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See detailData Fauna-Flora 1.0. Guide d’utilisation.
Barbier, Yvan; Rasmont, Pierre; Dufrêne, Marc ULg et al

Software (2000)

Detailed reference viewed: 5 (1 ULg)
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See detailData Fusion by Belief Propagation for Multi-Camera Tracking
Du, Wei ULg; Piater, Justus ULg

in The 9th International Conference on Information Fusion (2006)

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See detailA Data Imputation Method with Support Vector Machines for Activity-Based Transportation Models
Yang, Banghua; Janssens, Davy; Ruan, Da et al

in Wang, Y.; Li, T. (Eds.) Foundations of Intelligent Systems: Proceedings of the Sixth International Conference on Intelligent Systems and Knowledge Engineering, Shanghai, China, Dec 2011 (ISKE 2011) (2011)

In this paper, a data imputation method with a Support Vector Machine (SVM) is proposed to solve the issue of missing data in activity-based diaries. Here two SVM models are established to predict the ... [more ▼]

In this paper, a data imputation method with a Support Vector Machine (SVM) is proposed to solve the issue of missing data in activity-based diaries. Here two SVM models are established to predict the missing elements of ‘number of cars’ and ‘driver license’. The inputs of the former SVM model include five variables (Household composition, household income, Age oldest household member, Children age class and Number of household members). The inputs of the latter SVM model include three variables (personal age, work status and gender). The SVM models to predict the ‘number of cars’ and ‘driver license’ can achieve accuracies of 69% and 83% respectively. The initial experimental results show that missing elements of observed activity diaries can be accurately inferred by relating different pieces of information. Therefore, the proposed SVM data imputation method serves as an effective data imputation method in the case of missing information. [less ▲]

Detailed reference viewed: 43 (2 ULg)
See detailData in astronomy -- Carlos Jaschek
Manfroid, Jean ULg

in Ciel et Terre (1990), 106

Not Available

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See detailData Interpolating Empirical Orthogonal Functions (DINEOF): a tool for geophysical data analyses
Alvera Azcarate, Aïda ULg; Barth, Alexander ULg; Sirjacobs, Damien ULg et al

in Mediterranean Marine Science (2011), 12(3), 5-11

An overview of the technique called DINEOF (Data Interpolating Empirical Orthog- onal Functions) is presented. DINEOF reconstructs missing information in geophys- ical data sets, such as satellite imagery ... [more ▼]

An overview of the technique called DINEOF (Data Interpolating Empirical Orthog- onal Functions) is presented. DINEOF reconstructs missing information in geophys- ical data sets, such as satellite imagery or time series. A summary of the technique is given, with its main characteristics, recent developments and future research di- rections. DINEOF has been applied to a large variety of oceanographic variables in various domains of different sizes. This technique can be applied to a single variable (monovariate approach), or to several variables together (multivariate approach), with no complexity increase in the application of the technique. Error fields can be computed to establish the accuracy of the reconstruction. Examples are given to illustrate the capabilities of the technique. DINEOF is freely offered to download, and help is provided to users in the form of a wiki and through a discussion email list. [less ▲]

Detailed reference viewed: 180 (26 ULg)
See detailData mining
Martin, Didier ULg

Scientific conference (2011, June 15)

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See detailA Data Mining Analysis Applied to a Straightening Process Database
Caprace, Jean-David ULg; Losseau, N.; Bair, Frédéric ULg et al

in Ship Technology Research = Schiffstechnik (2007), 54(4), 177-183

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See detailA Data Mining Analysis Applied to a Straightening Process Database
Caprace, Jean-David ULg; Losseau, Nicolas ULg; Archambeau, Dominique et al

in Conference on Computer Applications and Information Technology in the Maritime Industries (COMPIT) (2007, April)

The paper presents the results of a data mining analysis aiming to improve the cost knowledge of the labour intensive straightening process. The data mining approach yields a formula linking the ... [more ▼]

The paper presents the results of a data mining analysis aiming to improve the cost knowledge of the labour intensive straightening process. The data mining approach yields a formula linking the straightening cost to the sections scantlings (plate thickness, dimension and inter-distance of longitudinal stiffeners, dimension and inter-distance of transversal frames) and to other section characteristics. [less ▲]

