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optical Fish Trajectory Measurement in Fishways through Computer Vision and Artificial neural networks

JOURNAL OF computing IN CIVIL ENGINEERING 2011年第4期25卷 291-301页

作者： Rodriguez, Alvaro Bermudez, Maria Rabunal, Juan R. Puertas, Jeronimo Dorado, Julian Pena, Luis Balairon, Luis Univ A Coruna Ctr Technol Innovat Construct & Civil Engn CITEEC La Coruna 15071 Spain Univ A Coruna Fac Informat Dept Informat & Commun Technol La Coruna 15071 Spain Univ A Coruna Dept Hydraul Engn ETSEC La Coruna 15071 Spain Ctr Hydrog Studies CEH CEDEX Madrid 28005 Spain

Vertical slot fishways are hydraulic structures that allow the upstream migration of fish through obstructions in rivers. The appropriate design of a vertical slot fishway depends on the interplay between hydraulic and biological variables because the hydrodynamic properties of the fishway must match the requirements of the fish species for which it is intended. One of the primary difficulties associated with studies of real fish behavior in fishway models is that the existing mechanisms to measure the behavior of the fish in these assays, such as direct observation or placement of sensors on the specimens, are impractical or unduly affect the animal behavior. This paper proposes a new procedure for measuring the behavior of the fish. The proposed technique uses artificial neural networks and computer vision techniques to analyze images obtained from the assays by means of a camera system designed for fishway integration. It is expected that this technique will provide detailed information about the fish behavior, and it will help to improve fish passage devices, which is currently a subject of interest in the area of civil engineering. A series of assays has been performed to validate this new approach in a full-scale fishway model with living fish. We have obtained very promising results that allow accurate reconstruction of the movements of the fish within the fishway. DOI: 10.1061/(ASCE)CP.1943-5487.0000092. (C) 2011 American Society of Civil Engineers.

关键词： Computer vision neural network Fishway Fish trajectory

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A neural network approach for a single-camera based Artificial Compound Eye System (ACES)

A neural network approach for a single-camera based Artifici...

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10th IEEE International Conference on Cognitive Informatics and Cognitive computing, ICCI*CC 2011

作者： Li, Weiming Fu, Siyao Yang, Guosheng Kuai, Xinkai City University of Hong Kong Kowloon Hong Kong School of Information and Engineering Central University of Nationalities Beijing 100081 China

ISBN: (纸本)9781457716966

This work studies an Artificial Compound Eye System (ACES) which consists of a camera, a main lens, and a lenslet array. Similar to the compound eye systems in arthropods, one single shot of the ACES can simultaneously capture the visual image of an object from hundreds of different viewpoints, which essentially record the object's 3D information. Due to the special optics of the ACES, traditional imaging models based on a pinhole assumption can not be directly applied to the system. In this work, we propose to use artificial neural network (NN) to model the mapping from the image observation of the ACES to the object's 3D information. To deal with the incomplete observation problem in the ACES, a sliding frame based method is proposed to construct the input and target data to train the neural network. Experimental results show that with the proposed method, position of an object in 3D can be effectively extracted from a single image input and the object's motion in 3D can be extracted from a 2D optical flow. This work may inspire related efforts in exploring the ACES for novel 3D imaging applications. © 2011 IEEE.

关键词： neural networks

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11th International Work-Conference on Artificial neural networks, IWANN 2011

11th International Work-Conference on Artificial Neural Netw...

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11th International Work-Conference on Artificial neural networks, IWANN 2011

