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ieee 7th International conference on Advanced Technologies, Signal and Image Processing (ATSIP)

作者： Abada, Wafia Bouramoul, Abdelkrim Univ Abdelhamid Mehri Constantine 2 MISC Lab Constantine 25000 Algeria

ISBN: (纸本)9798350351491;9798350351484

This article discusses the effects of data quality on studies conducted in the mental health field. Specifically, it examines how the quality of data used in addiction and substance dependence research can impact the results of these studies. The importance of data collection and the diversity and quality of data sets are emphasized, particularly in the context of machine learning applications in substance abuse research. The article explores the potential of machine learning to revolutionize addiction research and improve treatment strategies for individuals struggling with addiction. It also explores the role of machine learning algorithms and data synthesis techniques in advancing research on substance dependencies. The exploratory study conducted within this research has allowed us to assert that through the use of these data-driven approaches, researchers and practitioners can make significant progress in managing addiction-related issues.

关键词： addiction research data collection dataset variability machine learning data quality data synthesis Mental Health

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data-driven Injection Attack Strategy for Linear Cyber-Physical systems: An Input-Output data-Based Approach

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ieee TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING 2023年第6期10卷 4082-4095页

作者： Gao, Sheng Zhang, Hao Wang, Zhuping Huang, Chao Yan, Huaicheng Tongji Univ Coll Elect & Informat Engn Dept Control Sci & Engn Shanghai 200092 Peoples R China East China Univ Sci & Technol Key Lab Adv Control & Optimizat Chem Proc Minist Educ Shanghai 200237 Peoples R China East China Univ Sci & Technol Sch Informat Sci & Engn Shanghai 200237 Peoples R China

The system security of linear cyber-physical systems (CPSs) with repetitive movement characteristics under injection attacks is investigated in this article. The optimal data-driven injection attack strategies through iterative learning are proposed without utilizing the knowledge of the system model. A switching attack strategy with high concealment, flexibility, and low energy consumption is established. Finding the optimal attack strategy that maximizes the quadratic cost function of the system is the main objective of this article. Different from the existing attack strategies, the proposed one applies the attack input data from the previous iteration at the same sampling time to update the current attack input, thereby improving the attack effect. Furthermore, in order to overcome the difficulty that the attacker is unaware of the attacked system model, a system identification method based on input and output (I/O) data is used to construct a virtual model. An improved projection estimation approach is developed to estimate the parameters of the virtual model with the assistance of the matrix inverse lemma. Finally, a networked direct current (DC) motor simulation is applied to verify the effectiveness of the presented schemes.

关键词： Switches Wireless communication Iterative methods Cyber-physical systems data models Security Estimation Input-output programs FDI attack iterative learning linear cyber-physical systems switching sequence virtual linear I/O data attacked model

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Lightweight and Adaptive data-driven Intrusion Detection System for Autonomous Vehicles

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ieee TRANSACTIONS ON INTELLIGENT TRANSPORTATION systems 2025年第2期26卷 2282-2292页

作者： Aljabri, Wael Hamid, Md. Abdul Mosli, Rayan King Abdulaziz Univ Dept Informat Technol Jeddah 21589 Saudi Arabia

Autonomous vehicles are equipped with numerous electronic control units (ECUs). These ECUs are interconnected via in-vehicle networks to ensure safety, assist drivers, and perform various functions. A controller area network (CAN) facilitates real-time data transmission between ECUs. CAN provides sufficient reliability for communication within vehicles. Modern vehicles also utilize vehicle-to-everything (V2X) technologies. V2X enables vehicles to link to external networks, facilitating communication with other vehicles, infrastructures, and electronic devices. Modern vehicles' enhanced functionality and connections make them more susceptible to cyberattacks on both in-vehicle and external networks because of their extensive attack surfaces. To address this issue, this research presents a novel data-driven intrusion detection system (IDS) designed for in-vehicle and external communication networks. The proposed IDS can effectively detect many types of intrusions beyond the constraints of binary classifications. This system underwent a comprehensive evaluation using three in-vehicle datasets: car-hacking, car-hacking: attack & defense Challenge, and the ROAD datasets. Additionally, it was evaluated on two external communication network datasets: CICIDS2017 and CSE-CIC-IDS2018. The evaluation results demonstrated the IDS's adaptability, achieving significant accuracy, recall, and F1 scores. It attained up to 99.99% accuracy with the in-vehicle datasets. Moreover, it exhibited an exceptionally low false-positive rate, as low as 0, in the Car-Hacking dataset. The proposed IDS architecture is a lightweight approach compared to state-of-the-art models. It significantly reduced complexity in terms of Floating-Point Operations per Second (FLOPs), memory usage, and learning parameters, all without compromising performance.

