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16th IEEE Conference on Industrial Electronics and Applications (ICIEA)

作者： Wang, Wei Jiang, Qilong Heng, Xidan Liu, Dong Li, Zhenwei Xie, Le Southwest Jiaotong Univ Sch Elect Engn Chengdu Peoples R China

ISBN: (纸本)9781665422482

Fault diagnosis of the subway plug door plays an essential role in the safe operation of the city subway. To improve the diagnosis accuracy of the subway plug door, fault diagnosis of the plug door based on Ensemble Empirical Mode Decomposition (EEMD) and adaptive feature extraction was presented in this paper. Firstly, EEMD was used for decomposition of raw data, and the intrinsic mode function (IMF) after decomposition was selected by correlation coefficient criteria. Then, the fault features in IMFs was extracted and the sensitive features among which was selected by the sensitive index. Finally, the faults were classfied by Gray Wolf optimized Support Vector Machine (GWO-SVM). The experiment with the measured data of a subway door shows that this fault diagnosis method can adaptively extract the relative optimal characteristic quantity . , identify the normal state and four different fault states effectively . with the recognition accuracy of 89.35%, which is valuable in the engineering application.

关键词： subway plug door fault diagnosis EEMD feature extraction algorithm GWO-SVM

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Anomaly detection based on feature extraction of unknown protocol payload format 5

Anomaly detection based on feature extraction of unknown pro...

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IEEE 5th Information Technology and Mechatronics Engineering Conference (ITOEC)

作者： Song, Zefan Wu, Bin Beijing Univ Posts & Telecommun Sch Cyberspace Secur Beijing Peoples R China Beijing Univ Posts & Telecommun Natl Disaster Recovery Technol Engn Lab Beijing Peoples R China

ISBN: (纸本)9781728143231;9781728143224

Intrusion detection technology is mainly divided into two categories: Misuse Detection and Anomaly Detection. Misuse Detection cannot detect unknown attacks, and the rate of false negatives is high. Abnormal Detection's false alarms rate is high, and practical applications are few. The current mainstream intrusion detection systems (IDS) consider application scenarios that are common network environments and pursue high cost performance. To make up for the deficiencies of IDS, this paper proposes an attack detection in special network, which exist a large number of user-defined unknown protocols. By extracting features from the unknown protocols, the characteristics of the unknown protocol format are obtained, and anomaly detection is performed based on this characteristic. This paper first demonstrates the feature extraction technology of unknown protocols, explains mathematically how to obtain the protocol format information of each layer of the unknown protocol, then conducts simulation experiments and compares the detection results with ordinary misuse detection and anomaly detection.

关键词： network security anomaly detection unknown protocol feature extraction algorithm

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Robust feature extraction for the Composite Surface Mesh from STL File

Robust Feature Extraction for the Composite Surface Mesh fro...

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9th International Conference for Young Computer Scientists

作者： Yang, Shengyuan Shu, Shi Xiangtan Univ Sch Math & Computat Sci Xiangtan 411105 Peoples R China Xiangtan Univ Coll Informat Engn Xiangtan 411105 Peoples R China

ISBN: (纸本)9781424441983

In this paper, we propose an algorithm of feature extraction for the composite surface triangle mesh from the stereo lithography (STL) file. By creating a database for quickly determining the dihedral angle threshold of the composite surface mesh, we give a simple feature extraction algorithm based on the dihedral angle threshold. Furthermore, combining with other techniques such as perimeter ratio and principal curvatures, we get an improved feature extraction algorithm for the composite surface triangle mesh. Numerical experiments demonstrate that this algorithm is robust and effective.

关键词： composite surface mesh triangle dihedral angle database feature extraction algorithm

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A Generalized Preprocessing and feature extraction Platform for Scalp EEG Signals on FPGA 3

A Generalized Preprocessing and Feature Extraction Platform ...

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IEEE INTERNATIONAL CONFERENCE ON BIOMEDICAL ENGINEERING AND SCIENCES

作者： Wijesinghe, L. P. Wickramasuriya, D. S. Pasqual, Ajith A. Univ Moratuwa Dept Elect & Telecommun Engn Moratuwa 10400 Sri Lanka

ISBN: (纸本)9781479940844

Brain-computer interfaces (BCIs) require real-time feature extraction for translating input EEG signals recorded from a subject into an output command or decision. Owing to the inherent difficulties in EEG signal processing and neural decoding, many of the feature extraction algorithms are complex and computationally demanding. Presently, software does exist to perform real-time feature extraction and classification of EEG signals. However, the requirement of a personal computer is a major obstacle in bringing these technologies to the home and mobile user affording ease of use. We present the FPGA design and novel architecture of a generalized platform that provides a set of predefined features and preprocessing steps that can be configured by a user for BCI applications. The preprocessing steps include power line noise cancellation and baseline removal while the feature set includes a combination of linear and nonlinear, univariate and bivariate measures commonly utilized in BCIs. We provide a comparison of our results with software and also validate the platform by implementing a seizure detection algorithm on a standard dataset and obtained a classification accuracy of over 96%. A gradual transition of BCI systems to hardware would prove beneficial in terms of compactness, power consumption and much faster response to stimuli.

