Modeling the Grain Cleaning Process of a Stationary Sorghum Thresher

Rosana G. Moreira, Editor-in-Chief; Texas A&M UniversityThis is a paper from International Commission of Agricultural Engineering (CIGR, Commission Internationale du Genie Rural) E-Journal Volume 8 (2006): Modeling the Grain Cleaning Process of a Stationary Sorghum Thresher. Manuscript PM 06 012. Vol. VIII. August, 2006

Investigating Grain Separation and Cleaning Efficiency Distribution of a Conventional Stationary Rasp-bar Sorghum Thresher

A stationary grain thresher was developed and used to study grain separation and cleaning efficiency distribution of the cleaning unit, fractionated by sieve and horizontal air stream, along the sieve length. The influence of feed rate, m, air speed, VA and sieve oscillation frequency, FS on cleaning efficiency of sorghum was explored. Grain separation along the sieve can be divided into three sections: increasing, peak and decreasing sections. Results showed that cleaning efficiency decreased with increasing sieve oscillations frequency and feed rate respectively. Cleaning loss increased with increasing sieve oscillation frequency, feed rate and air speed

Evaluating Two-Stream CNN for Video Classification

Videos contain very rich semantic information. Traditional hand-crafted features are known to be inadequate in analyzing complex video semantics. Inspired by the huge success of the deep learning methods in analyzing image, audio and text data, significant efforts are recently being devoted to the design of deep nets for video analytics. Among the many practical needs, classifying videos (or video clips) based on their major semantic categories (e.g., "skiing") is useful in many applications. In this paper, we conduct an in-depth study to investigate important implementation options that may affect the performance of deep nets on video classification. Our evaluations are conducted on top of a recent two-stream convolutional neural network (CNN) pipeline, which uses both static frames and motion optical flows, and has demonstrated competitive performance against the state-of-the-art methods. In order to gain insights and to arrive at a practical guideline, many important options are studied, including network architectures, model fusion, learning parameters and the final prediction methods. Based on the evaluations, very competitive results are attained on two popular video classification benchmarks. We hope that the discussions and conclusions from this work can help researchers in related fields to quickly set up a good basis for further investigations along this very promising direction.Comment: ACM ICMR'1

Efficient On-the-fly Category Retrieval using ConvNets and GPUs

We investigate the gains in precision and speed, that can be obtained by using Convolutional Networks (ConvNets) for on-the-fly retrieval - where classifiers are learnt at run time for a textual query from downloaded images, and used to rank large image or video datasets. We make three contributions: (i) we present an evaluation of state-of-the-art image representations for object category retrieval over standard benchmark datasets containing 1M+ images; (ii) we show that ConvNets can be used to obtain features which are incredibly performant, and yet much lower dimensional than previous state-of-the-art image representations, and that their dimensionality can be reduced further without loss in performance by compression using product quantization or binarization. Consequently, features with the state-of-the-art performance on large-scale datasets of millions of images can fit in the memory of even a commodity GPU card; (iii) we show that an SVM classifier can be learnt within a ConvNet framework on a GPU in parallel with downloading the new training images, allowing for a continuous refinement of the model as more images become available, and simultaneous training and ranking. The outcome is an on-the-fly system that significantly outperforms its predecessors in terms of: precision of retrieval, memory requirements, and speed, facilitating accurate on-the-fly learning and ranking in under a second on a single GPU.Comment: Published in proceedings of ACCV 201

Receptive Field Block Net for Accurate and Fast Object Detection

Current top-performing object detectors depend on deep CNN backbones, such as ResNet-101 and Inception, benefiting from their powerful feature representations but suffering from high computational costs. Conversely, some lightweight model based detectors fulfil real time processing, while their accuracies are often criticized. In this paper, we explore an alternative to build a fast and accurate detector by strengthening lightweight features using a hand-crafted mechanism. Inspired by the structure of Receptive Fields (RFs) in human visual systems, we propose a novel RF Block (RFB) module, which takes the relationship between the size and eccentricity of RFs into account, to enhance the feature discriminability and robustness. We further assemble RFB to the top of SSD, constructing the RFB Net detector. To evaluate its effectiveness, experiments are conducted on two major benchmarks and the results show that RFB Net is able to reach the performance of advanced very deep detectors while keeping the real-time speed. Code is available at https://github.com/ruinmessi/RFBNet.Comment: Accepted by ECCV 201

