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17th International Multi-Conference on Systems, Signals and Devices, SSD 2020

作者： Gharbi, A. Kallel, A.Y. Bouhamed, A. Al-Hamry, A. Kanoun, O. Derbel, N. University of Paris 1 LSPM CNRS-UPR 9001 Villetaneuse France Technische Universittä T Chemnitz Measurement and Sensor Technology Chemnitz Germany University of Sfax Control Energy Management Laboratory National Engineering School of Sfax Tunisia

ISBN: (纸本)9781728110806

Two biomaterial types have been the focus of sensor applications: Fluorosilicate bioactive Glass \text{BG}-\mathrm{F}-{6} crude and compact. Of the two, \text{BG}-\mathrm{F}-{6} compact material show the greater mechanical strength, and electrical behaviour provoke greater humidity sensing capacity. Thus, the glassy discs show great potential for deployment in a new generation of biosensors and environment requires. The aim of this paper is to develop a material that combines an interesting mechanical strength, monophasic stability with potential thermic resistance and great electrical capacity. By returning to its high temperature synthesis protocol, makes this fluorosilicate glass an attractive material for sensors development topic and environmental precautions. © 2020 IEEE.

关键词： Capacitive sensors

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Opportunity and shift of nitrogen use in China

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Geography and Sustainability 2024年第1期5卷 33-40页

作者： Wangzheng Shen Jing He Sisi Li Yanhua Zhuang Hongyuan Wang Hongbin Liu Liang Zhang Andreas Kappler Hubei Provincial Engineering Research Center of Non-Point Source Pollution Control Innovation Academy for Precision Measurement Science and TechnologyChinese Academy of SciencesWuhan 430077China University of Chinese Academy of Sciences Beijing 100049China School of Environmental Studies China University of Geosciences(Wuhan)Wuhan 430078China State Environmental Protection Key Laboratory of Source Apportionment and Control of Aquatic Pollution Ministry of Ecology and Environment of the People's Republic of ChinaWuhan 430078China Hubei Key Laboratory of Yangtze River Basin Environmental Aquatic Science Wuhan 430078China Institute of Agricultural Resources and Regional Planning Chinese Academy of Agricultural SciencesBeijing 100081China Geomicrobiology Group Center for Applied Geosciences(ZAG)University of TuebingenTuebingen D-72076Germany

It is never an easy task for China to feed 1.4 billion people with only 7%of the world's arable *** nearly 30%of the world's nitrogen(N)fertilizer applied,China achieves high crop yields while facing N pollution result-ing from excessive N ***,we calculate the farmland N budget on the national and regional *** N use efficiency(NUE)in China increased by 28.0%during *** improvement is due to the reduction in fertilization and the improvement of crop *** fragmented farmland is changing to large-scale farmland with the increase in cultivated land area per rural population and the development of agricultural *** opportunity brings more possibilities for precision farmland management,thus further improving *** goal of an NUE of 0.6 may be achieved in the 2040s based on the current development *** striking N use shift in China has important implications for other developing countries.

关键词： Environmental Kuznets curve Nitrogen surplus Greenhouse gas emission Climate change Non-point source pollution

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Gas turbine rotor system fault diagnosis method based on improved deep convolutional generative adversarial networks

Research Square

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Research Square 2021年

作者： Liu, Shucong Wang, Hongjun Han, Fengxia Zhang, Xiang School of Mechanical and Electrical Engineering Beijing Information Science and Technology University Beijing100192 China College of Mechanical Engineering and Applied Electronics Technology Beijing University of Technology Beijing100124 China Key Laboratory of Modern Measurement and Control Technology of Ministry of Education Beijing Information Science and Technology University Beijing100192 China

