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Recognition of Moving Objects by Intelligent distributed Information and Measurement System

Recognition of Moving Objects by Intelligent Distributed Inf...

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International conference on Industrial Engineering, Applications and Manufacturing (ICIEAM)

作者： Zakasovskaya, E. Tarasov, V FEFU Far Eastern Reg Educ & Res Ctr Informat Secur Vladivostok Russia

ISBN: (纸本)9781538643075

The problem of pattern recognition for a distributed fiber-optic measuring system is considered in the article. At present, this complex problem of recognizing moving images has no general solution, but in the case of a number of applied problems, the research of approaches based on the use of methods and technologies of artificial intelligence can be useful for this purpose. When monitoring moving objects for pattern recognition, an integrated approach to solving data processing problems is advisable, which was implemented in this work by creating an expert system with the use of neural networks for a distributed fiber optic measurement network (DFOMN). The system for rapid decision making with reference to the object class and its parameters has a software module that extracts the characteristics of the object from the output data from the measuring lines and forms the models of the image. A comparative analysis of the results obtained with the reference data is made.

关键词： expert system neural networks distributed fiber-optic measuring systems pattern recognition wireless sensor networks perimeter monitoring big data

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Tibetan Location Name Recognition Based on BiLSTM-CRF Model

Tibetan Location Name Recognition Based on BiLSTM-CRF Model

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International conference on Cyber-Enabled distributed Computing and Knowledge Discovery, CyberC

作者： Wei Ma Hongzhi Yu Kun Zhao Deshun Zhao Jun Yang Jing Ma Key Laboratory of China’s Ethnic Languages and Information Technology of Ministry of Education Northwest Minzu University Lanzhou P.R. China Personnel Department Gansu Political Science and Law University Lanzhou P.R. China

Tibetan location name recognition is an important research topic in Tibetan information processing. In this paper, we attempt to construct a neural network model based on BiLSTM-CRF framework for location name recognition. In the experiment, for the public dataset of Tibetan location name, the model obtains the context information of the current word in the BiLSTM layer, decodes the output of the BiLSTM layer in the CRF layer, and finally outputs the tag sequence. The experiment proves the validity of the framework and obtains good experimental results, and the results show that the model outperforms competitive the traditional method, and improved the F-measure without resorting to any language-specific knowledge or resources.

关键词： Hidden Markov models Task analysis Training Logic gates Recurrent neural networks Computer architecture

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Automatic vehicle type classification with convolutional neural networks 25

Automatic vehicle type classification with convolutional neu...

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25th International conference on Systems, Signals and Image processing (IWSSIP)

作者： Roecker, Max N. Costa, Yandre M. G. Almeida, Joao L. R. Matsushita, Gustavo H. G. Univ Estadual Maringa Dept Informat Maringa Parana Brazil Univ Fed Parana Dept Informat Curitiba Parana Brazil

ISBN: (纸本)9781538669792

This paper proposes a convolutional neural network model for classification of vehicle types with low-resolution images from a frontal perspective. This characteristic can be useful to the development of systems with limited resources, like embedded systems. We trained the model as a multinomial logistic regression where cross-entropy of the ground truth labels and the model's prediction estimates the error. To prevent overfitting, we performed data augmentation in the training dataset and regularized the model using the dropout method. Experimental results in a subset of the BIT-Vehicle Dataset with samples uniformly distributed between classes shows that the model achieves an accuracy of 93.90%. We conclude that the model is discriminative and capable of generalizing the patterns of the vehicle type classification task.

关键词： vehicle classification convolutional neural networks machine learning computer vision pattern recognition

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Recurrent neural networks Based Online Learning Algorithms for distributed Systems 26

Recurrent Neural Networks Based Online Learning Algorithms f...

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26th IEEE Signal processing and Communications Applications conference (SIU)

作者： Ergen, Tolga Sahin, S. Onur Kozat, S. Serdar Ihsan Dogramaci Bilkent Univ Elek & Elekt Muhendisligi Bolumu Ankara Turkey

ISBN: (纸本)9781538615010

In this paper, we investigate online parameter learning for Long Short Term Memory (LSTM) architectures in distributed networks. Here, we first introduce an LSTM based structure for regression. Then, we provide the equations of this structure in a state space form for each node in our network. Using this form, we then learn the parameters via our distributed Particle Filtering based (DPF) training method. Our training method asymptotically converges to the optimal parameter set provided that we satisfy certain trivial requirements. While achieving this performance, our training method only causes a computational load that is similar to the efficient first order gradient based training methods. Through real life experiments, we show substantial performance gains compared to the conventional methods.