ISBN: (纸本)9783642215001

The proceedings contain 154 papers. The special focus in this conference is on Visual computing. The topics include: A Geometrical Method of Diffuse and Specular Image Components Separation;optical Flow Reliability Model Approximated with RBF;video and Image Processing with Self-Organizing neural networks;parallelism in Binary Hopfield networks;Multi-parametric Gaussian Kernel Function Optimization for Ε-SVMr Using a Genetic Algorithm;face Recognition System in a Dynamical Environment;memetic Pareto Differential Evolutionary neural network for Donor-Recipient Matching in Liver Transplantation;Studying the Hybridization of Artificial neural networks in HECIC;processing Acyclic Data Structures Using Modified Self-Organizing Maps;visual Features Extraction Based Egomotion Calculation from a Infrared Time-of-Flight Camera;On the Performance of the μ-GA Extreme Learning Machines in Regression Problems;a Hybrid Evolutionary Approach to Obtain Better Quality Classifiers;neural network Ensembles with Missing Data Processing and Data Fusion Capacities: Applications in Medicine and in the Environment;hybrid Artificial neural networks: models, Algorithms and Data;automatic Recognition of Daily Living Activities Based on a Hierarchical Classifier;prediction of Functional Associations between Proteins by Means of a Cost-Sensitive Artificial neural network;Hybrid (Generalization-Correlation) Method for Feature Selection in High Dimensional DNA Microarray Prediction Problems;Model Selection with PLANN-CR-ARD;Gender Recognition Using PCA and DCT of Face Images;efficient Face Recognition Fusing Dynamic Morphological Quotient Image with Local Binary Pattern;feature Weighting in Competitive Learning for Multiple Object Tracking in Video Sequences;a Growing neural Gas Algorithm with Applications in Hand Modelling and Tracking;object Representation with Self-Organising networks;visual Mining of Epidemic networks.

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11th International Work-Conference on Artificial neural networks, IWANN 2011

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11th International Work-Conference on Artificial neural networks, IWANN 2011

ISBN: (纸本)9783642214974

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Regularized Back-Propagation neural network for Rainfall-Runoff Modeling

Regularized Back-Propagation Neural Network for Rainfall-Run...

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International Conference on network computing and Information Security (NCIS)

作者： Xian Luo You-peng Xu Jin-tao Xu School of Geographic and Oceanographic Sciences Nanjing University Nanjing China

In this study, we applied regularized back-propagation neural network (BPNN), which made use of a performance function different from normal BPNN, to predict daily flow. On the other hand, Broyden-Fletcher-Goldfarb-Shanno (BFGS) -algorithm-based BPNN was also used to compare its prediction performance with that of regularized BPNN. From 1979 to 1998, precipitation and stream flow data in Xitiaoxi watershed for 20 years were collected. All these data were divided into 2 sets: one was the training set (1979-1988), and the other was the testing set (1989-1998). The mean absolute error (MAE), mean square error (MSE) and coefficient of efficiency (CE) were used to evaluate the performance of these two algorithms. The results indicated that regularized BPNN could enhance generalization ability and avoid over fitting effectively, and it outperformed BFGS-algorithm-based BPNN during training and testing stages. From this study, it could be found that regularized BPN is appropriate for rainfall-runoff modeling due to its simple structure and high accuracy.

关键词： Training Artificial neural networks Testing Hydrology Approximation algorithms Data models Predictive models

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A neural network approach for a single-camera based Artificial Compound Eye System (ACES)

A neural network approach for a single-camera based Artifici...

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IEEE International Conference on Cognitive Informatics

作者： Weiming Li Siyao Fu Guosheng Yang Xinkai Kuai City University of Hong Kong Hong Kong China School of Information and Engineering Central University for Nationalities Beijing China

关键词： Three dimensional displays Transforms Image color analysis Artificial neural networks Estimation Unified modeling language Colored noise

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The transmittance estimations of TP film with Cr and Cr2O3 coating

The transmittance estimations of TP film with Cr and Cr2O3 c...

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IEEE International Conference on Signal Processing, Communications and computing (ICSPCC)

作者： Shuming T. Wang Du-Jou Huang Jen-Pin Yang Yu-Ju Chen Huang-Chu Huang Rey-Chue Hwang Electrical Engineering Department I-Shou University Taiwan Information Management Department Cheng Shiu University Taiwan Electric Communication Department National Kaohsiung Marine University Taiwan

This paper presents the transmittance estimations for touch panel (TP) film with Cr and Cr 2 O 3 coating by using neural network (NN) model. The NN model with quasi-Newton learning method was used to obtain the mapping between TP transmittance and its all possible influencing factors. This study tries to develop an artificial intelligent (AI) evaporation decision mechanism which can help the technician to set the related control parameters before the film's evaporation process is taken. The transmittance is one of important determination factors used for checking whether the quality of TP is qualified or not. Thus, a smart decision mechanism not only can help technician to improve the work efficiency, but also can reduce the running cost of the company due to the defective products.

