The massive integration of communication and information technology with the large-scale power grid has enhanced the efficiency, safety, and economical operation of cyber-physical systems. However, the open and divers...
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The massive integration of communication and information technology with the large-scale power grid has enhanced the efficiency, safety, and economical operation of cyber-physical systems. However, the open and diversified communication environment of the smart grid is exposed to cyber-attacks. Data integrity attacks that can bypass conventional security techniques have been considered critical threats to the operation of the grid. Current detection techniques cannot learn the dynamic and heterogeneous characteristics of the smart grid and are unable to deal with non-euclidean data types. To address the issue, we propose a novel Deep-Q-Network scheme empowered with a graph convolutional network (GCN) framework to detect data integrity attacks in cyber-physical systems. The simulation results show that the proposed framework is scalable and achieves higher detection accuracy, unlike other benchmark techniques.
As a result of its aggressive nature and late identification at advanced stages, lung cancer is one of the leading causes of cancer-related deaths. Lung cancer early diagnosis is a serious and difficult challenge that...
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Temporal knowledge graph(TKG) reasoning, has seen widespread use for modeling real-world events, particularly in extrapolation settings. Nevertheless, most previous studies are embedded models, which require both enti...
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Temporal knowledge graph(TKG) reasoning, has seen widespread use for modeling real-world events, particularly in extrapolation settings. Nevertheless, most previous studies are embedded models, which require both entity and relation embedding to make predictions, ignoring the semantic correlations among different entities and relations within the same timestamp. This can lead to random and nonsensical predictions when unseen entities or relations occur. Furthermore, many existing models exhibit limitations in handling highly correlated historical facts with extensive temporal depth. They often either overlook such facts or overly accentuate the relationships between recurring past occurrences and their current counterparts. Due to the dynamic nature of TKG, effectively capturing the evolving semantics between different timestamps can be *** address these shortcomings, we propose the recurrent semantic evidenceaware graph neural network(RE-SEGNN), a novel graph neural network that can learn the semantics of entities and relations simultaneously. For the former challenge, our model can predict a possible answer to missing quadruples based on semantics when facing unseen entities or relations. For the latter problem, based on an obvious established force, both the recency and frequency of semantic history tend to confer a higher reference value for the current. We use the Hawkes process to compute the semantic trend, which allows the semantics of recent facts to gain more attention than those of distant facts. Experimental results show that RE-SEGNN outperforms all SOTA models in entity prediction on 6 widely used datasets, and 5 datasets in relation prediction. Furthermore, the case study shows how our model can deal with unseen entities and relations.
Software security poses substantial risks to our society because software has become part of our life. Numerous techniques have been proposed to resolve or mitigate the impact of software security issues. Among them, ...
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Software security poses substantial risks to our society because software has become part of our life. Numerous techniques have been proposed to resolve or mitigate the impact of software security issues. Among them, software testing and analysis are two of the critical methods, which significantly benefit from the advancements in deep learning technologies. Due to the successful use of deep learning in software security, recently,researchers have explored the potential of using large language models(LLMs) in this area. In this paper, we systematically review the results focusing on LLMs in software security. We analyze the topics of fuzzing, unit test, program repair, bug reproduction, data-driven bug detection, and bug triage. We deconstruct these techniques into several stages and analyze how LLMs can be used in the stages. We also discuss the future directions of using LLMs in software security, including the future directions for the existing use of LLMs and extensions from conventional deep learning research.
The emergence of multimodal disease risk prediction signifies a pivotal shift towards healthcare by integrating information from various sources and enhancing the reliability of predicting susceptibility to specific d...
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The disease that contains the highest mortality and morbidity across the world is cardiac disease. Annually millions of people are affected and deaths take place due to cardiac diseases worldwide. There are various di...
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Anticipating the imminent surge of retired lithium-ion batteries(R-LIBs)from electric vehicles,the need for safe,cost-effective and environmentally friendly disposal technologies has *** paper seeks to offer a compreh...
