The transformation of traditional electric power systems into smart grids has allowed for improved monitoring and control capabilities. However, this evolution has also brought about new challenges in the form of malw...
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Quantum machinelearning (QML) has emerged as a promising intersection of quantum computing and classical machinelearning, anticipated to drive breakthroughs in computational tasks. This paper discusses the question ...
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ISBN:
(纸本)9798331541378
Quantum machinelearning (QML) has emerged as a promising intersection of quantum computing and classical machinelearning, anticipated to drive breakthroughs in computational tasks. This paper discusses the question which security concerns and strengths are connected to QML by means of a systematic literature review. We categorize and review the security of QML models, their vulnerabilities inherent to quantum architectures, and the mitigation strategies proposed. The survey reveals that while QML possesses unique strengths, it also introduces novel attack vectors not seen in classical systems. We point out specific risks, such as cross-talk in superconducting systems and forced repeated shuttle operations in ion-trap systems, which threaten QML's reliability. However, approaches like adversarial training, quantum noise exploitation, and quantum differential privacy have shown potential in enhancing QML robustness. Our review discuss the need for continued and rigorous research to ensure the secure deployment of QML in real-world applications. This work serves as a foundational reference for researchers and practitioners aiming to navigate the security aspects of QML.
In the dynamic construction industry, traditional robotic integration has primarily focused on automating specific tasks, often overlooking the complexity and variability of human aspects in construction workflows. Th...
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ISBN:
(纸本)9798350377712;9798350377705
In the dynamic construction industry, traditional robotic integration has primarily focused on automating specific tasks, often overlooking the complexity and variability of human aspects in construction workflows. This paper introduces a human-centered approach with a "work companion rover" designed to assist construction workers within their existing practices, aiming to enhance safety and workflow fluency while respecting construction labor's skilled nature. We conduct an in-depth study on deploying a robotic system in carpentry formwork, showcasing a prototype that emphasizes mobility, safety, and comfortable worker-robot collaboration in dynamic environments through a contextual Reinforcement learning (RL)-driven modular framework. Our research advances robotic applications in construction, advocating for collaborative models where adaptive robots support rather than replace humans and underscores the potential for an interactive and collaborative human-robot workforce.
The progress in machinelearning and advancements in measurement and computational capabilities in modern wave control systems has spurred interest in learning-based control techniques, including model predictive cont...
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ISBN:
(纸本)9798350376357;9798350376340
The progress in machinelearning and advancements in measurement and computational capabilities in modern wave control systems has spurred interest in learning-based control techniques, including model predictive control. The primary challenge in model predictive control lies in addressing the inherent uncertainty in complex and dynamic system models. This paper explores the effectiveness of online system model learning to use Gaussian processes. Recent years have witnessed significant results by combining non-parametric learning models with Gaussian processes and model-based predictive control. This research focuses on assessing the effectiveness of Gaussian processes, considering a stochastic model predictive controller (SMPC). To evaluate the proposed method, simulations on a quadruple tank process are conducted, aiming to regulate liquid levels. Gaussian processes are utilized for real-time estimation of system hyperparameters, incorporated into the predictive control cost function, and account for input and output constraints. The results show the stability and efficacy of the proposed control system.
Traditional Intrusion Detection systems (IDS) often struggle to effectively address the evolving landscape of cyber threats. This research explores the potential of machinelearning techniques to improve the accuracy,...
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In the realm of digital entertainment, where the abundance of choices can overwhelm consumers, movie recommendation systems emerge as indispensable tools. These systems act as personalized guides, leveraging sophistic...
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Novelty Detection (ND) plays a crucial role in machinelearning by identifying new or unseen data during model inference. This capability is especially important for the safe and reliable operation of automated system...
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ISBN:
(纸本)9798350344868;9798350344851
Novelty Detection (ND) plays a crucial role in machinelearning by identifying new or unseen data during model inference. This capability is especially important for the safe and reliable operation of automated systems. Despite advances in this field, existing techniques often fail to maintain their performance when subject to adversarial attacks. Our research addresses this gap by marrying the merits of nearest-neighbor algorithms with robust features obtained from models pretrained on ImageNet. We focus on enhancing the robustness and performance of ND algorithms. Experimental results demonstrate that our approach significantly outperforms current state-of-the-art methods across various benchmarks, particularly under adversarial conditions. By incorporating robust pretrained features into the k-NN algorithm, we establish a new standard for performance and robustness in the field of robust ND. This work opens up new avenues for research aimed at fortifying machinelearningsystems against adversarial vulnerabilities.
In many settings, fleets of assets must perform series of missions with in-between finite breaks. For such fleets, a widely used maintenance strategy is the fleet selective maintenance (FSM). Under resource constraint...
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In many settings, fleets of assets must perform series of missions with in-between finite breaks. For such fleets, a widely used maintenance strategy is the fleet selective maintenance (FSM). Under resource constraints, the FSM problem selects an optimal subset of feasible maintenance actions to be performed on a subset of components to minimise the maintenance cost while ensuring high system reliability during the upcoming mission. The majority of extant FSMP models are focussed on traditional physics-based reliability models. With recent advances in machinelearning (ML) and Deep learning (DL) algorithms, data-driven methods have shown accuracy in predicting remaining useful life (RUL). This paper proposes a predictive FSM strategy for fleets of complex and large multicomponent systems. It relies on a concurrent ML/DL and optimisation approach where a clustering algorithm is used to determine the health states of components and a probabilistic RUL prognostics model is used for component reliability assessment. An optimisation model is developed to solve the predictive FSM problem to ensure high reliability of all systems within the fleet. An efficient two-phase solution approach is developed to solve this complex optimisation problem. Numerical experiments show the validity of the approach and highlight the improved maintenance plans achieved.
Paper builds a next-day traffic forecasting system using machinelearning. By the use of historical information, it can correctly predict what will happen in the case of traffic conditions. It also provides dynamic ro...
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Phishing attacks are a common and under-protected security hazard in today's digital ecosystem. Phishing, which first surfaced in 1996, has grown into an extremely severe and dangerous kind of cybercrime on the in...
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