2,076 research outputs found
Smart Traction Control Systems for Electric Vehicles Using Acoustic Road-type Estimation
The application of traction control systems (TCS) for electric vehicles (EV)
has great potential due to easy implementation of torque control with
direct-drive motors. However, the control system usually requires road-tire
friction and slip-ratio values, which must be estimated. While it is not
possible to obtain the first one directly, the estimation of latter value
requires accurate measurements of chassis and wheel velocity. In addition,
existing TCS structures are often designed without considering the robustness
and energy efficiency of torque control. In this work, both problems are
addressed with a smart TCS design having an integrated acoustic road-type
estimation (ARTE) unit. This unit enables the road-type recognition and this
information is used to retrieve the correct look-up table between friction
coefficient and slip-ratio. The estimation of the friction coefficient helps
the system to update the necessary input torque. The ARTE unit utilizes machine
learning, mapping the acoustic feature inputs to road-type as output. In this
study, three existing TCS for EVs are examined with and without the integrated
ARTE unit. The results show significant performance improvement with ARTE,
reducing the slip ratio by 75% while saving energy via reduction of applied
torque and increasing the robustness of the TCS.Comment: Accepted to be published by IEEE Trans. on Intelligent Vehicles, 22
Jan 201
Application of a Mamdani-type fuzzy rule-based system to segment periventricular cerebral veins in susceptibility-weighted images
This paper presents an algorithm designed to segment veins in the periventricular region of the brain in susceptibility-weighted magnetic resonance images. The proposed algorithm is based on a Mamdani-type fuzzy rule-based system that enables enhancement of veins within periventricular regions of interest as the first step. Segmentation is achieved after determining the cut-off value providing the best trade-off between sensitivity and specificity to establish the suitability of each pixel to belong to a cerebral vein. Performance of the algorithm in
susceptibility-weighted images acquired in healthy volunteers showed very good segmentation, with a small number of false positives. The results were not affected by small changes in the size and location of the regions of interest. The
algorithm also enabled detection of differences in the visibility of periventricular veins between healthy subjects and multiple sclerosis patients. © Springer International Publishing Switzerland 2016.Postprint (author's final draft
Use of Multispectral Aerial Videography for Jurisdictional Delineation of Wetland Areas
Multispectral aerial videography was used to reproduce the jurisdictional delineation of wetland area of approximately 50 hectares in Davis County, Utah Imagery from the system consisted of three-band composite with wavelengths covering 550 nm (±10 nm), 650 nm (±10 nm), and 850 nm (±10 nm). The site was overflown at three different flight dates during the 1992 growing season (June 2, July 22, October 1). Imagery resolution varied from 0.56 m to 0.81 m. Mosaiced images were analyzed with a Supervised clustering/maximum likelihood classifier, ISODATA clustering/Euclidan classifier, statistical clustering/maximum likelihood classifier, and fuzzy c-means clustering. Overall accuracies for wetland/upland designations as compared to ground truth data varied from 60% to 75%. The ISODATA method was the poorest performer for all dates and both of two accuracy testing techniques. Supervised clustering and statistical clustering were comparable with a slight edge in accuracy to the supervised clustering. The best all-round performer was the fuzzy c-means algorithm in terms of time spent and accuracy
Feature Evaluation for Effective Bearing Prognostics.