关键词： brain-computer interfaces electroencephalography feature extraction field programmable gate arrays medical disorders medical signal detection neurophysiology signal classification signal denoising BCI application BCI systems BCIs EEG signal classification EEG signal processing FPGA design baseline removal bivariate measures classification accuracy decision feature extraction algorithm feature extraction platform feature set generalized platform generalized preprocessing home input EEG signals mobile user neural decoding nonlinear measures output command personal computer power line noise cancellation preprocessing steps real-time feature extraction scalp EEG signals seizure detection algorithm standard dataset univariate measures Computer architecture Discrete wavelet transforms Electroencephalography feature extraction Field programmable gate arrays Finite impulse response filters Histograms

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Study on Image Search Engine Based on Color feature algorithm

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International Conference on Materials Engineering for Advanced Technologies (ICMEAT2011)

作者： Huang, Xianying Chen, Weiwei Chongqing Univ Technol Coll Comp Sci & Engn Chongqing 400054 Peoples R China

ISBN: (纸本)9783037851517

Considering the expansion, stability and the maneuverability of a search engine, a modular search engine based on the combination of keywords, image characteristics and content was designed. For the characteristics of image, a quantified method of color characteristics based on the maximum size of connected regions was raised. Experiments show that the method improves the retrieval efficiency and can get an expected search result more accurately, so as to satisfy the customer's needs.

关键词： Image Retrieval Search Engine feature extraction algorithm connected area quantify

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Research on Robustness of Emotion Recognition Under Environmental Noise Conditions

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IEEE ACCESS 2019年 7卷 142009-142021页

作者： Huang, Yongming Xiao, Jing Tian, Kexin Wu, Ao Zhang, Guobao Southeast Univ Minist Educ Sch Automat Lab Measurement & Control Complex Syst Engn Nanjing 210096 Jiangsu Peoples R China

Noise is an unneglectable problem in emotion recognition if we want to put it into practice. First, aiming at the problem of noise in speech, we design a new acoustic feature, Long time frame Analysis Weighted Wavelet Packet Cepstral Coefficient (LW-WPCC), for better robustness. To extract LW-WPCC feature, first the best wavelet packet basis is constructed. On the basis of this, a robust wavelet packet Cepstral Coefficient is extracted by combining short time frame analysis with long time frame analysis. After that, we introduce a sub-band spectral center-of-mass parameter with good robustness to additive noise and propose an extraction algorithm of LW-WPCC. Through experiments on speech emotion recognition of different SNR levels, it is shown that our proposed method shows better noise robustness and performance on speech emotion recognition. Whats more, as facial expressions will not be affected by noise, we do bio-modal emotion recognition based on audio-visual data to improve robustness by making a decision-level fusion. Experiments based on audio-visual data are conducted to evaluate efficiency of our method. Results show that bio-modal emotion recognition based on audio-visual data can improve robustness and achieve better performance by benefiting from different kinds of emotion data.

关键词： Emotion recognition Speech recognition Wavelet packets feature extraction Robustness Wavelet analysis Acoustics Robust noise speech emotion recognition LW-WPCC feature feature extraction algorithm bio-modal emotion recognition

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Recognition of humans based on radar micro-Doppler shape spectrum features

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IET RADAR SONAR AND NAVIGATION 2015年第9期9卷 1216-1223页

作者： Ricci, Roberto Balleri, Alessio Univ Padua I-36100 Vicenza Italy Cranfield Univ Def Acad United Kingdom Ctr Elect Warfare Swindon SN6 8LA Wilts England

In this study, a feature extraction algorithm is presented which automatically generates a set of shape spectrum features based on the cadence velocity diagram of the human micro-Doppler signature. Recognition performance between humans undertaking the same activity is assessed on a set of experimental data collected with a continuous wave radar operating at X-band using a Naive Bayesian classifier and a shape-similarity-spectrum classifier. Recognition performance is analysed as a function of key parameters, such as the dwell time on the target and the size of the training set, to investigate the level of robustness of the proposed features. Results show that high level recognition performance can be achieved for both the walking and running activities.

