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Search Results: 1 - 10 of 223797 matches for " R. Furferi "
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Colour Classification Method for Recycled Melange Fabrics
R. Furferi
Journal of Applied Sciences , 2011,
Abstract: Classification of wasted woollen textiles on the basis of their colour is a basic approach for the supply of a raw material which does not involve the cost of the colouring process. Colour classification is a very difficult task, especially when a fabric is composed by differently coloured fibre (melange fabric). Many systems have been developed in the last years for colour classification of textiles. Unfortunately such colour classification systems are not able to correctly classify melange fabrics. In the present work a method for real-time classification of melange colour woollen fabrics is proposed. The provided approach, that is suitable also for classifying solid colour fabrics, integrates a Machine Vision (MV) system, able to acquire high resolution images, with a clustering algorithm capable of mapping the colour pixel of fabric images into a series of colour classes. The proposed system provides a colour classification with a misclassification less than 10% when compared with the classification resulting from a panel of expert human operators. A comparison between the proposed method and some tools stated in scientific literature is also afforded.
An As-Short-as-Possible Mathematical Assessment of Spectrophotometric Color Matching
R. Furferi,M. Carfagni
Journal of Applied Sciences , 2010,
Abstract: Color match prediction is one of the most important aspects to be considered by industries dealing with colorants. Several generally applicable theoretical models have been proposed so far for helping the colorists in achieving an exact color match. Such approaches, often based on extensive experimental tests and provided of exhaustive results, are differentiated by a specific range of application (textiles, study, paintings, etc.). Therefore, the results are subjected to restrictions or constraints (number of colorants, reliability of the prediction, etc.). The present paper describes, into a mathematical form, three widely known techniques adopted in the scientific literature for evaluating the spectrophotometric color match prediction of a target shade: Kubelka-Munch, Stearns-Noechel and Artificial Neural Networks. The proposed method starts from such wide known methodologies and by means of mathematical assessment provides some useful equations to be straightforwardly used for color matching. Moreover an Artificial Neural Network based formulation is provided. The results of the work shows that the expected color distance between the predicted the real color of a shade is less than 0.8, in terms of CIE L*a*b* distance.
A Genetic Algorithms-based Procedure for Automatic Tolerance Allocation Integrated in a Commercial Variation Analysis Software
L. Governi,R. Furferi,Y. Volpe
Journal of Artificial Intelligence , 2012,
Abstract: In the functional design process of a mechanical component, the tolerance allocation stage is of primary importance to make the component itself responding to the functional requirements and to cost constraints. Present state-of-the-art approach to tolerance allocation is based on the use of Statistical Tolerance Analysis (STA) software packages which, by means of Monte Carlo simulation, allow forecasting the result of a set of user-selected geometrical and dimensional tolerances. In order to completely automate and optimize this process, this work presents a methodology to allow an automatic tolerance allocation, capable to minimize the manufacturing cost of a single part or assembly. The proposed approach is based on the Monte Carlo method to compute the statistical distribution of the critical to quality characteristics and uses an optimization technique based on Genetic Algorithms. The resulting procedure has been integrated in an off-the-shelf variation analysis software: eM-TolMate (by Siemens AG). Both the description of the optimization algorithm and some practical applications are presented in order to demonstrate the effectiveness of the proposed methodology.
