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Batch Microfabrication of Boron-Doped Diamond-Based Microsensor for Seconds Level Measurement of Dual Parameters of Chemical Oxygen Demand and Conductivity in Tap Water

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IEEE Sensors Journal 2025年第9期25卷 14606-14616页

作者： Han, Yunzhe Li, Jianqian Lu, Shengkang Yang, Ping Zhang, Jin Yu, Zhengyin Yin, Jiawen Jin, Qinghui Ningbo University Faculty of Electrical Engineering and Computer Science Ningbo315211 China Shanghai Institute of Microsystem and Information Technology Chinese Academy of Sciences State Key Laboratory of Transducer Technology Shanghai200050 China

With increasing concern about water safety, particularly the impact of tap water quality on human health, there is an urgent need for a biologically safe, multiparameter microsensor for rapid, online monitoring of tap water quality. This work reports on a batch fabricable miniature sensor based on microelectromechanical system (MEMS) technology for rapid detection of chemical oxygen demand (COD) and conductivity in tap water. The sensor is a silicon-glass structure comprising a boron-doped diamond (BDD) working electrode with biosafety properties. Three electrodes are integrated into the silicon wafer for the detection of COD, two of which can also be constructed as a conductivity sensing unit. Additionally, a microchamber is integrated into the glass. The excellent linearity (0.992 for COD and 0.996 for conductivity) and ideal detection range of the sensor (5–150 mgL-1 for COD and 98– 890 μS cm-1 for conductivity) are witnessed after a systematic performance investigation, and a satisfactory response time (2 s) was obtained by using a microchamber as the detection unit. As the sensors were prepared in a batch using the MEMS, the response of multiple sensors was verified through experimentation and found to be in good agreement. Furthermore, the impact of conductivity on the detection of COD was examined. In this article, a miniature ( 12 x 12 mm), low-cost, biologically safe dual-parameter sensor for COD and conductivity has been designed and prepared using the MEMS. It provides a core sensor component for online monitoring of multiple parameters in tap water. © 2001-2012 IEEE.

关键词： Silicon wafers

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A Novel Finite Mixture Model Based on the Generalized t Distributions with Two-Sided Censored Data

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Annals of Data science 2025年第1期12卷 341-379页

作者： Guan, Ruijie Rong, Yaohua Cheng, Weihu Xin, Zhenyu Key Laboratory of Network Information System Technology Aerospace Information Research Institute Chinese Academy of Sciences Beijing100094 China Faculty of Science Beijing University of Technology Beijing100124 China Master of management EBS Universität für Wirtschaft und Recht Oestrich-Winkel65375 Germany

In light of the rapid technological advancements witnessed in recent decades, numerous disciplines have been inundated with voluminous datasets characterized by multimodality, heavy-tailed distributions, and prevalent missing information. Consequently, the task of effectively modeling such intricate data poses a formidable yet indispensable challenge. This paper endeavors to address this challenge by introducing a novel finite mixture model predicated upon the generalized t distribution, tailored specifically to accommodate two-sided censored observations, thereby establishing a foundational framework for modeling this complex data structure. To facilitate parameter estimation within this model, we devise a variant of the EM-type algorithm, amalgamating the profile likelihood approach with the classical Expectation Conditional Maximization algorithm. Notably, this hybridized methodology affords analytical expressions in the E-step and a tractable M-step, thereby substantially enhancing computational expediency and efficiency. Furthermore, we furnish closed-form expressions delineating the observed information matrix, pivotal for approximating the asymptotic covariance matrix of the MLEs within this mixture model. To empirically evaluate the efficacy of the proposed algorithm, a series of simulation studies are conducted, demonstrating promising performance across various artificial datasets. Additionally, the practical applicability of the proposed methodology is elucidated through its deployment on two real-world datasets, thereby underscoring its feasibility and utility in practical settings. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Expectation maximization algorithm

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Dictionary-Theory-Based Orthogonal Chirp Division Multiplexing Signal for Integrated Sonar and Communication system

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IEEE Transactions on Vehicular technology 2025年第5期74卷 7005-7019页

作者： Wang, Junlong Wang, Qing Tao, Quan Fu, Xiaomei Tianjin University School of Electrical and Information Engineering Tianjin300072 China Tianjin University School of Marine Science and Technology The Key Laboratory of Ocean Observation Technology of Ministry of Natural Resources Tianjin300072 China

