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2nd IEEE International Conference on signal, Information and Data processing, ICSIDP 2024

作者： Ma, Rui Zhou, Shenghua Ma, Hui Qing, Jingshu Li, Jizhuan Li, Aihua Xidian University National Key Laboratory of Radar Signal Processing Xi'an China Beijing Institute of Remote Sensing Equipment Science and Technology on Millimeter-Wave Laboratory Beijing China

ISBN: (纸本)9798331515669

Electronic attack (EA) equipment can interfere radar systems by emitting interference signals with the assistant of the electronic support measurement (ESM) equipment. In this case, masking signalss can be designed to deceive ESM equipment and prevent EA equipment from emitting interference. This paper proposes an active anti-interference signal design method for Multiple-input Multiple-output (MIMO) radar. Designed masking signals cover a radar signal in the time domain, but the frequency band of the masking signals in the frequency domain does not completely overlap with the radar signal, making it difficult for an enemy ESM equipment to determine whether the obtained signal is a radar signal or interference masking signals. To reduce the cross correlation sidelobes between radar signals and masking signals, the masking signals are designed with upper and lower frequency bands with an identical center frequency, and then the spectrum of the masking signals can be efficient shifted to achieve the spectrum overlap, therefore, low cross correlation sidelobes can be ensured. Simulation results indicate that the propsoed algorithm can reduce the cross-correlation sidelobe level to the level of -100 dB, and it is also shown that the proposed algorithm is robust to the radar transmission delay. © 2024 IEEE.

关键词： Radar interference

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Transformative Techniques in Medical Imaging: Advancing Precision through Computer Vision and image Analysis 24

Transformative Techniques in Medical Imaging: Advancing Prec...

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7th International Conference on Digital Medicine and image processing, DMIP 2024

作者： Swathi, A. Prasad, Siva Madhavi, M. Gopal, Venu Nihaal Ahmed, K. Medical Image Processing Research Laboratory QIS College of Engineering and Technology Andhra Pradesh Ongole India School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Vellore India

ISBN: (纸本)9798400709586

As computer vision and image analysis technologies rapidly mature, they can revolutionize medical imaging, ushering in a new era of precision in diagnosis and treatment. In this research study, we explore innovative methodologies that leverage these technologies to enhance the image interpretation process and mitigate human error. Our findings reveal a significant increase in segmentation, classification, and anomaly detection accuracies achieved through deep learning-based models across various medical imaging modalities. These results underscore the transformative power of computer vision in healthcare, offering a promising path for more targeted and accurate interventions and instilling confidence in the effectiveness of these technologies. Copyright © 2024 held by the owner/author(s). Publication rights licensed to ACM.

关键词： Computer Vision

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Recognition of Micromotion Space Targets at Low SNR Based on Complex-Valued Time Convolutional Attention Denoising Recognition Network

IEEE Transactions on Radar Systems

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IEEE Transactions on Radar Systems 2025年 3卷 193-202页

作者： Bai, Xueru Mao, Xuchen Tian, Xudong Zhou, Feng Xidian University National Key Laboratory of Radar Signal Processing Xi'an710071 China Beijing Institute of Astronautical Systems Engineering Beijing100076 China Xidian University School of Aerospace Science and Technology Xi'an710071 China

For a micromotion space target, its narrowband radar cross section (RCS) series reflects the characteristics of target shape and motion. In practical scenarios, however, the RCS series of distant targets with weak scattering coefficients suffers from low signal-to-noise ratio (SNR), and performing separate noise suppression and recognition purely on the amplitude results in degraded recognition performance. To tackle this issue, an end-to-end complex-valued (CV) time convolutional attention denoising recognition network, dubbed as CV-TCANet, is proposed. Specifically, the denoising module captures temporal correlation by the CV attention mechanism and calculates the noise mask for denoising;and the recognition module utilizes the CV temporal convolutional network (CV-TCN) for feature extraction and recognition. In addition, a hybrid loss is designed to realize the integration of denoising and recognition, thus preserving target information while denoising and improving the recognition accuracy. Experimental results have proved that the proposed method could achieve satisfying recognition performance at low SNR. © 2025 IEEE.

关键词： signal to noise ratio

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Competition on robust deep learning

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National science Review 2023年第6期10卷 13-15页

作者： Yinpeng Dong Chang Liu Wenzhao Xiang Hang Su Jun Zhu Department of Computer Science and Technology Institute for AI Tsinghua-Bosch Joint ML Center THBI Lab BNRist Center Tsinghua University Institute of Image Communication and Networks Engineering in the Department of Electronic Engineering (EE) Shanghai Jiao Tong University Key Laboratory of Intelligent Information Processing of Chinese Academy of Sciences (CAS) Institute of Computing Technology CAS Peng Cheng Laboratory Pazhou Laboratory (Huangpu)

PROBLEM In recent years,the rapid development of artificial intelligence (AI) technology,especially machine learning and deep learning, is profoundly changing human production and *** various fields,such as robotics,face recognition,autonomous driving and healthcare,AI is playing an important ***,although AI is promoting the technological revolution and industrial progress,its security risks are often *** studies have found that the wellperforming deep learning models are extremely vulnerable to adversarial examples [1-3].The adversarial examples are crafted by applying small,humanimperceptible perturbations to natural examples,but can mislead deep learning models to make wrong *** vulnerability of deep learning models to adversarial examples can raise security and safety threats to various realworld applications.

