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Practical Rendering of Thin Layered Materials with Extended Microfacet Normal Distributions

Practical Rendering of Thin Layered Materials with Extended ...

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26th Eurographics Symposium on Rendering, Rendering - Experimental Ideas and Implementations, EGSR 2015

作者： Guo, Jie Qian, Jinghui Pan, Jingui State Key Lab for Novel Software Technology Nanjing University China College of Computer Science and Technology Nanjing Tech University China

ISBN: (纸本)9783905674880

We propose a practical reflectance model for rendering thin transparent layers with different sides varying in roughness and levels of gloss. To capture the effect of subsurface reflection, previous methods rely on importance sampling for each light-surface interaction. This soon becomes a computationally demanding task since a recursive sampling scheme is required to handle multiple internal reflections. In this paper, we first provide a comprehensive analysis of the relationship between the directional distribution of scattered light and the roughness of each layer boundary using joint spherical warping. Based on the analysis, we generalize the traditional microfacet theory for layered materials and introduce the extended normal distribution function (ENDF) to accurately model the behavior of subsurface reflection. With the ENDF, the number of sampling processes can be reduced to only once for each bounce of subsurface reflection. We demonstrate that our BSDF model based on the ENDF is easy to be implemented on top of Monte Carlo sampling based offline renderers and it incurs little computational overhead. Moreover, it can be also efficiently used in real-time applications with the help of GPU acceleration. © The Eurographics Association 2015.

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An ant colony optimization algorithm for learning brain effective connectivity network from fMRI data

An ant colony optimization algorithm for learning brain effe...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Jinduo Liu Junzhong Ji Aidong Zhang Peipeng Liang Beijing Municipal Key Laboratory of Multimedia and Intelligent Software the College of Computer Science and Technology Beijing University of Technology Beijing China Department of Computer Science and Engineering University at Buffalo The State University of New York Buffalo America Beijing Key Lab of MRI and Brain Informatics Department of Radiology Capital Medical University Beijing China

ISBN: (纸本)9781509016129

Identifying brain effective connectivity networks from functional magnetic resonance imaging (fMRI) data is an important advanced subject in neuroinformatics in recent years, where the learning method based on bayesian networks (BN) has become a new hot topic in the field. This paper proposes a new method to learn the brain effective connectivity network structure by combining ant colony optimization (ACO) with BN method, named as ACOEC. In the proposed algorithm, a brain effective connectivity network is first mapped onto an ant, and then the ant colony optimization by simulating real ants looking for food is employed to construct network structures and finally an ant with the highest score is obtained as the optimal solution. The experimental results on simulated and real fMRI data sets show that the new method can not only accurately identify the connections and directions of the brain networks, but also quantitatively describe the connection strength of the brain networks, which has a good clinical application prospects.

关键词： Ant colony optimization Bayes methods Measurement Mathematical model Brain modeling Optimization Electronic mail

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Improving the accuracy of integer signedness error detection using data flow analysis 27

Improving the accuracy of integer signedness error detection...

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27th International Conference on software Engineering and Knowledge Engineering, SEKE 2015

作者： Sun, Hao Su, Chao Wang, Yue Zeng, Qingkai State Key Laboratory for Novel Software Technology Nanjing University Nanjing210023 China Department of Computer Science and Technology Nanjing University Nanjing210023 China

ISBN: (纸本)1891706373

Integer signedness error can be exploited by attackers to cause severe damages to computer systems. Despite of the significant advances in automating the detection of integer signedness errors, accurately differentiating exploitable and harmful signedness errors from unharmful ones still remains an open problem. In this paper, we present the design and implementation of SignFlow, an instrumentation-based integer signedness error detector to reduce the reports for unharmful signedness errors without sacrificing the completeness (i.e. no false negatives). SignFlow utilizes static data flow analysis to identify unharmful integer signedness conversions from the view of where the operands originate and whether the data after conversions can propagate to security-related operations, and then inserts security checks for the remaining conversions so as to accomplish runtime protection. We evaluated SignFlow on 7 real-world harmful integer signedness bugs, SPECint 2006 benchmarks together with 5 real-world applications. Experimental results show that SignFlow successfully detected all harmful integer signedness bugs and achieved a reduction of 41% in false positives over IntFlow, the state-of-the-art signedness error detector.

