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检索条件"任意字段=Proceedings of the 2019 2nd International Conference on Signal Processing and Machine Learning"

共 1347 条记录，以下是741-750 订阅

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Cloud-Assisted IoT-Enabled Smoke Monitoring System (e-Nose) Using machine learning Techniques 2nd

Cloud-Assisted IoT-Enabled Smoke Monitoring System (e-Nose) ...

引用

2nd international conference on Smart IoT Systems: Innovations and Computing, SSIC 2019

作者： Goyal, Somya Bhatia, Pradeep K. Parashar, Anubha GJUS & T Hisar India Manipal University Jaipur India

ISBN: (纸本)9789811384059

E-nose is a self-controlled smoke detector developed using Arduino microcontroller and sensors. It is capable of sensing the smell in the ambiance via gas sensors and programmed to alert the smokers giving red signal with buzzer. Smoke detection algorithm facilitates the intelligent warning to the cigarette smokers. Nowadays, smokers’ count is increasing with a high-speed raising health and moral issues to the people especially in case of passive smoking at public places. Today "No Smoking" rules are only written, but to make these rules being followed this work would be a milestone. To implement e-Nose, a cigarette smoke detector with red/green LED and buzzer using Arduino uno board R3 is developed. MQ-2 gas sensor is used to detect cigarette smoke and combination of buzzer and LEDs is used to warn smokers. This work demonstrates the development of e-Nose and evaluation of the performance of developed product. In nutshell, this e-Nose which is cigarette smoke detector is designed to achieve high degree of awareness among public and to reduce the smokers’ count especially at public places. We detect the smoke through e-Nose and upload on the cloud. The cloud analysis is done using various machine learning techniques to categorize the smoke with pollution data. And result shows smoking percentage. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Microcontrollers

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Research on Improved machine learning for Distribution Network Fusion Control 2

Research on Improved Machine Learning for Distribution Netwo...

引用

2nd international conference on Computer Network, Electronic and Automation, ICCNEA 2019

作者： Li, Bo Zhang, Yu Chen, Long Hong, Jie He, Shuyi Liu, Xiaomin System Operating Department Hainan Power Grid Co. Ltd. China Communications Operations and Maintenance Department Hainan Power Grid Co. Ltd. Information and Communication Branch China

ISBN: (纸本)0769566847

In order to improve the accuracy of test sample data of distribution communication network fusion control, an improved machine learning technology for distribution communication network fusion control was established. The distribution communication network port is identified based on the network protocol, and the number of neurons controlled by the fusion of the distribution communication network is determined according to the sample information sent by the port number. After the feature selection layer by layer, the traffic characteristics of the machine learning protocol are selected according to the neural network structure. Moreover, the Relief algorithm is used to calculate the actual number of machine learning protocols and realize the design of improved machine learning technology for the fusion control of power distribution and communication networks. Compared with other three traditional techniques, it is concluded that this technique has obvious advantages in sample processing accuracy. © 2019 IEEE.

关键词： Internet protocols

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Semi-supervised learning through adversary networks for baseline detection 2

Semi-supervised learning through adversary networks for base...

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15th IAPR international conference on Document Analysis and Recognition (ICDAR) / 2nd Workshop of machine learning (WML)

作者： Karpinski, Romain Belaid, Abdel Univ Lorraine LORIA Campus Sci F-54500 Vandoeuvre Les Nancy France

ISBN: (纸本)9781728150543

The aim of this paper is to propose a new strategy adapted to the semantic segmentation of document images in order to extract baselines. Inspired by the work of Gruning [7], we used a convolutional model with residual layers enriched by an attention mechanism, called ARU-Net, a post-processing for the agglomeration of predictions and a data augmentation to enrich the database. Then, to consolidate the ARU-Net and help explicitly model dependencies between feature maps, we added a module of "Squeeze and Excitation" as proposed by Hu et al. [9]. Finally, to exploit the amount of unrated data available, we used a semi-supervised learning, based on ARU-Net, through the use of adversary networks. This approach has shown some interesting predictive qualities, compared to Gruning's work, with easier processing and less task-specific error correction. The resulting performance improvement is a success.