关键词： long short term memory networks distributed systems online training sequential regression

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Convolutional neural network-Based Approach for Citrus Diseases Recognition

Convolutional Neural Network-Based Approach for Citrus Disea...

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IEEE International conference on Big Data and Cloud Computing (BdCloud)

作者： Caixia Dong Zheling Xu Luanyuan Dai Weinan Liu Quan Chen Yizhang Liu Changcai Yang Tengyue Zou College of Computer and Information Sciences Fujian Agriculture and Forestry University Fuzhou China International Business Department Fuzhou Melbourne Polytechnic Fuzhou China

ISBN: (数字)9781728143286

ISBN: (纸本)9781728143293

For the development of agricultural modernization, an automatic recognition network for crop diseases with the advantages of high efficiency, non-destructiveness and continuity is indispensable. In this paper, we construct a 13-layers convolutional neural network for common citrus diseases recognition, which improving the efficiency and reducing overfitting by using a stack of small kernel and dropout, etc. Finally, we conduct comparative experiments with other networks. The experiment results show that the performances of our network are better than CNN and AlexNet. It indicates that the network we constructed is an effective citrus diseases recognition method, which can provide technical support for the identification and prevention of citrus diseases.

关键词： Diseases Agriculture Kernel Feature extraction Forestry Convolutional neural networks

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Efficient ConvNet-based Object Detection for Unmanned Aerial Vehicles by Selective Tile processing 18

Efficient ConvNet-based Object Detection for Unmanned Aerial...

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12th International conference on distributed Smart Cameras (ICDSC)

作者： Plastiras, George Kyrkou, Christos Theocharides, Theocharis Univ Cyprus Dept Elect & Comp Engn KIOS Res & Innovat Ctr Excellence Nicosia Cyprus Univ Cyprus KIOS Res & Innovat Ctr Excellence Nicosia Cyprus

ISBN: (纸本)9781450365116

Many applications utilizing Unmanned Aerial Vehicles (UAVs) require the use of computer vision algorithms to analyze the information captured from their on-board camera. Recent advances in deep learning have made it possible to use single-shot Convolutional neural network (CNN) detection algorithms that process the input image to detect various objects of interest. To keep the computational demands low these neural networks typically operate on small image sizes which, however, makes it difficult to detect small objects. This is further emphasized when considering UAVs equipped with cameras where due to the viewing range, objects tend to appear relatively small. This paper therefore, explores the trade-offs involved when maintaining the resolution of the objects of interest by extracting smaller patches (tiles) from the larger input image and processing them using a neural network. Specifically, we introduce an attention mechanism to focus on detecting objects only in some of the tiles and a memory mechanism to keep track of information for tiles that are not processed. Through the analysis of different methods and experiments we show that by carefully selecting which tiles to process we can considerably improve the detection accuracy while maintaining comparable performance to CNNs that resize and process a single image which makes the proposed approach suitable for UAV applications.

关键词： Object Detection Convolutional neural networks Aerial Cameras Pedestrian Detection

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A Power Efficient neural network Implementation on Heterogeneous FPGA and GPU Devices

A Power Efficient Neural Network Implementation on Heterogen...

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IEEE International conference on Information Reuse and Integration (IRI)

作者： Yuexuan Tu Saad Sadiq Yudong Tao Mei-Ling Shyu Shu-Ching Chen Department of Electrical and Computer Engineering University of Miami Coral Gables FL USA School of Computing and Information Sciences Florida International University Miami FL USA

Deep neural networks (DNNs) have seen tremendous industrial successes in various applications, including image recognition, machine translation, audio processing, etc. However, they require massive amounts of computations and take a lot of time to process. This quickly becomes a problem in mobile and handheld devices where real-time multimedia applications such as face detection, disaster management, and CCTV require lightweight, fast, and effective computing solutions. The objective of this project is to utilize specialized devices such as Field Programmable Gate Arrays (FPGAs) and Graphics processing Units (GPUs) in a heterogeneous computing environment to accelerate the deep learning computations with the constraints of power efficiency. We investigate an efficient DNN implementation and make use of FPGA for fully-connected layer and GPU for floating-point operations. This requires the deep neural network architecture to be implemented in a model parallelism system where the DNN model is broken down and processed in a distributed fashion. The proposed heterogeneous framework idea is implemented using an Nvidia TX2 GPU and a Xilinx Artix-7 FPGA. Experimental results indicate that the proposed framework can achieve faster computation and much lower power consumption.