The integrated sonar and communication (ISC) system plays an important role in smart ocean engineering, owing to its convenience in environmental perception and communication. The characteristics of the integrated signal and the signal processing methods are critical to the ISC system's performance. Orthogonal chirp division multiplexing (OCDM) signal is considered as a potential integrated signal due to its robustness in communication as well as excellent detection performance. However, insufficient bandwidth and subcarrier spacing can significantly degrade the performance of OCDM-based ISC system due to the severe Doppler expansion of the underwater acoustic (UWA) channel. In this paper, we introduce the dictionary theory into the multicarrier signal design and propose a new Dictionary-theory-based orthogonal chirp division multiplexing (Dic-OCDM) signal. We consider the chirp signal as the basis function. The geometric transformation in structure dictionary design is implemented to rearrange the basis function, resulting in the dictionary-theory-based discrete Fresnel transform (Dic-DFnT) matrix. The Dic-DFnT matrix is regarded as the modulation matrix of Dic-OCDM and provides a larger subcarrier spacing without requiring additional bandwidth. Besides, a Dic-OCDM based ISC system is designed, and corresponding signal processing methods for detection and communication are given. Simulation and lake experiment results attest to the effectiveness of our proposed Dic-OCDM-based ISC system for underwater active detection and communication. © 1967-2012 IEEE.

关键词： Sonar

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Influence of different anodised nanoporous structures on the anti-icing and electrical properties of transmission Al lines

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High Voltage 2025年第1期10卷 167-177页

作者： Xu Dai Yuan Yuan Jie Xiao Chenghao Jiang Xujiang Hua Huiying Xiang Tao Zhu Guoyong Liu Jiang Zhou Ruijin Liao State Key Laboratory of Power Transmission Equipment&System Security and New Technology Chongqing UniversityChongqingChina College of Materials Science and Engineering Chongqing UniversityChongqingChina Dongfang Electric Machinery Co. LtdDeyangChina

Ice accumulation of overhead transmission lines can lead to serious damage to power *** nanostructured Al conductors are proposed to replace the de-icing or ice-melting equipment for economic advantages,good anti-icing properties and robust electrical *** under different direct current densities is adopted to fabricate two nanostructures on Al conductors,including the reticular and honeycomb-like nanoporous *** to pure Al conductors,the anodised surface of honeycomb-like nanoporous structures exhibits lower ice adhesion(3.82 kPa)and ice *** conductor steel reinforced treated in the proposed method has also been validated to exhibit a significant anti-glaze icing ***-tionally,corona performance and line loss are experimentally measured and calculated to prove good electrical *** 0.27 A Al strand shows the highest corona inception voltage(27.86 kV)and the lowest AC resistance(4.65Ω/km),which is attributed to the good dielectric property and heat ***,the proposed anti-icing transmission conductors show profound application potential for power systems.

关键词： electrical structures properties

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FDDSGCN: Fractional Decoupling Dynamic Spatiotemporal Graph Convolutional Network for Traffic Forecasting

FDDSGCN: Fractional Decoupling Dynamic Spatiotemporal Graph ...

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

作者： Xu, Jinpeng Zhao, Chunna Yang, Jing Huang, Yaqun Yang, Yaoyuan Yee, Por Lip School of Information Science & Engineering Yunnan University Kunming China Department of Computer System and Technology Universiti Malaya Kuala Lumpur Malaysia

ISBN: (纸本)9798350368741

Urban traffic flow management faces increasing challenges due to accelerating urbanization. Traffic data collected from roadside sensors contain complex temporal and spatial dependencies that interact simultaneously. Although Graph Neural Networks and Recurrent Neural Networks have been successful in capturing these dependencies, two critical issues remain: 1) Treating all traffic signals equally fails to capture the nuanced spatiotemporal dependencies hidden in time series data;2) Dynamic traffic conditions hinder the accurate capture of local spatial dependencies, thereby limiting prediction accuracy and reliability. To address these challenges, we propose an innovative model, FDDSGCN. To resolve the first issue, we introduce Fractional Residual Decomposition, which effectively separates traffic data into spatial and temporal signals. For the second issue, we employ Dynamic Spatial-Temporal Graph Convolution with fractional-order weight adjustments to dynamically capture local dependencies. Additionally, the Long Short-Term Dependency module analyzes both long-term and short-term dependencies. Extensive experiments on three public datasets demonstrate the superior performance and practical value of our model. © 2025 IEEE.

关键词： dynamic graph learning Fractional Residual decomposition spatial-temporal graph Traffic prediction

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A Cross-Scale Multi-Branch Feature Fusion Based Method for Smoke and Fire Detection in Warehouses 24

A Cross-Scale Multi-Branch Feature Fusion Based Method for S...

