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Small Signal Stability-Guaranteed Moving Target Defense Against Load Redistribution Attack On IoT-based Smart Grid

IEEE Internet of Things Journal 2025年第11期12卷 17413-17429页

作者： Wang, Bingdong Zhang, Zhenyong Wang, Mufeng Liu, Mengxiang Ye, Rui Deng, Ruilong Zhang, Xin Guizhou University State Key Laboratory of Public Big Data College of Computer Science and Technology Guiyang550025 China China Industrial Control Systems Cyber Emergency Response Team Beijing100040 China University of Sheffield Department of Automatic Control and System Engineering Sheffield United Kingdom Guizhou Power Grid Co. Ltd. Intelligent Operation Center China Zhejiang University State Key Laboratory of Industrial Control Technology College of Control Science and Engineering Hangzhou310027 China Huzhou Institute of Industrial Control Technology Huzhou313000 China

Moving target defense (MTD) is a promising approach to defend against load redistribution attacks on the internet-of-things (IoT)-based smart grid networks by probing the distorted state estimates with the distributed flexible AC transmission system. However, existing studies mainly focus on optimizing the performance of MTD and ignore the safety effect of it on the system’s operation. In this paper, we fill this gap by deeply analyzing the effect of MTD on the small signal stability and aim to alleviate the negative impact and guarantee its defending performance simultaneously. Firstly, the stability is formally described using the eigenvalue sensitivity. The relationship between the MTD-induced perturbation (MTDper) and the stability criteria is derived. Secondly, a new indicator is proposed to measure the effectiveness of MTDper. Thirdly, a constrained optimization problem is formulated to compute the bound of MTDper for guaranteeing the small signal stability. In addition, a surprising finding is that the stability margin can be improved and enhanced by optimizing the value of MTDper without losing the MTD’s effectiveness. Finally, we evaluate the performance of MTDper and its impact on the small signal stability with extensive simulations on the IEEE 30-bus, 39-bus, and 68-bus test power systems. © 2014 IEEE.

关键词： Constrained optimization

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Automatic speaker identification system based on MLP network and deep learning in the presence of severe interference

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Multimedia Tools and Applications 2025年 1-42页

作者： Shafik, Amira Sedik, Ahmed El-Shafai, Walid Khalaf, Ashraf A. M. El-Rabaie, El-Sayed M. El-Samie, Fathi E. Abd Department of Electronics and Electrical Communications Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt Department of Electrical Engineering Faculty of Engineering Minia University Minia61519 Egypt Smart Systems Engineering Laboratory College of Engineering Prince Sultan University Riyadh11586 Saudi Arabia Department of the Robotics and Intelligent Machines Faculty of Artificial Intelligence Kafr Elsheikh University Kafr El‑Shaikh Egypt Computer Science Department Prince Sultan University Riyadh11586 Saudi Arabia Department of Information Technology College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia

Automatic Speaker Identification (ASI) is so crucial for security. Current ASI systems perform well in quiet and clean surroundings. However, in noisy situations, the robustness of an ASI system against additive noise and interference is a crucial factor. An investigation of the impact of interference on ASI system performance is presented in this paper, which introduces algorithms for achieving high ASI system performance. The objective is to resist the interference of various forms. This paper presents two models for the ASI task in the presence of interference. The first one depends on Normalized Pitch Frequency (NPF) and Mel-Frequency Cepstral Coefficients (MFCCs) as extracted features and Multi-Layer Perceptron (MLP) as a classifier. In this model, we investigate the utilization of a Discrete Transform (DT), such as Discrete Wavelet Transform (DWT), Discrete Cosine Transform (DCT) and Discrete Sine Transform (DST), to increase the robustness of extracted features against different types of degradation through exploiting the sub-band decomposition characteristics of DWT and the energy compaction property of DCT and DST. This is achieved by extracting features directly from contaminated speech signals in addition to features extracted from discrete transformed signals to create hybrid feature vectors. The enhancement techniques, such as Spectral Subtraction (SS), Winer Filter, and adaptive Wiener filter, are used in a preprocessing stage to eliminate the effect of the interference on the ASI system. In the second model, we investigate the utilization of Deep Learning (DL) based on a Convolutional Neural Network (CNN) with speech signal spectrograms and their Radon transforms to increase the robustness of the ASI system against interference effects. One of this paper goals is to introduce a comparison between the two models and build a more robust ASI system against severe interference. The experimental results indicate that the two proposed models lead to satisfa

