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Incremental Learning of Radio Modulation Classification Based on Sample Recall

China communications 2023年第7期20卷 258-272页

作者： Yan Zhao Shichuan Chen Tao Chen Weiguo Shen Shilian Zheng Zhijin Zhao Xiaoniu Yang School of Communication Engineering Hangzhou Dianzi UniversityHangzhou 310018China Science and Technology on Communication Information Security Control Laboratory Jiaxing 314033China Institute of Cyberspace Security Zhejiang University of TechnologyHangzhou 310023China

Radio modulation classification has always been an important technology in the field of *** difficulty of incremental learning in radio modulation classification is that learning new tasks will lead to catastrophic forgetting of old *** this paper,we propose a sample memory and recall framework for incremental learning of radio modulation *** data with different signal-to-noise ratios,we use a partial memory strategy by selecting appropriate samples for *** compare the performance of our proposed method with three baselines through a large number of simulation *** show that our method achieves far higher classification accuracy than finetuning method and feature extraction ***,it performs closely to joint training method which uses all old data in terms of classification accuracy which validates the effectiveness of our method against catastrophic forgetting.

关键词： radio modulation classification incremen-tal learning deep learning convolutional neural net-work.

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Research on Identification Method of Toroidal Propeller

Research on Identification Method of Toroidal Propeller

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2024 International Conference on measurement, communication, and Virtual Reality, MCVR 2024

作者： Han, Chuang Hu, Aiai Wu, Haibin Heilongjiang Province Key Laboratory of Laser Spectroscopy Technology and Application Harbin University of Science and Technology Harbin China School of Measurement - Control Technology and Communication Engineering Harbin University of Science and Technology Harbin China

ISBN: (纸本)9781510691773

With the continuous development of marine science and technology, propeller shapes are developing in a more complex direction. Among them, the toroidal propeller represents the latest research progress in propeller technology. This design features a ring-shaped geometry for the blades, which are similar to the catenary curve of a hanging chain. This propeller offers the advantages of higher efficiency and lower noise. In this paper, starting from the geometric shape analysis study of toroidal propeller, we introduce the geometric parameters such as wheelbase, outboard angle, lateral inclination angle among others, derive the mathematical formula of the coordinates of toroidal propeller in detail, simulate the modulation waveform and spectrogram of the toroidal propeller, and compare with those of the ordinary propeller, and according to the experimental data, the geometric shape of toroidal propeller can be recognized by using the proposed method. This lays a firm foundation for further research on the physical properties and scientific problems of toroidal propellers. © 2025 SPIE.

关键词： Geometry

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Impact Damage Identification of Aluminum Alloy Reinforced Plate Based on GWO-ELM Algorithm

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Structural Durability & Health Monitoring 2023年第6期17卷 485-500页

作者： Wei Li Benjian Zou Yuxiang Luo Ning Yang Faye Zhang Mingshun Jiang Lei Jia School of Control Science and Engineering Shandong UniversityJinan250061China Communication Technology Division Shandong Institute of Space Electronic TechnologyYantai264000China

As a critical structure of aerospace equipment,aluminum alloy stiffened plate will influence the stability of spacecraft in orbit and the normal operation of the *** this study,a GWO-ELM algorithm-based impact damage identification method is proposed for aluminum alloy stiffened panels to monitor and evaluate the damage condition of such stiffened panels of ***,together with numerical simulation,the experimental simulation to obtain the damage acoustic emission signals of aluminum alloy reinforced panels is performed,to establish the damage ***,the amplitude-frequency characteristics of impact damage signals are extracted and put into an extreme learning machine(ELM)model to identify the impact location and damage degree,and the Gray Wolf Optimization(GWO)algorithm is employed to update the weight parameters of the ***,experiments are conducted on the irregular aluminum alloy stiffened plate with the size of 2200 mm×500 mm×10 mm,the identification accuracy of impact position and damage degree is 98.90% and 99.55% in 68 test areas,*** experiments with ELM and backpropagation neural networks(BPNN)demonstrate that the impact damage identification of aluminum alloy stiffened plate based on GWO-ELM algorithm can serve as an effective way to monitor spacecraft structural damage.

