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arXiv 2024年

作者： Jiang, Tianqi Luo, Haoxiang Yang, Kun Sun, Gang Yu, Hongfang Huang, Qi Vasilakos, Athanasios V. Glasgow College University of Electronic Science and Technology of China Chengdu611731 China Key Laboratory of Optical Fiber Sensing and Communications Ministry of Education University of Electronic Science and Technology of China Chengdu611731 China Guangyuan Power Supply Company State Grid Sichuan Electric Power Company Guangyuan628033 China Peng Cheng Laboratory Shenzhen518066 China Sichuan Provincial Key Laboratory of Power System Wide-Area Measurement and Control University of Electronic Science and Technology of China Chengdu611731 China School of Information Engineering Southwest University of Science and Technology Mianyang621010 China Center for AI Research University of Agder Grimstad4879 Norway

The energy market encompasses the behavior of energy supply and trading within a platform system. By utilizing centralized or distributed trading, energy can be effectively managed and distributed across different regions, thereby achieving market equilibrium and satisfying both producers and consumers. Additionally, the energy market can address future production control and environmental concerns such as energy shortages and environmental deterioration. However, recent years have presented unprecedented challenges and difficulties for the development of the energy market. These challenges include regional energy imbalances, volatile energy pricing, high computing costs, and issues related to transaction information disclosure. These factors have hindered the smooth operation of the energy market. Researchers widely acknowledge that the security features of blockchain technology can enhance the efficiency of energy transactions and establish the fundamental stability and robustness of the energy market. This type of blockchain-enabled energy market is commonly referred to as an energy blockchain. Currently, there is a burgeoning amount of research in this field, encompassing algorithm design, framework construction, and practical application. It is crucial to organize and compare these research efforts to facilitate the further advancement of energy blockchain. This survey aims to comprehensively review the fundamental characteristics of blockchain and energy markets, highlighting the significant advantages of combining the two. Moreover, based on existing research outcomes, we will categorize and compare the current energy market research supported by blockchain in terms of algorithm design, market framework construction, and the policies and practical applications adopted by different countries. Finally, we will address current issues and propose potential future directions for improvement, to provide guidance for the practical implementation of blockchain in the

关键词： Deterioration

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A Combined Alignment Method for Strapdown Inertial Navigation System on Stationary Base

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Journal of Harbin Institute of technology(New Series) 2020年第6期27卷 57-71页

作者： Shuai Chen Xinzhi Liu Qian Sun Ya Zhang School of Information and Control Engineering Liaoning Shihua UniversityFushun 113001LiaoningChina Department of Applied Mathematics University of WaterlooWaterloo N2L3G1Canada College of Information and Communication Engineering Harbin Engineering UniversityHarbin 150001China School of Electrical Engineering&Automation Harbin Institute of TechnologyHarbin 150001China

Owing to the weak observability of the azimuth misalignment angle,alignment accuracy and time are always the contradictory issues in the initial alignment process of Strapdown Inertial Navigation System(SINS),which requires a compromise between *** this paper,a combined alignment mechanism is proposed to construct an observable and controllable system model,which can effectively achieve higher azimuth alignment accuracy during the fixed time ***,the Reduced Order Kalman Filter(ROKF)alignment algorithm was utilized to calculate the misalignment angles in parallel with the classical gyrocompass alignment ***,the misalignment angles calculated by the gyrocompass alignment method were used to formulate the augmented measurement model with zero velocity ***,the zero velocity model of the ROKF method was switched into the augmented measurement model when the azimuth misalignment angle of the gyrocompass alignment method was close to steady *** combined alignment method was analyzed reasonably by the observability and the mathematical *** comparison results of the numerical simulation and the experimental data test showed that the combined method had good performance in terms of estimation accuracy and consistency of the alignment results.

关键词： inertial navigation instrumentation and measurement gyroscopes accelerometers Kalman filter measurement errors

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Robust Beamforming Based on Covariance Matrix Reconstruction and ADMM

Robust Beamforming Based on Covariance Matrix Reconstruction...

