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2016 ieee international conference on progress in informatics and computing

作者： Liying Jiang Li Zhuo Haixia Long Xiaochen Hu Jing Zhang Signal & Information Processing Laboratory Beijing University of Technology Collaborative Innovation Center of Electric Vehicles in Beijing

Vehicle classification plays an important part in Intelligent Transport System(ITS). However, the existing vehicle classification methods are not very robust to various changes such as lighting, weathers, noises, and the classification accuracy has been requiring to be improved. Sparse Representation-based Classifier(SRC) is not sensitive to the shortage and damage of data, the feature selection methods, is robust to lighting changes of the images, and can achieve excellent performance in multiclass classification problems. Therefore, this paper presents a new method based on spatial location information and SRC. Firstly, taken the specific characteristics of vehicles into consideration, the features of HOG(Histogram of Oriented Gradient) and HU moments are extracted to characterize the property of vehicles. In addition, the spatial location information is added to HU moment features in this paper to improve its ability to distinguish and describe vehicles. Then, vehicles are classified into six classes(large bus, car, motorcycle, minibus, truck and van) by SRC. The performance evaluation is carried out on the dataset which consists of 13300 vehicle images extracted from the real highway surveillance videos. The experimental results show that, using the proposed method, the time cost of classification can be speeded up by about 1.57 times and the average classification precision can achieve 96.53%, which is drastically improved by more than 2.7% compared to other existing methods.

关键词： Vehicle classification HOG feature HU moments Spatial location information SRC

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Issues of Signaling Process Design for Coordinated Multipoint Transmission/Reception

Issues of Signaling Process Design for Coordinated Multipoin...

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2011 ieee international conference on Intelligent computing and Intelligent Systems(ICIS 2011)

作者： Xianqi LU MIIT Ministerial Key Laboratory ICN&SEC Center Beijing University of Posts and Telecommunications Beijing,China Si CHEN Dept. of Wireless Pre-research ZTE Corporation Shenzhen,China Wen-an ZHOU MIIT Ministerial Key Laboratory ICN&SEC Center Beijing University of Posts and Telecommunications Beijing,China Junde SONG MIIT Ministerial Key Laboratory ICN&SEC Center Beijing University of Posts and Telecommunications Beijing,China

Research of coordinated multipoint (CoMP) transmission/reception in LTE-Advanced to improve the cell- edge throughput has made considerable progress these *** implement and deploy CoMP in the real network and achieve the expected performance,it is essential to design feasible and effective signaling *** this contribution,we discuss some affecting factors that may lead to variability of the CoMP signaling process design,including the network structure,the scheduling strategy,and the activation and deactivation *** also present some common requirements that keep the generality of the CoMP signaling process design,during the sub process of activation,data transmission,and *** the end,we provide a brief summary of the contribution and an outlook of the future studies.

关键词： CoMP signaling process affecting factors requirements

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Mass Data Storage Platform for Smart Grid

Mass Data Storage Platform for Smart Grid

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2016 ieee international conference on progress in informatics and computing

作者： Huahui Lyu Ping Li Yuning Xiao Hongjie Qian Bin Sheng Ruimin Shen Information Department China Southern Power Grid Department of Mathematics and Information Technology The Education University of Hong Kong Department of Computer Science and Engineering Shanghai Jiao Tong University State Key Lab of Computer Science Institute of Software Chinese Academy of Sciences

With the continuous development of power systems, the scale of power grid is increasingly expanding, and the structure of it is becoming more and more complex. Traditional electrical network management cannot meet the current requirements. In this paper, we use load forecasting as a starting point, carrying on the exploration regarding heterogeneous multi-source data of the smart grid and doing research on shortterm load forecasting. In the process of building the application platform of smart grid, we propose some solutions to solve these problems. In the study of heterogeneous multi-source data fusion, we proposed a fusion method of grid data based on CIM/XML after comparing the current method and do numerical simulation on our application platform of smart grid to test its feasibility. At the same time, we also do a lot research on the current situation of load forecasting. After analyzing the advantage and disadvantage of some normal forecasting methods, for example, the peer-fold ratio method, the ration smoothing method, grey prediction and variation coefficient method, we developed two comprehensive approaches, the method of weighted mean and the ratio method based on grey prediction, which will increase the accuracy, general applicability and stability of load forecasting. Through these studies and efforts, we build a friendly and visually interactive application platform, which will lay the foundation of further development about the application platform of smart grid.

