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24th IEEE International Conference on Web Services (ICWS)

作者： Liu, Ruilin Xu, Xiaofei Wang, Zhongjie Sheng, Quan Z. Xu, Hanchuan Harbin Inst Technol Sch Comp Sci & Technol Harbin 150001 Heilongjiang Peoples R China Univ Adelaide Sch Comp Sci Adelaide SA 5005 Australia

ISBN: (纸本)9781538607527

With more and more Web services flooded on the Internet, the scale of Web services and complexity of connections among them are growing rapidly. This phenomenon has brought great challenges to service selection. Due to the huge search space, existing research approaches are hardly feasible in dynamic real-time scenarios under a stringent time limit with a large number of potential Web services involved. In order to deal with this problem, the focus of this paper is to improve the efficiency of QoS-aware web service selection in real-time considering a priori knowledge from historical log, which can reduce the search space effectively. We first analyse and discover the distribution of customer requests to identify request clusters, and we mine valuable fragments or service patterns from historical service solutions. Then, we build a probability matrix to improve the efficiency of service selection algorithm, which contains the request-solution mapping relationships between request clusters and service patterns based on statistical method. A series of experiments using both real and synthetic data demonstrate that our approach improves Global Planning optimisation algorithm (GP) and Artificial Bee Colony algorithm (ABC) by 36.20% and 41.98% respectively.

关键词： service pattern request clustering probability matrix request-solution mapping

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Analysis of probability matrix model for seismic damage vulnerability of highway bridges

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GEOMATICS NATURAL HAZARDS & RISK 2022年第1期13卷 1395-1421页

作者： Li, Si-Qi Liu, Hong-Bo Heilongjiang Univ Sch Civil Engn Harbin Peoples R China Longjian Rd & Bridge Co Ltd Harbin Peoples R China Harbin Inst Technol Sch Transportat Sci & Engn Harbin Peoples R China Heilongjiang Univ Key Lab Funct Inorgan Mat Chem Minist Educ Harbin Peoples R China

The present study was aimed at assessing more accurately the seismic vulnerability and performance of highway bridges in an earthquake-damaged area. Based on mathematical statistics (data collection) and a probability model, (i) survey data for 2134 bridges in 22 highway sections (47 sub-sections) affected by the Wenchuan earthquake on May 12, 2008 were obtained and analysed, (ii) Gaussian and exponential regression models were developed, (iii) the empirical seismic-damage vulnerability function, plane, surface and curve model of the 22 highway sections with sample number and failure ratio were established, and (iv) a regression parameter matrix was constructed considering the empirical seismic-damage data. Using the method of probability exceedance, the bridge vulnerability curve models considering the seismic-damage exceedance probability of each section were analysed, and a parameter matrix model (mean seismic-damage index) was proposed to evaluate and predict the seismic vulnerability of regional bridges. Based on creating the actual seismic-damage sample database, vulnerability point-cloud models of the 22 highway bridges were constructed, and the vulnerability probability matrix, function, curve and point-cloud model of each highway section can be used to evaluate and estimate the vulnerability of regional bridges in the future.

关键词： Highway bridge empirical vulnerability analysis probability matrix sample number failure ratio damage exceedance probability

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Automatic brain tissue segmentation in MR images using hybrid atlas forest based on confidence-weighted probability matrix

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INTERNATIONAL JOURNAL OF IMAGING SYSTEMS AND TECHNOLOGY 2019年第2期29卷 97-109页

作者： Xu, Lijun Liu, Hong Song, Enmin Jin, Renchao Hung, Chih-Cheng Huazhong Univ Sci & Technol Sch Comp Sci & Technol Wuhan 430074 Hubei Peoples R China Educ Minist Image Proc & Intelligent Control Key Lab Wuhan Hubei Peoples R China Kennesaw State Univ Marietta GA USA