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IEEE International Conference on Radar

作者： Pengcheng Gong Zhaobin Wang Kaiyan Xu School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu China School of Computer Science and Engineering Wuhan Institute of Technology Wuhan China Hubei Key Laboratory for High-efficiency Utilization of Solar Energy and Operation Control of Energy Storage System Hubei University of Technology Wuhan China

ISBN: (纸本)9781665468893

Considering at the issue that the performance of the traditional beamforming algorithm will decrease sharply when the disturbance occurs at the disturbed position and the steering vector mismatch occurs, a new robust beamforming method based on covariance matrix reconstruction and Alternating Direction Method of Multipliers (ADMM) is proposed. Firstly, based on the maximum output power criterion of beamformers, an optimization model is designed to solve the optimal steering vector. Then, the covariance matrix is reconstructed by using the defined interference range to widen the nullity and enhance the anti-motion interference ability of the system. Then, for the quadratic inequality constraint problem of the steering vector, this paper uses ADMM to solve the model iteratively, and obtains the specific solution of the steering vector in each iteration. In addition, the complexity of the algorithm is also analyzed. The experimental results show that, compared with the existing beamforming algorithms, the proposed method Widen the width of zero notch in the interference area, improves the anti-interference performance of the beam, and can well correct the mismatched steering vector.

关键词： Systematics Array signal processing Interference Radar Resists Convex functions Robustness

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A Spectrum Status Duration Prediction Method Based on Homotopy Theory for UAV communication 6

A Spectrum Status Duration Prediction Method Based on Homoto...

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6th IEEE International Conference on Computer and communications, ICCC 2020

作者： Zhou, Tao Luo, Shan Zheng, Peixiang Lin, Rongping School of Aeronautics and Astronautics University of Electronic Science and Technology of China Chengdu China Aircraft Swarm Intelligent Sensing and Cooperative Control Key Laboratory of Sichuan Province University of Electronic Science and Technology of China Chengdu China School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu China

ISBN: (纸本)9781728186351

With the development of wireless communication technology, a large number of spectrum resources are occupied and become increasingly scarce. If drones want to communicate, they need to share spectrum with others. The method of spectrum prediction in cognitive radio can efficiently achieve this. The traditional spectrum status duration prediction method based on the non-stationary hidden Markov model only predict the duration of one fixed geographic location and cannot meet the requirement of UAV's communications. We intend to apply the homotopy theory to the non-stationary hidden Markov model to obtain the spectrum status duration of different locations so that it can apply to UAV's communications. We also conducted experiments to predict the duration of the spectrum status at different locations. The experimental results show that our model can work well. © 2020 IEEE.

关键词： Hidden Markov models

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Multi-scale pseudo-labels filtering and key pixels adversarial alignment for domain adaptive object detection

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engineering Applications of Artificial Intelligence 2025年 157卷

作者： Wei, Xing Xia, Jiong Yang, Fan Zhao, Chong Liu, Cang Wang, Ge Chen, Yanke Lu, Yang School of Computer and Information Hefei University of Technology China Intelligent Manufacturing Technology Research Institute of Hefei University of Technology China Engineering Research Center of Safety Critical Industrial Measurement and Control Technology Ministry of Education China Anhui Mine IOT and Security Monitoring Technology Key Laboratory China

Domain Adaptive Object Detection (DAOD) aims to adapt a detector trained on the labeled source domain to perform effectively on the unlabeled target domain. Most existing DAOD methods commonly employ self-training strategy and adversarial learning. However, the methods based on the self-training strategy often generate unreliable pseudo-labels (e.g., missing detections and false positives) due to the absence of target domain semantics, resulting in suboptimal models. Meanwhile, aligning a large number of unimportant pixels hampers adversarial learning's ability to capture domain-invariant semantic information. Therefore, we propose the Multi-scale Adversarial Teacher Framework (MATF) based on the common self-training framework (teacher–student framework). Specifically, to mitigate the impact of unreliable pseudo-labels, we propose the Multi-scale Negatives Filtering (MNF) module in the teacher model, which prevents missing detections from damaging the model by filtering out unreliable negative samples at multiple feature scales. In addition, we propose the Multi-scale Key-Pixel Discriminator (MKPD), which predicts the distributions of key pixels at multiple feature scales emphasizing the adversarial alignment of key pixels rather than unimportant pixels. Finally, to generate higher-quality pseudo-labels, we introduce the discriminator into the student model to reduce the model's bias towards the source domain. Unlike most methods, we construct our method on a computationally efficient but less work-intensive one-stage detector. Extensive experiments conducted on DAOD benchmark datasets such as Cityscapes, FoggyCityscapes, KITTI, and Sim10k demonstrate the strong adaptability and effectiveness of MATF. © 2025 Elsevier Ltd

关键词： Object detection

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A Novel Multi-Points Laser Monitoring of Building Settlement and Its Risk Evaluation

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Wuhan University Journal of Natural Sciences 2019年第5期24卷 442-454页

