Aspect-oriented programming provides a convenient high-level model to define several kinds of dynamic analyses, in particular thanks to recent advances in exhaustive weaving in core libraries. Casting dynamic analyses...
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Aspect-oriented programming provides a convenient high-level model to define several kinds of dynamic analyses, in particular thanks to recent advances in exhaustive weaving in core libraries. Casting dynamic analyses as aspects allows the use of a single weaving infrastructure to apply different analyses to the same base program, simultaneously. However, even if dynamic analysis aspects are mutually independent, their mere presence perturbates the observations of others: this is due to the fact that aspectual computation is potentially visible to all aspects. Because current aspect composition approaches do not address this kind of computational interference, combining different analysis aspects yields at best unpredictable results. It is also impossible to flexibly combine various analyses, for instance to analyze an analysis aspect. In this paper we show how the notion of execution levels makes it possible to effectively address these composition issues. In order to realize this approach, we explore the practical and efficient integration of execution levels in a mainstream aspect language, AspectJ. We report on a case study of composing two out-of-the-box analysis aspects in a variety of ways, highlighting the benefits of the approach.
the wind integration is on the rise in modern grids to generate cleaner energy due to increasing environmental concerns. this further escalates the problem of low frequency oscillations (LFOs) in power system. thus, r...
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ISBN:
(纸本)9781538649978;9781538649961
the wind integration is on the rise in modern grids to generate cleaner energy due to increasing environmental concerns. this further escalates the problem of low frequency oscillations (LFOs) in power system. thus, real-time monitoring of oscillations becomes even more important in present interconnected power systems. the conventional methods are offline and time consuming. In this work, a wide-area based method employing Phasor Measurement Units (PMUs) and Artificial Neural Network (ANN) is proposed to predict the system oscillatory status in real-time. the PMUs are optimally placed using modified Integer Linear programming. the PMU data is dimensionally reduced using Principal component Analysis before using it to train the ANN. the ANN predicts the LFO related information like damping ratio and frequency and an index to access the mode's localness. the proposed methodology is verified on IEEE New England benchmark system. the suggested method is very fast and accurate in predicting the required information in real-time for different operating conditions involving topological variations with very less computational requirement.
Over the past 15 years large systems integrators have grown in size by an order of magnitude. During this time the nature of the systems we build, the manner in which they are built, and the clients for whom they are ...
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ISBN:
(纸本)9781605582672
Over the past 15 years large systems integrators have grown in size by an order of magnitude. During this time the nature of the systems we build, the manner in which they are built, and the clients for whom they are built have seen corresponding growth. During this talk I will review some of these changes and discuss some of the challenges we see on the horizon as these trends continue. What kind of systems would a systems integrator with 2 million people develop? How would they be built?
We present VAEL, a neuro-symbolic generative model integrating variational autoencoders (VAE) withthe reasoning capabilities of probabilistic logic (L) programming. Besides standard latent subsymbolic variables, our ...
ISBN:
(纸本)9781713871088
We present VAEL, a neuro-symbolic generative model integrating variational autoencoders (VAE) withthe reasoning capabilities of probabilistic logic (L) programming. Besides standard latent subsymbolic variables, our model exploits a probabilistic logic program to define a further structured representation, which is used for logical reasoning. the entire process is end-to-end differentiable. Once trained, VAEL can solve new unseen generation tasks by (i) leveraging the previously acquired knowledge encoded in the neural component and (ii) exploiting new logical programs on the structured latent space. Our experiments provide support on the benefits of this neuro-symbolic integration both in terms of task generalization and data efficiency. To the best of our knowledge, this work is the first to propose a general-purpose end-to-end framework integrating probabilistic logic programming into a deep generative model.
Wide Area Monitoring System (WAMS) is an inevitable component in todays power system since a small catastrophe like fluctuations caused by the renewable energy sources, fast changing loads, electric vehicles, etc. wil...
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ISBN:
(纸本)9781538649978;9781538649961
Wide Area Monitoring System (WAMS) is an inevitable component in todays power system since a small catastrophe like fluctuations caused by the renewable energy sources, fast changing loads, electric vehicles, etc. will lead to the collapse of the whole system. this paper addresses the optimal planning of WAMS by Nash Differential Evolution (NashDE) Algorithm, in which the variables of the problem are clustered into number of clusters, and will evolve parallelly by Differential Evolution (DE) towards the global objective of the problem. For optimization problems which are based on the connectivity of variables involved as in most of the power system planning processes, the convergence speed will greatly improve if variables in a cluster are physically connected. Further, this will ensure global convergence of the problem. Hence, this paper proposes a NashDE algorithm in which the variables are clustered based on their connectivity. Markov Clustering Algorithm (MCL) is used for clustering the variables based on their connectivity. this is the first paper of such kind which addresses WAMS planning problem by Evolutionary Game theory. Simulations are carried out for IEEE 14 and 30 bus test system using Python programming and the results are presented graphically.
the ship’s magnetic field is a significant source of exposure, and obtaining the characteristics of its magnetic field distribution in the entire space is a prerequisite for enhancing the ship’s magnetic stealth per...
