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A detection mechanism on malicious nodes in IoT

COMPUTER COMMUNICATIONS 2020年第0期151卷 51-59页

作者： Li, Bohan Ye, Renjun Gu, Gao Liang, Ruochen Liu, Wei Cai, Ken Nanjing Univ Aeronaut & Astronaut Coll Comp Sci & Technol Nanjing 211106 Peoples R China Minist Ind & Informat Technol Key Lab Safety Crit Software Nanjing 211106 Peoples R China Collaborat Innovat Ctr Novel Software Technol & I Nanjing Jiangsu Peoples R China Nanjing Inst Technicians Nanjing 210046 Peoples R China Zhongkai Univ Agr & Engn Coll Automat Guangzhou 510225 Peoples R China

The increasing scale of the Internet of Things (IoT) makes systems vulnerable to serious security threats, especially when the attacks of malicious nodes exist in these networks. Different malicious nodes will launch different attacks, but most of them are based on tampering, re-transmission, and discarding methods. For such attacks, an effective method to detect malicious nodes focuses on the received and sent messages of each node. However, gathering messages about each node in the network is time-consuming, as well as collecting all the message in the network would consume the limited resources in the IoT. In this paper, we propose a novel method to detect malicious nodes based on an online learning algorithm. We first calculate the credibility of each path in the network based on the collected packets., then modeled the got path reputation by the online learning algorithm, finally, calculated the trust of each node in the IoT environment and detected the malicious node by a clustering algorithm. To make the model have a good performance when the network scale is small, we perform some processing on the network topology based on the general online learning detection algorithm and get an enhanced online learning detection algorithm. The result of the experiment proves that the methods we proposed can detect malicious nodes with high accuracy and work well with good stability.

关键词： Malicious nodes Network topology online learning algorithm IoT

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Intelligent Positioning Approach for High Speed Trains Based on Ant Colony Optimization and Machine learning algorithms

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2019年第10期20卷 3737-3746页

作者： Cheng, Ruijun Song, Yongduan Chen, Dewang Ma, Xiaoping Beijing Jiaotong Univ State Key Lab Rail Traff Control & Safety Beijing 100044 Peoples R China Chongqing Univ Sch Automat Chongqing 400044 Peoples R China Fuzhou Univ Coll Math & Comp Sci Key Lab Spatial Data Min & Informat Sharing MOE Fuzhou 350116 Fujian Peoples R China

For high-speed train (HST), high-precision of train positioning is important to guarantee train safety and operational efficiency. For improving train positioning accuracy, we develop a mathematical positioning model by analyzing the wireless position report created by HST. To begin with, k-means algorithm is integrated with the least square support vector machine (LSSVM) to differentiate the position data and establish the corresponding prediction model for each position data class. Then, the ant colony optimization (ACO) algorithm is introduced to adaptively optimize the clustering number of position data and solve the over-fitting problem of the single k-means algorithm. So, a better classification of position data can be obtained by ACO-k-means than the single k-means algorithm. Furthermore, the online learning algorithms are designed for improving the adaptability and real-time performance of established positioning model. Finally, the field data of Beijing-Shanghai high-speed railway (BS_HSR) is used to test the performance of the established positioning models. Experiments on real-world positioning data sets from BS_HSR illustrate that the proposed methods can enhance the real-time performance in online updating process on the premise of reducing the positioning error.

关键词： High-speed train train positioning error LSSVM ant colony optimization (ACO) K-means algorithm online learning algorithm

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Sliding mode learning control of uncertain nonlinear systems with Lyapunov stability analysis

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TRANSACTIONS OF THE INSTITUTE OF MEASUREMENT AND CONTROL 2019年第6期41卷 1750-1760页

作者： Kayacan, Erkan MIT Senseable City Lab 77 Massachusetts Ave Cambridge MA 02139 USA MIT Comp Sci & Artificial Intelligence Lab 77 Massachusetts Ave Cambridge MA 02139 USA

This paper addresses the Sliding Mode learning Control (SMLC) of uncertain nonlinear systems with Lyapunov stability analysis. In the control scheme, a conventional control term is used to provide the system stability in compact space while a type-2 neuro-fuzzy controller (T2NFC) learns system behaviour so that the T2NFC completely takes over overall control of the system in a very short time period. The stability of the sliding mode learning algorithm has been proven in the literature;however, it is restrictive for systems without overall system stability. To address this shortcoming, a novel control structure with a novel sliding surface is proposed in this paper, and the stability of the overall system is proven for nth-order uncertain nonlinear systems. To investigate the capability and effectiveness of the proposed learning and control algorithms, the simulation studies have been carried out under noisy conditions. The simulation results confirm that the developed SMLC algorithm can learn the system behaviour in the absence of any mathematical model knowledge and exhibit robust control performance against external disturbances.

