Hesselbergsoelberg5437

With our model-based analysis, we showed that changes in illumination affect recognition approaches that use Deep Convolutional Neural Network to encode features for object recognition. Using data augmentation, we were able to show that such a system can be modified toward a more robust recognition without retraining the network. Additionally, we have shown that using simple brightness change models can help to improve the recognition across all training set sizes.Natural language is inherently a discrete symbolic representation of human knowledge. Recent advances in machine learning (ML) and in natural language processing (NLP) seem to contradict the above intuition discrete symbols are fading away, erased by vectors or tensors called distributed and distributional representations. However, there is a strict link between distributed/distributional representations and discrete symbols, being the first an approximation of the second. A clearer understanding of the strict link between distributed/distributional representations and symbols may certainly lead to radically new deep learning networks. In this paper we make a survey that aims to renew the link between symbolic representations and distributed/distributional representations. This is the right time to revitalize the area of interpreting how discrete symbols are represented inside neural networks.Self-folding technologies have been studied by many researchers for applications to various engineering fields. Most of the self-folding methods that use the physical properties of materials require complex preparation, and usually take time to complete. In order to solve these problems, we focus on the elasticity of a material, and propose a model for forming a 3D structure using its characteristics. Our proposed model achieves high-speed and high-precision self-folding with a simple structure, by attaching rigid frames to a stretchable elastomer. The self-folded structure is applied to introduce a self-assembled actuator by exploiting a dielectric elastomer actuator (DEA). We develop the self-assembled actuator driven with the voltage application by attaching stretchable electrodes on the both side of the elastomer. We attempt several experiments to investigate the basic characteristics of the actuator. We also propose an application of the self-assembled actuator as a gripper based on the experimental results. The gripper has three joints with the angle of 120°, and successfully grabs objects by switching the voltage.Repertoire-based learning is a data-efficient adaptation approach based on a two-step process in which (1) a large and diverse set of policies is learned in simulation, and (2) a planning or learning algorithm chooses the most appropriate policies according to the current situation (e.g., a damaged robot, a new object, etc.). In this paper, we relax the assumption of previous works that a single repertoire is enough for adaptation. Instead, we generate repertoires for many different situations (e.g., with a missing leg, on different floors, etc.) and let our algorithm selects the most useful prior. Our main contribution is an algorithm, APROL (Adaptive Prior selection for Repertoire-based Online Learning) to plan the next action by incorporating these priors when the robot has no information about the current situation. We evaluate APROL on two simulated tasks (1) pushing unknown objects of various shapes and sizes with a robotic arm and (2) a goal reaching task with a damaged hexapod robot. We compare with "Reset-free Trial and Error" (RTE) and various single repertoire-based baselines. The results show that APROL solves both the tasks in less interaction time than the baselines. selleck chemical Additionally, we demonstrate APROL on a real, damaged hexapod that quickly learns to pick compensatory policies to reach a goal by avoiding obstacles in the path.Dielectric elastomers (DEs) consist of highly compliant electrostatic transducers which can be operated as actuators, by converting an applied high voltage into motion, and as sensors, since capacitive changes can be related to displacement information. Due to large achievable deformation (on the order of 100%) and high flexibility, DEs appear as highly suitable for the design of soft robotic systems. An important requirement for robotic systems is the possibility of generating a multi degree-of-freedom (MDOF) actuation. By means of DE technology, a controllable motion along several directions can be made possible by combining different membrane actuators in protagonist-antagonist configurations, as well as by designing electrode patterns which allow independent activation of different sections of a single membrane. However, despite several concepts of DE soft robots have been presented in the recent literature, up to date there is still a lack of systematic studies targeted at optimizing the design of the sy subsequently performed, with the aim of studying the influence of the regarded parameters on the rotation angle. Finally, based on the results of the performed study, a model-based optimization of the prototype geometry is performed.In the immediate aftermath following a large-scale release of radioactive material into the environment, it is necessary to determine the spatial distribution of radioactivity quickly. At present, this is conducted by utilizing manned aircraft equipped with large-volume radiation detection systems. Whilst these are capable of mapping large areas quickly, they suffer from a low spatial resolution due to the operating altitude of the aircraft. They are also expensive to deploy and their manned nature means that the operators are still at risk of exposure to potentially harmful ionizing radiation. Previous studies have identified the feasibility of utilizing unmanned aerial systems (UASs) in monitoring radiation in post-disaster environments. However, the majority of these systems suffer from a limited range or are too heavy to be easily integrated into regulatory restrictions that exist on the deployment of UASs worldwide. This study presents a new radiation mapping UAS based on a lightweight (8 kg) fixed-wing unmanned aircraft and tests its suitability to mapping post-disaster radiation in the Chornobyl Exclusion Zone (CEZ).