关键词： robustness adversarial example adversarial training deep learning

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Spoken Digit Recognition Using Convolutional Neural Network

Spoken Digit Recognition Using Convolutional Neural Network

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2022 Applied Informatics International Conference, AiIC 2022

作者： Safie, S.I. Universiti Kuala Lumpur Malaysian Institute of Industrial Technology Signal and Image Processing Laboratory Johor Bahru Malaysia

ISBN: (数字)9781728184289

ISBN: (纸本)9781728184289

This paper presents a spoken digit recognition study based on features extracted from Bark spectrogram and classifies it using convolutional neural network (CNN). In this work, training and test databases of the spoken digits were developed from TI20 corpus. 6,506 spoken digits from 16 individuals have been used in the study. A 198 x 50 feature map has been extracted from each spoken digit to be fed as input to the CNN. A 5-layer convolutional filters have been added as layers in the CNN architecture. Adam optimization algorithm is then used to train the CNN. A confusion matrix resulting from classification on the test database has been presented in this paper. It is shown in this paper that the proposed CNN architecture performing on the test database achieved an average accuracy of over 99% for the known spoken digits and wrongly classify as digit up to 3.4% for the unknown spoken words. © 2022 IEEE.

关键词： Speech recognition

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A Novel Construction of Two-Dimensional Z-Complementary Array Code Sets Based on Extended Boolean Functions

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IEEE Transactions on Vehicular technology 2025年第5期74卷 7821-7834页

作者： Tian, Liying Song, Xiaoshi Li, Yubo Zhang, Haijun Northeastern University School of Computer Science and Engineering Shenyang110819 China Northeastern University Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Shenyang110189 China Yanshan University School of Information Science and Engineering Qinhuangdao066004 China Hebei Key Laboratory of Information Transmission and Signal Processing Qinhuangdao066004 China University of Science and Technology Beijing School of Computer and Communication Engineering Beijing100083 China

Two-dimensional (2-D) array sets with good 2-D correlation properties have received considerable attention in wireless communication systems. This paper focuses on 2-D Z-complementary array code sets (ZCACSs), which have ideal 2-D correlation properties within a rectangular zone around the origin. By developing a theory linking 2-D extended Boolean functions with 2-D arrays, we establish an efficient construction of 2-D ZCACSs. In contrast to previous work, the proposed construction is capable of producing 2-D ZCACSs with new flexible parameters. Constructions of three-dimensional (3-D) and higher-dimensional ZCACSs are further investigated. We remark that 2-D and higher-dimensional complete complementary codes (CCCs) can also be generated from the proposed constructions. © 1967-2012 IEEE.

关键词： Codes (symbols)

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Supervised machine learning-based salp swarm algorithm for fault diagnosis of photovoltaic systems

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Journal of Engineering and Applied science 2024年第1期71卷 12页

作者： Hichri, Amal Hajji, Mansour Mansouri, Majdi Nounou, Hazem Bouzrara, Kais Research Unit Advanced Materials and Nanotechnologies Higher Institute of Applied Sciences and Technology of Kasserine Kairouan University Kairouan Tunisia Electrical and Computer Engineering Program Texas A&M University at Qatar Doha Qatar Laboratory of Automatic Signal and Image Processing National School of Engineers of Monastir University of Monastir Monastir5019 Tunisia

The diagnosis of faults in grid-connected photovoltaic (GCPV) systems is a challenging task due to their complex nature and the high similarity between faults. To address this issue, we propose a wrapper approach called the salp swarm algorithm (SSA) for feature selection. The main objective of SSA is to extract only the most important features from the raw data and eliminate unnecessary ones to improve the classification accuracy of supervised machine learning (SML) classifiers. Subsequently, the selected features are used to train supervised machine learning (SML) techniques in distinguishing between various operating modes. To evaluate the efficiency of the technique, we used healthy and faulty data from GCPV systems that have been injected with frequent faults, 20 different types of faults were introduced, including line-to-line, line-to-ground, connectivity faults, and those affecting the operation of bay-pass diodes. These faults present diverse conditions, such as simple and multiple faults in the PV arrays and mixed faults in both arrays. The performances of the developed SSA-SML are compared with those using principal component analysis (PCA) and kernel PCA (KPCA) based SML techniques through different criteria (i.e., accuracy, recall, precision, F1 score, and computation time). The experimental findings demonstrated that the proposed diagnosis paradigm outperformed the other techniques and achieved a high diagnostic accuracy (an average accuracy greater than 99%) while significantly reducing computation time. © 2023, The Author(s).