关键词： Additive noise

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Relational Clustering-Based Parallel Spaces Construction and Embedding for Dynamic Knowledge Graph

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IEEE Transactions on Big Data 2025年

作者： Liu, Yao Zhang, Yongfei Beihang University Beijing Key Laboratory of Digital Media School of Computer Science and Engineering Beijing100191 China Beihang University School of Computer Science and Engineering China Beihang University State Key Laboratory of Virtual Reality Technology and Systems Beijing100191 China Pengcheng Laboratory Shenzhen518055 China

With the increasing amount of data in various domains, knowledge graphs (KGs) have become powerful tools for representing complex and heterogeneous information in a structured way, and for extracting valuable information from knowledge graphs through embedding techniques to support downstream tasks such as recommendation and Q&A systems. Knowledge graphs consist of triples that are continuously added as knowledge is updated. However, most existing embedding models are designed for static graphs, requiring the entire model to be retrained for each update, which is time-consuming. Existing global dynamic embedding models focus on exploiting the structural and relational information of the whole graph to achieve embedding quality, resulting in reduced dynamic efficiency. To address this problem, we propose a relational clustering-based parallel space model in which knowledge from different domains is embedded in different subspaces, allowing each subspace to focus on the data characteristics of a specific domain, thereby improving the quality of knowledge. Second, the new data only affects some subspaces but not the performance of other spaces, improving the model's adaptability to dynamics. Furthermore, we employ two incremental approaches based on the type of added data to improve the efficiency of dynamic embedding while ensuring that the added data preserves the characteristics of the parallel space. The experimental results show that the dynamic embedding efficiency of our model is improved by an average of 50.3% compared to the SOTA dynamic embedding model for the link prediction task. Particularly on FB15K, our model not only improves the efficiency by 41% but also increases the accuracy by 7.5%, demonstrating the accuracy and efficiency of our model. © 2015 IEEE.

关键词： Knowledge graph

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ACDV: Adaptive Content Delivery for Vehicular Digital Twin Networks

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IEEE Transactions on Vehicular Technology 2025年第5期74卷 7084-7098页

作者： Zheng, Jinkai Luan, Tom H. Li, Guanjie Yin, Zhisheng Wu, Yuan Dong, Mianxiong Dongguan University of Technology School of Electrical Engineering and Intelligentization Dongguan523808 China Xi'an Jiaotong University School of Cyber Science and Engineering Xi'an710049 China Xidian University School of Cyber Engineering Xi'an710071 China Singapore University of Technology and Design Information Systems Technology and Design Pillar 487372 Singapore Xidian University State Key Laboratory of Isn Xi'an710071 China University of Macau State Key Laboratory of Internet of Things for Smart City 999078 China University of Macau Department of Computer Information Science 999078 China Muroran Institute of Technology Department of Sciences and Informatics Muroran05-08585 Japan

