Dynamic Detection Method of Micro-blog Topic Based on Time Series

2018 ◽  
pp. 192-200 ◽  
Author(s):  
Deyang Zhang ◽  
Yiliang Han ◽  
Xiaolong Li
Keyword(s):  
Time Series ◽  
Detection Method ◽  
Dynamic Detection

An Anomaly Detection Method with Exemplar Subsequence for Time Series Data

2016 ◽  
Vol 136 (3) ◽  
pp. 363-372
Author(s):  
Takaaki Nakamura ◽  
Makoto Imamura ◽  
Masashi Tatedoko ◽  
Norio Hirai
Keyword(s):  
Time Series ◽  
Anomaly Detection ◽  
Time Series Data ◽  
Detection Method ◽  
Series Data

A Vehicle Dimensions Dynamic Detection Method Based on Image Recognition

2021 ◽  
Author(s):  
Feng’an Zhao ◽  
Gangfeng Tan ◽  
Bin Luo ◽  
Wenchao Sun ◽  
Jiaming Feng ◽  
...  
Keyword(s):  
Image Recognition ◽  
Detection Method ◽  
Dynamic Detection

scholarly journals Application of Residual-Based EWMA Control Charts for Detecting Faults in Variable-Air-Volume Air Handling Unit System

2016 ◽  
Vol 2016 ◽  
pp. 1-7 ◽  
Author(s):  
Haitao Wang
Keyword(s):  
Time Series ◽  
Fault Detection ◽  
Control Chart ◽  
Detection Method ◽  
Time Data ◽  
Air Handling Unit ◽  
Ewma Control Chart ◽  
Robust Fault Detection ◽  
Modeling Errors ◽  
Control Limits

An online robust fault detection method is presented in this paper for VAV air handling unit and its implementation. Residual-based EWMA control chart is used to monitor the control processes of air handling unit and detect faults of air handling unit. In order to provide a level of robustness with respect to modeling errors, control limits are determined by incorporating time series model uncertainty in EWMA control chart. The fault detection method proposed was tested and validated using real time data collected from real VAV air-conditioning systems involving multiple artificial faults. The results of validation show residual-based EWMA control chart with designing control limits can improve the accuracy of fault detection through eliminating the negative effects of dynamic characteristics, serial correlation, normal transient changes of system, and time series modeling errors. The robust fault detection method proposed can provide an effective tool for detecting the faults of air handling units.

A Novel Change Detection Method using Remotely Sensed Image Time Series Value and Shape based Dynamic Time Warping

2021 ◽  
pp. 1-16
Author(s):  
Huaqiao Xing ◽  
Linye Zhu ◽  
Bingyao Chen ◽  
Liguo Zhang ◽  
Dongyang Hou ◽  
...  
Keyword(s):  
Time Series ◽  
Change Detection ◽  
Dynamic Time Warping ◽  
Detection Method ◽  
Remotely Sensed ◽  
Time Warping ◽  
Remotely Sensed Image ◽  
Dynamic Time

Robot Collision Detection Without External Sensors Based on Time-Series Analysis

2020 ◽  
Vol 143 (4) ◽  
Author(s):  
Tie Zhang ◽  
Peizhong Ge ◽  
Yanbiao Zou ◽  
Yingwu He
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Collision Detection ◽  
Degrees Of Freedom ◽  
Detection Method ◽  
Industrial Robot ◽  
Detection Algorithm ◽  
Detection Accuracy ◽  
External Torque ◽  
Series Analysis

Abstract To ensure the human safety in the process of human–robot cooperation, this paper proposes a robot collision detection method without external sensors based on time-series analysis (TSA). In the investigation, first, based on the characteristics of the external torque of the robot, the internal variation of the external torque sequence during the movement of the robot is analyzed. Next, a time-series model of the external torque is constructed, which is used to predict the external torque according to the historical motion information of the robot and generate a dynamic threshold. Then, the detailed process of time-series analysis for collision detection is described. Finally, the real-machine experiment scheme of the proposed real-time collision detection algorithm is designed and is used to perform experiments with a six degrees-of-freedom (6DOF) articulated industrial robot. The results show that the proposed method helps to obtain a detection accuracy of 100%; and that, as compared with the existing collision detection method based on a fixed symmetric threshold, the proposed method based on TSA possesses smaller detection delay and is more feasible in eliminating the sensitivity difference of collision detection in different directions.

Anomaly detection of bridge health monitoring data based on KNN algorithm

2020 ◽  
Vol 39 (4) ◽  
pp. 5243-5252
Author(s):  
Zhen Lei ◽  
Liang Zhu ◽  
Youliang Fang ◽  
Xiaolei Li ◽  
Beizhan Liu
Keyword(s):  
Time Series ◽  
Anomaly Detection ◽  
Health Monitoring ◽  
Detection Method ◽  
Detection Algorithm ◽  
Monitoring Data ◽  
Data Detection ◽  
Multiple Time ◽  
Time Zone ◽  
Bridge Health Monitoring

Pattern recognition technology is applied to bridge health monitoring to solve abnormalities in bridge health monitoring data. Testing is of great significance. For abnormal data detection, this paper proposes a single variable pattern anomaly detection method based on KNN distance and a multivariate time series anomaly detection method based on the covariance matrix and singular value decomposition. This method first performs compression and segmentation on the original data sequence based on important points to obtain multiple time subsequences, then calculates the pattern distance between each time subsequence according to the similarity measure of the time series, and finally selects the abnormal mode according to the KNN method. In this paper, the reliability of the method is verified through experiments. The experimental results in this paper show that the 5/7/9 / 11-nearest neighbors point to a specific number of nodes. Combined with the original time series diagram corresponding to the time zone view, in this paragraph in the time, the value of the temperature sensor No. 6 stays at 32.5 degrees Celsius for up to one month. The detection algorithm controls the number of MTS subsequences through sliding windows and sliding intervals. The execution time is not large, and the value of K is different. Although the calculated results are different, most of the most obvious abnormal sequences can be detected. The results of this paper provide a certain reference value for the study of abnormal detection of bridge health monitoring data.

A near real-time water surface detection method based on HSV transformation of MODIS multi-spectral time series data

2014 ◽  
Vol 140 ◽  
pp. 704-716 ◽  
Author(s):  
J.-F. Pekel ◽  
C. Vancutsem ◽  
L. Bastin ◽  
M. Clerici ◽  
E. Vanbogaert ◽  
...  
Keyword(s):  
Time Series ◽  
Real Time ◽  
Water Surface ◽  
Time Series Data ◽  
Detection Method ◽  
Series Data ◽  
Surface Detection
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