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An Improved Algorithm Based on Convolutional neural network for Smoke Detection

An Improved Algorithm Based on Convolutional Neural Network ...

引用

IEEE International Symposium on Parallel and distributed processing with Applications and IEEE International conference on Ubiquitous Computing and Communications (ISPA/IUCC)

作者： Hang Yin Yurong Wei Shenyang Aerospace University Shenyang China

ISBN: (纸本)9781728152103

As an essential method of fire prevention and disaster control, smoke detection is of great significance to production and life. At present, the convolutional neural network (CNN) has achieved good results in the research of smoke detection. However, the detection accuracy is not high for some scenes. For example, the wind speed is tremendous, and the shape of the smoke changes rapidly. In order to deal with this problem better, this paper proposes an improved algorithm based on cascading classification and deep convolutional neural network. In the cascading classification part, we improve the cascading structure and make it select the appropriate parameter threshold for the smoke generated in different scenes. The convolutional neural network structure is trained to extract the variation characteristics of smoke better. Also, we optimize the parameters on the target data set. The experimental results show that the algorithm has achieved excellent results in accuracy and speed on the selected smoke detection data sets.

关键词： convolutional neural nets disasters emergency management fires learning (artificial intelligence) object detection smoke Smoke crisis management Object detection Fire Disasters neural network Emergency Services

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Optimizing Edge Weights for distributed Inference with Gaussian Belief Propagation 1

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14th International conference on Intelligent Computing (ICIC)

作者： Halloran, Brendan Premaratne, Prashan Vial, Peter James Univ Wollongong Sch Elect Comp & Telecommun Engn Wollongong NSW 2522 Australia

ISBN: (数字)9783319959306

ISBN: (纸本)9783319959290;9783319959306

distributed processing is becoming more important in robotics as low-cost ad hoc networks provide a scalable and robust alternative to tradition centralized processing. Gaussian belief propagation (GaBP) is an effective message-passing algorithm for performing inference on distributed networks, however, its accuracy and convergence can be significantly decreased as networks have higher connectivity and loops. This paper presents two empirically derived methods for weighting the messages in GaBP to minimize error. The first method uses uniform weights based on the average node degree across the network, and the second uses weights determined by the degrees of the nodes at either end of an edge. Extensive simulations show that this results in greatly decreased error, with even greater effects as the network scales. Finally, we present a practical application of this algorithm in the form of a multi-robot localization problem, with our weighting system improving the accuracy of the solution.

关键词： Gaussian belief propagation distributed algorithms Markov random fields Factor graphs Localization

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Improving Short Text Classification Using Fast Semantic Expansion on Multichannel Convolutional neural network 19

Improving Short Text Classification Using Fast Semantic Expa...

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19th IEEE/ACIS International conference on Software Engineering, Artificial Intelligence, networking and Parallel/distributed Computing (SNPD)

作者： Sotthisopha, Natthapat Vateekul, Peerapon Chulalongkom Univ Fac Engn Dept Comp Engn Big Data Analyt & IoT Ctr CUBIC Bangkok Thailand

ISBN: (纸本)9781538658895

Nowadays, text classification is recognized as one of crucial tools for business users to gain more insights from customers. However, textual data from customers such as comments are usually short. Thus, there are two main issues in short text categorization: (1) insufficient contextual information and (2) noisy data due to misspellings. Recently, there is a prior attempt to propose a deep learning approach for short text categorization by expanding semantic using word embeddings clustering via an algorithm based on density peaks searching. Since the number of words in word embeddings is usually large, the clustering algorithm does not scale with the size of data set, thus demanding unacceptable computational cost. In this paper, we aim to propose a fast short-text categorization framework. Rather than using a CNN with k- max pooling layer, we propose to use a faster version of Convolutional neural network (CNN) called "multichannel CNN." To speed up the semantic expansion process, we propose to employ mini batch K-Means++, which is considerably faster and scales well with the size of data set. Furthermore, we also introduce an additional preprocessing step to increase vocabulary coverage rate on word embeddings. We conducted experiments on four public data sets: Google Snippets, TREC, MR, and Subj. The results showed that the proposed framework does not only improve an accuracy on all data sets, but also reduces computational costs from several days to a few hours.

关键词： Short Texts Natural Language processing Text Classification Clustering Convolutional neural network Word Embeddings Deep Learning

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Evaluating Forecasting Techniques for Integrating Household Energy Prosumers into Smart Grids 14

Evaluating Forecasting Techniques for Integrating Household ...

