Search

Offshore Jacket Platforms (OJPs) are often affected by environmental components that lead to damage, and the early detection system can help prevent serious failures, ensuring safe operations and mining conditions, and reducing maintenance costs. In this study, we proposed a prediction model based on Convolutional Neural Networks (CNNs) aimed at determining the early stage of the OJP structure’s abnormal status. Additionally, the EfficientNet-B0 Deep Neural Network classifies normal and abnormal states, which may cause problems, by using displacement signal analysis at specific areas taken into account throughout the test. Displacement data is transferred to a 2D scalogram image by applying a continuous Wavelet converter that shows the state of the work. Finally, the scalogram image data set is used as the input of the neural network, and feasibility experimental results compared with other typical neural networks such as GoogLeNet and ResNet-50 have verified the effectiveness of the approach.

Many offshore projects, such as offshore oil and gas exploration and offshore wind farm development, have required controlling the position of elevating the hull in a stable and balanced manner in recent years. The Jack-up Rig (JuR) jacking control systems are a revolutionary innovation that is already being employed in offshore drilling and other maritime structures. The system is utilized automatically to control and stabilize the position of the JuR during sea state disturbances to keep platforms from being displaced. As a result, developing an improved control theory for the Jacking system (JS) of a JuR is very important. In this paper, first, we investigate how to adapt to the effects of external forces and hydrodynamic amplification using a particle swarm optimization strategy based on a fuzzy controller. Then, using the fuzzy controller as the foundation, we proposed the PSO - Self Adaptive Fuzzy Controller (PSO-SAFC) guaranteed by Lyapunov criteria and compares it with other fuzzy-based algorithms to show the benefit of adaptively and stability of the system. To verify the proposed algorithm, embedded STM32F746VG central processor used to build the algorithm and run Simulink models on Matlab, the collected data on the positions of the legs and the tilt compared it to the setting command values to move the Rig body and provided control signals to the Drive system. Eventually, the advantages of the proposed approach are demonstrated by the simulation and experiment outcomes.

Offshore Jacket Platforms (OJPs) are often affected by environmental components that lead to damage, and the early detection system can help prevent serious failures, ensuring safe operations and mining conditions, and reducing maintenance costs. In this study, we proposed a prediction model based on Convolutional Neural Networks (CNNs) aimed at determining the early stage of the OJP structure's abnormal status. Additionally, the EfficientNet-B0 Deep Neural Network classifies normal and abnormal states, which may cause problems, by using displacement signal analysis at specific areas taken into account throughout the test. Displacement data is transferred to a 2D scalogram image by applying a continuous Wavelet converter that shows the state of the work. Finally, the scalogram image data set is used as the input of the neural network, and feasibility experimental results compared with other typical neural networks such as GoogLeNet and ResNet-50 have verified the effectiveness of the approach. [ABSTRACT FROM AUTHOR]

Objectives: The aim of this study is to determine the prevalence of the first lower molars that have two roots or three roots and the number of the root canals of the mandibular first molars in the Vietnamese subpopulation using cone-beam computed tomography (CBCT)., Materials and Methods: The study was conducted on 166 patients who had CBCT as indicated by dentists in Nguyen Trai Dental CT Center, Ho Chi Minh City using the Picasso Trio (Ewoo Vatech, Korea). The number of root canals of the first lower molars was examined by moving cross-sectional slices from the pulpal floor to the apex. The orifices, middle thirds, and apical thirds of the canals of the first lower molar were observed, and the root canals of each root of the mandibular first molars were observed in three planes., Results: The prevalence of two, three, and four root canals of the mandibular first molars was 4.5%, 66.8%, and 28.9%, respectively. For the distal roots of these molars, a classification of Vertucci type I was the most common at a rate of 80.8%-97.6%. Whenever these teeth had three roots, a Vertucci type I was the classification of 100% of distolingual roots., Conclusion: Majority of the mandibular first molars has two roots and three canals. CBCT is appropriate equipment useful in investigating the complex root canal morphology of human teeth., Competing Interests: There are no conflicts of interest.