Deep Learning in the Automotive Industry: Applications and Tools

Deep Learning refers to a set of machine learning techniques that utilize neural networks with many hidden layers for tasks, such as image classification, speech recognition, language understanding. Deep learning has been proven to be very effective in these domains and is pervasively used by many Internet services. In this paper, we describe different automotive uses cases for deep learning in particular in the domain of computer vision. We surveys the current state-of-the-art in libraries, tools and infrastructures (e.\,g.\ GPUs and clouds) for implementing, training and deploying deep neural networks. We particularly focus on convolutional neural networks and computer vision use cases, such as the visual inspection process in manufacturing plants and the analysis of social media data. To train neural networks, curated and labeled datasets are essential. In particular, both the availability and scope of such datasets is typically very limited. A main contribution of this paper is the creation of an automotive dataset, that allows us to learn and automatically recognize different vehicle properties. We describe an end-to-end deep learning application utilizing a mobile app for data collection and process support, and an Amazon-based cloud backend for storage and training. For training we evaluate the use of cloud and on-premises infrastructures (including multiple GPUs) in conjunction with different neural network architectures and frameworks. We assess both the training times as well as the accuracy of the classifier. Finally, we demonstrate the effectiveness of the trained classifier in a real world setting during manufacturing process.Comment: 10 page

Composting Swine Manure from High Rise Finishing Facilities

Swine production has restructured considerably in recent years with increased production on fewer farms (Key et al., 2011). Most swine production facilities manage manure in liquid form either in deep pits underneath production facilities or in lagoons adjacent to the production facilities (Key et al., 2011). This management uses water to rinse manure from the facilities, which dilutes the nutrient concentration and value of the manure. The liquid forms are applied to land through irrigation systems or by liquid manure spreaders. Liquid manure management can have some operational constraints that composting eliminates (Bernal et al., 2009). The most common issue with handling liquid manure is that the manure has diluted nutrients and it is often not economical to transport large volumes of lagoon effluent to off‐site locations. Surface spreading through an irrigator is commonly used, but wet environments can delay application. Odor can be a concern if liquid manure is surface applied and not incorporated; and although soil incorporation does reduce manure odors, they can still be a concern

The design of a sampling mill to treat a south east Missouri lead ore

The purpose of the work that was undertaken in preparing this thesis was to provide plans and specifications for a sampling mill to be erected by the St. Louis Smelting and Refining Co. at their plant near St. Francois, Mo... It was intended at the outset to design the plant completely and to provide full working drawings and specifications for all parts of the mill. On account of the lack of time, however, it has been found necessary to somewhat reduce the amount of work. As a result only general plans and specifications with a few of the more important details are contained in the finished thesis --Thesis Subject, pages 1-2

Repetitive behavior profiles: Consistency across autism spectrum disorder cohorts and divergence from Prader–Willi syndrome

Restricted and repetitive behavior (RRB) is a group of heterogeneous maladaptive behaviors. RRB is one of the key diagnostic features of autism spectrum disorders (ASDs) and also commonly observed in Prader–Willi syndrome (PWS). In this study, we assessed RRB using the Repetitive Behavior Scale-Revised (RBS-R) in two ASD samples (University of Illinois at Chicago [UIC] and University of Florida [UF]) and one PWS sample. We compared the RBS-R item endorsements across three ASD cohorts (UIC, UF and an ASD sample from Lam, The Repetitive Behavior Scale-Revised: independent validation and the effect of subject variables, PhD thesis, 2004), and a PWS sample. We also compared the mean RBS-R subscale/sum scores across the UIC, UF and PWS samples; across the combined ASD (UIC + UF), PWS-deletion and PWS-disomy groups; and across the combined ASD sample, PWS subgroup with a Social Communication Questionnaire (SCQ) score ≥15, and PWS subgroup with a SCQ score <15. Despite the highly heterogeneous nature, the three ASD samples (UIC, UF and Lam’s) showed a similar pattern of the RBS-R endorsements, and the mean RBS-R scores were not different between the UIC and UF samples. However, higher RRB was noted in the ASD sample compared with the PWS sample, as well as in the PWS subgroup with a SCQ score ≥15 compared with the PWS subgroup with a SCQ score <15. Study limitations include a small sample size, a wide age range of our participants, and not controlling for potential covariates. A future replication study using a larger sample and further investigation into the genetic bases of overlapping ASD and RRB phenomenology are needed, given the higher RRB in the PWS subgroup with a SCQ score ≥15