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14th IEEE International conference on Intelligent Computer Communication and processing (ICCP)

作者： Petrican, Teodor Vcsa, Andreea Valeria Antal, Marcel Pop, Claudia Cioara, Tudor Anghel, Ionut Salomie, Ioan Tech Univ Cluj Napoca Dept Comp Sci Distributed Syst Res Lab Cluj Napoca Romania

ISBN: (纸本)9781538684450

This paper tackles the problem of integrating household energy prosumers in Smart Energy Grids by analyzing a set of state-of-the-art energy forecasting techniques that allow individual or aggregated prosumers to evaluate their future energy demand and inform the distributed System Operator (DSO) about potential grid imbalances. Thus, the DSO can perform a proactive strategy to manage the grid and avoid problems before they appear. The key element of this approach is the prediction technique, that must be accurate enough such that the resulting grid imbalances can be compensated in real-time. The paper evaluates a set of state-of-the-art statistical and Machine Learning (ML) prediction techniques, such as SARIMA, feed-forward and recurrent neural networks, support vector regression or ensemble prediction models, on real household historical energy demand logs by performing a feature selection process for each ML algorithm as to identify the best elements that influence the energy demand of a house. A set of experiments are performed on the REFIT Electrical Load Measurements data set evaluating each model's performance with respect to the selected features. Among the evaluated algorithms, the Ensemble Prediction Model gives best prediction accuracy, showing a Mean Absolute Percentage Error (MAPE) of 14.4% followed by the SVM model with a MAPE of 15.4%.

关键词： Smart Grids Energy Demand Forecasting Short-Term Load Forecasting Multi-layer neural network Recurrent neural networks Support Vector Regression

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DSDR: Dynamic Semantic Discard Reader for Open-Domain Question Answering

DSDR: Dynamic Semantic Discard Reader for Open-Domain Questi...

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International Joint conference on neural networks (IJCNN)

作者： Wang, Xu Huang, Zhen Zhang, Yiming Tan, Ludan Liu, Yang Natl Univ Def Technol Coll Comp Sci & Technol Parallel & Distributed Lab Changsha Peoples R China

ISBN: (纸本)9781509060146

Open-domain question answering (QA) is an important problem in natural language processing. A QA system usually consists of an information retrieve (IR) model for finding the relevant passages that may have answers and a read comprehension (RC) model for generating answers from the selected passages. The state-of-the-art RC model uses all the passages for feature extraction when matching semantic information of text pairs. In this paper, however, we argue that the texts of passages often have different importance for specific questions and some even provide negative information that might affect the correctness of final prediction. To address this problem, we design dynamic semantic discard reader (DSDR), a deep model that tries to drop negative information irrelevant to the questions. The result of our experiment shows that DSDR outperforms existing methods in the exact-match and FI scores on open-domain QA datasets.

关键词： question-answering neural network dynamic semantic discard

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Hyaline: A Transparent distributed Computing Framework for CUDA

Hyaline: A Transparent Distributed Computing Framework for C...

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International conference for Convergence of Technology (I2CT)

作者： Akshat Jain Owais Kazi Raviraj Joshi Shraddha Basantwani Yogita Bang Rahul Khengare Computer Engineering PICT Pune Computer Science IIT Madras

ISBN: (数字)9781538680759

ISBN: (纸本)9781538680766

The GPU usually handles the homogenous data parallel work, by taking advantage of its massive number of cores. In most of the applications, we use CUDA programming for utilizing the power of GPU. In data intensive high computational applications like neural networks, utilizing the GPU on a single machine is time consuming. Instead if multiple GPUs are used in a network the amount of time required will be significantly reduced. Traditionally to enable a set of program to be run in a distributed environment, programmer has to accordingly design components to make his system dynamic and resilient to changes in number of systems in cluster, which is a daunting task. This work distribution can be a poor solution as it may underutilize the GPUsIn our approach, we developed a framework which transparently distributes data parallel kernels across multiple GPUs in a distributed network. The programmer is responsible for developing a single data parallel kernel in CUDA while the framework automatically distributes the workload across an arbitrary set of CUDA enabled GPUs. Depending on current workload on GPUs and the amount of data to be processed optimal distribution is done. The goal is to maximally utilize the available resources with minimal programming complexity. The systems not compatible with CUDA can also utilize our solution. We expect our framework to reduce the processing time along with simplifying the task of programmers.

关键词： Graphics processing units Kernel distributed databases Computational modeling Programming Task analysis Computer architecture

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Design and Optimization of Edge Computing distributed neural Processor for Biomedical Rehabilitation with Sensor Fusion 18

Design and Optimization of Edge Computing Distributed Neural...

