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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 1333 条记录，以下是41-50 订阅

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Application of Multi-channel Impedance Measurement Device in Teaching of "signal Analysis and processing" 2

Application of Multi-channel Impedance Measurement Device in...

引用

2nd international conference on Artificial Intelligence and Education, ICAIE 2021

作者： Sun, Jiangtao Lu, Xupeng Sun, Shijie Wang, Rui Xie, Yuedong Beihang University School of Instrument and Opto-Electronic Engineering Beijing China

ISBN: (纸本)9781665424929

This article develops a low-cost multi-channel impedance measurement device as a teaching tool based on the electrical impedance tomography (EIT) technique. The device is applied for the teaching of "signal analysis and processing"related courses in the subject of instrumentation science and technology, which can reconstruct images of conductivity distributions within a domain of interest and recognize a variety of human gestures based on machine learning. This auxiliary teaching tool could help students deepen the understanding of core knowledge in the course, such as analog signal processing by operational amplification, signal sampling and discretization, discrete Fourier transform, and data classification through machine learning. The proposed teaching tool is easy to demonstrate during classes, which can help improve teaching efficiency and enhance students' interests towards related knowledge. © 2021 IEEE.

关键词： Electric impedance measurement

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Autonomous Intelligence for FMV ISR Sensors With A Human In The Loop Decision Support System 2018

Autonomous Intelligence for FMV ISR Sensors With A Human In ...

引用

2nd international conference on Vision, Image and signal processing (ICVISP)

作者： Lewis, Rory Univ Colorado Colorado Springs Dept Comp Sci Colorado Springs CO 80918 USA

ISBN: (纸本)9781450365291

As the United States Air Force moves towards autonomous labelling of FMV from ISR sensors, it has experienced unforeseen technical and legal challenges. In terms of the technical challenges, this research effort identifies these obstacles and presents solutions for them with detailed step-by-step analysis of the processes, its testing and prototypes. In terms of the legal challenges, the USAF's goals of infusing artificial intelligence into autonomous labelling of FMV is also being challenged by a formidable, looming legal threat of new laws that will force the USAF to include 'humans in the loop' of its artificial intelligence and machine learning systems [20], [7], [15]. Again, we analyze these legal threats and present solutions to allow inclusion of a human in the loop. It is important to note that our solution to these technical and legal challenges form a two-pronged solution that yields a Bench to Battlefield, Government off-the-shelf (GOTS) autonomous FMV labelling system that will, as time goes by, learn and grow in its ISR identification abilities.

关键词： Artificial intelligence machine learning unmanned aircraft systems

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Prediction and Data Analysis of Price Based on PSO with Extreme learning machine Algorithm and Particle Swarm Optimization 2

Prediction and Data Analysis of Price Based on PSO with Extr...

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2nd international signal processing, Communications and Engineering Management conference, ISPCEM 2022

作者： Chen, Huanyun Zhao, Weiming Private Hualian College Guangzhou China

ISBN: (纸本)9781665492713

The changes in the trading market are affected by many factors, and traditional forecasting methods are more and more difficult to meet people's needs. In order to improve the accuracy of prediction, this paper proposes an extreme learning machine algorithm based on particle swarm optimization for prediction and analysis. First, the fluctuation data of previous years are collected, and the closing price of the stock is determined as an experiment and simulated to establish a learning sample, and then the learning sample is input. Based on the online extreme learning machine model optimized by particle swarm optimization, the prediction model is established. The results show that the optimized extreme learning machine algorithm, compared with models such as ELM, PSO-BP and DE-ELM, has better surface prediction accuracy and fitting effect, and can more accurately describe the change trend, which is more efficient than other methods. © 2022 IEEE.

关键词： Forecasting

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ISR-Brain machine Intelligence for Unmanned Aircraft Systems 2018

ISR-Brain Machine Intelligence for Unmanned Aircraft Systems

引用

2nd international conference on Vision, Image and signal processing (ICVISP)

作者： Lewis, Rory Univ Colorado Colorado Springs Dept Comp Sci Colorado Springs CO 80918 USA