关键词： adaptive control neuro-fuzzy control online learning algorithm sliding mode control theory uncertain nonlinear systems

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Prediction and identification of discrete-time dynamic nonlinear systems based on adaptive echo state network

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NEURAL NETWORKS 2019年 113卷 11-19页

作者： Yao, Xianshuang Wang, Zhanshan Zhang, Huaguang Northeastern Univ Coll Informat Sci & Engn Shenyang 110819 Liaoning Peoples R China

In this paper, a new prediction and identification method based on adaptive echo state network (AESN) is proposed to identify a class of discrete-time dynamic nonlinear systems (DDNS). Firstly, according to the characteristics of input signals, the reservoir state update equation of AESN can be adaptively adjusted. In order to guarantee the echo state property of AESN, a sufficient condition for echo state property is given. Secondly, the reservoir parameters of AESN are optimized to improve the identification and prediction performance of AESN. Thirdly, an improved online output weights learning method based on historical reservoir state and output error is given. Finally, the effectiveness of the proposed method is verified by simulation examples. (C) 2019 Elsevier Ltd. All rights reserved.

关键词： Adaptive echo state network Prediction and identification online learning algorithm Discrete-time dynamic nonlinear systems

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online Portfolio Selection Based on Kalman Adaptive Moving Averages

World Scientific Research Journal

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World Scientific Research Journal 2020年第7期6卷 166-172页

作者： Xiaochan Xie

online portfolio selection based on Markowitz's mean-variance theory and Kelly's capital growth theory is a basic problem in financial engineering, which has attracted more and more attention and discussion in the fields of artificial intelligence and machine learning. Its main purpose is to distribute the existing wealth among investable assets in order to achieve the goal of maximizing the cumulative return. In view of the existing average inversion strategy without fully considering the noise data, slow trend and the characteristics of lagging, we use adaptive kaufman average to predict the price of the stock, which is based on stock market volatility, to avoid the short-term noise produced by false signals and lag of long-term trends, and use the PA of portfolio allocation algorithm. Theoretical analysis and empirical results shows that AMA strategy has superior performance in indicators such as cumulative income, CR, SR, DMM, etc. in DIJA data.

关键词： Kraft Man adaptive moving average online portfolio selection online learning algorithm

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Fault Diagnosis of Components and Sensors in HVAC Air Handling Systems With New Types of Faults

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IEEE ACCESS 2018年 6卷 21682-21696页

作者： Yan, Ying Luh, Peter B. Pattipati, Krishna R. Univ Connecticut Elect & Comp Engn Storrs CT 06269 USA

Air handling systems are the key sub-systems of heating ventilation and air conditioning (HVAC) systems. They condition and deliver air to satisfy human thermal comfort requirements and provide acceptable indoor air quality. Faults in their components and sensors may lead to high-energy consumption, poor thermal comfort, and unacceptable indoor air quality. Additionally, new types of faults may falsely be identified as known types. Identifying failure modes and their severities with low false identification rates is thus critical to know what faults occur and how severe they are. However, this is challenging, since 1) classifying both failure modes and fault severities generates many categories of failures, leading to high computational requirements;2) updating model parameters to adapt to changing environments requires accurate recursive equations that are hard to obtain;and 3) model errors and measurement noise may cause high false identification rates in detecting new types of faults. In this paper, failure modes are identified by hidden Markov models (HMMs) and fault severities are estimated by filtering methods, leading to a decrease in the number of HMM states and low computational requirements. To adapt to changing environments, a new online learning algorithm is developed. In this algorithm, HMM parameters are obtained based on their posterior distributions given new observations, thereby avoiding the need for accurate recurrence equations. To identify new fault types with low false identification rates, a robust statistical method is developed to compare current HMM observations with those expected from existing states to obtain potential new types, and then confirm new types by checking whether observations have a significant change. Physical knowledge is then used to find the reason for the new fault type. Experimental results show that failure modes and fault severities of both known and new types of faults are identified with high accuracy.

关键词： Fault diagnosis HVAC air handling system online learning algorithm hidden Markov model new fault types

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A self-learning disturbance observer for nonlinear systems in feedback-error learning scheme

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2017年 62卷 276-285页

作者： Kayacan, Erkan Peschel, Joshua M. Chowdhary, Girish Univ Illinois Coordinated Sci Lab Urbana IL 61801 USA Iowa State Univ Dept Agr & Biosyst Engn Ames IA 50011 USA

This paper represents a novel online self-learning disturbance observer (SLDO) by benefiting from the combination of a type-2 neuro-fuzzy structure (T2NFS), feedback-error learning scheme and sliding mode control (SMC) theory. The SLDO is developed within a framework of feedback-error learning scheme in which a conventional estimation law and a T2NFS work in parallel. In this scheme, the latter learns uncertainties and becomes the leading estimator whereas the former provides the learning error to the T2NFS for learning system dynamics. A learning algorithm established on SMC theory is derived for an interval type-2 fuzzy logic system. In addition to the stability of the learning algorithm, the stability of the SLDO and the stability of the overall system are proven in the presence of time-varying disturbances. Thanks to learning process by the T2NFS, the simulation results show that the SLDO is able to estimate time-varying disturbances precisely as distinct from the basic nonlinear disturbance observer (BNDO) so that the controller based on the SLDO ensures robust control performance for systems with time-varying uncertainties, and maintains nominal performance in the absence of uncertainties.

