检索结果-内蒙古大学图书馆

SSRN 2024年

作者： Wang, Litian Chen, Jie Li, Dan Qian, Lirong Xiong, Yang Miao, Lin Zhang, Rong Li, Cuiping Tianjin Key Laboratory of Film Electronic and Communication Devices School of Integrated Circuit Science and Engineering Tianjin University of Technology Tianjin300384 China School of Integrated Circuit Science and Engineering Tianjin University of Technology Tianjin300384 China Tianjin Key Laboratory for Advanced Mechatronic System Design and Intelligent Control School of Mechanical Engineering Tianjin University of Technology Tianjin300384 China National Demonstration Resonant for Experimental Mechanical and Electrical Engineering Education Tianjin University of Technology China

This study presents a novel microwave humidity sensor that utilizes a complementary interdigital capacitor resonator (CIDCR), an advanced design derived from the traditional complementary split ring resonator (CSRR) structure. The CIDCR design enhances the sensor's performance by concentrating the electric field in a more compact region, leading to improved sensitivity and miniaturization. The sensor is coated with a composite film of chitosan (CS) and polyacrylamide-glycerol (PAMG), combining the high moisture sensitivity of PAMG with the stability of CS. The CIDCR-based sensor demonstrates superior performance, including high sensitivity, a broad detection range, rapid response/recovery times, and excellent long-term stability. Density functional theory (DFT) calculations further elucidate the moisture-sensitive mechanisms at the molecular level, highlighting the role of hydrogen bonding in the composite film. Application tests confirm the sensor's potential for use in respiratory monitoring, non-contact sensing, and health monitoring, showcasing the advantages of the CIDCR structure in achieving enhanced humidity sensing capabilities. © 2024, The Authors. All rights reserved.

关键词： Humidity sensors

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Non-Stoichiometric Protic Ionic Liquids for Efficient Carbon Dioxide Capture and Subsequent Conversion Under Mild Conditions

SSRN

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SSRN 2024年

作者： Wang, Litian Chen, Jie Wang, Shuai Zhou, Tongyao Li, Dan Li, Cuiping Qian, Lirong Xiong, Yang Miao, Lin Xie, Zhiyong Tianjin Key Laboratory of Film Electronic and Communication Devices School of Integrated Circuit Science and Engineering Tianjin University of Technology Tianjin300384 China School of Integrated Circuit Science and Engineering Tianjin University of Technology Tianjin300384 China Tianjin Key Laboratory for Advanced Mechatronic System Design and Intelligent Control School of Mechanical Engineering Tianjin University of Technology Tianjin300384 China China Tianjin Bohua Yongli Chemical Industry CO. Ltd Tianjin30045 China

Protic ionic liquids (PILs) have emerged as innovative solvents with significant potential for CO2 capture and transformation, presenting a sustainable alternative to conventional methods which often involve energy-intensive processes or environmentally detrimental chemicals. Herein, a novel strategy for CO2 capture and conversion at mild conditions using non-stoichiometric protic ionic liquids (NPILs) was demonstrated for the first time. These NPILs, employed as sorbents, solvents, and catalysts, were composed of imidazole (Im) and 1,8-diazabicyclo [5.4.0] undec-7-ene (DBU) in various ratios. Correlations between the mole ratio of DBU/Im and physicochemical properties such as viscosity, conductivity, thermal stability, and the distribution of neutral and ionic species were systematically investigated. Effects of the absorption temperature and CO2 partial pressure on the absorption capacity were also studied. Different absorption mechanisms, including carbonate and carbamate pathways, were analyzed using FT-IR and NMR spectroscopy. Moreover, the fixed CO2 could be catalytically converted to quinazoline-2,4(1H,3H)-diones under exceptionally mild reaction conditions (1atm, 50 °C, metal-free process). A plausible reaction mechanism involving simultaneous CO2 activation and substrate activation for the transformation of CO2 into quinazoline-2,4(1H,3H)-diones was verified through NMR spectra. It is believed that the new insights into the CO2 mechanism will facilitate the rationaldesign of functional ionic liquids for CO2 capture, utilization, and storage in the future. © 2024, The Authors. All rights reserved.

