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Chinese Journal of Electronics 2023年第2期19卷 275-278页

作者： Zhijun Wu Zhen Shi School of Electronics and Information Engineering Civil Aviation University of China Tianjin China

Low-rate denial of service (LDoS) attack is a new class of DoS, which exploits TCP's congestion control mechanism. LDoS attack can reduce the throughput of the attacked link by sending low-rate packets in the way of periodic pulse. Because of its low-rate characteristic, it can elude the monitor of traditional detection approach. Most applications in the Internet use TCP protocol, so the damage of LDoS attack is not inferior to the traditional flood type DDoS attack. The characteristics of LDoS attack in frequency domain were analyzed, by adopting digital signal processing technology. The filtering algorithm was designed as a Finite impulse response (FIR) filter to eliminate the illegitimate frequencies in frequency domain. Experiments result show that 92.88% of LDoS attack energy is filtered, and 19.75% of normal energy is eliminated.

关键词： Protocols Finite impulse response filters filtering Frequency-domain analysis Telecommunication traffic filtering algorithms Throughput

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Enhancing Autonomous Underwater Vehicle Sensor Precision with Adaptive Genetic Algorithm aided Kalman filtering

Enhancing Autonomous Underwater Vehicle Sensor Precision wit...

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International Conference on Computing and Networking Technology (ICCNT)

作者： Jegadit S Saravanan U S R Nithish R Giri Prasath B Balaji V Anantha Narayanan Tata Sudhakar Department of Computer Science and Engineering Amrita Vishwa Vidyapeetham Coimbatore India National Institute of Ocean Technology Chennai India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Accurate sensor data is crucial for the successful operation of Autonomous Underwater Vehicles (AUVs) in various underwater missions. However, achieving precision in sensor data pose significant challenges due to dynamic underwater environments and inherent sensor limitations. This paper investigates the utilization of the Kalman filter, a mathematical model, to enhance sensor data accuracy in AUV navigation. But the filter performance is highly dependent on accurate noise covariance matrices (process noise covariance Q and measurement noise covariance R). In dynamic environments, these noise characteristics can change rapidly and unpredictably, making it difficult to maintain optimal filter performance. Furthermore, it explores the integration of Genetic algorithms (GAs) with the Kalman filter to optimize its performance in dynamic conditions. The proposed hybrid approach, termed Adaptive Genetic Algorithm based Kalman Filter (AGAKF), dynamically adjusts filter parameters using GAs, ensuring optimal performance in real-time. The study evaluates the efficacy of the AGAKF method through extensive simulations and comparisons with traditional Kalman filtering coupled with genetic algorithm in MATLAB. Results demonstrate that the AGAKF approach achieves superior noise reduction and preserves signal characteristics more effectively, offering promising potential for enhancing AUV navigation accuracy in diverse underwater environments.

关键词： Autonomous underwater vehicles Accuracy filtering Navigation Noise Noise reduction filtering algorithms Kalman filters Vehicle dynamics Genetic algorithms

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Maneuvering Targets Fusion filtering for Multi-Source Heterogeneous Sensor Using GRU-Fusion Net

Maneuvering Targets Fusion Filtering for Multi-Source Hetero...

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International Conference on Vision, Image and Signal Processing (ICVISP)

作者： Wenwen Zhang Hao Huang Shaopeng Wei Song Wei Peng Ren College of Ocean and Space Information China University of Petroleum (East China) Qingdao China College of Electrical Engineering and Automation Shandong University of Science and Technology Qingdao China

ISBN: (数字)9798331541460

ISBN: (纸本)9798331541477

The ISAR imaging principle requires continuous time observation of maneuvering targets. The premise of continuous time observation is to track the target robustly and continuously. The data fusion technology of multi-source heterogeneous sensors is the key to improving the robustness of maneuvering target tracking. However, multi-source heterogeneous sensors face challenges in maneuvering target fusion tracking, including errors from time registration of data from different sources and incomplete accuracy in model representation. To address these issues, this paper proposes a GRU-Fusion Net method that combines multi-source heterogeneous sensor maneuvering target tracking algorithms with GRU networks and FC layers. This method applies Kalman filtering in each local filter to obtain the state estimation and error covariance matrix of the maneuvering target. Based on multi-source heterogeneous sensor fusion tracking algorithms and the maneuvering target state model, different network modules are designed to adapt to the cases where varying numbers of sensors have observations at the sampling points, thus processing local filter results to obtain the tracking results of the maneuvering target. Simulation results show that, compared to traditional multi-source heterogeneous sensor fusion tracking algorithms, the GRU-Fusion Net method effectively overcomes errors arising from time registration of data from different sources and inaccuracies in model representation, and improves the tracking accuracy of maneuvering targets.

关键词： Target tracking Accuracy filtering Signal processing algorithms Sensor fusion filtering algorithms Data models Sensors Kalman filters State estimation

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Robust Subband Adaptive filtering Algorithm Based on Fair Estimator

Robust Subband Adaptive Filtering Algorithm Based on Fair Es...