关键词： Artificial neural networks Coatings Estimation optical films Data models Mathematical model

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The structures of optical neural nets based on new matrix-tensor equivalental models (MTEMs) and results of modeling

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optical Memory and neural networks (Information Optics) 2010年第1期19卷 31-38页

作者： Krasilenko, V.G. Nikolskyy, A.I. Flavitskaya, J.A. The Vinnitsa Social Economic Institute of University Ukraine Vinnitsa National Technical University Vinnitsa Russia

The structures of optical neural nets (NN) based on new matrix-tensor equivalental models (MTEMs) and algorithms are described in this article. MTE models are neuroparadigm of non-iterative type, which is a generalization of Hopfield and Hamming networks. The adaptive multi-layer networks, auto-associative and hetero-associative memory of 2-D images of high order can be built on the basis of MTEMs. The capacity of such networks in comparison with capacity of Hopfield networks is increased (including capacity for greatly correlated images). The results of modeling show that the number of neurons in neural network MTEMs is 10-20 thousand and more. The problems of training of such networks, different modifications, including networks with double adaptive-equivalental auto-weighing of weights, organization of computing process in different modes of network are discussed. The basic components of networks: matrix-tensor "equivalentors" and variants of their realization on the basis of liquid-crystal structures and optical multipliers with spatial and time integration are considered. The efficiency of proposed optical neural networks on the basis of MTEMs is evaluated for both variants on the level of 109 connections per second. Modified optical connections are realized as liquid-crystal television screens. © Allerton Press, Inc., 2010.

关键词： Matrix algebra

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Photonic Reservoir computing: a new approach to optical information processing

Photonic Reservoir Computing: a new approach to optical info...

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Conference on Photonics North 2010

作者： Vandoorne, Kristof Fiers, Martin Verstraeten, David Schrauwen, Benjamin Dambre, Joni Bienstman, Peter Univ Ghent IMEC Photon Res Grp Dept Informat Technol Sint Pietersnieuwstr 41 B-9000 Ghent Belgium Univ Ghent Dept Elect & Informat Syst PARIS B-9000 Ghent Belgium

ISBN: (纸本)9780819482419

Despite ever increasing computational power, recognition and classification problems remain challenging to solve. Recently, advances have been made by the introduction of the new concept of reservoir computing. This is a methodology coming from the field of machine learning and neural networks that has been successfully used in several pattern classification problems, like speech and image recognition. Thus far, most implementations have been in software, limiting their speed and power efficiency. Photonics could be an excellent platform for a hardware implementation of this concept because of its inherent parallelism and unique nonlinear behaviour. Moreover, a photonic implementation offers the promise of massively parallel information processing with low power and high speed. We propose using a network of coupled Semiconductor optical Amplifiers (SOA) and show in simulation that it could be used as a reservoir by comparing it to conventional software implementations using a benchmark speech recognition task. In spite of the differences with classical reservoir models, the performance of our photonic reservoir is comparable to that of conventional implementations and sometimes slightly better. As our implementation uses coherent light for information processing, we find that phase tuning is crucial to obtain high performance. In parallel we investigate the use of a network of photonic crystal cavities. The coupled mode theory (CMT) is used to investigate these resonators. A new framework is designed to model networks of resonators and SOAs. The same network topologies are used, but feedback is added to control the internal dynamics of the system. By adjusting the readout weights of the network in a controlled manner, we can generate arbitrary periodic patterns.

关键词： photonic reservoir computing integrated optics semiconductor optical amplifiers nonlinear optics optical neural networks speech recognition dynamics

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Differential Recurrent neural network Based Model Predictive Control for the Control of MAV Attitude

Differential Recurrent Neural Network Based Model Predictive...

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International Workshop on Intelligent Systems and Applications, ISA

作者： Xiangjian Chen Zhijun Xu Di Li Kehui Long Chang Chun Institute of Optics Fine Mechanics and Physics Chinese Academy and Sciences Changchun China

ISBN: (纸本)9781424458721;9781424458745

An efficient differential recurrent neural network is developed in this paper, and the trained network can be used in the nonlinear model predictive control, and also predict the future dynamic behavior of the nonlinear process in real time. In the new training network, use Taylor series expansion and automatic differentiation techniques. The effectiveness of the differential recurrent neural network predictive model training and predictive controller demonstrated through the MAV attitude control. The differential recurrent neural network-based NMPC approach results in good control performance.

关键词： Recurrent neural networks Predictive models Predictive control Attitude control Automatic control Nonlinear optics optical computing Physics neural networks optical fiber networks

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