Yarn Strength Prediction: A Practical Model Based on Artificial Neural Networks
Rocco Furferi,Maurizio Gelli
Advances in Mechanical Engineering , 2010, DOI: 10.1155/2010/640103
Abstract: Yarn strength is one of the most significant parameters to be controlled during yarn spinning process. This parameter strongly depends on both the rovings' characteristics and the spinning process. On the basis of their expertise textile technicians are able to provide a raw and qualitative prediction of the yarn strength by knowing a series of fiber parameters like length, strength, and fineness. Nevertheless, they often need to perform many tests before producing a yarn with a desired strength. This paper describes a Feed Forward Back Propagation Artificial Neural Network-based model able to help the technicians in predicting the yarn strength without the need of physically spinning the yarn. The model performs a reliable prediction of the yarn strength on the basis of a series of roving parameters, commonly measured by the technicians before the yarn spinning process starts. The model has been trained with 98 training data and validated with 50 new tests. The mean error in prediction of yarn strength, using the validation set, is less than 4%. The results have been compared with the one obtained by means of a classical method: the multiple regression. Nowadays, the developed model is running in the laboratory of New Mill S.p.A., an important textile company that operates in Prato (Italy). 1. Introduction During yarn spinning, textile experts commonly controls a series of parameters like the fiber strength, the fiber length, the twist yarn, the yarn count, and the fineness. Strength parameters of yarns are especially important for rotor-spun yarns. More in detail a very important parameter that technicians want to control is the yarn strength. This is defined as the breaking force of a spinning yarn, and it is commonly measured in cN. On the basis of their skill, the expert operators are capable of giving a qualitative, raw prediction of the yarn strength; unfortunately the empirical estimation of the actual value of the yarn strength is not straightforward. The assessment of such parameter is essential for obtaining high quality of the yarn. Accordingly, in the last two decades, the modeling of yarn properties has become one of the most important and decisive tasks in the textile research field. A considerable number of predictive models have been implemented to evaluate some yarn properties like strength, elongation, evenness, and hairiness. The relationship between fiber properties and yarn properties has been the focal point of several works [1–3]. The studies in literature have shown that the relationship between yarn strength and fiber
Colour Mixing Modelling and Simulation: Optimization of Colour Recipe for Carded Fibres
Rocco Furferi,Monica Carfagni
Modelling and Simulation in Engineering , 2010, DOI: 10.1155/2010/487678
Abstract: Colour matching between carded and finished fibres is an important challenge for textile industry. The straightforward approach for mixing together some differently coloured fibres in order to obtain a blend of a desired colour is to perform a trial and error approach starting from a given colour recipe and optimizing it with several attempts. Unfortunately, dyeing process so as the carding procedure may result in a carded fibre whose colour is different from the desired one. As a consequence textile companies have to modify the original recipe in order to reduce the gap between the colour of the final product and the desired one. The present work describes a model able to simulate the colour mixing of fibres in order to assess the best recipe. The model consists in two modules: a “prediction module” predicts the colour of a blend obtained by mixing together several fibres; an “optimization module” is used to optimize the final recipe. The devised system has been tested for optimizing the recipe of a set of 200 blends. The mean error in predicting the blend colour is about 15% with a variance of 0.165. The time for optimizing the recipe is reduced by 92%. 1. Background The production of a carded fibre is often performed by mixing together a series of differently coloured fibres according to a formula known as “recipe”. A recipe is a list of percentages of raw materials that have to be mixed together so as to obtain a carded fibre with a desired colour. The raw material is mixed by using a machine called “carder.” By means of this machine, the textile companies produce a carded fibre (also called “blend”) whose colour has to be compared with the one provided by a customer. The main problem is that different dye processes may lead to different reproduction of the colour. Accordingly, the use of a recipe for mixing the raw material does not guarantee that the colour of the blend is strictly “close” to the desired colour. This means that the colorimetric distance between a reference and the blend, measured by means of the CMC distance [1, 2] under different standard illuminants [3], may result too greatly. As widely known, the CMC tolerancing system has been developed by the Colour Measurement Committee of the Society of Dyers and Colourists in Great Britain and may be defined as a modification of CIELAB [4] which provides better agreement between visual assessment and instrumentally measured colour difference. The colour distance supplied by the CMC tolerancing system, called CMC distance DECMC, is expressed by the following equation: where(i) , , and are,
The Colorimetric Measurement of Melange Woollen Yarns: A New Optical Tool
Rocco Furferi,Monica Carfagni
Journal of Engineering and Applied Sciences , 2012,
Abstract: One of the most critical phases of the production of woollen yarns for knitting and weaving is the comparison between the colour of the finished product and the colour desired by a customer or provided by a catalogue. Accordingly the companies that produce yarns perform a colorimetric control of their products by evaluating the colorimetric distance between the final product, obtained by mixing the coloured raw material and the desired one. This colorimetric control is mainly performed using a calibrated reflectance spectrophotometer thus allowing an accurate measurement of the spectrum of a woollen yarn. This is performed only in the case of uniform colour. Being the area of the spectrophotometer acquisition sensor very small (20 mm2) when a woollen yarn is composed by two or more colours (i.e., is a meange woollen yarn) the spectrophotometer is not able to discriminate all the colours reliably. The objective of the present research, developed by the Dipartimento di Meccanica e Tecnologie Industriali of the University of Florence in collaboration with the company Newmill S.p.A., is to develop an optical system (tool) able to perform an accurate colorimetric measurement of melange woollen yarns on the basis of the combination of flat scanner acquisition and colorimetric techniques.
