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Study on the Method of Underwater Weak signal Detection in Lévy Noise Based on Stochastic Resonance 3

Study on the Method of Underwater Weak Signal Detection in L...

引用

3rd IEEE international conference on Information Technology, Big data and Artificial Intelligence, ICIBA 2023

作者： Liu, Xiaodong Zhang, Xiangjin Gu, Yingyan Ding, Jun Nanjing University of Science and Technology School of Mechanical Engineering Nanjing210094 China Jiangsu Automation Research Institute Lianyungang222061 China

ISBN: (纸本)9781665490788

For underwater weak signal detection, a lot of research focuses on extracting useful signal information under strong noise that using stochastic resonance, but it is basically carried out in the Gaussian white noise environment, and there is little research on stochastic resonance under Lévy noise excitation. In this paper, the generation method of Lévy noise is introduced, the numerical solution method of bistable system under Lévy noise is given, and the output of bistable system is analyzed by FFT. By analyzing the output of the bistable system, it is found that the bistable system will also have stochastic resonance under the excitation of Lévy noise, and the signal information submerged in the Lévy noise can also be identified. To prove the effectiveness of the above method, we did a set of computer simulation experiments. This method can realize the weak signal detection underwater the environment of Lévy nosie. © 2023 IEEE.

关键词： Fast Fourier transforms

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A Review on Optimization and Feature Selection Techniques for data Security in IoT 3

A Review on Optimization and Feature Selection Techniques fo...

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3rd international conference on Pervasive Computing and Social Networking, ICPCSN 2023

作者： Jose, Jisha Judith, J.E. Noorul Islam Centre for Higher Education Kumarakovil Department of Computer Science and Engineering Kanyakumari District Tamil Nadu Thuckalay India

ISBN: (纸本)9798350322842

Since IoT devices are becoming increasingly widespread, the security of IoT-dependent networks gets highly significant. Because of the Internet of Things' fast global proliferation, there has been a massive surge in cyber-attacks on it. Such products are connected to end-user over a public platform for flexibility of use, thereby increasing the scope of threat and endangering the confidentiality of users' data. Anyone may utilize the flaw in IoT nodes virtually anywhere that emphasizes processing resources confidentiality and safety. Optimization algorithms with the strongest feature selection approach were proposed as a potential option for minimizing different threats and to ensure data security. This paper examines and reviews Machine Learning and Evolutionary Swarm Intelligence based Optimization Algorithms with existing feature selection techniques. Further it discusses the significance of these techniques in IoT security and gives a thorough comparison of several optimization models along with the feature selection strategies used to build the models. © 2023 IEEE.

关键词： Optimization

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The Node2vec Algorithm of Network Structure Based on Wavelet Coefficients Sampling 3

The Node2vec Algorithm of Network Structure Based on Wavelet...

引用

3rd international signal processing, Communications and Engineering Management conference, ISPCEM 2023

作者： Li, Baozhen Su, Yuwei Sun, Ning School of Statistics and Data Science Nanjing Audit University Nanjing China School of Computer Science Nanjing Audit University Nanjing China College of Tourism and Service Management Nankai University Tianjin China

ISBN: (纸本)9798350307023

As information technology is constantly developing towards networking, various network embedding methods have emerged to deeply explore the inherent characteristics of network data. There is limited research using network topology information and node attribute information in the N ode2vec algorithm of network structure. This study develops a new sampling method to use the ability of wavelet coefficients to describe structure information, introducing wavelet coefficients sampling to replace traditional sampling methods. The new sampling measurement method combined with spatial neural networks can simultaneously consider topology information and node attributes. This study conduct a series of node classification experiments using samples with different sizes and neighborhoods from publicly available datasets in various fields to validate the effectiveness of the algorithm. The findings of this study suggest that we can effectively improve algorithm performance by using the characteristics of wavelet coefficients to sample nodes and combining it with spatial neural networks as a new sampling method. © 2023 IEEE.

关键词： Neural networks

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Fault Diagnosis of INFO-SVM Transformer Based on ADASYN 3

Fault Diagnosis of INFO-SVM Transformer Based on ADASYN

引用

3rd international conference on Electrical Engineering and Control Science, IC2ECS 2023

作者： Feng, Xin Wu, Weili School of Electrical and Control Engineering Xi'an University of Science and Technology Xi'an China

ISBN: (纸本)9798350382426

The imbalance between different types of DGA data in transformers makes power transformer fault diagnosis models tend to identify the tested samples as categories with a higher proportion in the training samples, which can lead to a decrease in consistency for imbalanced datasets. At the same time, common methods have the problem of low accuracy in fault diagnosis. In this regard, an ADASYN-INFO-SVM transformer fault diagnosis model is proposed, which can synthesize samples of the original few class data while retaining the characteristics of the few class data, so that the number of different types of samples in the training set reaches equilibrium. At the same time, the vector weighted average algorithm (INFO) is used to optimize the support vector machine (SVM) while achieving automatic optimization of SVM parameters, it can also improve the overall diagnostic accuracy. The results show that ADASYN can effectively reduce the imbalance phenomenon, fully extract fault information, and the fault diagnosis accuracy of the INFO optimized SVM model after imbalance processing reaches 96.58%, which is better than the optimization results of other commonly used algorithms. © 2023 IEEE.

