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An er algorithm-Based Method for Removal of Adherent Water Drops from Images Obtained by a Rear View Camera Mounted on a Vehicle in Rainy Conditions

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IEICE TRANSACTIONS ON FUNDAMENTALS OF ELECTRONICS COMMUNICATIONS AND COMPUTer SCIENCES 2009年第8期E92A卷 1939-1949页

作者： Hiramatsu, Tomoki Ogawa, Takahiro Haseyama, Miki Hokkaido Univ Grad Sch Informat Sci & Technol Sapporo Hokkaido 0600814 Japan

In this paper, an er (error-Reduction) algorithm-based method for removal of adherent water drops from images obtained by a rear view camera mounted on a vehicle in rainy conditions is proposed. Since Fourier-domain and object-domain constraints are needed for any er algorithm-based method, the proposed method introduces the following two novel constraints for the removal of adherent water drops. The first one is the Fourier-domain constraint that utilizes the Fourier transform magnitude of the previous frame in the obtained images as that of the target frame. Noting that images obtained by the rear view camera have the unique characteristics of objects moving like ripples because the rear view camera is generally composed of a fish-eye lens for a wide view angle, the proposed method assumes that the Fourier transform magnitudes of the target frame and the previous frame are the same in the polar coordinate system. The second constraint is the object-domain constraint that utilizes intensities in an area of the target frame to which water drops have adhered. Specifically, the proposed method models a deterioration process of intensities that are corrupted by the water drop adhering to the rear view camera lens. By utilizing these novel constraints, the proposed er algorithm can remove adherent water drops from images obtained by the rear view camera. Experimental results that verify the performance of the proposed method are represented.

关键词： image restoration er algorithm water drop rear view camera in-vehicle system

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Applied Research on er algorithm of gray modeling

Applied Research on ER algorithm of gray modeling

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International Conference on Materials Science and Engineering Science

作者： Li Yong Tan Liping Xu Baoru Liaoning Prov Coll Commun Shenyang Peoples R China Elect Power Surv & Design Inst Liaoning Prov Shenyang Peoples R China

ISBN: (纸本)9783037850169

To improve the precision of gray modeling in forest fire and solve the problem of small date modeling, er algorithm is proposed. Based on the senior introduced the robust estimation to gray modeling, this method interpolate the modeling date again. The method can achieve small date (3 dates) modeling. This research compared with three calculation methods: least squares method, least squares interpolation method and er algorithm. According to the fitting precision, least squares method is 10.21%, least squares interpolation method is 1.08% and er algorithm is 0.00%. That can be obtained by calculating er algorithm has a good fitting effect.

关键词： er algorithm robust estimation gray modeling

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Modelling of extended objects using sparse multi-aspect High Range Resolution radar data set

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IET RADAR SONAR AND NAVIGATION 2011年第7期5卷 756-768页

作者： Fasoula, A. van Genderen, P. Delft Univ Technol IRCTR MTS Radar Delft Netherlands

The two-dimensional (2D) geometry of extended objects is modelled as a discrete constellation of a small number of scattering centres. Each scattering centre is characterised by its 2D location and extension, as well as by its observability from aspect angles spanning a wide angular interval. The considered input information of the modelling consists of high range resolution (HRR) data sets, which are measured by a network of scanning surveillance radars from a limited set of aspect angles of the object. During the short time-on-target of the antennas within each radar scan, inverse synthetic aperture radar (ISAR) imaging is performed. The Expectation-Maximisation (er) algorithm is applied in order to fit each ISAR image to a Gaussian mixture model (GMM), pertaining to the aspect angle applicable to the respective radar. A merging algorithm for clustering all the partial GMMs into a unique 2D model of the extended object is designed. Cluster validation criteria are applied for correct alignment of the multi-radar multi-scan object model estimates. A Mixture of Gaussians classifier (MOGC), which is based on representation of each object by the estimated 2D model, is discussed. Results from application of the algorithms to two measured HRR radar data sets are presented.