Digital Twins (DTs), serving personalized cloud-based digital assistants, hold great promise for supporting infotainment applications of the Internet of Vehicles (IoVs), in which personalized content such as high-definition maps, system updates, and social video streaming can be delivered from individualized DTs to vehicular users (VUEs) to enhance the intelligence of IoV services and improve their driving experience. This paper proposes a novel content caching framework tailored to DT-enabled IoVs, where DTs selectively cache on-demand contents on edge devices (i.e., cellular base stations and roadside units) managed by the edge service manager (ESM) along the VUE's trip to avoid backbone congestion yet save download time. However, ESMs are inherently selfish and reluctant to contribute resources to cache contents without benefits. Furthermore, considering the diverse trajectories of vehicles, it is inefficient to cache data along a single path. To address these challenges, we propose ACDV, which first models the interactions between the ESMs and DTs as a Stackelberg game to incentivize ESMs to actively participate in the content caching process. We then deploy a Markov model to predict the VUE distribution, which enables DTs to cache contents with a focus on predicted trajectories. To save storage costs, we derive an upper bound of content sizes that the VUE can download within a limited network connection time. Considering the lack of network information and users' private utility model in practical scenarios, we further develop a learning-based algorithm to find the optimal pricing scheme and content size strategies of ESMs and DTs. Through extensive simulations, we show that our proposal can effectively find the optimal strategy and achieve a fast convergence speed and high-level performance compared to the baselines. © 1967-2012 IEEE.

关键词： Markov processes

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Learning for predictive control: A Dual Gaussian Process approach

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Automatica 2025年 177卷

作者： Liu, Yuhan Wang, Pengyu Tóth, Roland Control Systems Group Department of Electrical Engineering Eindhoven University of Technology Eindhoven Netherlands Flight Dynamics and Control Laboratory Department of Aerospace Engineering Korea Advanced Institute of Science and Technology Daejeon Korea Republic of Systems and Control Laboratory Institute for Computer Science and Control Budapest Hungary

An important issue in model-based control design is that an accurate dynamic model of the system is generally nonlinear, complex, and costly to obtain. This limits achievable control performance in practice. Gaussian Process (GP) based estimation of system models is an effective tool to learn unknown dynamics directly from input/output data. However, conventional GP-based control methods often ignore the computational cost associated with accumulating data during the operation of the system and how to handle forgetting in continuous adaption. In this paper, we present a novel Dual Gaussian Process (DGP) based Model Predictive Control (MPC) strategy that enables efficient use of online learning based predictive control without the danger of catastrophic forgetting. The bio-inspired DGP structure is a combination of a long-term GP and a short-term GP, where the long-term GP is used to keep the learnt knowledge in memory and the short-term GP is employed to rapidly compensate unknown dynamics during online operation. A novel recursive online update strategy for the short-term GP is proposed to successively improve the learnt model during online operation without a "dictionary" update and re-computation of the Gram matrix at each time step. Effectiveness of the proposed strategy is demonstrated via numerical simulations. © 2025

关键词： Predictive control systems

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Prescriptive Analytics for Logistics: Bridging Predictive Models and Actionable Decisions Through Advanced Optimization Techniques

Prescriptive Analytics for Logistics: Bridging Predictive Mo...

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Innovative Research in Applied science, engineering and Technology (IRASET),Conference on

作者： Oussama El Azzouzy Tarik Chanyour Said Jai Andaloussi Department of Mathematics and Computer Science Laboratory of Data Engineering and Intelligent Systems(LIDSI) Hassan II University Casablanca Morocco

ISBN: (数字)9798331532970

ISBN: (纸本)9798331532987

Modern logistics is facing increasing challenges due to the growing complexity of supply chains, high consumer expectations, and economic and environmental pressures. While predictive analytics, powered by artificial intelligence (AI) and massive data, has transformed the ability to forecast trends and risks, its application remains limited by the lack of effective mechanisms to convert these predictions into actionable decisions in dynamic and constrained environments. This paper proposes an innovative hybrid approach that combines advanced predictive models, notably the Temporal Fusion Transformer (TFT), with prescriptive algorithms, such as linear programming and deep reinforcement learning (DRL). The TFT demonstrated superior performance in terms of predictive accuracy (significant reduction in MAE, RMSE, and MAPE), while the prescriptive algorithms optimized complex logistics decisions in real time, even in uncertain environments. This integrated methodology was validated through use cases simulating critical logistics scenarios, such as delivery route optimization. Experimental results show a substantial improvement in operational efficiency, with reductions in costs, lead times and resource utilization. The prescriptive approach made it possible to automate complex decisions, while taking into account multiple constraints, such as costs, lead times and sustainability objectives. By integrating real-time data collected via IoT devices and logistics management systems, this architecture offers a robust, adaptable and scalable solution. It thus meets the requirements of modern logistics by enabling proactive, optimized and resilient decision-making. This research makes a significant contribution by bridging the gap between predictive and prescriptive analysis, while laying the foundations for new applications in the field of intelligent logistics.