关键词： Accuracy Computer crime Vehicle-to-everything Training Long short term memory Automobiles Safety Intrusion detection Deep learning data models Autonomous vehicle CAN-bus in-vehicle network car-hacking

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Collaborative Decision-Making Technique for Wireless Communication Means Based on Multi-Agent Reinforcement learning 13

Collaborative Decision-Making Technique for Wireless Communi...

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13th ieee data driven control and learning systems conference, DDCLS 2024

作者： Zhang, Kun Hu, Chao Huang, Deqing Shen, Jiachen School of Electrical Engineering Southwest Jiaotong University Chengdu611756 China Southwest Jiaotong University Key Laboratory of Advanced Energy Traction and Comprehensive Energy Saving Railway Industry Chengdu610031 China

ISBN: (纸本)9798350361674

Wireless communication is a pivotal domain within modern information and communication technology. With the proliferation of mobile devices and the Internet of Things, wireless communication methods have become ubiquitous across diverse industries. Challenges arise in collaborative decision-making technology that leverages wireless communication, including heightened information variability, increased time sensitivity, and a significant need for manual intervention. This paper introduces, for the first time, the application of multi -agent reinforcement learning technology to develop wireless communication agents. This approach involves distributed training, real-time simulation interaction, and other key technologies rooted in the pertinent wireless communication mechanism. By leveraging a mechanism of iterative trial and error, the optimal collaborative decision-making strategy is derived, significantly enhancing efficiency. Experimental validation demonstrates the effectiveness of the proposed method. By leveraging a mechanism of iterative trial and error, the optimal collaborative decision-making strategy is derived, significantly enhancing efficiency. Experimental validation demonstrates the effectiveness of the proposed method. © 2024 ieee.

关键词： Reinforcement learning

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Toward Next-Generation Signal Intelligence: A Hybrid Knowledge and data-driven Deep learning Framework for Radio Signal Classification

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ieee TRANSACTIONS ON COGNITIVE COMMUNICATIONS AND NETWORKING 2023年第3期9卷 564-579页

作者： Zheng, Shilian Zhou, Xiaoyu Zhang, Luxin Qi, Peihan Qiu, Kunfeng Zhu, Jiawei Yang, Xiaoniu Sci & Technol Commun Informat Secur Control Lab Res Ctr 11 Jiaxing 314033 Peoples R China Xidian Univ State Key Lab Integrated Serv Networks Xian 710071 Peoples R China

Automatic modulation classification (AMC) can generally be divided into knowledge-based methods and data-driven methods. In this paper, we explore combining the knowledge-based method and data-driven technology to take full advantage of both and propose a hybrid knowledge and data-driven deep learning framework (HKDD) for AMC. To make the handcrafted features more discriminative, various traditional features are adopted, including instantaneous features, statistical features, and spectral features. In the HKDD framework, a feature fusion mechanism is proposed to integrate the features learned from the original signal with those processed by a fully connected network from the handcrafted features. Besides, an attention mechanism is implemented on the fused features to neglect immature features and highlight important features. To evaluate the performance of the proposed method, we construct two modulation classification datasets containing both traditional features and raw IQ data. The bigger one contains 36 modulation categories, which is greater than the number of categories of any AMC dataset currently available. Simulation results show that our proposed method has significant performance gain in both adequate-sample classification scenario and few-shot classification scenario.

关键词： Modulation Feature extraction Knowledge based systems Deep learning Phase shift keying Training Quadrature amplitude modulation Automatic modulation classification few-shot classification Index Terms deep learning attention mechanism traditional features

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Structured Neural Network Modeling for Developing Digital Twins Models of Hydropower Generation Units 19

Structured Neural Network Modeling for Developing Digital Tw...

