scholarly journals Filter design using data fusion for a pneumatic control valve

2021 ◽  
Vol 18 (1) ◽  
pp. 49-61
Author(s):  
Bhagya Navada ◽  
Santhosh Venkata
Keyword(s):  
Data Fusion ◽  
Filter Design ◽  
Control Valve ◽  
Structural Deformation ◽  
Normal System ◽  
Multiple Time ◽  
Time Warping ◽  
Pneumatic Control ◽  
Using Data ◽  
Dynamic Time

This paper presents a filter design technique for a pneumatic valve using data fusion techniques. The objective of this paper is to examine the suppression of the effect of parameters causing deviation from normal system performance using the technique of data fusion over time. The output of a system affected by inherited noise is processed by applying operations such as finding the statistical variance, time warping, interpolation, and extrapolation. These techniques are used to compute the transfer function of the filter, which when cascaded with the system will suppress the effect of noise on the process. The operation of the control valve is affected by characteristics such as stiction, structural deformation, etc. The characteristics of the system are studied and data for multiple time instances are extracted to carry out fusion across time by dynamic time warping. Tests show that the filter presented here can suppress the effects of stiction and mechanical deformation on the output signal.

scholarly journals Multi-Temporal Data Fusion in MS and SAR Images Using the Dynamic Time Warping Method for Paddy Rice Classification

Agriculture ◽  
2022 ◽  
Vol 12 (1) ◽  
pp. 77
Author(s):  
Tsu Chiang Lei ◽  
Shiuan Wan ◽  
You Cheng Wu ◽  
Hsin-Ping Wang ◽  
Chia-Wen Hsieh
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Decision Tree ◽  
Data Fusion ◽  
Dynamic Time Warping ◽  
Image Data ◽  
Synthetic Aperture ◽  
Time Warping ◽  
Radar Images ◽  
Dynamic Time

This study employed a data fusion method to extract the high-similarity time series feature index of a dataset through the integration of MS (Multi-Spectrum) and SAR (Synthetic Aperture Radar) images. The farmlands are divided into small pieces that consider the different behaviors of farmers for their planting contents in Taiwan. Hence, the conventional image classification process cannot produce good outcomes. The crop phenological information will be a core factor to multi-period image data. Accordingly, the study intends to resolve the previous problem by using three different SPOT6 satellite images and nine Sentinel-1A synthetic aperture radar images, which were used to calculate features such as texture and indicator information, in 2019. Considering that a Dynamic Time Warping (DTW) index (i) can integrate different image data sources, (ii) can integrate data of different lengths, and (iii) can generate information with time characteristics, this type of index can resolve certain classification problems with long-term crop classification and monitoring. More specifically, this study used the time series data analysis of DTW to produce “multi-scale time series feature similarity indicators”. We used three approaches (Support Vector Machine, Neural Network, and Decision Tree) to classify paddy patches into two groups: (a) the first group did not apply a DTW index, and (b) the second group extracted conflict predicted data from (a) to apply a DTW index. The outcomes from the second group performed better than the first group in regard to overall accuracy (OA) and kappa. Among those classifiers, the Neural Network approach had the largest improvement of OA and kappa from 89.51, 0.66 to 92.63, 0.74, respectively. The rest of the two classifiers also showed progress. The best performance of classification results was obtained from the Decision Tree of 94.71, 0.81. Observing the outcomes, the interference effects of the image were resolved successfully by various image problems using the spectral image and radar image for paddy rice classification. The overall accuracy and kappa showed improvement, and the maximum kappa was enhanced by about 8%. The classification performance was improved by considering the DTW index.

scholarly journals Data Fusion Using Improved Support Degree Function in Aquaculture Wireless Sensor Networks

Sensors ◽  
2018 ◽  
Vol 18 (11) ◽  
pp. 3851 ◽  
Author(s):  
Pei Shi ◽  
Guanghui Li ◽  
Yongming Yuan ◽  
Liang Kuang
Keyword(s):  
Time Series ◽  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Data Fusion ◽  
Dynamic Time Warping ◽  
Wireless Sensor ◽  
Time Warping ◽  
Segmentation Strategy ◽  
Time Series Segmentation ◽  
Dynamic Time

For monitoring the aquaculture parameters in pond with wireless sensor networks (WSN), high accuracy of fault detection and high precision of error correction are essential. However, collecting accurate data from WSN to server or cloud is a bottleneck because of the data faults of WSN, especially in aquaculture applications, limits their further development. When the data fault occurs, data fusion mechanism can help to obtain corrected data to replace abnormal one. In this paper, we propose a data fusion method using a novel function that is Dynamic Time Warping time series strategy improved support degree (DTWS-ISD) for enhancing data quality, which employs a Dynamic Time Warping (DTW) time series segmentation strategy to the improved support degree (ISD) function. We use the DTW distance to replace Euclidean distance, which can explore the continuity and fuzziness of data streams, and the time series segmentation strategy is adopted to reduce the computation dimension of DTW algorithm. Unlike Gauss support function, ISD function obtains mutual support degree of sensors without the exponent calculation. Several experiments were finished to evaluate the accuracy and efficiency of DTWS-ISD with different performance metrics. The experimental results demonstrated that DTWS-ISD achieved better fusion precision than three existing functions in a real-world WSN water quality monitoring application.

Application of Mobile Terrestrial LiDAR Scanning Systems for Identification of Potential Pavement Rutting Locations

2021 ◽  
pp. 036119812110057
Author(s):  
Afshin Famili ◽  
Wayne A. Sarasua ◽  
Alireza Shams ◽  
William J. Davis ◽  
Jennifer H. Ogle
Keyword(s):  
Grid Cell ◽  
Ground Truth ◽  
Pavement Management ◽  
Time Warping ◽  
Terrestrial Lidar ◽  
Transverse Profile ◽  
Cell Groups ◽  
Raster Cell ◽  
Using Data ◽  
Dynamic Time

Periodic measurement and identification of the presence and severity of pavement rutting are crucial for pavement management programs conducted by state transportation agencies. This paper proposes a novel analytical method for identifying pavement rutting locations using data collected by mobile terrestrial LiDAR scanning (MTLS). Four vendor MTLS systems were evaluated based on their ability to accurately reproduce a roadway’s transverse profile. To establish ground-truth measurements, 2 in. interval pavement transverse profiles, which included rutting sections, were collected using traditional surveying techniques. MTLS transverse profiles were evaluated using partial curve mapping, Fréchet distance, area, curve length, and dynamic time warping techniques. Resultant pavement transverse profiles were compared between vendors and a profile created from traditional surveying. Results indicate that calibrated MTLS systems can provide accurate transverse profiles for potential identification of pavement rut areas. Based on this determination, a novel method was developed for use in identifying locations of pavement rutting through analysis of the curvature of MTLS raster surfaces. After evaluating three grid cell sizes for elevation raster surfaces, a raster grid cell size of 1 ft × 1 ft was determined to be most suitable for identifying continuous concave raster cell groups along wheel path trajectories. These cell groupings were found to reliably identify pavement rutting locations. The analytical procedures employed through application of this method consist of an efficient workflow process that is not reliant on a time-consuming continuous comparison with an MTLS-modeled uniform surface.

Enhancing Athlete Tracking Using Data Fusion in Wearable Technologies

2021 ◽  
Vol 70 ◽  
pp. 1-13
Author(s):  
Adnan Waqar ◽  
Iftekhar Ahmad ◽  
Daryoush Habibi ◽  
Nicolas Hart ◽  
Quoc Viet Phung
Keyword(s):  
Data Fusion ◽  
Wearable Technologies ◽  
Using Data
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