关键词： Costs Supply chains Predictive models Prediction algorithms Linear programming Transformers Real-time systems Thin film transistors Resource management Optimization

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Refining Boundary Sample Space for Differentiable Rendering 16

Refining Boundary Sample Space for Differentiable Rendering

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2024 16th International Conference on Graphics and Image Processing, ICGIP 2024

作者： Gao, Ruicheng Qi, Yue State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang University Beijing100191 China Jingdezhen Research Institute Beihang University Jingdezhen333000 China Qingdao Research Institute Beihang University Qingdao266104 China

ISBN: (数字)9781510688780

ISBN: (纸本)9781510688773

The objective of physics-based differentiable rendering (PBDR) is to propagate gradients between scene parameters and the intensities of image pixels in a manner that is physically correct. The gradients obtained can be applied in optimization algorithms for the reconstruction of 3D geometry or materials, or they can be further propagated into neural network to learn neural representations of the scene. However, applying automatic differentiation techniques directly to the primary rendering process will result in biased gradients, as the rendering integral contains moving high-dimensional discontinuities. Based on how these discontinuities are managed-either implicitly or explicitly-existing PBDR methods can be categorized into two groups: reparameterization methods and boundary sampling methods. Boundary sampling methods need to construct paths that have one segment tangent to the geometry being differentiated in order to estimate a boundary integral to address the discontinuities explicitly. Such paths are usually constructed by sampling the tangent segment first and then extending it to complete the paths for subsequent processing. Fortunately, the number of dimensions in the space composed of such tangent segments is only three. In scenes comprised solely of triangle meshes, the first dimension is used to parameterize all the edges on the mesh, which determines a point on the tangent segment. The remaining two dimensions are used to parameterize the direction of the tangent segment. However, state-of-the-art boundary sampling methods parameterize the first dimension uniformly, which is inefficient because only a small portion of the edges contributes to the boundary integral, resulting in wasted parameter space. In this paper, we parameterize the first dimension by considering both edge length and contributions, thereby allocating more parameter space to important edges. Experiments demonstrate that our methods achieve lower variance gradients in the forward dif

关键词： Mesh generation

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SPRGAN: Streamlined Progressive Refinement for Adversarial Point Cloud Video Upsampling

SPRGAN: Streamlined Progressive Refinement for Adversarial P...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Zhang, Zhiyong Liu, Ruyu Wang, Chaochao Zhang, Xianchao Zhang, Jianhua Liu, Xiufeng School of Computer Science and Engineering Tianjin University of Technology Tianjin China School of Information Science and Technology Hangzhou Normal University Hangzhou China Provincial Key Laboratory of Multimodal Perceiving and Intelligent Systems Jiaxing University Zhejiang China Department of Technology Management and Economics Technical University of Denmark Lyngby Denmark

ISBN: (纸本)9798350368741

Getting dense, uniform, time-series point cloud data is critical for effective rendering. However, due to the limited computational power of edge devices, existing methods cannot achieve real-time results, which affects the visual quality of the consumer experience. To effectively address this issue, this paper presents a self-supervised adversarial upsampling method for point cloud video streams called SPR-GAN. In the generator, we design the Temporal Iterative Graph module to learn local features for each frame and captures long-range spatial information using three iterations of graph convolution operations. Then the Contextual Temporal Fusion module is developed to merge information between different frames, synthesizing temporal information and enriching the dynamic feature representation of the point cloud. Meanwhile, in the discriminator, we introduce the Efficient Shape module. Through dynamic graph convolution operations and stacked learning, it significantly improves the resolution efficiency of global shape information in point clouds. The final experiments show that the proposed method exhibits high practicality and superiority. The model achieves a good result on both the D-FAUST and DeformingThing4D-Animals datasets. © 2025 IEEE.