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ieee 19th conference on Industrial Electronics and Applications (ICIEA)

作者： Wang, Hong Ou, Shiqi Oak Ridge Natl Lab Bldg & Transportat Sci Div Knoxville TN 37932 USA

ISBN: (纸本)9798350360875;9798350360868

Dynamic modeling is a key part in the development of digital twin (DT) for dynamic systems. This is true for hydropower systems, where whole system modeling including penstock, turbine and generators, etc is important in realizing actuate modeling for the real systems. On the other hand, in response to the large variations of the power demand due to increased penetration of renewables such as wind and solar, hydropower systems are now required to operate in a large power generation range. This situation triggers the nonlinear characteristics of the generation unit with respect to its models. As such, it is imperative to use data driven modeling such as neural networks to learn the nonlinear dynamics of the hydropower generation unit. To achieve this objective, this study constructs a modeling and learning algorithm integrated with multiple structured neural network models for the modeling of turbine shaft speed, penstock pressure, and generator power output based on the generator power control setpoint, field current, and field voltage. In addition, the study uses the hydropower data from Tacoma Public Utilities to train and validate the proposed neural network algorithm. The results have shown that this structured neural network modeling approach can learn the system dynamics effectively by using the real-time data collected from the hydropower system with the desired modeling results.

关键词： Hydropower systems Francis turbine synchronous generator neural network

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Enhancing Cyber-Physical System Analysis with Structure-Aware Modular Neural Networks 7

Enhancing Cyber-Physical System Analysis with Structure-Awar...

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ieee 7th International conference on Industrial Cyber-Physical systems (ICPS)

作者： Vranjes, Daniel Niggemann, Oliver Helmut Schmidt Univ Inst Automat Technol Hamburg Germany

ISBN: (纸本)9798350363029;9798350363012

In a wide array of engineering disciplines, modular designs serve as a fundamental approach for constructing intricate systems from simpler constituent modules. Domain-specific knowledge about the resulting structures can be formalized, paving the way for informed machine learning, a methodology that leverages prior knowledge to augment data-driven techniques with symbolic insights. One of many possible ways to include prior knowledge into a machine learning pipeline is through incorporation into the design of neural network architectures. We propose a novel algorithm for the learning of modular neural networks on modular cyber-physical systems where structural knowledge is used to build network architectures which resemble technical modules and the information flow is based on semantic relations between data and modules. We investigate potential benefits of modular neural networks compared to classical monolithic approaches based on anomaly detection experiments on data sets of mechanical pendulums with variable numbers of joints which are provided with this research. We define and investigate different levels of knowledge integration and modularity and show that modular designs can have a positive impact on anomaly detection performance. All models and code are published as open source.

关键词： machine learning deep learning informed machine learning neural architecture search modular neural networks cyber-physical systems

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Deep learning-Based State-Dependent ARX Modeling and Predictive control of Nonlinear systems

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ieee ACCESS 2023年 11卷 32579-32594页

作者： Kang, Tiao Peng, Hui Xu, Wenquan Sun, Yapeng Peng, Xiaoyan Cent South Univ Sch Automat Changsha 410083 Peoples R China Hunan Inst Engn Engn Training Ctr Xiangtan 411101 Peoples R China Hunan Univ Coll Mech & Vehicle Engn Changsha 410082 Peoples R China

For many practical industrial objects with time-varying operating points, strong nonlinearity, and difficulty in obtaining analytical models, the data-driven identification method is usually used to model such nonlinear systems. However, it is difficult for traditional modeling algorithms to effectively extract the dynamic characteristics of nonlinear systems from data and obtain accurate mathematical models. In this paper, we consider using the deep learning network combined with the state-dependent exogenous variable autoregressive (SD-ARX) model framework to build the nonlinear system model, so as to effectively and accurately learn the space-time characteristics of the nonlinear system from the sample data. Based on the idea, the hybrid models, i.e., the RNN-ARX model, CNN-ARX model, and RNN-CNN-ARX model are built, which use recurrent neural networks (RNN), convolutional neural networks (CNN) and their combination to fit the function-type coefficients of SD-ARX model, respectively. SD-ARX model based on deep learning has the advantages of local linearity and global nonlinearity. Compared with the other two models, the RNN-CNN-ARX model has a stronger ability to extract the multidimensional spatiotemporal dynamic characteristics of nonlinear systems, because it combines the advantages of RNN in mining temporal features and CNN in extracting spatial features. According to the structural characteristics of these models, three model-based predictive control (MPC) strategies are designed, i.e., RNN-ARX-MPC, CNN-ARX-MPC, and RNN-CNN-ARX-MPC. The real-time control comparative experiment on an actual multi-water-tank object shows that the proposed modeling and MPC method is feasible and effective for the modeling and predictive control of the nonlinear system.