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13th International Conference on Computing and Pattern Recognition, ICCPR 2024

作者： Bai, Yanbing Liu, Pengyu Liu, Weilong Deng, Yiyang Kong, Lili Chen, Shanji Chen, Jiali Yuan, Bowen School of Information Science and Technology Beijing Laboratory of Advanced Information Networks Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing University Of Technology Beijing China Information Technology Co. Ltd Dingzhou China Beijing-Dublin International College at BJUT Beijing University Of Technology Beijing China College of Physics and Electronic Information Engineering Qinghai Minzu University Qinghai China

ISBN: (纸本)9798400717482

To address the challenges of leakage, misdetection, and low accuracy caused by environmental complexity, object occlusion, and insufficient lighting in smoke and fire detection within warehouse scenarios, this paper proposes a cross-scale multi-branch feature fusion network for smoke and fire detection, named SF-YOLO. Firstly, a multi-branch feature enhancement module, Res2Net-C, is proposed to introduce an attention mechanism using a multi-branch feature fusion network, which not only extends the sensory field of the network, but also enhances the ability of extracting effective information, while effectively suppressing the interference of irrelevant information. It effectively improves detection performance under complex environmental conditions and in the presence of object occlusion. Secondly, a cross-scale feature fusion network structure, MSFPN, is proposed, which achieves effective complementation and fusion of high-level and low-level information by establishing hopping connections between features at different scales, and significantly improves the recognition accuracy of the type and location of the smoke and fire in low-light conditions. In addition, the added small target detection layer further improves the network's ability to detect small targets. Finally, in order to better adapt to pyrotechnic targets with variable morphology, EIoU is adopted as the bounding box loss function. Test results on a self-built dataset show that SF-YOLO achieves a mean accuracy rate (mAP) of 87.5% in the task of detecting smoke and fire under multiple environmental factors in a warehouse scenario, proving its effectiveness and usefulness. © 2024 Copyright held by the owner/author(s).

关键词： Deep learning

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Data-Driven Prediction of Coefficient of Friction in Wet Friction Components: A Model Development and Interpretability Analysis

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Journal of Tribology 2025年第7期147卷 074601页

作者： Wu, Jianpeng Zhao, Peng Cui, Jiahao Wang, Liyong Yang, Chengbing Ouyang, Jianping Key Laboratory of Modern Measurement and Control Technology Ministry of Education Beijing Information Science and Technology University 12 Xiaoying East Road Haidian District Beijing100192 China School of Mechanical and Engineering University of Science and Technology Beijing 30 Xueyuan Road Haidian District Beijing100083 China Mechanical Electrical Engineering School Beijing Information Science and Technology University 12 Xiaoying East Road Haidian District Beijing100192 China

Predicting the coefficient of friction (COF) is essential for enhancing the efficiency and reliability of mechanical systems. Nevertheless, traditional mechanistic models relying on fixed values or fitted curves fail to accurately capture this complexity. To address this issue, this paper proposes a model for predicting the COF of wet friction components using an extreme gradient boosting (XGBoost) algorithm optimized by the sparrow search algorithm (SSA). This model effectively captures the nonlinear relationships among relative speed, pressure, temperature, and COF. As a result, the proposed SSA-XGBoost model exhibits excellent predictive performance with a root mean square error (RMSE) of only 0.063, and 88.3% of the COF predictions have a relative error of less than 1%, significantly outperforming other deep-learning algorithms. Additionally, to enhance the understanding of the COF prediction results for wet friction components, the SHapley Additive exPlanations (SHAP) model is used to explore the influence of relative speed, pressure, and temperature on the predicted COF values. Copyright © 2024 by ASME.

关键词： Deep learning

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Infrared Dim Small Target Detection Algorithm Based on Adaptive Attention Transformer Network

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Journal of Shanghai Jiaotong University (science) 2025年 1-13页

作者： Ding, Leqi Xia, Yilin Wang, Biyun Cai, Yunze Department of Automation Shanghai Jiao Tong UniversityKey Laboratory of System Control and Information Processing of Ministry of Education Shanghai200240 China National Key Laboratory of Air-based Information Perception and Fusion Henan Luoyang471009 China Shanghai Institute of Aerospace Control Technology Infrared Detection Technology R & D Center of China Aerospace Science and Technology Corporation Shanghai201109 China

