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检索条件"任意字段=Conference on Image Processing - Algorithms and Systems VI"

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Fuzzy Neural Networks (FNNs) Training Algorithm with Dropout via Its Equivalent Fully Connected Fuzzy Inference systems (F-CONFIS)

Fuzzy Neural Networks (FNNs) Training Algorithm with Dropout...

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2018 International conference on Security, Pattern Analysis, and Cybernetics, SPAC 2018

作者： Wang, Jing Chen, Philip Ma, Zhenyuan Xiao, Zhenghong College of Computer Science Guangdong Polytechnic Normal University Guangdong China Faculty of Science and Technology University of Macau MSAR China

ISBN: (纸本)9781728105512

Fuzzy neural network (FNN) often suffers from overfitting problem, especially when FNN has large number of parameters. In the FNN system, there are two types of adjustable parameters, one is control parameters, and the other is link weights of consequent part. To improve convergent rate, Dropout technique is first adopted for Fuzzy neural network. A new training algorithm with dropout technique for FNN is proposed via its equivalent F-CONFIS. Illustrative examples are provided for checking the validity of the proposed method. Simulation attained satisfactory results. Proposed method for Fuzzy neural network via F-CONFIS has its rising values in all practical applications, such as system identification, expert System and image information processing system..., etc. © 2018 IEEE.

关键词： Fuzzy neural networks

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Analysis of multiple features and classifier techniques combination for image pattern recognition 2nd

Analysis of multiple features and classifier techniques comb...

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2nd International conference on Intelligent Computing and Communication, ICICC 2017

作者： Shinde, Ashish Shinde, Abhijit Sinhgad College of Engineering PuneMaharashtra41 India Bhima Institute of Management and Technology Kagal KolhapurMaharashtra416216 India

ISBN: (纸本)9789811072444

Automatic visual pattern recognition is complex and highly researched area of image processing. This research aims to study various pattern recognition algorithms, cloth pattern recognition is presented as research problem and to find out best combination suited for the cloth pattern recognition problem. The dataset is collected from CCNY clothing pattern dataset and contains 150 samples of each category (Patternless, Striped, Plaid, and Irregular). The presented study compares all combinations of three different feature extraction techniques and three classifier techniques. Feature extraction techniques used here are Radon Feature Extraction, projection of rotated gradient, and quantized histogram of gradients. The classifiers used are KNN, neural network, and SVM classifier. The highest recognition rate is achieved using Radon Signature feature and KNN classifier combination which reaches to 93.7% of accuracy. © 2018, Springer Nature Singapore Pte Ltd.

关键词： Feature extraction

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Optimizing super-resolution reconstruction using a genetic algorithm 10

Optimizing super-resolution reconstruction using a genetic a...

引用

10th International conference on Agents and Artificial Intelligence, ICAART 2018

作者： Kawulok, Michal Kostrzewa, Daniel Benecki, Pawel Skonieczny, Lukasz Future Processing Bojkowska 37A Gliwice44-100 Poland Institute of Informatics Silesian University of Technology Akademicka 16 Gliwice44-100 Poland

ISBN: (纸本)9789897582752

Super-resolution reconstruction (SRR) is aimed at increasing spatial resolution given a single image or multiple images presenting the same scene. The existing methods are underpinned with a premise that the observed low resolution images are obtained from a hypothetic high resolution image by applying a certain imaging model (IM) which degrades the image and decreases its resolution. Hence, the reconstruction consists in applying an inverse IM to recover the high resolution data. Such an approach has been found effective, if the IM is known and controlled, in particular when the low resolution images are indeed obtained from a high resolution one. However, in a real-world scenario, when SRR is performed from images originally captured at low resolution, finding appropriate IM and tuning its hyperparameters is a challenging task. In this paper, we propose to optimize the SRR hyperparameters using a genetic algorithm, which has not been reported in the literature so far. We argue that this may substantially improve the capacities of learning the relation between low and high resolution images. Our initial, yet highly encouraging, experimental results reported in the paper allow us to outline our research pathways to deploy the developed techniques in practice. Copyright © 2018 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved

关键词： Genetic algorithms

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Black-Box Model Explained Through an Assessment of Its Interpretable Features 22nd

Black-Box Model Explained Through an Assessment of Its Inter...

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22nd East-European conference on Advances in Databases and Information systems (ADBIS)

作者： Ventura, Francesco Cerquitelli, Tania Giacalone, Francesco Politecn Torino Corso Duca Abruzzi 24 I-10129 Turin Italy

ISBN: (纸本)9783030000639;9783030000622

algorithms are powerful and necessary tools behind a large part of the information we use every day. However, they may introduce new sources of bias, discrimination and other unfair practices that affect people who are unaware of it. Greater algorithm transparency is indispensable to provide more credible and reliable services. Moreover, requiring developers to design transparent algorithm-driven applications allows them to keep the model accessible and human understandable, increasing the trust of end users. In this paper we present EBANO, a new engine able to produce prediction-local explanations for a black-box model exploiting interpretable feature perturbations. EBANO exploits the hypercolumns representation together with the cluster analysis to identify a set of interpretable features of images. Furthermore two indices have been proposed to measure the influence of input features on the final prediction made by a CNN model. EBANO has been preliminary tested on a set of heterogeneous images. The results highlight the effectiveness of EBANO in explaining the CNN classification through the evaluation of interpretable features influence.