关键词： GWO-ELM aluminum alloy stiffened plate damage identification amplitude-frequency characteristic

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Exploiting the Direct Link in IRS Assisted NOMA Networks with Hardware Impairments

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Computer Modeling in engineering & Sciences 2023年第7期136卷 767-785页

作者： Ziwei Liu Xinwei Yue Shuo Chen Xuliang Liu Yafei Wang Wanwei Tang Key Laboratory of Modern Measurement&Control Technology Ministry of EducationSchool of Information and Communication EngineeringBeijing Information Science and Technology UniversityBeijing100101China The Party Committee Office Yellow River Conservancy Technical InstituteKaifeng475001China Tangshan University Tangshan063000China

Hardware impairments(HI)are always present in low-cost wireless *** paper investigates the outage behaviors of intelligent reflecting surface(IRS)assisted non-orthogonal multiple access(NOMA)networks by taking into account the impact of ***,we derive the approximate and asymptotic expressions of the outage probability for the IRS-NOMA-HI *** on the asymptotic results,the diversity orders under perfect self-interference cancellation and imperfect self-interference cancellation scenarios are obtained to evaluate the performance of the considered *** addition,the system throughput of IRS-NOMA-HI is discussed in delay-limited *** obtained results are provided to verify the accuracy of the theoretical analyses and reveal that:1)The outage performance and system throughput for IRS-NOMA-HI outperforms that of the IRS-assisted orthogonal multiple access-HI(IRS-OMA-HI)networks;2)The number of IRS elements,the pass loss factors,the Rician factors,and the value of HI are pivotal to enhancing the performance of IRS-NOMAHI networks;and 3)It is recommended that effective methods of reducing HI should be used to ensure system performance,in addition to self-interference cancellation techniques.

关键词： Hardware impairments imperfect SIC intelligent reflecting surface non-orthogonal multiple access outage probability

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Reliability Allocation of Cruise Ship Automation System Based on BBWM-AHP 6

Reliability Allocation of Cruise Ship Automation System Base...

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6th International Conference on System Reliability and Safety engineering, SRSE 2024

作者： Xiang, Jiaxin Sun, Yongquan Wang, Hao Chen, Hongke Harbin University of Science and Technology School of Measurement and Control Technology and Communication Engineering Harbin China China Institute of Marine Technology & Economy Beijing China Xiamen University Tan Kah Kee College School of Mechanical and Electrical Engineering & Automation Zhangzhou China

ISBN: (纸本)9798350356083

Limited reliability data during the development of cruise ship automation systems often leads to subjective weight allocation. This paper proposes an reliability allocation method based on the Bayesian best-worst method and analytic hierarchy process to address this challenge. By considering the system reliability indices, influencing factors, and units need to be allocated, a hierarchical model is established for reliability allocation based on the analytic hierarchy process. The Bayesian best-worst method compares elements in each layer, generating best and worst matrices based on decision-makers. These matrices determine optimal weights for each layer's elements. The analytic hierarchy process determines relative importance weights among the system reliability index, influencing factors, and units. Consequently, the derived optimal weights are converted into allocation factors, which enable the allocation of reliability for cruise ship automation systems. This fusion-based approach presents a novel and effective method for optimizing reliability allocation during the development stage of complex systems. © 2024 IEEE.

关键词： Decision making

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Hyperspectral Image Cross Scene Classification Combined Domain Adaptation with XGBoost

Hyperspectral Image Cross Scene Classification Combined Doma...

