In recent years, renewable energy resources have accounted for an increasing share of electricity energy. Among them, photovoltaic (PV) power generation has received broad attention due to its economic and environmental benefits. Accurate PV generation forecasts can reduce power dispatch from the grid, thus increasing the supplier's profit in the day-ahead electricity market. The power system of a PV site is affected by solar radiation, PV plant properties and meteorological factors, resulting in uncertainty in its power output. This study used multiple seasonal-trend decomposition using LOESS (MSTL) and temporal fusion transformer (TFT) to perform day-ahead PV prediction on the desert knowledge Australia solar centre (DKASC) dataset. We compare the decomposition algorithms (VMD, EEMD and VMD-EEMD) and prediction models (BP, LSTM and XGBoost, etc.) which are commonly used in PV prediction presently. The results show that the MSTL-TFT method is more accurate than the aforementioned methods, which have noticeable improvement compared to other recent day-ahead PV predictions on desert knowledge Australia solar centre (DKASC).

The U.S. energy grid loses about 5% of its power due to resistive losses in its transmission lines, according to an estimate from the EIA. What if we could find a way to eliminate all of that? As it turns out, there's a really cool class of materials called superconductors -- materials that conduct electricity with 0 resistance. I'll admit, I'm no expert on how exactly the superconducting phenomenon happens. What I do know is that it only happens when the given material gets really cold -- we're talking down to single digits of Kelvin.

A Northumbria University physicist has been awarded more than half a million pounds to develop artificial intelligence which will protect the Earth from devastating space storms. Activity from the Sun such as solar eruptions, known as Coronal Mass Ejections, results in plasma being fired towards Earth at supersonic speeds, which can result in serious disruption to power and communication systems. With our increasing reliance on technology, solar storms pose a serious threat to our everyday lives, leading to severe space weather being added to the UK National Risk Assessment for the first time in 2011. Northumbria's Dr Andy Smith has recently been awarded a Research Fellowship from the Natural Environment Research Council (NERC) to explore how physics-inspired machine learning could be used to forecast space weather more accurately and predict serious space storms. During the Next Generation, Physics-Inspired AI for Space Weather Forecasting project, Dr Smith and his team will analyse huge amounts of data from satellites and space missions over the last 20 years to gain a better understanding of the conditions under which storms are likely to occur.

San Francisco city officials have sent letters to the California Public Utilities Commission (CPUC) asking to slow or halt the expansion of Cruise and Waymo robotaxi services in the city, NBC News has reported. San Francisco Transportation Authority (SFTA) officials wrote that unlimited expansion would be "unreasonable" in light of recent safety incidents in which vehicles blocked traffic and interfered with emergency vehicles. Alphabet's Waymo and Cruise, owned by GM, both operate fully driverless services (without backup drivers) in the city. Last June, Cruise gained permission to charge for rides in set areas of the city between the hours of 10PM and 6AM. Waymo is allowed to give driverless vehicle rides but is waiting for another permit before it can charge for them.

"Multiple adulterants detection in turmeric powder using VIS-SWNIR hyperspectral imaging followed by multivariate curve resolution and classification techniques" LINK "Application of visible-near-infrared hyperspectral imaging technology coupled with wavelength selection algorithm for rapid determination of moisture content of …" LINK

A recently developed computational approach based on AI can improve the understanding of different states of carbon, helping guide the search for materials yet to be discovered. We address the applications around us by using materials to create solutions, and everything we make is by definition made up of them. We discover some, and we create some, but commercializing materials for mainstream manufacturing can be tedious, expensive, and often based on trial and error. A material's atomic structure establishes its electronic, thermal, and mechanical properties. Scientists in this field are always looking for ways to arrange atoms to develop useful materials, often using high pressures and temperatures.