关键词： Fault diagnosis INFO-SVM Transformer Unbalanced data processing

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An Overview of GPT-4's Characteristics through the Lens of 10V's of Big data 3

An Overview of GPT-4's Characteristics through the Lens of 1...

引用

3rd international conference on Intelligent Cybernetics Technology and Applications, ICICyTA 2023

作者： Alyasiri, Osamah Mohammed Akhtom, Dua'a Alrasheedy, Mashary N. Al-Furat Al-Awsat Technical University Karbala Technical Institute Karbala Iraq Universiti Sains Malaysia School of Computer Sciences Penang Malaysia University of Ha'il Department of Computer Science Applied College Ha'il Saudi Arabia Universiti Kebangsaan Malaysia Faculty of Information Science and Technology Bangi Malaysia

ISBN: (纸本)9798350394559

The vast reservoirs of archival and consequential data within Big data present an invaluable resource for organizations, capable of fortifying business decisions on factual evidence rather than subjective perceptions. The comprehensive framework of the 10 Vs - volume, veracity, velocity, variety, value, validity, variability, venue, vocabulary, and vagueness - represents the most sophisticated approach to defining Big data. This research explores the potential of integrating GPT-4, a pre-trained Natural Language processing (NLP) model, into the handling of Big data within the context of these 10V's. Highlighting GPT-4's pivotal role in the era of massive information, the study confirms its alignment with key aspects of Big data processing, including volume, variety, velocity, value, validity, vocabulary, and addressing vagueness, despite its limitation to textual and image data. Consequently, this research serves as a foundational guide for both researchers and industries, proposing GPT-4 as an effective AI tool for handling extensive and diverse Big data with enhanced efficiency. © 2023 IEEE.

关键词： data Analytics

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A Spatio-Temporal Multi-Subgraph Convolutional Network for Parkinson’s Disease Detection Using Gait data 24

A Spatio-Temporal Multi-Subgraph Convolutional Network for P...

引用

3rd international conference on Artificial Intelligence and Intelligent Information processing, AIIIP 2024

作者： Wu, Huimin Liu, Yongcan Wang, Xianquan Yu, Xinglin Zhao, Aite College of Computer Science and Technology Qingdao University Shandong Qingdao China Institute of Advanced Technology University of Science and Technology of China Anhui Hefei China

ISBN: (纸本)9798400707308

Gait disturbance is a key motor symptom of Parkinson’s disease (PD) and has been widely studied for diagnosis and severity assessment using non-invasive sensor data. However, most existing methods for PD detection struggle to adaptively model sensor positions and inter-signal correlations within a multi-sensor system. Additionally, these sensors form a complex system with multiple subsystems monitoring local regions, and their interrelations are often overlooked. In this work, we propose a spatio-temporal multi-subgraph convolutional network (ST-MSGCN), a hybrid framework using graph neural networks to capture gait differences between healthy individuals and PD patients from multi-sensor data. We first decouple the overall sensor system and encode inter-subsystem correlations into multiple subgraphs. The ST-MSGCN then employs a spatio-temporal graph convolutional network to analyze 3D-graph representations of foot force signals, exploring spatial and temporal characteristics within each subgraph. We also introduce channel-wise topology and temporal attention mechanisms into the graph convolutional layers to dynamically learn discriminative features. Finally, a decision-making layer using a voting strategy fuses observations and state estimations from multiple graphs. The proposed method is validated on the PDgait dataset and compared with several existing models. Additionally, it is evaluated on a normal skeleton-based dataset to explore the relationships among body parts. © 2024 Copyright held by the owner/author(s).

关键词： Neurodegenerative diseases

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SSI-Net: A Multi-Stage Speech signal Improvement System for ICASSP 2023 SSI Challenge 48

SSI-Net: A Multi-Stage Speech Signal Improvement System for ...

引用

48th IEEE international conference on Acoustics, Speech and signal processing, ICASSP 2023

作者： Zhu, Weixin Wang, Zilin Lin, Jiuxin Zeng, Chang Yu, Tao Tencent Shenzhen China Tsinghua University Shenzhen International Graduate School Shenzhen China Sokendai Kanagawa Japan

ISBN: (纸本)9781728163277

The ICASSP 2023 Speech signal Improvement (SSI) Challenge concentrates on improving the speech signal quality of real-time communication (RTC) systems. In this paper, we introduce the speech signal improvement network (SSI-Net) submitted to the ICASSP 2023 SSI Challenge, which satisfies the real-time condition. The proposed SSI-Net has a multi-stage architecture. We present the time-domain restoration generative adversarial network (TRGAN) in the first restoration stage for speech restoration. Regarding the second enhancement stage, we employ a lightweight multi-scale temporal frequency convolutional network with axial self-attention (MTFAA-Net) called MTFAA-Lite to enhance the fullband speech. In the subjective test on the SSI Challenge blind test set, our proposed SSI-Net yields a P.835 overall mean opinion score (MOS) of 3.190 and a P.804 overall MOS of 3.178, which eventually takes the 3rd place in tracks 1&2. © 2023 IEEE.

