Universal access to affordable, reliable, and sustainable modern energy is a Sustainable Development Goal (SDG). However, lack of sufficient power generation, poor transmission and distribution infrastructure, affordability, uncertain climate concerns, diversification and decentralization of energy production, and changing demand patterns are creating complex challenges in power generation. According to the 2019 International Energy Agency (IEA) report, 860 million people lack access to electricity, and three billion people use open fires and simple stoves fueled by kerosene, biomass, or coal for cooking. As a result, over four million people die prematurely of the illnesses associated. Artificial intelligence (AI) offers a great potential to lower energy costs, cut energy waste, and facilitate and accelerate the use of renewable and clean energy sources in power grids worldwide. In addition, it can help improve the planning, operation, and control of power systems.

Mixed integer convex and nonlinear programs, MICP and MINLP, are expressive but require long solving times. Recent work that combines data-driven methods on solver heuristics has shown potential to overcome this issue allowing for applications on larger scale practical problems. To solve mixed-integer bilinear programs online with data-driven methods, several formulations exist including mathematical programming with complementary constraints (MPCC), mixed-integer programming (MIP). In this work, we benchmark the performances of those data-driven schemes on a bookshelf organization problem that has discrete mode switch and collision avoidance constraints. The success rate, optimal cost and solving time are compared along with non-data-driven methods. Our proposed methods are demonstrated as a high level planner for a robotic arm for the bookshelf problem.

The advances in data science and machine learning have resulted in significant improvements regarding the modeling and simulation of nonlinear dynamical systems. It is nowadays possible to make accurate predictions of complex systems such as the weather, disease models or the stock market. Predictive methods are often advertised to be useful for control, but the specifics are frequently left unanswered due to the higher system complexity, the requirement of larger data sets and an increased modeling effort. In other words, surrogate modeling for autonomous systems is much easier than for control systems. In this paper we present the framework QuaSiModO (Quantization-Simulation-Modeling-Optimization) to transform arbitrary predictive models into control systems and thus render the tremendous advances in data-driven surrogate modeling accessible for control. Our main contribution is that we trade control efficiency by autonomizing the dynamics - which yields mixed-integer control problems - to gain access to arbitrary, ready-to-use autonomous surrogate modeling techniques. We then recover the complexity of the original problem by leveraging recent results from mixed-integer optimization. The advantages of QuaSiModO are a linear increase in data requirements with respect to the control dimension, performance guarantees that rely exclusively on the accuracy of the predictive model in use, and little prior knowledge requirements in control theory to solve complex control problems.

Robotic manipulation of deformable linear objects (DLOs) has broad application prospects in many fields. However, a key issue is to obtain the exact deformation models (i.e., how robot motion affects DLO deformation), which are hard to theoretically calculate and vary among different DLOs. Thus, shape control of DLOs is challenging, especially for large deformation control which requires global and more accurate models. In this paper, we propose a coupled offline and online data-driven method for efficiently learning a global deformation model, allowing for both accurate modeling through offline learning and further updating for new DLOs via online adaptation. Specifically, the model approximated by a neural network is first trained offline on random data, then seamlessly migrated to the online phase, and further updated online during actual manipulation. Several strategies are introduced to improve the model's efficiency and generalization ability. We propose a convex-optimization-based controller, and analyze the system's stability using the Lyapunov method. Detailed simulations and real-world experiments demonstrate that our method can efficiently and precisely estimate the deformation model, and achieve large deformation control of untrained DLOs in 2D and 3D dual-arm manipulation tasks better than the existing methods. It accomplishes all 24 tasks with different desired shapes on different DLOs in the real world, using only simulation data for the offline learning.

Simulations of high energy density physics are expensive, largely in part for the need to produce nonlocal thermodynamic equilibrium opacities. High-fidelity spectra may reveal new physics in the simulations not seen with low-fidelity spectra, but the cost of these simulations also scale with the level of fidelity of the opacities being used. Neural networks are capable of reproducing these spectra, but neural networks need data to to train them which limits the level of fidelity of the training data. This paper demonstrates that it is possible to reproduce high-fidelity spectra with median errors in the realm of 3% to 4% using as few as 50 samples of high-fidelity Krypton data by performing transfer learning on a neural network trained on many times more low-fidelity data. K. D. Humbird is with Lawrence Livermore National Laboratory, 7000 East Ave, Livermore, CA, 94550 USA, email: humbird1@llnl.gov. In this case, higher fidelity opacity calculations are necessary Inertial confinement fusion (ICF) is currently to capture important physical processes accurately one of the experimental approaches to controlled [4], [5]. In this work, we focus on improving the nuclear fusion.

