Decomposing user-defined tasks in a reinforcement learning setup using TextWorld.

The current paper proposes a hierarchical reinforcement learning (HRL) method to decompose a complex task into simpler sub-tasks and leverage those to improve the training of an autonomous agent in a simulated environment. For practical reasons (i.e.

CoFly-WeedDB: A UAV image dataset for weed detection and species identification.

The CoFly-WeedDB contains 201 RGB images (∼436 MB) from the attached camera of DJI Phantom Pro 4 from a cotton field in Larissa, Greece during the first stages of plant growth. The 1280 × 720 RGB images were collected while the Unmanned Aerial Vehicle (UAV) was performing a coverage mission over the field's area. During the designed mission, the camera angle was adjusted to -87°, vertically with the field.

PLUG-N-HARVEST Architecture for Secure and Intelligent Management of Near-Zero Energy Buildings.

Building Automation (BA) is key to encourage the growth of more sustainable cities and smart homes. However, current BA systems are not able to manage new constructions based on Adaptable/Dynamic Building Envelopes (ADBE) achieving near-zero energy-efficiency. The ADBE buildings integrate Renewable Energy Sources (RES) and Envelope Retrofitting (ER) that must be managed by new BA systems based on Artificial Intelligence (AI) and Internet of Things (IoT) through secure protocols.

Intrajejunal migration of adjustable gastric band: a case report.

Introduction. Laparoscopic gastric banding is a first line bariatric procedure that is performed worldwide and can achieve substantial weight loss. Despite its many advantages, as the least invasive bariatric procedure, it has several complications like gastric prolapse, stoma obstruction and migration of the gatstric band.

Control of unknown nonlinear systems with efficient transient performance using concurrent exploitation and exploration.

Learning mechanisms that operate in unknown environments should be able to efficiently deal with the problem of controlling unknown dynamical systems. Many approaches that deal with such a problem face the so-called exploitation-exploration dilemma where the controller has to sacrifice efficient performance for the sake of learning "better" control strategies than the ones already known: during the exploration period, poor or even unstable closed-loop system performance may be exhibited. In this paper, we show that, in the case where the control goal is to stabilize an unknown dynamical system by means of state feedback, exploitation and exploration can be concurrently performed without the need of sacrificing efficiency.

Large scale nonlinear control system fine-tuning through learning.

Despite the continuous advances in the fields of intelligent control and computing, the design and deployment of efficient large scale nonlinear control systems (LNCSs) requires a tedious fine-tuning of the LNCS parameters before and during the actual system operation. In the majority of LNCSs the fine-tuning process is performed by experienced personnel based on field observations via experimentation with different combinations of controller parameters, without the use of a systematic approach. The existing adaptive/neural/fuzzy control methodologies cannot be used towards the development of a systematic, automated fine-tuning procedure for general LNCS due to the strict assumptions they impose on the controlled system dynamics; on the other hand, adaptive optimization methodologies fail to guarantee an efficient and safe performance during the fine-tuning process, mainly due to the fact that these methodologies involve the use of random perturbations.

Universal stabilization using control Lyapunov functions, adaptive derivative feedback, and neural network approximators.

In this paper, the problem of stabilization of unknown nonlinear dynamical systems is considered. An adaptive feedback law is constructed that is based on the switching adaptive strategy proposed by the author and uses linear-in-the-weights neural networks accompanied with appropriate robust adaptive laws in order to estimate the time-derivative of the control Lyapunov function (CLF) of the system. The closed-loop system is shown to be stable; moreover, the state vector of the controlled system converges to a ball centered at the origin and having a radius that can be made arbitrarily small by increasing the high gain K and the number of neural network regressor terms.

Correction on 'Dynamical neural networks that ensure exponential identification error convergence'.

In the article by [Kosmatopoulos et al. (1997)] (Neural Networks 10(2) 299-314) the Theorem 4.1 was incorrect.

Dynamical Neural Networks that Ensure Exponential Identification Error Convergence.

Classical adaptive and robust adaptive schemes, are unable to ensure convergence of the identification error to zero, in the case of modeling errors. Therefore, the usage of such schemes to "black-box" identification of nonlinear systems ensures-in the best case-bounded identification error. In this paper, new learning (adaptive) laws are proposed which when applied to recurrent high order neural networks (RHONN) ensure that the identification error converges to zero exponentially fast, and even more, in the case where the identification error is initially zero, it remains equal to zero during the whole identification process.

Convergence properties of a class of learning vector quantization algorithms.

A mathematical analysis of a class of learning vector quantization (LVQ) algorithms is presented. Using an appropriate time-coordinate transformation, we show that the LVQ algorithms under consideration can be transformed into linear time-varying stochastic difference equations. Using this fact, we apply stochastic Lyapunov stability arguments, and we prove that the LVQ algorithms under consideration do indeed converge, provided that some appropriate conditions hold.

High-order neural network structures for identification of dynamical systems.

Several continuous-time and discrete-time recurrent neural network models have been developed and applied to various engineering problems. One of the difficulties encountered in the application of recurrent networks is the derivation of efficient learning algorithms that also guarantee the stability of the overall system. This paper studies the approximation and learning properties of one class of recurrent networks, known as high-order neural networks; and applies these architectures to the identification of dynamical systems.