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International Conference on Computers, Communications, and systems (ICCCS)

作者： Guoqing Deng Yangguang Zhao Long Zhang Zhigang Li Yong Liu Yi Zhang Bin Li Opto-electronics Applied Technology Research Centre Institute of Applied Technology and Hefei Institutes of Physical Science Chinese Academy of Sciences Hefei China Jiangxi Branch of China Tobacco Industrial Corporation Nanchang China Key Laboratory of Tobacco Processing Technology China National Tobacco Corporation Zhengzhou China

ISBN: (数字)9781728161365

ISBN: (纸本)9781728161372

In order to guide the production of cigarette products and improve the quality of cigarette products, this paper proposes a classification method for cigarette combustion cones based on deep convolutional neural network model. The method is optimized based on the Inception Resnet v2 model and is innovatively used in the detection of cigarette burning cones. The classification accuracy of combustion cone fallout is characterized by the overall classification accuracy (OA) and the Kappa coefficient (Kappa). The experimental results show that the overall classification accuracy is 97.22%, and the Kappa coefficient is 0.9583. The deep convolutional neural network has better classification effect. Based on the classification method of deep convolutional neural network, the cigarette burning cone can be accurately identified.

关键词： Combustion Training Classification algorithms Convolutional neural networks Force Head Manuals

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validation of finite-element models using full-field experimental data: Levelling finite-element analysis data through a digital image correlation engine

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STRAIN 2020年第4期56卷 e12350-e12350页

作者： Lava, Pascal Jones, Elizabeth M. C. Wittevrongel, Lukas Pierron, Fabrice MatchID NV Ghent Belgium Sandia Natl Labs Diagnost Sci & Engn POB 5800 Albuquerque NM 87185 USA Univ Southampton Engn & Phys Sci Southampton Hants England

Full-field data from digital image correlation (DIC) provide rich information for finite-element analysis (FEA) validation. However, there are several inherent inconsistencies between FEA and DIC data that must be rectified before meaningful, quantitative comparisons can be made, including strain formulations, coordinate systems, data locations, strain calculation algorithms, spatial resolutions and data filtering. In this paper, we investigate two full-field validation approaches: (1) the direct interpolation approach, which addresses the first three inconsistencies by interpolating the quantity of interest from one mesh to the other, and (2) the proposed DIC-levelling approach, which addresses all six inconsistencies simultaneously by processing the FEA data through a stereo-DIC simulator to 'level' the FEA data to the DIC data in a regularisation sense. Synthetic 'experimental' DIC data were generated based on a reference FEA of an exemplar test specimen. The direct interpolation approach was applied, and significant strain errors were computed, even though there was no model form error, because the filtering effect of the DIC engine was neglected. In contrast, the levelling approach provided accurate validation results, with no strain error when no model form error was present. Next, model form error was purposefully introduced via a mismatch of boundary conditions. With the direct interpolation approach, the mismatch in boundary conditions was completely obfuscated, while with the levelling approach, it was clearly observed. Finally, the 'experimental' DIC data were purposefully misaligned slightly from the FEA data. Both validation techniques suffered from the misalignment, thus motivating continued efforts to develop a robust alignment process. In summary, direct interpolation is insufficient, and the proposed levelling approach is required to ensure that the FEA and the DIC data have the same spatial resolution and data filtering. Only after the FEA data hav

关键词： digital image correlation (DIC) DIC-levelling approach finite-element analysis (FEA) verification and validation (v&v)

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Adaptive interpolation of multidimensional signals for compression on board an aircraft 5

Adaptive interpolation of multidimensional signals for compr...

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5th Information Technology and Nanotechnology 2019: image processing and Earth Remote Sensing, ITNT 2019

作者： Glumov, N.I. Gashnikov, M.v. Samara National Research University Moskovskoe Shosse 34 Samara443086 Russia Image Processing Systems Institute of RAS - Branch of the FSRC "Crystallography and Photonics" RAS Molodogvardejskaya street 151 Samara443001 Russia

We consider the compression of multidimensional signals on the aircraft board. We describe the data of such signals as a hypercube, which is "rotated" in a special way. To compress this hypercube, we use a hierarchical compression method. As one of the stages of this method, we use an adaptive interpolation algorithm. The adaptive algorithm automatically switches between different interpolating functions at each signal point. We perform computational experiments in real-world multidimensional signals. Computational experiments confirm that the use of proposed adaptive interpolator allows increasing (up to 31%) the compression ratio of the "rotated" hypercube corresponding to multidimensional hyperspectral signals. © 2019 CEUR-WS. All rights reserved.

关键词： Adaptive algorithms

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Anomaly Detection and Identification Using visual Techniques in Streaming video

Anomaly Detection and Identification Using Visual Techniques...

