This paper presents a novel method for tomographic measurement and data analysis based on crowdsourcing. X-ray radiography imaging was initially applied to determine silo flow parameters. We used traced particles immersed in the bulk to investigate gravitational silo flow. The reconstructed images were not perfect, due to inhomogeneous silo filling and nonlinear attenuation of the X-rays on the way to the detector. Automatic processing of such data is not feasible. Therefore, we used crowdsourcing for human-driven annotation of the trace particles. As we aimed to extract meaningful flow parameters, we developed a modified crowdsourcing annotation method, focusing on selected important areas of the silo pictures only. We call this method “targeted crowdsourcing”, and it enables more efficient crowd work, as it is focused on the most important areas of the image that allow determination of the flow parameters. The results show that it is possible to analyze volumetric material structure movement based on 2D radiography data showing the location and movement of tiny metal trace particles. A quantitative description of the flow obtained from the horizontal and vertical velocity components was derived for different parts of the model silo volume. Targeting the attention of crowd workers towards either a specific zone or a particular particle speeds up the pre-processing stage while preserving the same quality of the output, quantified by important flow parameters.
Abstract-Proper break taking during office work iss necessary to prevent musculoskeletal disorders and reduce the risk of heart disease. We present APOEW -an avatar for preventing continuous office work without taking breaks. APOEW is a system that uses a personalized robot avatar to encourage proper break behaviour during office work. The avatar signals the need for a break by stooping. The system was designed to be unobtrusive and blend well with the office environment. The avatars are customisable in order to enable users to design their work environment freely. We conducted a user study where we observed developers working in front of their computers next to the avatar. Preliminary results indicate it has no negative impact on the work environment and users are intrigued by the system. Moreover, a survey on attitude to our concept reveals interesting and positive feedback that will help to develop an APOEW system further.
This paper presents an overview of what Big Data can bring to the modern industry. Through following the history of contemporary Big Data frameworks the authors observe that the tools available have reached sufficient maturity so as to be usable in an industrial setting. The authors propose the concept of a system for collecting, organising, processing and analysing experimental data obtained from measurements with process tomography. Process tomography is used for noninvasive flow monitoring and data acquisition. The measurement data is collected, stored and processed to identify process regimes and process threats. Further general examples of solutions that aim to take advantage of the existence of such tools are presented as proof of viability of such approach. As the first step in the process of creating the proposed system, a scalable, distributed, containerisation-based cluster has been constructed, with consumer-grade hardware.
The presented paper proposes a hybrid neural architecture that enables intelligent data analysis efficacy to be boosted in smart sensor devices, which are typically resource-constrained and application-specific. The postulated concept integrates prior knowledge with learning from examples, thus allowing sensor devices to be used for the successful execution of machine learning even when the volume of training data is highly limited, using compact underlying hardware. The proposed architecture comprises two interacting functional modules arranged in a homogeneous, multiple-layer architecture. The first module, referred to as the knowledge sub-network, implements knowledge in the Conjunctive Normal Form through a three-layer structure composed of novel types of learnable units, called L-neurons. In contrast, the second module is a fully-connected conventional three-layer, feed-forward neural network, and it is referred to as a conventional neural sub-network. We show that the proposed hybrid structure successfully combines knowledge and learning, providing high recognition performance even for very limited training datasets, while also benefiting from an abundance of data, as it occurs for purely neural structures. In addition, since the proposed L-neurons can learn (through classical backpropagation), we show that the architecture is also capable of repairing its knowledge.
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The presented paper proposes a novel, hybrid neuromorphic computational architecture for visual data classification aimed at implementation in energy-efficient applicationspecific, FPGA or ASIC-based edge computing devices. The architecture combines a convolutional neural extractor that produces comprehensive representations of input patterns with a Hyperdimensional Computing (HDC) module that enables complex data analyses, including vector and vector sequence classification. As the biologically inspired HDC paradigm operates on holistic representations of concepts, we accordingly design a convolutional extractor to summarize various aspects of objects' appearance. As low energy consumption is the key design constraint, we assume that input images are delivered by energy-efficient dynamic vision sensors (event cameras). The extractor is pretrained using a three-head Convolutional Neural Network (CNN). The different CNN heads: classifier, decoder, and clusterer implement optimization objectives essential for the "holographic" concept representation. Feature vectors produced by the extractor are projected onto hyperdimensional binary vectors using an encoding unit, and they are subject to classification in the HDC module. The neural extractor is trained in limited precision mode to account for ASIC/FPGA hardware constraints. We apply the proposed architecture to classify objects (pedestrians, cars, and cyclists) from two different traffic datasets: VIRAT and KAIST. We show that the proposed concept enables solving classification problems with an accuracy that matches the performance of deep neural classifiers while being feasible for implementation in energy-efficient applicationspecific hardware.
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