with principal component analysis (PCA) and factor analysis (FA). The results obtained show that the overall performance of the proposed method outperforms PCA and FA while improving the computational efficiency to a certain extent.

In mixed-criticality (MC) systems, tasks with varying criticality levels share resources, leading to challenges in resource management during mode transitions. Existing approaches often result in suboptimal performance due to resource contention and criticality level inheritance. This paper introduces a novel learner-based resource management strategy that predicts optimal mode switching times and prevents low-criticality tasks from acquiring resources during critical periods. By combining vector autoregressive (VAR) and feed-forward neural network (FNN) techniques, our approach effectively anticipates system state changes and optimises resource allocation. Specifically, the method extracts key system features, including processor temperature, soft error rate, cache miss rate, and task slack time. A hybrid forecasting model then predicts the probability of a mode transition within a specified time horizon. Based on these predictions, the system proactively denies resource requests from low-criticality tasks during periods of high probability of mode transition, ensuring the availability of resources for high-criticality tasks. Comprehensive simulations demonstrate significant reductions in blocking time (up to 75%), miss rate (up to 9.35%), and energy consumption (up to 12.15%) compared to state-of-the-art methods. These improvements enhance system reliability and efficiency, making it suitable for safety-critical applications.

To achieve low-carbon development, the ladder-type carbon trading mechanism is proved to be beneficial to reduce carbon emissions while increasing the operation cost of the integrated energy system (IES). In this paper, an IES optimal operation strategy considering the ladder-type carbon trading mechanism is proposed, with the help of the dynamic characteristics of the heat network to compensate for the increased operation cost. First, the transmission model of the heat network is established, and the dynamic characteristic of the heat network during the heat transfer process is analysed Then, the ladder-type carbon trading mechanism is introduced, and the impact on IES operation is analysed accordingly. Finally, the IES optimal operation model considering the ladder-type carbon trading mechanism and the dynamic characteristics of the heat network is established. The programming model is expressed as mixed-integer quadratic programming (MIQP). Simulation experiments are carried out for validation. The results show that considering the ladder-type carbon trading mechanism and the dynamic characteristics of the heat network in the IES can improve the wind power consumption rate and reduce the system operation cost and carbon emissions.

Current analytical methods are inadequate in uncovering the internal propagation mechanisms of disturbances and the interconnections between subsystems in the wind turbine-storage integrated grid connected system, which faces stability issues. Therefore, this paper employs a damping module modelling approach to conduct a dynamic analysis of the dynamic interactions in wind turbine-storage storage integrated systems, focusing on the damping path analysis with the phase-locked loop (PLL) as the oscillation mode. The research initiates with the linearisation of the doubly-fed induction generator (DFIG) and energy storage system (ESS) models. The closed-loop structure of the system is then presented to expose the disturbance propagation paths between subsystems. Subsequently, the damping coefficients of the second-order dynamic equation are expanded to include the dynamic equations of the most prominent oscillation mode, which establishes stability criteria for the system. Finally, by performing damping decomposition and reconstruction, the damping coefficients of each subsystem as well as the total damping coefficient of the interconnection system are obtained. An analysis is conducted on how the proportional-integral parameters of the PLL affect the damping of the interconnection system. The results suggest that the damping paths of the DFIG and the ESS can be expressed as a closed-loop structure diagram. By decreasing the proportional or integral coefficients of the PLL, the overall damping coefficient is increased, resulting in an enhancement of the stability of the grid-connected system.

XSPA vulnerability can be attacked by stealing the cookie's information. In this case, it becomes utmost necessary to secure the information written in a cookie. A cookie contains a session ID that is a unique number generated by the server. This session ID must be a large random number so that no one can guess a valid session ID in real-time. Numerous research studies have been accomplished on the same but the area still persist gaps in view of emerging threats, such as phishing, pharming, and DoS. This paper proposes a new random-number generator that produces unique numbers in bulk. This helps the server to match the high demand of unique session IDs from different clients. The proposed generator is suitable for all types of web applications, because it requires the smallest area of only 134 Gate Equivalent on the application specific integrated circuit (ASIC) for its execution. Additionally, the proposed generator passed all tests of EPCglobal. Total time delay of digital circuit and power analysis results presented in the subsequent sections are also in the favour of proposed generator. With the implementation of this proposed technique cookies are expected to be more secure as evident from try-out results.