The segmentation of specific tissues in an MR brain image for quantitative analysis can assist the disease diagnosis and medical research. Therefore, a robust and accurate method for automatic segmentation is necessary. Atlas-based-method is a common and effective method of automatic segmentation where an atlas refers to a pair of image consist of an intensity image and its corresponding label image. Apart from the general multi-atlas-based methods, which propagate labels through the single atlas then fuse them, we proposed a hybrid atlas forest based on confidence-weighted probability matrix to consider the atlases set as a whole and treat each voxel differently. In the framework, we first register the atlas to the image space of target and calculate the confidence of voxels in the registered atlas. Then, a confidence-weighted probability matrix is generated and it augments to the intensity image of the atlas or target for providing spatial information of the target tissue. Third, a hybrid atlas forest is trained to gather the features and correlation information among the atlases in the dataset. Finally, the segmentation of the target tissues is predicted by the trained hybrid atlas forest. The segment performance and the components efficiency of the proposed method are evaluated on the two public datasets. Based on the experiment results and quantitative comparisons, our method can gather spatial information and correlation among the atlases to obtain an accurate segmentation.

关键词： brain segmentation label fusion magnetic resonance imaging probability matrix random forest

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Computing the expected subtree number of random hexagonal and phenylene chains based on probability matrices

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DISCRETE APPLIED MATHEMATICS 2023年第1期339卷 184-201页

作者： Yang, Yu Jin, Bang-Bang Lu, Mei Hui, Zhi-Hao Zhao, Lu-Xuan Wang, Hua Henan Univ Technol Coll Informat Sci & Engn Zhengzhou 450001 Peoples R China Pingdingshan Univ Sch Software Pingdingshan 467000 Peoples R China Pingdingshan Univ Sch Math & Stat Pingdingshan 467000 Peoples R China Tsinghua Univ Dept Math Sci Beijing 100084 Peoples R China WuXi AppTec 288 Fute Zhong Rd Shanghai 200131 Peoples R China Georgia Southern Univ Statesboro Dept Math Sci Statesboro GA 30460 USA

With structural analysis and probability matrix, this paper studies the subtree number index (number of non-labeled subtrees) of random hexagonal and phenylene chains, and presents exact recursive formulas for the expected values of subtree number index of the random hexagonal and phenylene chains in terms of probability matrices, as appli-cations, we obtain the subtree number of hexagonal linear chain, hexagonal helicence chain, phenylene linear chain and phenylene helicence chain. Moreover, the proposed matrix method also builds a bridge to explore the deep correlation between structural-based and distance-based topological indices, and provides important theoretical support for prediction new properties of chemical compounds.& COPY;2023 Elsevier B.V. All rights reserved.

关键词： Expected subtree number index Random hexagonal chain Random phenylene chain probability matrix

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A parallelizable compression scheme for Monte Carlo scatter system matrices in PET image reconstruction

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PHYSICS IN MEDICINE AND BIOLOGY 2007年第12期52卷 3421-3437页

作者： Rehfeld, Niklas Alber, Markus Univ Klinikum Tubingen Sekt Biomed Phys Klin Radioonkol D-72076 Tubingen Germany

Scatter correction techniques in iterative positron emission tomography ( PET) reconstruction increasingly utilize Monte Carlo ( MC) simulations which are very well suited to model scatter in the inhomogeneous patient. Due to memory constraints the results of these simulations are not stored in the system matrix, but added or subtracted as a constant term or recalculated in the projector at each iteration. This implies that scatter is not considered in the back-projector. The presented scheme provides a method to store the simulated Monte Carlo scatter in a compressed scatter system matrix. The compression is based on parametrization and B-spline approximation and allows the formation of the scatter matrix based on low statistics simulations. The compression as well as the retrieval of the matrix elements are parallelizable. It is shown that the proposed compression scheme provides sufficient compression so that the storage in memory of a scatter system matrix for a 3D scanner is feasible. Scatter matrices of two different 2D scanner geometries were compressed and used for reconstruction as a proof of concept. Compression ratios of 0.1% could be achieved and scatter induced artifacts in the images were successfully reduced by using the compressed matrices in the reconstruction algorithm.