关键词： Field programmable gate arrays Graphics processing units Computational modeling Real-time systems Computer architecture Parallel processing Acceleration

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Convolutional Model for Predicting SNP Interactions 1

引用

25th International conference on neural Information processing (ICONIP)

作者： Uppu, Suneetha Krishna, Aneesh Curtin Univ Sch Elect Engn Comp & Math Sci Perth WA Australia

ISBN: (数字)9783030042219

ISBN: (纸本)9783030042219;9783030042202

Single-nucleotide polymorphisms (SNPs) are genetic markers that empower researchers to examine for genes associated with complex diseases. Several efforts have been contributed by researchers to study the interaction effects between multi-locus SNPs for discerning the status of complex diseases. However, the current conventional machine learning techniques are still left with several caveats. Deep learning is a new breed of machine learning technique that elucidates the hidden structure of the raw data by transforming it into multiple high levels of abstractions, using the power of parallel and distributed computing. It promises empirical success in the number of applications including bioinformatics to drive insights of biological complexities. The deep learning approach in the multi-locus interaction studies is yet to meet its potential achievements. In this paper, a convolutional neural network is trained to identify true causative two-locus SNP interactions. The performance of the method is evaluated on hypertension data. Highly ranked two-locus SNP interactions are identified for the manifestation of hypertension.

关键词： Convolutional neural network SNP-SNP interactions Deep learning Multi-locus Epistasis Gene-gene interactions

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GF-3 PolSAR Marine Aquaculture Recognition Based on Complex Convolutional neural networks

GF-3 PolSAR Marine Aquaculture Recognition Based on Complex ...

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International conference on Intelligent Control and Information processing (ICICIP)

作者： Jianchao Fan Xinxin Wang Xiang Wang Xiaoxin Liu Jianhua Zhao Qinghui Meng National Mar. Environ. Monitor. Cent. Dalian China Computer Science and Engineering Washington University in St. Louis Saint Louis USA

ISBN: (数字)9781728100159

ISBN: (纸本)9781728100166

Marine floating raft aquaculture is widely distributed along the coast in China. Polarimetric synthetic aperture radar (PoISAR) images can distinguish marine aquaculture targets from sea water background, but optical satellite remote sensing images cannot detect these effectively and completely. In this paper, considering the complex character of PoISAR data, a complex-value convolutional neural network is utilized for marine aquaculture recognition, which makes the most of phase information implicit in original complex data to improve detection accuracy. Experiments on actual GF-3 PoISAR images substantiate the effectiveness of the proposed approach.

关键词： Aquaculture Convolutional neural networks Synthetic aperture radar Image recognition Radar polarimetry Hydrological techniques

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12th International conference on Intelligent Robotics and Applications, ICIRA 2019

12th International Conference on Intelligent Robotics and Ap...

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12th International conference on Intelligent Robotics and Applications, ICIRA 2019

ISBN: (纸本)9783030275402

The proceedings contain 378 papers. The special focus in this conference is on Intelligent Robotics and Applications. The topics include: Intelligent Robot Arm: Vision-Based Dynamic Measurement System for Industrial Applications;research on Autonomous Face Recognition System for Spatial Human-Robotic Interaction Based on Deep Learning;KPCA-Based Visual Fault Diagnosis for Nonlinear Industrial Process;data Denosing processing of the Operating State of the Robotic Arm of Coal Sampling Robot;a Study on Step-by-Step Calibration of Robot Based on Multi-vision Measurement;characteristic Frequency Input neural network for Inertia Identification of Tumbling Space Target;An FFT-based Method for Analysis, Modeling and Identification of Kinematic Error in Harmonic Drives;real-Time Human-Posture Recognition for Human-Drone Interaction Using Monocular Vision;HSVM-Based Human Activity Recognition Using Smartphones;stability Analysis and Fixed Radius Turning Planning of Hexapod Robot;Human-AGV Interaction: Real-Time Gesture Detection Using Deep Learning;Coverage Path Planning for Complex Structures Inspection Using Unmanned Aerial Vehicle (UAV);development of Four Rotor Fire Extinguishing System for Synchronized Monitoring of Air and Ground for Fire Fighting;A Small Envelope Gait Control Algorithm Based on FTL Method for Snake-Like Pipe Robot;trajectory Tracking Control of Wheeled Mobile Robots Using Backstepping;the Design of Inspection Robot Navigation Systems Based on distributed Vision;mobile Robot Autonomous Navigation and Dynamic Environmental Adaptation in Large-Scale Outdoor Scenes;movement Analysis of Rotating-Finger Cable Inspection Robot;autonomous Indoor Mobile Robot Exploration Based on Wavefront Algorithm;multi-robot Path Planning for Complete Coverage with Genetic Algorithms;the Mechanical Design and Torque Control for the Ankle Exoskeleton During Human Walking.

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