Student motivation for professional self-improvement

Problems of arousing student motivation in vocational education are discussed. The authors suggest dealing with these problems by specifying the learner’s vocational self-determination and stimulating their professional growth. The notion of “professional self-improvement of students in the system of secondary vocational education” is introduced. Relations between students’ and teachers’ motivation problems are revealed.Раскрываются проблемы формирования мотивации студентов к профессиональному обучению. Предлагается методика разрешения этих проблем средствами уточнения профессионального самоопределения и профессионального самосовершенствования. Вводится определение понятия «профессиональное саморазвитие студентов профессиональных образовательных организаций среднего профессионального образования». Обозначаются связи между проблемами мотивации у студентов и преподавателей

Towards Bottom-Up Analysis of Social Food

in ACM Digital Health Conference 201

The Charity Beauty Premium: Satisfying Donors’ “Want” versus “Should” Desires

Despite widespread conviction that neediness is the most important criterion for charitable allocations, we observe a “charity beauty premium” in which donors often favor beautiful, but less needy charity recipients. We propose that donors hold simultaneous, yet incongruent preferences of wanting to support beautiful recipients (who tend to be judged as less needy) yet believing they should support needy recipients instead. We additionally posit that preferences for beautiful recipients are most likely to emerge when decisions are intuitive whereas preferences for needy recipients are most likely to emerge when decisions are deliberative. We test these propositions in several ways. First, when a beautiful recipient is introduced to basic choice sets, it becomes the most popular option and increases donor satisfaction. Second, heightening deliberation steers choices away from beautiful recipients and toward needier ones. Third, donors explicitly state that they “want” to give to beautiful recipients but “should” give to less beautiful, needier ones. Taken together, these findings reconcile and extend previous and sometimes conflicting results about beauty and generosity

An early resource characterization of deep learning on wearables, smartphones and internet-of-things devices

Detecting and reacting to user behavior and ambient context are core elements of many emerging mobile sensing and Internet-of-Things (IoT) applications. However, extracting accurate infer-ences from raw sensor data is challenging within the noisy and complex environments where these systems are deployed. Deep Learning { is one of the most promising approaches for overcom-ing this challenge, and achieving more robust and reliable infer-ence. Techniques developed within this rapidly evolving area of machine learning are now state-of-the-art for many inference tasks (such as, audio sensing and computer vision) commonly needed by IoT and wearable applications. But currently deep learning al-gorithms are seldom used in mobile/IoT class hardware because they often impose debilitating levels of system overhead (e.g., memory, computation and energy). Efforts to address this bar-rier to deep learning adoption are slowed by our lack of a system-atic understanding of how these algorithms behave at inference time on resource constrained hardware. In this paper, we present the-rst { albeit preliminary { measurement study of common deep learning models (such as Convolutional Neural Networks and Deep Neural Networks) on representative mobile and embed-ded platforms. The aim of this investigation is to begin to build knowledge of the performance characteristics, resource require-ments and the execution bottlenecks for deep learning models when being used to recognize categories of behavior and context. The results and insights of this study, lay an empirical foundation for the development of optimization methods and execution envi-ronments that enable deep learning to be more readily integrated into next-generation IoT, smartphones and wearable systems

Structural Material Property Tailoring Using Deep Neural Networks

Advances in robotics, artificial intelligence, and machine learning are ushering in a new age of automation, as machines match or outperform human performance. Machine intelligence can enable businesses to improve performance by reducing errors, improving sensitivity, quality and speed, and in some cases achieving outcomes that go beyond current resource capabilities. Relevant applications include new product architecture design, rapid material characterization, and life-cycle management tied with a digital strategy that will enable efficient development of products from cradle to grave. In addition, there are also challenges to overcome that must be addressed through a major, sustained research effort that is based solidly on both inferential and computational principles applied to design tailoring of functionally optimized structures. Current applications of structural materials in the aerospace industry demand the highest quality control of material microstructure, especially for advanced rotational turbomachinery in aircraft engines in order to have the best tailored material property. In this paper, deep convolutional neural networks were developed to accurately predict processing-structure-property relations from materials microstructures images, surpassing current best practices and modeling efforts. The models automatically learn critical features, without the need for manual specification and/or subjective and expensive image analysis. Further, in combination with generative deep learning models, a framework is proposed to enable rapid material design space exploration and property identification and optimization. The implementation must take account of real-time decision cycles and the trade-offs between speed and accuracy