关键词： Carbon dioxide

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ChartKG: A Knowledge-Graph-Based Representation for Chart Images

arXiv

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arXiv 2024年

作者： Zhou, Zhiguang Wang, Haoxuan Zhao, Zhengqing Zheng, Fengling Wang, Yongheng Chen, Wei Wang, Yong School of Media and Design Hangzhou Dianzi University CO Hangzhou310018 China School of Media and Design Hangzhou Dianzi University China Big Data Visualization and Human Computer Collaborative Intelligent Laboratory Hangzhou Dianzi University China Research Center of Big Data Intelligence Zhejiang Lab Hangzhou China State Key Lab of CAD&CG Zhejiang University China College of Computing and Data Science Nanyang Technological University Singapore

Chart images, such as bar charts, pie charts, and line charts, are explosively produced due to the wide usage of data visualizations. Accordingly, knowledge mining from chart images is becoming increasingly important, which can benefit downstream tasks like chart retrieval and knowledge graph completion. However, existing methods for chart knowledge mining mainly focus on converting chart images into raw data and often ignore their visual encodings and semantic meanings, which can result in information loss for many downstream tasks. In this paper, we propose ChartKG, a novel knowledge graph (KG) based representation for chart images, which can model the visual elements in a chart image and semantic relations among them including visual encodings and visual insights in a unified ***, we develop a general framework to convert chart images to the proposed KG-based representation. It integrates a series of image processing techniques to identify visual elements and relations, e.g., CNNs to classify charts, yolov5 and optical character recognition to parse charts, and rule-based methods to construct graphs. We present four cases to illustrate how our knowledge-graph-based representation can model the detailed visual elements and semantic relations in charts, and further demonstrate how our approach can benefit downstream applications such as semantic-aware chart retrieval and chart question answering. We also conduct quantitative evaluations to assess the two fundamental building blocks of our chart-to-KG framework, i.e., object recognition and optical character recognition. The results provide support for the usefulness and effectiveness of ChartKG. © 2024, CC BY.

关键词： Knowledge graph

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Vibration Characteristics Analysis Of A Double-Suction Centrifugal Pump Identified By intelligent Algorithms 12

Vibration Characteristics Analysis Of A Double-Suction Centr...

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12th IEEE Global Reliability and Prognostics and Health Management, PHM-Nanjing 2021

作者： Gu, Zhenyue Zhang, Mian Li, Decai Kang, Tianbo Tang, Xiuyang Wang, Zhiyuan Pei, Zhenlong Wang, Yiqiao Tianjin Key Laboratory For Advanced Mechatronic System Design And Intelligent Control School Of Mechanical Engineering Tianjin University Of Technology Tianjin300384 China China Tianjin Todelon Scicnce &TECHOLOGY CO. Ltd Tianjin China State Key Lab Of Water Resources And Hydropower Engineering Science Wuhan University Wuhan China

ISBN: (纸本)9781665401302

Due to the merits of high flow rate and high lift, double-suction centrifugal pumps are widely used for agricultural irrigation along the Yellow River in northern China. The operational conditions of the pump are highly related to the silt concentration and flow rates. Therefore, it is crucial to identify the vibration characteristics corresponding to different silt concentrations and flow rates to provide a prior guideline for pump condition monitoring. To this end, this paper explores the identification capabilities of typical intelligent models, including convolutional neural network (CNN), deep neural network (DNN), and support vector machine (SVM), to intelligently establish the connection between vibration characteristics and different flow rates and silt concentrations. Experimental studies on a double-section pump covering a range of flow rates and different silt concentrations are conducted. The results show that CNN features higher identification accuracy than DNN and SVM. © 2021 IEEE.

关键词： Vibration analysis

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Hand Pose Estimation Using Convolutional Neural Networks and Support Vector Regression 12th

Hand Pose Estimation Using Convolutional Neural Networks and...

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12th International Conference on e-Learning and Games, Edutainment 2018

作者： Dong, Yufeng Lu, Jian Zhang, Qiang Key Laboratory of Advanced Design and Intelligent Computing Dalian University Ministry of Education Dalian116622 China

ISBN: (纸本)9783030237110

In order to improve the accuracy of hand pose estimation from a depth image, a method based on convolutional neural network (CNN) is proposed in this paper. First of all, we modify the structure of traditional CNN to recognize the 3D joint locations from a depth image. By appending some shortcuts between layers, the proposed network increases the correlation between the front and back layers. This structure can avoid the information loss caused by the simple layer-by-layer transmission, and can improve the estimation accuracy effectively. Afterwards, the estimated joint locations continue to be inputted into a support vector regression (SVR) phase. The use of SVR can introduce the constraint of local joint information, which can get rid of those abnormal estimations further. Extensive experiments show that our method enables significant performance improvement over the-state-of-arts in the accuracy of hand pose estimation. © 2019, Springer Nature Switzerland AG.

