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A novel rough set approach for classification

A novel rough set approach for classification

引用

2006 IEEE International Conference on Granular Computing

作者： Zhang, Li-Juan Li, Zhou-Jun National Laboratory for Parallel and Distributed Processing Changsha 410073 China School of Computer Science and Engineering Beihang University Beijing 100083 China

ISBN: (纸本)1424401348

Rough set theory has been widely and successfully used in data mining, especially in classification field. But most existing rough set based classification approaches require computing optimal attribute reduction, which is usually intractable and many problems related to it have been shown to be NT-hard. Although approximate algorithms exist, they also tend to be computationally expensive. This paper presents a novel rough set method for classification, which does not require computing attribute reduction. It stepwise investigates condition attributes and outputs the classification rules induced by them, which is just like the strategy of "on the fly". The theoretical analysis and the empirical study show that the proposed method is effective and efficient. © 2006 IEEE.

关键词： Rough set theory

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Coordinate strip-mining and kernel fusion to lower power consumption on GPU

Coordinate strip-mining and kernel fusion to lower power con...

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作者： Wang, Guibin National Laboratory for Parallel and Distributed Processing School of Computer National University of Defense Technology Changsha Hunan China

ISBN: (纸本)9783981080179

Although general purpose GPUs have relatively high computing capacity, they also introduce high power consumption compared with general purpose CPUs. Therefore low-power techniques targeted for GPUs will be one of the most hot topics in the future. On the other hand, in several application domains, users are unwilling to sacrifice performance to save power. In this paper, we propose an effective kernel fusion method to reduce the power consumption for GPUs without performance loss. Different from executing multiple kernels serially, the proposed method fuses several kernels into one larger kernel. Owing to the fact that most consecutive kernels in an application have data dependency and could not be fused directly, we split large kernel into multiple slices with strip-mining method, then fuse independent sliced kernels into one kernel. Based on the CUDA programming model, we propose three different kernel fusion implementations, with each one targeting for a special case. Based on the different strip-ming methods, we also propose two fusion mechanisms, which are called invariant-slice fusion and variant-slice fusion. The latter one could be better adapted to the requirements of the kernels to be fused. The experimental results validate that the proposed kernel fusion method could effectively reduce the power consumption for GPU. © 2011 EDAA.

关键词： Electric power utilization

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SAWSDL service discovery based on Fine-Grained data semantics

SAWSDL service discovery based on Fine-Grained data semantic...

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作者： Wei, Dengping Wang, Ting Wang, Ji School of Computer National University of Defense Technology 410073 Changsha China National Laboratory for Parallel and Distributed Processing 410073 Changsha China

With the aim to improve the effectiveness of SAWSDL service discovery, this paper proposes a novel discovery method for SAWSDL services, which is based on the matchmaking of so-called finegrained data semantics that is defined via sets of atomic elements with built-in data types. The fine-grained data semantics can be obtained by a transformation algorithm that decomposes parameters at message level into a set of atomic elements, considering the characteristics of SAWSDL service structure and semantic annotations. Then, a matchmaking algorithm is proposed for the matching of fine-grained data semantics, which avoids the complex and expensive structural matching at the message level. The fine-grained data semantics is transparent to the specific data structure of message-level parameters, therefore, it can help to match successfully similar Web services with different data structures of parameters. Moreover, a comprehensive measure is proposed by considering together several important components of SAWSDL service descriptions at the same time. Finally, this method is evaluated on SAWSDL service discovery test collection SAWSDL-TC2 and compared with other SAWSDL matchmakers. The experimental results show that our method can improve the effectiveness of SAWSDL service discovery with low average query response time. The results imply that fine-grained parameters fit to represent the data semantics of SAWSDL services, especially when data structures of parameters are not important for semantics. © 2011 The Institute of Electronics, Information and Communication Engineers.

关键词： Data structures

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YOLOv5_CSL_F: YOLOv5's Loss Improvement and Attention Mechanism Application for Remote Sensing Image Object Detection 3

YOLOv5_CSL_F: YOLOv5's Loss Improvement and Attention Mechan...

