التفاصيل البيبلوغرافية
| العنوان: |
一种基于多阈值模板的快速分类在线检测方法. (Chinese) |
| Alternate Title: |
A Fast Classification Online Detection Method Based on Multi - Threshold Template. (English) |
| المؤلفون: |
薛宇鑫, 齐金鹏, 贾灿, 袁傲, 黄莉娜 |
| المصدر: |
Electronic Science & Technology; 2024, Vol. 37 Issue 6, p77-83, 7p |
| Abstract (English): |
The traditional off-line data analysis method has many shortcomings in processing the data with high immediacy and large flow, while the online detection model can meet the real time requirements of data flow analysis. This study proposes an online detection method based on the multi - threshold template. The proposed method combines TSTKS (Ternary Search Tree and Kolmogorov - Smirnov) algorithm for online detection, and updates the window length based on the mutation point density to improve the mutation point detection accuracy. Self-learning, matching and classification of time series data are realized by equal grading strategy, so as to detect and predict the status of large scale lesion data. The experimental results of simulation experiment and lesion data show that the proposed method has the advantages of high efficiency and accurate classification, which provides a new method for the rapid classification of large scale time series data. [ABSTRACT FROM AUTHOR] |
| Abstract (Chinese): |
传统离线数据分析方法对于处理即时性高和流量大的数据存在缺陷,而在线检测模型可以满足数据流分 析的实时性要求。文中提出了一种基于多阈值模板的在线检测方法。该方法结合多路搜索树突变点检测(Ternary Search Tree and Kolmogorov - Smirnov, TSTKS)算法进行在线检测,基于突变点密度更新窗口长度从而提高了突变点检测 精度。采用等量分级策略实现对时序数据的自学习、匹配和分类,进而对大规模病变数据进行状态检测和预测。仿真实 验和病变数据的实验结果表明,所提方法具有效果高、分类准确等优点,为大规模时序数据进行快速分类研究提供了新 方法。 [ABSTRACT FROM AUTHOR] |
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| قاعدة البيانات: |
Complementary Index |