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Impact of the chaotic semiconductor laser output power on time-delay-signature of chaos and random bit generation

Chinese Physics B 2025年第3期34卷 325-330页

作者： Chenpeng Xue Xu Wang Likai Zheng Haoyu Zhang Yanhua Hong Zuxing Zhang College of Electronic and Optical Engineering and College of Flexible Electronics(Future Technology) Nanjing University of Posts and TelecommunicationsNanjing 210023China School of Computer Science and Engineering Bangor UniversityBangor LL571UTUK

We experimentally analyze the effect of the optical power on the time delay signature identification and the random bit generation in chaotic semiconductor laser with optical *** to the inevitable noise during the photoelectric detection and analog-digital conversion,the varying of output optical power would change the signal to noise ratio,then impact time delay signature identification and the random bit *** results show that,when the optical power is less than-14 dBm,with the decreasing of the optical power,the actual identified time delay signature degrades and the entropy of the chaotic signal ***,the extracted random bit sequence with lower optical power is more easily pass through the randomness testing.

关键词： chaos time delay signature entropy estimation random bit generation

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HIGH-FREQUENCY RESONANCE SUPPRESSION OF MMC-HVDC SYSTEMS UNDER FREQUENCY DIVIDED ACTIVE DAMPING CONTROL 20

HIGH-FREQUENCY RESONANCE SUPPRESSION OF MMC-HVDC SYSTEMS UND...

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20th International Conference on AC and DC Power Transmission 2024, ACDC 2024

作者： Li, Yunfeng Wen, Tao Zhang, Yuhang Wei, Hangyu Xia, Siyi Ye, Huirong Wang, Yuming School of Electrical and Information Engineering Changsha University of Science and Technology Changsha China

ISBN: (纸本)9781837242672

In the modular multilevel converter (MMC) based high voltage direct current (HVDC) transmission system, the high-frequency negative damping effect of the MMC output impedance induces high frequency resonances. The existing active damping controls, such as the virtual series impedance, change the range of high-frequency negative damping effects, thereby suppressing the high-frequency resonances (HFRs). However, it can only be applied to the single operating condition of AC power grid because the single damping controller cannot effectively reshape the impedance characteristics of MMC in multiple HFR risk regions simultaneously. Therefore, a frequency divided active damping control (FDADC) is proposed to address the suppression requirements of multiple HFR risk regions. Based on the simplified model of the MMC in high-frequency band, the analytical calculation method and expressions for the parameters' selection ranges of the frequency divided damping controller are presented. The parameters of damping controller suitable for multiple operating conditions of AC systems are given, and their effectiveness is verified through electromagnetic transient simulation models. © The Institution of engineering & technology 2024.

关键词： Damping

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Cross-modal Gaussian Localization Distillation for Optical information guided SAR Object Detection

Cross-modal Gaussian Localization Distillation for Optical I...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Wang, Chao Luo, Lei Fang, Wenxuan Yang, Jian School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China

ISBN: (纸本)9798350368741

Synthetic Aperture Radar (SAR) images contain a dense clutter of objects that can be better characterized using bounding boxes with angles. However, accurately detecting the angles of objects remains challenging due to the imaging mechanism of SAR. To address this issue, we propose a novel knowledge distillation method called cross-modal Gaussian Localization Distillation (GaLD). It aims to improve SAR object detection performance by utilizing the angle information from optical images. Specifically, we convert the oriented bounding box into a Gaussian distribution and design a Gaussian Angle Distillation (GAD) loss function to align the angle information between optical and SAR images. In addition, to mitigate the negative impact of low-quality angle information on the network, we design an Adaptive Weighting Strategy (AWS) to guide the student network to prioritize high-quality angle information. The lack of high-quality oriented labels and objects in the OGSOD-1.0 dataset has hindered the progress in related fields. Therefore, we have added high-quality oriented labels and images to the OGSOD-1.0 dataset to construct a new dataset. Extensive experiments demonstrate the effectiveness and superiority of our proposed GaLD over existing methods. The dataset and code are available at: https://***/wchao0601/GaLD. © 2025 IEEE.