关键词： Semi-supervised learning Semantic segmentation ARU-Net Adversary networks

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machine learning for Strategic Urban Planning 2

Machine Learning for Strategic Urban Planning

引用

2nd international conference of the IEEE-Nigeria-Computer-Chapter (NigeriaComputConf)

作者： Odaudu, S. N. Umoh, I. J. Mu'azu, M. B. Ahmadu Bello Univ Dept Comp Engn Zaria Kaduna Nigeria

ISBN: (纸本)9781728107134

Data mining is an important part of strategic planning for the development of modern urban settlement with capacities to accommodate population explosion. Developing countries arefast becoming urbanized giving the developments and opportunities that are lacking in rural areas. Data regarding urban development such as satellite image need to be analysed to ascertain the possibilities for further development or opening up of new settlements. This work presents a binary sub-pixel and feature based method of classification to detect water bodies and vegetation in earth observatory images. In this work, the images were subjected data pre-processing, feature extraction, and analysed the data using machine learning classification method to detection regions that support urban expansion or development of new settlement The proposed method achieved 88.93% accuracy and 0.06% RAISE

关键词： Satellite image object detection machine learning Haar-cascade feature engineering

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signal Demodulation without Channel Equalizer Using machine learning Techniques 2

Signal Demodulation without Channel Equalizer Using Machine ...

引用

2nd international conference on Intelligent Computing, Instrumentation and Control Technologies, ICICICT 2019

作者： Muthulakshmi, S. Jose, Renu ACIS ECE Rajiv Gandhi Institute of Technology Kottayam India

ISBN: (纸本)9781728102832

With the unprecedented availability of computing and data resources, there has been a widespread trend in using machine learning techniques in the field of communication as well. In the existing communication system, signal demodulation in multipath channel works with the aid of equalizers to mitigate the effect of inter-code cross talk and inter-symbol interference. Owing to the ability other machine learning models to learn the complex structure in feature space, deep learning architectures such as Deep Belief Network(DBN) and Stacked Auto Encoders(SAE) are used for demodulation in this paper. The experiments were conducted in a multipath Rayleigh channel. Simulation results shows the feasibility and hegemony of the deep learning model over the conventional demodulation techniques using equalization. © 2019 IEEE.

关键词： Demodulation

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Voltage Controlled Stimulator for Nerve Conduction Study 2

Voltage Controlled Stimulator for Nerve Conduction Study

引用

2nd international conference on Innovations in Electronics, signal processing and Communication, IESC 2019

作者： Talukdar, Champak Hazarika, Anil Singh, Amarprit Barthakur, Mausumi Bhuyan, Manabendra Tezpur University Department of ECE Assam India GNRC Hospital GuwahatiAssam India

ISBN: (纸本)9781728107448

Use of an appropriate stimulus is essential to acquire proper nerve conduction signal. This paper presents a voltage controlled stimulator that generates proper voltage to be applied over the skin for nerve conduction study (NCS). Unlike conventional stimulator, the designed stimulator is based on battery which is suitable for portable nerve study machines. The recording and signal processing involve signal conditioning circuit that consisting of filter and amplifier circuits. The conditioning circuit amplifies and filters low-level biomedical signal, usually, falls in mV. Experimental setup has been tested and demonstrated using real-Time data collected in laboratory. The system is adaptable to the subjects with safety. © 2019 IEEE.

关键词： signal conditioning circuits

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Multi-scale Principle Manifold learning Noise Reduction Method for Telemetry Vibration signal 2

Multi-scale Principle Manifold Learning Noise Reduction Meth...

引用

IEEE 2nd international conference on Electronic Information and Communication Technology (ICEICT)

作者： Liu, Xue Sun, Ao Xu, Hongzhou Wei, Long PLA 91550 Dalian Peoples R China Being Inst Struct & Environm Engn Beijing Peoples R China

ISBN: (纸本)9781538692981

Telemetry vibration signal contains a great deal of information that reflects the status of the aircraft during the test, and has characteristics of strong noise, non-linear, non-stationary, transient impact and so on. How to effectively denoise the signal without changing the dynamic characteristics of the system directly related to the accuracy of flight state analysis. To solve this problem, a phase space multi-scale principle manifold learning noise reduction method based on double tree complex wavelets was proposed. Firstly, the noisy signal was orthogonally decomposed into each scale band by the double-tree complex wavelet transform. Then according to the spatial distribution of signal and noise in each scale band, the wavelet coefficients in each scale were respectively identified by the phase space principle manifold learning algorithm, which could remove noise by projecting the scale coefficients from the high dimensional phase space to the intrinsic dimensional space projection. Finally, the denoised wavelet coefficients were reconstructed to obtain the vibration signal. Simulation and experimental results showed the effectiveness of the method.

