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20th Chinese Intelligent Systems Conference

作者： Fu, Yihao Mo, Lipo Beijing Technol & Business Univ Sch Math & Stat Beijing 100048 Peoples R China

ISBN: (纸本)9789819786565;9789819786541;9789819786534

This paper explores online noncooperative games (NGs) with constrained involving multi-agent systems on unbalanced directed graphs (digraphs), where players try to minimize their objective functions selfishly, and the objective functions and decisions of it are vary with time. Additionally, the players are bound to time-varying constraints. To seek the stable sequence of the game online, that is the generalized Nash equilibrium (GNE) sequence, we developed a distributed online learning algorithm, which utilizing primal-dual, gradient descent, and projection methods. This approach achieved sublinear bounded dynamic regrets and constraint violations. Ultimately, the example of online electricity market games demonstrates the effectiveness of the introduced algorithm.

关键词： Generalized nash equilibrium Multi-agent systems online learning algorithm Noncooperative games Unbalanced digraphs

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Automated human emotion recognition using hybrid approach based on sensitivity analysis on residual time-frequency plane with online learning algorithm

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BIOMEDICAL SIGNAL PROCESSING AND CONTROL 2023年第1期84卷

作者： Sharma, Rahul Teerthanker Mahaveer Univ Moradabad Dept Elect & Commun Engn Moradabad 244001 Uttar Pradesh India

Emotions are neurophysiological changes that are modulated by central , peripheral nerve modulators that cause transient agitation due to the synchronized firing of nerve cells. Hence, the electroencephalogram (EEG) signal perfectly reflected the stimulated excitations of emotions. The excitation of these neurons corresponding to different emotions varies with instantaneous time. The focus of this work is to develop effective, efficient , scalable data mining tools to study brain signals. The sensitivity analysis technique is employed to extract the valuable knowledge embedded in the vast amounts of brain data. It measures dilation parameters as a function of spectral domain changes without compromising the integrity of the original data. A residual time-frequency wavelet analogy is introduced to explore the EEG signals in the high-dimensional plane. Various nonlinear statistics are measured to extract the underlying complexity and variance discrepancy from the residual time-frequency domain. The feature matrix is further processed by a feature reduction and selection algorithm followed by a classifier to maximize the classification probability. This emotionally labeled feature matrix is augmented to enhance the dimension of the feature space. A bi-LSTM based deep learning network is used to secure a maximum probability of classification in a short time. The results reveal that the proposed algorithm maintains multiple potentials to achieve an excellent classification accuracy of order 78.37%, which helps researchers to understand the EEG patterns associated with each emotion.

关键词： Electroencephalogram signal Spectral domain Residual wavelet transform Statistical variants online learning algorithm

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Vibration-based damage detection using online learning algorithm for output-only structural health monitoring

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STRUCTURAL HEALTH MONITORING-AN INTERNATIONAL JOURNAL 2018年第4期17卷 727-746页

作者： Jin, Seung-Seop Jung, Hyung-Jo Korea Adv Inst Sci & Technol Dept Civil & Environm Engn 291 Daehak Ro Daejeon 305701 South Korea

Damage-sensitive features such as natural frequencies are widely used for structural health monitoring;however, they are also influenced by the environmental condition. To address the environmental effect, principal component analysis is widely used. Before performing principal component analysis, the training data should be defined for the normal condition (baseline model) under environmental variability. It is worth noting that the natural change of the normal condition may exist due to an intrinsic behavior of the structural system. Without accounting for the natural change of the normal condition, numerous false alarms occur. However, the natural change of the normal condition cannot be known in advance. Although the description of the normal condition has a significant influence on the monitoring performance, it has received much less attention. To capture the natural change of the normal condition and detect the damage simultaneously, an adaptive statistical process monitoring using online learning algorithm is proposed for output-only structural health monitoring. The novelty aspect of the proposed method is the adaptive learning capability by moving the window of the recent samples (from normal condition) to update the baseline model. In this way, the baseline model can reflect the natural change of the normal condition in environmental variability. To handle both change rate of the normal condition and non-linear dependency of the damage-sensitive features, a variable moving window strategy is also proposed. The variable moving window strategy is the block-wise linearization method using k-means clustering based on Linde-Buzo-Gray algorithm and Bayesian information criterion. The proposed method and two existing methods (static linear principal component analysis and incremental linear principal component analysis) were applied to a full-scale bridge structure, which was artificially damaged at the end of the long-term monitoring. Among the three methods, t