关键词： knowledge distillation object detection Remote sensing synthetic aperture radar (SAR)

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FEMD: Feature Enhancement-aided Multimodal Feature Fusion Approach for Smart Contract Vulnerability Detection 30

FEMD: Feature Enhancement-aided Multimodal Feature Fusion Ap...

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30th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2024

作者： Li, Juan Li, Rui Lu, Youshui Cai, Bowen Cao, Yulin Lin, Hang Li, Xingxing Li, Chan Qinghai Normal University School of Computer Science Xining China Xidian University The State Key Laboratory of Tibetan Intelligent Information Processing and Application School of Computer Science Qinghai Normal University School of Computer Science and Technology Xian China School of the Instrument Science and Technology School of Electrical Engineering Xi'an Jiaotong University Xian China School of Science and Engineering University of Minnesota - Twin City Minneapolis United States

ISBN: (纸本)9798331515966

Smart contracts, due to their immutability and transparency upon deployment, entail significant economic and systemic risks from any vulnerabilities present. Traditional vulnerability detection methods suffer from low automation and high false positive rates, while existing deep learning-based approaches inadequately extract contract features, thereby limiting detection accuracy. To address these issues, this paper proposes FEMD: a feature-enhanced aided multimodal feature fusion method for smart contract vulnerability detection. Building on the foundation of addressing the low automation of traditional detection tools, our method improves the model's feature extraction performance and detection capabilities. Specifically, we construct a contract graph through Comprehensive Expert-Graph Fusion, combining multi-modal feature fusion using a multi-head attention mechanism with expert patterns to ensure the capture and effective preservation of all critical information during the fusion process. To delve deeper into potential information within smart contract graphs, we employ a Feature Enhancer that leverages transpose operations on feature matrices to extract complex interaction patterns across different dimensions. Our approach is validated through batches of experiments on the Ethereum open dataset focusing on reentrancy and timestamp dependency vulnerabilities, demonstrating significant improvements in detection accuracy and robustness. © 2024 IEEE.

关键词： Deep learning

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Assessing Deep Learning Approaches for Time Series Analysis to Detect Uterine Sarcoma

Assessing Deep Learning Approaches for Time Series Analysis ...

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2024 International Conference on Optimization Computing and Wireless Communication, ICOCWC 2024

作者： Shukla, Gaurav Dheer, Meenakshi Krishnamoorthy, Ramkumar Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Vivekananda Global University Department of Agriculture Jaipur India Department of Computer Science and Information Technology Bangalore India

ISBN: (纸本)9798350383348

This paper aims to evaluate the performance of numerous deep-gaining knowledge of fashions for detecting Uterine Sarcoma via Time series evaluation. Uterine Sarcoma is a malignant tumor that influences the uterus and different parts of the woman's reproductive machine. Time collection analysis techniques have been broadly used in scientific fact mining, specifically for clinical records, because of their capability to capture temporal traits of the data. In this look, quite several deeps getting to know fashions which include Convolutional Neural Networks (CNNs), long brief-time period reminiscence (LSTM), and Self-Organizing Maps (SOMs), were evaluated at the MIMIC-III database-the use of metrics such as accuracy, precision and bear in mind. The results showed that the CNN had the highest accuracy (zero.99%) and precision (zero.75%) and did not forget (0.90%) in predicting Uterine Sarcoma when compared with the opposite models. This examination serves as a starting point for a similar investigation into the potential capabilities of deep mastering for detecting Uterine Sarcoma and other illnesses in medical statistics. This paper evaluates deep learning processes for time series evaluation to hit upon uterine sarcoma. The strategies used in this examination are Recurrent Neural Networks (RNNs) and Convolutional Neural Networks (CNNs). To assess the performance of the networks, the dataset from the yank university of Radiology (ACR) Uterine Sarcoma Imaging and Research Database changed used. The networks were evaluated for accuracy, sensitivity, and specificity. Moreover, the RNNs and CNNs were compared to evaluate their performance. The results show that the CNN performs better than the RNN with an accuracy of ninety-seven. 50%, a sensitivity of 95.05%, and specificity of ninety-nine. 25%. It is steady with previous studies implementing deep learning techniques for medical photograph evaluation. The outcomes of this observation reveal that both RNN and CNN are appr

关键词： assignment excellent influences records reproductive

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BWO-LSSVR Model-Based Aeroengine Combustor Multi-Objective Optimum Design 3

BWO-LSSVR Model-Based Aeroengine Combustor Multi-Objective O...