关键词： EMISSION-TOMOGRAPHY probability matrix COMPENSATION ALGORITHM NOISE SPECT

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Exploring privileged information from simple actions for complex action recognition

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NEUROCOMPUTING 2020年第0期380卷 236-245页

作者： Liu, Fang Xu, Xiangmin Zhang, Tong Guo, Kailing Wang, Lin Guangdong Univ Finance Sch Internet Finance & Informat Engn Guangzhou Peoples R China South China Univ Technol Sch Elect & Informat Engn Guangzhou Peoples R China South China Univ Technol Sch Comp Sci & Engn Guangzhou Peoples R China

Complex action recognition is an important yet challenging problem in computer vision. Sufficient labeled training data are required for learning a robust model. However, labeling complex actions is often time-consuming and expensive. Considering that each complex action is composed of a sequence of simple actions, we propose a new learning framework for complex action recognition by using a sequence of existing simple actions. A matrix is first designed as the probability matrix by manual annotation, which encodes the occurrence of simple actions in complex actions. As the probability matrix depends on label information, it is only available for the training data and is regarded as privileged information. A new framework is proposed, which is called latent task learning with privileged information (LTL-PI). An efficient algorithm is also presented for solving the proposed LTL-PI formulation, which obtains the optimal sparse weight parameters. To validate the proposed LTL-PI algorithm, extensive experiments are carried out on two challenging complex action datasets: the Olympic Sports dataset and the UCF50 dataset. Experimental results show that LTL-PI is a promising development that improves the performance of complex action recognition, and the designed privileged information can offer promising enhancement. (C) 2019 Elsevier B.V. All rights reserved.

关键词： Privileged information probability matrix Simple action Complex action recognition

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A new EDA algorithm combined with Q-learning for semiconductor final testing scheduling problem

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COMPUTERS & INDUSTRIAL ENGINEERING 2024年 193卷

作者： Zhang, Long Lin, Yi Xu, Chuanpei Liu, Min Guilin Univ Elect Technol Guangxi Key Lab Cognit Radio & Informat Proc Minist Educ Guilin Peoples R China Tsinghua Univ Dept Automat BNRIST Beijing Peoples R China

In recent years, the Semiconductor Final Testing Scheduling Problem (SFTSP), recognized as a unique multiresource scheduling challenge, attracts increasingly attention of academia and industry in the semiconductor manufacturing process. In this paper, a novel estimation of distribution algorithm combined with Q-learning (QEDA) is proposed to solve the SFTSP. According to the characteristics of the used operation encoding, a new probability matrix update mechanism is proposed for enhancing the priority relationships among operations. Considering that the traditional EDA is not in favor of local exploitation compared with its global exploration, a reinforcement learning is designed to improve the performance of the proposed algorithm. Furthermore, for the challenge of the resource allocation in SFTSP, four actions are introduced based on the variance of individual objectives. Extensive numerical simulations and comparative experiments show that the proposed QEDA algorithm exhibits much better performance than the state-of-the-art algorithms in the literature for solving the SFTSP.

关键词： Semiconductor final testing scheduling problem Q-learning EDA probability matrix

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Improved Mutual Information Maximizing Input Clustering Algorithm for Permutation Flow Shop Scheduling Problem

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SENSOR LETTERS 2012年第8期10卷 1862-1866页

作者： Gao, Huimin Wang, Xiaoping Jiaxing Univ Mech & Elect Engn Coll Jiaxing 314001 Peoples R China Jiaxing Univ Sch Business Jiaxing 314001 Peoples R China

Improved Mutual Information Maximizing Input Clustering algorithm is a kind of discrete estimation of distribution algorithm, which is convenient to solve permutation flow shop scheduling problem. In this paper, the encoding mode and probability model are improved, new individual strategy is proposed, greedy algorithm is introduced at the initial phase of the probability matrix, and crossover operator, mutation operator, insert operator and swap operator are adopted during the process of evolution, dynamic adjusted method is employed to determine the population size. These improvements gurantee the population diversity even in small population. Experiment results show that the improved Mutual Information Maximizing Input Clustering algorithm is effective and stable.

