It is well known that conservative mechanical systems exhibit local oscillatory behaviours due to their elastic and gravitational potentials, which completely characterise these periodic motions together with the inertial properties of the system. The classification of these periodic behaviours and their geometric characterisation are in an on-going secular debate, which recently led to the so-called eigenmanifold theory. The eigenmanifold characterises nonlinear oscillations as a generalisation of linear eigenspaces. With the motivation of performing periodic tasks efficiently, we use tools coming from this theory to construct an optimization problem aimed at inducing desired closed-loop oscillations through a state feedback law. We solve the constructed optimization problem via gradient-descent methods involving neural networks. Extensive simulations show the validity of the approach.

The utilization of large-scale distributed renewable energy promotes the development of the multi-microgrid (MMG), which raises the need of developing an effective energy management method to minimize economic costs and keep self energy-sufficiency. The multi-agent deep reinforcement learning (MADRL) has been widely used for the energy management problem because of its real-time scheduling ability. However, its training requires massive energy operation data of microgrids (MGs), while gathering these data from different MGs would threaten their privacy and data security. Therefore, this paper tackles this practical yet challenging issue by proposing a federated multi-agent deep reinforcement learning (F-MADRL) algorithm via the physics-informed reward. In this algorithm, the federated learning (FL) mechanism is introduced to train the F-MADRL algorithm thus ensures the privacy and the security of data. In addition, a decentralized MMG model is built, and the energy of each participated MG is managed by an agent, which aims to minimize economic costs and keep self energy-sufficiency according to the physics-informed reward. At first, MGs individually execute the self-training based on local energy operation data to train their local agent models. Then, these local models are periodically uploaded to a server and their parameters are aggregated to build a global agent, which will be broadcasted to MGs and replace their local agents. In this way, the experience of each MG agent can be shared and the energy operation data is not explicitly transmitted, thus protecting the privacy and ensuring data security. Finally, experiments are conducted on Oak Ridge national laboratory distributed energy control communication lab microgrid (ORNL-MG) test system, and the comparisons are carried out to verify the effectiveness of introducing the FL mechanism and the outperformance of our proposed F-MADRL.

Industry 4.0, also known as the Fourth Industrial Revolution, refers to the integration of advanced technologies such as artificial intelligence, the Internet of Things (IoT), and automation into manufacturing and other industries. This integration is expected to lead to significant changes and improvements in the way businesses operate. Some of the potential impacts of Industry 4.0 on business include: Overall, Industry 4.0 has the potential to bring about significant changes and improvements in the way businesses operate, and companies that are able to effectively leverage these technologies are likely to have a competitive advantage. The Internet of Things (IoT) refers to the interconnectedness of physical devices, such as sensors, actuators, and other electronic devices, through the internet. The Industrial Internet of Things (IIoT) refers to the application of IoT in industrial environments, such as manufacturing plants, oil and gas facilities, and other industrial settings.

In recent years, we have witnessed a time of global instability marked by pandemics, inflation, geopolitical turmoil, supply problems, blockades, etc. This challenging landscape has also resulted in an ideal time to change how we do business and a turning point to adopt new technology solutions that help companies deal with these difficult times and turn them into a competitive advantage. We review the technology trends that will transform business in 2023, which you should follow closely. Digital twins have been positioning themselves for several years as one of the leading technological trends of the moment, especially if we talk about the industrial sector. These are not only digital replicas of objects, spaces, physical systems, or processes with which to obtain more accurate products, reduce costs or predict possible errors.

Model estimates obtained from traditional subspace identification methods may be subject to significant variance. This elevated variance is aggravated in the cases of large models or of a limited sample size. Common solutions to reduce the effect of variance are regularized estimators, shrinkage estimators and Bayesian estimation. In the current work we investigate the latter two solutions, which have not yet been applied to subspace identification. Our experimental results show that our proposed estimators may reduce the estimation risk up to $40\%$ of that of traditional subspace methods.