关键词： Structural health monitoring environmental variability adaptive statistical process monitoring online learning algorithm variable moving window strategy

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online learning of order flow and market impact with Bayesian change-point detection methods

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QUANTITATIVE FINANCE 2025年第2期25卷 307-322页

作者： Tsaknaki, Ioanna-Yvonni Lillo, Fabrizio Mazzarisi, Piero Scuola Normale Super Pisa Piazza Cavalieri 7 I-56126 Pisa Italy Univ Bologna Dept Math Piazza Porta San Donato 5 I-40126 Bologna Italy Univ Siena Dept Econ & Stat Banchi Sotto 55 I-53100 Siena Italy

Financial order flow exhibits a remarkable level of persistence, wherein buy (sell) trades are often followed by subsequent buy (sell) trades over extended periods. This persistence can be attributed to the division and gradual execution of large orders. Consequently, distinct order flow regimes might emerge, which can be identified through suitable time series models applied to market data. In this paper, we propose the use of Bayesian online change-point detection (BOCPD) methods to identify regime shifts in real-time and enable online predictions of order flow and market impact. To enhance the effectiveness of our approach, we have developed a novel BOCPD method using a score-driven approach. This method accommodates temporal correlations and time-varying parameters within each regime. Through empirical application to NASDAQ data, we have found that: (i) Our newly proposed model demonstrates superior out-of-sample predictive performance compared to existing models that assume i.i.d. behavior within each regime;(ii) When examining the residuals, our model demonstrates good specification in terms of both distributional assumptions and temporal correlations;(iii) Within a given regime, the price dynamics exhibit a concave relationship with respect to time and volume, mirroring the characteristics of actual large orders;(iv) By incorporating regime information, our model produces more accurate online predictions of order flow and market impact compared to models that do not consider regimes.

关键词： Regime switching models Bayesian methods Price impact Metaorders online learning algorithm

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online DDoS attack detection using Mahalanobis distance and Kernel-based learning algorithm

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JOURNAL OF NETWORK AND COMPUTER APPLICATIONS 2020年 168卷 102756-102756页

作者： Cakmakci, Salva Daneshgadeh Kemmerich, Thomas Ahmed, Tarem Baykal, Nazife Middle East Tech Univ METU Grad Sch Informat Ankara Turkey Norwegian Univ Sci & Technol NTNU Dept Informat Secur & Commun Technol Gjovik Norway Independent Univ Bangladesh IUB Dept Comp Sci & Engn Dhaka Bangladesh Univ Bremen Dept Comp Sci Grp Comp Architecture Bremen Germany

Distributed denial-of-service (DDoS) attacks are constantly evolving as the computer and networking technologies and attackers' motivations are changing. In recent years, several supervised DDoS detection algorithms have been proposed. However, these algorithms require a priori knowledge of the classes and cannot automatically adapt to frequently changing network traffic trends. This emphasizes the need for the development of new DDoS detection mechanisms that target zero-day and sophisticated DDoS attacks. In this paper, we propose an online, sequential, DDoS detection scheme that is suitable for use with multivariate data. The proposed algorithm utilizes a kernel-based learning algorithm, the Mahalanobis distance, and a chi-square test. Initially, we extract four entropy-based and four statistical features from network flows per minute as detection metrics. Then, we employ the kernel-based learning algorithm using the entropy features to detect input vectors that were suspected to be DDoS. This algorithm assumes no model for network traffic or DDoS. It constructs and adapts a dictionary of features that approximately span the subspace of normal behavior. Every T minutes, the Mahalanobis distance between suspicious vectors and the distribution of dictionary members is measured. Subsequently, the chi-square test is used to evaluate the Mahalanobis distance. The proposed DDoS detection scheme was applied to the CICIDS2017 dataset, and we compared the results with those given by existing algorithms. It was demonstrated that the proposed online detection scheme outperforms almost all available DDoS classification algorithms with an offline learning process.

