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SSRN 2023年

作者： Mao, Guosheng Zhang, Yanmei Xu, Yang Li, Xiaoyu Xu, Min Zhang, Yiyi Jia, Pengfei School of Electrical Engineering Guangxi University Nanning China Guangxi Key Laboratory of Intelligent Control and Maintenance of Power Equipment Guangxi University Nanning China College of Food Science and Bioengineering Xihua University Chengdu China Guangxi Key Laboratory of Power System Optimization and Energy Technology China

Among harmful gases, carbon monoxide (CO) has posed a significant threat to human safety due to its concealment and harmfulness. Therefore, it’s imperative for the preservation of human safety to implement early detection for CO concentration. However, traditional methods are plagued by the limitations of insufficient accuracy and the incapacity to make early prediction. To address this issue, a novel technique, namely H-CRNN, is presented for electronic nose (E-nose) to perform early prediction of CO concentration, leveraging the strengths of convolutional and recurrent neural network to efficiently capture long-term dependencies within data. Also, shortcut connection and gated attention mechanism is proposed to enhance the capacity to capture crucial features. Furthermore, a linear process unit has been incorporated to handle the scale insensitivity issue. The experimental results show that the proposed H-CRNN outperforms RNN with a notable average reduction of 50.51% in the root relative squared error and 53.8% in the relative absolute error, and exhibits varying degrees of improvement when compared to state-of-the-art algorithms (TCN, TPA-LSTM, STCN, LSTNet, LSTM). Additionally, the H-CRNN achieved an average accuracy rate of 96.42%, surpassing all other algorithms and demonstrating the highest level of performance. Therefore, our work substantiates the proposed H-CRNN as an applicable method for early prediction of CO concentration. © 2023, The Authors. All rights reserved.

关键词： Carbon monoxide

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Mesenchymal stem cells loaded on 3D-printed gradient poly(e-caprolactone)/methacrylated alginate composite scaffolds for cartilage tissue engineering

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Regenerative Biomaterials 2021年第3期8卷 70-83页

作者： Yanyan Cao Peng Cheng Shengbo Sang Chuan Xiang Yang An Xiaochun Wei Zhizhong Shen Yixia Zhang Pengcui Li Key Lab of Advanced Transducers and Intelligent Control System of the Ministry of Education MicroNano System Research CenterCollege of Information and ComputerTaiyuan University of TechnologyTaiyuan 030024China College of Information Science and Engineering Hebei North UniversityZhangjiakou 075000China Shanxi Key Laboratory of Bone and Soft Tissue Injury Repair Department of OrthopedicsThe Second Hospital of Shanxi Medical UniversityTaiyuan 030001China Department of Plastic Surgery Peking University Third HospitalBeijing 100191China Department of Biomedical Engineering College of Biomedical EngineeringTaiyuan University of TechnologyTaiyuan 030024China

Cartilage has limited self-repair ability due to its avascular,alymphatic and aneural *** combination of three-dimensional(3D)printing and tissue engineering provides an up-and-coming approach to address this ***,we designed and fabricated a tri-layered(superficial layer(SL),middle layer(ML)and deep layer(DL))stratified scaffold,inspired by the architecture of collagen fibers in native cartilage *** scaffold was composed of 3D printed depth-dependent gradient poly(e-caprolactone)(PCL)impregnated with methacrylated alginate(ALMA),and its morphological analysis and mechanical properties were *** prove the feasibility of the composite scaffolds for cartilage regeneration,the viability,proliferation,collagen deposition and chondrogenic differentiation of embedded rat bone marrow mesenchymal stem cells(BMSCs)in the scaffolds were assessed by Live/dead assay,CCK-8,DNA content,cell morphology,immunofluorescence and real-time reverse transcription polymerase chain ***-loaded gradient PCL/ALMA scaffolds showed excellent cell survival,cell proliferation,cell morphology,collagen II deposition and hopeful chondrogenic differentiation compared with three individual-layer ***,our study demonstrates the potential use of the gradient PCL/ALMA construct for enhanced cartilage tissue engineering.

