In this post, we will build a license plate (LP) OCR model with Catalyst. There are different approaches to this issue, and we will build a multi-head classification model. We are going to use a Russian LP dataset gathered by Nomeroff Net. The model takes LP images and returns their texts as strings. It consists of a feature extractor backbone and several classification heads.

The process of steelmaking has been the same for thousands of years, using the traditional, coal-fired blast furnace. But SSAB is bringing steelmaking into a sustainable future. Using electricity, hydrogen and new digital tools, the highly specialized global steel manufacturer plans to produce fossil-free steel products in 2026. By 2045, SSAB's vision is to create a complete, fossil-free value chain from customers to end-users. To achieve this goal, almost all SSAB's processes need to have a digital component – and many of the decisions made in daily production need to be driven by analytics.

Machine learning is currently overhyped, but in the long term it will deliver dramatic improvements in our jobs, lives and societies. Organizations are getting disappointed that their investments in machine learning algorithms are not paying off because of their lack of understanding. Machine learning (ML) is an iceberg of massive proportions with a lot of potential to change the world if used correctly. Machine learning is a branch of artificial intelligence (AI) and computer science which focuses on the use of data and algorithms to imitate the way that humans learn, gradually improving its accuracy. Machine learning is an important component of the growing field of data science.

Maybe you've heard of the power of the Internet of Things (IoT) to transform industries, automate processes, and improve ROI. No industry is more ripe for change than construction and IoT has the potential to increase productivity, on-site safety, and operational efficiency. Through the deployment of low-power sensors, managers can improve worksite visibility at every stage of a project in real-time, from planning to construction, and even operation post-construction. While the construction industry is changing at a glacial pace, construction companies who are adopting technology to successfully address common workplace concerns and streamline processes are benefitting from increased efficiencies and improved responsiveness to the increasing demands of the industry. Flat productivity, decreased margins, more schedule overruns and increased competition are some of the obvious reasons construction companies should consider the adoption of IoT technology and digitization.

US and EU officials opened their two-day, high-level meetings in Pittsburgh on Wednesday, an effort to repair relations damaged under the administration of former president Donald Trump and boost cooperation on technology issues. The inaugural meeting of the Trade and Technology Council (TTC) comes as industries worldwide grapple with shortages of crucial semiconductors and is being held in Pittsburgh, a Pennsylvania city that was once the heart of the American steel industry and has since evolved into a tech hub. The ministers met at Mill 19, a massive World War II-era munitions factory and later steel mill on the shores of the Monongahela River that has been reborn as an advanced robotics facility for researchers from Carnegie Mellon University. The shadow of steel hangs over the meetings in other ways as well, especially as the two sides have yet to resolve a conflict over Trump-era tariffs on steel and aluminum. The former president cited US national security concerns in June 2018 when he imposed punitive tariffs of 25 percent on steel imports and 10 percent on aluminum, which have been a thorn in the side of trans-Atlantic relations since.

At the Goodwood festival of speed near Chichester, the crowds gathered at the hill-climb circuit to watch the world's fastest cars roar past, as they do every year. But not far from the high-octane action, there was a new, and quieter, attraction: a display of the latest electric vehicles, from the £28,000 Mini Electric to the £2m Lotus Evija hypercar. Even here, at one of the biggest events in Britain's petrolhead calendar, it's clear the days of the internal combustion engine are numbered. As countries strive to meet stringent carbon-emission targets, and vehicle-makers phase out combustion engines, 145m electric vehicles are predicted to be on the roads within a decade, up from 11m last year. The car batteries they require, along with storage batteries for solar and wind power, have sent demand for metals soaring, taking mining firms to the bottom of the sea in the hunt for those metals.

Classification and adulterant detection in" LINK "Agronomy: Phenotyping and Validation of Root Morphological Traits in Barley (Hordeum vulgare L.)" LINK "Comparison of wavelength selected methods for improving of prediction performance of PLS model to determine aflatoxin B1 (AFB1) in wheat samples during storage" LINK "A Brief History of Whiskey Adulteration and the Role of Spectroscopy Combined with Chemometrics in the Detection of Modern Whiskey Fraud" LINK Equipment for Spectroscopy "Feasibility study of detecting palm oil adulteration with recycled cooking oil using handheld Near-infrared spectrometer" LINK Environment NIR-Spectroscopy Application "Spatial Prediction of Calcium Carbonate and Clay Content in Soils using Airborne Hyperspectral Data" LINK "Monitoring the soil copper pollution degree based on the reflectance spectrum of an arid desert plant" LINK "Micromachines: Visualization of Local Concentration and Viscosity Distribution during Glycerol-Water Mixing in a Y-Shape ...

Machine learning can pinpoint "genes of importance" that help crops to grow with less fertilizer, according to a new study published in Nature Communications. It can also predict additional traits in plants and disease outcomes in animals, illustrating its applications beyond agriculture. Using genomic data to predict outcomes in agriculture and medicine is both a promise and challenge for systems biology. Researchers have been working to determine how to best use the vast amount of genomic data available to predict how organisms respond to changes in nutrition, toxins, and pathogen exposure-;which in turn would inform crop improvement, disease prognosis, epidemiology, and public health. However, accurately predicting such complex outcomes in agriculture and medicine from genome-scale information remains a significant challenge. In the Nature Communications study, NYU researchers and collaborators in the U.S. and Taiwan tackled this challenge using machine learning, a type of artificial intelligence used to detect patterns in data.

Abstract-- Future Moon bases will likely be constructed using resources mined from the surface of the Moon. The difficulty of maintaining a human workforce on the Moon and communications lag with Earth means that mining will need to be conducted using collaborative robots with a high degree of autonomy. In this paper, we explore the utility of robotic vision towards addressing several major challenges in autonomous mining in the lunar environment: lack of satellite positioning systems, navigation in hazardous terrain, and delicate robot interactions. The competition provided a simulated lunar environment that exhibits the complexities alluded to above. This argues for a high degree of intelligence on each agent and a robust multi-robot The need to transport resources from Earth is a serious coordination system to ensure long-term operation. In-Situ Resource some of the key challenges towards autonomous robots Utilisation (ISRU), where resources are extracted on for collaborative space mining: lack of satellite positioning other astronomical objects and exploited to support longer systems, navigation in hazardous terrain, and the need for and deeper space missions, has been proposed as a way to delicate robot interactions.

A transition to a low-carbon electricity supply is crucial to limit the impacts of climate change. Reducing carbon emissions could help prevent the world from reaching a tipping point, where runaway emissions are likely. Runaway emissions could lead to extremes in weather conditions around the world -- especially in problematic regions unable to cope with these conditions. However, the movement to a low-carbon energy supply can not happen instantaneously due to the existing fossil-fuel infrastructure and the requirement to maintain a reliable energy supply. Therefore, a low-carbon transition is required, however, the decisions various stakeholders should make over the coming decades to reduce these carbon emissions are not obvious. This is due to many long-term uncertainties, such as electricity, fuel and generation costs, human behaviour and the size of electricity demand. A well choreographed low-carbon transition is, therefore, required between all of the heterogenous actors in the system, as opposed to changing the behaviour of a single, centralised actor. The objective of this thesis is to create a novel, open-source agent-based model to better understand the manner in which the whole electricity market reacts to different factors using state-of-the-art machine learning and artificial intelligence methods. In contrast to other works, this thesis looks at both the long-term and short-term impact that different behaviours have on the electricity market by using these state-of-the-art methods.