关键词： Telemetry vibration signal Double-tree complex wavelet Multi-scale analysis principle manifold learning

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Deep Convolutional Auto-encoders Based Fault Diagnosis for Diesel Generator Set 2

Deep Convolutional Auto-encoders Based Fault Diagnosis for D...

引用

2nd IEEE international conference on Information Communication and signal processing (ICICSP)

作者： Jia, Shuli Hu, Feng Feng, Xijia Shanghai Marine Diesel Engine Res Inst Automat Engn Dept Shanghai Peoples R China Harbin Inst Technol Sch Informat Sci & Engn Weihai Peoples R China

ISBN: (纸本)9781728151021

As a kind of power supply equipment, diesel generator set has the characteristics of good mobility, fast start, stable power supply, convenient operation and maintenance. Diesel generator set is very important for power supply applications. The research on automatic fault diagnosis of diesel generator set is of great significance for monitoring the operation status of diesel generator and timely maintenance. A deep convolutional auto-encoders based fault diagnosis for diesel generator set is developed. Convolutional auto-encoders replace the full-connected layers with convolutional layers, and provide a lower dimension latent representation to extract the invariable features for fault classification. The sensor data collected from diesel generator set are processed to form a dataset for training and test, and an architecture for deep convolutional auto-encoders is designed. The experimental results show that the deep convolutional auto-encoders based method has the best fault diagnosis performance in recall, precision, accuracy and F1-score than other learning based methods.

关键词： fault diagnosis diesel generator set deep concolutional auto-encoders deep learning machine learning

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Adaptive fast composite splitting algorithm for MR image reconstruction 1

引用

2nd international conference on machine Intelligence and signal processing, MISP 2019

作者： Mathew, Raji Susan Paul, Joseph Suresh Medical Image Computing and Signal Processing Lab Indian Institute of Information Technology and Management TrivandrumKerala India

ISBN: (数字)9789811513664

ISBN: (纸本)9789811513657

Reconstruction of an image from undersampled k-space data using conventional sparsity priors use any of the pre-specified transforms like wavelet, which utilizes the global sparsity of images. Alternatively, methods such as block-matching and 3D filtering (BM3D) utilize the non-local sparsity of images, resulting in better preservation of finer details. However, use of either of the methods cannot utilize global and non-local sparsity at the same time. In this paper, we propose an efficient combination of the global and non-local sparsity realized using wavelet and BM3D, implemented using the fast composite splitting denoising (FCSD) algorithm. Furthermore, we use an adaptive regularization parameter for the wavelet to yield the best achievable performance using adaptive thresholded Landweber (ATL). The proposed adaptive fast composite splitting denoising algorithm (AFCSD) combines the advantages of both BM3D with adaptive TL through the use of FCSD and thus exploits the self-repetitive structure in images by enforcing non-local sparsity as well as entire consistency of the image. Experimental results reveal that the proposed method is superior to any of the individual methods in terms of reconstruction accuracy and computation complexity. Additionally, when used as an auxiliary constraint in dictionary learning (DL), this can result in further improvements in reconstruction with significant reduction in the computation time as compared to a conventional DL reconstruction. © Springer Nature Singapore Pte Ltd. 2020.

关键词： Image reconstruction

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A Gamified learning App for Children with ADHD 19

A Gamified Learning App for Children with ADHD

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2nd international conference on Image and Graphics processing (ICIGP) / 5th international conference on Virtual Reality

作者： Supangan, Renz Anthony Acosta, Leo Alfred S. Amarado, Jose Lorenzo S. Blancaflor, Eric B. Samonte, Mary Jane C. Mapua Univ Makati 1200 Philippines

ISBN: (纸本)9781450360920

Special Education is an Educational Service provided by private or public schools that cater students with disabilities. Attention Deficit Hyperactivity Disorder (ADHD) is a common behavioral disorder that begins at infancy stage. But it can also develop during teenage state and even in adulthood. This study focused on providing an interactive supplementary tool in assisting ADHD children in learning Mathematics, Language and Basic Hygiene. This gamified system was designed for an Android mobile application of Level 1 lectures in animated presentation. This tool was made available in order for the parents and teachers track students or ADHD children's progress through different activities taken in the e-tutor system. In conclusion, the user acceptance testing showed that the Android application was approved in content and suitable to use for special education services.

关键词： Gamified learning ADHD e-tutor system Android mobile app Special education

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