Analyzing urban sprawl patterns through fractal geometry: the case of Istanbul metropolitan area

Over the last decade, there has been a rapid increase in the amount of literature on the measurement of urban sprawl. Density gradients, sprawl indexes which are based on a series of measurable indicators and certain simulation techniques are some quantitative approaches used in previous studies. Recently, fractal analysis has been used in analyzing urban areas and a fractal theory of cities has been proposed. This study attempts to measure urban sprawl using a sprawl index and analyses urban form through fractal analysis for characterizing urban sprawl in Istanbul which has not been measured or characterized yet. In this study, measures of sprawl were calculated at each neighborhood level and then integrated within sprawl index through “density” and “proximity” factors. This identifies the pattern of urban sprawl during six periods from 1975 to 2005, and then the urban form of Istanbul is quantified through fractal analysis in given periods in the context of sprawl dynamics. Our findings suggest that the fractal dimension of urban form is positively correlated with the urban sprawl index score when urban growth pattern is more likely “concentrated”. However, a negative relationship has been observed between fractal dimension and sprawl index score when the urban growth pattern changes from the concentrated to the semi-linear form

Multivariate SCADA data analysis methods for real-world wind turbine power curve monitoring

Due to the stochastic nature of the source, wind turbines operate under non-stationary conditions and the extracted power depends non-trivially on ambient conditions and working parameters. It is therefore difficult to establish a normal behavior model for monitoring the performance of a wind turbine and the most employed approach is to be driven by data. The power curve of a wind turbine is the relation between the wind intensity and the extracted power and is widely employed for monitoring wind turbine performance. On the grounds of the above considerations, a recent trend regarding wind turbine power curve analysis consists of the incorporation of the main working parameters (as, for example, the rotor speed or the blade pitch) as input variables of a multivariate regression whose target is the power. In this study, a method for multivariate wind turbine power curve analysis is proposed: it is based on sequential features selection, which employs Support Vector Regression with Gaussian Kernel. One of the most innovative aspects of this study is that the set of possible covariates includes also minimum, maximum and standard deviation of the most important environmental and operational variables. Three test cases of practical interest are contemplated: a Senvion MM92, a Vestas V90 and a Vestas V117 wind turbines owned by the ENGIE Italia company. It is shown that the selection of the covariates depends remarkably on the wind turbine model and this aspect should therefore be taken in consideration in order to customize the data-driven monitoring of the power curve. The obtained error metrics are competitive and in general lower with respect to the state of the art in the literature. Furthermore, minimum, maximum and standard deviation of the main environmental and operation variables are abundantly selected by the feature selection algorithm: this result indicates that the richness of the measurement channels contained in wind turbine Supervisory Control And Data Acquisition (SCADA) data sets should be exploited for monitoring the performance as reliably as possible

LSTM neural networks: Input to state stability and probabilistic safety verification

The goal of this paper is to analyze Long Short Term Memory (LSTM) neural networks from a dynamical system perspective. The classical recursive equations describing the evolution of LSTM can be recast in state space form, resulting in a time-invariant nonlinear dynamical system. A sufficient condition guaranteeing the Input-to-State (ISS) stability property of this class of systems is provided. The ISS property entails the boundedness of the output reachable set of the LSTM. In light of this result, a novel approach for the safety verification of the network, based on the Scenario Approach, is devised. The proposed method is eventually tested on a pH neutralization process

Wind turbine systematic yaw error: Operation data analysis techniques for detecting IT and assessing its performance impact