关键词： RNN-ARX model CNN-ARX model RNN-CNN-ARX model model predictive control deep learning

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Chaos quasi-opposition sea-horse based modified new tilt controller designed for multi-area deregulated AGC using deep learning against cyber-attacks

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CHAOS SOLITONS & FRACTALS 2024年 188卷

作者： Vidyarthi, Prabhat Kumar Kumar, Ashiwani Raj, Saurav Natl Inst Technol Patna Dept Elect Engn Patna 800005 India Inst Chem Technol Dept Elect Engn Jalna India

This article highlights the intermittent character of RESs and reveals how deep learning techniques reduce the frequency deviation in automatic generation control (AGC) of the deregulated Modern Power System (MPS). With increasing MPS complexity comes more difficulty controlling frequency deviation and frequency security. The stability of an MPS is significantly affected by the robustness of the controller. The proposed MPS consists of thermal, hydro, and nuclear power plants, which include generation rate constraint (GRC) and governor dead band (GRB). The incorporation of wind and solar photovoltaic systems, together with their intermittent nature, has been considered to provide a more reliable control mechanism for enhanced frequency regulation in multiarea deregulated environments. The PID, FOPID, and TID basic AGC controllers are insufficient to offer a plant with optimal performance because of MPS hybridization. So, a new modified Cascaded tilted-FO derivative with filter (CPD mu F - TI) controller has been suggested, and its performance has been checked by comparing it with different existing controllers, which provide better performance in terms of overshoot, undershoot, and settling time. To optimize the different controller parameters, a newly modified chaos quasi-opposition-based sea horse optimization (CQOSHO) approach has been suggested to demonstrate its superiority it compared with several popular existing Meta-heuristic optimization. Moreover, an extensive investigation of the proposed AGC has been investigated and successfully implemented. A data-driven deep-learning-based forecasting technique is used to forecast real load with renewable energy (wind and solar) in MPS. The flexibility and reliability of the proposed controller in deregulated systems have been evaluated in various situations, including step, multi-step disturbance, and modified ieee-118 bus. This research proposes a learning-based attack detection technique for the cyber-physical mod

关键词： Automatic generation control (AGC) Chaos quasi opposition-based Sea-horse opti- mization (CQOSHO) Renewable energy sources (RESs) Deep learning technique Modified ieee-118 bus

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data-driven Active Power Dispatch for Wind Farms 13

Data-Driven Active Power Dispatch for Wind Farms

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13th ieee data driven control and learning systems conference, DDCLS 2024

作者： Jiao, Xuguo Zhang, Daoyuan Wang, Xin Ma, Zhaoxing Zhang, Zhenyong Liu, Wenfeng School of Information and Control Engineering Qingdao University of Technology Qingdao266520 China College of Control Science and Engineering Zhejiang University State Key Laboratory of Industrial Control Technology Hangzhou310027 China Ministry of Education Shandong Computer Science Center Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Jinan250014 China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan250014 China College of Computer Science and Technology Guizhou University State Key Laboratory of Public Big Data Guiyang550025 China School of Civil Engineering Qingdao University of Technology Qingdao266520 China

ISBN: (纸本)9798350361674

Active power dispatch of wind farms plays an important role in power grid scheduling. In this paper, a data-driven active power dispatch strategy for wind farms is proposed, which uses the key point of minimizing the standard deviation of wind turbines' output power series to reduce their fluctuations. First, to build the optimization problem, the historical active power output data of each wind turbine is collected from the Supervisory control And data Acquisition (SCADA) system. Then, the objective function is developed based on the idea of minimizing the standard deviation of the output power time series. Meanwhile, considering the constraints of wind turbines' output power and active power balance of the grid, the feasible domain of the cost function is constructed. Further, to solve the proposed nonlinear optimization problem with equality and inequality constraints, the trust region interior point method (TRIPM) is employed to explore optimal solution within the feasible domain. Finally, actual data collected from wind farms is used to test effectiveness of the designed approach and experimental results show that our method can achieve better output power smoothing performance than conventional Proportional Dispatch (PD) method. © 2024 ieee.

关键词： Cost functions

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