The identification of nonlinear dynamical systems from experimental time series and image series data became an important research theme in the early 1990s [25, 37, 36]. After lapsing for almost two decades, it is now experiencing a spectacular rebirth. A key element of the older work was the use of neural architectures [14, 37] (recurrent, convolutional, ResNet) motivated by traditional numerical analysis algorithms. Importantly, such architectures allow researchers to identify effective, coarse-grained, mean-field type evolution models from fine-scale (atomistic, molecular, agent-based) data [29, 5]. In this paper, we identify coarse-grained, effective stochastic differential equations (eSDE) for colloidal particle selfassembly based onfine-grained, Brownian dynamics simulations under the influence of electric fields [51, 11]. We demonstrate that the identified eSDE encodes accurately the physics of the Brownian Dynamic simulations and captures the dynamics of corresponding experimental data. Those experiments have previously been shown to quantitatively match to BD simulations at equilibrium in terms of time-averaged distribution functions [11, 18, 20]. Figure 1 shows a sample path of a latent space trajectory {t, φ(t)}

In general, optimal motion planning can be performed both locally and globally. In such a planning, the choice in favour of either local or global planning technique mainly depends on whether the environmental conditions are dynamic or static. Hence, the most adequate choice is to use local planning or local planning alongside global planning. When designing optimal motion planning both local and global, the key metrics to bear in mind are execution time, asymptotic optimality, and quick reaction to dynamic obstacles. Such planning approaches can address the aforesaid target metrics more efficiently compared to other approaches such as path planning followed by smoothing. Thus, the foremost objective of this study is to analyse related literature in order to understand how the motion planning, especially trajectory planning, problem is formulated, when being applied for generating optimal trajectories in real-time for Multirotor Aerial Vehicles (MAVs), impacts the listed metrics. As a result of the research, the trajectory planning problem was broken down into a set of subproblems, and the lists of methods for addressing each of the problems were identified and described in detail. Subsequently, the most prominent results from 2010 to 2022 were summarized and presented in the form of a timeline.

Deep learning is a topic of considerable current interest. The availability of massive data collections and powerful software resources has led to an impressive amount of results in many application areas that reveal essential but hidden properties of the observations. System identification learns mathematical descriptions of dynamic systems from input-output data and can thus benefit from the advances of deep neural networks to enrich the possible range of models to choose from. For this reason, we provide a survey of deep learning from a system identification perspective. We cover a wide spectrum of topics to enable researchers to understand the methods, providing rigorous practical and theoretical insights into the benefits and challenges of using them. The main aim of the identified model is to predict new data from previous observations. This can be achieved with different deep learning based modelling techniques and we discuss architectures commonly adopted in the literature, like feedforward, convolutional, and recurrent networks. Their parameters have to be estimated from past data trying to optimize the prediction performance. For this purpose, we discuss a specific set of first-order optimization tools that is emerged as efficient. The survey then draws connections to the well-studied area of kernel-based methods. They control the data fit by regularization terms that penalize models not in line with prior assumptions. We illustrate how to cast them in deep architectures to obtain deep kernel-based methods. The success of deep learning also resulted in surprising empirical observations, like the counter-intuitive behaviour of models with many parameters. We discuss the role of overparameterized models, including their connection to kernels, as well as implicit regularization mechanisms which affect generalization, specifically the interesting phenomena of benign overfitting ...

In context of laser powder bed fusion (L-PBF), it is known that the properties of the final fabricated product highly depend on the temperature distribution and its gradient over the manufacturing plate. In this paper, we propose a novel means to predict the temperature gradient distributions during the printing process by making use of neural networks. This is realized by employing heat maps produced by an optimized printing protocol simulation and used for training a specifically tailored recurrent neural network in terms of a long short-term memory architecture. The aim of this is to avoid extreme and inhomogeneous temperature distribution that may occur across the plate in the course of the printing process. In order to train the neural network, we adopt a well-engineered simulation and unsupervised learning framework. To maintain a minimized average thermal gradient across the plate, a cost function is introduced as the core criteria, which is inspired and optimized by considering the well-known traveling salesman problem (TSP). As time evolves the unsupervised printing process governed by TSP produces a history of temperature heat maps that maintain minimized average thermal gradient. All in one, we propose an intelligent printing tool that provides control over the substantial printing process components for L-PBF, i.e.\ optimal nozzle trajectory deployment as well as online temperature prediction for controlling printing quality.