关键词： expectation-maximisation algorithm 2D geometry radar imaging er algorithm sparse multiaspect high range resolution radar data set multiradar multiscan object model cluster validation criteria scanning surveillance radar Optical, image and video signal processing ISAR imaging MOGC discrete constellation Combinatorial mathematics Radar equipment, systems and applications search radar GMM HRR radar data set inverse synthetic aperture radar imaging Gaussian processes two-dimensional geometry synthetic aperture radar geometry Gaussian mixture model radar antennas wide angular interval antennas Other topics in statistics

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Econometrics with Privacy Preservation

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OPerATIONS RESEARCH 2019年第4期67卷 905-926页

作者： Cai, Ning Kou, Steven Hong Kong Univ Sci & Technol Dept Ind Engn & Decis Analyt Hong Kong Peoples R China Boston Univ Questrom Sch Business Dept Finance Boston MA 02215 USA

Many data are sensitive in areas such as finance, economics, and other social sciences. We propose an er (encryption and recovery) algorithm that allows a central administration to do statistical inference based on the encrypted data, while still preserving each party's privacy even for a colluding majority in the presence of cyber attack. We demonstrate the applications of our algorithm to linear regression, logistic regression, maximum likelihood estimation, the method of moments, and estimation of empirical distributions. Moreover, our algorithm can help to address another practically significant issue-privacy preservation for distributed statistical inference when data are allocated to different parties who are unwilling to share their own data with others. Finally, we provide two extensions of the applications of our algorithm, including the combination of our algorithm and Fourier transforms and the development of a modified root-finding method for recovering quantiles with privacy preservation.

关键词： er algorithm invariant equidistribution privacy preservation distributed statistical inference Fourier transforms quantile functions

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Forest fire spread model based on the grey system theory

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JOURNAL OF SUPerCOMPUTING 2020年第5期76卷 3602-3614页

作者： Lv, Chundong Wang, Jia Zhang, Fanfei Beijing Forestry Univ Beijing Key Lab Precis Forestry 35 Qinghua East RdBox 111 Beijing 100083 Peoples R China Beijing Forestry Univ Coll Forestry 35 Qinghua East RdBox 111 Beijing 100083 Peoples R China

Accurate prediction of forest fire spread is very essential for minimizing its effects. Although many models have been developed to predict the forest fire spread, all these models require several parameters, sometimes, cannot be obtained in a real time. In this paper, the grey system theory was applied for forest fire spread model developing. By preprocessing and fusing the MODIS remote sensing data, the sequence data of the grey model can be confirmed. After making precision comparison among least square estimation algorithm, least square interpolation algorithm and er algorithm, forest fire spread GM(1, 1) model was developed with er algorithm and precision of the model was evaluated at the same time. The results showed that the fitting precision and predicting precision of the model were both high, of which the one-level model made up 50%, the two-level 25% and the model between the one-level and the two-level 25%. The prediction accuracy of forest fire spreading model was tested to meet the requirement of modeling. GM(1, 1) model provided a new approach for the study of forest fire spread simulation.

关键词： Least square estimation Least square estimation interpolation er algorithm MODIS image GM(1 1)

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Improved document ranking in ontology-based document search engine using evidential reasoning

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IET SOFTWARE 2014年第1期8卷 33-41页

作者： Tang, Wenhu Yan, Long Yang, Zhen Wu, Qinghua Henry S China Univ Technol Sch Elect Power Guangzhou Guangdong Peoples R China Univ Liverpool Dept Elect Engn & Elect Liverpool L69 3GJ Merseyside England

This study presents a novel approach to document ranking in an ontology-based document search engine (ODSE) using evidential reasoning (er). Firstly, a domain ontology model, used for query expansion, and a connection interface to an ODSE are developed. A multiple attribute decision making (MADM) tree model is proposed to organise expanded query terms. Then, an er algorithm, based on the Dempster-Shafer theory, is used for evidence combination in the MADM tree model. The proposed approach is discussed in a generic frame for document ranking, which is evaluated using document queries in the domain of electrical substation fault diagnosis. The results show that the proposed approach provides a suitable solution to document ranking and the precision at the same recall levels for ODSE searches have been improved significantly with er embedded, in comparison with a traditional keyword-matching search engine, an ODSE without er and a non-randomness-based weighting model.