This paper presents a novel algorithm for detecting infrared dim small targets in complex backgrounds by integrating both global and local feature information. The proposed method employs an adaptive attention transformer network which addresses challenges associated with computational complexity and information redundancy in traditional self-attention mechanisms. A global context aggregation module is designed to address the loss of small target features in high-level feature maps. This module compensates for spatial distribution information during the multi-scale feature fusion process, ensuring the preservation of target features. Comparison experiments were conducted on two datasets, NUAA-SIRST and IRSTD-1k. The proposed algorithm achieved 0.784 for mIoU and 0.878 for F-measure on NUAA-SIRST, 0.632 for mIoU and 0.807 for F-measure on IRST-1k, all of which are the highest values. The proposed algorithm shows a significant improvement over other existing detection algorithms, demonstrating its capability to accurately extract both local and global features of targets with high recall rates and low false alarm rates across various scenarios. The ablation study results highlight the significant improvements contributed by the key modules to the proposed algorithm. (Figure presented.) © Shanghai Jiao Tong University 2025.

关键词： A adaptive attention global context aggregation module infrared dim small target detection TP391.4 Transformer network

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High-frequency enhanced ultrafast compressed active photography

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Opto-Electronic Advances 2025年第1期8卷 32-43页

作者： Yizhao Meng Yu Lu Pengfei Zhang Yi Liu Fei Yin Lin Kai Qing Yang Feng Chen State Key Laboratory for Manufacturing System Engineering and Shaanxi Key Laboratory of Photonics Technology for Information School of Electronic Science and EngineeringXi’an Jiaotong UniversityXi’an 710049China School of Mechanical Engineering Xi’an Jiaotong UniversityXi’an 710049China

Single-shot ultrafast compressed imaging(UCI)is an effective tool for studying ultrafast dynamics in physics,chemistry,or material science because of its excellent high frame rate and large frame ***,the random code(Rcode)used in traditional UCI will lead to low-frequency noise covering high-frequency information due to its uneven sampling interval,which is a great challenge in the fidelity of large-frame ***,a high-frequency enhanced compressed active photography(H-CAP)is *** uniformizing the sampling interval of R-code,H-CAP capture the ultrafast process with a random uniform sampling *** sampling mode makes the high-frequency sampling energy dominant,which greatly suppresses the low-frequency noise blurring caused by R-code and achieves high-frequency information of image *** superior dynamic performance and large-frame reconstruction ability of H-CAP are verified by imaging optical self-focusing effect and static object,*** applied H-CAP to the spatial-temporal characterization of double-pulse induced silicon surface ablation dynamics,which is performed within 220 frames in a single-shot of 300 ps.H-CAP provides a high-fidelity imaging method for observing ultrafast unrepeatable dynamic processes with large frames.

关键词： ultrafast compressed imaging high-frequency enhanced sampling spectral-temporal transform transient processes high-fidelity reconstruction

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Hybrid-Vector Deadbeat Model Predictive Torque Control for SPMSM With Improved Switching Loss and Synthesis Accuracy

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IEEE Access 2025年 13卷 81666-81676页

作者： Hou, Weiyang Miao, Yiru He, Ruizhi Liao, Wu Xu, Qiwei Huang, Shoudao Tu, Yuxiaoying Hunan University College of Electrical and Information Engineering Changsha410082 China Chongqing University State Key Laboratory of Power Transmission Equipment Technology Chongqing400044 China Changsha University of Science and Technology School of Electrical and Information Engineering Changsha410076 China Wenzhou Polytechnic Wenzhou325035 China

To improve the performance of the dual-vector model predictive torque control (MPTC) scheme for SPMSM, for sectors II, IV, and VI, this paper attempts to improve the traditional dual-vector modulation with three-segment symmetrical sequences from two aspects: switching loss and synthesis accuracy. First, the switching state of the zero vector is changed from 000 to 111, thereby reducing the number of switching actions by half within a PWM cycle. However, theoretical analysis and simulation results indicate that this dual-vector modulation will introduce more switching actions and losses between two consecutive PWM cycles. Therefore, the traditional dual-vector modulation has the minimum switching actions and cannot be further improved in switching loss. Then, the commutation instants are modified to increase another applied active vector, and the three-vector modulation with five-segment symmetrical sequences and no vector synthesis error is adopted in sectors II, IV, and VI. For other sectors, the dual-vector modulation with three-segment symmetrical sequences is adopted, which has only one switching action within a PWM cycle. Therefore, a hybrid-vector MPTC is formed. Finally, the feasibility and effectiveness of the proposed hybrid-vector MPTC for PMSM are verified by the experimental results, compared to the dual-vector MPTC with three-segment symmetrical sequences and the three-vector MPTC with five-segment symmetrical sequences, under the same switching frequency, the best control performance can be obtained with the minimum switching losses. © 2013 IEEE.

关键词： Switching (rolling stock)

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