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6th International Conference on Frontier Computing, FC 2018

作者： Liu, Bo Shi, Chao Li, Jianqiang Li, Yong Lang, Jianlei Gu, Rentao Beijing Advanced Innovation Center for Future Internet Technology Beijing University of Technology Beijing100124 China Faculty of Information Technology School of Software Engineering Beijing University of Technology Beijing100124 China Key Laboratory of Beijing on Regional Air Pollution Control Beijing University of Technology Beijing100124 China College of Environmental and Energy Engineering Beijing University of Technology Beijing100124 China Beijing Laboratory of Advanced Information Networks School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing100876 China

ISBN: (纸本)9789811336478

In recent years, more and more people have been plagued by respiratory diseases. The air quality, which is characterized by inhalable particles and fine particles, has attracted increasing attention. Accurately monitor and forecast the quality of air could not only help the government conduct interventions to alleviate the air pollution earlier, but also alert relevant people who suffer from respiratory diseases. In order to develop effective Air Quality Index (AQI) prediction models, this paper compared the performance of different Machine Learning (ML) methods and feature selection methods. First the air quality data in Beijing from 2016 to 2017 were collected. Then Multi-Linear Regression (MLR), Random forest Regression (RFR), BP Neural Network (BPNN) and Support Vector Regression (SVR) algorithm were trained on 10-fold cross validation. Correlation coefficient (R), mean absolute error (MAE) and root mean square error (RMSE) were used as evaluation metrics. The experimental results showed that the performance of SVR and BPNN were similarly well. MLR had the worst performance, which was possibly caused by a small feature dimension, and RFR had higher accuracy and better generalization capability than the other models, probably because the algorithm of regression tree in random forest included the interaction of variables. © 2019, Springer Nature Singapore Pte Ltd.

关键词： Decision trees

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[email protected] nanorods：Synthesis and their wide controllable range for the local surface plasmon

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Optical Materials 2022年 134卷

作者： Wenlong Yang Xingchen Meng Xinmei Liu Liuyang Zhang Chenwei Wang Xiaoyang Yu Ying Yang School of Measurement and Communication Engineering Harbin University of Science and Technology Harbin 150080 PR China Key Laboratory of Laser Spectroscopy Technology and Application of Heilongjiang Province Harbin 150080 PR China State Key Laboratory of Mechanics and Control of Mechanical Structures Nanjing University of Aeronautics and Astronautics Nanjing 210016 PR China School of Material Science and Chemical Engineering Harbin University of Science and Technology Harbin 150080 PR China

This work demonstrated the wide controllable range of the Local Surface Plasmon Resonance (LSPR) characteristic peak of the KTa x-1 Nb x O 3 @Ag ( [email protected] ). [email protected] nanocubes ( [email protected] NCs) and [email protected] nanorods ( [email protected] NRs) with different Ag loading rate have been prepared. Compared with [email protected] NCs, the LSPR characteristic peak of [email protected] NRs showed a wider controllable range and the enhanced factor could reach 5.48. This result could be derived from the higher aspect ratio of [email protected] NRs, resulting in an enhanced the coupling effect between KTN and Ag. This viewpoint could also be verified by the FDTD (Finite-Difference Time-Domain) calculation. With the increase of Ag loading rate, the coupling between KTN and Ag was weakened. And the characteristic peak of [email protected] show a blue shift, which shifts to the characteristic peak of Ag nanocrystal gradually. With the increase of Ag loading rate from 11 to 21 wt%, the LPSR characteristic peak of [email protected] NCs showed a blue shift from 569 to 547 nm, whereas peak for [email protected] NRs was shifted from 762 to 646 nm. This work combined the coupling effect of nanostructured KTN and Ag nanocrystals. And the anisotropy would be beneficial to broaden the LSPR controllable range of the characteristic extinction spectra of [email protected] Considering the low price of KTN, the [email protected] NRs should be a promising material applied in optical detection devices.