关键词： case-based reasoning decision making document handling fault diagnosis inference mechanisms ontologies (artificial intelligence) query processing search engines substation protection trees (mathematics) ontology-based document search engine evidential reasoning document ranking multiple attribute decision making tree model MADM tree model domain ontology model query expansion connection interface expanded query terms er algorithm Dempster-Shafer theory evidence combination generic frame document queries electrical substation fault diagnosis ODSE searches

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Imputation and Deletion Methods Under The Presence of Missing Values and Outliers: A Comparative Study

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GAZI UNIVerSITY JOURNAL OF SCIENCE 2016年第4期29卷 799-809页

作者： Toka, Onur Cetin, Meral Hacettepe Univ Dept Stat TR-06800 Ankara Turkey

Missing data and imputation methods are studied in many disciplines. However, the methods have some different properties and some constraints according to missingness mechanism. In this paper, we examine some deletion and imputation methods' behaviors under the presence of outliers. We obtain a mean vector and covariance matrix with missing and contaminated data and compare the results of imputation methods using mean square errors. As an application, we use the regression data and examine the effect of missingness on regression model's parameters. We compare the imputed values with real values and explain the results of classical and robust imputation methods.

关键词： er algorithm missing data outliers robust imputation sequential imputation

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Diabetes Diagnosis Expert System by using Belief Rule Base with Evidential Reasoning 2

Diabetes Diagnosis Expert System by using Belief Rule Base w...

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2nd International Conference on Electrical Engineering and Information and Communication Technology (ICEEICT)

作者： Rahaman, Saifur Int Islamic Univ Chittagong Dept Comp Sci & Engn Chittagong Bangladesh Univ Chittagong Chittagong Bangladesh

ISBN: (纸本)9781467366762

Diabetes is a knotty disease and very common in the world. It can affect almost every organ of the body. The diagnosis of diabetes is determining from some medical and clinical data associate with diabetes. However, all the data are associated various types of uncertainty, which can cause for time delay, inaccuracy of the diagnosis. A system for automated medical diagnosis would enhance the accuracy of the diagnosis and reduce the cost effects. This paper presents an effective approach for diagnosing diabetes using Belief Rule Base (BRB) with evidential reasoning, which can handle the errors and uncertainties. This paper used the medical and clinical real data to develop and test this system. It has been observed that, this system provides user interactive and reliable results of diabetes diagnosis in percentage.

关键词： Diabetes Belief Rule Base er algorithm Decision Support System Diagnosis

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DEA Malmquist productivity index based on a double-frontier slacks-based model: Iranian road safety assessment

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EUROPEAN TRANSPORT RESEARCH REVIEW 2019年第1期11卷 4-4页

作者： Ganji, S. S. Rassafi, A. A. Imam Khomeini Int Univ Fac Engn Dept Transportat Qazvin Iran

Many governments in the developing world face the social and economic consequences of road accidents and mortalities. Hence, more precise evaluation of regional programs to reduce road fatalities has been a concern for many safety *** safety performance is often measured using various extensions of Data Envelopment Analysis (DEA), in particular the model proposed by Charnes, Cooper and Rhodes (CCR), which deals only with the radial efficiency as the objective function neither taking into account input excesses nor output shortfalls. The Slacks-Based Measure (SBM) of efficiency overcomes this shortcoming by taking both measurements mentioned above simultaneously. In this regard, the current study aims to employ the SBM in analyzing road safety performance. It is noteworthy that the efficiency of each Decision Making Unit (DMU) can be pessimistically measured using the slacks-based measure of inefficiency such that the anti-efficient DMUs provide the anti-efficient frontier. The results obtained from the optimistic and pessimistic frontiers are nonlinearly aggregated by means of the Evidential Reasoning (er) ***, a Double-Frontier SBM-based Malmquist Productivity Index (DF-SBM- MPI) is provided to analyze the efficiency and technological changes in safety performance from 2014 to 2016. For this purpose, the standard SBM and Super-SBM models are used to compute the optimistic Malmquist Productivity Index (MPI);similarly, the pessimistic MPI is determined by means of the inverted SBM and Super-SBM models. Finally, the obtained MPIs from the two different points of view are geometrically combined to obtain the overall MPI.

关键词： Double-frontier slacks-based measurement Iranian provinces Road safety performance er algorithm DF-SBM-MPI

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Diabetes Diagnosis Expert System by using Belief Rule Base with Evidential Reasoning

Diabetes Diagnosis Expert System by using Belief Rule Base w...

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International Conference on Electrical Engineering and Information Communication Technology

作者： Saifur Rahaman Dept. of Computer Science & Engineering International Islamic University Chittagong Chittagong Bangladesh University of Chittagong Chittagong Bangladesh

ISBN: (纸本)9781467366779

关键词： Diabetes Belief Rule Base er algorithm Decision Support System Diagnosis

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