关键词： three-dimensional printing cartilage tissue engineering PCL methacrylated alginate bone marrow mesenchymal stem cells

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Coarray Tensor Train Decomposition for Bistatic MIMO Radar with Uniform Planar Array

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IEEE Transactions on Antennas and Propagation 2025年

作者： Xie, Qianpeng Wang, Zhanling Wen, Fangqing He, Jin Truong, Trieu-Kien National University of Defense Technology State Key Laboratory of Complex Electromagnetic Environment Effects on Electronics and Information System Changsha410073 China Hubei University of Automotive Technology Institute of Vehicle Information Control and Network Technology Shiyan442002 China China Three Gorges University Hubei Key Laboratory of Intelligent Vision Based Monitoring for Hydroelectric Engineering Yichang443002 China Shanghai Jiao Tong University Shanghai Key Laboratory of Navigation and Location Based Services School of Electronic Information and Electrical Engineering Shanghai200240 China

Recently, tensor network decomposition has attracted increasing attention due to its high efficacy in modeling multi-dimensional correlations in high-order tensors. Considering this advantage, this study employs tensor train decomposition (TTD) to achieve joint two-dimensional direction-of-departure (2D-DOD) and direction-of-arrival (2D-DOA) estimation in bistatic multiple-input multiple-output (MIMO) radar systems with uniform planar array (UPA) configurations. First, a five-dimensional (5D) tensor is defined to incorporate the two-dimensional (2D) spatial information from transmit and receive UPAs, which is then combined with temporal information. Next, a low-rank four-dimensional (4D) tensor is generated by performing an autocorrelation operation along the temporal dimension of the 5D tensor, forming a structure that encapsulates the difference coarrays of the transmit and receive UPAs. Then, a TTD framework is designed to decompose the 4D tensor into interconnected lower-order TT-cores. Furthermore, a precise bidirectional mapping is performed between the TT-cores and the original Vandermonde factor matrices. Finally, leveraging distinct combinations of TT-cores, two innovative methods are developed to recover the Vandermonde factor matrices. The proposed methods conduct automatic pairing of 2D-DOD and obtain 2D-DOA estimates by enforcing consistent permutation orders across all factor matrices. The proposed methods are verified by simulation experiments. The results confirm that the proposed methods can surpass the existing tensor-based methods in terms of estimation accuracy. © 1963-2012 IEEE.

关键词： Matrix algebra

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An Empirical Evaluation of Meta Adaptive Residual Network for Classifying Sensor Drift Samples

SSRN

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SSRN 2023年

作者： Zhu, Zhengyang Ling, Haikui Zhang, Yiyi Liu, Jiefeng Shuang, Feng Xu, Min Jia, Pengfei School of Electrical Engineering Guangxi University Nanning China Guangxi Key Laboratory of Intelligent Control and Maintenance of Power Equipment Guangxi University Nanning China College of Food Science and Bioengineering Xihua University Chengdu China Guangxi Key Laboratory of Power System Optimization and Energy Technology China

With the advancements in materials and pattern recognition algorithms, electronic nose (E-nose) has been continuously developing and finding increasingly widespread applications. However, due to factors including but not limited to variations in temperature and humidity, as well as chemical and physical interactions among analytes and gases, E-nose is prone to drift during practical usage, leading to decreased accuracy of pattern recognition algorithms. But now, this challenge can be overcome with the help of meta-learning. We applied the model-agnostic meta-learning (MAML) architecture from meta-learning and designed a residual classifier with learnable per-parameter learning rates. Leveraging the ability of ‘learning to learn’, our method do not require a large amount of target domain data and there is no need to repeat multiple training iterations when transferring to any other target domain compared to other unsupervised algorithms, resulting it highly deployable. Additionally, we present a Gini value-based approach to enhance the certainty of model output labels in a multi-task setting. Through the evaluation of multiple experiments, we demonstrate the feasibility of our approach and achieve very high classification accuracy on a publicly available long-term drift dataset compared with other methods. © 2023, The Authors. All rights reserved.