关键词： FDTD calculation LSPR Coupling effect Loading rate Blue shift

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A novel airplane detection algorithm based on deep CNN 4th

A novel airplane detection algorithm based on deep CNN

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4th International Conference of Pioneer Computer Scientists, Engineers and Educators, ICPCSEE 2018

作者： Wang, Ying Wang, Aili Hu, Changyu Higher Education Key Lab for Measure and Control Technology and Instrumentations of Heilongjiang Harbin University of Science and Technology Harbin150080 China Department of Communication Engineering Nanjing University of Aeronautics and Astronautics Nanjing210016 China

ISBN: (纸本)9789811322020

Driven by the massive natural scene image and computer’s high speed computation ability, deep CNN becomes the mainstream method for completing the computer vision task by the power of feature representation capability. Because object detections based on deep CNN are unable to achieve optimization due to lack of training set of remote sensing images, a novel method based on transfer learning and deep CNN combined with SVM is proposed in this paper. Firstly, we obtain the deep CNN model AlexNet trained on a large-scale data set. Then, we truncate the first five convolution layers and get the initial parameters through transfer learning. Then, Deep CNN is used as feature extractor to extract the depth feature for training SVM, and the final remote sensing image airplane detection model is obtained. Experimental results show that the average precision of the proposed algorithm outperforms other traditional algorithms. © Springer Nature Singapore Pte Ltd. 2018.

关键词： Remote sensing

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3D facial geometry recovery from a depth view with attention guided generative adversarial network

arXiv

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arXiv 2020年

作者： Cai, Xiaoxu Yu, Hui Lou, Jianwen Zhang, Xuguang Li, Gongfa Dong, Junyu School of Creative Technologies University of Portsmouth PortsmouthPO1 2DJ United Kingdom School of Communication Engineering Hangzhou Dianzi University Hangzhou310018 China Key Laboratory of Metallurgical Equipment and Control Technology Ministry of Education Wuhan University of Science and Technology Wuhan430081 China College of Information Science and Engineering Ocean University of China Qingdao266100 China

We present to recover the complete 3D facial geometry from a single depth view by proposing an Attention Guided Generative Adversarial Networks (AGGAN). In contrast to existing work which normally requires two or more depth views to recover a full 3D facial geometry, the proposed AGGAN is able to generate a dense 3D voxel grid of the face from a single unconstrained depth view. Specifically, AGGAN encodes the 3D facial geometry within a voxel space and utilizes an attention-guided GAN to model the ill-posed 2.5D depth-3D mapping. Multiple loss functions, which enforce the 3D facial geometry consistency, together with a prior distribution of facial surface points in voxel space are incorporated to guide the training process. Both qualitative and quantitative comparisons show that AGGAN recovers a more complete and smoother 3D facial shape, with the capability to handle a much wider range of view angles and resist to noise in the depth view than conventional methods. Copyright © 2020, The Authors. All rights reserved.

关键词： Generative adversarial networks

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Coherent H ∞ control for Markovian jump linear quantum systems

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IFAC-PapersOnLine 2020年第2期53卷 269-274页

作者： Yanan Liu Daoyi Dong Ian R. Petersen Qing Gao Steven X. Ding Hidehiro Yonezawa School of Engineering and Information Technology University of New South Wales Canberra ACT 2600 Australia Center for Quantum Computation and Communication Technology Australian Research Council Canberra ACT 2600 Australia Institute for Automatic Control and Complex Systems (AKS) University of Duisburg-Essen. 47057 Duisburg Germany Research School of Electrical Energy and Materials Engineering The Australian National University Canberra ACT 2601 Australia School of Automation Science and Electrical Engineering State Key Laboratory of Software Development Environment Beijing and also with Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing 100191 China

The purpose of this paper is to design a coherent feedback controller for a Markovian jump linear quantum system suffering from a fault signal. The control objective is to bound the effect of the disturbance input on the output for the time-varying quantum system. We prove the relation between the H ∞ control problem, the dissipation properties, and the solutions of Riccati differential equations, by which the H ∞ controller of the Markovian jump linear quantum system is given by the solutions of Linear Matrix Inequalities (LMIs).

关键词： Coherent quantum feedback H∞ control fault-tolerant control Markovian jump linear quantum systems

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Corrigendum to ‘Dodecahydrododecaborate anion cluster-reduced nano-Au-decorated graphene oxide for 100% hydrogenation of nitroaromatics’[Colloid Interface Sci. Commun. 50 (2022) 100672]

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Colloid and Interface Science communications 2023年 55卷

作者： Yuwen Wang Xue Zhao Jianbing Chen Shuxing Zhou Xianxi Zhang Xinzhong Wang Thomas Wågberg Guangzhi Hu School of Electronic Communication Technology Shenzhen Institute of Information Technology Shenzhen 518172 China School of Materials and Energy Institute for Ecological Research and Pollution Control of Plateau Lakes Yunnan University Kunming 650504 China Research Academy of Non-metallic Mining Industry Development Materials and Environmental Engineering College Chizhou University Chizhou 247000 China Hubei Key Laboratory of Low Dimensional Optoelectronic Materials and Devices Hubei University of Arts and Science Xiangyang 441053 China School of Chemistry and Chemical Engineering Liaocheng University Liaocheng 252000 China Department of Physics Umeå University Umeå 901 87 Sweden

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Weak Signal Detection with Duffing Oscillator Based on Circular Boundary Counting Method and STFT State Identification Parameter

Weak Signal Detection with Duffing Oscillator Based on Circu...