Particularly, The smart energy community has attracted significant interest the problem of regulating prosumers and consumers with from the research community recently. It consists of energy optimality constraints, wherein the overall cost to the energy prosumers and consumers. Energy prosumers are the users that community is minimized, is not well studied. Our work can consume and produce energy; for example, households contributes toward addressing this problem. We assume that connected to a power grid to consume energy and have members (prosumers) in the smart energy community have solar photovoltaic panels on their rooftops to produce energy heterogeneous renewable energy sources, some prosumers locally. In smart energy communities, members of a particular install solar panels, and others install wind turbines in their geographical location make a cooperative group to achieve a households. The prosumers provide excess produced energy common goal. Moreover, in the smart energy communities, the to some community members, called energy consumers. Costs prosumers provide the surplus produced energy to community are associated with the installation and transmission of energy members or a grid for monetary benefits or to store energy in from renewable sources.

Forecasts by the European Centre for Medium-Range Weather Forecasts (ECMWF; EC for short) can provide a basis for the establishment of maritime-disaster warning systems, but they contain some systematic biases.The fifth-generation EC atmospheric reanalysis (ERA5) data have high accuracy, but are delayed by about 5 days. To overcome this issue, a spatiotemporal deep-learning method could be used for nonlinear mapping between EC and ERA5 data, which would improve the quality of EC wind forecast data in real time. In this study, we developed the Multi-Task-Double Encoder Trajectory Gated Recurrent Unit (MT-DETrajGRU) model, which uses an improved double-encoder forecaster architecture to model the spatiotemporal sequence of the U and V components of the wind field; we designed a multi-task learning loss function to correct wind speed and wind direction simultaneously using only one model. The study area was the western North Pacific (WNP), and real-time rolling bias corrections were made for 10-day wind-field forecasts released by the EC between December 2020 and November 2021, divided into four seasons. Compared with the original EC forecasts, after correction using the MT-DETrajGRU model the wind speed and wind direction biases in the four seasons were reduced by 8-11% and 9-14%, respectively. In addition, the proposed method modelled the data uniformly under different weather conditions. The correction performance under normal and typhoon conditions was comparable, indicating that the data-driven mode constructed here is robust and generalizable.

Inspired by biological neurons, the activation functions play an essential part in the learning process of any artificial neural network commonly used in many real-world problems. Various activation functions have been proposed in the literature for classification as well as regression tasks. In this work, we survey the activation functions that have been employed in the past as well as the current state-of-the-art. In particular, we present various developments in activation functions over the years and the advantages as well as disadvantages or limitations of these activation functions. We also discuss classical (fixed) activation functions, including rectifier units, and adaptive activation functions. In addition to discussing the taxonomy of activation functions based on characterization, a taxonomy of activation functions based on applications is presented. To this end, the systematic comparison of various fixed and adaptive activation functions is performed for classification data sets such as the MNIST, CIFAR-10, and CIFAR- 100. In recent years, a physics-informed machine learning framework has emerged for solving problems related to scientific computations. For this purpose, we also discuss various requirements for activation functions that have been used in the physics-informed machine learning framework. Furthermore, various comparisons are made among different fixed and adaptive activation functions using various machine learning libraries such as TensorFlow, Pytorch, and JAX.

Today we see clear movement and momentum to decarbonization and the green energy transition. In parallel, the rise in digital technology and advanced analytics provide unique opportunities to not only migrate to new energy technologies, but to monitor progress, predict performance, integrate systems, ensure reliability and resiliency – and improve sustainability by optimizing products, solutions, and services like never before. At the same time, we have changing dynamics in the sector that increase its complexity. Grids are moving from centralized to decentralized models. Energy producers have multi-OEM (original equipment manufacturer) solutions that must be monitored as a system to ensure uptime and output.

In 2022, ESG & Technology have been the norm across all sectors and businesses. Technology advancement has made it easier for companies to identify, assess and mitigate environmental and social risks. The confluence of ESG with tech space has created new opportunities for investments and created social impact in core industries like agriculture, energy and utilities(power, telecom etc..), healthcare and transportation. Companies across the spectrum are leveraging technology to track their supply chains, source sustainable materials and understand how to improve their overall impact on the planet and community. These changes have paved the way for more responsible practices by companies across industries and promoted greater transparency in the corporate sector.