Improvement of Large Data Acquisition Method without the Interference on the CPU Load for Automotive Software Testing

Author(s):  
Jong-Hwan Shin ◽  
Ki-Yong Choi ◽  
Jeong-Woo Lee ◽  
Jung-Won Lee
Author(s):  
Ping Yi ◽  
Bin Ran

This research examines a streamlined accident data acquisition, communications, and analysis system to improve the Chinese highway safety program. A data logger compatible with the Global Positioning System and geographic information system is proposed to identify highway accident locations and organize the data into a database format. A data encoding concept is used to transform Chinese characters into numbers, so that the encoded data are easy to integrate into a large data system. A three-tier client–server networking system is set up as the backbone framework for data communications between the central database and distributed local offices. Using local database functions, traffic police at the client level can view crash data through data mapping and attribute listing and analyze the data through nested query and sorting operations. A data graphing and analysis module was tested for automatically constructing a collision diagram on selected data. The proposed approach to crash data acquisition and analysis was found to be feasible and effective and will help to enhance China’s highway safety program after full implementation.


2021 ◽  
Author(s):  
Han Chunyu ◽  
Fang Jiandong ◽  
Li Bajin ◽  
Zhao Yudong

2021 ◽  
Author(s):  
Peter Lukacs ◽  
Theodosia Stratoudaki ◽  
Geo Davis ◽  
Anthony Gachagan

Abstract This study introduces a novel data acquisition method, the Selective Matrix Capture (SMC), that can adapt the array geometry during data acquisition, to the demands of the inspected structure, such as the defects encountered. The adaptive data acquisition method is enabled by the use of Laser Induced Phased Arrays (LIPAs). We have previously demonstrated high-resolution ultrasonic images of the interior of components using Full Matrix Capture (FMC) and the Total Focusing Method (TFM). However, capturing the FMC requires long synthesis time due to signal averaging and mechanical laser scanning, compromising the application potential of LIPAs. Given that most components are defect free, significant time savings can be obtained by only acquiring high-fidelity data when a defect is indicated. The paper presents the Selective Matrix Capture that acquires data more efficiently without a priori knowledge of the location of the defects, while still achieving the superior imaging quality provided by an FMC data set.


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