The building industry, and especially the concrete industry, consumes a considerable proportion of the natural resources extracted from the lithosphere and is responsible for a large part of the ...solid waste generated worldwide. Unfortunately, this fact has not yet generated a movement to counter the trend; rather, a huge increase in the use of concrete in new structures can be observed. It is imperative to counteract this development by optimally exploiting the material properties of concrete in order to improve its efficiency. One strategy being pursued at the University of Natural Resources and Life Sciences, Vienna and TU Wien is to reduce the amount of concrete and reinforcement in structures by using high‐performance materials such as carbon‐fibre‐reinforced polymers (CFRP) in ultra‐high‐performance concrete (UHPC) members. This permits a crucial reduction in the self‐weight and allows for the design of very lightweight precast concrete elements. The authors' goal is to use CFRP rods as bending reinforcement and a combination of flat and preformed CFRP textiles as shear and structural reinforcement. In the first part of this paper, the research approach is introduced and a comprehensive overview of the state of the art of UHPC and CFRP reinforcement is given. The second part contains a description of the conceptual design of some structural members (one floor element and two T‐beams), starting with the development of a suitable UHPC mixture. Furthermore, preliminary uniaxial tensile tests of textile‐reinforced UHPC strips are described and evaluated. In the final part of this paper, the authors present parameter studies for different reinforcement types, values, additional prestressing of the rod reinforcement and the first prototypes. They are used to give an estimation of the feasibility, the load‐bearing and deflection behavior of the proposed building components.
After identifying the biblical and Calderonian sources of the play, a dramatic, stylistic, and symbolic analysis of the work follows, with the aim of reflecting on the intentions of the author in ...reusing certain elements, combined with notable innovations. In order to do so, we will take into account three aspects of the use of the sources: 1) the development of the wicked character of Baltasar, who creates space for the conflict; 2) the preparation of the great banquet as the decisive moment of the dramatic tension and of Baltasar's irreverent behaviour; and 3) the dramatic construction of his final punishment. En los ejemplos que siguen él mismo nos pone de manifiesto cuál es su papel en la comedia: «mandé» (v. 1891), «ordené» (v. 1699), «he de lograr» (v. 1703), «robé» (v. 126), «ejecutarlo» (v. 1717), «oprime mi planta» (v. 129), «faltando al concierto» (v. 1035), «morirá» (v. 1604), «cerrad la torre» (v. 1609), «idos, villanos, de aquí» (v. 1677), «pues viva yo y muera Ciro» (v. 1806), «ha de desposarse» (v. 1950), «has de ser mía / aunque los dioses se agravien» (vv. 1961-1962), «han de morir a mis manos» (v. 2193), «vengaré» (v. 2194), «empiece .../ el desprecio deste dios» (v. 2856), «pasad al punto a cuchillo / mueran todos al instante» (vv. 2242-2243), «llevadlos; mueran allí» (v. 2251), «te mato yo» (v. 1256); «a mi mano has de morir» (v. 2292), «has de ser mi esposa ya» (v. 2333), «pues mañana eres mía» (v. 2346), «tu dios quiero despreciar» (v. 2372), etc. Salen todos los que pudieren con fuentes y aguamaniles y jarros y la más vajilla dorada que se pueda juntar (v. 2734 acot.) Descúbrense las mesas y aparadores con luces (v. 2799 acot.) Sale toda la compañía, damas por una puerta, hombres por otra y unos niños hebreos de gala con toallas y salvillas (v. 2819 acot.) El momento más efectista, sin duda, y el desencadenante del final de Baltasar, es cuando este se atreve a brindar con los vasos sagrados por su matrimonio forzado con Fénix: Siéntanse todos y en tanto tocan chirimías y luego canta la música mientras empiezan a cenar y Bato al pie de la mesa toma un plato ...
