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The Research of Data Mining Based on Application Data Pool
Minjie Bian,Jue Gao,JiePin Xu,Honghao Gao 보안공학연구지원센터 2014 International Journal of u- and e- Service, Scienc Vol.7 No.5
Today, people use various kinds of information technology applications to deal with applications in our daily life, which generates lots of information. However,most of the informationis just stored in many Distributed Heterogeneous Databases (DHDs) as log records, instead of being used abundantly and effectively. In this paper, we mainly discusses about how to use these data in useful ways by Data Mining (DM). Relative to thetraditional Data Mining based on Data Warehouse (WD),we propose a definition named Application Data Pool (ADP)replacing WD in this paper. And we design a KnowledgeDiscovery in Databases (KDD) model with ADP to use these data more efficiently. At last we use an application in Shanghai University ID Card designed with the ADP to prove the effectiveness and feasibility in KDD.
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Li Ruizhi,Gao Jue,Gao Honghao,Bian Minjie,Xu Huahu 보안공학연구지원센터 2016 International Journal of Grid and Distributed Comp Vol.9 No.11
With the development of cloud computing technology, people not only want to pursue the shortest time to complete the tasks by using cloud computing, but also hope to take into the running costs of machines. Existing task scheduling algorithm in the cloud computing environment has been unable to meet people's needs. As an extension and generalization of the model checking theory, probability model checking is also used in many fields, such as random distributed algorithm and other areas. The task scheduling algorithm based on the particle swarm optimization algorithm combined with probability model is proposed in this paper. The algorithm defines the fitness functions of the time cost and the running cost. The fitness functions can improve the efficiency of the cloud computing platform. At the same time, the probability model can be used to analyze the running states of machines and the computing capability of the nodes in the cloud cluster. The probability, which is calculated by the probability model, provides the basis for changing particle swarm algorithm’s the inertia factor and the learning factor, so as to solve the drawback that the inertia factor and the learning factor solely depend on the fixed value.
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Research on Improved Hadoop Distributed File System in Cloud Rendering
Ren Qin,Gao Jue,Gao Honghao,Bian Minjie,Xu Huahu,Feng Weibing 보안공학연구지원센터 2016 International Journal of Database Theory and Appli Vol.9 No.11
With the rapid development of cloud computing technology, it’s the cloud rendering that cloud computing is applied to render the job in CG (Computer Graphic) industry. The cloud rendering can handle a large number of rendering requests which are enormous pressure for back-end servers in system. Facing with massive data and computing resources, the bottleneck of original HDFS (Hadoop Distributed File System) based on cloud computing has become more and more prominent, such as the failure of single Namenode, scalability issues. Therefore, the paper proposed an improved HDFS which evolved a single Namenode into multi-Namenode. In HDFS, Metadata management is very important. So this paper presented a two-level Metadata distribution algorithm. The two-level algorithm was based on the principle that different distribution strategies were used to different categories of Metadata. The experiments verified that the improved HDFS effectively improved the performance of the system.
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Fast Pedestrian Detection with Adaboost Algorithm Using GPU
Chong Chao Cai,Jue Gao,Bian Minjie,Peicheng Zhang,Honghao Gao 보안공학연구지원센터 2015 International Journal of Database Theory and Appli Vol.8 No.6
Pedestrian detection is one of the hot research problems in computer vision field. The Cascade AdaBoost System is a commonly used algorithm in this region. However, when the training datasets become larger, it is still a time consuming process to build one Adaboost classifier. In this paper we detail an implementation of the AdaBoost algorithm using the NVIDIA CUDA framework based on the haar features as feature vectors, and downscaling with integral image. The result shows that we can get nearly 6x from the standard code to with our CPU implementation to achieve a near real-time performance and ensure better classification results in misjudgment.
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3.2-kW 9.7-GHz Polarization-maintaining Narrow-linewidth All-fiber Amplifier
Hang Liu,Yujun Feng,Xiaobo Yang,Yao Wang,Hongming Yu,Jue Wang,Wanjing Peng,Yanshan Wang,Yinhong Sun,Yi Ma,Qingsong Gao,Chun Tang 한국광학회 2024 Current Optics and Photonics Vol.8 No.1
We present a Yb-doped narrow-linewidth polarization-maintaining (PM) all-fiber amplifier that achieves a high mode-instability (MI) threshold, high output power, and 9.7-GHz spectral linewidth. Six wavelength-multiplexed laser diodes are used to pump this amplifier. First, we construct a high-power fiber amplifier based on a master oscillator-power amplifier (MOPA) configuration for experiments. Subsequently, we examine the MI threshold by individually pumping the amplifier with wavelengths of 976, 974, 981, 974, and 981 nm respectively. The experimental results demonstrate that the amplifier exhibits a high MI threshold (>3.5 kW) when pumped with a combination of wavelengths at 974 and 981 nm. Afterward, we inject an optimized phase-modulated seed with a nearly flat-top spectrum into this amplifier. Ultimately, laser output of 3.2 kW and 9.7 GHz are obtained.
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