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3rd International Symposium on Aerospace engineering and Systems, ISAES 2024

作者： Tang, Fei Ma, Yue Guo, Mingming Wang, Heng Zhang, Hua School of Information Engineering Southwest University of Science and Technology Swust Mianyang China

ISBN: (纸本)9798350350418

As one of the core components of aeroengine, high efficiency and high performance combustor design is the key to the high reliability operation of aeroengine and even the whole aircraft. In order to avoid the problems of long cycle and high calculation cost of traditional combustor design, the optimization design of combustor based on machine learning has achieved some results, but these methods are limited by the number of samples and are easy to fall into local optimal. In this paper, based on an aeroengine combustor model, combined with BWO-LSSVR (Beluga Optimization - least square support vector regression) model and MOSSA (multi-objective sparrow search algorithm) algorithm, the multi-objective optimization design of combustor is carried out to maximize combustion efficiency and minimize total pressure loss. The results show that, compared with SVR, LSSVR, Kriging and other common models, BWO-LSSVR model has the lowest root-mean-square error and average absolute percentage error, and has the highest prediction accuracy under the condition of small sample. The Pareto solution set and optimal design space are obtained through multi-objective optimization, which provides physical insights for advanced aero engine combustor design meeting performance indexes, and can guide the optimization design of combustor and accelerate the development of aeroengine. © 2024 IEEE.

关键词： Combustors

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Application of VGIS Digital Twin Platform for Enhanced Oilfield Disaster Management and Environmental Protection

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Informatica (Slovenia) 2024年第14期48卷 27-42页

作者： Alabbasi, Awab Aljabery, Maalim A. Bölük, Pınar Sarısaray Faculty of Engineering and Natural Sciences Electrical and Computer Engineering Dept. Bahcesehir University Istanbul34353 Turkey Faculty of Computer Science and Information Technology Computer Science Dept. University of Basrah Basrah61004 Iraq Faculty of Engineering and Natural Sciences Software Engineering Dept. Bahçeşehir University Istanbul34353 Turkey

VGIS (Virtual Geographic information System) Platform is a unified oilfield operations management platform based on MaaS (Management as a Service) that integrates advanced technologies such as AIoT (Artificial Intelligence & Internet of Things), GIS (Geographic information System), Digital Twin, and AR (Augmented Reality)/ VR (Virtual Reality). The VGIS platform connects, collects, integrates, analyses, and acts on disparate data sources to provide the oilfield with a centralized view to help make informed decisions fast, bring operational visibility across all assets to improve safety and operational efficiency, reduce costs and improve margins, provide comprehensive analytics and decision support, increase business agility, and reduce management complexity. This is a case study of the application of the VGIS platform Majnoon oilfield, Basrah, Iraq. VGIS digital transformation, production process, and KPIs (Key performance indicators) are highly managed taking oilfield management to a higher level globally, in this study, the objective is to address and resolve the issue of high costs associated with using cloud services by utilising a local Data Center (a large group of networked computer servers typically used by organizations for the remote storage, processing, or distribution of large amounts of data). This approach involves directly uploading 3D maps to ensure faster service on the intranet, eliminating the need to pay for cloud *** aims providing a platform for researching and implementing monitoring and controlling processes in a more convenient and remote manner. The vGIS platform has emerged as a crucial instrument in the realm of disaster response and crisis management. This technology enables responders to rapidly evaluate, analyse, and respond to crucial information in real-time. This research examined the application of vGIS technology in disaster response and analyse the potential advantages and obstacles associated with its utilisation. © 2

关键词： information management

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Interrupted-sampling Repeater Jamming Recognition based on the PC-LFM Waveform 16

Interrupted-sampling Repeater Jamming Recognition based on t...