A surge of digital technologies adopted into Industrial Control Systems (ICS) exposes critical infrastructures to increasingly hostile and well-organised cybercrime. The increased need for flexibility and convenient administration expands the attack surface. Likewise, an insider with authorised access reveals a difficult-to-detect attack vector. Because of the range of critical services that ICS provide, disruptions to operations could have devastating consequences making ICS an attractive target for sophisticated threat actors. Hence, the authors introduce a novel anomalous behaviour detection model for ICS data streams from physical plant sensors. A model for one-class classification is developed, using stream rebalancing followed by adaptive machine learning algorithms coupled with drift detection methods to detect anomalies from physical plant sensor data. The authors’ approach is shown on ICS datasets. Additionally, a use case illustrates the model's applicability to post-incident investigations as part of a defence-in-depth capability in ICS. The experimental results show that the proposed model achieves an overall Matthews Correlation Coefficient score of 0.999 and Cohen's Kappa score of 0.9986 on limited variable single-type anomalous behaviour per data stream. The results on wide data streams achieve an MCC score of 0.981 and a K score of 0.9808 in the prevalence of multiple types of anomalous instances.

There are complex correlations between facial expression and facial landmarks in facial images. The facial landmarks detection technology is more mature than the facial expression recognition methods. Considering this, in order to better address the problem of interclass similarity and intraclass discrepancy in facial expressions recognition (FER), facial landmarks are used to supervise the learning of facial expression features in our work, and a multiscale and multilevel fusion network based on ResNet and MobileFaceNet (MMFRM) is proposed for FER. Specifically, the authors designed a triple CBAM feature fusion module (TCFFM) that characterises the correlation between facial expression and facial landmarks to better guide the learning of expression features. Furthermore, the proposed loss function of removing facial residual features (RFLoss) can suppress facial features and highlight expression features. We extensively validate our proposed MMFRM on two public facial expression datasets, demonstrating the effectiveness of our method.

Coupled multidisciplinary systems are fundamental to many complex engineering systems, such as those in cyber–physical systems, aerospace engineering, automotive systems, energy networks, and robotics. Accurate analysis, control, and monitoring of these systems depend on effectively inferring their inherent uncertainties. However, the dynamic nature of these systems, along with the interconnectivity of various disciplines, poses significant challenges for uncertainty estimation. This paper presents a framework for learning uncertainty distributions in partially observed coupled multidisciplinary systems. By employing a non-linear/non-Gaussian hidden Markov model (HMM) representation, the authors capture the stochastic nature of system states and observations. The proposed methodology leverages particle filtering techniques and Bayesian optimisation for efficient parameter estimation, accounting for the inherent uncertainties in input statistics. Numerical experiments on a coupled aerodynamics-structures system and a power converter system demonstrate the efficacy of the proposed method in estimating input distribution statistics. The results highlight the critical importance of accounting for non-stationary behaviours in coupled multidisciplinary systems for capturing the true variability of input statistics and showcase the superiority of our method over approaches that assume data derive from the stationary state of the system.

With the ongoing development of Distributed Energy Resources (DER) communication networks, the imperative for strong cybersecurity and data privacy safeguards is increasingly evident. DER networks, which rely on protocols such as Distributed Network Protocol 3 and Modbus, are susceptible to cyberattacks such as data integrity breaches and denial of service due to their inherent security vulnerabilities. This paper introduces an innovative Federated Learning (FL)-based anomaly detection system designed to enhance the security of DER networks while preserving data privacy. Our models leverage Vertical and Horizontal Federated Learning to enable collaborative learning while preserving data privacy, exchanging only non-sensitive information, such as model parameters, and maintaining the privacy of DER clients' raw data. The effectiveness of the models is demonstrated through its evaluation on datasets representative of real-world DER scenarios, showcasing significant improvements in accuracy and F1-score across all clients compared to the traditional baseline model. Additionally, this work demonstrates a consistent reduction in loss function over multiple FL rounds, further validating its efficacy and offering a robust solution that balances effective anomaly detection with stringent data privacy needs.

Electric power systems are composed of physical and cyber sub-systems. The sub-systems depend on each other. If the cyber sub-system is compromised by a cyber threat, what is the impact on the physical system? This paper presents a case study that shows the steps of a multi-stage cyber threat involving a database injection attack, and what happens to the power system if this threat is not detected in its early stages. The threat first affects one utility but it can spread to the balancing authority, which is responsible for keeping the voltage and frequency stable in the power grid. During the cyber threat, the authors also show defence tools, such as a cyber-physical data fusion tool that displays and analyses power and cyber telemetry.