Search results for "Machine"
showing 10 items of 2592 documents
Modelling Dependencies Between Classifiers in Mobile Masquerader Detection
2004
The unauthorised use of mobile terminals may result in an abuse of sensitive information kept locally on the terminals or accessible over the network. Therefore, there is a need for security means capable of detecting the cases when the legitimate user of the terminal is substituted. The problem of user substitution detection is considered in the paper as a problem of classifying the behaviour of the person interacting with the terminal as originating from the user or someone else. Different aspects of behaviour are analysed by designated one-class classifiers whose classifications are subsequently combined. A modification of majority voting that takes into account some of the dependencies …
A Learning Automata Local Contribution Sampling Applied to Hydropower Production Optimisation
2017
Learning Automata (LA) is a powerful approach for solving complex, non-linear and stochastic optimisation problems. However, existing solutions struggle with high-dimensional problems due to slow convergence, arguably caused by the global nature of feedback. In this paper we introduce a novel Learning Automata (LA) scheme to attack this challenge. The scheme is based on a parallel form of Local Contribution Sampling (LCS), which means that the LA receive individually directed feedback, designed to speed up convergence. Furthermore, our scheme is highly decentralized, allowing parallel execution on GPU architectures. To demonstrate the power of our scheme, the LA LCS is applied to hydropower…
A General Frame for Building Optimal Multiple SVM Kernels
2012
The aim of this paper is to define a general frame for building optimal multiple SVM kernels. Our scheme follows 5 steps: formal representation of the multiple kernels, structural representation, choice of genetic algorithm, SVM algorithm, and model evaluation. The computation of the optimal parameter values of SVM kernels is performed using an evolutionary method based on the SVM algorithm for evaluation of the quality of chromosomes. After the multiple kernel is found by the genetic algorithm we apply cross validation method for estimating the performance of our predictive model. We implemented and compared many hybrid methods derived from this scheme. Improved co-mutation operators are u…
Multi-pass execution of functional logic programs
1994
An operational semantics for functional logic programs is presented. In such programs functional terms provide for reduction of expressions, provided that they ground. The semantics is based on multi-pass evaluation techniques originally developed for attribute grammars. Program execution is divided into two phases: (1) construction of an incomplete proof tree, and (2) its decoration into a complete proof tree. The construction phase applies a modified SLD-resolution scheme, and the decoration phase a partial (multi-pass) traversal over the tree. The phase partition is generated by static analysis where data dependencies are extracted for the functional elements of the program. The method g…
Extension and Tuning of Virtual Synchronous Machine to Avoid Oscillatory Instability in Isolated Power Networks
2020
The risk of oscillatory instability introduced by virtual synchronous machines (VSM) control in isolated power networks is investigated. The considered control is a common scheme widely studied in literature, in several configurations and for different case studies. The impact of the VSM control on the system stability is examined for the existing power network of a Mediterranean island. The VSM control is implemented within the power converters interfacing the energy storage systems (ESS) installed in the network. Simulations and analysis show the occurrence of oscillatory instability in the system, with the ESS-VSM and the synchronous machines of the network progressively swinging against…
Toward Self-Supervised Feature Learning for Online Diagnosis of Multiple Faults in Electric Powertrains
2021
This article proposes a novel online fault diagnosis scheme for industrial powertrains without using historical faulty or labeled training data. The proposed method combines a one-class support vector machine (SVM) based anomaly detection and supervised convolutional neural network (CNN) algorithms to online detect multiple faults and fault severities under variable speeds and loads. The one-class SVM algorithm is to derive a score for defining faults or health classes in the first stage, and the resulting health classes are used as the training data for the CNN-based classifier in the second stage. Within this framework, the self-supervised learning of the proposed CNN algorithm allows the…
A Cognitive-based scheme for user reliability and expertise assessment in Q&A social networks
2011
Q&A social media has gained a great deal of attention during recent years. People rely on these sites to obtain information due to the number of advantages they offer as compared to conventional sources of knowledge (e.g., asynchronous and convenient access). However, for the same question one may find highly contradictory answers, causing ambiguity with respect to the correct information. This can be attributed to the presence of unreliable and/or non-expert users. In this work, we propose a novel approach for estimating the reliability and expertise of a user based on human cognitive traits. Every user can individually estimate these values based on local pairwise interactions. We examine…
Multi-cloud privacy preserving schemes for linear data mining
2015
This paper presents an approach to privacy-preserving data mining that relies upon a relatively simple secret sharing scheme. Its main feature is that users, sensitive data owners, are engaged in the secret sharing operations that protect their privacy. They are grouped in independent clouds connected to a central unit, the data miner, that only manages the aggregated data of each cloud, therefore avoiding the disclosure of information belonging to single nodes. We propose two privacy preserving schemes, with different privacy levels and communication costs. When designing them, we assume that some users' data might become inaccessible during the operation of the privacy preserving protocol…
Survival Prediction in Intrahepatic Cholangiocarcinoma: A Proof of Concept Study Using Artificial Intelligence for Risk Assessment
2021
Several scoring systems have been devised to objectively predict survival for patients with intrahepatic cholangiocellular carcinoma (ICC) and support treatment stratification, but they have failed external validation. The aim of the present study was to improve prognostication using an artificial intelligence-based approach. We retrospectively identified 417 patients with ICC who were referred to our tertiary care center between 1997 and 2018. Of these, 293 met the inclusion criteria. Established risk factors served as input nodes for an artificial neural network (ANN). We compared the performance of the trained model to the most widely used conventional scoring system, the Fudan score. Pr…
Optimization of cogging force in a linear permanent magnet generator for the conversion of sea waves energy
2015
In this paper an approach to the optimization of cogging force in a linear permanent magnet generator for the conversion of sea waves energy is presented. We have optimized the cogging force of a linear permanent magnet generator by using a 3D FEM parametric simulation. Several possible design solutions have been computed. A generator has been built and the results of the minimization procedure has been experimentally validated.