关键词： Failure analysis

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Enhanced Genetic Algorithms Based Optimization of ARX-Laguerre Model Parameters: Application to 2-Dof Helicopter

Enhanced Genetic Algorithms Based Optimization of ARX-Laguer...

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2024 IEEE International Conference on Artificial Intelligence and Green Energy, ICAIGE 2024

作者： Guerfel, Mohamed Abdelwahed, Imen Ben Jaffel, Ines Bouzrara, Kais Higher Institute of Applied Sciences and Technology of Sousse University of Sousse Electronics department Tunisia National Engineering School of Monastir University of Monastir Laboratory of Automatic Control Signal and Image Processing Tunisia Higher Institute of Applied Sciences and Technology of Kairouan University of Kairouan Tunisia

ISBN: (纸本)9798350389838

This paper explores the application of genetic algorithms to optimize ARX-Laguerre model parameters. It outlines the ARX-Laguerre modeling process and genetic algorithm principles. A fitness function is chosen and the impact of noise on it is addressed. A new methodology for optimizing Laguerre poles using genetic algorithms is proposed. It is used in modeling a real 2-Degree-of-Freedom helicopter offering valuable insights for real-world applications. © 2024 IEEE.

关键词： Genetic algorithms

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Deep Learning-based Serialized Model and Feature Extraction Algorithm for Solid Oxide Fuel Cell State Prediction 43

Deep Learning-based Serialized Model and Feature Extraction ...

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43rd Chinese Control Conference, CCC 2024

作者： Wu, Jiajian Zhu, Kanglin Wu, Han Xiang, Guohui Li, Xi Huazhong University of Science and Technology School of Artificial Intelligence and Automation Key Laboratory of Image Processing and Intelligent Control of Education Ministry Hubei Wuhan China Shenzhen Huazhong University of Science and Technology Research Institute Guangdong Shenzhen China

ISBN: (纸本)9789887581581

Deep learning-based methods have shown their wide application prospects in the field of solid oxide fuel cell(SOFC) prediction. However, the irrationality of the prediction object and the lack of prediction accuracy have hindered the progress of the prediction methods for SOFC systems. Therefore, two algorithms are proposed in this study, a feature selection algorithm based on maximum information coefficient and Bayesian information criterion for identifying key variables that are sensitive to changes in the SOFC system, and a prediction model based on a genetic algorithm optimized for sequence to sequence architecture. The M/BIC algorithm reduces the number of features by 62.5% (from 40 to 15), which contributes to the acceleration of the algorithm training and the subsequent sensor layout optimization. The prediction model incorporates a two-dimensional convolutional neural network and a multi-head attention mechanism to capture more information in the input sequence, an encoder-decoder architecture and a Gated recurrent unit to better predict performance trends. Tests with real experimental data show significant changes in feature dimensionality reduction and prediction accuracy. © 2024 Technical Committee on Control Theory, Chinese Association of Automation.

关键词： Dimensionality reduction

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Microgrid energy management strategy considering microgrid economy and rSOC stability: A model-based study 43

Microgrid energy management strategy considering microgrid e...

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43rd Chinese Control Conference, CCC 2024

作者： Huang, Haizhou Xia, Zhiping Shan, Xinkai Wang, Jie Li, Xi Huazhong University of Science and Technology School of Artificial Intelligence and Automation Key Laboratory of Image Processing and Intelligent Control of Education Ministry Wuhan430074 China Shenzhen Huazhong University of Science and Technology Research Institute Shenzhen518055 China

ISBN: (纸本)9789887581581

Microgrids possess the ability to use renewable energy efficiently and play an increasingly significant role in environmental protection and sustainable development. Meanwhile, reversible solid oxide fuel cell (rSOC) is currently a new type of energy conversion and storage. Combining with microgrid systems is a great way to benefit from its high capacity, high efficiency, and cleanliness. However, rSOC's complex system of coupled heat, electricity, and gas presents a significant challenge in finding energy management strategies for microgrid applications containing rSOC. In this study, for a microgrid system combining rSOC, battery energy storage system (BESS), solar photovoltaic (PV), and wind turbine generator (WTG), we propose a model-based energy management strategy that simultaneously takes into account the economy of the microgrid system and the stability of the rSOC system. First of all, the microgrid is modeled with great detail, including the wind turbine, solar panels, battery storage system, and rSOC. In addition, the microgrid energy management problem is framed as a mathematical programming problem, with the attention focusing on the costs of BESS aging and operational energy loss of rSOC. In particular, this study also proposes a penalty strategy to reduce the power fluctuation amplitude of the rSOC system and reduce the mode switching frequency of the rSOC system. Using the solver, solving for each device's target power in a microgrid. The actual power operating curve is output by tracking the target power under slope constraints using the Simulink model. We then conduct a case study using data from Wuhan, China. Our research shows that compared with the control strategy based on rule control, the control strategy proposed in this study can reduce the power variation amplitude of the rSOC system by 79%, reduce the mode switching frequency by 85%, reduce the system operating cost by 4.5% and increase the energy self-sufficiency rate by 9%. © 2024 Techn

关键词： Wind turbines

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