Modification of Polypropylene/Polystyrene blend using Nanoclay and Sisal Cellulose nano fiber

Cochin University of Science and Technology

Design and Development of Fiber Grating Based Chemical and Bio-Sensors

International School of Photonics

Verbesserung der seismischen Kapazität von Mauerwerkswänden durch Verwendung von Elastomerlagern

Ein großer Teil der Schäden wie auch der Verluste an Gesundheit und Leben im Erdbebenfall hat mit dem frühzeitigen Versagen von Mauerwerksbauten zu tun. Unbewehrtes Mauerwerk, wie es in vielen Ländern üblich ist, weist naturgemäß einen begrenzten Erdbebenwiderstand auf, da Zugspannungen und Zugkräfte nicht wie bei Stahlbeton- oder Stahlbauten aufgenommen werden können. Aus diesem Grund wurde bereits mit verschiedenen Methoden versucht, die Tragfähigkeit von Mauerwerk im Erdbebenfall zu verbessern. Modernes Mauerwerk kann auch als bewehrtes oder eingefasstes Mauerwerk hergestellt werden. Bei bewehrtem Mauerwerk kann durch die Bewehrung der Widerstand bei Beanspruchung als Scheibe wie als Platte verbessert werden, während durch Einfassung mit Stahlbetonelementen in erster Linie die Scheibentragfähigkeit sowie die Verbindung zu angrenzenden Bauteilen verbessert wird. Eine andere interessante Möglichkeit ist das Aufbringen textiler Mauerwerksverstärkungen oder von hochfesten Lamellen. In dieser Arbeit wird ein ganz anderer Weg beschritten, indem weiche Fugen Spannungsspitzen reduzieren sowie eine höhere Verformbarkeit gewährleiten. Dies ist im Erdbebenfall sehr hilfreich, da die Widerstandfähigkeit eines Bauwerks oder Bauteils letztlich von der Energieaufnahmefähigkeit, also dem Produkt aus Tragfähigkeit und Verformbarkeit bestimmt wird. Wenn also gleichzeitig durch die weichen Fugen keine Schwächung oder sogar eine Tragfähigkeitserhöhung stattfindet, kann der Erdbebenwiderstand gesteigert werden. Im Kern der Dissertation steht die Entwicklung der Baukonstruktion einer Mauerwerkstruktur mit einer neuartigen Ausbildung der Mauerwerksfugen, nämlich Elastomerlager und Epoxydharzkleber anstatt üblichem Dünnbettmörtel. Das Elastomerlager wird zwischen die Steinschichten einer Mauerwerkswand eingefügt und damit verklebt. Die Auswirkung dieses Ansatzes auf das Verhalten der Mauerwerkstruktur wird unter dynamischer und quasi-statischer Last numerisch und experimentell untersucht und dargestellt.

Punching Shear Behavior of UHPC Flat Slabs

At the Institute of Structural Engineering of the Faculty of Civil Engineering, Kassel University, series tests of slab-column connection were carried out, subjected to concentrated punching load. The effects of steel fiber content, concrete compressive strength, tension reinforcement ratio, size effect, and yield stress of tension reinforcement were studied by testing a total of six UHPC slabs and one normal strength concrete slab. Based on experimental results; all the tested slabs failed in punching shear as a type of failure, except the UHPC slab without steel fiber which failed due to splitting of concrete cover. The post ultimate load-deformation behavior of UHPC slabs subjected to punching load shows harmonic behavior of three stages; first, drop of load-deflection curve after reaching maximum load, second, resistance of both steel fibers and tension reinforcement, and third, pure tension reinforcement resistance. The first shear crack of UHPC slabs starts to open at a load higher than that of normal strength concrete slabs. Typically, the diameter of the punching cone for UHPC slabs on the tension surface is larger than that of NSC slabs and the location of critical shear crack is far away from the face of the column. The angle of punching cone for NSC slabs is larger than that of UHPC slabs. For UHPC slabs, the critical perimeter is proposed and located at 2.5d from the face of the column. The final shape of the punching cone is completed after the tension reinforcement starts to yield and the column stub starts to penetrate through the slab. A numerical model using Finite Element Analysis (FEA) for UHPC slabs is presented. Also some variables effect on punching shear is demonstrated by a parametric study. A design equation for UHPC slabs under punching load is presented and shown to be applicable for a wide range of parametric variations; in the ranges between 40 mm to 300 mm in slab thickness, 0.1 % to 2.9 % in tension reinforcement ratio, 150 MPa to 250 MPa in compressive strength of concrete and 0.1 % to 2 % steel fiber content. The proposed design equation of UHPC slabs is modified to include HSC and NSC slabs without steel fiber, and it is checked with the test results from earlier researches.