The widespread availability of wind turbine operation data has considerably boosted the research and the applications for wind turbine monitoring. It is well established that a systematic misalignment of the wind turbine nacelle with respect to the wind direction has a remarkable impact in terms of down-performance, because the extracted power is in first approximation proportional to the cosine cube of the yaw angle. Nevertheless, due to the fact that in the wind farm practice the wind field facing the rotor is estimated through anemometers placed behind the rotor, it is challenging to robustly detect systematic yaw errors without the use of additional upwind sensory systems. Nevertheless, this objective is valuable because it involves the use of data that are available to wind farm practitioners at zero cost. On these grounds, the present work is a two-steps test case discussion. At first, a new method for systematic yaw error detection through operation data analysis is presented and is applied for individuating a misaligned multi-MW wind turbine. After the yaw error correction on the test case wind turbine, operation data of the whole wind farm are employed for an innovative assessment method of the performance improvement at the target wind turbine. The other wind turbines in the farm are employed as references and their operation data are used as input for a multivariate Kernel regression whose target is the power of the wind turbine of interest. Training the model with pre-correction data and validating on post-correction data, it is estimated that a systematic yaw error of 4◦ affects the performance up to the order of the 1.5% of the Annual Energy Production

In situ atomic force microscopy in the study of electrogeneration of polybithiophene on Pt electrode

Electrochemical AFM technique has been used for the in situ study of the electrogeneration-deposition process of polybithiophene at varying the polymerisation conditions, such as supporting electrolyte, i.e., LiClO4 or tetrabutylammonium hexafluorophosphate, and polymerisation procedure, i.e., either potentiostatic or potentiodynamic method. In order to better follow the evolution of the morphology of the deposit, particularly during the early stages of the polymer film growth, a suitable home-made electrochemical cell has been used

Developing a Technology Readiness Level Template for Model-Based Design Methods and Tools in a Collaborative Environment

Currently, companies in the manufacturing field are experiencing the need to go digital, compelled by rising competitivity and efficiency requirements. Digitalization implies the development and implementation of complex systems in manufacturing plants as well as in the delivery of product-service systems and solutions, asking both for the adoption of Model Based Design (MBD) tools and methods. In this context, the assessment of suitability of MBD tools is vital for the companies that try to digitalise their operations. Due to the high relevance that this characteristic has for users and providers, a vital part of the implementation process is assesing the level of development or maturity of the tools. This paper presents and proposes a Technology Readiness Level (TRL) template developed in the HUBCAP project. This template aims to support MBD tools providers (guiding them in the description of the tool added on the platform), the platform management (easing governance tasks) and its users (clarifying the tool description for them) along the upload, update and control processes of the MBD tools in the collaborative platform

In vitro fermentation of ten cultivars of barley silage.

The fermentation characteristics of whole-crop barley silages from ten different cultivars were evaluated by the in vitro gas production technique. The organic matter degradability of barley silage (62.9% in average) was comparable to those reported in our previous trials for oat (59.7%) and sorghum silages (65.5%); while the maximum gas production rate (5.38 ml/h in average) was slightly lower respect to oat (6.71 ml/h) and sorghum silage (6.74 ml/h). The mean nutritive value (4.00 MJ/kg DM) calculated on the basis of both chemical composition and in vitro fermentation data was comparable to that (4.16 MJ/kg DM) obtained in our previous research performed on corn silage, from crop sowed in the same area

Food industry digitalization: from challenges and trends to opportunities and solutions

Over the last years, manufacturing companies have to face several challenges, mainly related to the volatility of the demand and to the continuously changing requirements, both from the customers and suppliers. In the meantime, new technological roadmaps and suggested interventions in manufacturing systems have been implemented. These solutions aim to exploit the high innovation and economic potential resulting from the continuing impact of rapidly advancing information and communication technology (ICT) in industry. This paper explores these topics focusing on the food sector. Indeed, companies belonging to this industry are facing global challenges, which can be met with the support of the information technologies (IT). The overall goal of this study is to help food companies toward digitalization, with a particular focus on the design and manufacturing processes. From the methodological point of view, Case Study has been used as research method. Furthermore, a questionnaire characterized by the different elements of the Manufacturing Value Modelling Methodology (MVMM) has been developed and used to gather information from companies. A framework for the digitalization process in the food industry has been developed basing on the results of a preliminary literature review and of different focus groups. On completion of the aforementioned framework, a list of enabling technologies has been discussed. These represent the technological solutions for the specific food issues highlighted by the framework. Finally, a case study has been accomplished in order to test and validate the contents' framework. (C) 2018, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved