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Articles by Juanjuan Zhao
Total Records ( 4 ) for Juanjuan Zhao
  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.
  Yan Qiang , Yue Li , Wei Wei and Juanjuan Zhao
  The rapid growth of mobile apps gives rise to the challenge of discovering apps in the mobile systems while it concurrently brings the problem of intensive screen space consumption for mobile devices. To be specified, users always need scrolling screens of apps just for discovering and accessing one of them. The traditional forest fire recognition algorithm couldn't achieve low time-consuming and high accuracy at the same time. To the features of forest fire image, discussed the significance of forest fire prevention combining sea computing, studied and proposed the forest fire recognition algorithm based on sea computing. Used Qt/Embedded to design the algorithm according to the characteristics of the sensor node and then completed the judgment of color and region by this algorithm through additive color model and Sobel operator. Finally, the results were automatically fed back to the sink node. The experiments showed that, this algorithm not only completed the instant detection and recognition of forest fire but also synchronously achieved higher accuracy rate and lower time-consuming compared with the traditional methods.
  Juanjuan Zhao , Guohua Ji , Wei Wei , Jin Wang , Yongxing Liu , Quan Wang and Yan Qiang
  This study presents a new wavelet-based noise reduction scheme based on the lifting scheme and genetic algorithms, which is a novel approach by using a Genetic Algorithm and lifting wavelet framework for threshold selection. There are two folders in this approach. Firstly, it adapts itself to various types of noises without any prior knowledge of noise; secondly, it suppresses noises while preserving the dynamics of the signals. The experimental evaluation is conducted to compare the performances of the new method with existing approaches and the applications for signal denoising are investigated in this study.
  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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