This study explores a model of a multilinear queueing system (QS) with channel switching under uncertainty, where the statistical characteristics of the homogeneous Markov chain, which governs the transition probabili...
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With the rapid advancements in network technology and automation processes, the threats posed by cyberattacks have become increasingly significant. To address these threats, numerous researchers have developed various...
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Determinacy is a desirable but difficult-to-achieve behavioural property in scalable distributedsystems. Deterministic Models of Computation range from the asynchronous Kahn Process networks to synchronous reactive l...
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ISBN:
(纸本)9798350378030;9798350378023
Determinacy is a desirable but difficult-to-achieve behavioural property in scalable distributedsystems. Deterministic Models of Computation range from the asynchronous Kahn Process networks to synchronous reactive languages such as Lustre, where logical clocks enforce the synchrony hypothesis. These models have well-founded data-flow semantics where computations are viewed as the least fixed point solutions of simultaneous equations defined by continuous functions on streams of discrete values. However, scalable and efficient implementations of the Kahn model are challenging to construct, while the synchrony hypothesis in Lustre makes distributed implementations difficult. Moreover, determinacy is a consequence of specific assumptions built into the computational model. The notion of Logical Synchrony, proposed by Lall et al., and explored further by Kenwright et al., suggests that synchronisation issues may be decoupled from computation, leading to a distributed model where computations at independent nodes are related by invariant logical delays. We provide a semantic notion of behaviour for functional processes running on such Logical Synchrony networks (extension graphs), and an appropriate and robust notion of logical observational equivalence (wavefront equivalence) retaining semantic aspects of KPNs, specifically determinacy. Further, we propose extending the versatile notion of the synchronous observer, exploited in the Lustre toolset, to a network of located synchronous observers with the same invariant logical delays as the distributed system. Thus we will be able to use the same logically synchronous model of computation for checking or monitoring a class of (safety) properties of programs, specifying axioms and assumptions on behaviour, constraining models and specifying test cases, etc.
Neural networks have become a leading model in modern machine learning, able to model even the most complex data. For them to be properly trained, however, a lot of computational resources are required. With the carbo...
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distributed Sagnac loop (SI) is a simple vibration-based sensing system, which is cost-effective and simple to implement. Because of its simplicity, its performance is low compared to other complex structure sensors. ...
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With the vigorous development and utilization of new energy, the amount of distributed photovoltaic access in the distribution network has increased significantly, and it has also brought a series of problems when it ...
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While digital displays and glass cockpits have become widespread in modern aircraft, analog instruments remain. These gauges can be challenging to digitize or integrate into automated safety systems. This work investi...
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ISBN:
(纸本)9783031773884;9783031773891
While digital displays and glass cockpits have become widespread in modern aircraft, analog instruments remain. These gauges can be challenging to digitize or integrate into automated safety systems. This work investigates the application of computer vision to evaluate aircraft instruments of varying complexity. For ease of acquisition, training data was recorded from a flight simulator and used to train neural networks. The resulting models have high accuracy when evaluating single pointer gauges in lighting conditions similar to the training data set, as well as with entirely different lighting conditions. Performance remains robust even with more complex instruments, such as dual pointer airspeed gauges and attitude indicators. Potential future work on this system includes applying it to real-life aircraft and integration with safety systems, including detection of instrument display failures.
Emerging mobile applications often require the execution of computer vision (CV) tasks based on compute- and memory-intensive deep neural networks (DNNs). Although offloading CV tasks to edge servers can decrease reso...
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ISBN:
(纸本)9798350386066;9798350386059
Emerging mobile applications often require the execution of computer vision (CV) tasks based on compute- and memory-intensive deep neural networks (DNNs). Although offloading CV tasks to edge servers can decrease resource consumption at the mobile devices, it poses the challenge of handling multiple concurrent tasks with limited computing and memory capacity. In stark opposition with the existing state of the art, we tackle this challenge by jointly optimizing (i) the utilization of resources at the edge, among which memory - so far widely overlooked - and the radio resources used for task offloading;(ii) which and how many offloaded tasks should be executed;and (iii) the structure of the DNNs. First, we formulate the DNN for scalable Offloading of Tasks (DOT) problem, prove that it is NP-hard, and envision a weighted-tree-based heuristic solution, named OffloaDNN, that efficiently solves the DOT problem. We evaluate OffloaDNN through extensive numerical analysis using state-of-the-art image classification ResNet-18, as well as real-world experiments on the Colosseum emulator. The numerical results show that, in small-scale scenarios, OffloaDNN matches the optimum very closely, and, in larger-scale scenarios, increases the number of admitted offloaded tasks by 26.9% with respect to the state of the art, while saving 82.5% memory and 77.4% per-inference computing time. The numerical results are confirmed by the real-world validation on Colosseum.
In high-volume, high-velocity contexts, threat identification requires effective real-time data stream analysis. This study offers a novel architecture - real-time processing of high-speed data streams - that is criti...
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Machine Learning approaches have been successfully used for the creation of high-performance control components of cyber-physical systems, where the control dynamics result from the combination of many subsystems. How...
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ISBN:
(纸本)9783031606977;9783031606984
Machine Learning approaches have been successfully used for the creation of high-performance control components of cyber-physical systems, where the control dynamics result from the combination of many subsystems. However, these approaches may lack the trustworthiness required to guarantee their reliable application in a safety-critical context. In this paper, we propose an approach to automatically translate entire feed-forward fully-connected neural networks into first-order logic formal models that can be used to analyse the prediction of the network. The approach exploits the Prototype Verification System theorem prover to model neural networks based on non-linear activation functions and prove the safety bounds of the output under safety-critical conditions. Finally, we show the application of the proposed approach to a model-predictive controller for autonomous driving.
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