We have demonstrated the effectiveness of reinforcement learning (RL) in bluff body flow control problems both in experiments and simulations by automatically discovering active control strategies ...for drag reduction in turbulent flow. Specifically, we aimed to maximize the power gain efficiency by properly selecting the rotational speed of two small cylinders, located parallel to and downstream of the main cylinder. By properly defining rewards and designing noise reduction techniques, and after an automatic sequence of tens of towing experiments, the RL agent was shown to discover a control strategy that is comparable to the optimal strategy found through lengthy systematically planned control experiments. Subsequently, these results were verified by simulations that enabled us to gain insight into the physical mechanisms of the drag reduction process. While RL has been used effectively previously in idealized computer flow simulation studies, this study demonstrates its effectiveness in experimental fluid mechanics and verifies it by simulations, potentially paving the way for efficient exploration of additional active flow control strategies in other complex fluid mechanics applications.
Since its introduction, the reward prediction error theory of dopamine has explained a wealth of empirical phenomena, providing a unifying framework for understanding the representation of reward and ...value in the brain
. According to the now canonical theory, reward predictions are represented as a single scalar quantity, which supports learning about the expectation, or mean, of stochastic outcomes. Here we propose an account of dopamine-based reinforcement learning inspired by recent artificial intelligence research on distributional reinforcement learning
. We hypothesized that the brain represents possible future rewards not as a single mean, but instead as a probability distribution, effectively representing multiple future outcomes simultaneously and in parallel. This idea implies a set of empirical predictions, which we tested using single-unit recordings from mouse ventral tegmental area. Our findings provide strong evidence for a neural realization of distributional reinforcement learning.
Power system emergency control is generally regarded as the last safety net for grid security and resiliency. Existing emergency control schemes are usually designed offline based on either the ...conceived "worst" case scenario or a few typical operation scenarios. These schemes are facing significant adaptiveness and robustness issues as increasing uncertainties and variations occur in modern electrical grids. To address these challenges, this paper developed novel adaptive emergency control schemes using deep reinforcement learning (DRL) by leveraging the high-dimensional feature extraction and non-linear generalization capabilities of DRL for complex power systems. Furthermore, an open-source platform named Reinforcement Learning for Grid Control (RLGC) has been designed for the first time to assist the development and benchmarking of DRL algorithms for power system control. Details of the platform and DRL-based emergency control schemes for generator dynamic braking and under-voltage load shedding are presented. Robustness of the developed DRL method to different simulation scenarios, model parameter uncertainty and noise in the observations is investigated. Extensive case studies performed in both the two-area, four-machine system and the IEEE 39-bus system have demonstrated excellent performance and robustness of the proposed schemes.
Chillers are responsible for almost half of the total energy demand in buildings. Hence, the obligation of control systems of multi-chiller due to changes indoor environments is one of the most ...significant parts of a smart building. Such a controller is described as a nonlinear and multi-objective algorithm, and its fabrication is crucial to achieving the optimal balance between indoor thermal comfort and running a minimum number of chillers. This work proposes deep clustering of cooperative multi-agent reinforcement learning (DCCMARL) as well-suited to such system control, which supports centralized control by learning of agents. In MARL, since the learning of agents is based on discrete sets of actions and stats, this drawback significantly affects the model of agents for representing their actions with efficient performance. This drawback becomes considerably worse when increasing the number of agents, due to the increased complexity of solving MARL, which makes modeling policy very challenging. Therefore, the DCCMARL of multi-objective reinforcement learning is leveraging powerful frameworks of a hybrid clustering algorithm to deal with complexity and uncertainty, which is a critical factor that influences to the achievement of high levels of a performance action. The results showed that the ability of agents to manipulate the behavior of the smart building could improve indoor thermal conditions, as well as save energy up to 44.5% compared to conventional methods. It seems reasonable to conclude that agents' performance is influenced by what type of model structure.