关键词： Transparent mining Neural networks image processing

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Effect of Aft Rotor on Forward Rotor Blade Wakes in Open Rotor Propulsion systems

引用

ASME Turbo Expo: Turbomachinery Technical conference and Exposition

作者： DeLorenzo, Charles Deleon, Jared Slaboch, Paul E. Univ Hartford Hartford CT 06117 USA

ISBN: (纸本)9780791850985

NASA's Environmentally Responsible Aviation (ERA) project supported the collection of a large stereoscopic Particle image Velocimetry (PIV) dataset of the Open Rotor Propulsion Rig (ORPR) in the 9 x 15 Low Speed Wind Tunnel at the NASA Glenn Research Center (GRC). The data collection effort acquired a volume of three component velocity measurements composed of 30 planes from near the hub radially outward towards the tip. The PIV cameras and laser were mounted to a traverse that allowed the entire data acquisition system to move from plane to plane. The PIV data acquisition was triggered on the front rotor such that the front rotor was always in the same position for each acquisition event. The aft rotor position was not recorded and varied randomly during the acquisition. Because the position of the aft rotor was not synchronized to either the forward rotor or the camera it was necessary to separate individual PIV images based off of the phase of the aft rotor before they could be processed. The phase of the aft rotor was determined by locating the outline of the rotor in the PIV images and determining its position relative to a known point. This process was conducted by an image processing algorithm. Previous algorithms were able to make a relatively accurate 3D model of the wake between the forward and aft rotors, howeiTer some small inaccuracies were present. Improvements to this algorithm allowed for more accurate phase averaging, which yielded an improved PIV dataset. Both rotors were set to the same nominal rotational speed, however variations in motor control and other physical mechanisms allowed for some differences in the true RPM of the two rotors. The effects of the aft rotor on the front rotor blade wakes within the inter-rotor flow field of the ORPR were examined. The aft rotor potential field was shown to have significant upstream impact on the front rotor wakes, altering their topology approaching the aft rotor. The wake strength was quantified throug

关键词： Wakes Propulsion systems Rotors Blades

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Differential Architecture Search in Deep Learning for Genomic Applications

Differential Architecture Search in Deep Learning for Genomi...

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IEEE GCC conference & Exhibition

作者： Shabir Moosa Sabri Boughorbel Abbes Amira Dept. of Computer Science and Engineering Qatar University Doha Qatar Dept. of Systems Biology Sidra Medicine Doha Qatar

ISBN: (数字)9781538694770

ISBN: (纸本)9781538694787

The data explosion caused by unprecedented advancements in the field of genomics is constantly challenging the conventional methods used in the interpretation of the human genome. The demand for robust algorithms over the recent years has brought huge success in the field of Deep Learning (DL) in solving many difficult tasks in image, speech and natural language processing by automating the manual process of architecture design. This has been fueled through the development of new DL architectures. Yet genomics possesses unique challenges as we expect DL to provide a super human intelligence that easily interprets a human genome. In this paper, the state-of-the art DL approach based on differential search mechanism was adapted for interpretation of biological sequences. This method has been applied on the splice site recognition task on raw DNA sequences to discover high-performance convolutional architectures by automated *** discovered architecture achieved comparable accuracy when evaluated with a fixed Recurrent Neural Network (RNN) architecture. The results have shown a potential of using this automated architecture search mechanism for solving other problems in genomics.

关键词： Deep Learning Splice Site Genomics Neural Architecture Search Convolutional Neural Networks Genomics Human Genome ARCHITECTURE Building construction recurrent neural nets Automation

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Design of a Voice Control 6DoF Grasping Robotic arm Based on Ultrasonic Sensor, Computer vision and Alexa Voice Assistance

Design of a Voice Control 6DoF Grasping Robotic arm Based on...

引用

IEEE International Symposium on Information (IT) in Medicine and Education, ITME

作者： Zhiheng Wang Daqing Chen Perry Xiao Department of Electrical and Electronics Engineering London South Bank University London U.K

The article presents a study to design a 6-degree of freedom robotic arm which can pick up objects in random positions on a 2D surface based on Arduino microcontroller, ultrasonic sensors and picamera. The robotic arm is able to recognise objects based on computer vision algorithm for shape detection. The ultrasonic sensor measures the distance between the objects and the robotic arm, and the position of the objects in the real world will be detected by its mass centre in the image to improve the accuracy of the pick-up movement. Arduino microcontroller will calculate the rotation angles for the joints of the robotic arm by using inverse kinematics algorithms. The movement of the robotic arm also can be controlled by an Amazon Alexa voice assistance device. The experiment of applying the artificial neural network to control the robotic arm pick-up movement is achieved. The artificial neural network can manipulate the position of the robotic arm to pick up objects after training using the values which are calculated by inverse kinematics equations. The Raspberry Pi is used for processing the computer vision data, and voice commands from Alexa Voice Service based on cloud service.