关键词： smart grid load forecasting data fusion CIM XML

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Intelligent decision making and Consensus

Intelligent decision making and Consensus

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2017 international conference on progress in informatics and computing(PIC)

作者： Enrique Herrera-Viedma Department of Computer Science and Artificial Intelligence University of Granada

Societal and technological trends make the decision environments more and more *** emergence of the intelligent decision making technology provides a good decision support to deal with these complex decision making problems, and Artificial Intelligence tools play a core role in the intelligent decision making technology. Nowadays we find many real world problems in which intelligent decision making processes are developed by automatic decision making systems that support individual or organizational decision making processes supported by different information technologies as the Web, Social Networks, Internet of the Things, Big Data, and Artificial Intelligence tools(as Computational Intelligence tools). In many cases, we find that the intelligent decision making processes involve the use of both preference modelling and consensus processes. The preference modelling deals with the representation and models of the preferences provided by the experts in the problems. The fuzzy logic is a computational intelligence tool that provides an adequate framework to deal with the uncertainty presented in the user opinions. The fuzzy preference modelling has been satisfactory applied in intelligent decision making. On the other hand, consensus is an important area of research in intelligent decision making. Consensus is defined as a state of mutual agreement among members of a group where all opinions have been heard and addressed to the satisfaction of the group. A consensus reaching process is a dynamic and iterative process composed by several rounds where the experts express, discuss and modify their preferences. In this talk we show the main elements of the decision making activity in presence of vague information and uncertainty. In particular, we present different concepts to understand the research developed in the area of fuzzy decision making as fuzzy preference relations to represent expert preferences, aggregation operators, selection processes of alternativ

关键词： Intelligent decision making and Consensus

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Classification of Edible Vegetable Oils by Laser Near-infrared Spectra

Classification of Edible Vegetable Oils by Laser Near-infrar...

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2014 ieee international conference on progress in informatics and computing

作者： Zhiqiang Song Bin Tu Lulu Zeng Cheng Yin Heng Zhang Xiao Zheng Dong-ping He Peishi Qi School of Mechanical Engineering Wuhan Polytechnic University College of Food Science and Engineering Wuhan Polytechnic University

The Support Vector Classification(SVC) model was constructed in this study by applying the near infrared(NIR) analysis technology combined with the chemometrics method toclassify and distinguish 11 types of vegetable oil, including "restaurant waste oil". Partial least squaresdiscriminate analysis(PLS-DA) and GA-SVC classification models were constructed, respectively, after the data were preprocessed with Savitzky-Golay(SG) smoothing, Standardized normal variate and De-trending(SNV-DT) and unit vector normalization(UVN). The results indicated that the pre-processing effect of SNV-DT was the best, PLS-DA failed tocomplete the classification, while GA-SVC model was able tomake accurate prediction, the penalty parameter of SVC classifier C=642.5 was toolarge, and the model had poor generalizability and the modeling was time consuming. Back Interval Partial Least Squares(Bi PLS), Successive Projection Algorithm(SPA), and Bi PLS-SPA were adopted respectively toextract the characteristic wavelength toconstruct GA-SVC classification model. Of these three methods, the wavelength selected with Bi PLS-SPA was 17, accounting for only 3.77% of the full wave, the penalty parameter was C=32.53, and the identification rates of prediction set and calibration set reached 95% and 99.4%, respectively. This model alsohad the shortest modeling time, and better generalizability. This research indicated that the method of NIR combined with GA-SVC could classify and distinguish edible vegetable oils rapidly, accurately and effectively. BiP LS-SPA could effectively reduce the collinearity among variables, allowing the model tobe more robust with high generalizability.