作者： CHAI Caiping WU Zejian WANG Huifeng GUAN Limin HUANG He MU Kenan CUI Wenfen HAO Ting School of Highway & Railway Engineering Shaanxi College of Communication Technology Xi'an 714018 ShaanxiChina School of Electronic & Control Engineering Chang'an University Xi'an 710064 Shaanxi China

Aiming at the defects of routine settlement measurement methods, such as complicated procedures, time-consuming and labor-intensive, high cost and low measurement accuracy, based on the analysis of existing engineering measurement technical requirements and specifications, a multi-point high precision and high efficiency based on laser reference is proposed. The automatic building settlement real-time monitoring system program gives the principle and system model of single-point settlement observation. The model of multi-point scanning settlement monitoring system and the model of multi-point network settlement monitoring system are designed, and their advantages and disadvantages are analyzed. We focus on the networked multi-point settlement monitoring system for network cumulative error analysis, and propose related evaluation and correction methods. The hardware schematic and software block diagram of the laser reference measurement and measurement system of the single point settlement acquisition system are given. Finally, the risk of subsidence state is quantitatively evaluated based on multi-point settlement monitoring data. The measurement error of this method is less than 300 μm, which can realize the monitoring and evaluation of the overall settlement.

关键词： measurement technology laser reference settlement chain linear array Charge-Coupled Device(CCD)

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Simultaneous sensing of gas-liquid two-phase flow rate based on optical carrier microwave interferometry

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Energy 2025年 331卷

作者： Songlin Li Ting Xue Yan Wu Zhuping Li Bin Wu School of Electrical and Information Engineering Tianjin University Tianjin Key Laboratory of Process Measurement and Control Tianjin 300072 China State Key Laboratory of Precision Measuring Technology and Instruments Tianjin University Tianjin 300072 China

Gas-liquid two-phase flow is widespread in energy systems, and accurate measurement of its two-phase flow rate is vital to ensure process safety and improve efficiency. In this paper, a fiber-optic gas-liquid two-phase flow rate simultaneous sensing method based on optical carrier microwave interferometry (OCMI) is proposed. Since the gas-liquid two-phase flow has complex characteristics, the commonly used OCMI demodulation methods (including dip frequency tracking and phase demodulation methods) face challenges in measuring the two-phase flow rates simultaneously. Therefore, an artificial neural network (ANN) based demodulation method is developed. The impact of different inputs to the ANN model on the prediction results is investigated, where utilizing the amplitudes of the spatiotemporal reflection peaks as the input achieves the optimal performance. The experimental results of the 5-fold cross-validation demonstrate that the proposed method achieves gas flow rate prediction with a mean absolute error (MAE) of 0.94 ± 0.15 m 3 /h and a mean absolute percentage error (MAPE) of 3.91 ± 0.51 %, while the MAE and MAPE for predicting liquid flow rate are 0.13 ± 0.04 m 3 /h and 4.87 ± 0.61 %, respectively. In conclusion, the proposed fiber-optic gas-liquid two-phase flow rate sensing method based on OCMI and ANN provides new insights for the measurement and characteristic analysis of complex flows.

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Black-box domain adaptation for cross-domain on-device machinery fault diagnosis via hierarchical debiased self-supervised learning

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Knowledge-Based Systems 2025年 324卷

作者： Zhu, Mengliang Liu, Jie Lu, Yanglong Hu, Zhongxu Yuan, Li Zhou, Kaibo MOE Key Laboratory of Image Information Processing and Intelligent Control Belt and Road Joint Laboratory on Measurement and Control Technology School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China National Center of Technology Innovation for Digital Construction School of Civil and Hydraulic Engineering Huazhong University of Science and Technology Wuhan430074 China Department of Mechanical and Aerospace Engineering The Hong Kong University of Science and Technology Clear Water Bay Hong Kong SAR China School of Mechanical Science and Engineering Huazhong University of Science and Technology Wuhan430074 China

