The COVID-19 outbreak from the SARS-CoV-2 virus has shocking us with its fast transmission and deadly complication. Due to that, the movement restriction has been enforced to contain this pandemic. Recently, there is an increasing pressure to restart and resurrect social and economic sectors, and to allow people to get back to work. This must be well planned before the movement restriction is lifted. Because of that, this paper aims to review and make recommendations on the new normal for our daily activities and works. Firstly, the social and economic sectors must be opening in phases by emphasizing safety and health than an economic recovery. In the meantime, the WHO recommendations on social distancing and personal hygiene must be adapted and become a new normal. Because of that, the government and local authorities should develop a soft landing approach based on the WHO recommendations. Next, the community must be engaged and empowered to do their parts in preventing the spread of COVID-19. From the new normal recommendations, the people can continue their daily routines, and at the same time can reduce COVID-19 transmission. However, medical possibilities are not considered while compiling these new normals and procedures. The population must adapt and embrace the new normal to control, reduce and prevent the spreading of COVID-19, as it could be with us for a long time.
Fish image classification tool is important in the field of ichthyology. In this paper, we present a fish image classification benchmark comparison across different types of convolutional neural network (CNN). CNN extracts features from labeled image data to solve classification problems. CNN models were trained to classify fish images using transfer learning with data augmentation. CNN models consisting of AlexNet, GoogLeNet, and ResNet were incorporated in the benchmark tests. A dataset of 18,000 fish images across 18 categores, were split into 5,400 images for validation (30%) and 12,600 images for training (70%) dataset. Such neural network models show high accuracy up to 99.85% (AlexNet), 96.39% (GoogLeNet) and 99.51% (ResNet-50). To evaluate the performance of each framework, the analysis presented consists of classification accuracy, learning curve, validation test, top-five prediction and confusion matrix. The work presented here has shown its potential to contribute towards accurate development of state-of-the-art fish classification tools. It is envisioned that these CNN algorithms have the potential to assist in fish image classification problems with high accuracy despite visually similar features of images in the dataset.
High speed planing craft as a unique vessel type play key commercial roles in niche passenger ferrying and high value cargo transport. In addition, they are used to support several critical maritime activities such as coastal surveillance, reconnaissance, and life-saving operations and many recreational pursuits. Formal optimization frameworks, despite their significant use across a range of domains, have rarely been proposed and developed to deal with the design challenges of high speed planing craft. Highlighted in this paper is an optimization framework drawing on both domain dependent and domain independent elements for the conceptual and preliminary design of high speed planing craft. A summary of the principal components of the optimization framework are presented, followed by several case study examples. The solvers developed and employed are classified as being population based, evolutionary and stochastic in nature. These characteristics are well suited to design space exploration in all engineering and decision making contexts.
Within the case studies presented, the sample key performance indicators include calm water resistance, resistance in waves, seakeeping and manoeuvring. The concept of scenario-based hydrodynamic design optimization is introduced using an example of a small rescue craft operating in a predefined sea-state. Finally, a multi-objective optimization case study considering total resistance, steady turning diameter and vertical impact acceleration is presented to demonstrate the capability to explore trade-offs while at the same time providing an understanding of the design intent of a basis ship. This work has significant purpose and relevance in both ab-initio and reverse engineering contexts. It also has natural extensions in both depth of analysis and breadth of application.
Shape representation plays a major role in any shape optimization exercise. The ability to identify a shape with good performance is dependent on both the flexibility of the shape representation scheme and the efficiency of the optimization algorithm. In this article, a memetic algorithm is presented for 2D shape matching problems. The shape is represented using B-splines, in which the control points representing the shape are repaired and subsequently evolved within the optimization framework. The underlying memetic algorithm is a multi-feature hybrid that combines the strength of a real coded genetic algorithm, differential evolution and a local search. The efficiency of the proposed algorithm is illustrated using three test problems, wherein the shapes were identified using a mere 5000 function evaluations. Extension of the approach to deal with problems of unknown shape complexity is also presented in the article.
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<p>This article begins with a dynamical analysis of the Permanent Magnet Synchronous Generator (PMSG) in a wind turbine system with quadratic nonlinearities. The dynamical behaviors of the PMSG are analyzed and examined using Poincare map, bifurcation model, and Lyapunov spectrum. Finally, an adaptive type-2 fuzzy controller is designed for different flow configurations of the PMSG. An analysis of the performance for the proposed approach is evaluated for effectiveness by simulating the PMSG. In addition, the proposed controller uses advantages of adaptive type-2 fuzzy controller in handling dynamic uncertainties to approximate unknown non-linear actions.</p>
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