Getting up from an arbitrary fallen state is a basic human skill. Existing methods for learning this skill often generate highly dynamic and erratic get-up motions, which do not resemble human get-up strategies, or are based on tracking recorded human get-up motions. In this paper, we present a staged approach using reinforcement learning, without recourse to motion capture data. The method first takes advantage of a strong character model, which facilitates the discovery of solution modes. A second stage then learns to adapt the control policy to work with progressively weaker versions of the character. Finally, a third stage learns control policies that can reproduce the weaker get-up motions at much slower speeds. We show that across multiple runs, the method can discover a diverse variety of get-up strategies, and execute them at a variety of speeds. The results usually produce policies that use a final stand-up strategy that is common to the recovery motions seen from all initial states. However, we also find policies for which different strategies are seen for prone and supine initial fallen states. The learned get-up control strategies often have significant static stability, i.e., they can be paused at a variety of points during the get-up motion. We further test our method on novel constrained scenarios, such as having a leg and an arm in a cast.

During applying data-oriented diagnosis systems to distinguishing the type of and evaluating the severity of nuclear power plant initial events, it is of vital importance to decide which parameters to be used as the system input. However, although several diagnosis systems have already achieved acceptable performance in diagnosis precision and speed, hardly have the researchers discussed the method of monitoring point choosing and its layout. For this reason, redundant measuring data are used to train the diagnostic model, leading to high uncertainty of the classification, extra training time consumption, and higher probability of overfitting while training. In this study, a method of choosing thermal hydraulics parameters of a nuclear power plant is proposed, using the theory of post-hoc interpretability theory in deep learning. At the start, a novel Time-sequential Residual Convolutional Neural Network (TRES-CNN) diagnosis model is introduced to identify the position and hydrodynamic diameter of breaks in LOCA, using 38 parameters manually chosen on HPR1000 empirically. Afterwards, post-hoc interpretability methods are applied to evaluate the attributions of diagnosis model's outputs, deciding which 15 parameters to be more decisive in diagnosing LOCA details. The results show that the TRES-CNN based diagnostic model successfully predicts the position and size of breaks in LOCA via selected 15 parameters of HPR1000, with 25% of time consumption while training the model compared the process using total 38 parameters. In addition, the relative diagnostic accuracy error is within 1.5 percent compared with the model using parameters chosen empirically, which can be regarded as the same amount of diagnostic reliability.

Digital transformation has gained significant momentum in recent years. The fact that digitalisation in the construction industry is pointing upwards is not only due to the Covid-19 pandemic and a general increase in interest in digitalisation topics, but above all to the convincing added values that can be achieved along the entire value chain with the help of digital tools. These include, for example, better overall planning of projects and the associated resources, increased cost and schedule reliability through simulations with the digital twin, improved communication and collaboration between the numerous trades involved – especially in large projects, faster coordination through paperless processes and stringent quality assurance across all project phases. There are benefits in terms of sustainability, too – the construction sector is, after all, responsible for almost 40% of global carbon emissions – enormous potential is available through increased efficiency in construction and operation, better planning of materials, improved recycling and the reduced production of waste. While digitalisation has already made inroads in the construction industry, another technology is now well on its way to becoming an integral part of the construction process: artificial intelligence (AI).

After spending many hours completing Spider-Man Remastered main story and being unable to tear ourselves away from the screen, editors at BabelTechReviews (BTR) recommend it as a great game with a few flaws. Mark Poppin, Mario Vasquez, and I have collaborated on this review, each of us playing the game for 20 or more hours. We cover the gameplay, updated performance with Thursday's patch, and IQ (image quality), which includes ray tracing and testing AMD's and Nvidia's upscaling solutions. Spider-Man Remastered was released originally as a PS4 exclusive in 2018 and two years later was remastered for PS5. Sony then gave it a complete makeover when they ported it to the PC, complete with ray-traced reflections, and together with all the downloadable content, it was released on August 12, 2022.

With much of the world now focused on the goal of net zero carbon emissions by the year 2050, industry leaders are asking: How can businesses be more sustainable? At the same time, another revolution has sprung: digital transformation. Companies are busy adopting tools and technologies to make processes more efficient and competitive. In the pursuit of global optimization, businesses have a lot to gain from thinking about these two movements in conjunction. Let's say a company with sustainability baked into its business model has decided to invest time and focus on driving more efficient operations and reducing waste.