关键词： NIR Support Vector Classification Characteristic wavelength extraction Bi PLS-SPA

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Preface

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Advances in Intelligent Systems and computing 2020年 921卷 vii-viii页

作者： Hassanien, Aboul Ella Azar, Ahmad Taher Tolba, Mohamed F. Bhatnagar, Roheet Gaber, Tarek Faculty of Computers and Information Cairo University Giza Egypt School of Computing Science and Engineering University of Salford Salford Greater Manchester United Kingdom Faculty of Computers and Informatics Suez Canal University Ismailia Egypt Faculty of Computer and Information Science Ain Shams University Cairo Egypt Faculty of Computers and Information Benha University Benha Egypt Department of Computer Science and Engineering School of Computing and IT Faculty of Engineering Manipal University Jaipur JaipurRajasthan India

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The Brain Tumor Sequence Registration (BraTS-Reg) Challenge: Establishing Correspondence Between Pre-Operative and Follow-up MRI Scans of Diffuse Glioma Patients

arXiv

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arXiv 2021年

作者： Baheti, Bhakti Chakrabarty, Satrajit Akbari, Hamed Bilello, Michel Wiestler, Benedikt Schwarting, Julian Calabrese, Evan Rudie, Jeffrey Abidi, Syed Mousa, Mina Villanueva-Meyer, Javier Fields, Brandon K.K. Kofler, Florian Shinohara, Russell Takeshi Iglesias, Juan Eugenio Mok, Tony C.W. Chung, Albert C.S. Wodzinski, Marek Jurgas, Artur Marini, Niccolò Atzori, Manfredo Müller, Henning Grosbröhmer, Christoph Siebert, Hanna Hansen, Lasse Heinrich, Mattias P. Canalini, Luca Klein, Jan Gerken, Annika Heldmann, Stefan Hering, Alessa Hahn, Horst K. Meng, Mingyuan Bi, Lei Feng, Dagan Kim, Jinman Zeineldinal, Ramy A. Karar, Mohamed E. Mathis-Ullrich, Franziska Burgert, Oliver Abderezaei, Javid Pionteck, Aymeric Chopra, Agamdeep Kurt, Mehmet Yan, Kewei Yan, Yonghong Tang, Zhe Ma, Jianqiang Nasser, Sahar Almahfouz Kurian, Nikhil Cherian Meena, Mohit Shamsi, Saqib Sethi, Amit Tustison, Nicholas J. Avants, Brian B. Cook, Philip Gee, James C. Tian, Lin Greer, Hastings Niethammer, Marc Hoopesar, Andrew Hoffmannas, Malte Dalcaar, Adrian V. Christodoulidis, Stergios Estiene, Théo Vakalopoulou, Maria Paragios, Nikos Marcus, Daniel S. Davatzikos, Christos Sotiras, Aristeidis Menze, Bjoern Bakas, Spyridon Waldmannstetter, Diana Division of Computational Pathology Department of Pathology and Laboratory Medicine Indiana University School of Medicine IndianapolisIN United States Department of Electrical and Systems Engineering Washington University in St. Louis St. LouisMO United States GE HealthCare San RamonCA United States University of Pennsylvania PhiladelphiaPA United States Department of Radiology Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Mallinckrodt Institute of Radiology Washington University School of Medicine St. LouisMO United States Institute for Informatics Data Science & Biostatistics Washington University School of Medicine St. LouisMO United States Department of Bioengineering Santa Clara University Santa ClaraCA United States Department of Quantitative Biomedicine University of Zurich Zurich Switzerland Department of Computer Science TUM School of Computation Information and Technology Technical University of Munich Munich Germany Department of Diagnostic and Interventional Neuroradiology School of Medicine Klinikum rechts der Isar Technical University of Munich Munich Germany Department of Nuclear Medicine University Hospital Regensburg Regensburg Germany University Hospital Regensburg Regensburg Germany Department of Radiology & Biomedical Imaging University of California San FranciscoCA United States Department of Radiology Duke University Medical Center DurhamNC United States Department of Radiology University of California San DiegoCA United States Helmholtz AI Helmholtz Munich Neuherberg Germany Penn Statistics in Imaging and Visualization Center Department of Biostatistics Epidemiology and Informatics University of Pennsylvania Philadelphia United States Massachusetts General Hospital Harvard Medical School United States Centre for Medical Imaging Computing University College London United Kingdom The Hong Kong University of Science and Technology Hong Kong University of Applied Sciences West