关键词： Data flow analysis

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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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Smart Grid Data Mining and Visualization

Smart Grid Data Mining and Visualization

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2016 IEEE International Conference on Progress in Informatics and Computing

作者： Yingyao Zhou Ping Li Yuning Xiao Anum Masood Qichen Yu Bin Sheng 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 Department of Computer Science COMSATS Institute of Information Technology Department of Electrical Engineering and Automation Shanghai Jiao Tong University

The power industry innovation has increasingly become a top concern for current reforms. Power systems feature scattered data storage, incapable data analysis ability, poor computing capability, and ineffective interaction interface. To resolve these issues, we need multiple data mining techniques to extract information for analytical capacity improvement. Secondly, we need visualization techniques to analyze and optimize interaction. Lastly, we need distributed technologies for unified data management to increase computing capability and system scalability. Considering China's smart grid information, this paper proposes solutions to problems, such as the existing underdeveloped power management systems, a lack of automation methods, low data visualization, and poor data management. The electric power industry has functional requirements for this research. Based on existing data mining, visualization and understanding of distributed technologies, we discussed the functions of each part of the implementation in a smart grid management system: the data mining module, visualization module and data management module.

关键词： smart grid data mining visualization geographic information systems distributed computing

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Performance optimization of Jacobi stencil algorithms based on POWER8 architecture

Performance optimization of Jacobi stencil algorithms based ...

引用

International Conference on Application Specific Systems (ASAP), Architectures and Processors

作者： Jingheng Xu Haohuan Fu Lin Gan Yu Song Hongbo Peng Wen Shi Guangwen Yang Department of Computer Science & Technology Tsinghua University National Supercomputer Center at Wuxi Ministry of Education Key Lab. for Earth System Modeling Tsinghua University IBM China Systems and Technology Laboratory

In this paper we choose the widely used Jacobi stencil algorithm as our target program to evaluate the effectiveness of tuning techniques based on the latest POWER8 processor, thus to provide optimization guidelines t... 详细信息

ISBN: (纸本)9781509015047

关键词： Optimization Jacobian matrices Hardware Instruction sets Tuning computer architecture software performance

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Omnidirectional chargability with directional antennas

Omnidirectional chargability with directional antennas

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International Conference on Network Protocols

作者： Haipeng Dai Xiaoyu Wang Alex X. Liu Fengmin Zhang Yang Zhao Guihai Chen State Key Laboratory for Novel Software Technology Nanjing University Nanjing Jiangsu CHINA Department of Computer Science and Engineering Michigan State University East Lansing MI USA Shanghai key Laboratory of Scalable Computing and Systems Shanghai Jiao Tong University Shanghai CHINA

ISBN: (纸本)9781509032822

Wireless Power Transfer (WPT) has received more and more attentions because of its convenience and reliability. In this paper, we first propose the notion of omnidirectional charging by which an area is omnidirectionally charged if a device with directional antennas at any position in the area with any orientation can be charged by directional chargers with power being no smaller than a given threshold. We present our empirical charging model based on field experimental results using off-the-shelf WPT products. Next, we consider the problem of detecting whether the target area achieves omnidirectional charging given a deterministic deployment of chargers. We develop piecewise constant approximation and area discretization techniques to partition the target area into subareas and approximate powers from chargers as constants. Then we propose the Minimum Coverage Set extraction technique which reduces the continuous search space to a discrete one and thereby allows a fast detection algorithm. Moreover, we consider the problem of determining the probability that the target area achieves omnidirectional charging given a random deployment of chargers. We first replace the target area by grid points on triangular lattices to reduce the search space from infinite to finite, then approximate chargers' power with reasonable relaxation, and derive an upper bound of the omnidirectional charging probability. Finally, we conduct both simulation and field experiments, and the results show that our algorithm outperforms comparison algorithms by at least 120%, and the consistency degree of our theoretical results and field experimental results is larger than 93.6%.