关键词： online learning algorithm DDoS KOAD E-KOAD Mahalanobis distance Chi-square test

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Robust Tracking Control for Quadrotor UAV With External Disturbances and Uncertainties Using Neural Network Based MRAC

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IEEE ACCESS 2024年 12卷 36183-36201页

作者： Madebo, Muluken Menebo Abdissa, Chala Merga Lemma, Lebsework Negash Negash, Dereje Shiferaw Addis Ababa Univ Sch Elect & Comp Engn Addis Ababa 1000 Ethiopia

In this paper, an intelligent Model Reference Adaptive Control (MRAC) based on a neural network is proposed for robust tracking control of quadrotor UAV under external disturbances and parameter variations. First, the singularity-free dynamic model of the quadrotor is developed using Newton-Quaternion formalism. Then, conventional MRAC is designed to generate training data. With the generated data, the Feed Forward Neural Network (FFNN) and Recurrent Neural Network (RNN) are trained offline to get an initial set of network parameters for position controller parameters estimation and attitude control of the quadrotor, respectively, and an online learning algorithm is developed to update those network parameters in real-time. Finally, the performance of the designed Neural network-based MRAC has been evaluated using a numerical simulation in a nominal scenario and by introducing parametric variation and external disturbances as matched and unmatched uncertainties into the system. The simulation results show that the proposed controller has a better tracking performance and disturbance rejection capability compared with the Linear Quadratic Regulator (LQR) and conventional MRAC. Furthermore, the utilized control efforts are minimal and smooth proving functional safety and economical use of the controller. Therefore, the suggested controller is feasible for real-time implementation of the quadrotor UAV.

关键词： Feed forward neural network LQR model reference adaptive control newton-quaternion formalism online learning algorithm recurrent neural network quadrotor UAV

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An Improved online learning algorithm and its Applications on Leak Points Prediction of Gas Pipe in Petrochemical Industries

An Improved Online Learning Algorithm and its Applications o...

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Annual Conference of the IEEE Industrial Electronics Society

作者： Linchao Zhuo Kun Wang Lei Shu Chunsheng Zhu Zhiyou Ouyang Nanjing University of Posts and Telecommu University of California Guangdong University of Petrochemical Technology University of British Columbia

ISBN: (纸本)9781479940318

In petrochemical industries, one of the most concerned problems is the leaking of toxic gas. Once leaking occurs, the safety of equipments located in production site is greatly threatened, thereby affecting surrounding environment. In order to solve this problem, it is necessary to predict the possible location of leak points from sensors which are located in gas pipe. On the other hand, data from sensors of petrochemical industries need to be timely operated because of time sensitivity, and it is hard to achieve associated information from sensors located in production site. To this end, an OLA-IBP (online learning algorithm based on Improved Back Propagation) is proposed. The adaptive structure of this algorithm is settled on-line. Meanwhile, real-time data streams are parallelly processed according to arriving time in input layer. Simulation results show that OLA-IBP can efficiently improve learning time and accuracy rate. Finally, the adaptability of OLA-IBP is verified in leak points prediction of petrochemical equipments from processed data.

关键词： BP algorithm online learning algorithm Leak points prediction Petrochemical industries

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An online Zero-Forcing Precoder for Weighted Sum-Rate Maximization in Green CoMP Systems

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IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS 2022年第9期21卷 7566-7581页

作者： Dong, Yanjie Zhang, Haijun Li, Jianqiang Yu, F. Richard Guo, Song Leung, Victor C. M. Shenzhen Univ Coll Comp Sci & Software Engn Shenzhen 518060 Peoples R China Univ Sci & Technol Beijing Beijing Adv Innovat Ctr Mat Genome Engn Beijing Engn & Technol Res Ctr Convergence Networ Inst Artificial Intelligence Beijing 100083 Peoples R China Carleton Univ Dept Syst & Comp Engn Ottawa ON K1S 5B6 Canada Hong Kong Polytech Univ Dept Comp Hong Kong Peoples R China Univ British Columbia Dept Elect & Comp Engn Vancouver BC V6T 1Z4 Canada