关键词： Electronic nose

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Supervised Drift Sensor Sample Classification Method Based on Meta-Learning

SSRN

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SSRN 2023年

With the advancements in materials and pattern recognition algorithms, electronic nose (E-nose) has been continuously developing and finding increasingly widespread applications. However, due to factors including but not limited to variations in temperature and humidity, as well as chemical and physical interactions among analytes and gases, E-nose is prone to drift during practical usage, leading to decreased accuracy of pattern recognition algorithms. Currently, the primary methods for addressing the issue of sensor drift in sample classification are semi-supervised learning and unsupervised learning. Supervised learning has been limited in the past due to the need for large amounts of up-to-date samples to retrain the models. But now, this challenge can be overcome with the help of meta-learning. Meta-learning, also known as "learning to learn", aims to equip models with the ability to acquire a learning capability and automatically learn some meta-knowledge. To tackle the challenge of few-shot learning, we applied the model-agnostic meta-learning (MAML) architecture from meta-learning and designed a residual classifier with learnable per-parameter learning rates. Additionally, we present a Gini value-based approach to enhance the certainty of model output labels in a multi-task setting. As a result, we achieved high classification accuracy on a publicly available long-term drift dataset. © 2023, The Authors. All rights reserved.

关键词： Electronic nose

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A Novel Magnetoelastic Biosensor Based on Pdms/Fesib/Qds Composite Film for African Swine Fever Virus Detections

SSRN

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SSRN 2024年

作者： Liu, Yuanhang Sang, Shengbo Zhao, Dong Ge, Yang Xue, Juanjuan Duan, Qianqian Guo, Xing Shanxi Key Laboratory of Micro Nano Sensors & Artificial Intelligence Perception College of Electronic Information and Optical Engineering Taiyuan University of Technology Taiyuan030024 China Key Lab of Advanced Transducers and Intelligent Control System of The Ministry of Education Taiyuan University of Technology Taiyuan030024 China Shanxi Research Institute of 6D Artificial Intelligence Biomedical Science Taiyuan030024 China

African swine fever (ASF) is a highly contagious and severe hemorrhagic disease caused by the African swine fever virus (ASFV). The continuous spread of ASFV affects the safety of global meat supply, so the establishment of sensitive and specific detection methods for ASFV has become an important hot spot in food safety. Herein, we developed a flexible magnetoelastic (ME) biosensor based on PDMS/FeSiB/QDs composite films for the detection of ASFV. Based on the high luminescence performance of CsPbBr3 quantum dots and the excellent magnetoelastic effect of FeSiB, flexible ME biosensors convert stress signals generated by antibody-antigen-specific binding into optical and electromagnetic signals. The nanostructures covalently linked by quantum dots and PDMS provide biomodification sites for ASFV antibodies, simplifying the functionalization modification process compared to conventional biosensors. The deformation of the PDMS film is amplified and conversion of surface stress signal to electrical signal is enhanced by exposing the biosensor to a uniform magnetic field. The experimental results proved that the flexible ME biosensor has wide linear range of 10 ng/mL - 100 μg/mL, and the detection limit is as low as 0.7933 ng/mL. Moreover, the flexible ME biosensor have also shown to good stability, sensitivity and specificity. © 2024, The Authors. All rights reserved.

关键词： Biosensors

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Lightweight Vision Transformer for Small Data Sets

Lightweight Vision Transformer for Small Data Sets

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Youth Academic Annual Conference of Chinese Association of Automation (YAC)

作者： Wenxing Fang Yanxu Su Institutes of Physical Science and Information Technology Anhui University Hefei China School of Artificial Intelligence Anhui University Hefei P. R. China Key Laboratory of Measurement and Control of Complex System of Engineering Ministry of Education Southeast University Nanjing P. R. China

With the rapid development of Transformers in the field of computer vision, models based on Transformers have become highly competitive architectures in the area. Although variants of Transformer models have achieved increasing accuracy on image classification tasks, the size of the training set and the number of parameters required by the models have increased dramatically. When dealing with small datasets, such models face problems such as overfitting and undergeneralization, leading to poor accuracy on the test set. We propose a new lightweight vision transformer (LVT) to address these issues. We reconstructed the backbone network, which learns the relationship between pixels through local window self-attention and global self-attention computation. We also use the attention pooling approach to fuse the token sequences generated by the backbone network more meticulously. We trained on the CIFAR-10 and CIFAR-100 datasets from scratch and compared them with a modern convolutional neural network. The experimental results show that LVT outperforms the modern convolutional neural network in terms of accuracy and efficiency. On the test set of CIFAR-10, we have obtained an accuracy of 96.83%, which indicates that our model can effectively solve the problems facing the training of small datasets and has a wide range of application prospects.

