Optimal Control for Torpedo Motion based on Fuzzy-PSO Advantage Technical

The torpedo is a nonlinear object which is very difficult to control. Via to manage the rudder angle yaw, the diving plane angle, and the fin shake reduction, the torpedo yaw horizontal, the depth vertical and roll damping of the system are controlled accurately and steadily. In this paper, the particle swarm optimization is used to correct the imprecision of architecture fuzzy parameters. The coverage width of membership function and the overlap degree influence of neighboring membership functions are considered in the method to adjust dynamically from the system errors. Thereby optimizing the control signal and enhancing the torpedo motion quality. The proposed method results in a better performance compared to the other control method such as adaptive fuzzy-neural that proved effective of the proposed controller

Analyzing the Sea Weather Effects to the Ship Maneuvering in Vietnam’s Sea from BinhThuan Province to Ca Mau Province Based on Fuzzy Control Method

Vietnam is located in the tropical monsoon climate, so there are many storms affecting the marine environment each year. However, Vietnam’s sea also has distinct characteristics due to the continental shelf factors, salinity, sea currents and viscosity water. In this paper, the sea weather effects to the ship in the sea area from BinhThuan province to Ca Mau province are analyzed. Specifically, wave, wind and current which are the three main factors affecting the safety of ship are thoroughly examined. Importantly, the survey parameters have been built from the actual operating environment. In addition, maintaining the stability of dynamic positioning system in Vietnam weather conditions is the main point of this study

Applying convolutional neural networks for limited-memory application

Currently, convolutional neural networks (CNN) are considered as the most effective tool in image diagnosis and processing techniques. In this paper, we studied and applied the modified SSDLite_MobileNetV2 and proposed a solution to always maintain the boundary of the total memory capacity in the following robust bound and applied on the bridge navigational watch & alarm system (BNWAS). The hardware was designed based on raspberry Pi-3, an embedded single board computer with CPU smartphone level, limited RAM without CUDA GPU. Experimental results showed that the deep learning model on an embedded single board computer brings us high effectiveness in application

Maritime Data Mining for Marine Safety Based on Deep Learning: Southern Vietnam Case Study

High-speed passenger vessels, integrated river and sea vessels, container vessels, oil tankers, and other underwater vehicles operating in maritime traffic are among the types of vessels that must be equipped with AIS and VHF. The safety of navigation is one of the major problems in the maritime sector, particularly in Vietnam. Furthermore, marine traffic in the seaport zone is a common and difficult issue to manage in areas with a high volume of vessel traffic, mostly in places where the infrastructure supporting navigation is inadequately developed to meet the rapidly growing demands of the contemporary world. Therefore, it is necessary to create an integrated maritime management system to improve the efficiency of data exploitation and support maritime safety. To address this challenge, this study suggests a Maritime Traffic State Prediction (MTSP) model to predict traffic conditions in the channels where real-time data collection is insufficient in some specific locations. We recommend a deep learning method using Long Short-Term Memory (LSTM) networks to predict the safe path of the vessel in case of missing data segments. The findings have shown that the proposed approach encourages the mining of historical vessel data for maritime traffic, is ready to be applied, and can easily be implemented in a computer program or a web-based app

No Tissue Expression of KRAS or BRAF Mutations in 61 Adult Patients Treated for Esophageal Atresia in Early Childhood

Background Previous studies have reported an association among esophageal atresia (EA), Barrett's esophagus, and esophageal adenocarcinoma later in life. Objective The objective of the article is to evaluate KRAS and BRAF mutations as potential genetic markers for early detection of malignant transformation, we used an ultrasensitive technique to detect tissue expression of KRAS and BRAF mutations in endoscopic biopsies from 61 adult patients under follow-up after treatment for EA. Materials and Methods RNA was extracted from 112 fresh-frozen endoscopic tissue biopsies from 61 adult patients treated for EA in early childhood. RNA was reverse transcribed using the extendable blocking probe reverse transcription method. KRAS codons 12 and 13, as well as BRAF mutations were detected by quantitative polymerase chain reaction. Results No mutations of KRAS codon 12, KRAS codon 13, or BRAF were found in 112 endoscopic biopsy samples from 61 patients. Conclusion Despite the presence of histological findings indicating long-standing gastroesophageal reflux in 25%, as well as symptomatic gastroesophageal reflux in more than 40%, there was no detectable tissue expression of KRAS or BRAF mutations in this cohort of patients.Peer reviewe

