A Platform-independent Programming Environment for Robot Control

The development of robot control programs is a complex task. Many robots are different in their electrical and mechanical structure which is also reflected in the software. Specific robot software environments support the program development, but are mainly text-based and usually applied by experts in the field with profound knowledge of the target robot. This paper presents a graphical programming environment which aims to ease the development of robot control programs. In contrast to existing graphical robot programming environments, our approach focuses on the composition of parallel action sequences. The developed environment allows to schedule independent robot actions on parallel execution lines and provides mechanism to avoid side-effects of parallel actions. The developed environment is platform-independent and based on the model-driven paradigm. The feasibility of our approach is shown by the application of the sequencer to a simulated service robot and a robot for educational purpose

TSEM: Temporally Weighted Spatiotemporal Explainable Neural Network for Multivariate Time Series

Deep learning has become a one-size-fits-all solution for technical and business domains thanks to its flexibility and adaptability. It is implemented using opaque models, which unfortunately undermines the outcome trustworthiness. In order to have a better understanding of the behavior of a system, particularly one driven by time series, a look inside a deep learning model so-called posthoc eXplainable Artificial Intelligence (XAI) approaches, is important. There are two major types of XAI for time series data, namely model-agnostic and model-specific. Model-specific approach is considered in this work. While other approaches employ either Class Activation Mapping (CAM) or Attention Mechanism, we merge the two strategies into a single system, simply called the Temporally Weighted Spatiotemporal Explainable Neural Network for Multivariate Time Series (TSEM). TSEM combines the capabilities of RNN and CNN models in such a way that RNN hidden units are employed as attention weights for the CNN feature maps temporal axis. The result shows that TSEM outperforms XCM. It is similar to STAM in terms of accuracy, while also satisfying a number of interpretability criteria, including causality, fidelity, and spatiotemporality

TSEM: Temporally Weighted Spatiotemporal Explainable Neural Network for Multivariate Time Series

Deep learning has become a one-size-fits-all solution for technical and business domains thanks to its flexibility and adaptability. It is implemented using opaque models, which unfortunately undermines the outcome trustworthiness. In order to have a better understanding of the behavior of a system, particularly one driven by time series, a look inside a deep learning model so-called posthoc eXplainable Artificial Intelligence (XAI) approaches, is important. There are two major types of XAI for time series data, namely model-agnostic and model-specific. Model-specific approach is considered in this work. While other approaches employ either Class Activation Mapping (CAM) or Attention Mechanism, we merge the two strategies into a single system, simply called the Temporally Weighted Spatiotemporal Explainable Neural Network for Multivariate Time Series (TSEM). TSEM combines the capabilities of RNN and CNN models in such a way that RNN hidden units are employed as attention weights for the CNN feature maps temporal axis. The result shows that TSEM outperforms XCM. It is similar to STAM in terms of accuracy, while also satisfying a number of interpretability criteria, including causality, fidelity, and spatiotemporality

Precise pointing target recognition for human-robot interaction

This work presents a person independent pointing gesture recognition application. It uses simple but effective features for the robust tracking of the head and the hand of the user in an undefined environment. The application is able to detect if the tracking is lost and can be reinitialized automatically. The pointing gesture recognition accuracy is improved by the proposed fingertip detection algorithm and by the detection of the width of the face. The experimental evaluation with eight different subjects shows that the overall average pointing gesture recognition rate of the system for distances up to 250 cm (head to pointing target) is 86.63% (with a distance between objects of 23 cm). Considering just frontal pointing gestures for distances up to 250 cm the gesture recognition rate is 90.97% and for distances up to 194 cm even 95.31%. The average error angle is 7.28◦

Impact of stratospheric major warmings and the quasi-biennial oscillation on the variability of stratospheric water vapompo

Based on simulations with the Chemical Lagrangian Model of the Stratosphere for the 1979–2013 period, driven by the European Centre for Medium-Range Weather Forecasts ERA-Interim reanalysis, we analyze the impact of the quasi-biennial oscillation (QBO) and of Major Stratospheric Warmings (MWs) on the amount of water vapor entering the stratosphere during boreal winter. The amplitude of H2O variation related to the QBO amounts to 0.5 ppmv. The additional effect of MWs reaches its maximum about 2–4 weeks after the central date of the MW and strongly depends on the QBO phase. Whereas during the easterly QBO phase there is a pronounced drying of about 0.3 ppmv about 3 weeks after the MW, the impact of the MW during the westerly QBO phase is smaller (about 0.2 ppmv) and more diffusely spread over time. We suggest that the MW-associated enhanced dehydration combined with a higher frequency of MWs after the year 2000 may have contributed to the lower stratospheric water vapor after 2000