Flexible Mild Heaters in Structural Conservation of Paintings: State of the Art and Conceptual Design of a New Carbon Nanotubes-based Heater
Tomas Markevicius,Nina Olsson,Rocco Furferi,Helmut Meyer
Journal of Applied Sciences , 2012,
Abstract: Thermal treatments constitute the core in the success for most structural treatments, such as consolidation, treating planar deformations, reinforcing degraded support and others. Among the wide range of devices for thermal treatments of paintings proposed in scientific and technical literature, flexible heaters appear to be the most promising technology, especially for working with large painting or in situ. The present study provides a comprehensive review of flexible mild heater systems devised for structural conservation of paintings in the last decades, bringing forward the issues related to the instrumentation used for thermal treatments, stressing the importance of accurate control and the inadequateness of available devices. By highlighting the actual limitations of existing devices, a different approach, which employs Carbon Nanotubes-based flexible heaters is then proposed in its conceptual form. The design of such device, called IMAT (Intelligent Mobile Accurate Thermo-electrical device) is supported by the European Community in the context of the EC-FP7 Environment Theme (ENV-NMP.2011.2.2-5) into a three-year project started on November 2011.
Marco Daou,Rocco Furferi,Lucia Recchia,Enrico Cini
Journal of Agricultural Engineering , 2007, DOI: 10.4081/jae.2007.4.11
Abstract: In the present work is described a feasibility assessment for a new approach in virgin olive oil production control system. A predicting or simulating algorithm is implemented as artificial neural network based software, using literature found data concerning parameters related to olive grove, process, machine. Test and validation proved this tool is able to answer two different frequently asked questions by olive oil mill operators, using few agronomic and technological parameters with time and cost saving: – which quality level is up to oil extracted from defined olive lot following a defined process (predicting mode); – which process and machine parameters set would determine highest quality level for oil extracted from a defined olive lot (simulating mode).
Power Aware Routing Protocol (PARP) for Wireless Sensor Networks  [PDF]
R. Prema, R. Rangarajan
Wireless Sensor Network (WSN) , 2012, DOI: 10.4236/wsn.2012.45019
Abstract: Several wireless sensor network applications ought to decide the intrinsic variance between energy efficient communication and the requirement to attain preferred quality of service (QoS) such as packet delivery ratio, delay and to reduce the power consumption of wireless sensor nodes. In order to address this challenge, we propose the Power Aware Routing Protocol (PARP), which attains application-specified communication delays at low energy cost by dynamically adapting transmission power and routing decisions. Extensive simulation results prove that the proposed PARP attains better QoS and reduced power consumption.
An Innovative Low Cost EM Pollution Measurement System  [PDF]
R. Sittalatchoumy, R. Seetharaman
Circuits and Systems (CS) , 2016, DOI: 10.4236/cs.2016.78176
Abstract: Mobile phones and other electronic devices are emitting radiations that will provide harmful effects to the human health. In order to measure the radiation, an innovative low cost measurement system is proposed in this paper. The ideology is to simplify the circuit’s value by converting a voltage detecting circuit to a field detecting circuit by finding an optimum resistance on trial and error basis. The requirement for a trial and error technique is to not allow too high or too low resistance which can be either short or open, resulting provides more damage to the circuit.
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