In gas turbine rotor system fault diagnosis intelligent method based on data-driven is an important means to monitor the health status of gas turbine, it is necessary to obtain sufficient effective fault data to train the intelligent diagnosis model. In the actual operation of gas turbine, the collected gas turbine fault data is limited, and the small and imbalanced fault samples seriously affect the accuracy of fault diagnosis method. Aiming at the imbalance of gas turbine fault data, an Improved Deep Convolutional Generative Adversarial Network (Improved DCGAN) suitable for gas turbine signal is proposed, an structural optimization on generator of Deep Convolutional Generative Adversarial Network (DCGAN) and gradient penalty improvement on the loss function are introduced to generate effective fault data and improve the classification accuracy. The experiment results of gas turbine test bench demonstrated that the proposed method generated effective fault samples as a supplementary set of fault samples to balance the dataset, effectively improved the fault classification and diagnosis performance of gas turbine rotor in the case of small samples, The proposed method can be used as a solution to the problems of small unbalanced fault samples, and provides an effective method for gas turbine fault diagnosis. © 2021, CC BY.

关键词： Failure analysis

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Semi-supervised Early Event Detection 5th

Semi-supervised Early Event Detection

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5th Asian Conference on Pattern Recognition, ACPR 2019

作者： Xie, Liping Gong, Chen Zhang, Jinxia Shan, Shuo Wei, Haikun Key Laboratory of Measurement and Control of CSE Ministry of Education School of Automation Southeast University Nanjing210096 China Jiangsu Key Laboratory of Image and Video Understanding for Social Safety School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing210094 China

ISBN: (纸本)9783030412982

Early event detection is among the keys in the field of event detection due to its timeliness in widespread applications. The objective of early detection (ED) is to identify the specified event of the video sequence as early as possible before its ending. This paper introduces semi-supervised learning to ED, which is the first attempt to utilize the domain knowledge in the field of ED. In this setting, some domain knowledge in the form of pairwise constraints is available. Particularly, we treat the segments of complete events as must-link constraints. Furthermore, some segments do not overlap with the event are put together with the complete events as cannot-link constraints. Thus, a new algorithm termed semi-supervised ED (SemiED) is proposed, which could make better early detection for videos. The SemiED algorithm is a convex quadratic programming problem, which could be resolved efficiently. We also discuss the computational complexity of SemiED to evaluate its effectiveness. The superiority of the proposed method is validated on two video-based datasets. © 2020, Springer Nature Switzerland AG.

关键词： Domain Knowledge

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Kinetic modelling and steady-state optimization of cooling crystallization by continuous oscillatory baffled crystallizer

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Chemical engineering Journal 2025年 519卷

作者： Mingyan Zhao Tao Liu Bo Song Ji Fan Xiongwei Ni Zoltan K. Nagy Rolf Findeisen Key Laboratory of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education Dalian University of Technology Dalian 116024 China Institute of Advanced Measurement & Control Technology Dalian University of Technology Dalian 116024 China School of Engineering and Physical Science Heriot-Watt University Edinburgh UK Davidson School of Chemical Engineering Purdue University West Lafayette IN 47907 United States Control and Cyber-physical Systems Laboratory Technical University of Darmstadt Darmstadt 64283 Germany

To address an open issue of sufficiently describing crystallization kinetics in a continuous oscillatory baffled crystallizer (COBC), a comprehensive kinetic modelling method is proposed in this paper, along with a steady-state optimization approach (SOA) for operating the COBC. Taking into account the axial dispersion of crystal quantity (ADCQ), velocity dispersion of crystal population (VDCP), and growth rate dispersion (GRD), a non-ideal plug flow micro-distribution model (NPF-MDM) is firstly established, which could be used to predict the crystal size distribution (CSD) and mean crystal size (MCS) in each zone of COBC. The model parameters are estimated by heterogeneous tracer experiments and continuous cooling crystallization (CCC) experiments in a real COBC named DN15. Based on the established NPF-MDM, an SOA is provided for operating the COBC. The tube length distribution across different temperature zones of COBC is optimized to determine the maximum attainable region of product MCS. By introducing an objective function related to the target crystal size and the CSD width of product crystals, a sensitivity analysis (SA) is presented to identify the critical operating conditions (COCs), including the seed recipe and net flow rate. Subsequently, the SA-based SOA is carried out. A growth optimizer algorithm is offered to solve the related nonconvex optimization problems. Experiments on the CCC of L-glutamic acid (LGA) via DN15 are performed to validate the proposed modelling and SOA.