Registration of longitudinal brain Magnetic Resonance Imaging (MRI) scans containing pathologies is challenging due to dramatic changes in tissue appearance. Although there has been considerable progress in developing general-purpose medical image registration techniques, they have not yet attained the requisite precision and reliability for this task, highlighting its inherent complexity. Here we describe the Brain Tumor Sequence Registration (BraTS-Reg) challenge, as the first public benchmark environment for deformable registration algorithms focusing on estimating correspondences between pre-operative and follow-up scans of the same patient diagnosed with a diffuse brain glioma. The challenge was conducted in conjunction with both the ieee international Symposium on Biomedical Imaging (ISBI) 2022 and the international conference on Medical Image computing and Computer-Assisted Intervention (MICCAI) 2022. The BraTS-Reg data comprise de-identified multi-institutional multi-parametric MRI (mpMRI) scans, curated for size and resolution according to a canonical anatomical template, and divided into training, validation, and testing sets. Clinical experts annotated ground truth (GT) landmark points of anatomical locations distinct across the temporal domain. The training data with their GT annotations, were publicly released to enable the development of registration algorithms. The validation data, without their GT annotations, were also released to allow for algorithmic evaluation prior to the testing phase, which only allowed submission of containerized algorithms for evaluation on hidden hold-out testing data. Quantitative evaluation and ranking was based on the Median Euclidean Error (MEE), Robustness, and the determinant of the Jacobian of the displacement field. The top-ranked methodologies yielded similar performance across all evaluation metrics and shared several methodological commonalities, including pre-alignment, deep neural networks, inverse consistency

关键词： Magnetic resonance imaging

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Why is the Winner the Best?

Why is the Winner the Best?

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conference on Computer Vision and Pattern Recognition (CVPR)

