21,476 research outputs found
Feasibility of using neural networks to obtain simplified capacity curves for seismic assessment
The selection of a given method for the seismic vulnerability assessment of buildings is mostly dependent on the scale of the analysis. Results obtained in large-scale studies are usually less accurate than the ones obtained in small-scale studies. In this paper a study about the feasibility of using Artificial Neural Networks (ANNs) to carry out fast and accurate large-scale seismic vulnerability studies has been presented. In the proposed approach, an ANN was used to obtain a simplified capacity curve of a building typology, in order to use the N2 method to assess the structural seismic behaviour, as presented in the Annex B of the Eurocode 8. Aiming to study the accuracy of the proposed approach, two ANNs with equal architectures were trained with a different number of vectors, trying to evaluate the ANN capacity to achieve good results in domains of the problem which are not well represented by the training vectors. The case study presented in this work allowed the conclusion that the ANN precision is very dependent on the amount of data used to train the ANN and demonstrated that it is possible to use ANN to obtain simplified capacity curves for seismic assessment purposes with high precision.info:eu-repo/semantics/publishedVersio
Big Data Analysis-based Security Situational Awareness for Smart Grid
Advanced communications and data processing technologies bring great benefits to the smart grid. However, cyber-security threats also extend from the information system to the smart grid. The existing security works for smart grid focus on traditional protection and detection methods. However, a lot of threats occur in a very short time and overlooked by exiting security components. These threats usually have huge impacts on smart gird and disturb its normal operation. Moreover, it is too late to take action to defend against the threats once they are detected, and damages could be difficult to repair. To address this issue, this paper proposes a security situational awareness mechanism based on the analysis of big data in the smart grid. Fuzzy cluster based analytical method, game theory and reinforcement learning are integrated seamlessly to perform the security situational analysis for the smart grid. The simulation and experimental results show the advantages of our scheme in terms of high efficiency and low error rate for security situational awareness
Emerging Challenges in Smart Grid Cybersecurity Enhancement: A Review
In this paper, a brief survey of measurable factors affecting the adoption of cybersecurity enhancement methods in the smart grid is provided. From a practical point of view, it is a key point to determine to what degree the cyber resilience of power systems can be improved using cost-effective resilience enhancement methods. Numerous attempts have been made to the vital resilience of the smart grid against cyber-attacks. The recently proposed cybersecurity methods are considered in this paper, and their accuracies, computational time, and robustness against external factors in detecting and identifying False Data Injection (FDI) attacks are evaluated. There is no all-inclusive solution to fit all power systems requirements. Therefore, the recently proposed cyber-attack detection and identification methods are quantitatively compared and discusse
Advancements in Enhancing Resilience of Electrical Distribution Systems: A Review on Frameworks, Metrics, and Technological Innovations
This comprehensive review paper explores power system resilience, emphasizing
its evolution, comparison with reliability, and conducting a thorough analysis
of the definition and characteristics of resilience. The paper presents the
resilience frameworks and the application of quantitative power system
resilience metrics to assess and quantify resilience. Additionally, it
investigates the relevance of complex network theory in the context of power
system resilience. An integral part of this review involves examining the
incorporation of data-driven techniques in enhancing power system resilience.
This includes the role of data-driven methods in enhancing power system
resilience and predictive analytics. Further, the paper explores the recent
techniques employed for resilience enhancement, which includes planning and
operational techniques. Also, a detailed explanation of microgrid (MG)
deployment, renewable energy integration, and peer-to-peer (P2P) energy trading
in fortifying power systems against disruptions is provided. An analysis of
existing research gaps and challenges is discussed for future directions toward
improvements in power system resilience. Thus, a comprehensive understanding of
power system resilience is provided, which helps in improving the ability of
distribution systems to withstand and recover from extreme events and
disruptions
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Behavioural inhibition and valuation of gain/loss are neurally distinct from approach/withdrawal
Gain or omission/termination of loss produces approach; while loss or omission/termination of gain produces withdrawal. Control of approach/withdrawal motivation is distinct from valuation of gain/loss and does not entail learning – making “reward” and “punishment” ambiguous. Approach-withdrawal goal conflict engages a neurally distinct Behavioural Inhibition System, which controls “anxiety” (conflict/passive avoidance) but not “fear” (withdrawal/active avoidance)
Comparative analysis of spring flood risk reduction measures in Alaska, United States and the Sakha Republic, Russia