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IEEE Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON)

作者： T.A. Wanigaaratchi v.G.T.N. vidanagama Faculty of Applied Sciences Wayamba University of Sri Lanka

ISBN: (数字)9781728196565

ISBN: (纸本)9781728196572

There are many intelligent systems and tools which uses highly efficient processing models to identify different anomalies with high accuracy. The anomaly detection is of high importance and mostly will come as an absolute requirement at high risk environments and situations. The amount of processing involved in quick decision taking systems bare high deployment costs which restricts the anomaly detection only to a selected few who are capable of building such resource centered systems. Modern world uses drones and other video feeds in order to find and keep track of any anomalous events around a specific area. But most such detection requires absolute manual attention as well as processing power to keep up with real time detection and recognition. The proposed research solution aims to automate this process and includes a two-step anomaly detection system which gives a quicker anomaly detection in an average processing unit time with an advanced recognition model with up to 90% accuracy. The deep learning model (vGG 16) together with alert system and comparison techniques on videos leads into unsupervised anomaly detection of a landscape. The system generates alerts and recognizes anomalies on the alerted video frames. The proposed solution can also be used by any source and does not require high capacity of capability system to get the optimal output. Moreover, the solution brings a simple yet sophisticated technique to address modern anomaly detection and quick alerting system.

关键词： Anomaly detection Neural networks Hidden Markov models Data models Training image color analysis Clustering algorithms

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Memory-Latency-Accuracy Trade-Offs for Continual Learning on a RISC-v Extreme-Edge Node

Memory-Latency-Accuracy Trade-Offs for Continual Learning on...

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IEEE Workshop on Signal processing systems (SIPS)

作者： Leonardo Ravaglia Manuele Rusci Alessandro Capotondi Francesco Conti Lorenzo Pellegrini vincenzo Lomonaco Davide Maltoni Luca Benini University of Bologna Bologna Italy University of Modena and Reggio Emilia Modena Italy

ISBN: (纸本)9781728180991

AI-powered edge devices currently lack the ability to adapt their embedded inference models to the ever-changing envi ronment. To tackle this issue, Continual Learning (CL) strategies aim at incrementally improving the decision capabilities based on newly acquired data. In this work, after quantifying memory and computational requirements of CL algorithms, we define a novel HW/SW extreme-edge platform featuring a low power RISC-v octa-core cluster tailored for on-demand incremental learning over locally sensed data. The presented multi-core HW/SW architecture achieves a peak performance of 2.21 and 1.70 MAC/cycle, respectively, when running forward and backward steps of the gradient descent. We report the trade-off between memory footprint, latency, and accuracy for learning a new class with Latent Replay CL when targeting an image classification task on the CORe50 dataset. For a CL setting that retrains all the layers, taking 5h to learn a new class and achieving up to 77.3% of precision, a more efficient solution retrains only part of the network, reaching an accuracy of 72.5% with a memory requirement of 300 MB and a computation latency of 1.5 hours. On the other side, retraining only the last layer results in the fastest (867 ms) and less memory hungry (20 MB) solution but scoring 58% on the CORe50 dataset. Thanks to the parallelism of the low-power cluster engine, our HW/SW platform results 25× faster than typical MCU device, on which CL is still impractical, and demonstrates an 11× gain in terms of energy consumption with respect to mobile-class solutions.

关键词： Training Task analysis Memory management Computational modeling Adaptation models Inference algorithms

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Dualize, Split, Randomize: Toward Fast Nonsmooth Optimization algorithms

arXiv

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arXiv 2020年

作者： Salim, Adil Condat, Laurent Mishchenko, Konstantin Richtárik, Peter Thuwal23955-6900 Saudi Arabia

We consider minimizing the sum of three convex functions, where the first one F is smooth, the second one is nonsmooth and proximable and the third one is the composition of a nonsmooth proximable function with a linear operator L. This template problem has many applications, for instance, in image processing and machine learning. First, we propose a new primal–dual algorithm, which we call PDDY, for this problem. It is constructed by applying Davis–Yin splitting to a monotone inclusion in a primal–dual product space, where the operators are monotone under a specific metric depending on L. We show that three existing algorithms (the two forms of the Condat–vũ algorithm and the PD3O algorithm) have the same structure, so that PDDY is the fourth missing link in this self-consistent class of primal–dual algorithms. This representation eases the convergence analysis: it allows us to derive sublinear convergence rates in general, and linear convergence results in presence of strong convexity. Moreover, within our broad and flexible analysis framework, we propose new stochastic generalizations of the algorithms, in which a variance-reduced random estimate of the gradient of F is used, instead of the true gradient. Furthermore, we obtain, as a special case of PDDY, a linearly converging algorithm for the minimization of a strongly convex function F under a linear constraint;we discuss its important application to decentralized optimization. Copyright © 2020, The Authors. All rights reserved.

