Adaptive density deconvolution with dependent inputs

In the convolution model $Z\_i=X\_i+ \epsilon\_i$, we give a model selection procedure to estimate the density of the unobserved variables $(X\_i)\_{1 \leq i \leq n}$, when the sequence $(X\_i)\_{i \geq 1}$ is strictly stationary but not necessarily independent. This procedure depends on wether the density of $\epsilon\_i$ is super smooth or ordinary smooth. The rates of convergence of the penalized contrast estimators are the same as in the independent framework, and are minimax over most classes of regularity on ${\mathbb R}$. Our results apply to mixing sequences, but also to many other dependent sequences. When the errors are super smooth, the condition on the dependence coefficients is the minimal condition of that type ensuring that the sequence $(X\_i)\_{i \geq 1}$ is not a long-memory process

Adaptive density estimation for general ARCH models

We consider a model $Y\_t=\sigma\_t\eta\_t$ in which $(\sigma\_t)$ is not independent of the noise process $(\eta\_t)$, but $\sigma\_t$ is independent of $\eta\_t$ for each $t$. We assume that $(\sigma\_t)$ is stationary and we propose an adaptive estimator of the density of $\ln(\sigma^2\_t)$ based on the observations $Y\_t$. Under various dependence structures, the rates of this nonparametric estimator coincide with the minimax rates obtained in the i.i.d. case when $(\sigma\_t)$ and $(\eta\_t)$ are independent, in all cases where these minimax rates are known. The results apply to various linear and non linear ARCH processes

Anomaly Detection Based on Aggregation of Indicators

Automatic anomaly detection is a major issue in various areas. Beyond mere detection, the identification of the origin of the problem that produced the anomaly is also essential. This paper introduces a general methodology that can assist human operators who aim at classifying monitoring signals. The main idea is to leverage expert knowledge by generating a very large number of indicators. A feature selection method is used to keep only the most discriminant indicators which are used as inputs of a Naive Bayes classifier. The parameters of the classifier have been optimized indirectly by the selection process. Simulated data designed to reproduce some of the anomaly types observed in real world engines.Comment: 23rd annual Belgian-Dutch Conference on Machine Learning (Benelearn 2014), Bruxelles : Belgium (2014

Interpretable Aircraft Engine Diagnostic via Expert Indicator Aggregation

Detecting early signs of failures (anomalies) in complex systems is one of the main goal of preventive maintenance. It allows in particular to avoid actual failures by (re)scheduling maintenance operations in a way that optimizes maintenance costs. Aircraft engine health monitoring is one representative example of a field in which anomaly detection is crucial. Manufacturers collect large amount of engine related data during flights which are used, among other applications, to detect anomalies. This article introduces and studies a generic methodology that allows one to build automatic early signs of anomaly detection in a way that builds upon human expertise and that remains understandable by human operators who make the final maintenance decision. The main idea of the method is to generate a very large number of binary indicators based on parametric anomaly scores designed by experts, complemented by simple aggregations of those scores. A feature selection method is used to keep only the most discriminant indicators which are used as inputs of a Naive Bayes classifier. This give an interpretable classifier based on interpretable anomaly detectors whose parameters have been optimized indirectly by the selection process. The proposed methodology is evaluated on simulated data designed to reproduce some of the anomaly types observed in real world engines.Comment: arXiv admin note: substantial text overlap with arXiv:1408.6214, arXiv:1409.4747, arXiv:1407.088

Anomaly Detection Based on Indicators Aggregation

Automatic anomaly detection is a major issue in various areas. Beyond mere detection, the identification of the source of the problem that produced the anomaly is also essential. This is particularly the case in aircraft engine health monitoring where detecting early signs of failure (anomalies) and helping the engine owner to implement efficiently the adapted maintenance operations (fixing the source of the anomaly) are of crucial importance to reduce the costs attached to unscheduled maintenance. This paper introduces a general methodology that aims at classifying monitoring signals into normal ones and several classes of abnormal ones. The main idea is to leverage expert knowledge by generating a very large number of binary indicators. Each indicator corresponds to a fully parametrized anomaly detector built from parametric anomaly scores designed by experts. A feature selection method is used to keep only the most discriminant indicators which are used at inputs of a Naive Bayes classifier. This give an interpretable classifier based on interpretable anomaly detectors whose parameters have been optimized indirectly by the selection process. The proposed methodology is evaluated on simulated data designed to reproduce some of the anomaly types observed in real world engines.Comment: International Joint Conference on Neural Networks (IJCNN 2014), Beijing : China (2014). arXiv admin note: substantial text overlap with arXiv:1407.088

Bi-acylation of cellulose: determining the relative reactivities of the acetyl and fatty-acyl moieties

The global reaction between acetic anhydride and a fatty acid yields, at equilibrium, an asymmetric acetic-aliphatic anhydride in a medium containing finally: acetic-fatty anhydride, acetic anhydride, fatty acid, acetic acid and fatty anhydride. No solvent or catalyst was used to evaluate the impact of the actual reactivity of the anhydrides. The competition between the formation of acetyl and fatty acyl ester functions was evaluated by determining the ratio of acetyl/fatty acyl groups grafted on solid cellulose. The influence of temperature, reaction time, and length of fatty chain on the total degree of substitution and on the ratio of acetyl/fatty acyl ester functions was investigated. For the first time, a correlation has been established between esterification and the length of the aliphatic chain of the fatty acid. Reactivity of the medium decreased with the number of carbons in the fatty acid, raised to the power 2.37

Multi-slot Coded ALOHA with Irregular Degree Distribution

This paper proposes an improvement of the random multiple access scheme for satellite communication named Multislot coded ALOHA (MuSCA). MuSCA is a generalization of Contention Resolution Diversity Slotted ALOHA (CRDSA). In this scheme, each user transmits several parts of a single codeword of an error correcting code instead of sending replicas. At the receiver level, the decoder collects all these parts and includes them in the decoding process even if they are interfered. In this paper, we show that a high throughput can be obtained by selecting variable code rates and user degrees according to a probability distribution. With an optimal irregular degree distribution, our system achieves a normalized throughput up to 1.43, resulting in a significant gain compared to CRDSA and MuSCA. The spectral efficiency and the implementation issues of the scheme are also analyzed.Comment: 6 pages, 8 figure

Search Strategies for Binary Feature Selection for a Naive Bayes Classifier

We compare in this paper several feature selection methods for the Naive Bayes Classifier (NBC) when the data under study are described by a large number of redundant binary indicators. Wrapper approaches guided by the NBC estimation of the classification error probability out-perform filter approaches while retaining a reasonable computational cost

Interactions with water of mixed acetic-fatty cellulose esters

Cellulose powder was acylated with mixtures containing acetic, fatty and acetic-fatty anhydrides to form acetic-fatty cellulose esters. The total degree of substitution (DS) of the mixed cellulose esters (MCE) ranged from 2x10-2 to 2.92. MCE were characterized by their interactions with water. Static contact angles with water were measured on a regular smooth surface. The values found were dependent on the fatty acyl content and independent of the acetyl content. In the case of acetic-oleic cellulose esters, the minimum DS of the oleoyl moiety required to obtain permanent water repellency was 3x10-4. The microporosity of the samples may account for this exceptional hydrophobic character. Nevertheless, water vapor adsorption measurements on powder samples revealed only a limited increase in hydrophobicity of the MCE compared to cellulose acetate with the same acetyl content. It was thus demonstrated that water repellency and vapor water adsorption are not correlated