3,049 research outputs found

    An intelligent sales assistant for configurable products

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    Some of the recent proposals of web-based applications are oriented to provide advanced search services through virtual shops. Within this context, this paper proposes an advanced type of software application that simulates how a sales assistant dialogues with a consumer to dynamically configure a product according to particular needs. The paper presents the general knowl- edge model that uses artificial intelligence and knowledge-based techniques to simulate the configuration process. Finally, the paper illustrates the description with an example of an application in the field of photography equipment

    Memory and information processing in neuromorphic systems

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    A striking difference between brain-inspired neuromorphic processors and current von Neumann processors architectures is the way in which memory and processing is organized. As Information and Communication Technologies continue to address the need for increased computational power through the increase of cores within a digital processor, neuromorphic engineers and scientists can complement this need by building processor architectures where memory is distributed with the processing. In this paper we present a survey of brain-inspired processor architectures that support models of cortical networks and deep neural networks. These architectures range from serial clocked implementations of multi-neuron systems to massively parallel asynchronous ones and from purely digital systems to mixed analog/digital systems which implement more biological-like models of neurons and synapses together with a suite of adaptation and learning mechanisms analogous to the ones found in biological nervous systems. We describe the advantages of the different approaches being pursued and present the challenges that need to be addressed for building artificial neural processing systems that can display the richness of behaviors seen in biological systems.Comment: Submitted to Proceedings of IEEE, review of recently proposed neuromorphic computing platforms and system

    Sustainable product development strategies: Business planning and performance implications

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    Copyright © 2012 by Institution of Mechanical Engineers. This is the author's accepted manuscript. The final published article is available from the link below.Manufacturing firms are under many financial and competitive pressures which focus attention on the performance of their manufacturing processes. In this paper the opportunities for improving the environmental impact of products within the constraints of existing manufacturing infrastructure are examined. Approaches which support sustainability in two aspects are proposed, firstly, the provision of products to the users in ways which extend the product life and secondly, manufacturing approaches which reduce resource usage. This paper outlines three different sustainable development strategies for different product types and describes the cost implications for manufacturers across the life-cycle. The performance measures affected by these strategies are examined drawing on product development case studies from a number of high technology sectors to highlight the different approaches taken. The results are intended to aid manufacturers during the earliest stages of business planning to consider alternative product development approaches which are more sustainable

    Visualizing Large Business Process Models: Challenges, Techniques, Applications

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    Large process models may comprise hundreds or thousands of process elements, like activities, gateways, and data objects. Presenting such process models to users and enabling them to interact with these models constitute crucial tasks of any process-aware information systems (PAISs). Existing PAISs, however, neither provide adequate techniques for visualizing and abstracting process models nor for interacting with them. In particular, PAISs do not provide tailored process visualizations as needed in complex application environments. This paper presents examples of large process models and discusses some of the challenges to be tackled when visualizing and abstracting respective models. Further, it presents a comprehensive framework that allows for personalized process model visualizations, which can be tailored to the specific needs of the different user groups. First, process model complexity can be reduced by abstracting the models, i.e., by eliminating or aggregating process elements not relevant in the given visualization context. Second, the appearance of process elements can be customized independent of the process modeling language used. Third, different visualization formats (e.g., process diagrams, process forms, and process trees) are supported. Finally, it will be discussed how tailored visualizations of process models may serve as basis for changing and evolving process models at a high level of abstraction

    Parallel Tempering Simulation of the three-dimensional Edwards-Anderson Model with Compact Asynchronous Multispin Coding on GPU

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    Monte Carlo simulations of the Ising model play an important role in the field of computational statistical physics, and they have revealed many properties of the model over the past few decades. However, the effect of frustration due to random disorder, in particular the possible spin glass phase, remains a crucial but poorly understood problem. One of the obstacles in the Monte Carlo simulation of random frustrated systems is their long relaxation time making an efficient parallel implementation on state-of-the-art computation platforms highly desirable. The Graphics Processing Unit (GPU) is such a platform that provides an opportunity to significantly enhance the computational performance and thus gain new insight into this problem. In this paper, we present optimization and tuning approaches for the CUDA implementation of the spin glass simulation on GPUs. We discuss the integration of various design alternatives, such as GPU kernel construction with minimal communication, memory tiling, and look-up tables. We present a binary data format, Compact Asynchronous Multispin Coding (CAMSC), which provides an additional 28.4%28.4\% speedup compared with the traditionally used Asynchronous Multispin Coding (AMSC). Our overall design sustains a performance of 33.5 picoseconds per spin flip attempt for simulating the three-dimensional Edwards-Anderson model with parallel tempering, which significantly improves the performance over existing GPU implementations.Comment: 15 pages, 18 figure
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