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2019 2nd International Conference on Electrical and Electronic engineering（EEE 2019）

作者： Zhi-qiang ZHU Rui-li JIA Xiao-peng YAN Ke WANG Jian HOU Science and Technology on Electromechanical Dynamic Control Laboratory School of Mechatronical Engineering Beijing Institute of Technology Beijing Jinghang Research Institute of Computing and Communication

To solve the problem of quantitative identification of the state of Duffing oscillator and determination of the critical threshold of reference amplitude, a combined weak signal detection method is proposed in this paper. The STFT statistical characteristics of Duffing oscillator in chaotic state, intermittent state and large-scale periodic state is analyzed and based on it the state identification parameter is selected to quantitatively present the state of Duffing oscillator. The step change of the number of the phase points on the boundary of circular regions in the phase plane is shown and the circular boundary counting method is proposed to determine the critical threshold. Based on above research, the algorithm of the combined weak signal detection is given, which uses Runge-Kutta algorithm to calculate the Duffing system. Simulation results show that this combined method is not only accurate at low SNR, but also robust when noise condition is variable.

关键词： Weak signal detection Duffing oscillator Circular boundary counting method State identification Determining critical value

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An Efficiency Enhanced Graphene/n-Si Schottky Junction for Solar Cells

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Chinese Physics Letters 2018年第7期35卷 58-61页

作者：李刚程宏伟郭丽芳王开鹰程再军 MicroNano System Research Center College of Information and Computer & Key Lab of Advanced Transducers and Intelligent Control System of Ministry of Education Taiyuan University of Technology Taiyuan 030024 School of Optoelectronic and Communication Engineering Xiamen University of Technology Xiamen 361024

A novel and facile oxidation-induced self-doping process of graphene-silicon Schottky junction by nitric acid（HNO3） vapor is reported. The HNO3 oxidation process makes graphene p-type self-doped, and leads to a higher built-in potential and conductivity to enhance charge transfer and to suppress charge carrier recombination at the graphene-silicon Schottky junction. After the HNO3 oxidation process, the open-circuit voltage is increased from the initial value of 0.36 V to the maximum value of 0.47 V, the short-circuit current is greatly increased from 0.80μA to 7.71μA, and the ideality factor is optimized from 4.4 to 1.0. The enhancement of the performance of graphene-Si solar cells may be due to oxidation-induced p-type self-doping of graphene-Si junctions.

关键词： Graphene

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Empirical and Comparative Study of Various Classifiers with Forecast Deformity Prone Software Models

Empirical and Comparative Study of Various Classifiers with ...

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International Conference on Cybernetics, Robotics and control (CRC)

作者： Maaz Rasheed Malik Liu Yining Salahuddin Shaikh School of Information Communication Engineering Guilin University of Electronic Technology Guilin China School of Control & Computer Engineering North China Electric Power University Beijing China

ISBN: (数字)9781728146201

ISBN: (纸本)9781728146218

The main principle thought of this research is to give a general outline about Deformity Prone Software Datasets Models utilizing machine learning classifiers. Deformity Prone Software Datasets Models are also classification problems so it is needed to used Classifiers and analysis the defected datasets models. The evaluation measure unit is used to evaluate the performance of defect prone model datasets. TP-Rate, F-measure, ROC and CCI these we have used as evaluation measure unit. We have used NASA PROMISE repository Models as Forecast Deformity Prone Software Models. We have selected 17 NASA PROMISE repositories. These datasets files all are with class interests which are Defective and Non-Defective Class. In this research paper, our class interest is already fixed because we are interested in Defective Class so Defective Class is assign. A Comparative study of these classifiers utilized inside the Deformity Prone Software Models are also covered in this research. Experimental Analysis results showed that stacking is worst classifiers and cannot enhanced the efficiency and accuracy of Deformity Prone Software Datasets Models but LMT, Multiclass, Navie Bayes Updateable and Multilayer Perceptron have increased the positive accuracy of defected models and enhanced the efficiency in correctly classified instances.