International audienceRolling element bearing failure is one of the foremost causes of breakdown in rotating machinery. It is not uncommon to replace a defected/used bearing with a new one that has shorter remaining useful life than the defected one. Thus, prognostics of bearing plays critical role for increased availability and reduced cost. Effective prognostics highly depend on the quality of the extracted features. Diagnostics is basically a classification problem, whereas the prognostics is the process of forecasting the future health states. The quality of the features for classification has been studied thoroughly. However, evaluation of the quality of features for prognostics is a relatively new problem. This paper presents an evaluation method for the goodness of the features for prognostics and presents results on bearings run until failure in a lab environment
Non-invasive Techniques Towards Recovering Highly Secure Unclonable Cryptographic Keys and Detecting Counterfeit Memory Chips
Due to the ubiquitous presence of memory components in all electronic computing systems, memory-based signatures are considered low-cost alternatives to generate unique device identifiers (IDs) and cryptographic keys. On the one hand, this unique device ID can potentially be used to identify major types of device counterfeitings such as remarked, overproduced, and cloned. On the other hand, memory-based cryptographic keys are commercially used in many cryptographic applications such as securing software IP, encrypting key vault, anchoring device root of trust, and device authentication for could services. As memory components generate this signature in runtime rather than storing them in memory, an attacker cannot clone/copy the signature and reuse them in malicious activity. However, to ensure the desired level of security, signatures generated from two different memory chips should be completely random and uncorrelated from each other. Traditionally, memory-based signatures are considered unique and uncorrelated due to the random variation in the manufacturing process. Unfortunately, in previous studies, many deterministic components of the manufacturing process, such as memory architecture, layout, systematic process variation, device package, are ignored. This dissertation shows that these deterministic factors can significantly correlate two memory signatures if those two memory chips share the same manufacturing resources (i.e., manufacturing facility, specification set, design file, etc.). We demonstrate that this signature correlation can be used to detect major counterfeit types in a non-invasive and low-cost manner. Furthermore, we use this signature correlation as side-channel information to attack memory-based cryptographic keys. We validate our contribution by collecting data from several commercially available off-the-shelf (COTS) memory chips/modules and considering different usage-case scenarios
The Fuzziness in Molecular, Supramolecular, and Systems Chemistry
Fuzzy Logic is a good model for the human ability to compute words. It is based on the theory of fuzzy set. A fuzzy set is different from a classical set because it breaks the Law of the Excluded Middle. In fact, an item may belong to a fuzzy set and its complement at the same time and with the same or different degree of membership. The degree of membership of an item in a fuzzy set can be any real number included between 0 and 1. This property enables us to deal with all those statements of which truths are a matter of degree. Fuzzy logic plays a relevant role in the field of Artificial Intelligence because it enables decision-making in complex situations, where there are many intertwined variables involved. Traditionally, fuzzy logic is implemented through software on a computer or, even better, through analog electronic circuits. Recently, the idea of using molecules and chemical reactions to process fuzzy logic has been promoted. In fact, the molecular word is fuzzy in its essence. The overlapping of quantum states, on the one hand, and the conformational heterogeneity of large molecules, on the other, enable context-specific functions to emerge in response to changing environmental conditions. Moreover, analog input–output relationships, involving not only electrical but also other physical and chemical variables can be exploited to build fuzzy logic systems. The development of “fuzzy chemical systems” is tracing a new path in the field of artificial intelligence. This new path shows that artificially intelligent systems can be implemented not only through software and electronic circuits but also through solutions of properly chosen chemical compounds. The design of chemical artificial intelligent systems and chemical robots promises to have a significant impact on science, medicine, economy, security, and wellbeing. Therefore, it is my great pleasure to announce a Special Issue of Molecules entitled “The Fuzziness in Molecular, Supramolecular, and Systems Chemistry.” All researchers who experience the Fuzziness of the molecular world or use Fuzzy logic to understand Chemical Complex Systems will be interested in this book
An Intelligent Decision Support System for Leukaemia Diagnosis using Microscopic Blood Images
This research proposes an intelligent decision support system for acute lymphoblastic leukaemia
diagnosis from microscopic blood images. A novel clustering algorithm with stimulating discriminant
measures (SDM) of both within- and between-cluster scatter variances is proposed to produce robust
segmentation of nucleus and cytoplasm of lymphocytes/lymphoblasts. Specifically, the proposed
between-cluster evaluation is formulated based on the trade-off of several between-cluster measures
of well-known feature extraction methods. The SDM measures are used in conjuction with Genetic
Algorithm for clustering nucleus, cytoplasm, and background regions. Subsequently, a total of eighty
features consisting of shape, texture, and colour information of the nucleus and cytoplasm subimages
are extracted. A number of classifiers (multi-layer perceptron, Support Vector Machine (SVM)
and Dempster-Shafer ensemble) are employed for lymphocyte/lymphoblast classification. Evaluated
with the ALL-IDB2 database, the proposed SDM-based clustering overcomes the shortcomings of
Fuzzy C-means which focuses purely on within-cluster scatter variance. It also outperforms Linear
Discriminant Analysis and Fuzzy Compactness and Separation for nucleus-cytoplasm separation.
The overall system achieves superior recognition rates of 96.72% and 96.67% accuracies using
bootstrapping and 10-fold cross validation with Dempster-Shafer and SVM, respectively. The results
also compare favourably with those reported in the literature, indicating the usefulness of the
proposed SDM-based clustering method
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