The advent of the digital age and the internet has recently seen a corresponding increase in security concerns. Intrusion detection systems are one of the crucial factors to consider in today's digital world. This...
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ISBN:
(纸本)9781665494250
The advent of the digital age and the internet has recently seen a corresponding increase in security concerns. Intrusion detection systems are one of the crucial factors to consider in today's digital world. This paper proposes a metaheuristic algorithm based on quantum differential evolution with multiple strategies. This algorithm proposes a new differential mutation strategy approach to improve the search capability and convergence speed. Then, a quantum rotation gate is used to perform the secondary evolution of the population. Finally, various benchmark functions are chosen to demonstrate the optimization ability of the algorithm. The experimental results show that the model outperforms differential evolution and quantum differential evolution. And has better optimization capability, efficiency and stability. This evolutionary technique plays an important role in identifying network intrusions and security attacks.
As the penetration of photovoltaic (PV) solar generation increases, numerous residential and commercial solar PV systems without meters are being installed. The majority of these systems, however, are not monitored by...
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ISBN:
(纸本)9781665409186
As the penetration of photovoltaic (PV) solar generation increases, numerous residential and commercial solar PV systems without meters are being installed. The majority of these systems, however, are not monitored by power system operators. Therefore, the uncertainty of net load owing to these invisible solar power generation will raise additional challenges for power system operation. To reduce the above-mentioned impact, this work proposes a novel method to estimate the total solar power generation in a large region from a small representative subset. The proposed method is capable of capturing all relevant information that assists in the identification of representative subsets. Moreover, different optimization algorithms are utilized and evaluated to select the optimal number of clusters and representative subsets. As a case study, the power generation of 166 PV sites in Taiwan was collected and analyzed. The proposed method demonstrates a significant improvement in estimating the aggregated power generation compared to other existing studies.
Featured Application The maximal vertical distance (MVD) algorithm is intended to be used for the discretization of stress-strain metallic material curves for defining a multilinear material model when conducting a fi...
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Featured Application The maximal vertical distance (MVD) algorithm is intended to be used for the discretization of stress-strain metallic material curves for defining a multilinear material model when conducting a finite element analysis of engineering components with included material *** The maximal vertical distance (MVD) recursive algorithm, a novel approach for the optimal discretization of stress-strain material curves, is proposed. The algorithm simplifies the process of defining multilinear curves from material stress-strain curves when conducting a finite element analysis (FEA) of components. By directly selecting points on the material curve, the MVD algorithm eliminates the requirement for initial discretization, thereby minimizing information loss. As the measure of goodness of fit of the simplified polyline to the original curve, the percentage of stress deviation (SD) is proposed. The algorithm can generate multiple multilinear curves while keeping the stress deviation of each curve within a predefined limit. This feature is particularly beneficial during the finite element analysis of components exhibiting complex and position-dependent material properties, such as surface-hardened components, ensuring consistent modelling accuracy of material properties across the components' geometry. Consistent accuracy also proves advantageous when exploring multiple differing material states of quenched and tempered steel, ensuring fair and reliable comparisons. The MVD algorithm was compared with existing algorithms from the literature, consistently maintaining the accuracy of the multilinear curves within predetermined limits using the fewest possible points.
The mass introduction of renewable energy sources (RESs) presents numerous challenges for transmission system operators (TSOs). The Italian TSO, Terna S.p.A., aims to assess the impact of inverter-based generation on ...
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The mass introduction of renewable energy sources (RESs) presents numerous challenges for transmission system operators (TSOs). The Italian TSO, Terna S.p.A., aims to assess the impact of inverter-based generation on system inertia, primary regulating energy and short-circuit power for the year 2030, characterized by a large penetration of these sources. The initial working point of the Italian transmission network has to be defined through load flow (LF) calculations before starting dynamical analyses and simulations of the power system. Terna 2030 development plan projections enable the estimation of active power generation and load for each hour of that year in each Italian market zone, as well as cross-zonal active power flows;this dataset differs from conventional LF assignments. Therefore, in order to set up a LF analysis for the characterization of the working point of the Italian transmission network, LF assignments have to be derived from the input dataset provided by Terna. For this purpose, this paper presents two methods for determining canonical LF assignments for each network bus, aligning with the available data. The methodologies are applied to a simplified model of the Italian network, but they are also valid for other transmission networks with similar topology and meet the future needs of TSOs. The methods are tested at selected hours, revealing that both approaches yield satisfactory results in terms of compliance with the hourly data provided.
