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2024 OES China Ocean Acoustics, COA 2024

作者： Li, Hui Cao, Zhongyi Sun, Dajun Harbin Engineering University National Key Laboratory of Underwater Acoustic Technology Harbin150001 China Ministry of Industry and Information Technology Key Laboratory of Marine Information Acquisition and Security Harbin150001 China Harbin Engineering University College of Underwater Acoustic Engineering Harbin150001 China

ISBN: (纸本)9798350340297

Estimation process in radial velocity for underwater acoustic navigation is affected by time-varying channel. The channel has uncertainty with complex randomness and multi parameter coupling. Understanding accurately and quantifying effectively the measure uncertainty can provide matching quality evaluation auxiliary information for acoustic velocity measurement and input accurate measurement noise in real-time for multiple information fusion processes. It is an important way to improve navigation accuracy. This article decomposes the environmental factors that affect the estimation process of radial velocity into two parts: the spatial distribution of reverberation intensity and radial velocity, for analyzing the uncertainty in radial velocity under typical environmental parameters. Preliminary difference analysis of measure uncertainty under different environments is completed through the distribution statistics of velocity estimation output by acoustic Doppler velocity log in experiments. Then, a simulation model is used to analyze the characteristics of radial velocity distribution under several typical environmental parameters further. It can be concluded that the radial velocity distribution with significant environmental and system parameter dependence has certain non-Gaussian characteristic. © 2024 IEEE.

关键词： Velocity distribution

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Att-TCN: An Advertising Attribution Method Based on Temporal Convolutional Network with Attention Mechanism 8

Att-TCN: An Advertising Attribution Method Based on Temporal...

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8th IEEE International Conference on Computer and Communications, ICCC 2022

作者： Xie, Bowen Sun, Yi Lin, Zhaowen Ma, Baoquan Beijing University of Posts and Telecommunications Beijing China National Engineering Laboratory for Mobile Network Technologies Beijing China National Engineering Laboratory for Industry Control System Information Security Technology Beijing China National Computer System Engineering Research Institute of China Beijing China

ISBN: (纸本)9781665450515

Advertising attribution is an effective method to measure the effect of various advertisements in the era of Internet advertising. It enables scientific tracking back and refined operation of advertising by quantitatively evaluating the contribution of different advertising channels to reach the target. It is challenging to predict conversion results based on users' journey data accurately, reflect the impact of ads on user conversions, and appropriately assign attribution values to advertising channels. To achieve the above objectives, we propose an advertising attribution method based on a temporal convolutional network with an attention mechanism (Att-TCN). By mining the hidden information in time and attribution channels using the attention mechanism, our proposed method can improve the accuracy of conversion prediction and thus more rationally assign attribution values across channels. Experimental validation on a real dataset obtained from an operational commercial advertising monitoring company shows that our proposed attribution method has better accuracy than other state-of-the-art attribution methods and gives an open-ended approach to attribution value assignment. © 2022 IEEE.

关键词： Marketing

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Oblique shock train motion based on schlieren image processing

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Chinese Journal of Aeronautics 2023年第3期36卷 30-41页

作者： Longsheng XUE Chuan CHENG Chengpeng WANG Lantian ZHANG Kang LI Keming CHENG College of Aerospace Engineering Nanjing University of Aeronautics and AstronauticsNanjing 210016China Key Laboratory of Unsteady Aerodynamics and Flow Control Ministry of Industry and Information TechnologyNanjing University of Aeronautics and AstronauticsNanjing 210016China Beijing Institute of Tracking and Telecommunication Technology Beijing 100094China Aerospace System Engineering Shanghai Shanghai 201109China

In this paper,shock train motion in a Mach number 2.7 duct is studied experimentally,and large numbers of schlieren images are obtained by a high-speed *** image processing method based on Maximum Correlation Detection(MCD)is proposed to detect shock train motion from the schlieren images,based on which the key structures,e.g.,separation positions and separation shock angles on the top and bottom walls,can be analysed in *** oscillations of the shock train are generated by rhombus and ellipse shafts at various excitation *** to the analysis of MCD results,the distributions of the frequency components of shock train oscillation generated by the two shafts are distinctly different,in which the motion generated by the ellipse shaft is much smoother;shock train motion is mainly characterized by the oscillation of separation position while the separation shock strength is not so sensitive to downstream disturbance;there is a hysteresis loop relation between the downstream pressure and separation position.