A Reinforcement-Learning Approach to Power Management

We describe an adaptive, mid-level approach to the wireless device power management problem. Our approach is based on reinforcement learning, a machine learning framework for autonomous agents. We describe how our framework can be applied to the power management problem in both infrastructure and ad~hoc wireless networks. From this thesis we conclude that mid-level power management policies can outperform low-level policies and are more convenient to implement than high-level policies. We also conclude that power management policies need to adapt to the user and network, and that a mid-level power management framework based on reinforcement learning fulfills these requirements.

Reinforcement Learning by Policy Search

One objective of artificial intelligence is to model the behavior of an intelligent agent interacting with its environment. The environment's transformations can be modeled as a Markov chain, whose state is partially observable to the agent and affected by its actions; such processes are known as partially observable Markov decision processes (POMDPs). While the environment's dynamics are assumed to obey certain rules, the agent does not know them and must learn. In this dissertation we focus on the agent's adaptation as captured by the reinforcement learning framework. This means learning a policy---a mapping of observations into actions---based on feedback from the environment. The learning can be viewed as browsing a set of policies while evaluating them by trial through interaction with the environment. The set of policies is constrained by the architecture of the agent's controller. POMDPs require a controller to have a memory. We investigate controllers with memory, including controllers with external memory, finite state controllers and distributed controllers for multi-agent systems. For these various controllers we work out the details of the algorithms which learn by ascending the gradient of expected cumulative reinforcement. Building on statistical learning theory and experiment design theory, a policy evaluation algorithm is developed for the case of experience re-use. We address the question of sufficient experience for uniform convergence of policy evaluation and obtain sample complexity bounds for various estimators. Finally, we demonstrate the performance of the proposed algorithms on several domains, the most complex of which is simulated adaptive packet routing in a telecommunication network.

Hybrid coordination of reinforcement learning-based behaviors for AUV control

This paper proposes a hybrid coordination method for behavior-based control architectures. The hybrid method takes advantages of the robustness and modularity in competitive approaches as well as optimized trajectories in cooperative ones. This paper shows the feasibility of applying this hybrid method with a 3D-navigation to an autonomous underwater vehicle (AUV). The behaviors are learnt online by means of reinforcement learning. A continuous Q-learning implemented with a feed-forward neural network is employed. Realistic simulations were carried out. The results obtained show the good performance of the hybrid method on behavior coordination as well as the convergence of the behaviors

A behavior-based scheme using reinforcement learning for autonomous underwater vehicles

This paper presents a hybrid behavior-based scheme using reinforcement learning for high-level control of autonomous underwater vehicles (AUVs). Two main features of the presented approach are hybrid behavior coordination and semi on-line neural-Q_learning (SONQL). Hybrid behavior coordination takes advantages of robustness and modularity in the competitive approach as well as efficient trajectories in the cooperative approach. SONQL, a new continuous approach of the Q_learning algorithm with a multilayer neural network is used to learn behavior state/action mapping online. Experimental results show the feasibility of the presented approach for AUVs

Policy gradient based Reinforcement Learning for real autonomous underwater cable tracking

This paper proposes a field application of a high-level reinforcement learning (RL) control system for solving the action selection problem of an autonomous robot in cable tracking task. The learning system is characterized by using a direct policy search method for learning the internal state/action mapping. Policy only algorithms may suffer from long convergence times when dealing with real robotics. In order to speed up the process, the learning phase has been carried out in a simulated environment and, in a second step, the policy has been transferred and tested successfully on a real robot. Future steps plan to continue the learning process on-line while on the real robot while performing the mentioned task. We demonstrate its feasibility with real experiments on the underwater robot ICTINEU AUV

Autonomous underwater vehicle control using reinforcement learning policy search methods

Autonomous underwater vehicles (AUV) represent a challenging control problem with complex, noisy, dynamics. Nowadays, not only the continuous scientific advances in underwater robotics but the increasing number of subsea missions and its complexity ask for an automatization of submarine processes. This paper proposes a high-level control system for solving the action selection problem of an autonomous robot. The system is characterized by the use of reinforcement learning direct policy search methods (RLDPS) for learning the internal state/action mapping of some behaviors. We demonstrate its feasibility with simulated experiments using the model of our underwater robot URIS in a target following task

Towards Direct Policy Search Reinforcement Learning for Robot Control

This paper proposes a high-level reinforcement learning (RL) control system for solving the action selection problem of an autonomous robot. Although the dominant approach, when using RL, has been to apply value function based algorithms, the system here detailed is characterized by the use of direct policy search methods. Rather than approximating a value function, these methodologies approximate a policy using an independent function approximator with its own parameters, trying to maximize the future expected reward. The policy based algorithm presented in this paper is used for learning the internal state/action mapping of a behavior. In this preliminary work, we demonstrate its feasibility with simulated experiments using the underwater robot GARBI in a target reaching task