REGULAR ACTIVITIES OF ADULT PTYAS MUCOSA (LINNAEUS, 1758) IN A FARMING CONDITIONS OF NGHE AN PROVINCE, VIET NAM

In the feeding condition in Cua Lo Town, Nghe An province, Viet Nam, adult Ptyas mucosa (P. mucosa) operates seasonally and day and night with the rule: The active season is from March to December and hibernating in January and February every year. For the rule of day and night operation: In March and April, they are mainly active from 6-18h; from May to July, they are operates at the 2 time periods: 7-11h (at most 8-10h), and 15-20h; In August, they are also operates 2 time periods, but from 5-11h, and 14 - 20h; From September to December, it operates at one period (6 or 7-19h), not active at night. The activity of adult P. mucosa is closely dependent on the temperature and humidity of the environment (20-40oC, 40-100%), the most favorable is 27- 31oC, 76-92%. They officially hibernate in January and February when the ambient temperature is below 17oC for many days

Early State Prediction Model for Offshore Jacket Platform Structural Using EfficientNet-B0 Neural Network

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

Nonlinear Stability Analysis of Eccentrically Stiffened Functionally Graded Truncated Conical Sandwich Shells with Porosity

This paper analyzes the nonlinear buckling and post-buckling characteristics of the porous eccentrically stiffened functionally graded sandwich truncated conical shells resting on the Pasternak elastic foundation subjected to axial compressive loads. The core layer is made of a porous material (metal foam) characterized by a porosity coefficient which influences the physical properties of the shells in the form of a harmonic function in the shell’s thickness direction. The physical properties of the functionally graded (FG) coatings and stiffeners depend on the volume fractions of the constituents which play the role of the exponent in the exponential function of the thickness direction coordinate axis. The classical shell theory and the smeared stiffeners technique are applied to derive the governing equations taking the von Kármán geometrical nonlinearity into account. Based on the displacement approach, the explicit expressions of the critical buckling load and the post-buckling load-deflection curves for the sandwich truncated conical shells with simply supported edge conditions are obtained by applying the Galerkin method. The effects of material properties, core layer thickness, number of stiffeners, dimensional parameters, semi vertex angle and elastic foundation on buckling and post-buckling behaviors of the shell are investigated. The obtained results are validated by comparing with those in the literature

Downlink Resource Sharing and Caching Helper Selection Control Maximized Multicast Video Delivery Capacity in Dense D2D 5G Networks

In 5G ultra-dense networks, a large number of mobile users (MUs) request a huge amount of high data rate video traffic causing a peak congestion situation at the macro base station (MBS) and small-cell base stations. This situation certainly reduces the total video capacity delivered to the MUs. In this paper, we exploit the available spectrum and storage resources of the MUs as well as the wireless broadcast nature of device-to-device (D2D) communications to propose a joint downlink resource sharing and caching helper selection (DRS-CHS) control to maximize the multicast video delivery capacity in dense D2D 5G networks. We assume that the MUs are divided into different clusters in which they can communicate with each other by D2D communications. There are two types of MUs in each cluster including the requesting users (RUs) that request the video and the caching helpers (CHs) that have cached the video. In addition, there are some sharing users (SUs) that can share their downlink resources with the CHs and the RUs for D2D multicast communications. A DRS-CHS optimization problem is then formulated and solved for an optimal control process of how to select a CH in each cluster and how to assign an SU to share its downlink resource with the selected CH such that the total video delivery capacity multicasted from the CHs to the RUs in all clusters is maximized. Simulation results demonstrate the benefits of the proposed DRS-CHS control solution compared to other conventional benchmarks