关键词： Frequency components Hysteresis loop Maximum Correlation Detection(MCD) Schlieren image processing Shock train oscillation

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Anti-Multipath Underwater Acoustic Time-Delay Detection Algorithm Based on Dual-Delta Correlators

Anti-Multipath Underwater Acoustic Time-Delay Detection Algo...

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2023 International Conference on Frontiers of Ocean Science and technology, ICFOST 2023

作者： Yue, Yang Zheng, Cuie Han, Yunfeng Ouyang, Yujie National Key Laboratory of Underwater Acoustic Technology Harbin Engineering University Harbin150001 China Key Laboratory of Marine Information Acquisition and Security Harbin Engineering University Ministry of Industry and Information Technology Harbin150001 China College of Underwater Acoustic Engineering Harbin Engineering University Harbin150001 China

For the underwater acoustic (UAC) positioning system based on continuous waveform signal, the time-delay estimation of the Line-of-sight (Los) is not robust under the UAC strongly time-varying multipath channel. So an anti-multipath time-delay detection algorithm based on dual-delta (DD) correlators is proposed in this paper to address the issue. The algorithm uses a differential processing code-phase discriminator model to improve the traditional delay-locked-loop (DLL) time-delay detection algorithm, and combines it with the signal tracking loop adapted to the UAC channel condition to estimate time-delay. Theoretical analysis indicates that due to the reduction of local code interval and the differential processing operation, the proposed algorithm effectively mitigates the effect of time-varying multipath signals on time-delay estimation compared with conventional methods. Simulation analysis further demonstrates that the proposed algorithm can robustly detect the Los and achieve high-precision time-delay estimation under the time-varying multipath channel. The algorithm shows great potential for practical applications. © Published under licence by IOP Publishing Ltd.

关键词： Time delay

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Research on Smart Photovoltaic Power Station control system Based on DCS Architecture

Research on Smart Photovoltaic Power Station Control System ...

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2024 International Conference on Computer Communication, Networks and information Science, CCNIS 2024

作者： Zhan, Lei Chn Energy Zhishen Control Technology Co. Ltd. Beijing Engineering Research Center of Power Station Automation State R&D Center of Control System and Information Security Technologies for Energy Industry Beijing102211 China

ISBN: (纸本)9798331507046

Aiming at the operation and maintenance management and control issues of large-scale photovoltaic power stations, a smart photovoltaic power station control system based on DCS architecture was studied. In terms of system architecture design, the design idea based on the 'cloud-edge-end' architecture is adopted, and some core equipment and data storage devices in the site are placed in the cloud to achieve centralized management and storage of data. At the same time, some computationally intensive tasks are processed in the cloud to reduce the computing pressure on edge devices. Applying this system to the control of photovoltaic power plants can achieve precise control of operation and maintenance data, and enable the identification and accurate processing of different faults. Comparing the fault identification algorithm used in this system with other algorithms, the results prove that the fault identification accuracy of this algorithm can reach 100℅, creating a large amount of economic benefits for the stable operation of photovoltaic power plants. © 2024 IEEE.

关键词： Ferroelectric RAM

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Fast resistivity imaging of transient electromagnetic using an extreme learning machine

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Soft Computing 2025年第2期29卷 1121-1131页

作者： Li, Ruiyou Zhang, Yong Li, Guang Li, Ruiheng Hu, Jia Li, Min School of Software and Internet of Things Engineering Jiangxi University of Finance and Economics NanChang330013 China School of Geophysics and Measurement Control Technology East China University of Technology NanChang330013 China School of Information Engineering Hubei University of Economics Wuhan430205 China The State Key Laboratory of Power Transmission Equipment and System Security and New Technology Chongqing University Chongqing400044 China