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2024 International Conference on measurement, communication, and Virtual Reality, MCVR 2024

作者： Liu, Chengyang Wu, Haibin Li, Jiarong Liu, Xinyu Yan, Siqi Wang, Aili Heilongjiang Province Key Laboratory of Laser Spectroscopy Technology and Application College of Measurement and Control Technology and Communication Engineering Harbin University of Science and Technology Harbin150080 China School of Electronics and Information Engineering Harbin Institute of Technology Harbin150001 China

ISBN: (纸本)9781510691773

To address the issues of domain discrepancies and limited labeled samples in hyperspectral image classification of cross scene, this paper proposes an ensemble learning framework that integrates eXtreme Gradient Boosting (XGBoost) with confidence domain adaptation strategies to improve classification performance. First, the Houston dataset from different scenes is input separately into a deep embedding model. The hyperspectral images are embedded into a low-dimensional space, ensuring that similar spectra are represented more closely in this space, thereby capturing the intrinsic structure and distribution of the hyperspectral image dataset. Subsequently, these samples are mapped into a discriminator model, where domain adaptation techniques are employed to reduce differences in feature distributions across different scenes. Additionally, confidence-based learning is used to identify and remove noisy samples in the Houston dataset, thereby enhancing the alignment of feature distributions among various scenes. Finally, through ensemble learning, a weighted voting strategy is employed to combine multiple base classifiers into a strong classification model. This approach better captures the complex features within the data, thereby reducing the bias of individual classifiers and enhancing the overall generalization ability and robustness of the model. Experimental results on the Houston dataset demonstrate that the proposed method outperforms popular domain adaptation learning models and traditional methods in terms of classification accuracy and robustness, showcasing its potential and advantages in practical applications. This result confirms the effectiveness of combining XGBoost and confidence domain adaptation strategies in handling hyperspectral image classification. © 2025 SPIE.

关键词： Photointerpretation

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DESIGN AND ANALYSIS OF A SLOTTED PLANAR ANTENNA WITH PARASITIC RING PATCH FOR EFFICIENT 5G MICROWAVE APPLICATIONS

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Telecommunications and Radio engineering (English translation of Elektrosvyaz and Radiotekhnika) 2025年第1期84卷 71-82页

作者： Yadav, Swati Varun Yadav, Manish Varun Gupta, Ridhi Yadav, Dinesh Bhati, Kirti Panwar Ali, Tanweer Singh, Vikas Department of Instrumentation and Control Engineering Manipal Institute of Technology Manipal Academy of Higher Education Manipal Karnataka India Department of Aeronautical and Automobile Engineering Manipal Institute of Technology Manipal Academy of Higher Education Manipal Karnataka India Department of Electronics and Communication Engineering Manipal University Jaipur Rajasthan Jaipur India School of Electronics Devi Ahilya University M.P Indore India Department of Electronics and Communication Engineering Manipal Institute of Technology Manipal Academy of Higher Education Manipal Karnataka India

Design and analysis of a slotted planar antenna with a parasitic ring patch for efficient 5G microwave applications is presented. The antenna design aims to strike a balance between complexity and size efficiency. Its front side features a rectangular patch at the center, comprising L-shaped, T-shaped, and inverse T-shaped elements, alongside four parasitic rings on top. The central feed utilizes a 50-ohm strip line connected to a microstrip line. On the back side, the antenna incorporates three parasitic rings on top, four horizontal strips arranged in an increasing order, and a ground plane with a small rectangular slot. To evaluate its performance, the antenna is simulated using CST Microwave Studio on an FR-4 substrate with overall dimensions measuring 13 × 15 × 1.5 mm3. The results demonstrate an impressive impedance bandwidth of 60.8% and a return loss of –28 dB within the frequency range of 3.2–6 GHz, with a central frequency of 4.6 GHz. Throughout its operational range, the radiation patterns remain constant, exhibiting stable polar patterns and efficient performance with a wide gain of 4.81 dBi and an efficiency of 82%;the proposed antenna proves to be suitable for 5G microwave applications. © 2025 by Begell House, Inc.

关键词： Microwave antennas

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EDSA-Ensemble: an Event Detection Sentiment Analysis Ensemble Architecture

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IEEE Transactions on Affective Computing 2024年第2期16卷 1-18页

作者： PETRESCU, Alexandru TRUICA, Ciprian-Octavian APOSTOL, Elena-Simona PASCHKE, Adrian Computer Science and Engineering Department Faculty of Automatic Control and Computers National University of Science and Technology Politehnica Bucharest Bucharest Romania Fraunhofer Institute for Open Communication Systems Berlin Germany