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37th IEEE/ACM International conference on Computer-Aided Design (ICCAD)

作者： Otseidu, Kofi Jia, Tianyu Bryne, Joshua Hargrove, Levi Gu, Jie Northwestern Univ Dept EECS Evanston IL 60208 USA Shirley Ryan Abil Lab Chicago IL USA

ISBN: (纸本)9781450359504

Modem biomedical devices use sensor fusion techniques to improve the classification accuracy of motion intent of users for rehabilitation application. The design of motion classifier observes significant challenges due to the large number of channels and stringent communication latency requirement. This paper proposes an edge-computing distributed neural processor to effectively reduce the data traffic and physical wiring congestion. A special local and global networking architecture is introduced to significantly reduce traffic among multi-chips in edge computing. To optimize the design space of the features selected, a systematic design methodology is proposed. A novel mixed-signal feature extraction approach with assistance of neural network distortion recovery is also provided to significantly reduce the silicon area. A 12-channel 55nm CMOS test chip was implemented to demonstrate the proposed systematic design methodology. The measurement shows the test chip consumes only 20uW power, more than 10,000X less power than the current clinically used microprocessor and can perform edge-computing networking operation within 5ms time.

关键词： neural network Low power edge processing Mixed signal feature extraction Inter-chip communication Biomedical devices

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SparseNN: An Energy-Efficient neural network Accelerator Exploiting Input and Output Sparsity

SparseNN: An Energy-Efficient Neural Network Accelerator Exp...

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Design, Automation and Test in Europe conference and Exhibition (DATE)

作者： Zhu, Jingyang Jiang, Jingbo Chen, Xizi Tsui, Chi-Ying Hong Kong Univ Sci & Technol Dept Elect & Comp Engn Hong Kong Peoples R China

ISBN: (纸本)9783981926309

Contemporary Deep neural network (DNN) contains millions of synaptic connections with tens to hundreds of layers. The large computational complexity poses a challenge to the hardware design. In this work, we leverage the intrinsic activation sparsity of DNN to substantially reduce the execution cycles and the energy consumption. An end-to-end training algorithm is proposed to develop a lightweight (less than 5% overhead) run-time predictor for the output activation sparsity on the fly. Furthermore, an energy-efficient hardware architecture, SparseNN, is proposed to exploit both the input and output sparsity. SparseNN is a scalable architecture with distributed memories and processing elements connected through a dedicated on-chip network. Compared with the state-of-the-art accelerators which only exploit the input sparsity, SparseNN can achieve a 10%-70% improvement in throughput and a power reduction of around 50%.

关键词： Training Computer architecture Scheduling Hardware System-on-chip Prediction algorithms neural networks

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Analysis of Hyperparameter Tuning in neural Style Transfer 5

Analysis of Hyperparameter Tuning in Neural Style Transfer

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5th IEEE International conference on Parallel, distributed and Grid Computing (PDGC)

作者： Khandelwal, Siddhant Pandey, Kavita Rana, Sarthak Kaushik, Prashant Jaypee Inst Informat Technol Dept CSE & IT Noida India

ISBN: (纸本)9781728106465

Most of the notable artworks of all time are hand drawn by great artists. But, now with the advancement in image processing and huge computation power, very sophisticated synthesised artworks are being produced. Since mid-1990's, computer graphics engineers have come up with algorithms to produce digital paintings, but the results were not visually appealing. Recently, neural networks have been used to do this task and the results seen are like never before. One such algorithm for this purpose is the neural style transfer algorithm, which imparts the pattern from one image to another, producing marvellous pieces of art. This research paper focuses on the roles of various parameters involved in the neural style transfer algorithm. An extensive analysis of how these parameters influence the output, in terms of time, performance and quality of the style transferred image produced is also shown in the paper. A concrete comparison has been drawn on the basis of different time and performance metrics. Finally, optimal values for these discussed parameters have been suggested.

关键词： neural Style Transfer hyperparameter tuning content cost style cost VGG Convolutional neural network

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Dilated Deep Residual network for Post-processing in TPG Based Image Coding 11th

Dilated Deep Residual Network for Post-processing in TPG Bas...

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11th International conference on Internet and distributed Computing Systems (IDCS)

作者： Yuan, Yuan Sun, Jingtao Wang, Miaohui Shenzhen Univ Shenzhen 518060 Peoples R China Natl Inst Informat Tokyo 1018430 Japan

ISBN: (纸本)9783030027384;9783030027377

Lossy image compression algorithms like JPEG usually introduce visually annoying artifacts on decoded images, such as blocking artifacts, blurring and ringing effects. The tiny portable graphics (TPG) based image/video compression technique is proposed to improve JPEG compression performance. However, the lossy compression artifacts cannot be fully removed, especially at low coding bit-rates. Recently, some shallow convolutional neural network (CNN) models have been proposed as post-processing techniques to reduce compression artifacts. Learning from the fact that deep CNNs have shown extraordinary ability in high-level vision problems, we propose to investigate how a deeper CNN can further enhance the quality of decoded images. Specifically, we adopt a network with 16 residual blocks. In order to increase the receptive field, we change the first convolution layer in the first five residual blocks to dilated convolution with size 2. The primary experimental results show that the proposed model can outperform existing CNN based post-processing methods.

关键词： Post-processing Artifacts reduction TPG coding Deep residual network

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