Predictive tracking with improved motion models for optical belt sorting

Optical belt sorters are a versatile means to sort bulk materials. In previous work, we presented a novel design of an optical belt sorter, which includes an area scan camera instead of a line scan camera. Line scan cameras, which are well-established in optical belt sorting, only allow for a single observation of each particle. Using multitarget tracking, the data of the area scan camera can be used to derive a part of the trajectory of each particle. The knowledge of the trajectories can be used to generate accurate predictions as to when and where each particle passes the separation mechanism. Accurate predictions are key to achieve high quality sorting results. The accuracy of the trajectories and the predictions heavily depends on the motion model used. In an evaluation based on a simulation that provides us with ground truth trajectories, we previously identified a bias in the temporal component of the prediction. In this paper, we analyze the simulation-based ground truth data of the motion of different bulk materials and derive models specifically tailored to the generation of accurate predictions for particles traveling on a conveyor belt. The derived models are evaluated using simulation data involving three different bulk materials. The evaluation shows that the constant velocity model and constant acceleration model can be outperformed by utilizing the similarities in the motion behavior of particles of the same type

Simulation study and experimental validation of a neural network-based predictive tracking system for sensor-based sorting

Die sensorgestützte Sortierung bietet zukunftsweisende Lösungen für die Trennung von körnigen Materialien. Die derzeit in solchen Systemen verwendeten Zeilensensoren liefern nur eine einzige Beobachtung jedes Objekts und keine Daten über dessen Bewegung. Jüngsten Studien zufolge hat die Verwendung einer Flächenkamera das Potenzial, sowohl den Charakterisierungs- als auch den Trennungsfehler in einem Sortierprozess zu verringern. Ein prädiktiver Tracking-Ansatz auf der Grundlage von Kalman-Filtern ermöglicht die Schätzung der verfolgten Pfade und die Parametrisierung eines individuellen Bewegungsmodells für jedes Objekt in einem Multiobjekt-Tracking-System. Während sich frühere Studien auf physikalisch motivierte Bewegungsmodelle konzentrierten, hat sich gezeigt, dass moderne Ansätze des maschinellen Lernens genauere Vorhersagen ermöglichen. In diesem Beitrag beschreiben wir die Entwicklung eines prädiktiven Trackingsystems auf Basis neuronaler Netze. Der neue Algorithmus wird auf ein experimentelles Sortiersystem und auf ein numerisches Modell des Sortierers angewendet. Zwar erreicht der neue Ansatz noch nicht ganz die Sortierqualität der bestehenden Ansätze, jedoch ermöglicht er die Anwendung von prädiktivem Tracking, ohne dass hierfür Expertenwissen oder ein grundlegendes Verständnis der Parametrisierung des Partikelbewegungsmodells erforderlich sind

Real-time multitarget tracking for sensor-based sorting – A new implementation of the auction algorithm for graphics processing units

Utilizing parallel algorithms is an established way of increasing performance in systems that are bound to real-time restrictions. Sensor-based sorting is a machine vision application for which firm real-time requirements need to be respected in order to reliably remove potentially harmful entities from a material feed. Recently, employing a predictive tracking approach using multitarget tracking in order to decrease the error in the physical separation in optical sorting has been proposed. For implementations that use hard associations between measurements and tracks, a linear assignment problem has to be solved for each frame recorded by a camera. The auction algorithm can be utilized for this purpose, which also has the advantage of being well suited for parallel architectures. In this paper, an improved implementation of this algorithm for a graphics processing unit (GPU) is presented. The resulting algorithm is implemented in both an OpenCL and a CUDA based environment. By using an optimized data structure, the presented algorithm outperforms recently proposed implementations in terms of speed while retaining the quality of output of the algorithm. Furthermore, memory requirements are significantly decreased, which is important for embedded systems. Experimental results are provided for two different GPUs and six datasets. It is shown that the proposed approach is of particular interest for applications dealing with comparatively large problem sizes

Multitarget Tracking Using Orientation Estimation for Optical Belt Sorting

In optical belt sorting, accurate predictions of the bulk material particles’ motions are required for high-quality results. By implementing a multitarget tracker tailored to the scenario and deriving novel motion models, the predictions are greatly enhanced. The tracker’s reliability is improved by also considering the particles’ orientations. To this end, new estimators for directional quantities based on orthogonal basis functions are presented and shown to outperform the state of the art

Inline Bulk-lifetime Prediction on as-cut Multicrystalline Silicon Wafers

AbstractSolar cell production using multicrystalline silicon is rife with uncertainties about material quality and its impact on subsequent processing steps. However, inline electrical metrology can provide predictive information about bulk silicon quality before processing resources are applied, allowing later metrology to focus on process control. In this work, we show an industrial algorithm which enables bulk lifetime prediction in as-cut multicrystalline wafers, agnostic of wafer origin, using an inline Quasi-Steady-State Photoconductance (QSSPC) measurement setup from Sinton Instruments. In addition, we demonstrate a robust method to extract emitter saturation current density from post-diffusion wafer measurements. Finally, we extend the use of inline QSSPC with a corrected doping measurement that utilizes the theory of grain-boundary potential barriers to interpret excess conductance

Teacher Quality and Student Inequality (Revised 2014)

This paper examines the extent to which the allocation of teachers within and across public high schools is contributing to inequality in student test score performance. Using ten years of administrative data from North Carolina public high schools, I estimate a flexible education production function in which student achievement reflects student inputs, teacher quality, school quality, and a school-specific scaling factor that allows the impact of teaching quality to vary across schools. The existence of nearly 3,000 teacher transfers, combined with a testable exogenous mobility assumption, allows separate identification of each teacher’s quality from both school quality and school sensitivity to teacher quality. I find that teaching quality is surprisingly equitably distributed both within and across high schools. Schools predominantly serving underprivileged students employ teachers who are only slightly below average, and most students receive a mix of their school’s good and bad teachers. Overall, I find that the allocation of teacher and school inputs at the high school level contributes only 4% to the achievement gap between the top and bottom deciles of an index of student background. Finally, I find that schools that disproportionately serve disadvantaged students tend to be more sensitive to teacher quality

Automated Design and Optimization of Metallic Alloys

The design and optimization of metallic alloys poses a significant engineering challenge. The search space of all possible alloys is sufficiently large that it is impossible to fully explore by traditional methods. In order to address this challenge, physics based computational frameworks linked to advanced machine learning algorithms can serve to automate this process with computational efficiency such that the state of the industry may be rapidly advanced. The work herein presents a suite of computational frameworks leveraged to automate the design and optimization process of advanced alloys. An ab initio alloy thermodynamics system, Molecular Dynamics simulations, a Convolutional-Neural Network system, and a coupled Neural Network and Multi-objective Genetic Algorithm. These algorithms are validated over the set of binary nanocrystalline Al-X alloys, and multi-component High Entropy Alloys (HEA)