Special issue on Industrial Applications of Neural Networks

This special issue contains 14 papers selected from the first International Symposium on Computational Intelligence and Industrial Applications (ISCIIA'04), held in Haikou, China, December 20-24, 2004. Of the 82 papers from 8 countries submitted to the symposium, 62 were accepted for the proceedings. Based on reviewer's recommendations and guest editor's careful consideration, the authors of 14 papers have revised and extended their symposium papers for this issue. Computational intelligence is the study of the design of "intelligent" systems, which is flexible in changing environments and changing goals with uncertainty, and covers artificial intelligence, neural networks, fuzzy systems, evolutionary computation, and hybrid systems. The objective of this special issue is to reveal current challenges, research topics, and technology solutions critical to algorithms and applications involving computational intelligence. These 14 papers cover such important research areas as neural networks, image processing, control, financial engineering, robotics, and related technologies in computational intelligence. We believe that the information in this issue will become a valuable new resource for the computational intelligence community. We thank the authors and referees whose selfless work and valuable comments have made this special issue possible and improved the overall quality of the papers.

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Editorial: Special Issue on Compact Deep Neural Networks With Industrial Applications

IEEE Journal of Selected Topics in Signal Processing ◽

10.1109/jstsp.2020.3006323 ◽

2020 ◽

Vol 14 (4) ◽

pp. 605-608

Author(s):

Lixin Fan ◽

Diana Marculescu ◽

Werner Bailer ◽

Yurong Chen

Keyword(s):

Neural Networks ◽

Deep Neural Networks ◽

Industrial Applications ◽

Special Issue ◽

Editorial Special Issue

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Deep Convolutional Neural Network with RNNs for Complex Activity Recognition Using Wrist-Worn Wearable Sensor Data

Electronics ◽

10.3390/electronics10141685 ◽

2021 ◽

Vol 10 (14) ◽

pp. 1685

Author(s):

Sakorn Mekruksavanich ◽

Anuchit Jitpattanakul

Keyword(s):

Neural Networks ◽

Activity Recognition ◽

Human Activities ◽

Recognition Performance ◽

Confusion Matrix ◽

Experimental Studies ◽

Industrial Applications ◽

Sensor Data ◽

Complex Activity ◽

Activity Data

Sensor-based human activity recognition (S-HAR) has become an important and high-impact topic of research within human-centered computing. In the last decade, successful applications of S-HAR have been presented through fruitful academic research and industrial applications, including for healthcare monitoring, smart home controlling, and daily sport tracking. However, the growing requirements of many current applications for recognizing complex human activities (CHA) have begun to attract the attention of the HAR research field when compared with simple human activities (SHA). S-HAR has shown that deep learning (DL), a type of machine learning based on complicated artificial neural networks, has a significant degree of recognition efficiency. Convolutional neural networks (CNNs) and recurrent neural networks (RNNs) are two different types of DL methods that have been successfully applied to the S-HAR challenge in recent years. In this paper, we focused on four RNN-based DL models (LSTMs, BiLSTMs, GRUs, and BiGRUs) that performed complex activity recognition tasks. The efficiency of four hybrid DL models that combine convolutional layers with the efficient RNN-based models was also studied. Experimental studies on the UTwente dataset demonstrated that the suggested hybrid RNN-based models achieved a high level of recognition performance along with a variety of performance indicators, including accuracy, F1-score, and confusion matrix. The experimental results show that the hybrid DL model called CNN-BiGRU outperformed the other DL models with a high accuracy of 98.89% when using only complex activity data. Moreover, the CNN-BiGRU model also achieved the highest recognition performance in other scenarios (99.44% by using only simple activity data and 98.78% with a combination of simple and complex activities).

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A Survey of Encoding Techniques for Signal Processing in Spiking Neural Networks

Neural Processing Letters ◽

10.1007/s11063-021-10562-2 ◽

2021 ◽

Author(s):

Daniel Auge ◽

Julian Hille ◽

Etienne Mueller ◽

Alois Knoll

Keyword(s):

Artificial Intelligence ◽

Neural Networks ◽

Industrial Applications ◽

Spiking Neural Networks ◽

Biologically Inspired ◽

Power Efficient ◽

Signal Encoding ◽

Uniform Nomenclature ◽

Theoretical Foundations ◽

Do So

AbstractBiologically inspired spiking neural networks are increasingly popular in the field of artificial intelligence due to their ability to solve complex problems while being power efficient. They do so by leveraging the timing of discrete spikes as main information carrier. Though, industrial applications are still lacking, partially because the question of how to encode incoming data into discrete spike events cannot be uniformly answered. In this paper, we summarise the signal encoding schemes presented in the literature and propose a uniform nomenclature to prevent the vague usage of ambiguous definitions. Therefore we survey both, the theoretical foundations as well as applications of the encoding schemes. This work provides a foundation in spiking signal encoding and gives an overview over different application-oriented implementations which utilise the schemes.

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