Benchmarking flexible job-shop scheduling and control systems

Benchmarking is comparing the output of different systems for a given set of input data in order to improve the system’s performance. Faced with the lack of realistic and operational benchmarks that can be used for testing optimization methods and control systems in flexible systems, this paper proposes a benchmark system based on a real production cell. A three-step method is presented: data preparation, experimentation, and reporting. This benchmark allows the evaluation of static optimization performances using traditional operation research tools and the evaluation of control system's robustness faced with unexpected events.     

MODIS Collection 5 global land cover: Algorithm refinements and characterization of new datasets

Information related to land cover is immensely important to global change science. In the past decade, data sources and methodologies for creating global land cover maps from remote sensing have evolved rapidly. Here we describe the datasets and algorithms used to create the Collection 5 MODIS Global Land Cover Type product, which is substantially changed relative to Collection 4. In addition to using updated input data, the algorithm and ancillary datasets used to produce the product have been refined. Most importantly, the Collection 5 product is generated at 500-m spatial resolution, providing a four-fold increase in spatial resolution relative to the previous version. In addition, many components of the classification algorithm have been changed. The training site database has been revised, land surface temperature is now included as an input feature, and ancillary datasets used in post-processing of ensemble decision tree results have been updated. Further, methods used to correct classifier results for bias imposed by training data properties have been refined, techniques used to fuse ancillary data based on spatially varying prior probabilities have been revised, and a variety of methods have been developed to address limitations of the algorithm for the urban, wetland, and deciduous needleleaf classes. Finally, techniques used to stabilize classification results across years have been developed and implemented to reduce year-to-year variation in land cover labels not associated with land cover change. Results from a cross-validation analysis indicate that the overall accuracy of the product is about 75% correctly classified, but that the range in class-specific accuracies is large. Comparison of Collection 5 maps with Collection 4 results show substantial differences arising from increased spatial resolution and changes in the input data and classification algorithm.     

An intestinal surgery simulator: real-time collision processing and visualization

This research work is aimed toward the development of a VR-based trainer for colon cancer removal. It enables the surgeons to interactively view and manipulate the concerned virtual organs as during a real surgery. First, we present a method for animating the small intestine and the mesentery (the tissue that connects it to the main vessels) in real-time, thus enabling user interaction through virtual surgical tools during the simulation. We present a stochastic approach for fast collision detection in highly deformable, self-colliding objects. A simple and efficient response to collisions is also introduced in order to reduce the overall animation complexity. Second, we describe a new method based on generalized cylinders for fast rendering of the intestine. An efficient curvature detection method, along with an adaptive sampling algorithm, is presented. This approach, while providing improved tessellation without the classical self-intersection problem, also allows for high-performance rendering thanks to the new 3D skinning feature available in recent GPUs. The rendering algorithm is also designed to ensure a guaranteed frame rate. Finally, we present the quantitative results of the simulations and describe the qualitative feedback obtained from the surgeons.     

Software Architecture Reconstruction: A Process-Oriented Taxonomy

To maintain and understand large applications, it is important to know their architecture. The first problem is that unlike classes and packages, architecture is not explicitly represented in the code. The second problem is that successful applications evolve over time, so their architecture inevitably drifts. Reconstructing the architecture and checking whether it is still valid is therefore an important aid. While there is a plethora of approaches and techniques supporting architecture reconstruction, there is no comprehensive software architecture reconstruction state of the art and it is often difficult to compare the approaches. This paper presents a state of the art in software architecture reconstruction approaches.     

Spectral evolution in dynamic networks

We introduce and study the spectral evolution model, which characterizes the growth of large networks in terms of the eigenvalue decomposition of their adjacency matrices: In large networks, changes over time result in a change of a graph’s spectrum, leaving the eigenvectors unchanged. We validate this hypothesis for several large social, collaboration, rating, citation, and communication networks. Following these observations, we introduce two link prediction algorithms based on the learning of the changes to a network’s spectrum. These new link prediction methods generalize several common graph kernels that can be expressed as spectral transformations. The first method is based on reducing the link prediction problem to a one-dimensional curve-fitting problem which can be solved efficiently. The second algorithm extrapolates a network’s spectrum to predict links. Both algorithms are evaluated on fifteen network datasets for which edge creation times are known.     

Object Touch by a Humanoid Robot Avatar Induces Haptic Sensation in the Real Hand

Humanoid robot embodiment is a recently developed form of mediated embodiment. In 2 studies, we report and quantify a new haptic (touch) illusion during embodiment of a humanoid robot. Around 60% of the users in our studies reported haptic sensations in their real hand when they observed their robot avatar touching a curtain with its hand. Critically, our study shows for the first time that users can experience haptic sensations from a nonanthropomorphic embodied limb/agent with visual feedback alone (i.e. no haptic feedback provided). The results have important implications for the understanding of the cognitive processes governing mediated embodiment and the design of avatar scenarios.     

Atmospheric total ozone column evaluation with a smartphone image sensor

A new method for quantifying the total ozone column (TOC) using a smartphone image sensor has been developed and validated. The TOC has been evaluated for relatively cloud free days at high air masses for solar zenith angles between 49.7° and 76.7° at a sub-tropical site. The method is based on the evaluation of the direct solar irradiances at 305 and 312 nm using the red colour pixel values of the solar disc recorded at these wavelengths by a smartphone camera. Narrow bandpass filters of 2 nm full width at half maximum at each of the two wavelengths were used in turn placed over the camera sensor to directly image the solar disc. The calibration of the pixel values of the solar disc to provide the direct solar irradiances at each of these two wavelengths allowed evaluation of the TOC calibrated to a portable sun photometer. The root mean square error (RMSE) for the smartphone-derived ozone values calibrated to corresponding values from a portable sun photometer was 4.3 Dobson Units (DU). The validation measurements for the smartphone-derived ozone values provided an average residual of 3.5% (up to a maximum of 11%) compared to the corresponding portable sun photometer values, with an RMSE of 8.4 DU during days of intermittent inclement weather conditions. The evaluation of the TOC based on a widely available device such as a smartphone has the potential to increase current citizen science initiatives valued by the general public and school-aged learners by enhancing knowledge and awareness of ozone and the resulting influences on the solar ultraviolet environment.