scholarly journals Automotive Industrial Supply Chain Performance Evaluation under Uncertain Constraints on Cloud Computing System

2021 ◽  
Author(s):  
Suthep Butdee
Keyword(s):  
Cloud Computing ◽  
Supply Chain ◽  
Performance Evaluation ◽  
Real Time ◽  
Continuous Process ◽  
Computing System ◽  
Continuous Process Improvement ◽  
Daily Monitoring ◽  
Automotive Supply Chain ◽  
Cloud Computing System

Performance evaluation is a critical and complex task as well as uncertain demands for automotive supply chain. Several methods are applied and adopted to deal with current situations and maintain competitiveness such as fuzzy logic, neuro fuzzy, agent (multi) based evaluation, etc. However, such systems are not rapid enough to respond customer requirements by real-time on mobile cloud computing system. There are many companies that operate under the first tier company as subcontractors on the same goal. Cloud computing system is capable to monitor real-time production processes for every subcontractor to assist the 1st tier to make decision and respond customer effectively. Daily monitoring data of all subcontractors in the supply chain are stored in the central database and finally the performance evaluation can be done. The implication is cost reduction of the whole supply chain and increase competitiveness as well as continuous process improvement for all.

scholarly journals Performance Evaluation of Parallel Structure from Motion (SfM) Processing with Public Cloud Computing and an On-Premise Cluster System for UAS Images in Agriculture

2021 ◽  
Vol 10 (10) ◽  
pp. 677
Author(s):  
Anjin Chang ◽  
Jinha Jung ◽  
Jose Landivar ◽  
Juan Landivar ◽  
Bryan Barker ◽  
...  
Keyword(s):  
Cloud Computing ◽  
Performance Evaluation ◽  
Structure From Motion ◽  
Computing System ◽  
Unmanned Aircraft ◽  
High Temporal Resolution ◽  
Public Cloud ◽  
3D Point Clouds ◽  
Cloud Computing System ◽  
Agricultural Applications

Thanks to sensor developments, unmanned aircraft system (UAS) are the most promising modern technologies used to collect imagery datasets that can be utilized to develop agricultural applications in these days. UAS imagery datasets can grow exponentially due to the ultrafine spatial and high temporal resolution capabilities of UAS and sensors. One of the main obstacles to processing UAS data is the intensive computational resource requirements. The structure from motion (SfM) is the most popular algorithm to generate 3D point clouds, orthomosaic images, and digital elevation models (DEMs) in agricultural applications. Recently, the SfM algorithm has been implemented in parallel computing to process big UAS data faster for certain applications. This study evaluated the performance of parallel SfM processing on public cloud computing and on-premise cluster systems. The UAS datasets collected over cropping fields were used for performance evaluation. We used multiple computing nodes and centralized network storage with different network environments for the SfM workflow. In single-node processing, an instance with the most computing power in the cloud computing system performed approximately 20 and 35 percent faster than in the most powerful machine in the on-premises cluster. The parallel processing results showed that the cloud-based system performed better in speed-up and efficiency metrics for scalability, although the absolute processing time was faster in the on-premise cluster. The experimental results also showed that the public cloud computing system could be a good alternative computing environment in UAS data processing for agricultural applications.

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