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Chinese Automation Congress (CAC)

作者： Chenggang Li Xiaohong Yin Kaili Yin Shaoyuan Li College of Automation and Electronic Engineering Qingdao University of Science and Technology Qingdao China

ISBN: (数字)9798350368604

ISBN: (纸本)9798350368611

This paper tackles the challenge of identifying sparse systems in environments with impulsive noise and proposes the FNSAF algorithm, which combines the Fair estimator with normalized subband adaptive filter (NSAF) algorithm and exhibits high robustness to impulsive noise. To enhance the flexibility of the FNSAF algorithm and enable it to adaptively balance the trade-off between rapid convergence as well as low steady-state MSD, we propose the adaptive threshold FNSAF algorithm (AF-NSAF). In this algorithm, the threshold parameter of the Fair function is updated in real time, allowing for better handling of complex noise environments. Additionally, based on energy conservation argument, stability conditions for both AF-NSAF and NSAF algorithms are provided, along with the computational complexity for the algorithms used in the experiments. Ultimately, computer simulations in impulsive noise scenarios validate the effectiveness of our algorithms for sparse system identification.

关键词： Noise Adaptive filters filtering algorithms Stability analysis Robustness Real-time systems Steady-state System identification Computational complexity Convergence

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Improvement of Euler Angle Algorithm Based on Extended Kalman filtering

Improvement of Euler Angle Algorithm Based on Extended Kalma...

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Intelligent Control, Measurement and Signal Processing and Intelligent Oil Field (ICMSP), International Conference on

作者： Yitao Zhu Yu Wang Department of Mechanical Nanjing University of Science and Technology Nanjing China

ISBN: (数字)9798331516550

ISBN: (纸本)9798331516567

Euler angles are essential parameters to describe the attitude of a carrier, and the Euler angles computed by the Inertial Measurement Unit (IMU) are affected by the performance of the components. Traditional Kalman filtering algorithms struggle to effectively suppress nonlinear Gaussian noise in engineering applications. Therefore, this paper employs an improved Extended Kalman filtering algorithm(EKF), which linearizes the nonlinear system using the Jacobian matrix. It utilizes quaternions and gyroscope errors as state variables, while accelerometers serve as measurement inputs to update the quaternions, thereby obtaining the Euler angles. Building on the updated Euler angles, a magnetometer is incorporated to further refine the calculation, mitigating the impact of gyroscope z-axis drift on the heading angle and eliminating external magnetic interference. Experimental results demonstrate that, compared to the Madgwick algorithm and complementary filtering, the improved algorithm significantly reduces the influence of component errors on Euler angle calculations, minimizes bias drift, and better reflects the actual operational conditions of the carrier, effectively achieving filtering results.

关键词： Magnetometers filtering Magnetic separation Quaternions Signal processing algorithms Process control Interference filtering algorithms Gyroscopes Kalman filters

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Intelligent filtering Tuning in Edge Computing

Intelligent Filtering Tuning in Edge Computing

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Midwest Symposium on Circuits and Systems (MWSCAS)

作者： Nieves G. Hernandez-Gonzalez Juan Montiel-Caminos Javier Sosa Juan A. Montiel-Nelson Department of Electronics Engineering and Automation (DIEA) Institute for Applied Microelectronics (IUMA) University of Las Palmas de Gran Canaria Las Palmas de Gran Canaria Spain

ISBN: (数字)9798350387179

ISBN: (纸本)9798350387186

An Artificial Intelligence approach to determine the best narrow input filter for a fundamental frequency extractor is presented. The algorithms are implemented on board a water current velocity sensor node. The target sensor node is based on an ARM Cortex-M0+ without DSP and FPU hardware support. The implementation is studied in detail in domains of real and integer variables. The results demonstrate that the proposed ANN-based solution is at least 5.5 and 15 times better than the published FFT solutions when using real variables and integer variables, respectively, for an input bandwidth reduction factor of 7.

关键词： Time-frequency analysis Program processors Microcontrollers filtering Neural networks filtering algorithms Real-time systems

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Skincare Recommendation System based on User’s Skin Characteristics Similarity using Collaborative filtering Approach

Skincare Recommendation System based on User’s Skin Charact...

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International Conference on Electrical Engineering, Computer and Information Technology (ICEECIT)

作者： Agnes Rinawati Naulita Sianturi Ari Kusumadewi Henry Lucky Meiliana Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798331504373

ISBN: (纸本)9798331504380

In order to obtain the optimal result from skincare products, consumer must use the ones that are suitable for their skin. However, the large amount of skincare products that are available in the market might cause confusion to consumer in choosing the right skincare products for themselves. Therefore, this study proposes a skincare recommendation system that will provide skincare product recommendations to consumer based on their skin characteristics. The proposed model will implement collaborative filtering approach by finding other consumer with similar skin characteristics using cosine similarity method, read-through the review comments to determine the rating of the products and create the relationship between users and products using Singular Vector Decomposition (SVD) algorithm. As a result, the proposed model shows an excellent result with accuracy score of 92.8%, highest among all baseline models. The proposed model also successfully outperformed other system that employed different approach with precision score of 76%. Hence, it is proven that the proposed model can give better skincare product recommendation to consumer.