关键词：

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SPP-CNN: An Efficient Framework for Network Robustness Prediction

arXiv

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arXiv 2023年

作者： Wu, Chengpei Lou, Yang Wang, Lin Li, Junli Li, Xiang Chen, Guanrong The College of Computer Science Sichuan Normal University Chengdu610066 China The Key Laboratory of System Control and Information Processing Ministry of Education Shanghai200240 China The Department of Computer Science National Yang Ming Chiao Tung University Hsinchu300 Taiwan The Department of Automation Shanghai Jiao Tong University Shanghai200240 China The Institute of Complex Networks and Intelligent Systems Shanghai Research Institute for Intelligent Autonomous Systems Tongji University Shanghai201210 China The Department of Control Science and Engineering Tongji University Shanghai200240 China The Department of Electrical Engineering City University of Hong Kong Hong Kong

This paper addresses the robustness of a network to sustain its connectivity and controllability against malicious attacks. This kind of network robustness is typically measured by the time-consuming attack simulation, which returns a sequence of values that record the remaining connectivity and controllability after a sequence of node- or edge-removal attacks. For improvement, this paper develops an efficient framework for network robustness prediction, the spatial pyramid pooling convolutional neural network (SPP-CNN). The new framework installs a spatial pyramid pooling layer between the convolutional and fully-connected layers, overcoming the common mismatch issue in the CNN-based prediction approaches and extending its generalizability. Extensive experiments are carried out by comparing SPP-CNN with three state-of-the-art robustness predictors, namely a CNN-based and two graph neural networks-based frameworks. Synthetic and real-world networks, both directed and undirected, are investigated. Experimental results demonstrate that the proposed SPP-CNN achieves better prediction performances and better generalizability to unknown datasets, with significantly lower time-consumption, than its counterparts. © 2023, CC BY.

关键词： complex networks

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An Inherent Property-Based Rumor Dissemination Model in Online Social Networks

IEEE Networking Letters

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IEEE Networking Letters 2020年第1期2卷 43-46页

作者： Cui, Lei Xie, Gang Yu, Shui Zhai, Xuemeng Gao, Longxiang School of Information Technology Deakin University BurwoodVIC3125 Australia Shanxi Key Laboratory of Advanced Control and Intelligent Information System Taiyuan University of Science and Technology Taiyuan030024 China School of Software University of Technology Sydney SydneyNSW2007 Australia

Rumor propagation is an important issue in online social networks (OSNs), while modelling rumor propagation is critical and challenging. Existing studies are restrictive for unrealistic assumptions. We find that the inherent property of a rumor has determinant impact on propagation dynamics, and rumor propagates across different clustered communities with variable transmissibility. In this letter, we formulate the problem using information theory and establish a rigorous two layer epidemic model. As a result, the proposed model reflects the studied subject better than the previous models. Extensive experiments have been conducted through real-world twitter dataset, the results confirm our theoretical findings, and demonstrate that the proposed model outperforms the existing ones. © 2019 IEEE.

关键词： Social networking (online)

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The design of a displaying system for battlefield situation based on ArcGIS Engine

The design of a displaying system for battlefield situation ...

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Chinese Automation Congress (CAC)

作者： Jiaqi Wang Cheng Xu Hua Ji Science and Technology on Complex System Control and Ietelligent Agent Coopration Laboratory Beijing Beijing Electro-Mechanical Engineering Institute Beijing China

ISBN: (数字)9781665426473

ISBN: (纸本)9781665426480

To facilitate the precise analysis and judgment of battlefield situation for military stuffs inmodern warfare with abundant information, a modularized two-dimension battlefield situation displaysystem is designed based on ArcGIS Engine in this paper. The functions of each module are analyzed, andkey issues of the design are introduced. The real-time dynamic display of moving objects in the battlefield isachieved by reading data from the battlefield.

关键词： Computer languages Automation Real-time systems Software C# languages Engines

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