引用

3rd International Conference on Wireless Communications and Smart Grid, ICWCSG 2021

作者： Wang, Junhua Xiao, Tao Gu, Qinyi Chen, Qian National University of Defense Technology Laboratory of Parallel and Distributed Processing Changsha China No.61646 Troop Department of Information Beijing China

ISBN: (纸本)9781665425988

With the continuous improvement of the resolution of satellite remote sensing images and aerial remote sensing images, more and more useful data and information are obtained from remote sensing images. At the same time, compared with ordinary images, remote sensing images have the characteristics of variable directions, unbalanced categories, complex backgrounds, and difficult detection of small objects. All of these make remote sensing image object detection very challenging. In this paper, based on the deep learning framework and the YOLOv5 object detection algorithm, according to the characteristics of remote sensing images, adopting Circular Smooth Label (CSL) [1] to calculate the loss of the rotating object detection bounding box and introducing the FcaNet [2] attention mechanism to design new feature fusion modules, we propose the remote sensing image object detection algorithm YOLOv5_CSL_F. We tested the algorithm model on the DOTA dataset. Compared with the detection performance of the original YOLOv5 algorithm, our algorithm improves the detection accuracy by 0.6%. © 2021 IEEE.

关键词： Object detection

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OpenMP 4.0 based heterogeneous parallel optimization of an engine combustion simulation application 2

OpenMP 4.0 based heterogeneous parallel optimization of an e...

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2nd IEEE International Conference on Computer and Communications, ICCC 2016

作者： Meifang, Yang Che, Yonggang Gao, Xiang Science and Technology on Parallel and Distributed Processing Laboratory China School of Computer China National University of Defense Technology Changsha China School of Computer National University of Defense Technology Changsha China

ISBN: (纸本)9781467390262

LESAP is a combustion simulation application capable of simulating the chemical reactions and supersonic flows in the scramjet engines. It can be used to solve practical engineering problems and involves a large amount of computations. In this paper, we port and optimize LESAP with the OpenMP 4.0 accelerator model, targeting the heterogeneous many-core platform composed of general CPU and Intel Many Integrated Core (MIC). Based on the application characteristics, a series of techniques are proposed, including OpenMP 4.0 based task offloading, data movement optimization, grid-partition based load-balancing and SIMD optimization. The performance evaluation is done for a real combustion simulation configuration, with 532,0896 grid cells, on one Tianhe-2 supercomputer node. The results show that the resulting heterogenous code significantly outperforms the original CPU only code. When the heterogenous code runs on two Intel Xeon E5-2692 CPUs and three Intel Xeon Phi 31S1P coprocessors, it achieves a maximum speedup of 3.63x over the original code that only runs on the two Intel Xeon E5-2692 CPUs. © 2016 IEEE.

关键词： Program processors

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Minimum redundancy gene selection based on grey relational analysis

Minimum redundancy gene selection based on grey relational a...

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作者： Zhang, Li-Juan Li, Zhou-Jun Chen, Huo-Wang Wen, Jian National Laboratory for Parallel and Distributed Processing Changsha 410073 China School of Computer Science and Engineering Beihang University Beijing 100083 China

ISBN: (纸本)0769527027

In this article we describe a method for selecting informative genes from microarray data. The method is based on clustering, namely, it first find similar genes, group them and then select informative genes from these groups to avoid redundancy. A new gene similarity measure based on Grey Relational Analysis (GRA), called Grey Relational Grade (GRG), is used in clustering. Experiments on three public data sets demonstrate the effectiveness of our method. © 2006 IEEE.

关键词： Redundancy

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MTracer: A Trace-Oriented Monitoring Framework for Medium-Scale distributed Systems

MTracer: A Trace-Oriented Monitoring Framework for Medium-Sc...