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Reconfigurable Metagrating Enabled by Shape Memory Polymers

Reconfigurable Metagrating Enabled by Shape Memory Polymers

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IEEE MTT-S International Microwave Workshop Series on Advanced Materials and Processes for RF and THz Applications (IMWS-AMP)

作者： Sidi Wang Kai Feng Jianjia Yi Key Laboratory of Integrated Services Network Xidian University Xi’an China Science and Technology on Electro-Optical Information Security Control Laboratory Tianjin China School of Information and Communications Engineering Xi’an Jiaotong University Xi’an China State Key Laboratory of Millimeter Waves Southeast University Nanjing China

We propose and demonstrate a shape-controlled metagrating with adjustable energy distribution based on shape memory Polymers (SMPs). The design consists of a capacitance printed on the top of the FR4 substrate, SMPs substrate (VeroWhitePlus) in the middle layer, and a metallic ground at the bottom. Using the perfect diffraction theory to design reconfigurable metagratings based on shape memory polymers is. Furthermore, the functionalities of the designed shape-controlled tunable metagrating are investigated by full-wave simulations. Simulation results show that the reconfigurable metagrating can achieve energy bisection of +1, -1 order and the energy trisection of +1, 0, -1 order. And the proposed reconfigurable metagrating is experimentally demonstrated. The design breaks through the limitation that the traditional electrically tunable reconfigurable metagratings need external power supply, and provides a new design idea for the dynamic control of energy distribution of the reconfigurable metagratings.

关键词： Radio frequency Shape Diffraction Simulation Switched mode power supplies Process control Holography

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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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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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TSGN: Transaction Subgraph Networks Assisting Phishing Detection in Ethereum

arXiv

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arXiv 2022年

作者： Wang, Jinhuan Chen, Pengtao Xu, Xinyao Wu, Jiajing Shen, Meng Xuan, Qi Yang, Xiaoniu Institute of Cyberspace Security College of Information Engineering Zhejiang University of Technology Hangzhou310023 China School of Computer Science and Engineering Sun Yat-sen University Guangzhou510006 China School of Cyberspace Science and Technology Beijing Institute of Technology Beijing100081 China Shenzhen518066 China PCL Research Center of Networks and Communications Peng Cheng Laboratory Shenzhen518000 China Utron Technology Company Ltd. Hangzhou310056 China Institute of Cyberspace Security Zhejiang University of Technology Hangzhou310023 China Science and Technology on Communication Information Security Control Laboratory Jiaxing314033 China

Due to the decentralized and public nature of the Blockchain ecosystem, the malicious activities on the Ethereum platform impose immeasurable losses for the users. Existing phishing scam detection methods mostly rely only on the analysis of original transaction networks, which is difficult to dig deeply into the transaction patterns hidden in the network structure of transaction interaction. In this paper, we propose a Transaction SubGraph Network (TSGN) based phishing accounts identification framework for Ethereum. We first extract transaction subgraphs for target accounts and then expand these subgraphs into corresponding TSGNs based on the different mapping mechanisms. In order to make our model incorporate more important information about real transactions, we encode the transaction attributes into the modeling process of TSGNs, yielding two variants of TSGN, i.e., Directed-TSGN and Temporal-TSGN, which can be applied to the different attributed networks. Especially, by introducing TSGN into multi-edge transaction networks, the Multiple-TSGN model proposed is able to preserve the temporal transaction flow information and capture the significant topological pattern of phishing scams, while reducing the time complexity of modeling large-scale networks. Extensive experimental results show that TSGN models can provide more potential information to improve the performance of phishing detection by incorporating graph representation learning. Copyright © 2022, The Authors. All rights reserved.

关键词： Topology

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