The transient electromagnetic method (TEM) is widely used in geophysical exploration. In TEM data interpretation, nonlinear inversion plays an important role. However, traditional TEM nonlinear inversion adopts the OCCAM imaging method, which merely presents the approximate shape of the stratum model, with poor inversion accuracy and much iteration time. To solve the above problems, a novel nonlinear inversion approach based on extreme learning machine (ELM) is proposed in this paper. The ELM is required to establish the input–output mapping relationship of the inversion network only once through the analytical method, which is different from the traditional neural network method that demands iterative gradient learning and is prone to fall into the local optimum. Moreover, the ELM inversion network by randomly assigning the hidden layer parameters is capable of mapping the observed TEM data and quickly producing resistivity images, which avoids time-consuming iteration and inversion calculations. The presented approach is applied to both synthetic and field examples. The results show that compared with the traditional nonlinear inversion algorithms (BP and OCCAM), the proposed method achieves better inversion accuracy and significantly reduces the calculation time, which verifies the effectiveness of the ELM algorithm for TEM data interpretation. Additionally, the research provides a new method and technology for TEM data inversion. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： Electromagnetic transients

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Improved control Strategies for Totem-Pole PFC With True Full Range ZVS Operation

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IEEE Transactions on Industrial Electronics 2023年第3期70卷 2419-2430页

作者： Chen, Jie Tai, Weiyu Xun, Benxin Gong, Chunying Chen, Jiawei Nanjing University of Aeronautics and Astronautics Center for More-Electric-Aircraft Power System Ministry of Industry and Information Technology College of Automation Engineering Nanjing211106 China Chongqing University Key Laboratory of Complex System Safety and Control Ministry of Education College of Automation Chongqing400044 China

Zero voltage switching (ZVS) of power semiconductor devices is an effective way to improve the efficiency and power density of the power electronic converters with extremely high switching frequency. This article aims to analyze and realize the true full-range ZVS operation of a totem-pole power factor correction circuit. The available ZVS control strategies are investigated, and the conditions for the realization of full-range ZVS operation are analyzed. It is revealed that the existed control strategies did not consider the influence of the dead-time, resulting in the failure of ZVS operation in some input voltage ranges. To address this issue, two feasible retrofitted control strategies, namely the turn-off current optimization control and the adaptive dead-time control, are proposed. The merits and drawbacks of the two control strategies are carefully analyzed and compared. It is found that the adaptive dead-time control can not only achieve the true full range ZVS but also significantly reduce the loss of the freewheeling diodes. The design consideration of key parameters is discussed. Finally, the feasibility of the proposed control and the correctness of the theoretical analyses are verified by simulation and experiment. © 1982-2012 IEEE.

关键词： Zero voltage switching

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Tensor and Minimum Connected Dominating Set based Confident information Coverage Reliability Evaluation for IoT

IEEE Transactions on Sustainable Computing

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IEEE Transactions on Sustainable Computing 2024年第03期10卷 547-561页

作者： Xiao, Ziheng Zhu, Chenlu Feng, Wei Liu, Shenghao Deng, Xianjun Lu, Hongwei Yang, Laurence T. Park, Jong Hyuk Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology Hubei Wuhan430074 China Artificial Intelligence and Intelligent Transportation Joint Technical Center of HUST and Hubei Chutian Intelligent Transportation Company Ltd United States Network and Industrial Control Information Security Technology Department China Nuclear Power Operation Technology Company Ltd. 1011 Xiongchu Avenue Hubei Wuhan430070 China The Department of Computer Science and Engineering Seoul National University of Science and Technology Seoul01811 Korea Republic of

Internet of Things (IoT) reliability evaluation contributes to the sustainable computing and enhanced stability of the network. Previous algorithms usually evaluate the reliability of IoT by enumenating the states of nodes and networks, which are difficult to handle IoT with hundreds of nodes because the computational cost. In this paper, a novel algorithm, TMCRA, is proposed to evaluate the reliability of IoT in complex network environment, which consider both coverage and connectivity. For coverage, TMCRA employs the Confident information Coverage (CIC) model to divide the target area into independent grids and calculates the coverage rate. In terms of connectivity, TMCRA forming the Virtual Backbone Network (VBN) based on two proposed methods: TMA and MGIN, and evaluate connectivity by analyzing the VBN rather than the whole network. The TMA and MGIN are two algorithms for constructing Minimum Connected Dominant Sets (MCDS), which are suitable for different scale networks. Finally, based on the data of coverage and connectivity, TMCRA utilizes tensors for the unified modeling and representation of network structure, and calculates IoT reliability based on the tensors. Simulations are carried out for various sizes of IoT to show the advantages and effectiveness of the proposed approach in reliability evaluation. © 2016 IEEE.