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2014 IEEE 8th International Symposium on Service Oriented System Engineering

作者： Jingwen Zhou Zhenbang Chen Haibo Mi Ji Wang Science and Technology on Parallel and Distributed Processing Laboratory Changsha China

Trace-oriented runtime monitoring is a very effective method to improve the reliability of distributed systems. However, for medium-scale distributed systems, existing trace-oriented monitoring frameworks are either not powerful or efficient enough, or too complex and expensive to deploy and maintain. In this paper, we present MTracer, which is a lightweight trace-oriented monitoring system for medium-scale distributed systems. We have proposed and implemented several optimizations to improve the efficiency of the monitor server in MTracer. A web-based frontend is also provided to visualize a monitored system from different perspectives. We have validated MTracer in a real medium-scale environment. The results indicate that MTracer has a very lower overhead, and can handle more than 4000 events per second.

关键词： Monitoring Servers Optimization Databases Runtime Data mining Reliability

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Mixed Weighted Classification Loss Function and TFcaNet Attention Mechanism for Remote Sensing Images Object Detection 6

Mixed Weighted Classification Loss Function and TFcaNet Atte...

引用

2021 6th International Conference on Information science, Computer technology and Transportation, ISCTT 2021

作者： Wang, Junhua Jiang, Jingfei Xu, Jinwei Gu, Qinyi National Laboratory for Parallel and Distributed Processing National University of Defense Technology Changsha China Department of Image Technology No.95806 Troop Beijing China

ISBN: (纸本)9783800757282

With the development of intelligent technology, the intelligent processing of remote sensing images has become a research hotspot and the object detection for remote sensing images is becoming more and more extensive. The characteristics of sample imbalance in categories, numerous small objects, and blurry object edges that remote sensing images contains bring huge challenges to object detection. Aiming at the characteristics of sample imbalance in categories of remote sensing images, we designed a mixed weighted classification loss function. At the same time, the TFcaNet attention mechanism is proposed to improve the capabilities of small objects detection and localization of the deep learning model. We create a model named YOLOv5-FRD based on YOLOv5. Through training and testing, the mAP on DOTA dataset reached 73.53%. Compared with the current mainstream one-stage models, YOLOv5-FRD achieves the second place in mAP and the highest AP in the most categories. © VDE VERLAG GMBH · Berlin · Offenbach.

关键词： Object detection

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High performance and memory efficient implementation of matrix multiplication on FPGAs

High performance and memory efficient implementation of matr...

引用

2010 International Conference on Field-Programmable technology, FPT'10

作者： Wu, Guiming Dou, Yong Wang, Miao National Laboratory for Parallel and Distributed Processing National University of Defense Technology Changsha 410073 China Jiangnan Institute of Computing Technology Wuxi 214083 China

ISBN: (纸本)9781424489817

We present a high performance and memory efficient hardware implementation of matrix multiplication for dense matrices of any size on the FPGA devices. By applying a series of transformations and optimizations on the original serial algorithm, we can obtain an I/O and memory optimized block algorithm for matrix multiplication on FPGAs. A linear array of processing elements (PEs) is proposed to implement this block algorithm. We show significant reduction in hardware resources consuming compared to the related work while increasing clock frequency. Moreover, the memory requirement can be reduced to O(S) from O(S 2), where S is the block size. Therefore, more PEs can be integrated into the same FPGA devices. © 2010 IEEE.

关键词： Field programmable gate arrays (FPGA)

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Automatic synthesis of processor arrays with local memories on FPGAs

Automatic synthesis of processor arrays with local memories ...

引用

2010 International Conference on Field-Programmable technology, FPT'10

ISBN: (纸本)9781424489817

In this paper, we present an automatic synthesis framework to map loop nests to processor arrays with local memories on FPGAs. An affine transformation approach is firstly proposed to address space-time mapping problem. Then a data-driven architecture model is introduced to enable automatic generation of processor arrays by extracting this data-driven architecture model from transformed loop nests. Some techniques including memory allocation, communication generation and control generation are presented. Synthesizable RTL codes can be easily generated from the architecture model built by these techniques. A preliminary synthesis tool is implemented based on PLUTO, an automatic polyhedral source-to-source transformation and parallelization framework. © 2010 IEEE.

关键词： Field programmable gate arrays (FPGA)

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