Two-Step Wind Power Prediction Approach With Improved Complementary Ensemble Empirical Mode Decomposition and Reinforcement Learning

The strong stochastic nature of wind power generation makes it extremely challenging to accurately predict and support the planning and operation of modern power systems with significant penetration of renewable energy. This article proposes a two-step wind power prediction method, which consists of two phases: long time-scale coarse prediction and short time-scale fine correction. In the long time-scale phase, a complementary ensemble empirical mode decomposition-based sigma point Kalman filter approach is proposed to coarsely predict wind power merely with historical data. In the short time-scale phase, a deep deterministic policy gradient approach learns from real-time weather information to correct the coarse prediction result, which results in an improved prediction accuracy. A real-life case study confirms that the proposed method can properly predict wind power generation and have a better prediction accuracy than existing techniques, thus offering a viable and promising alternative for predicting wind power generation

Nonnegative coupled matrix tensor factorization for smart city spatiotemporal pattern mining

With the advancements in smartphones and inbuilt sensors, the day-to-day spatiotemporal activities of people can be recorded. With this available information, the automated extraction of spatiotemporal patterns is crucial to un-derstand the people’s mobility. These patterns can assist in improving the smart city environments like traffic control, urban planning, and transportation facili-ties. The smartphone generated spatiotemporal data is enriched with multiple contexts and efficiently utilizing them in a Machine Learning process is still a challenging task. In this paper, we propose a Nonnegative Coupled Matrix Tensor Factorization (CMTF) model to integrate and analyze additional contexts with spatiotemporal data to generate meaningful patterns. We also propose an efficient factorization algorithm based on variable selection to solve the Nonnegative CMTF model that yields accurate spatiotemporal patterns. Our empirical analysis highlights the efficiency of the proposed CMTF model in terms of accuracy and factor goodness

Towards secure distance bounding

Abstract. Relay attacks (and, more generally, man-in-the-middle attacks) are a serious threat against many access control and payment schemes. In this work, we present distance-bounding protocols, how these can deter relay attacks, and the security models formalizing these protocols. We show several pitfalls making existing protocols insecure (or at least, vulnerable, in some cases). Then, we introduce the SKI protocol which enjoys resistance to all popular attack-models and features provable security. As far as we know, this is the first protocol with such all-encompassing security guarantees. 1 Why Distance-Bounding? It is well known that a chess beginner can win against a chess grand-master easily by defeating two grand-masters concurrently, taking different colors in both games, and relaying the move of one master to the other. This is a pure relay attack where two masters play against each other while each of them thinks he is playing against a beginner. In real life, relay attacks find applications in access control. For instance, a car wit

Persuasive Cities for Sustainable Wellbeing: Quantified Communities

Can you imagine a city that feels, understands, and cares about your wellbeing? Future cities will reshape human behavior in countless ways. New strategies and models are required for future urban spaces to properly respond to human activity, environmental conditions, and market dynamics. Persuasive urban systems will play an important role in making cities more livable and resource-efficient by addressing current environmental challenges and enabling healthier routines. Persuasive cities research aims at improving wellbeing across societies through applications of socio-psychological theories and their integration with conceptually new urban designs. This research presents an ecosystem of future cities, describes three generic groups of people depending on their susceptibility to persuasive technology, explains the process of defining behavior change, and provides tools for social engineering of persuasive cities. Advancing this research is important as it scaffolds scientific knowledge on how to design persuasive cities and refines guidelines for practical applications in achieving their emergence

Flexicurity: a conceptual critique

In recent years 'flexicurity' has become an influential concept in academic and political discourse, in particular since the European Commission placed it at the core of the European Employment Strategy. However, both as an academic concept and as a policy concept flexicurity is underdeveloped and suffers from a number of serious shortcomings. In this paper we critically review the flexicurity concept and discuss a number of its problematic features. In particular, we focus on four aspects: the concept’s ambiguity and openness to political capture; its failure to problematise the creation of institutional complementarities; its lack of attention to conflicts of interest and to the heterogeneity of the labour market; and its reductionist view of the sources of flexibility and security. We illustrate this discussion with a series of empirical examples. Finally, we conclude that the flexicurity approach should or be abandoned, or substantially improved. We also provide a number of suggestions on how to strengthen the flexicurity approach

The real-time city? Big data and smart urbanism

Abstract: \u27Smart cities\u27 is a term that has gained traction in academia, business and government to describe cities that, on the one hand, are increasingly composed of and monitored by pervasive and ubiquitous computing and, on the other, whose economy and governance is being driven by innovation, creativity and entrepreneurship, enacted by smart people. This paper focuses on the former and how cities are being instrumented with digital devices and infrastructure that produce ‘big data’ which enable real-time analysis of city life, new modes of technocratic urban governance, and a re-imagining of cities. The paper details a number of projects that seek to produce a real-time analysis of the city and provides a critical reflection on the implications of big data and smart urbanism