Function approximation using non-normalized SISO fuzzy systems

AbstractIn this paper we propose an improvement in the field of fuzzy function approximation. It is well known that tuning the shape and the position of the membership functions, improves the approximation, but what about changing the heights of these functions? Usually the system is normalized so that the heights of the membership functions are set to 1, but an interesting result can be obtained if we make them variable, giving a further degree of freedom to the fuzzy system. We will use this feature in order to achieve a better function approximation, to build a second-order derivative approximation or to make the derivative of our approximation continuous. We will show also how to increase the spectral purity of the approximation function as in the case of sinusoidal functions. This approach will be analyzed under a theoretical point of view, comparing the results with those obtained with the classical approach

A Topology Optimization Method for Stochastic Lattice Structures

AbstractStochastic lattice structures are very powerful solutions for filling three-dimensional spaces using a generative algorithm. They are suitable for 3D printing and are well appropriate to structural optimization and mass distribution, allowing for high-performance and low-weight structures. The paper shows a method, developed in the Rhino-Grasshopper environment, to distribute lattice structures until a goal is achieved, e.g. the reduction of the weight, the harmonization of the stresses or the limitation of the strain. As case study, a cantilever beam made of Titan alloy, by means of SLS technology has been optimized. The results of the work show the potentiality of the methodology, with a very performing structure and low computational efforts

A Well-to-Wheel Comparative Life Cycle Assessment Between Full Electric and Traditional Petrol Engines in the European Context

AbstractAutomotive sector is crucial for the economic and social system. Conversely, it also plays an important role in the global emissions balance with strong consequences on the environment. Currently the Research world is engaged in the reduction of the emissions, especially in order to contrast the Climate Change and reduce toxicity on humans and the ecosystem. This study presents a comparative Life Cycle Assessment, Well-to-Wheel, between the most common technology used in the automotive sector, i.e. the traditional petrol Internal Combustion Engine and the full Battery Electric Vehicle. The different configurations have been analysed within 17 different impact categories in terms of climate change, human health, resourced depletion and ecosystems. The Well-to-Wheel approach allows to focus the attention on the use stage of the vehicle, considering the local effects due to the direct emissions in high density urban zones and it mitigates the dependence of usage hypotheses, different scenarios and intrinsic differences between the various models of cars in circulation

Extracting fuzzy classification rules from texture segmented hrct lung images

Automatic tools for detection and identification of lung and lesion from high-resolution CT (HRCT) are becoming increasingly important both for diagnosis and for delivering high-precision radiation therapy. However, development of robust and interpretable classifiers still presents a challenge especially in case of non-small cell lung carcinoma (NSCLC) patients. In this paper, we have attempted to devise such a classifier by extracting fuzzy rules from texture segmented regions from HRCT images of NSCLC patients. A fuzzy inference system (FIS) has been constructed starting from a feature extraction procedure applied on overlapping regions from the same organs and deriving simple if-then rules so that more linguistically interpretable decisions can be implemented. The proposed method has been tested on 138 regions extracted from CT scan images acquired from patients with lung cancer. Assuming two classes of tissues C1 (healthy tissues) and C2 (lesion) as negative and positive, respectively; preliminary results report an AUC 0.98 for lesions and AUC 0.93 for healthy tissue, with an optimal operating condition related to sensitivity 0.96, and specificity 0.98 for lesions and sensitivity 0.99, and specificity 0.94 for healthy tissue. Finally, the following results have been obtained: false-negative rate (FNR)06 % (C1), FNR 02 % (C2), false-positive rate (FPR) 04 % (C1), FPR 03 % (C2), true-positive rate (TPR) 0.94 %, (C1) and TPR 0.98 % (C2)

Automatic breast masses boundary extraction in digital mammography using spatial fuzzy c-means clustering and active contour models MeMeA 2011

Abstract—In this paper, we propose a novel approach for the automatic breast boundary segmentation using spatial fuzzy cmeans clustering and active contours models. We will evaluate the performance of the approach on screen film mammographic images digitized by specific scanner devices and full-field digital mammographic images at different spatial and pixel resolutions. Expert radiologists have supplied the reference boundary for the massive lesions along with the biopsy proven pathology assessment. A performance assessment procedure will be developed considering metrics such as precision, recall, F-measure, and accuracy of the segmentation results. A Montecarlo simulation will be also implemented to evaluate the sensitivity of the boundary extracted on the initial settings and on the image nois

Uncertainty modeling and propagation through RFVs for the assessment of CADx systems in digital mammography

In this paper, we consider uncertainty handling and propagation by means of random fuzzy variables (RFVs) through a computer-aided-diagnosis (CADx) system for the early diagnosis of breast cancer. In particular, the denoising and the contrast enhancement of microcalcifications is specifically addressed, providing a novel methodology for separating the foreground and the background in the image to selectively process them. The whole system is then assessed by metrological aspects. In this context, we assume that the uncertainty associated to each pixel of the image has both a random and a non-negligible systematic contribution. Consequently, a preliminary noise variance estimation is performed on the original image, and then, using suitable operators working on RFVs, the uncertainty propagation is evaluated through the whole system. Finally, we compare our results with those obtained by a Monte Carlo method