关键词： Software Deformable models Computer bugs Predictive models Analytical models Classification algorithms NASA

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Efficient MedSAMs: Segment Anything in Medical Images on Laptop

arXiv

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arXiv 2024年

作者： Ma, Jun Li, Feifei Kim, Sumin Asakereh, Reza Le, Bao-Hiep Nguyen-Vu, Dang-Khoa Pfefferle, Alexander Wei, Muxin Gao, Ruochen Lyu, Donghang Yang, Songxiao Purucker, Lennart Marinov, Zdravko Staring, Marius Lu, Haisheng Dao, Thuy Thanh Ye, Xincheng Li, Zhi Brugnara, Gianluca Vollmuth, Philipp Foltyn-Dumitru, Martha Cho, Jaeyoung Mahmutoglu, Mustafa Ahmed Bendszus, Martin Pflüger, Irada Rastogi, Aditya Ni, Dong Yang, Xin Zhou, Guang-Quan Wang, Kaini Heller, Nicholas Papanikolopoulos, Nikolaos Weight, Christopher Tong, Yubing Udupa, Jayaram K. Patrick, Cahill J. Wang, Yaqi Zhang, Yifan Contijoch, Francisco McVeigh, Elliot Ye, Xin He, Shucheng Haase, Robert Pinetz, Thomas Radbruch, Alexander Krause, Inga Kobler, Erich He, Jian Tang, Yucheng Yang, Haichun Huo, Yuankai Luo, Gongning Kushibar, Kaisar Amankulov, Jandos Toleshbayev, Dias Mukhamejan, Amangeldi Egger, Jan Pepe, Antonio Gsaxner, Christina Luijten, Gijs Fujita, Shohei Kikuchi, Tomohiro Wiestler, Benedikt Kirschke, Jan S. de la Rosa, Ezequiel Bolelli, Federico Lumetti, Luca Grana, Costantino Xie, Kunpeng Wu, Guomin Puladi, Behrus Martín-Isla, Carlos Lekadir, Karim Campello, Victor M. Shao, Wei Brisbane, Wayne Jiang, Hongxu Wei, Hao Yuan, Wu Li, Shuangle Zhou, Yuyin Wang, Bo AI Collaborative Centre University Health Network Department of Laboratory Medicine and Pathobiology University of Toronto Vector Institute Toronto Canada Peter Munk Cardiac Centre University Health Network Toronto Canada Toronto General Hospital Research Institute University Health Network Department of Computer Science University of Toronto University Health Network Vector Institute Toronto Canada University of Science Vietnam National University Ho Chi Minh City Viet Nam Institute of Computer Science University of Freiburg Freiburg Germany School of Medicine and Health Harbin Institute of Technology Harbin China Division of Image Processing Department of Radiology Leiden University Medical Center Leiden Netherlands Department of System and Control Engineering School of Engineering Institute of Science Tokyo Formerly Tokyo Institute of Technology Tokyo Japan Institute for Anthropomatics and Robotics Karlsruhe Institute of Technology Karlsruhe Germany School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu China School of Electrical Engineering and Computer Science University of Queensland Brisbane Australia School of Cyberspace Hangzhou Dianzi University Hangzhou China Division for Computational Radiology and Clinical AI The Department of Neuroradiology University Hospital Bonn Germany Division for Computational Radiology and Clinical AI The Department of Neuroradiology University Hospital Bonn Germany Department of Neuroradiology Heidelberg University Hospital Heidelberg Germany Division for Computational Radiology and Clinical AI Department of Neuroradiology University Hospital Bonn Germany School of Biomedical Engineering Shenzhen University Shenzhen China School of Biological Science and Medical Engineering Southeast University Nanjing China Department of Urology Cleveland Clinic Cleveland United States Department of Computer Science University of Minnesota Minneapolis United St

Promptable segmentation foundation models have emerged as a transformative approach to addressing the diverse needs in medical images, but most existing models require expensive computing, posing a big barrier to their adoption in clinical practice. In this work, we organized the first international competition dedicated to promptable medical image segmentation, featuring a large-scale dataset spanning nine common imaging modalities from over 20 different institutions. The top teams developed lightweight segmentation foundation models and implemented an efficient inference pipeline that substantially reduced computational requirements while maintaining state-of-the-art segmentation accuracy. Moreover, the post-challenge phase advanced the algorithms through the design of performance booster and reproducibility tasks, resulting in improved algorithms and validated reproducibility of the winning solution. Furthermore, the best-performing algorithms have been incorporated into the open-source software with a user-friendly interface to facilitate clinical adoption. The data and code are publicly available to foster the further development of medical image segmentation foundation models and pave the way for impactful real-world applications. © 2024, CC BY-NC-SA.

关键词： Open source software

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