关键词： Convolution

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Effects of Woody Plant Roots on Migration of Ciprofloxacin and Tylosin in Soil

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SSRN 2025年

作者： Li, Jingfang Ma, Huiting Hu, Xiaoming Chen, Liang Fallgren, Paul H. State Key Laboratory of Hydraulic Engineering Intelligent Construction and Operation Tianjin University Tianjin300350 China School of Civil Engineering Tianjin University Tianjin300350 China Shandong Province Traffic Planning and Design Institute Group Co. Ltd. Jinan Shandong250101 China Advanced Environmental Technologies LLC Fort CollinsCO80525 United States

Antibiotics are an emerging environmental concern that may impact water sources via transport through soil. Woody plants with well-developed root systems and longer root lengths change soil properties that may influence the migration of antibiotics. This study investigated the migration of ciprofloxacin (CIP) and tylosin (TYL) through soil with woody plant roots. Experiments were conducted with soil columns with three different woody plants with different root types, which included Sophora japonica (taproot), Malus baccata (fibrous root), and Malus pumila Mill. (characteristically both taproot and fibrous root). The woody plant roots increased the infiltration capacity by 39.53–56.29%, which was a factor in increasing antibiotic mass in the soil column discharge. The CIP discharge mass increased by 0.05–0.52 times and the TYL discharge mass increased by 1.36–2.59 times, where higher discharge mass was observed from the taproot system of S. japonica compared to the other root system types. The woody plant roots promoted CIP adsorption (increase of 30.41–101.22%) and inhibited TYL adsorption (decrease of 62.41–86.49%). The woody plant roots promoted antibiotic migration to the middle and lower root layers in the soil column. The correlation between CIP adsorption to soil and soil organic matter (SOM) was insignificant (p > 0.05, r = 0.41), while there was a negative correlation between TYL adsorption and SOM content (p © 2025, The Authors. All rights reserved.

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Pyramid Pixel Context Adaption Network for Medical Image Classification with Supervised Contrastive Learning

arXiv

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arXiv 2023年

作者： Zhang, Xiaoqing Xiao, Zunjie Wu, Xiao Chen, Yanlin Zhao, Jilu Hu, Yan Liu, Jiang Research Institute of Trustworthy Autonomous Systems Southern University of Science and Technology Shenzhen518055 China Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China Center for High Performance Computing Shenzhen Key Laboratory of Intelligent Bioinformatics Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation Southern University of Science and Technology Shenzhen518055 China Singapore Eye Research Institute 169856 Singapore

Spatial attention mechanism has been widely incorporated into deep neural networks (DNNs), significantly lifting the performance in computer vision tasks via long-range dependency modeling. However, it may perform poorly in medical image analysis. Unfortunately, existing efforts are often unaware that long-range dependency modeling has limitations in highlighting subtle lesion regions. To overcome this limitation, we propose a practical yet lightweight architectural unit, Pyramid Pixel Context Adaption (PPCA) module, which exploits multiscale pixel context information to recalibrate pixel position in a pixel-independent manner dynamically. PPCA first applies a well-designed cross-channel pyramid pooling to aggregate multiscale pixel context information, then eliminates the inconsistency among them by the well-designed pixel normalization, and finally estimates per pixel attention weight via a pixel context integration. By embedding PPCA into a DNN with negligible overhead, the PPCANet is developed for medical image classification. In addition, we introduce supervised contrastive learning to enhance feature representation by exploiting the potential of label information via supervised contrastive loss. The extensive experiments on six medical image datasets show that PPCANet outperforms state-of-the-art attention-based networks and recent deep neural networks. We also provide visual analysis and ablation study to explain the behavior of PPCANet in the decision-making process. © 2023, CC BY.

关键词： Pixels

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Research on flexible job shop scheduling under finite transportation conditions for digital twin workshop

Research on flexible job shop scheduling under finite transp...