With the proliferation of Internet of Things (IoT) devices and the generation of massive amounts of sensitive data, preserving privacy while enabling data sharing has become a critical concern. In this research, we pr...
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With the proliferation of Internet of Things (IoT) devices and the generation of massive amounts of sensitive data, preserving privacy while enabling data sharing has become a critical concern. In this research, we propose a novel approach for achieving differential privacy in IoT data sharing through an improved Particle Swarm optimization (PSO) algorithm. Our objective is to find the optimal configuration of privacy-preserving mechanisms that maximizes privacy while maintaining data utility. The research begins with a comprehensive overview of differential privacy in IoT data sharing, highlighting the limitations of existing optimization algorithms. We then present our proposed improvements to the traditional PSO algorithm, including the design of a suitable fitness function, the use of a dynamic inertia weight, exploration of different neighborhood topologies, and adaptive acceleration coefficients. We define a set of performance metrics, including privacy metrics (e.g. epsilon-differential privacy parameter) and utility measures (e.g. accuracy, utility loss), to assess the algorithm's effectiveness. The results of our experiments demonstrate that the improved PSO algorithm achieves higher privacy guarantees while maintaining competitive levels of data utility compared to existing approaches. The proposed algorithm exhibits faster convergence, better exploration of the search space, and improved scalability. Our research contributes to the field of privacy-preserving IoT data sharing by providing an efficient and effective optimization algorithm that enables secure and privacy-aware data sharing while facilitating valuable insights and analysis.
Cross -modal hashing has gained popularity in similarity search due to its excellent query efficiency and economical storage costs. However, current models frequently overlook the distinctive property of each modality...
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Cross -modal hashing has gained popularity in similarity search due to its excellent query efficiency and economical storage costs. However, current models frequently overlook the distinctive property of each modality, resulting in reduced accuracy due to inadequate utilization of these attributes. Moreover, there is a weak semantic relevance between modality attributes and multiple supervision knowledge (the labels and similarity constraints constructed by labels), accompanied by a cumulative quantization of the models. To address these issues, we propose an Individual Mapping and Asymmetric Dual Supervision method (IMADS). It merges specific and shared information to effectively learn a cross -modal representation space. Furthermore, we present an asymmetric dual supervision learning framework to produce discriminative hash codes. This framework achieves two primary goals: (1) Combing cross -modal representation and multiple supervision information to enhance the consistent relation of distinct modalities, and (2) developing a discrete optimization algorithm to mitigate the information loss caused by the hash code. Comprehensive experimental results illustrate that the introduced IMADS outperforms other stat-of-the-art hashing methods.
The rapid increase in Internet users has made web applications essential for daily services, rendering them targets for various cyber-attacks like path traversal, zero-day attacks, and injection attacks. While traditi...
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The rapid increase in Internet users has made web applications essential for daily services, rendering them targets for various cyber-attacks like path traversal, zero-day attacks, and injection attacks. While traditional security measures can prevent many familiar attacks, they are often ineffective against OPTIONS attacks, which involve injecting malicious code via hyperlinks to obstruct user access to legitimate webpage content. To address this novel challenge, we propose the OAD-WSN-MMRCNN technique, leveraging an Optimized Multitask MultiAttention Residual Shrinkage Convolutional Neural Network for OPTIONS attack detection in Wireless Sensor Networks (WSNs). This approach begins by selecting a CPU parameters dataset for attack detection, followed by pre-processing with a Variational Bayesian-Based Maximum Correntropy Cubature Kalman Filter to remove redundant data. Key features such as handles, threads, processor, context switch, deferred procedure call (DPC), interrupt delta, CPU socket, and core are extracted using a variable velocity strategy particle swarm optimization algorithm. The MMRCNN, optimized with the Tyrannosaurus optimization algorithm, is then employed to detect normal and OPTIONS attacks. Implemented in Python, the efficacy of OAD-WSN-MMRCNN is evaluated using metrics such as energy consumption, target window, accuracy, precision, F-measure, recall, and CPU utilization. Experimental results demonstrate that OAD-WSN-MMRCNN outperforms existing techniques, achieving a 20 % improvement in detection accuracy and a 25 % reduction in energy consumption, highlighting its effectiveness and potential for enhancing web application cyber security.