关键词： Robot sensing systems Manipulators Robot kinematics Acoustics Kinematics Computer vision

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Polynomial feature engineering for classification of textural images 4

Polynomial feature engineering for classification of textura...

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4th International conference on Information Technology and Nanotechnology, ITNT 2018

作者： Gaidel, A.V. Samara National Research University Moskovskoe Shosse 34 Samara443086 Russia Image Processing Systems Institute Branch of the Federal Scientific Research Centre Crystallography and Photonics Russian Academy of Sciences Molodogvardeyskaya str. 151 Samara443001 Russia

I consider a number of methods of automatic quadratic features adjustment for digital textural images of biological tissues in order to improve the quality of classification. The proposed approaches are based on optimization procedures that use various quality criteria of a feature space as target functions. I investigate the methods based on random search, genetic algorithm, simulation of annealing, as well as the original hybrid algorithm. I presented results of experimental studies of the proposed algorithms on sets of real X-ray images of bone tissue and the lung CT images. We show that the hybrid algorithm provides more stable results regardless of the chosen quality criterion of the feature space, which is expressed in decreasing of the average percentage of incorrectly recognized images in comparison with the use of the specific optimization methods individually. © 2018 Institute of Physics Publishing. All rights reserved.

关键词： image enhancement

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Offloading SLAM for Indoor Mobile Robots with Edge-Fog-Cloud Computing

Offloading SLAM for Indoor Mobile Robots with Edge-Fog-Cloud...

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International conference on Advances in Science, Engineering and Robotics Technology

作者： V. K. Sarker J. Pena Queralta T. N. Gia H. Tenhunen T. Westerlund Department of Future Technologies University of Turku Turku Finland Department of Electronics KTH Royal Institute of Technology Stockholm Sweden

Indoor mobile robots are widely used in industrial environments such as large logistic warehouses. They are often in charge of collecting or sorting products. For such robots, computation-intensive operations account for a significant percentage of the total energy consumption and consequently affect battery life. Besides, in order to keep both the power consumption and hardware complexity low, simple micro-controllers or single-board computers are used as onboard local control units. This limits the computational capabilities of robots and consequently their performance. Offloading heavy computation to Cloud servers has been a widely used approach to solve this problem for cases where large amounts of sensor data such as real-time video feeds need to be analyzed. More recently, Fog and Edge computing are being leveraged for offloading tasks such as image processing and complex navigation algorithms involving non-linear mathematical operations. In this paper, we present a system architecture for offloading computationally expensive localization and mapping tasks to smart Edge gateways which use Fog services. We show how Edge computing brings computational capabilities of the Cloud to the robot environment without compromising operational reliability due to connection issues. Furthermore, we analyze the power consumption of a prototype robot vehicle in different modes and show how battery life can be significantly improved by moving the processing of data to the Edge layer.

关键词： Robot sensing systems Logic gates Cloud computing Robot kinematics Task analysis Edge computing

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Simplifying the multi-GPU programming of a hyperspectral image registration algorithm

Simplifying the multi-GPU programming of a hyperspectral ima...

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International conference on High Performance Computing & Simulation (HPCS)

作者： Jorge Fernàndez-Fabeiro Arturo Gonzalez-Escribano Diego R. Llanos Departamento de Informaticá Universidad de Valladolid Valladolid Spain

ISBN: (数字)9781728144849

ISBN: (纸本)9781728144856

Hyperspectral image registration is a relevant task for real-time applications like environmental disasters management or search and rescue scenarios. Traditional algorithms for this problem were not really devoted to real-time performance. The HYFMGPU algorithm arose as a high-performance GPU-based solution to solve such a lack. Nevertheless, a single-GPU solution is not enough, as sensors are evolving and then generating images with finer resolutions and wider wavelength ranges. An MPI+CUDA multi-GPU implementation of HYFMGPU was previously presented. However, this solution shows the programming complexity of combining MPI with an accelerator programming model. In this paper we present a new and more abstract programming approach for this type of applications, which provides a high efficiency while simplifying the programming of the multi-device parts of the code. The solution uses Hitmap, a library to ease the programming of parallel applications based on distributed arrays. It uses a more algorithm-oriented approach than MPI, including abstractions for the automatic partition and mapping of arrays at runtime with arbitrary granularity, as well as techniques to build flexible communication patterns that transparently adapt to the data partitions. We show how these abstractions apply to this application class. We present a comparison of development effort metrics between the original MPI implementation and the one based on Hitmap, with reductions of up to 95% for the Halstead score in specific work redistribution steps. We finally present experimental results showing that these abstractions are internally implemented in a high efficient way that can reduce the overall performance time in up to 37% comparing with the original MPI implementation.

关键词： Programming Graphics processing units Hyperspectral imaging Real-time systems Libraries Principal component analysis

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