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作者： Yan, Jun Liu, Zhifeng Zhang, Caixia Zhang, Tao Zhang, Yueze Yang, Congbin Institute of Advanced Manufacturing and Intelligent Technology Faculty of Materials and Manufacturing Beijing University of Technology Beijing100124 China Key Laboratory of CNC Equipment Reliability Ministry of Education School of Mechanical and Aerospace Engineering Jilin University Jilin130012 China Mechanical Industry Key Laboratory of Heavy Machine Tool Digital Design and Testing Technology Beijing University of Technology Beijing100124 China

Flexible job shop scheduling is one of the most effective methods for solving multiple varieties and small batch production problems in discrete manufacturing enterprises. However, limitations of actual transportation conditions in the flexible job shop scheduling problem (FJSP) are neglected, which limits its application in actual production. In this paper, the constraint influence imposed by finite transportation conditions in the FJSP is addressed. The coupling relationship between transportation and processing stages is analyzed, and a finite transportation conditions model is established. Then, a three-layer encoding with redundancy and decoding with correction is designed to improve the genetic algorithm and solve the FJSP model. Furthermore, an entity-JavaScript Object Notation (JSON) method is proposed for transmission between scheduling services and Digital Twin (DT) virtual equipment to apply the scheduling results to the DT system. The results confirm that the proposed finite transportation conditions have a significant impact on scheduling under different scales of scheduling problems and transportation times. © 2021

关键词： Genetic algorithms

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Continuous Geodesic Self-Attention Models with Gated Fusion for Trajectory Prediction

Continuous Geodesic Self-Attention Models with Gated Fusion ...

引用

International Joint Conference on Neural Networks (IJCNN)

作者： Kexin Ke Zhengyu Li Huining Chen Hao Wang Xian Wei Jian Yang Xuan Tang MoE Engineering Research MOE Center of SW/HW Co-Design Technology and Application Shanghai Key Laboratory of Trustworthy Computing East China Normal University China School of Advanced Technology Xi’an Jiaotong-Liverpool University China School of Geospatial Information Information Engineering University China School of Communications and Electronic Engineering East China Normal University China

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Driven by the rapid development of intelligent driving vehicles, predicting the trajectories of pedestrians on the road is crucial for decision-making during driving and even road safety. In this paper, we propose a novel method for trajectory prediction, namely, Continuous Geodesic Self-Attention Models with Gated Fusion (CGSAG). We use geodesic attention to measure the similarity between trajectory points, and utilize a gating mechanism to fuse the geodesic features extracted by multi-layer graph convolution. We then use Neural Ordinary Differential Equations (Neural ODE) to model the continuous-time dynamics of the trajectory. We show that CGSAG improves state-of-the-art performances on several human trajectory prediction datasets, including ETH/UCY, SDD, and Ind. At the same time, we conduct ablation studies to prove the effectiveness and efficiency of our proposed method.

关键词： Pedestrians Logic gates Predictive models Feature extraction Mathematical models Time measurement Road safety

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Stfi: Structure-Aware and Time-Frequency Domain Interaction Network for Deformable Medical Image Registration

SSRN

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SSRN 2025年

作者： Hu, Rui Wang, Nian Zhang, Yuxin Li, Teng Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education School of Electrical Engineering and Automation Anhui University Hefei230601 China Advanced Imaging Research Center University of Texas Southwestern Medical Center 5323 Harry Hines Blvd Dallas75390 United States School of Cyber Science and Engineering Southeast University No.2 Southeast University Road Nanjing211189 China

Deformable image registration is crucial in medical image analysis. Recently, unsupervised methods using intensity matching have shown promising results without labeled data. However, these methods still face challenges: (1) They often encounter local optima when handling anatomical structures with significant variations in intensity distribution;(2) Time domain convolution in decoders leads to loss of high-frequency fine features, limiting the effectiveness of generating fine deformation vector fields. To address these issues, we propose a novel structure-aware and time-frequency domain interaction (STFI) network. STFI integrates a structure-aware frequency domain loss and a time-frequency domain interaction module. The former leverages principal structural information from the weighted phase spectrum to guide the registration process, thereby reducing the risk of local optima. The latter processes and amalgamates information from both the time and frequency domains, effectively preserving high-frequency details. The efficacy and adaptability of our approach have been demonstrated on two types of data, offering a new perspective on exploiting time-frequency domain information for medical image registration. © 2025, The Authors. All rights reserved.

关键词： Image registration

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