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See detailA Data Mining Analysis Applied to a Straightening Process Database
Caprace, Jean-David ULg; Losseau, Nicolas; Bair, Frédéric ULg et al

in Ship Technology Research = Schiffstechnik (2007), 54(4), 177-183

The complexity of modern manufacturing processes in a highly competitive environment forces the manufacturers to invest massively in automation and monitoring systems. The large data flows from these new ... [more ▼]

The complexity of modern manufacturing processes in a highly competitive environment forces the manufacturers to invest massively in automation and monitoring systems. The large data flows from these new installations are sources of valuable and hidden knowledge that is so far hardly used. Data mining methods through integrated data analysis tools give a solution to this situation, allowing easy retrieval of knowledge starting from a data base. This is also a unique opportunity to learn faster about the process and to detect hidden and complex relationships between parameters involved. Within this framework we have decided to apply this data analysis method to the straightening process in shipbuilding. We refer to Caprace et al. (2007) for additional illustrations. In shipbuilding, the assembly of elements by welding involves temperature gradients within the ma- terial. These cause deformations which sometimes have to be reduced to obtain an acceptable surface flatness. The straightening process to eliminate these distortions for esthetical or functional reasons is labour intensive. Estimating the straightening impact on the production workload is interesting in the context of production simulation, cost assessment of ship hull, structure optimization, design for production, etc. To reach these objectives, the idea was to elaborate, through a data mining approach, a formula linking the straightening cost to the sections scantlings (plate thickness, dimension and inter-distance of longitudinal stiffeners, dimension and inter-distance of transversal frames) and to other section characteristics. This paper describes each stage of the methodology: data description, analysis of data quality, data exploration and finally choice of discriminatory attributes and the generation of the data-driven models. [less ▲]

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See detailA Data Mining Analysis to evaluate the additional workloads caused by welding distortions
Losseau, Nicolas ULg; Caprace, Jean-David ULg; Aracil Fernandez, Francisco et al

in MARSTRUCT'09 (2009, March)

This paper presents a way to minimize cost in shipbuilding industry by using the results of a data mining analysis aiming to improve the cost knowledge of the additional operations caused by welding ... [more ▼]

This paper presents a way to minimize cost in shipbuilding industry by using the results of a data mining analysis aiming to improve the cost knowledge of the additional operations caused by welding distortions. This statistical analysis had the scope to establish assessment formulas of the supplementary workloads in function of scantlings and welding distortions. Those formulas can be useful to evaluate the profitability of new welding devices and can improve the research in the following domains: production simulation, cost assessment of ship hull, structure optimization, design for production, etc. [less ▲]

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See detailData Mining in Ship Construction and operation
Caprace, Jean-David ULg

Conference (2011, January)

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See detailData mining tools and application in power system engineering
Olaru, Cristina; Geurts, Pierre ULg; Wehenkel, Louis ULg

in Proceedings of the 13th Power System Computation Conference, PSCC99 (1999)

The power system field is presently facing an explosive growth of data. The data mining (DM) approach provides tools for making explicit some implicit subtle structure in data. Applying data mining to ... [more ▼]

The power system field is presently facing an explosive growth of data. The data mining (DM) approach provides tools for making explicit some implicit subtle structure in data. Applying data mining to power system engineering is an iterative and interactive process, requiring an acquainted user with the application specifics. The paper describes data mining tools like statistical methos, visualization, machine learning and neural networks, exemplifying by results obtained with a DM software developed for dynamic security assessment studies. Power system engineering applications where data mining would be useful are reviewed in the second part of the paper. [less ▲]

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See detailData networks integration
De Praetere, Julien; Maricau, Pḧilippe; Van Droogenbroeck, Marc ULg

in 37th European Telecommunications Congress, Volume 17 (1998, August)

This paper discusses the role of data networks integration on an asynchronous transfer mode (ATM) core and compares the advantages achieved by the usage of ATM with those given by emerging technologies ... [more ▼]

This paper discusses the role of data networks integration on an asynchronous transfer mode (ATM) core and compares the advantages achieved by the usage of ATM with those given by emerging technologies such Internet protocol (IP) over synchronous digital hierarchy (SDH) (giga-routers). [less ▲]

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See detailData normalization and supervised learning to assess the condition of patients with multiple sclerosis based on gait analysis
Azrour, Samir ULg; Pierard, Sébastien ULg; Geurts, Pierre ULg et al

in European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN) (2014, April)

