Poonpakdee, Pasu
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Poonpakdee, Pasu
Alternative Name
Poonpakdee, P.
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Email
pasu.po@kmitl.ac.th
4 results
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Item type:Publication, Robust and efficient membership management in large-scale dynamic networks(2017-10-01); Di Fatta, GiuseppeEpidemic protocols are a bio-inspired communication and computation paradigm for large-scale networked systems based on randomised communication. These protocols rely on a membership service to build decentralised and random overlay topologies. In large-scale, dynamic network environments, node churn and failures may have a detrimental effect on the structure of the overlay topologies with negative impact on the efficiency and the accuracy of applications. Most importantly, there exists the risk of a permanent loss of global connectivity that would prevent the correct convergence of applications. This work investigates to what extent a dynamic network environment may negatively affect the performance of Epidemic membership protocols. A novel Enhanced Expander Membership Protocol (EMP+) based on the expansion properties of graphs is presented. The proposed protocol is evaluated against other membership protocols and the comparative analysis shows that EMP+ can support faster application convergence and is the first membership protocol to provide robustness against global network connectivity problems. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Heat Transfer Intensification in a Heat Exchanger by Means of Twisted Tapes in Rib and Sawtooth Forms(2022-12-01); ; - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Accuracy of distributed systems towards industry 4.0: Smart grids and urban drainage systems case studies(2018-01-01); With significantly improving the overall manufacturing operations commonly known in smart factory practices, vertical and horizontal integrations of various components are introduced throughout the entire value chain. The proliferation of smart factory practices introduces the fourth industrial revolution called Industry 4.0. Industry 4.0 creates new challenges and the application of networked manufacturing systems is one of the important features required to handle the systems in an efficient way by communicating and processing information to each other. Distributed systems provide coordination to allow the global information to be available for a better decision making and consequently achieving high efficiency. This connectivity helps for a better decision making and therefore achieve high efficiency. However, the evaluation on the accuracy of global information between different architectures of distributed systems (centralized systems and decentralized systems) has little work mentioning in the state of current manufacturing systems under the conception of Industry 4.0. As a result, this research will fill the gap by providing an accuracy of the decentralized system over the centralized system together with their sensitivity analysis. In this study, the decentralized system is built based on the concept of Epidemic protocols or Gossip-based protocols, while the centralized system is a simple client-server. The epidemic protocol is a bio-inspired communication paradigm that imitates the behavior of virus when the outbreak occurs in a community. The accuracy in both systems has been monitored by means of simulations. The effects of message loss to the accuracy of centralized and decentralized systems are studied. By comparing the system accuracy between both systems, it was found that the accuracy of the decentralized system is generally more accurate when the system is used for a long period. The accuracy tends to be lower down especially when the information is not completely distributed, while the accuracy of the centralized system receives an excessive suffer from message loss. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Estimation of the Weight and Volume of Lime (Citrus aurantifolia (Christm.) Swingle) Fruit Using Computer Vision Based on Traditional Machine Learning and Deep Learning(2024-10-01); ; The post-harvest process is important to increasing the market value of limes and requires focus. During this process, limes are graded and categorized based on size, weight, and volume. Therefore, identifying efficient means of estimating these properties is very important and remains an open research area. This study applies the concept of computer vision based on traditional machine learning algorithms (partial least square regression (PLS), epsilon-support vector regression (ε-SVR), decision tree (DT), random forest (RF), adaptive boosting (AB), gradient boosting (GB), Bagging meta-estimator (BME), and extremely randomized trees (ERTs)) and pre-trained deep learning (InceptionV3, MoblieNetV2, ResNet50, and VGG-16) for estimating the weight and volume of limes. Our findings showed that the BME and ResNet50 could yield the highest performance for estimating the weight and volume of limes. The BME produced (Formula presented.) values of 0.954 and 0.882 for weight and volume, respectively, while the (Formula presented.) values of ResNet50 models were between 0.951 and 0.957 for weight and volume, respectively. This study concluded that computer vision based on both traditional machine learning and deep learning could be used to estimate the weight and volume of limes. The approach proposed in this study can be adopted for applications related to computer vision in the post-harvest process.