Improving the H2MLVQ algorithm by the Cross Entropy Method

This paper addresses the use of a stochastic optimization method called the Cross Entropy (CE) Method in the improvement of a recently proposed H2MLVQ (Harmonic to minimum LVQ) algorithm, this algorithm was proposed as an initialization insensitive variant of the well known Learning Vector Quantization (LVQ) algorithm. This paper has two aims, the first aim is the use of the Cross Entropy (CE) Method to tackle the initialization sensitiveness problem associated with the original (LVQ) algorithm and its variants and the second aim is to use a weighted norm instead of the Euclidean norm in order to select the most relevant features. The results in this paper indicate that the CE method can successfully be applied to this kind of problems and efficiently generate high quality solutions. Also, good competitive numerical results on several datasets are reported

Improving the H2MLVQ algorithm by the Cross Entropy Method

This paper addresses the use of a stochastic optimization method called the Cross Entropy (CE) Method in the improvement of a recently proposed H2MLVQ (Harmonic to minimum LVQ) algorithm, this algorithm was proposed as an initialization insensitive variant of the well known Learning Vector Quantization (LVQ) algorithm. This paper has two aims, the first aim is the use of the Cross Entropy (CE) Method to tackle the initialization sensitiveness problem associated with the original (LVQ) algorithm and its variants and the second aim is to use a weighted norm instead of the Euclidean norm in order to select the most relevant features. The results in this paper indicate that the CE method can successfully be applied to this kind of problems and efficiently generate high quality solutions. Also, good competitive numerical results on several datasets are reported

Système d'aide au diagnostic par apprentissage : application aux systèmes microélectroniques

AIX-MARSEILLE3-BU Sc.St Jérô (130552102) / SudocSudocFranceF

Application of global optimization methods to model and feature selection

Many data mining applications involve the task of building a model for predictive classification. The goal of this model is to classify data instances into classes or categories of the same type. The use of variables not related to the classes can reduce the accuracy and reliability of classification or prediction model. Superfluous variables can also increase the costs of building a model particularly on large datasets. The feature selection and hyper-parameters optimization problem can be solved by either an exhaustive search over all parameter values or an ptimization procedure that explores only a finite subset of the possible values. The objective of this research is to simultaneously optimize the hyperparameters and feature subset without degrading the generalization performances of the induction algorithm. We present a global optimization approach based on the use of Cross-Entropy Method to solve this kind of problem

Data collection and processing tools for naturalistic study of powered two-wheelers users' behaviours

Instrumented vehicles are key tools for in-depth understanding of drivers' behaviours, thus for the design of scientifically based countermeasures to reduce fatalities and injuries. The instrumentation of Powered Two-Wheelers (PTW) has been less widely implemented that for vehicles, in part due to the technical challenges involved. The last decade has seen the development in Europe of several tools and methodologies to study motorcycle riders' behaviours and motorcycle dynamics for a range of situations, including crash events involving falls. Thanks to these tools, a broad-ranging research programme has been conducted, from the design and tuning of real-time falls detection to the study of riding training systems, as well as studies focusing on naturalistic riding situations such as filtering and line splitting. The methodology designed for the in-depth study of riders' behaviours in naturalistic situations can be based upon the combination of several sources of data such as: PTW sensors, context-based video retrieval system, Global Positioning System (GPS) and verbal data on the riders' decisions making process. The goals of this paper are: (1) to present the methodological tools developed and used by INRETS-MSIS (now Ifsttar-TS2/Simu) in the last decade for the study of riders' behaviours in real-world environment as well as on track for situations up to falls, (2) to illustrate the kind of results that can be gained from the conducted studies, (3) to identify the advantages and limitations of the proposed methodology to conduct large scale naturalistic riding studies, and (4) to highlight how the knowledge gained from this approach will fill many of the knowledge gaps about PTWriders' behaviours and risk factors

Rollover risk prediction of heavy vehicles by reliability index and empirical modelling

This paper focuses on a combination of a reliability-based approach and an empirical modelling approach for rollover risk assessment of heavy vehicles. A reliability-based warning system is developed to alert the driver to a potential rollover before entering into a bend. The idea behind the proposed methodology is to estimate the rollover risk by the probability that the vehicle load transfer ratio (LTR) exceeds a critical threshold. Accordingly, a so-called reliability index may be used as a measure to assess the vehicle safe functioning. In the reliability method, computing the maximum of LTR requires to predict the vehicle dynamics over the bend which can be in some cases an intractable problem or time-consuming. With the aim of improving the reliability computation time, an empirical model is developed to substitute the vehicle dynamics and rollover models. This is done by using the SVM (Support Vector Machines) algorithm. The preliminary obtained results demonstrate the effectiveness of the proposed approach

Understanding the behaviour of motorcycle riders: An objective investigation of riding style and capability

Human errors are the primary cause of powered two-wheeler crashes worldwide due to the demanding control required and the often ineffective rider-training programs. Literature on rider behaviour is limited, partly due to the lack of standard investigation methodologies. This work investigated the differences in riding style and capability of a diverse set of riders. It explored the impact of familiarisation and riding instruction through objective metrics. Correlation with experience was a particular focus. Seven riders of various experience levels performed trials on an instrumented motorcycle, following three riding instructions: ‘Free Riding’, ‘Handlebar Riding’, and ‘Body Riding’. Objective metrics assessed rider familiarisation, capability and willingness to excite motorcycle dynamics, riding style, and input preference. Results indicated that riders asymptotically converged to their motorcycle dynamics intensity level after a specific distance; both intensity and distance were positively correlated with experience. Experienced riders achieved higher longitudinal acceleration and utilised combined dynamics to a higher degree. The negative longitudinal jerk during braking varied greatly among riders and correlated with experience. A clustering approach identified two prominent trial groups concerning the motorcycle response intensity. Higher diversity emerged in the inputs, leading to five clusters with distinct riding style meanings. Instructions influenced behaviour, particularly regarding input usage. The unsupervised approach and metrics proposed should make rider behaviour research more straightforward and objective. It could be applied to naturalistic riding sessions for more conclusive evidence of inter-driver differences. The diversity that emerged concerning the command inputs used warrants a revision of training practices to promote riding safety