Virtual power plants (VPPs) are susceptible to cyber anomalies due to their extensive communication layer. FL-trust, an improved federated learning (FL) approach, has been recently introduced as a mitigation system fo...
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Virtual power plants (VPPs) are susceptible to cyber anomalies due to their extensive communication layer. FL-trust, an improved federated learning (FL) approach, has been recently introduced as a mitigation system for cyber-attacks. However, current FL-trust enhancements, relying solely on proportional-integral (PI), exhibit drawbacks like sensitivity to controller gain fluctuations and a slow response to sudden disturbances, and conventional FL-trust is not directly applicable to the non-independent and identically distributed (non-IID) datasets common in VPPs. To address these limitations, we introduce an artificial neural network (ANN)-based technique to adapt FL-trust to non-IID datasets. The ANN is designed as an intelligent anomaly mitigation control method, employing a dynamic recurrent neural network with exogenous inputs. We consider the effects of the most common VPP attacks, poisoning attacks, on the distributed cooperative controller at the secondary control level. The ANN is trained offline and tested online in the simulated VPP. Using MATLAB simulations on a HOMER-modeled VPP, the proposed technique demonstrates its superior ability to sustain normal VPP operation amidst cyber anomalies, outperforming a PI-based mitigation system in accuracy and detection speed.
Skin cancer is a common type of malignant tumor that poses a serious threat to patients' lives and health, especially melanoma. It may spread to other body parts, resulting in serious complications and death. In t...
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Skin cancer is a common type of malignant tumor that poses a serious threat to patients' lives and health, especially melanoma. It may spread to other body parts, resulting in serious complications and death. In the medical field, accurate identification of skin lesion images is crucial for diagnosing different diseases. However, due to the similarity between different skin lesions, it brings some challenges to medical diagnosis. In this paper, a novel Ensemble Learning Model (EL-DLOA) based on deep learning and optimization algorithms is proposed, which uses four different deep neural network architectures to generate confidence levels for classes, and optimization algorithms are used to integrate these confidence levels to make the final predictions. To ensure the model's accuracy and reliability, it is first trained using three different learning rates to find the best classification performance of the model. Then, a new search method based on the grey wolf optimization algorithm is proposed to enhance the grey wolf search efficiency. The method improves the search mechanism by changing the grey wolf's individual position through random perturbation or adaptive mutation, which solves the problem that the grey wolf algorithm is easy to fall into local optimum. Finally, four different ensemble strategies are used to reduce individual model bias in the classification process. The proposed model is trained and evaluated using the publicly available dataset HAM10000. The experimental results show that the improved grey wolf optimization algorithm effectively avoids the premature convergence problem and improves the search combination efficiency. Furthermore, in the ensemble methods, the adaptive weight average ensemble strategy effectively improves the classification performance, yielding accuracy, precision, recall, and F1 scores of 0.888, 0.837, 0.897, and 0.862, respectively. These metrics show varying degrees of improvement over the best performing single model. I
Ship trajectory prediction plays an important role in ensuring ship safety;through accurate ship positioning, the future trajectory of ships and their encounter time and location can be obtained, which facilitates the...
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Ship trajectory prediction plays an important role in ensuring ship safety;through accurate ship positioning, the future trajectory of ships and their encounter time and location can be obtained, which facilitates the maritime regulatory authorities to assess the risks of ship encounters and implement effective traffic control. Meanwhile, with the rapid development of the shipping industry, the increasingly complex maritime traffic poses potential risks, which may cause serious traffic accidents and huge economic losses. To improve the accuracy of ship navigation risk prediction and ensure the safety of ship navigation, automatic identification system (AIS) data and deep learning models are used to extract the ship trajectory change feature pattern and apply it to ship trajectory prediction. This study builds the improved bidirectional long short-term memory network (Bi-LSTM) model based on rectified adaptive moment estimation (Radam) and lookahead, respectively. The AIS data of the Port of Tianjin area were selected for model training, and the results of comparison experiments show that the improved Bi-LSTM model has a stronger generalization ability, which further improves the trajectory prediction accuracy, and shows excellent predictive performance. The prediction model is feasible for the prediction of ship navigation trajectory.
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