关键词： Wireless sensor networks Wireless communication Directional antennas Adaptation models Joining processes Conferences Protocols

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Battery capacity anomaly detection and data fusion

Battery capacity anomaly detection and data fusion

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2015 Annual Conference of the Prognostics and Health Management Society, PHM 2015

作者： Weddington, John Yan, Wuzhao Dou, Wanchun Zhang, Bin Department of Electrical Engineering University of South Carolina ColumbiaSC United States Department of Computer Science and Technology State Key Lab. for Novel Software Technology Nanjing University NanjingJiangsu210093 China

ISBN: (纸本)9781936263202

Anomaly detection is a critical enabling technique of PHM, especially in safety critical applications. In order for the PHM system to begin prediction of remaining useful life of a given system or component, the fault must be detected. This paper presents an integrated anomaly detection system for state-of-health of lithium-ion batteries. Two algorithms for state estimation and anomaly detection are used: the extended Kalman filter and the particle filter. A Dempster-Shafer Theory-based fusion approach is implemented to reduce the uncertainty of detection. The results on battery data show that the fusion improves the detection results significantly. © 2015, Prognostics and Health Management Society. All rights reserved.

关键词： Kalman filters

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New texture-spatial features for keyword spotting in video images 3

New texture-spatial features for keyword spotting in video i...

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3rd IAPR Asian Conference on Pattern Recognition, ACPR 2015

作者： Shivakumara, Palaiahnakote Liang, Guozhu Roy, Sangheeta Pal, Umapada Lu, Tong Faculty of Computer Science and Information Technology University of Malaya Kuala Lumpur Malaysia National Key Lab for Novel Software Technology Nanjing University Nanjing China Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India

ISBN: (纸本)9781479961009

keyword spotting in video document images is challenging due to low resolution and complex background of video images. We propose the combination of Texture-Spatial-Features (TSF) for keyword spotting in video images without recognizing them. First, a segmentation method extracts words from text lines in each video image. Then we propose the set of texture features for identifying text candidates in the word image with the help of k-means clustering. The proposed method finds proximity between text candidates to study the spatial arrangement of pixels that result in feature vectors for spotting words in the input frame. The proposed method is evaluated on word images of different fonts, contrasts, backgrounds and font sizes, which are chosen from standard databases such as ICDAR 2013 video and our video data. Experimental results show that the proposed method outperforms the existing method in terms of recall, precision and f-measure. © 2015 IEEE.

关键词： Image segmentation

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Personalized location recommendation by aggregating multiple recommenders in diversity

Personalized location recommendation by aggregating multiple...

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Workshop on Location-Aware Recommendations, LocalRec 2015 - co-located with the 9th ACM Conference on Recommender Systems, RecSys 2015

作者： Lu, Ziyu Wang, Hao Mamoulis, Nikos Tu, Wenting Cheung, David W. Dept. of Computer Science Univ. of Hong Kong Hong Kong State Key Lab. for Novel Software Technology Nanjing University China

Location recommendation is an important feature of social network applications and location-based services. Most existing studies focus on developing one single method or model for all users. By analyzing real location-based social networks, in this paper we reveal that the decisions of users on place visits depend on multiple factors, and different users may be affected differently by these factors. We design a location recommendation framework that combines results from various recommenders that consider various factors. Our framework estimates, for each individual user, the underlying influence of each factor to her. Based on the estimation, we aggregate suggestions from different recommenders to derive personalized recommendations. Experiments on Foursquare and Gowalla show that our proposed method outperforms the state-of the-art methods on location recommendation.

关键词： Location

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