关键词： Disturbance observer Neural networks Neuro-fuzzy structure online learning algorithm Robustness Sliding mode control Type-2 fuzzy logic systems Uncertainty

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online learning algorithm of Gaussian Process Based on Adaptive Natural Gradient for Regression

Online Learning Algorithm of Gaussian Process Based on Adapt...

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International Conference on Manufacturing Engineering and Automation

作者： Shen, Qianqian Sun, Zonghai S China Univ Technol Coll Automat Sci & Engn Guangzhou 510640 Peoples R China

ISBN: (纸本)9780878492268

dGaussian Process (GP) is a new learning method on nonlinear system modeling. The most common way of model training is conjugate gradient method, but this method should compute Heisenberg matrix which needs much computing resource. It is not a suitable training method for online learning algorithm. There is one online learning algorithm of GP which is named sparse online GP now. This algorithm has constraint to the training data sets. In order to satisfy the real-time modeling without the limit of the training data sets, an online algorithm of GP based on adaptive natural gradient (ANG) is proposed in this paper. The algorithm is applied in Continuous Stirred Tank Reactor (CSTR) modeling and the sparse online GP is also applied in CSTR modeling for comparison. Obtained from the simulation results, the algorithm is effective and has higher Accuracy compared with the sparse online GP algorithm.

关键词： Gaussian process regression online learning algorithm Adaptive nature gradient CSTR modeling

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Fast intra coding unit decision for high efficiency video coding based on statistical information

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SIGNAL PROCESSING-IMAGE COMMUNICATION 2017年 55卷 121-129页

作者： Lee, Dokyung Jeong, Jechang Hanyang Univ Dept Elect & Comp Engn Seoul 133791 South Korea

The latest video coding compression standard is known as high efficiency video coding (HEVC). It supports high resolution video sequences and has better coding performance than the,previous standard H.264/AVC. A quad tree based coding unit (CU) partitioning process is one of the most efficient technologies used in an HEVC encoder. A coding tree unit (typically 64 x 64) can be split into smaller CUs based on rate-distortion optimization, allowing various types of video content to be adaptively compressed. In addition, intra prediction of the HEVC standard supports 35 prediction modes (planar, DC, and 33 angular modes) to improve coding efficiency. However, the computational complexity of HEVC encoder becomes a critical problem when implement with an encoder. Thus, a fast CU size decision algorithm for intra prediction of an HEVC encoder is proposed in this study. We utilize image complexity and an adaptive depth prediction for early split CU decision making. In addition, the Bayesian decision rule and quadratic discriminant analysis are used for early termination of the CU partitioning process. Experimental results show that our proposed algorithm considerably reduces encoding time by approximately 55.47% with only a small BD-BR loss (1.01%) compared to the HEVC reference software HM 16.0.

关键词： High-efficiency video coding (HEVC) Fast coding unit size decision online learning algorithm Bayes decision Quadratic discriminant analysis (QDA) Video compression

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Comparison of online Adaptive learning algorithms for Myoelectric Hand Control 9

Comparison of Online Adaptive Learning Algorithms for Myoele...

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9th International Conference on Human System Interactions (HSI)

作者： Zhang, Yue Wang, Zheng Zhang, Zhuo Fang, Yinfeng Liu, Honghai Zhejiang Univ Technol Coll Comp Sci & Technol Hangzhou Zhejiang Peoples R China Univ Portsmouth Sch Comp Portsmouth PO1 2UP Hants England Shanghai Jiao Tong Univ Sch Mech Engn Shanghai Peoples R China

ISBN: (纸本)9781509017294

Pattern recognition (PR) based myoelectric hand control has become a research focus in the field of rehabilitative engineer and intelligent control. However, the state of the art method is hardly adopted for clinical use because of signal interfered by shift, fatigue and user-unfriendly of retraining. The aim of this study is to evaluate the performance of different kinds of online algorithms in classifying the myoelectric hand motions, and reveal the key factors to classification accuracy of online learning algorithms. Two groups of experiments on intra-session and inter-session were designed to evaluate the classification and recognition performance of overall methods. The comparison results show that the second-order online learning algorithms outperformed the first-order algorithms in classification and recognition. Soft confidence-weighted learning performs best with 99% classification rate in same session and over 85% recognition rate in different session. This paper uncovers the online learning with large margin and confidence weight can always acquire a good property. In addition, online learning algorithms retrain the classification model by incorporating the testing data to the previous model by measuring the changes between the predicted label and true label which can improve the performance in long-term use.

关键词： Surface Electromyography Pattern Recogniton online learning algorithm Hand Motion

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