关键词： Data reliability

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General Simultaneous Localization and Mapping Scheme for mmWave Communication systems

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IEEE Internet of Things Journal 2024年第12期11卷 22521-22536页

作者： Du, Tao Yang, Jie Wen, Chao-Kai Xia, Shuqiang Jin, Shi Southeast University National Mobile Communications Research Laboratory Nanjing210096 China Southeast University Key Laboratory Of Measurement And Control Of Complex Systems Of Engineering Ministry Of Education The Frontiers Science Center For Mobile Information Communication And Security Nanjing210096 China Institute Of Communications Engineering National Sun Yat-sen University Kaohsiung80424 Taiwan Zte Corporation The State Key Laboratory Of Mobile Network And Mobile Multimedia Technology Shenzhen518055 China Southeast University National Mobile Communications Research Laboratory The Frontiers Science Center For Mobile Information Communication And Security Nanjing210096 China

Utilizing high-resolution antenna arrays and wide bandwidth of the millimeter-wave (mmWave) spectrum in 5G new radio (NR) mmWave communication systems holds the potential for high-throughput data transmission while enabling user localization and environmental mapping. However, the majority of existing simultaneous localization and mapping (SLAM) algorithms rely on methods akin to the extended Kalman filter for generating initial map features. These methods prove ineffective when the measurement dimension is insufficient. Furthermore, there is a notable absence of research exploring mmWave prototype systems to evaluate and compare the performance and viability of various SLAM algorithms. To address these challenges, we propose an innovative probability hypothesis density (PHD) generation scheme for birth events and have developed a prototype system. Our approach, referred to as PHD-SLAM, exhibits remarkable effectiveness even in scenarios where the measurement dimension falls short of map features. This means it can function seamlessly with only delay or angle information available. Additionally, we have designed a 28GHz mmWave beam scanning prototype system that leverages the 5G NR frame for accomplishing SLAM algorithms. Following this, we conducted extensive simulations and experimental evaluations to gauge the performance of several leading-edge SLAM algorithms under diverse mmWave circumstances, encompassing PHD-based and belief propagation (BP) SLAM algorithms. Our analysis reveals that both PHD and BP SLAM can achieve agent localization precision within a decimeter and mapping precision within a meter, capitalizing on the angle parameters of mmWave signals. While BP SLAM showcases reduced computational demand, its estimation precision is marginally inferior to that of PHD-SLAM. © 2014 IEEE.

关键词： Belief propagation

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Extended NNs-Based Adaptive Event-Triggered Asymptotic control for Nonlinear systems Against Actuator Faults 7

Extended NNs-Based Adaptive Event-Triggered Asymptotic Contr...

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7th International Symposium on Autonomous systems, ISAS 2024

作者： Liu, Shanlin Zhang, Yingwei Zhao, Xudong Wang, Hesheng Northeastern University College of Information Science and Engineering Shenyang China Northeastern University State Laboratory of Synthesis Automation of Process Industry Shenyang China Dalian University of Technology Faculty of Electronic Information and Electrical Engineering Dalian China Department of Automation Key Laboratory of System Control and Information Processing of Ministry of Education Key Laboratory of Marine Intelligent Equipment and System of Ministry of Education Shanghai Engineering Research Center of Intelligent Control and Management Shanghai Jiao Tong University Shanghai China

ISBN: (纸本)9798350363173

In this article, an adaptive event-triggered asymp¬totically fault-tolerant control (FTC) issue for nonlinear systems with actuator faults is investigated. An extended neural networks (NNs) technique is introduced to estimate unknown functions of the system under consideration. Compared with traditional NNs that ignore approximation errors, the proposed extended NNs further processes approximation errors, reducing the conservatism of the proposed algorithm. Then, within the framework of backstepping method, a new adaptive control strategy is developed by combining event-triggered mechanism (ETM) and bound estimation (BE) approach, which ensures the asymptotic convergence of system states while also reducing the transmission of unnecessary information, thus improving the utilization of communication resources. At the same time, the designed controller eliminates the adverse effects of actuator failures and system nonlinear terms. Finally, a simulation example of a single-link robot system is provided to demonstrate that the developed control strategy can ensure that all signals are bounded and system states are asymptotically convergent. © 2024 IEEE.

关键词： Approximation algorithms

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