ISBN: (纸本)9781450365291

This paper presents a system for extrapolating knowledge and classification rules from existing ISR FMV and creating an ISR-Brain. As combat operations have grown to depend upon assured, live ISR support during operations, US forces are presented with formidable challenges to integrate artificial intelligence (AI) capabilities with existing ISR systems. The common challenge being the variance at which advances in commercial and academic AI are deployed compared to rate of speed that innovative AI systems are developed and utilized in military domains. ISR, USAF and SOCOM need to develop a means to seamlessly integrate military and commercial state-of-the-art systems. The ISR-Brain presented will be capable of converting classifiers in existing ISR FMV to machine learning rules for real time ISR sensor, multi-source, multi-enclave data and adaptable with ongoing research efforts with A2, SOCOM, REDO, MITRE and Project MAVEN to develop and test and ISR-Brain to enable the system to integrate with all ISR sensors and predict future Troops in Contact events (TIC) and IED events.

关键词： Artificial intelligence machine learning unmanned aircraft systems

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Research on signal processing Methods for Gas Ultrasonic Flowmeters 2

Research on Signal Processing Methods for Gas Ultrasonic Flo...

引用

2nd international conference on machine learning, Cloud Computing, and Intelligent Mining, MLCCIM 2023

作者： Wan, Yong China Coal Technology and Engineering Group Chongqing Research Institute Chongqing400039 China State Key Laboratory of Gas Disaster Monitoring and Emergency Technology Chongqing400039 China

ISBN: (纸本)9798350328530

On the basis of the difficulty in determining the feature point in the signal processing of gas ultrasonic flowmeter,a variable threshold based zero-crossing detection signal processing method is proposed to determine a feature wave of the signal accurately. Thus the propagation time of ultrasonic signal is calculated,and the gas flow rate is measured *** method is realized real time in a signal processing system with DSP and FPGA as dualcore for the gas ultrasonic *** judging the 'step', the error caused by noise interference is eliminated,and the stability of the measurement results is enhanced. With piecewise linear calibration,the non-linear error is corrected,and the measurement accuracy of the system is improved. The experimental results of gas flow calibration verify the effectiveness of the method and the system. © 2023 IEEE.

关键词： Flow of gases

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Document Modeling with Hierarchical Deep learning Approach for Sentiment Classification 2018

Document Modeling with Hierarchical Deep Learning Approach f...

引用

2nd international conference on Digital signal processing (ICDSP) / international conference on Computer Graphics and Virtuality (ICCGV)

作者： Ghosh, Monalisa Sanyal, Goutam Natl Inst Technol Dept CSE Durgapur India

ISBN: (纸本)9781450364027

Sentiment analysis has recently been considered as most active research field in NLP domain. Deep learning is a growing trend of machine learning due to its automatic learning capability with impressive results across different NLP task. In this paper a model is proposed to analyze the deep sentiment representation based on CNN and LSTM (modified version of RNN) network. We aim to improve the performance of traditional machine learning method by merging them with deep learning techniques to tackle the challenge of sentiment prediction of massive amount of unsupervised product review dataset. We make our model first learn to sentence representation with CNN. Next, the semantics of sentences are encoded with LSTM network for document representation. We conduct experiments on two review datasets based on movie review with evaluation metric 'accuracy'. The result shows that proposed model outperformed traditional machine learning as well as baseline neural network model

关键词： Sentiment Analysis Deep learning traditional machine learning Convolutional neural network (CNN) recurrent neural networks LSTM embedding algorithm etc.

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Convolutional neural networks application in cardiovascular decision support systems 2

Convolutional neural networks application in cardiovascular ...

引用

2nd international conference on Image and Video processing, and Artificial Intelligence (IPVAI)

作者： Natalia, Konnova Mikhail, Basarab Michael, Khachatryan Anna, Domracheva Igor, Ivanov Bauman Moscow State Tech Univ Comp Sci & Control Syst Dept 2 Ya Baumanskaya St5 Moscow 105005 Russia

ISBN: (数字)9781510634107

ISBN: (纸本)9781510634107

The paper considers the possibilities of using neural network methods of machine learning to diagnose the states of the human cardiovascular system and support decision-making in cardiology and cardiac surgery. The issues of processing and preparation of electrocardiography signals, selection of architecture and tuning of neural network parameters for automation of diagnosis are discussed. Here, the results obtained with the help of multilayer perceptrons and convolutional neural networks to assign the submitted input cardiovascular data to one of the classes of states in the selected space are examined. Based on a specialized developed software, the proprietary numerical experiments with real clinical data were carried out. Given the above results, demonstrating the applicability of the used deep learning methods and algorithms to diagnostic automation, a model of a hierarchical decision support system is proposed.