关键词： Stochastic systems

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Metadata Extraction Using DeepLab v3 and Probabilistic Latent Semantic Analysis for Intelligent visual Surveillance systems

Metadata Extraction Using DeepLab V3 and Probabilistic Laten...

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IEEE International Conference on Consumer Electronics (ICCE)

作者： Heungmin Oh Minjung Lee Hyungtae Kim Joonki Paik Image Processing and Intelligent System Laboratory Chung-Ang University Seoul Korea

ISBN: (数字)9781728151861

ISBN: (纸本)9781728151878

Recently, surveillance cameras are ubiquitous for both real-time monitoring and recording important moments. Temporarily seamless surveillance using multiple cameras requires increasing amount of human efforts and enormous size of storage. The use of dynamic cameras further requires advanced computer vision algorithms, and is another challenge for intelligent visual surveillance. To solve those problems, we present an enhanced metadata extraction method for robust object search and a person re-identification. More specifically, the proposed method accurately extracts an object region using a modified DeepLab version 3, and then extracts metadata including representative color, size, aspect ratio, and moving trajectory of the object. The proposed metadata extraction method can be applied to a wide range of surveillance systems such as search for missing children in a large public space and crowd monitoring system.

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Document Management using Artificial Neural Network 4

Document Management using Artificial Neural Network

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4th International Conference on Communication and Electronics systems, ICCES 2019

作者： Pimparkar, Kaustubh Lulla, Rahul Rathod, Pratik Anirudh, v. Dedgaonkar, Suruchi G. BE-Information Technology VIIT Pune India

ISBN: (纸本)9781728112619

Document Classification is the problem of assigning documents into predefined category making it easier to manage and sort documents. There are several algorithms using which the problem of classification can be solved. We use feed forward neural network with multiple layers (also called multilayer perceptron) trained using backpropagation algorithm for the problem for categorization of resume to make management of these documents easier. Neural networks can learn and model non-linear and complex relationships and model generalize solutions that can predict on unseen data. Applications of neural networks are character recognition, image processing and stock market prediction. Backpropagation algorithm which trains the neural net is a supervised learning approach which learns from errors in order to minimize it, thus, requires a lot of training and storage to produce efficient results. © 2019 IEEE.

关键词： Support vector machines

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Determining the proximity of groups in social networks based on text analysis using big data 5

Determining the proximity of groups in social networks based...

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Data Science Session at the 5th International Conference on Information Technology and Nanotechnology, DS-ITNT 2019

作者： Mukhin, A.S. Rytsarev, I.A. Paringer, R.A. Kupriyanov, A.v. Kirsh, D.v. Samara National Research University Moskovskoe Shosse 34 Samara443086 Russia Image Processing Systems Institute of RAS Branch of the FSRC "Crystallography and Photonics" RAS Molodogvardejskaya street 151 Samara443001 Russia

The article is devoted to the definition of such groups in social networks. The object of the study was selected data social network vk. Text data was collected, processed and analyzed. To solve the problem of obtaining the necessary information, research was conducted in the field of optimization of data collection of the social network vk. A software tool that provides the collection and subsequent processing of the necessary data from the specified resources has been developed. The existing algorithms of text analysis, mainly of large volume, were investigated and applied. © 2019 CEUR Workshop Proceedings.

关键词： Big data

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An Approach Towards image Encryption and Decryption using Quaternary Fractional-Linear Operations 10

An Approach Towards Image Encryption and Decryption using Qu...

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10th International Conference on Emerging Ubiquitous systems and Pervasive Networks (EUSPN) / 9th International Conference on Current and Future Trends of Information and Communication Technologies in Healthcare (ICTH)

作者： Kovalchuk, Anatoliy Izonin, Ivan Gregushml, Mihal Lotoshynska, Natalia Lviv Polytech Natl Univ Dept Publishing Informat Technol S Bandera 12 UA-79013 Lvov Ukraine Comenius Univ Fac Management Odbojarov 10 Bratislava Slovakia

The problem of unauthorized access to various content is an important task for the present. It becomes even more acute when using the existing toolkit for the processing and transmission of encrypted images with the fluctuation intensity function. The authors developed a modification of the RSA algorithm to use it in relation to the above -mentioned images. It is proposed a conceptual view for the joint use of quaternary fractional -linear transformations with elements of the basic RSA algorithm. The application of this mathematical apparatus in the basic RSA algorithm avoids contours of image objects in an encrypted sample. In addition, this combination provides additional stability to the basic RSA algorithm for unauthorized decryption. The simulation of the method was carried out in two described by the authors algorithms: using one line and four lines of the image matrix, for grayscale and color images. The high efficiency of the developed method for avoiding contours of objects on encrypted images has been confirmed. In both cases, contours after applying encryption procedures do not appear. The reverse procedure allows to get an image without visible distortion. 2019 The Authors. Published by Elsevier B.v.

关键词： RSA modiffication encryption decryption image contour stability quaternary fractional -linear form

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