关键词： object recognition Doppler radar feature extraction CW radar Bayes methods pattern classification gait analysis feature extraction algorithm radar micro-Doppler shape spectrum features set human recognition cadence velocity diagram human micro-Doppler signature continuous wave radar X-band Naive Bayesian classifier shape similarity spectrum classifier walking activity running activity

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LearningADD: Machine learning based acoustic defect detection in factory automation

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JOURNAL OF MANUFACTURING SYSTEMS 2021年 60卷 48-58页

作者： Zhang, Tao Ding, Biyun Zhao, Xin Liu, Ganjun Pang, Zhibo Tianjin Univ Sch Elect & Informat Engn Tianjin 300072 Peoples R China ABB Corp Res Sweden Dept Automat Technol S-72178 Vasteras Sweden Royal Inst Technol KTH Dept Intelligent Syst S-11428 Stockholm Sweden

Defect inspection of glass bottles in the beverage industrial is of significance to prevent unexpected losses caused by the damage of bottles during manufacturing and transporting. The commonly used manual methods suffer from inefficiency, excessive space consumption, and beverage wastes after filling. To replace the manual operations in the pre-filling detection with improved efficiency and reduced costs, this paper proposes a machine learning based Acoustic Defect Detection (LearningADD) system. Moreover, to realize scalable deployment on edge and cloud computing platforms, deployment strategies especially partitioning and allocation of functionalities need to be compared and optimized under realistic constraints such as latency, complexity, and capacity of the platforms. In particular, to distinguish the defects in glass bottles efficiently, the improved Hilbert-Huang transform (HHT) is employed to extend the extracted feature sets, and then Shuffled Frog Leaping algorithm (SFLA) based feature selection is applied to optimize the feature sets. Five deployment strategies are quantitatively compared to optimize real-time performances based on the constraints measured from a real edge and cloud environment. The LearningADD algorithms are validated by the datasets from a real-life beverage factory, and the F-measure of the system reaches 98.48 %. The proposed deployment strategies are verified by experiments on private cloud platforms, which shows that the Distributed Heavy Edge deployment outperforms other strategies, benefited from the parallel computing and edge computing, where the Defect Detection Time for one bottle is less than 2.061 s in 99 % probability.

关键词： Acoustic defect detection Edge computing Factory automation feature extraction algorithm

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Reconfigurable hardware architecture for faster descriptor extraction in SURF

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ELECTRONICS LETTERS 2018年第4期54卷 210-212页

作者： Kim, Y. Jung, H. Sookmyung Womens Univ Dept Comp Sci Seoul South Korea

Speeded up robust features (SURFs) are considered to be the most efficient feature extraction algorithm and it has been implemented in powerful hardware for real-time operation due to its characteristics of data-intensive computation of high complexity. Especially, the computational load of the descriptor extraction procedure is very significant and the overall performance of SURF can be improved by speeding up the descriptor extraction step with increasing parallel hardware accelerators. However, simply increasing the hardware accelerators is burdensome because of causing significant area and power consumption. Therefore, a reconfigurable hardware architecture is proposed that enables achieving the maximum performance of the descriptor extraction step with making the best use of the existing accelerators without any additional ones. Experimental results show that the proposed architecture improves the performance of the descriptor extraction step by 24.77-47.45% with negligible area overheads when compared with the existing hardware implementations of the SURF algorithm.

关键词： real-time systems feature extraction reconfigurable architectures reconfigurable hardware architecture descriptor extraction procedure speeded up robust features feature extraction algorithm real-time operation data-intensive computation computational load parallel hardware accelerators SURF algorithm

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A novel Intrusion Detection System for Vehicular Ad Hoc Networks (VANETs) based on differences of traffic flow and position

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APPLIED SOFT COMPUTING 2019年 75卷 712-727页

作者： Liang, Junwei Chen, Jianyong Zhu, Yingying Yu, Richard Shenzhen Univ Sch Comp & Software Engn Shenzhen Peoples R China Carleton Univ Sch Informat Technol Ottawa ON Canada

Vehicle ad hoc networks (VANETs) have attracted great interests from both industry and academia, but a number of issues, particularly security, have not been readily addressed. Intrusion Detection System (IDS) as one of the most important approaches to protect network security has been studied adequately in previous literatures. However, the performance of IDSs still needs to be improved to adapt the scenario of VANETs which are very fast moving and highly dynamic. In this paper, we propose a novel IDS that is able to be appropriately used in the wireless and dynamic networks, like VANETs. It mainly contains a novel feature extraction algorithm and a classifier based on an improved growing hierarchical self-organizing map (I-GHSOM) for IDS in VANETs. The proposed feature extraction algorithm is used to quickly extract distinct features from vehicle messages for IDS's training and test. In the proposed algorithm, two key features including the differences of traffic flow and of position are extracted. The former feature is calculated according to the range of the distance between vehicles, while both a voting filter mechanism and a semi-cooperative mechanism are designed to get the latter feature. Furthermore, in the I-GHSOM-based classifier, for quickly attaining precise classification results, two novel mechanisms (relabeling and recalculating mechanisms) are proposed to relabel the units of GHSOM and check whether the balance of GHSOM structure is broken or not. Simulation results show that the proposed IDS is better than others in the measurement of accuracy, stability, processing efficiency and message scales. (C) 2018 Elsevier B.V. All rights reserved.

关键词： Vehicle Ad Hoc Networks (VANETs) Intrusion detection system (IDS) feature extraction algorithm Improved growing hierarchical self-organizing map (I-GHSOM)

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