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Articles by Bo Pei
Total Records ( 3 ) for Bo Pei
  Yan Qiang , Yue Li , Wei Wei , Bo Pei , Juanjuan Zhao and Hui Zhang
  In order to guarantee the stability of the system performance and the high Qos(quality of service) of users, a new method based on the HDFS (Hadoop Distributed File System) was proposed which including a job type classification method and a dynamic replica manage mechanism. The method uses the job type classification method to select the I/O intensive job, in order to achieve more accuracy of the classification taken the heterogeneity of the jobs into consideration. For the classified jobs, a dynamic replica manage mechanism was used to determine whether to increase or decrease the number of copies on the specific data node. For a test of a cluster with 1 namenode and 20 data nodes, the method has a high performance. The theoretical and experimental analyses in this paper prove that the proposed method has the ability to improve the performance of HDFS effectively.
  J. Juan Zhao , Jin Wang , Wei Wei , Xiao Min Chang and Bo Pei
  With the quick development of computer image detection technique, image boundary inspect method has become the field of image processing and computer vision research focus, digital image boundary inspection is image segmentation, image recognition, image analysis for instance area shape extracting the important foundation. Based on Canny operator manually select the threshold improperly, which result in edge detection has some ineffective shortcomings. In this study, the method is presented an reinforced Canny boundary inspection to automatically produc an adaptive threshold for Canny edge detection operator. In the algorithm, it can create the threshold parameter automatically, by the mean square error and average gray of the image. Therefore this method can avoid errors caused by manual input and obtain a desired edge effect.
  Yan Qiang , Bo Pei , Wei Wei , Jianfeng Yang and Juanjuan Zhao
  In order to improve the accuracy of the solitary pulmonary nodule diagnosis with medical signs in medical imaging diagnostics, a novel computer-aided classification method is developed. In the view of the existing problems in the lung cancer diagnosis such as the large number of data and the low diagnose efficiency. In order to solve the problem, a new classification method based on the Fuzzy Support Vector Machine (FSVM) was developed to choose the lung with suspicious lesion at an early stage. In this method, the membership function was improved based on the spectral clustering theory which ensures each sample has two membership degrees that guarantees the class of the specific sample more reasonably. The proposed method was used to classify benign and malignant of the pulmonary nodules, the parameters show this method can distinguish the noise and outliers samples more effectively, compared with the traditional fuzzy support vector machine method. Thus, the results illustrated the robust to noise capability and the effective classification ability of this method.
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