关键词： decision support system deep learning digital signal processing machine learning neural networks

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Multi-static Active Sonar Target Recognition Method Based on Bionic signal 2

Multi-static Active Sonar Target Recognition Method Based on...

引用

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

作者： Liu, Jiheng Zhou, Zemin Zeng, Xinwu Natl Univ Def Technol Coll Meteorol & Oceanog Changsha Peoples R China

ISBN: (纸本)9781728151021

In modern sonar systems, automatic recognition of underwater targets has always been one of the key technologies in research. In recent years, classification and recognition methods based on machine learning have been widely used in underwater acoustic field where good results have been achieved. Compared with monostatic active sonar, multi-static active sonar can simultaneously acquire the forward, lateral, and backscattering information of the target, and can obtain more accurate and stable target recognition result. Furthermore, the transmit waveform of active sonar effort the performance in complex ocean environment. As all known, the signals transmitted by cetaceans have the characteristics of strong anti-jamming ability, high positioning accuracy, et al. Accordingly, the performance of multi-static active sonar target recognition based on bionic signal is investigated in this paper. Besides, the machine learning methods are applied to the recognition of echo signals, so that further good results and conclusions are obtained.

关键词： Underwater target recognition Active sonar machine learning Bionic signal

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A Study on ELM(Election pledge management for Local governors Model) Based on machine learning -Focused on On-Nara Document System- 2019

A Study on ELM(Election pledge management for Local governor...

引用

2nd international conference on Software Engineering and Information Management (ICSIM) / 2nd international conference on Big Data and Smart Computing (ICBDSC)

作者： Lee, Hong-Jae Han, Kyeong-Seok Kwon, Tae-Hyun Han, Sang-Ung Soongsil Univ Dept IT Policy Management Seoul 06978 South Korea Soongsil Univ Dept Management Seoul 06978 South Korea

ISBN: (纸本)9781450366427

The background of this paper is new social trend of more public's interest in the implementation of the pledge of the local governors who were elected by citizens. In these days the election pledge for enhanced local governmental policies became more important. The objective of this paper is to suggest the model of election pledge management for local government heads based on machine learning focused on On-Nara document system. The system is currently used by Korean governmental organizations for document processes. The methods to prove a comparative advantage of the proposed model are the comparison tests between As-Is system and To-Be system based on a few criteria such as time, efficiency and extraction rate. Through this model, local governors could present systematic goals and road map of pledges in order to get closer to citizens and local residents. In other words, this study proposes a model, so called ELM(Election pledge management for Local governors Model), for efficiently extracting necessary data from planned and implemented details of pledge projects that are prepared in the form of unstructured documents. We carried out research to prove empirically our machine learning-based model is more efficient than current semi-manual system with some automated processes in order to manage efficiently the pledge project implementation of local governors to get the results. In conclusion, this research proved that the proposed model is more competitive than the existing models. In the 4 th industrial revolution era the new approach using machine learning and big data will become more popular.

关键词： machine learning Big Data Natural Language processing Unstructured Database Election pledge management Text mining

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Query-Based machine learning Model for Data Analysis of Infrasonic signals in Wireless Sensor Networks 2018

Query-Based Machine Learning Model for Data Analysis of Infr...

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2nd international conference on Digital signal processing (ICDSP) / international conference on Computer Graphics and Virtuality (ICCGV)

作者： Chang, Ray-I Huang, Chien-Chang Lai, Liang-Bin Lee, Chia-Yun Natl Taiwan Univ Dept Engn Sci & Ocean Engn Taipei 106 Taiwan Chunghwa Telecom Co Ltd Taipei 100 Taiwan

ISBN: (纸本)9781450364027

As infrasonic signals can through objects and propagate at a long distance, infrasound sensors are widely applied in wireless sensor networks to monitor environment events of a large area. The signal conditions are usually complex and have various characteristics while monitoring the large area. Different features in both time and frequency domains should be extracted and considered. Big data increases the computation complexity, and the wrong selection of features may decreases the accuracy in event prediction. To overcome this problem, a query-based learning method is applied to select the proper features for smart edge computing in machine learning. Experimental results show that the proposed method provides good performance when comparing with previous feature selection methods.

关键词： signal processing systems sensor networks feature selection data analytics machine learning

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