Following the roadmap of carbon neutrality, wireless communication systems are upgrading to use green energy that comes from renewable sources, e.g., sun, tide, and wind. Due to the volatile arrival of green energy, the on-grid energy is used as a backup for a green coordinated multiple point system. In this work, a weighted sum-rate maximization problem in the green coordinated multiple point system is investigated by expecting non-positive consumption of the on-grid energy in the long term. Motivated by the capacity-achieving property and simple implementation, an online zero-forcing dirty paper precixler is proposed to update the preceding matrices by combining statistical learning with the Lyapunov learning technique. A tradeoff relation is theoretically established to show that the long-term weighted sum rate approaches the O(V)-neighbor of optimal value while the long-term on-grid energy increases at a rate of O(log(2) (v)/root V), where V is an introduced control parameter. Numerical results are used to verify the performance of the proposed online adaptive precoder.

关键词： Adaptive precoder CoMP green communications online learning algorithm

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AN online THROUGHPUT MAXIMIZATION algorithm FOR GREEN COORDINATED MULTI-POINT SYSTEMS 47

AN ONLINE THROUGHPUT MAXIMIZATION ALGORITHM FOR GREEN COORDI...

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47th IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

作者： Dong, Yanjie Zhang, Haijun Li, Jianqiang Yu, F. Richard Guo, Song Leung, Victor C. M. Shenzhen Univ Shenzhen Peoples R China Univ Sci & Technol Beijing Beijing Peoples R China Carleton Univ Ottawa ON Canada Hong Kong Polytech Univ Hong Kong Peoples R China Univ British Columbia Vancouver BC Canada

ISBN: (纸本)9781665405409

Wireless systems are upgraded to use green energy (e.g., solar, wind, and tide energy) such that the greenhouse gas emission can be neutralized. This work incorporates the on-grid energy into a green coordinated multi-point (CoMP) system to handle the volatile arrival of green energy. In the green CoMP, the long-term weighted throughput maximization problem is investigated by expecting a non-positive consumption of the long-term on-grid energy. Motivated by the capacity-achieving property and simple implementation, an online zero-forcing dirty paper precoder is proposed to update the precoding matrices by combining statistical learning with the Lyapunov learning. A tradeoff relation is theoretically established to show that the long-term weighted throughput approaches the O(V)-neighbor of optimal value while the long-term consumed on-grid energy increases at a rate of O(log(2) (V)/root V), where V is an introduced control parameter. Numerical results are used to verify the performance of the online zero-forcing dirty paper precoder.

关键词： Coordinated multi-point transmission green communications online learning algorithm

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Spectrum sensing in cognitive radio networks: threshold optimization and analysis

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EURASIP JOURNAL ON WIRELESS COMMUNICATIONS AND NETWORKING 2020年第1期2020卷 1-19页

作者： Kockaya, Kenan Develi, Ibrahim Sivas Cumhuriyet Univ Dept Divrigi & Nuri Demirag Vocat High Sch TR-58300 Sivas Turkey Erciyes Univ Dept Elect & Elect Engn TR-38039 Kayseri Turkey

Cognitive radio is a technology developed for the effective use of radio spectrum sources. The spectrum sensing function plays a key role in the performance of cognitive radio networks. In this study, a new threshold determination method based on online learning algorithm is proposed to increase the spectrum sensing performance of spectrum sensing methods and to minimize the total error probability. The online learning algorithm looks for the optimum decision threshold, which is the most important parameter to decide the presence or absence of the primary user, using historical detection data. Energy detection- and matched filter-based spectrum sensing methods are discussed in detail. The performance of the proposed algorithm was tested over non-fading and different fading channels for low signal-to-noise ratio regime with noise uncertainty. In the conclusion of the simulation studies, improvement in spectrum sensing performance according to optimal threshold selection was observed.

关键词： Spectrum sensing Energy detection Threshold Machine learning algorithm online learning algorithm

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