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Anticipating the imminent surge of retired lithium-ion batteries(R-LIBs)from electric vehicles,the need for safe,cost-effective and environmentally friendly disposal technologies has *** paper seeks to offer a comprehensive overview of the entire disposal framework for R-LIBs,encompassing a broad spectrum of activities,including screening,repurposing and ***,we delve deeply into a thorough examination of current screening technologies,shifting the focus from a mere enumeration of screening methods to the exploration of the strategies for enhancing screening ***,we outline battery repurposing with associated key factors,summarizing stationary applications and sizing methods for R-LIBs in their second life.A particular light is shed on available reconditioning solutions,demonstrating their great potential in facilitating battery safety and lifetime in repurposing scenarios and identifying their techno-economic *** the realm of battery recycling,we present an extensive survey of pre-treatment options and subsequent material recovery ***,we introduce several global leading recyclers to illustrate their industrial processes and technical ***,relevant challenges and evolving trends are investigated in pursuit of a sustainable end-of-life management and disposal *** hope that this study can serve as a valuable resource for researchers,industry professionals and policymakers in this field,ultimately facilitating the adoption of proper disposal practices.
Lithium plating is a detrimental phenomenon in lithium-ion cells that compromises both functionality and *** study investigates electro-chemo-mechanical behaviors of lithium plating in lithium iron phosphate pouch cel...
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Lithium plating is a detrimental phenomenon in lithium-ion cells that compromises both functionality and *** study investigates electro-chemo-mechanical behaviors of lithium plating in lithium iron phosphate pouch cells under different external *** force microscopy nanoindentation is performed on the graphite electrode to analyze the influence of external pressure on solid-electrolyte interphase(SEI),revealing that the mechanical strength of SEI,indicated by Young's modulus,increases with the presence of external ***,an improved phase field model for lithium plating is developed by incorporating electrochemical parameterization based on nonequilibrium *** results demonstrate that higher pressure promotes lateral lithium deposition,covering a larger area of ***,electrochemical impedance spectroscopy and thickness measurements of the pouch cells are conducted during overcharge,showing that external pressure suppresses gas generation and thus increases the proportion of lithium deposition among galvanostatic overcharge *** integrating experimental results with numerical simulations,it is demonstrated that moderate pressure mitigates SEI damage during lithium plating,while both insufficient and excessive pressure may exacerbate *** study offers new insights into optimizing the design and operation of lithium iron phosphate pouch cells under external pressures.
For the diagnostics and health management of lithium-ion batteries,numerous models have been developed to understand their degradation *** models typically fall into two categories:data-driven models and physical mode...
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For the diagnostics and health management of lithium-ion batteries,numerous models have been developed to understand their degradation *** models typically fall into two categories:data-driven models and physical models,each offering unique advantages but also facing ***-informed neural networks(PINNs)provide a robust framework to integrate data-driven models with physical principles,ensuring consistency with underlying physics while enabling generalization across diverse operational *** study introduces a PINN-based approach to reconstruct open circuit voltage(OCV)curves and estimate key ageing parameters at both the cell and electrode *** parameters include available capacity,electrode capacities,and lithium inventory *** proposed method integrates OCV reconstruction models as functional components into convolutional neural networks(CNNs)and is validated using a public *** results reveal that the estimated ageing parameters closely align with those obtained through offline OCV tests,with errors in reconstructed OCV curves remaining within 15 *** demonstrates the ability of the method to deliver fast and accurate degradation diagnostics at the electrode level,advancing the potential for precise and efficient battery health management.
Platooning represents one of the key features that connected automated vehicles may possess as it allows multiple automated vehicles to be maneuvered cooperatively with small headways on roads. However, a critical cha...
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Platooning represents one of the key features that connected automated vehicles may possess as it allows multiple automated vehicles to be maneuvered cooperatively with small headways on roads. However, a critical challenge in accomplishing automated vehicle platoons is to deal with the effects of intermittent and sporadic vehicle-to-vehicle data transmissions caused by limited wireless communication resources. This paper addresses the co-design problem of dynamic event-triggered communication scheduling and cooperative adaptive cruise control for a convoy of automated vehicles with diverse spacing policies. The central aim is to achieve automated vehicle platooning under various gap references with desired platoon stability and spacing performance requirements, while simultaneously improving communication efficiency. Toward this aim, a dynamic event-triggered scheduling mechanism is developed such that the intervehicle data transmissions are scheduled dynamically and efficiently over time. Then, a tractable co-design criterion on the existence of both the admissible event-driven cooperative adaptive cruise control law and the desired scheduling mechanism is derived. Finally, comparative simulation results are presented to substantiate the effectiveness and merits of the obtained results.
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