关键词： point cloud sequence Point cloud upsampling self-supervised temporal aggregation

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Correction to: Automated explainable and interpretable framework for anomaly detection and human activity recognition in smart homes (Neural Computing and Applications, (2025), 10.1007/s00521-025-10991-3)

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Neural Computing and Applications 2025年 1-2页

作者： Kim, Tae Hoon Ojo, Stephen Krichen, Moez Alamro, Meznah A. Mihoub, Alaeddine Sampedro, Gabriel Avelino School of Information and Electronic Engineering Zhejiang University of Science and Technology No. 318 Zhejiang Hangzhou China Department of Electrical and Computer Engineering College of Engineering Anderson University AndersonSC29621 United States Al-Baha University Al-Baha65528 Saudi Arabia ReDCAD Laboratory University of Sfax Sfax3038 Tunisia Department of Information Technology College of Computer and Information Science Princess Nourah Bint Abdul Rahman University Riyadh11564 Saudi Arabia Department of Management Information Systems College of Business and Economics Qassim University P.O. Box: 6640 Buraidah51452 Saudi Arabia Department of Computer Science University of the Philippines Diliman Quezon City1101 Philippines

In this article, the author’s name Stephen Ojo was incorrectly written as Stepehn Ojo and the affiliation details for author Stephen Ojo was incorrectly given as ‘Department of Electrical and computer engineering, College of engineering Anderson, Anderson University, Anderson, SC 29621, USA’ but should have been ‘Department of Electrical and computer engineering, College of engineering, Anderson University, Anderson, SC 29621, USA’. In addition, the affiliation details for author Gabriel Avelino Sampedro were incorrectly given as ‘College of computer Studies, De La Salle University, 2401 Taft Ave., Malate, 1004 Manila, Philippines and Faculty of Information and Communication Studies, University of the Philippines Open University, 4031 Los Baños, Philippines’ but should have been ‘Department of computer science, University of the Philippines Diliman, 1101 Quezon City, Philippines’. The original article has been corrected. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

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A Globally Convergent Method for Computing B-stationary Points of Mathematical Programs with Equilibrium Constraints

arXiv

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arXiv 2025年

作者： Nurkanovic, Armin Leyffer, Sven Systems Control and Optimization Laboratory Department of Microsystems Engineering University of Freiburg Germany Mathematics and Computer Science Division Argonne National Laboratory United States

This paper introduces a method that globally converges to Bstationary points of mathematical programs with equilibrium constraints (MPECs) in a finite number of iterations. B-stationarity is necessary for optimality and means that no feasible first-order direction improves the objective. Given a feasible point of an MPEC, B-stationarity can be certified by solving a linear program with equilibrium constraints (LPEC) constructed at this point. The proposed method solves a sequence of LPECs, which either certify B-stationarity or provide an active-set estimate for the complementarity constraints, and nonlinear programs (NLPs) - referred to as branch NLPs (BNLPs) - obtained by fixing the active set in the MPEC. A BNLP is more regular than the MPEC, easier to solve, and with the correct active set, its solution coincides with the solution of the MPEC. The method has two phases: the first phase identifies a feasible BNLP or certifies local infeasibility, and the second phase solves a finite sequence of BNLPs until a B-stationary point of the MPECis found. The paper provides a detailed convergence analysis and discusses implementation details. In addition, extensive numerical experiments and an open-source software implementation are provided. The experiments demonstrate that the proposed method is more robust and faster than relaxation-based methods, while also providing a certificate of B-stationarity without requiring the usual too-restrictive *** Codes 90C30, 90C33, 49M37, 65K10, 90C11 © 2025, CC BY.

关键词： Nonlinear programming

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