关键词： Electrical engineering Accuracy Collaborative filtering filtering algorithms Information filters Skin Vectors Information technology Recommender systems Systematic literature review

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Encoding-Decoding-Based Recursive filtering for Nonlinear Systems Subject to Multiple Missing Measurements

Encoding-Decoding-Based Recursive Filtering for Nonlinear Sy...

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Chinese Automation Congress (CAC)

作者： Yongxu Liu Bo Jiang Fan Yang Jinnan Zhang Hongli Dong School of Electrical & Information Engineering Northeast Petroleum University Daqing China Artificial Intelligence Energy Research Institute Northeast Petroleum University Daqing China

ISBN: (数字)9798350368604

ISBN: (纸本)9798350368611

This paper investigates the recursive filtering (RF) problem for a class of time-varying nonlinear systems with an encoding-decoding mechanism. Multiple missing measurements (MMMs) phenomena are considered in the data transmission process, which is mainly caused by the transient failure of the sensor or external interference. A set of reciprocally independent Bernoulli random variables with uncertain probabilities are used to characterise the stochastic occurrence of MMMs. In wireless communication networks, a dynamic-quantization-based encoding-decoding mechanism is introduced to encrypt the measurements, safeguarding important data from potential theft. The connection between the real measurement and the decoded output is established through a bounded uncertainty. The purpose of this paper is to devise a RF algorithm for the considered phenomenon. Meanwhile, the filtering error covariance matrix is minimized by calculating the appropriate filtering gain. Finally, the effectiveness and practicality of the proposed filtering algorithm are verified by a simulation example.

关键词： Radio frequency Wireless communication Upper bound Uncertainty filtering filtering algorithms Random variables Cryptography Nonlinear systems Transient analysis

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Study of a Real-Time filtering Algorithm Suitable for LQR Control

Study of a Real-Time Filtering Algorithm Suitable for LQR Co...

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Artificial Intelligence and Automation Control (AIAC), International Conference on

作者： Xinpeng Yang Linghui Zhang Digital Manufacturing Laboratory Beijing Institute of Technology Beijing China China North Vehicle Research Institute Beijing China

ISBN: (数字)9798331529147

ISBN: (纸本)9798331529154

Linear quadratic regulator (LQR) is crucial in modern optimal control theory. However, its application to nonlinear unstable systems suffers from poor robustness and instability. To address this, this paper proposes a self-optimizing filtering algorithm from a signal processing perspective. The algorithm samples signal values via recursive queue, calculates averages, and limits them. Iterations find the optimal limit interval, and the average of interval values is the filtering result. Experimental verification on a first-order linear inverted pendulum shows this method outperforms others in accuracy, filtering quality, and stability, while meeting real-time control requirements. This improves LQR's robustness and control accuracy.

关键词： Accuracy Regulators filtering Noise Signal processing algorithms Process control Optimal control filtering algorithms Real-time systems Robustness

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A Novel Logarithmic Transformed Deep Galerkin Approach To Optimal filtering Problem

A Novel Logarithmic Transformed Deep Galerkin Approach To Op...

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IEEE Conference on Decision and Control

作者： Ji Shi Xiaopei Jiao Stephen S.-T. Yau Academy for Multidisciplinary Studies Capital Normal University Beijing Department of Applied Mathematics University of Twente Netherlands Department of Mathematical Sciences Tsinghua University and Beijing Institute of Mathematical Sciences and Application Beijing China

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

The optimal filtering problem for general nonlinear state-observation systems has garnered significant attention in control theory. At its core, optimal filtering involves determining the probability density function of the system state conditioned on historical observations. The Yau-Yau method [25], a pioneering framework, offers a viable approach with comprehensive theoretical guarantees and practical numerical implementation. Specifically, the Yau-Yau framework comprises two key components: offline solution of the forward Kolmogorov equation (FKE) and online data assimilation updates. The primary challenge lies in efficiently and accurately solving the FKE, as it directly impacts the real-time filtering process. To address this fundamental obstacle, we propose a highly efficient filtering algorithm that combines a FKE solver based on deep neural networks and a PDF approximator using generalized Legendre polynomials. By integrating advanced deep learning techniques with Galerkin approximation, we introduce the logarithmic transformed deep Galerkin approach (LTDG). The numerical simulations showcase the effectiveness and accuracy of our newly proposed algorithm. LTDG demonstrates superior performance compared to other methods, such as the extended Kalman filter and particle filter, and it successfully maintains the high accuracy of the Galerkin spectral method while having fewer online computational burdens.

关键词： Training Accuracy filtering Artificial neural networks filtering algorithms Probability density function Approximation algorithms Mathematical models Stability analysis Method of moments

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