关键词： Mutual Information Maximizing Input Clustering Algorithm Permutation Flow Shop Scheduling Problem Estimation of Distribution Algorithm probability matrix

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Graph Learning-Based Cooperative Spectrum Sensing in Cognitive Radio Networks

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IEEE WIRELESS COMMUNICATIONS LETTERS 2023年第1期12卷 138-142页

作者： Jiang, Tao Jin, Ming Guo, Qinghua Tian, Ye Liu, Juan Ningbo Univ Fac Elect Engn & Comp Sci Ningbo 315211 Peoples R China Univ Wollongong Sch Elect Comp & Telecommun Engn Wollongong NSW 2522 Australia

In cooperative spectrum sensing (CSS), received signal strengths (RSSs) of multiple secondary users (SUs) are combined to improve sensing performance. In existing CSS schemes, RSS levels are often assumed in the same order of magnitude, which, however, is not the case due to, e.g., shadowing in some scenarios, leading to performance degradation. To address this issue, in this letter, we first reveal that the expectation of a RSS matrix from SUs has the property of low rank. Then, using this property, we propose a graph learning method to select SUs of the same order of RSS levels. Specifically, a probability matrix of graph edges is used to represent the correlations of SUs, and a graph learning problem is formulated for learning the probability matrix, which is further solved by using alternating direction method of multipliers (ADMM). Then SUs with high correlations are collected for implementing CSS. Moreover, false-alarm and detection probabilities of the proposed detector are analyzed. Numerical results demonstrate the superiority of the proposed detector compared to state-of-the-art detectors.

关键词： Sensors Correlation Detectors Radio spectrum management Fading channels Topology Shadow mapping Cooperative spectrum sensing graph learning probability matrix received signal strength

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Graph Learning-Based Cooperative Spectrum Sensing With Corrupted RSSs in Spectrum-Heterogeneous Cognitive Radio Networks

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IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS 2024年第10期23卷 15447-15461页

作者： Jiang, Tao Jin, Ming Guo, Qinghua Yao, Junteng Ningbo Univ Fac Elect Engn & Comp Sci Ningbo 315211 Peoples R China Univ Wollongong Sch Elect Comp & Telecommun Engn Wollongong NSW 2522 Australia

Spatiotemporal spectrum sensing of multiple primary users (PUs) sharing the same channels with unknown and irregular coverage presents significant challenges. The receive signal strength (RSS) levels at secondary users (SUs) vary greatly due to path propagation loss and shadowing. Moreover, due to security concerns and limited energy at SUs, the reported RSS measurements to a fusion center are noisy and incomplete. These challenges seriously impact the performance of existing cooperative spectrum sensing (CSS) techniques. In this work, to address these issues, we propose a robust graph learning based CSS (RoGL-CSS) detector, after revealing the low rank property of the expectation of RSS matrix and formulating a graph learning problem. By solving the graph learning problem with the alternating direction method of multipliers (ADMM), a probability matrix (graph) representing the correlations among SUs is acquired, which is applied to recover the expectation of RSSs from corrupted measurements and select SUs for CSS. Specifically, a robust RSS recovery algorithm with a learned probability matrix is adopted, and the SUs with recovered RSSs of high correlations are collected for implementing CSS. Numerical results are provided to demonstrate the superiority of the proposed detector compared to state-of-the-art detectors. At a false-alarm probability of 10%, with measurement missing rate 20% and outlier rate 30%, RoGL-CSS achieves performance improvement of at least 16% and 9% in detection probability, compared to other detectors for scenarios of two and five PUs, respectively.

关键词： Cooperative spectrum sensing graph learning signal inpainting incomplete RSS measurements probability matrix Cooperative spectrum sensing graph learning signal inpainting incomplete RSS measurements probability matrix

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