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ISBN:
(数字)9798331506230
ISBN:
(纸本)9798331506247
the ship’s magnetic field is a significant source of exposure, and obtaining the characteristics of its magnetic field distribution in the entire space is a prerequisite for enhancing the ship’s magnetic stealth performance. To address the variability of the ship’s magnetic field across different orientations, a modeling method for the ship’s magnetic field based on the Particle Swarm Optimization (PSO) algorithm is proposed, leveraging theoretical magnetic field data from parametric models. An array model of magnetic dipoles for the ship’s magnetic field is established. Withthe root mean square error of the modeled magnetic field as the objective function, the PSO algorithm is employed to optimize the magnetic moment parameters, yielding an optimal array of magnetic dipoles. Experiments are then designed to validate the approach. the experimental results demonstrate that the proposed method can conveniently, effectively, and accurately simulate the target magnetic field of the ship, with a minimum similarity of $\mathbf{9 5. 2 \%}$ for the three-component magnetic field modeling.
Automatic program repair plays a crucial role in the software development and implementation. While deep learning-based approaches have made significant progress, one inherent challenge is the inefficiency in code rep...
Automatic program repair plays a crucial role in the software development and implementation. While deep learning-based approaches have made significant progress, one inherent challenge is the inefficiency in code representation, which hampers accurate patch generation. Furthermore, the training data used by these data-driven approaches may be limited, and they may not be able to capture the subtle differences between vulnerabilities and patches. To address these issues, FixGPT, we propose a three-tier deep learning model in the study. Specifically, a generative pre-trained transformer model is designed in the first tier to capture code characteristics and programming patterns. the second tier integrates a generation model based on the structure of neural machine translation, for the purpose of generating potential patches. Finally, a contrast model is introduced in the last tier to differentiate between the vulnerability and the patch. We also incorporate the Byte Pair Encoding approach to reduce the search space by converting identifiers into subwords. Detailed experimental studies have been carried out to evaluate the performance of FixGPT on two well-known benchmarking datasets: QuixBugs and Defects4J. the results demonstrated significant improvements in the effectiveness and accuracy in comparison with existing solutions. We complement these findings through the analysis of two case studies.
Humans have various kinds of needs, namely primary, secondary and tertiary needs. One of the activities to fulfill those needs is to carry out sales and purchase transaction. the development of knowledge and technolog...
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While dynamic code evolution in object-oriented systems is an important feature supported by dynamic languages, there is currently only limited support for dynamic code evolution in high-performance, state-of-the-art ...
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While dynamic code evolution in object-oriented systems is an important feature supported by dynamic languages, there is currently only limited support for dynamic code evolution in high-performance, state-of-the-art runtime systems for statically typed languages, such as the Java Virtual Machine. In this tool demonstration, we present the Dynamic Code Evolution VM, which is based on a recent version of Oracle's state-of-the-art Java HotSpot (TM) VM and allows unlimited changes to loaded classes at runtime. Based on the Dynamic Code Evolution VM, we developed an enhanced version of the Mantisse GUI builder (which is part of the NetBeans IDE) that allows adding GUI components without restarting the application under development. Furthermore, we redesigned the dynamic AOP framework HotWave to take advantage of the enhanced dynamic code evolution capabilities. the new version, HotWave2, now supports most AspectJ constructs, including around() advice and static cross-cutting. We will demonstrate boththe enhanced Mantisse GUI builder as well as HotWave2, weaving several aspects for dynamic analysis in sizable applications at runtime.
It is proposed a new approach based on a methodology, assisted by a tool, to create new products in the automobile industry based on previous defined processes and experiences inspired on a set of best practices or pr...
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It is proposed a new approach based on a methodology, assisted by a tool, to create new products in the automobile industry based on previous defined processes and experiences inspired on a set of best practices or principles: it is based on high-level models or specifications; it is component-based architecture centric; it is based on generativeprogramming techniques. this approach follows in essence the MDA (Model Driven Architecture) philosophy with some specific characteristics. We propose a repository that keeps related information, such as models, applications, design information, generated artifacts and even information concerning the development process itself (e.g., generation steps, tests and integration milestones). Generically, this methodology receives the users' requirements to a new product (e.g., functional, non-functional, product specification) as its main inputs and produces a set of artifacts (e.g., design parts, process validation output) as its main output, that will be integrated in the engineer design tool (e.g. CAD system) facilitating the work.
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