作者： M. Eisenmann A. Reinke V. Weru M. D. Tizabi F. Isensee T. J. Adler S. Ali V. Andrearczyk M. Aubreville U. Baid S. Bakas N. Balu S. Bano J. Bernal S. Bodenstedt A. Casella V. Cheplygina M. Daum M. De Bruijne A. Depeursinge R. Dorent J. Egger D. G. Ellis S. Engelhardt M. Ganz N. Ghatwary G. Girard P. Godau A. Gupta L. Hansen K. Harada M. Heinrich N. Heller A. Hering A. Huaulmé P. Jannin A. E. Kavur O. Kodym M. Kozubek J. Li H. Li J. Ma C. Martín-Isla B. Menze A. Noble V. Oreiller N. Padoy S. Pati K. Payette T. Rädsch J. Rafael-Patiño V. Singh Bawa S. Speidel C. H. Sudre K. Van Wijnen M. Wagner D. Wei A. Yamlahi M. H. Yap C. Yuan M. Zenk A. Zia D. Zimmerer D. Aydogan B. Bhattarai L. Bloch R. Brüngel J. Cho C. Choi Q. Dou I. Ezhov C. M. Friedrich C. Fuller R. R. Gaire A. Galdran Á. García Faura M. Grammatikopoulou S. Hong M. Jahanifar I. Jang A. Kadkhodamohammadi I. Kang F. Kofler S. Kondo H. Kuijf M. Li M. Luu T. Martinčič P. Morais M. A. Naser B. Oliveira D. Owen S. Pang J. Park S. Park S. Płotka E. Puybareau N. Rajpoot K. Ryu N. Saeed A. Shephard P. Shi D. Štepec R. Subedi G. Tochon H. R. Torres H. Urien J. L. Vilaça K. A. Wahid H. Wang J. Wang L. Wang X. Wang B. Wiestler M. Wodzinski F. Xia J. Xie Z. Xiong S. Yang Y. Yang Z. Zhao K. Maier-Hein P. F. Jäger A. Kopp-Schneider L. Maier-Hein Division of Intelligent Medical Systems German Cancer Research Center (DKFZ) Heidelberg Germany Helmholtz Imaging German Cancer Research Center (DKFZ) Heidelberg Germany Faculty of Mathematics and Computer Science Heidelberg University Heidelberg Germany Division of Biostatistics German Cancer Research Center (DKFZ) Heidelberg Germany Division of Medical Image Computing German Cancer Research Center (DKFZ) Heidelberg Germany Faculty of Engineering and Physical Sciences School of Computing University of Leeds Leeds UK Institute of Informatics School of Management HES-SO Valais-Wallis University of Applied Sciences and Arts Western Switzerland Sierre Switzerland Department of Nuclear Medicine and Molecular Imaging Lausanne University Hospital Lausanne Switzerland Technische Hochschule Ingolstadt Ingolstadt Germany Center for Artificial Intelligence and Data Science for Integrated Diagnostics (AI2D) and Center for Biomedical Image Computing and Analytics (CBICA) University of Pennsylvania Philadelphia PA USA Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Department of Radiology Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Department of Radiology University of Washington Seattle WA USA Department of Computer Science Wellcome/EPSRC Centre for Interventional and Surgical Sciences (WEISS) University College London London UK Universitat Autònoma de Barcelona & Computer Vision Center Barcelona Spain Division of Translational Surgical Oncology National Center for Tumor Diseases (NCT/UCC) Dresden Dresden Germany Department of Advanced Robotics Istituto Italiano di Tecnologia Italy Department of Electronics Information and Bioengineering Politecnico di Milano Milan Italy IT University of Copenhagen Copenhagen Denmark Department of General Visceral and Transplantation Surgery Heidelberg University Hospital Heidelberg Germany Department of Radiology and Nuc

international benchmarking competitions have become fundamental for the comparative performance assessment of image analysis methods. However, little attention has been given to investigating what can be learnt from these competitions. Do they really generate scientific progress? What are common and successful participation strategies? What makes a solution superior to a competing method? To address this gap in the literature, we performed a multicenter study with all 80 competitions that were conducted in the scope of ieee ISBI 2021 and MICCAI 2021. Statistical analyses performed based on comprehensive descriptions of the submitted algorithms linked to their rank as well as the underlying participation strategies revealed common characteristics of winning solutions. These typically include the use of multi-task learning (63%) and/or multi-stage pipelines (61%), and a focus on augmentation (100%), image preprocessing (97%), data curation (79%), and post-processing (66%). The “typical” lead of a winning team is a computer scientist with a doctoral degree, five years of experience in biomedical image analysis, and four years of experience in deep learning. Two core general development strategies stood out for highly-ranked teams: the reflection of the metrics in the method design and the focus on analyzing and handling failure cases. According to the organizers, 43% of the winning algorithms exceeded the state of the art but only 11% completely solved the respective domain problem. The insights of our study could help researchers (1) improve algorithm development strategies when approaching new problems, and (2) focus on open research questions revealed by this work.

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Preface

Lecture Notes in Computer Science (including subseries Lectu...

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Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 2017年 10477 LNBI卷 V-VI页

作者： Bracciali, Andrea Caravagna, Giulio Gilbert, David Tagliaferri, Roberto Computing Science and Mathematics University of Stirling Stirling United Kingdom School of Informatics University of Edinburgh Edinburgh United Kingdom Department of Computer Science Brunel University London Uxbridge Middlesex United Kingdom Department of Management and Innovation Systems DISA-MIS University of Salerno Fisciano Italy

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