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16th International Conference on Signal Processing Systems, ICSPS 2024

作者： Bu, Yi Yu, Xianxiang Cui, Guolong Yang, Xiaobo School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu China

ISBN: (纸本)9781510689251

The interrupted-sampling repeater jamming (lSRJ) can cause a series of false targets after pulse compression by quickly sampling and forwarding radar signals, which poses a significant threat to radar performance. This paper proposes a new electronic counter-countermeasures (ECCM) method based on the phase-coded linear frequency modulation (PC-LFM) waveform. In particular, by leveraging the de-chirping operation on the obtained PC-LFM waveform, a novel processing scheme is developed to explore the disparity features between the target and the ISRJ in the range profile. As a result, a target detection method is proposed to identify interference based on these feature differences. Simulation results verify the effectiveness of the proposed target recognition method. © 2025 SPIE.

关键词： Jamming

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Turbulent drag reduction by sector-shaped counter-flow dielectric barrier discharge plasma actuator

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Chinese Physics B 2025年第2期34卷 362-370页

作者： Borui Zheng Shaojie Qi Minghao Yu Jianbo Zhang Linwu Wang Dongliang Bian School of Automation and Information Engineering Xi’an University of TechnologyXi’an 710048China School of Mechanical and Precision Instrument Engineering Xi’an University of TechnologyXi’an 710048China Science and Technology on Plasma Dynamics Laboratory Air Force Engineering UniversityXi’an 710038China Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi’an 710048China Shaanxi University Key Laboratory of Photonic Power Devices and Discharge Regulation Xi’an 710048China

The primary objective in aircraft transportation is to minimize turbulent drag, thereby conserving energy and reducing emissions. We propose a sector-shaped counter-flow dielectric barrier discharge plasma actuator, which leverages jet synthesis for drag reduction. A drag control experiment was conducted in a low-speed wind tunnel with a controlled flow velocity of 9.6 m/s(Re = 1.445 × 10^(4)). This study investigated the effects of varying pulse frequencies and actuation voltages on the turbulent boundary layer. Using a hot-wire measurement system, we analyzed the pulsating and time-averaged velocity distributions within the boundary layer to evaluate the streamwise turbulent drag reduction. The results show that the local TDR decreases as the pulse frequency increases, reaching a maximum reduction of approximately 20.97% at a pulse frequency of 50 Hz. In addition, as the actuation voltage increases, the friction coefficient decreases, increasing the drag reduction rate. The maximum drag reduction of approximately 33.34% is achieved at an actuation voltage of 10 kV.

关键词： plasma flow control turbulent boundary layer turbulent drag reduction

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Design of a Compact Multiband Planar Printed Monopole Antenna

Design of a Compact Multiband Planar Printed Monopole Antenn...

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2024 International Conference on Optimization Computing and Wireless Communication, ICOCWC 2024

作者： Saha, Ananya Yadav, Rakesh Kumar Abhinav Department of Computer Science and Information Technology Bangalore India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Vivekananda Global University Department of Agriculture Jaipur India

ISBN: (纸本)9798350383348

the compact multiband planar published monopole antenna is a sort of antenna that is designed to operate over more than one frequency bands in a compact size. Its miles usually fabricated the use of printed circuit board (PCB) technology, making it appropriate for integration into compact gadgets including cell phones, capsules, and other wireless gadgets. The design of this antenna includes selecting a particular geometry and configuration of the antenna factors to obtain the desired frequency bands. The antenna usually includes a main radiating detail, along with a monopole, and further parasitic factors that help to create the preferred frequency reaction. One approach to reaching multiband operation is by using the usage of an unmarried radiating element with more than one parasitic factor placed at specific locations alongside the antenna. Those parasitic elements act as directors or reflectors, changing the radiation pattern and resonant frequency of the antenna. By means of optimizing the scale, spacing, and configuration of these parasitic factors, the antenna can be made to function over a wide frequency variety. Some other technique is to apply multiple radiating factors which might be every designed to function at a one of a kind frequency band. These factors are normally coupled together to shape a compact structure and also can be mixed with parasitic factors to enhance performance. © 2024 IEEE.

关键词： Alongside Changing Enhance Optimizing Performance Preferred Radiating

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