39,364 research outputs found

    Development of a heat transfer and artificial neural networks teaching laboratory practical for biotechnology students

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    The paper describes a newly developed laboratory practical that teaches students how to develop an Artificial Neural Network model and its possible use in bio-processing. An emphasis is placed on giving students "hands on" experience with bio-processing equipment, namely bio-reactors and data acquisition systems in an attempt to help prepare them for work in bio-processing and chemical engineering industries

    Data Mining by Soft Computing Methods for The Coronary Heart Disease Database

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    For improvement of data mining technology, the advantages and disadvantages on respective data mining methods should be discussed by comparison under the same condition. For this purpose, the Coronary Heart Disease database (CHD DB) was developed in 2004, and the data mining competition was held in the International Conference on Knowledge-Based Intelligent Information and Engineering Systems (KES). In the competition, two methods based on soft computing were presented. In this paper, we report the overview of the CHD DB and the soft computing methods, and discuss the features of respective methods by comparison of the experimental results

    GRASS: Generative Recursive Autoencoders for Shape Structures

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    We introduce a novel neural network architecture for encoding and synthesis of 3D shapes, particularly their structures. Our key insight is that 3D shapes are effectively characterized by their hierarchical organization of parts, which reflects fundamental intra-shape relationships such as adjacency and symmetry. We develop a recursive neural net (RvNN) based autoencoder to map a flat, unlabeled, arbitrary part layout to a compact code. The code effectively captures hierarchical structures of man-made 3D objects of varying structural complexities despite being fixed-dimensional: an associated decoder maps a code back to a full hierarchy. The learned bidirectional mapping is further tuned using an adversarial setup to yield a generative model of plausible structures, from which novel structures can be sampled. Finally, our structure synthesis framework is augmented by a second trained module that produces fine-grained part geometry, conditioned on global and local structural context, leading to a full generative pipeline for 3D shapes. We demonstrate that without supervision, our network learns meaningful structural hierarchies adhering to perceptual grouping principles, produces compact codes which enable applications such as shape classification and partial matching, and supports shape synthesis and interpolation with significant variations in topology and geometry.Comment: Corresponding author: Kai Xu ([email protected]

    Principles for Consciousness in Integrated Cognitive Control

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    In this article we will argue that given certain conditions for the evolution of bi- \ud ological controllers, these will necessarily evolve in the direction of incorporating \ud consciousness capabilities. We will also see what are the necessary mechanics for \ud the provision of these capabilities and extrapolate this vision to the world of artifi- \ud cial systems postulating seven design principles for conscious systems. This article \ud was published in the journal Neural Networks special issue on brain and conscious- \ud ness
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