39,364 research outputs found
Development of a heat transfer and artificial neural networks teaching laboratory practical for biotechnology students
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
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
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
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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