In this blog, we will show our approach to classifying images of ship using supervised models. We use a dataset obtained from Kaggle in order to perform our analyses. We discuss various data preprocesses we went through in order to reduce the dimensionality of the data, and to feed our models the best inputs possible. Ship or vessel detection has a wide range of applications, in the areas of maritime safety, fisheries management, marine pollution, defence and maritime security, protection from piracy, illegal migration, etc. Keeping this in mind, a Governmental Maritime and Coastguard Agency is planning to deploy a computer vision based automated system to identify ship type only from the images taken by the survey boats. You have been hired as a consultant to build an efficient model for this project.

Thousands of historical images from across Ireland are being brought to life in color for the first time, thanks to a new AI-led photo project. Combining digital technology with painstaking historical research, professors John Breslin and Sarah-Anne Buckley at the National University of Ireland, Galway, have been able to turn photos, originally shot in black in white, into rich color images. It includes portraits of key figures like Oscar Wilde and poet W.B. Yeats, as well as defining moments in history, like the Titanic setting sail from the Belfast shipyard where it was constructed. Yet, some of the most compelling photos depict everyday scenes -- people herding pigs, spinning wool or packed onto the back of horse-drawn carts. And while poverty is evident in pictures of barefoot villagers crowding around for a photo, or of Dublin's working-class tenement buildings, there are also well-to-do family shots and depictions of upper-class pastimes like fox hunting.

Daewoo Shipbuilding & Marine Engineering is the first global shipbuilding industry with artificial intelligence for hot processing A robot system that combines technology is applied. Daewoo Shipbuilding & Marine Engineering (CEO Lee Seong-Geun) has developed an artificial intelligent hot processing robot'Goknuri' that can produce high-quality products even with low-skilled people using standardized big data and artificial intelligence technology while improving the working environment and applied it to the field It was revealed on the 20th. The newly developed robot'Goknuri' contributes to maintaining high quality by standardizing work contents while storing and utilizing the know-how and performance of existing workers as data. In addition, the accumulated data can be used for the construction of other ships using artificial intelligence technology in the future. In addition, it is possible to dramatically improve the working environment of workers who have been exposed to noise and musculoskeletal diseases.

Daewoo Shipbuilding Company, one of the largest shipbuilders in the world, has experienced great deal of trouble with the planning and scheduling of its production process. To solve the problems, from 1991 to 1993, Korea Advanced Institute of Science and Technology (KAIST) and Daewoo jointly conducted the Daewoo Shipbuilding Scheduling (das) Project. To integrate the scheduling expert systems for shipbuilding, we used a hierarchical scheduling architecture. To automate the dynamic spatial layout of objects in various areas of the shipyard, we developed spatial scheduling expert systems. For reliable estimation of person-hour requirements, we implemented the neural network-based person-hour estimator.

In this work, we introduce a deep learning-based polar code construction algorithm. The core idea is to represent the information/frozen bit indices of a polar code as a binary vector which can be interpreted as trainable weights of a neural network (NN). For this, we demonstrate how this binary vector can be relaxed to a soft-valued vector, facilitating the learning process through gradient descent and enabling an efficient code construction. We further show how different polar code design constraints (e.g., code rate) can be taken into account by means of careful binary-to-soft and soft-to-binary conversions, along with rate-adjustment after each learning iteration. Besides its conceptual simplicity, this approach benefits from having the "decoder-in-the-loop", i.e., the nature of the decoder is inherently taken into consideration while learning (designing) the polar code. We show results for belief propagation (BP) decoding over both AWGN and Rayleigh fading channels with considerable performance gains over state-of-the-art construction schemes.

A cutting-edge, £1 million robot ship will cross the Atlantic Ocean unmanned next year to commemorate 400 years since the maiden voyage of the Mayflower to the USA. The Mayflower Autonomous Ship (MAS) will set off on its pioneering, 2,750-mile trip in September 2020, following in the trail of its namesake 400 years earlier. The 15-metre long, catamaran-style ship will be powered by state-of-the-art renewable energy. It will be unmanned but will have marine AI on board, and will be steered from a control room in Plymouth, Devon - where the original Mayflower set off from. It will carry three research pods, containing sensors and other equipment, which scientists hope will pave the way for ground-breaking research into ocean conditions for autonmous navigation.