Thesis (Ph.D.) University of Alaska Fairbanks, 2017River ice thaw and breakup are an annual springtime phenomena in the North. Depending on regional weather patterns and river morphology, breakups can result in catastrophic floods in exposed and vulnerable communities. Breakup flood risk is especially high in rural and remote northern communities, where flood relief and recovery are complicated by unique geographical and climatological features, and limited physical and communication infrastructure. Proactive spring flood management would significantly minimize the adverse impacts of spring floods. Proactive flood management entails flood risk reduction through advances in ice jam and flood prevention, forecasting and mitigation, and community preparedness. With the goal to identify best practices in spring flood risk reduction, I conducted a comparative case study between two flood-prone communities, Galena in Alaska, United States and Edeytsy in the Sakha Republic, Russia. Within a week from each other, Galena and Edeytsy sustained major floods in May 2013. Methods included focus groups with the representatives from flood managing agencies, surveys of families impacted by the 2013 floods, observations on site, and archival review. Comparative parameters of the study included natural and human causes of spring floods, effectiveness of spring flood mitigation and preparedness strategies, and the role of interagency communication and cooperation in flood risk reduction. The analysis revealed that spring flood risk in Galena and Edeytsy results from complex interactions among a series of natural processes and human actions that generate conditions of hazard, exposure, and vulnerability. Therefore, flood risk in Galena and Edeytsy can be reduced by managing conditions of ice-jam floods, and decreasing exposure and vulnerability of the at-risk populations. Implementing the Pressure and Release model to analyze the vulnerability progression of Edeytsy and Galena points to common root causes at the two research sites, including colonial heritage, unequal distribution of resources and power, top-down governance, and limited inclusion of local communities in the decision-making process. To construct an appropriate flood risk reduction framework it is important to establish a dialogue among the diverse stakeholders on potential solutions, arriving at a range of top-down and bottom-up initiatives and in conjunction selecting the appropriate strategies. Both communities have progressed in terms of greater awareness of the hazard, reduction in vulnerabilities, and a shift to more reliance on shelter-in-place. However, in neither community have needed improvements in levee protection been completed. Dialogue between outside authorities and the community begins earlier and is more intensive for Edeytsy, perhaps accounting for Edeytsy's more favorable rating of risk management and response than Galena's
REPUTATION MANAGEMENT ALGORITHMS IN DISTRIBUTED APPLICATIONS
Nowadays, several distributed systems and applications rely on interactions between unknown agents that cooperate in order to exchange resources and services.
The distributed nature of these systems, and the consequent lack of a single centralized point of control, let agents to adopt selfish and malicious behaviors in order to maximize their own utility. To address such issue, many applications rely on Reputation Management Systems (RMSs) to estimate the future behavior of unknown agents before establishing actual interactions.
The relevance of these systems is even greater if the malicious or selfish behavior exhibited by a few agents may reduce the utility perceived by cooperative agents, leading to a damage to the whole community.
RMSs allow to estimate the expected outcome of a given interaction, thus providing relevant information that can be exploited to take decisions about the convenience of interacting with a certain agent. Agents and their behavior are constantly evolving and becoming even more complex, so it is increasingly difficult to successfully develop the RMS, able to resist the threats presented.
A possible solution to this problem is the use of agent-based simulation software designed to support researchers in evaluating distributed reputation management systems since the design phase.
This dissertation presents the design and the development of a distributed simulation platform based on HPC technologies called DRESS. This solution allows researchers to assess the performance of a generic reputation management system and provides a comprehensive assessment of its ability to withstand security attacks. In the scientific literature, a tool that allows the comparison of distinct RMS and different design choices through a set of defined metrics, also supporting large-scale simulations, is still missing.
The effectiveness of the proposed approach is demonstrated by the application scenario of user energy sharing systems within smart-grids and by considering user preferences differently from other work.
The platform has proved to be useful for the development of an energy sharing system among users, which with the aim of maximizing the amount of energy transferred has exploited the reputation of users once learned their preferences
Fast seismic assessment of built urban areas with the accuracy of mechanical methods using a feedforward neural network
Capacity curves obtained from nonlinear static analyses are widely used to perform seismic assessments of structures as an alternative to dynamic analysis. This paper presents a novel ‘en masse’ method to assess the seismic vulnerability of urban areas swiftly and with the accuracy of mechanical methods. At the core of this methodology is the calculation of the capacity curves of low-rise reinforced concrete buildings using neural networks, where no modeling of the building is required. The curves are predicted with minimal error, needing only basic geometric and material parameters of the structures to be specified. As a first implementation, a typology of prismatic buildings is defined and a training set of more than 7000 structures generated. The capacity curves are calculated through push-over analysis using SAP2000. The results feature the prediction of 100-point curves in a single run of the network while maintaining a very low mean absolute error. This paper proposes a method that improves current seismic assessment tools by providing a fast and accurate calculation of the vulnerability of large sets of buildings in urban environments.info:eu-repo/semantics/publishedVersio
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