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•Clustering based hybrid network structure strategy is used to optimize chiller sequencing control.•Hybrid model structure systemized by logic to deal with the multi-agent continuous actions space.•Fuzzy rules systemizing multi-agent policy to sequencing control of three sequential chiller strategies.•Sequencing of novel hybrid intelligence model maintains the required margin of chilled water out temperature.•The investigation for chiller sequencing control shows saving more than 44% of HVAC energy.
3D Concrete Printing (3DcP) is an emerging technology with promises to transform concrete construction using additive manufacturing technologies. One of the major challenges for 3DcP technology is ...the reinforcement method, particularly in the interlayer direction (across the layers). Previously developed methods include (1) pre-installed reinforcement where the reinforcement is placed or 3D printed before the concrete printing and (2) post-installed reinforcement where reinforcement is inserted and grouted after the 3D printing of concrete with holes for the reinforcements. A new in-process method is presented in this paper to embed mesh reinforcement at the same time when concrete layers are printed. Reinforcements in each layer are lapped in the interlayer (across the layer) direction to simulate a continuous reinforcement. Tests and calculations show that lapped mesh reinforcement was effective in functioning as a continuous reinforcement.
•New “in process” method of laying reinforcement in 3D Concrete Printing is presented.•A new nozzle design and lapping of reinforcements to facilitate this are described.•Effectiveness of the reinforcement continuity across the layers was tested.•The bond between print mortar and reinforcement was tested.•The method provides an effective solution to a major problem in 3D Concrete Printing.
Volt-VAR control is critical to keeping distribution network voltages within allowable range, minimizing losses, and reducing wear and tear of voltage regulating devices. To deal with incomplete and ...inaccurate distribution network models, we propose a safe off-policy deep reinforcement learning algorithm to solve Volt-VAR control problems in a model-free manner. The Volt-VAR control problem is formulated as a constrained Markov decision process with discrete action space, and solved by our proposed constrained soft actor-critic algorithm. Our proposed reinforcement learning algorithm achieves scalability, sample efficiency, and constraint satisfaction by synergistically combining the merits of the maximum-entropy framework, the method of multiplier, a device-decoupled neural network structure, and an ordinal encoding scheme. Comprehensive numerical studies with the IEEE distribution test feeders show that our proposed algorithm outperforms the existing reinforcement learning algorithms and conventional optimization-based approaches on a large feeder.
Deep Reinforcement Learning: A Survey Wang, Xu; Wang, Sen; Liang, Xingxing ...
IEEE transaction on neural networks and learning systems,
04/2024, Volume:
35, Issue:
4
Journal Article
Deep reinforcement learning (DRL) integrates the feature representation ability of deep learning with the decision-making ability of reinforcement learning so that it can achieve powerful end-to-end ...learning control capabilities. In the past decade, DRL has made substantial advances in many tasks that require perceiving high-dimensional input and making optimal or near-optimal decisions. However, there are still many challenging problems in the theory and applications of DRL, especially in learning control tasks with limited samples, sparse rewards, and multiple agents. Researchers have proposed various solutions and new theories to solve these problems and promote the development of DRL. In addition, deep learning has stimulated the further development of many subfields of reinforcement learning, such as hierarchical reinforcement learning (HRL), multiagent reinforcement learning, and imitation learning. This article gives a comprehensive overview of the fundamental theories, key algorithms, and primary research domains of DRL. In addition to value-based and policy-based DRL algorithms, the advances in maximum entropy-based DRL are summarized. The future research topics of DRL are also analyzed and discussed.
Hill's biography of Sir Julius is filled with accounts of the squabbles between him and other prominent figures, usually over money, and even records Sir Julius's own complaints about the visits he ...received from the Queen, since such visits demanded the outlay of a gift, which he claimed was unlikely to ever be returned to him via direct financial reward in spite of the several preferments he received-it was common knowledge after 12 September 1598, for example, that during the Queen's visit to Caesar's estate at Mitcham, he had identified himself in his supplication as 'the eldest judge, the youngest and the poorest', in reference to his frequent pleas regarding financial hardship.7 One public squabble is particularly relevant:
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