关键词： multi-stage speech signal improvement SSI-Net

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VAG signal Classification Using Time Domain Statistical Features and Machine Learning 3

VAG Signal Classification Using Time Domain Statistical Feat...

引用

3rd IEEE international conference on Mobile Networks and Wireless Communications, ICMNWC 2023

作者： Patankar, Sanika Durge, Gaurav Joshi, Ashutosh Jaid, Atharva Kalambe, Khomesh Dhale, Hrishikesh Vishwakarma Institute of Technology Department of Instrumentation and Control Engineering Maharashtra Pune India

ISBN: (纸本)9798350317022

This research paper presents a novel approach for the classification of vibroarthrographic (VAG) signals to aid in the early diagnosis of knee arthritis, a disease whose prevalence is increasing worldwide. VAG signals are non-invasive and cost-effective, making them a promising tool for screening and detection. In this study, a time-domain statistical feature approach is employed to extract relevant features from VAG signals. Analytical measures like Kurtosis, skewness, Standard Deviation have been used in this work. These features are then fed into machine learning algorithms for the automatic detection of knee arthritis. The classification accuracy of 2 algorithms has been compared in this work, namely SVM and Random Forest. The latter resulted in an overall accuracy of 89% and an AUROC of. 90. The results indicate the potential of this approach to provide an accurate and reliable means for early knee arthritis detection, which would allow the patient to be treated ahead of time. © 2023 IEEE.

关键词： Classification Knee arthritis Machine learning signal processing Time-domain statistical features Vibroarthrography

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Refined Modeling and Simulation Method of Raw signal Returns for Space-based Radar 3

Refined Modeling and Simulation Method of Raw Signal Returns...

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3rd IEEE international conference on Power, Electronics and Computer Applications, ICPECA 2023

作者： Wang, Zhikang Xi, Xingjuan Liao, Guisheng Lan, Lan Chen, Hui Xidian University National Laboratory of Radar Signal Processing Xi'an China Jiangsu Cyber Space Science and Technology Co. Ltd Nanjing China Southeast University School of Information Science and Engineering Nanjing China

ISBN: (纸本)9781665472784

Radar echo refinement simulation is an important means to provide data for space-based radar design demonstration. Most of the current simulation methods ignore the real surface topography and complex geomorphic distribution, and the target characteristics are not sufficiently described, resulting in a shortage of refined radar echo data, which is difficult to meet the demonstration requirements of space-based radar early warning detection system. This paper proposes a more refined simulation method for space-based radar echo, mainly from the background clutter and target echo two aspects of fine modeling: Firstly, based on the real ground battlefield environment fluctuation model and complex geomorphic distribution attributes constructed by GIS (Geographic Information System) data, a refined space-time two-dimensional clutter simulation model of space-based radar is constructed which simultaneously includes the characteristics of terrain fluctuation scattering distribution, terrain occlusion and diffraction, earth-sea clutter distribution and earth rotation. Secondly, based on the efficient modeling technology of ray-tracing high-frequency electromagnetic scattering, the target characteristics in the detection scene are finely modeled, which can fully consider the changes of target characteristics under different conditions. Finally, the rationality of space-time two-dimensional echo signal is verified and analyzed by simulation cases. The echo generated by this method is closer to the real situation, and provide support for the research of space-based radar system design and clutter suppression algorithm. © 2023 IEEE.

关键词： Space-based radar

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VVC adaptive QP offset algorithm based on visual perception 3

VVC adaptive QP offset algorithm based on visual perception

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3rd international conference on signal Image processing and Communication, ICSIPC 2023

作者： Jin, Siyu Guan, XiaoHan Liu, Zhi North China University of Technology Beijing China

ISBN: (纸本)9781510670945

The latest video coding standard, the Universal Video Coding Standard (VVC), uses new coding tools to greatly improve compression efficiency. However, the adaptive QP module in the coding framework ignores the characteristics of the human visual system HVS, resulting in coding results that differ from the real perception of the human eye. To incorporate visual perception into QP selection, this paper proposes an adaptive QP offset algorithm based on visual perception metrics. The first step is to design a QP offset algorithm based on the luminance, frequency, and time domain characteristics of the HVS. The visual perception index is designed to reflect the real perception of human eyes, and then the index is used to guide the CTU into different levels according to visual sensitivity. Experiments show that the algorithm achieves 0.74% performance improvement in VMAF - based BD-Rate and about 8% improvement in VMAF score compared to the original VVC. © 2023 SPIE.

关键词： Video signal processing

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