As global digitization continues to grow, technology becomes more affordable and easier to use, and social media platforms thrive, becoming the new means of spreading information and news. Communities are built around sharing and discussing current events. Within these communities, users are enabled to share their opinions about each event. Using Sentiment Analysis to understand the polarity of each message belonging to an event, as well as the entire event, can help to better understand the general and individual feelings of significant trends and the dynamics on online social networks. In this context, we propose a new ensemble architecture, EDSAEnsemble (Event Detection Sentiment Analysis Ensemble), that uses Event Detection and Sentiment Analysis to improve the detection of the polarity for current events from Social Media. For Event Detection, we use techniques based on Information Diffusion taking into account both the time span and the topics. To detect the polarity of each event, we preprocess the text and employ several Machine and Deep Learning models to create an ensemble model. The preprocessing step includes several word representation models: raw frequency, TFIDF, Word2Vec, and Transformers. The proposed EDSA-Ensemble architecture improves the event sentiment classification over the individual Machine and Deep Learning models. Authors

关键词： Sentiment analysis

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Design of Vehicle Bus Signal Simulation Device 5

Design of Vehicle Bus Signal Simulation Device

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5th International Conference on Sensing, measurement and Data Analytics in the Era of Artificial Intelligence, ICSMD 2024

作者： Chen, Sirui Wang, Shiji Chen, Wenke Li, Baohua Qiao, Jiaqing School of Electronics and Information Engineering Harbin Institute of Technology Harbin China Beijing Aerospace Measurement and Control Technology Co. Ltd Beijing China

ISBN: (纸本)9798331529192

As the functions of automobiles continue to increase and become more intelligent, the data exchange and communication between onboard electronic devices have become increasingly important. To ensure the reliability and stability of these systems in practical applications, it is necessary to manually simulate the testing and verification of ECUs with erroneous bus data packets. This paper designs a vehicular bus signal simulation device that can simulate the generation of CAN, LIN, FlexRay, CXPI, and SENT bus signals and their fault data packets. The hardware part of the device consists of an arbitrary waveform generator and an upper computer, while the software part uses QT to design the user interface and C++ to write the programmable software. Finally, experiments have verified the performance and accuracy of the simulation device. The waveforms captured by the digital storage oscilloscope are consistent with the expected input parameters, proving the effectiveness of the device. This device has significant application value in the development, teaching, and training of automotive electronic systems, capable of reducing the reliance on real-vehicle testing, and reducing test costs and time. © 2024 IEEE.

关键词： C++ (programming language)

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PI-Based Set-Point Learning control for Batch Processes with Unknown Dynamics and Nonrepetitive Uncertainties

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IEEE Transactions on Automatic control 2024年第6期70卷 3649-3664页

作者： Hao, Shoulin Liu, Tao Rogers, Eric Wang, Youqing Paszke, Wojciech Dalian University of Technology Key Lab. of Intelligent Control and Optimization for Industrial Equipment of Ministry of Eduction The Institute of Advanced Measurement and Control Technology Dalian116024 China Beijing University of Chemical Technology College of Information Science and Technology Beijing100029 China University of Zielona Góra Institute of Automation Electronic and Electrical Engineering Zielona Góra65-246 Poland

For industrial batch processes with unknown dynamics subject to nonrepetitive initial conditions and disturbances, this paper develops a novel adaptive data-driven set-point learning control (ADDSPLC) scheme based on only the measured process input and output data, which has two loops, one for the dynamics within a batch and the other for the batch-to-batch dynamics. In the former case, a model-free tuning strategy is firstly presented for determining the closed-loop PI controller parameters. For the latter case, a set-point learning control law with adaptive set-point learning gain and gradient estimation is developed for batch run optimization. Robust convergence of the output tracking error is rigorously analyzed together with the boundedness of adaptive learning gain and real-time updated set-point command. Moreover, another iterative extended state observer based (ADDSPLC) scheme is developed with rigorous convergence and boundedness analysis, to enhance the robust tracking performance against nonrepetitive uncertainties. Finally, two illustrative examples from the literature are used to demonstrate the effectiveness and superiority of the new schemes over the recently developed data-driven learning control designs. © 1963-2012 IEEE.

关键词： Batch data processing

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