This paper explores black-box domain adaptation (BBDA) for cross-domain on-device machinery fault diagnosis. Specifically, a pre-trained black-box source model is deployed on a cloud platform with only its input-output API accessible, and a randomly initialized target model is trained locally utilizing unlabeled target data on an edge device. In BBDA, neither raw data nor model parameters are transmitted across domains, and the only available supervised information for adaptation is API-queried predictions on unlabeled target data. However, API-queried predictions are inevitably noisy and class-imbalanced due to cross-domain distribution discrepancy and inherent category bias. Directly utilizing them for on-device training easily leads to confirmation bias and exacerbates category bias, thereby severely degrading performance. This paper proposes a hierarchical debiased self-supervised learning framework for BBDA from a novel research perspective of concurrently alleviating both confirmation and category biases. Specifically, debiased knowledge distillation is proposed to gradually enhance discriminability of the randomly initialized target model by providing debiased training predictions. An adaptive category-unbiased sample division strategy is devised to divide unlabeled target data into certain-aware and uncertain-aware sets, enabling fully exploiting intrinsic data structures and utilizing underlying sample relationships within the target domain. On this basis, debiased prototypical self-training is proposed for the certain-aware set to obtain reliable pseudo-labels for self-training, leveraging global semantic structure. Hierarchical neighborhood adaptation is customized for the uncertain-aware set to enforce the cluster assumption for noise refinement, exploiting local clustering structure and utilizing underlying sample relationships. Extensive experiments and analyses on public and practical datasets validate the proposed framework's superiority in cross-dom

关键词： Self-supervised learning

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Temperature measurement based on electron spin resonance of magnetic nanoparticles

arXiv

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arXiv 2021年

作者： Wang, Shuai Zhong, Jing Liu, Wenzhong School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China Belt and Road Joint Laboratory on Measurement and Control Technology Huazhong University of Science and Technology Wuhan430074 China Key Laboratory of Image Processing and Intelligent Control Huazhong University of Science and Technology Ministry of Education Wuhan430074 China School of Instrumentation and Optoelectronic Engineering Beihang University Beijing100191 China

Magnetic nanoparticles (MNPs) have excellent magnetic-temperature characteristic. However, current temperature measurement based on MNPs is interfered by concentration. Utilizing the electron spin resonance (ESR), we propose a highly sensitive temperature measurement method without concentration coupling. The anisotropic field is affected by temperature, thus affecting the g-value. The influence of the MNP concentration, size, and the data analysis method on temperature estimation are studied. The optimal temperature sensitivity is achieved with 15-nm MNPs while Gaussian smoothing method allows an optimal accuracy at Fe concentration of 5 mg/ml with a standard deviation of 0.07 K. Copyright © 2021, The Authors. All rights reserved.

关键词： Magnetic moments

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Synergistic wedge effect for low-temperature doping in dual-phase dielectric ceramics

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Journal of Materials Science & technology 2025年

作者： Yanzhao Zhang GuoXiang Zhou Yuhang Zhang Ning Xie Lanlan Yang Kunpeng Lin Zhe Zhao Meiling Yang Huatay Lin Zhihua Yang Dechang Jia Yu Zhou Institute for Advanced Ceramics School of Materials Science and Engineering Harbin Institute of Technology Harbin 150001 China Chongqing Research Institute Harbin Institute of Technology Chongqing 401135 China Shandong Provincial Key Laboratory of Preparation and Measurement of Building Materials University of Jinan Jinan 250000 China School of Microelectronics and Communication Engineering Chongqing University Chongqing 400044 China School of Electromechanical Engineering Guangdong University of Technology Guangzhou 510006 China School of Materials Science and Engineering Harbin Institute of Technology (Shenzhen) Shenzhen 518055 China

Selective ion doping is a promising method to enhance the overall performance of perovskite dielectric ceramics. However, the confrontations among the dielectric constant ( ε r ), near-zero temperature coefficient of resonant frequency (TCF), and quality factor ( Q × f ) remain key challenges in high-performance dielectric ceramics. To leverage the trade-off conflicts, using dual-phase perovskite-based dielectric ceramic composites could be a potentially ideal solution. Unfortunately, the selected ions are hard to be doped into the perovskite phase due to the sintering temperature disparity of the two phases, thereby significantly impacting the modification effect. In this study, a “wedge” strategy was employed in a MgTiO 3 -CaTiO 3 dual-phase dielectric ceramic composite by using Ce as an auxiliary dopant to realize the main Hf doping in the CaTiO 3 phase due to the lattice distortion. This strategy also harmonizes the confrontation of the sintering temperature disparity between the two components of CaTiO 3 and MgTiO 3 . The Hf 4+ doping level of as high as 40 mol.% with a reduced sintering temperature of over 200 °C was successfully achieved by using this “wedge” strategy. Most importantly, the trade-off conflicts among the ε r , near-zero TCF, and Q × f have been considerably reduced and harmonized due to the successful introduction of a high dosage of Hf dopant in the CaTiO 3 phase without sacrificing the intact structure of MgTiO 3 . This strategy not only sheds light on manufacturing high-performance dual-phase dielectric ceramics with trade-off conflicts of properties but also provides an innovative pathway to address the challenge of mismatch of sintering temperature for selective ion doping in multiple-phase ceramic composites.

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