A hundred and thirty three years--that's how long Virginia-based Newport News Shipbuilding has been in the business of manufacturing ships. As the sole developer of U.S. Navy aircraft carriers, the company has constructed more than 30 warships, including the world's first and largest nuclear-powered carrier, which weighs 100,000 tons and comprises 300 million parts. Traditionally, building a ship like this might require 30 million to 40 million man-hours. Digital transformation has upended the ship building business, said Bharat Amin, VP and chief information officer at Newport News Shipbuilding, on stage at Siemens' Spotlight on Innovation, an annual technology conference held recently in Orlando. "You hear about smart cities, we want to be a smart shipyard," Amin told the audience.

The American Bureau of Shipping recently collaborated with Google Cloud and software engineers SoftServe to use artificial intelligence (AI) models to detect levels of corrosion and marine coatings breakdowns on brown- and bluewater vessels. The pilot project is aimed at developing image recognition software tools that can examine early signs of degradation in hull structures, to avoid unsafe working conditions, unscheduled maintenance and resulting operational downtime. The effort demonstrated how AI can support early detection of structural anomalies that are usually found through traditional, visual inspections. The project was focused on corrosion and coatings failures, but ABS engineers believe the new tools could also be used to detect stress fractures and larger hull deformations. These AI techniques -- in tandem with advanced data algorithms -- could be used to analyze images over time to understand the trends in corrosion and asset fatigue that would support a transition to more efficient class and maintenance regimes for everything from workboats to offshore structures.

We propose a new framework for constructing polar codes (i.e., selecting the frozen bit positions) for arbitrary channels, and tailored to a given decoding algorithm, rather than based on the (not necessarily optimal) assumption of successive cancellation (SC) decoding. The proposed framework is based on the Genetic Algorithm (GenAlg), where populations (i.e., collections) of information sets evolve successively via evolutionary transformations based on their individual error-rate performance. These populations converge towards an information set that fits both the decoding behavior and the defined channel. Using our proposed algorithm over the additive white Gaussian noise (AWGN) channel, we construct a polar code of length 2048 with code rate 0.5, without the CRC-aid, tailored to plain successive cancellation list (SCL) decoding, achieving the same error-rate performance as the CRC-aided SCL decoding, and leading to a coding gain of 1 dB at BER of $10^{-6}$. Further, a belief propagation (BP)-tailored construction approaches the SCL error-rate performance without any modifications in the decoding algorithm itself. The performance gains can be attributed to the significant reduction in the total number of low-weight codewords. To demonstrate the flexibility, coding gains for the Rayleigh channel are shown under SCL and BP decoding. Besides improvements in error-rate performance, we show that, when required, the GenAlg can be also set up to reduce the decoding complexity, e.g., the SCL list size or the number of BP iterations can be reduced, while maintaining the same error-rate performance.

We propose a new polar code construction framework (i.e., selecting the frozen bit positions) for the additive white Gaussian noise (AWGN) channel, tailored to a given decoding algorithm, rather than based on the (not necessarily optimal) assumption of successive cancellation (SC) decoding. The proposed framework is based on the Genetic Algorithm (GenAlg), where populations (i.e., collections) of information sets evolve successively via evolutionary transformations based on their individual error-rate performance. These populations converge towards an information set that fits the decoding behavior. Using our proposed algorithm, we construct a polar code of length 2048 with code rate 0.5, without the CRC-aid, tailored to plain successive cancellation list (SCL) decoding, achieving the same error-rate performance as the CRC-aided SCL decoding, and leading to a coding gain of 1 dB at BER of $10^{-6}$. Further, a belief propagation (BP)-tailored polar code approaches the SCL error-rate performance without any modifications in the decoding algorithm itself.