Gait impairment is considered as an important feature of disability in multiple sclerosis but its evaluation in the clinical routine remains limited. In this paper, we assess, by means of supervised ... [more ▼]

Gait impairment is considered as an important feature of disability in multiple sclerosis but its evaluation in the clinical routine remains limited. In this paper, we assess, by means of supervised learning, the condition of patients with multiple sclerosis based on their gait descriptors obtained with a gait analysis system. As the morphological characteristics of individuals influence their gait while being in first approximation independent of the disease level, an original strategy of data normalization with respect to these characteristics is described and applied beforehand in order to obtain more reliable predictions. In addition, we explain how we address the problem of missing data which is a common issue in the field of clinical evaluation. Results show that, based on machine learning combined to the proposed data handling techniques, we can predict a score highly correlated with the condition of patients. [less ▲]

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See detailData processing of vibrational chemical imaging for pharmaceutical applications.
Sacre, Pierre-Yves ULg; De Bleye, Charlotte ULg; Chavez, Pierre-François ULg et al

in Journal of Pharmaceutical & Biomedical Analysis (in press)

Vibrational spectroscopy (MIR, NIR and Raman) based hyperspectral imaging is one of the most powerful tools analyze pharmaceutical preparation. Indeed, it combines the advantages of vibrational ... [more ▼]

Vibrational spectroscopy (MIR, NIR and Raman) based hyperspectral imaging is one of the most powerful tools analyze pharmaceutical preparation. Indeed, it combines the advantages of vibrational spectroscopy to imaging techniques and allows therefore the visualization of distribution of compounds, crystallization processes. However, these techniques provide a huge amount of data that must be processed to extract the relevant information. This review presents fundamental concepts of hyperspectral imaging, the basic theory of the most used chemometric tools used to pre-process, process and post-process the generated data. The last part of the present paper focuses on pharmaceutical applications of hyperspectral imaging and highlights the data processing approaches to enable the reader making the best choice among the different tools available. [less ▲]

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See detailData quality of the serum analysis of PCDD, PCDF and PCB in the French dioxin and incinerators study
Pascal, M.; Eppe, Gauthier ULg; Zeghnoun, A. et al

in Organohalogen Compounds (2007), 69

Detailed reference viewed: 21 (3 ULg)
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See detailData reconciliation and sampling protocol design, case of a paper deinking process
Brown, David; Marechal, François; Heyen, Georges ULg et al

in Paperi Ja Puu-Paper and Timber (2004), 86(8), 565-570

An equation solver data reconciliation software has been used to build a validated model of a waste paper deinking mill, by combining control room measurements and process design specifications. An ... [more ▼]

An equation solver data reconciliation software has been used to build a validated model of a waste paper deinking mill, by combining control room measurements and process design specifications. An optimal sampling protocol to validate the model by using only control room measurements has been determined by identifying, with genetic algorithm programming, the additional sampling points and corresponding sensors required to compensate for the lack of redundant measurements. [less ▲]

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See detailData validation and missing data reconstruction using self-organizing map for water treatment
Lamrini, B, Lakhal, E K; Wehenkel, Louis ULg

in Neural Computing & Applications (2011), 20(4), 575-588

Applications in the water treatment domain generally rely on complex sensors located at remote sites. The processing of the corresponding measurements for generating higher-level information such as ... [more ▼]

Applications in the water treatment domain generally rely on complex sensors located at remote sites. The processing of the corresponding measurements for generating higher-level information such as optimization of coagulation dosing must therefore account for possible sensor failures and imperfect input data. In this paper, selforganizing map (SOM)-based methods are applied to multiparameter data validation and missing data reconstruction in a drinking water treatment. The SOM is a special kind of artificial neural networks that can be used for analysis and visualization of large high-dimensional data sets. It performs both in a nonlinear mapping from a high-dimensional data space to a low-dimensional space aiming to preserve the most important topological and metric relationships of the original data elements and, thus, inherently clusters the data. Combining the SOM results with those obtained by a fuzzy technique that uses marginal adequacy concept to identify the functional states (normal or abnormal), the SOM performances of validation and reconstruction process are tested successfully on the experimental data stemming from a coagulation process involved in drinking water treatment. [less ▲]

Detailed reference viewed: 24 (1 ULg)