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arXiv 2023年

作者： Beierling, Helen Richter, Phillip Brandt, Mara Terfloth, Lutz Schulte, Carsten Wersing, Heiko Vollmer, Anna-Lisa Bielefeld University Medical School EWL Interactive Robotics Universitätsstraße 25 Bielefeld33615 Germany Paderborn University Faculty of Computer Science Electrical Engineering and Mathematics Didactics of Informatics Warburger Straße 100 Paderborn33098 Germany Honda Research Institute Europe GmbH Carl-Legien-Str. 30 Offenbach am Main 63073 Germany

Nowadays, we are dealing more and more with robots and AI in everyday life. However, their behavior is not always apparent to most lay users, especially in error situations. As a result, there can be misconceptions about the behavior of the technologies in use. This, in turn, can lead to misuse and rejection by users. Explanation, for example, through transparency, can address these misconceptions. However, it would be confusing and overwhelming for users if the entire software or hardware was explained. Therefore, this paper looks at the 'enabling' architecture. It describes those aspects of a robotic system that might need to be explained to enable someone to use the technology effectively. Furthermore, this paper is concerned with the 'explanandum', which is the corresponding misunderstanding or missing concepts of the enabling architecture that needs to be clarified. We have thus developed and present an approach for determining this 'enabling' architecture and the resulting 'explanandum' of complex technologies. © 2023, CC BY.

关键词： robotics

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Activity With Gender Recognition Using Accelerometer and Gyroscope

Activity With Gender Recognition Using Accelerometer and Gyr...

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International Conference on Ubiquitous Information Management and Communication (IMCOM)

作者： Ahmed Sharshar Ahmed Fayez Yasser Ashraf Walid Gomaa Computer Science and Engineering Egypt-Japan University of Science and Technology Alexandria Egypt Mechatronics and Robotics Engineering Egypt-Japan University of Science and Technology Alexandria Egypt Egypt-Japan University of Science and Technology and Faculty of Engineering Alexandria University Alexandria Egypt

ISBN: (数字)9781665423182

ISBN: (纸本)9781665446198

Recently, the use of the inertia measurement units (IMU), especially the gyroscope and accelerometer sensors, has increased in the human activity recognition (HAR) due to the extensive use of smartwatches and smartphones. In addition to the high quality and efficiency result in by these sensors, they can capture the data of the body dynamic motion as function of time, then the stream of data is analyzed and processed to classify and predict the action being done, the gender, the health status and many other characteristics. Gender and activity recognition have been deeply studied recently, using various ways to recognize either of them through many interfaces, like voice, image, or inertia measurement motion data. All these types of classifications are crucial in many applications such as recommendation systems, speech recognition, sports tracking, security and most importantly in healthcare. In this research, we present two models (hierarchical model and joint distribution model) and compare between two datasets (MoVi and MotionSense), using only two IMU sensors on right and left hand and motion sense dataset using mobile phone, to predict gender with activity and see how every activity reflect on gender, and explore the efficiency on using autocorrelation function as a feature extractor and compare between three classifiers, Random Forest (RF), Support Vector Machine (SVM) and Convolution Neural Network (CNN).

关键词： Support vector machines Accelerometers Speech recognition Predictive models Activity recognition Feature extraction Gyroscopes

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Jet mixing enhancement with Bayesian optimization, deep learning, and persistent data topology

arXiv

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arXiv 2023年

作者： Li, Yiqing Noack, Bernd R. Wang, Tianyu Cornejo Maceda, Guy Y. Pickering, Ethan Shaqarin, Tamir Tyliszczak, Artur School of Mechanical Engineering and Automation Harbin Institute of Technology Shenzhen518055 China Guangdong Provincial Key Laboratory of Intelligent Morphing Mechanisms and Adaptive Robotics Harbin Institute of Technology Shenzhen518055 China Department of Mechanical Engineering Tafila Technical University Tafila66110 Jordan Faculty of Mechanical Engineering and Computer Science Czestochowa University of Technology Czestochowa42-201 Poland

We optimize the jet mixing using large eddy simulations (LES) at a Reynolds number of 3000. Key methodological enablers consist of Bayesian optimization, a surrogate model enhanced by deep learning, and persistent data topology for physical interpretation. The mixing performance is characterized by an equivalent jet radius (Req) derived from the streamwise velocity in a plane located 8 diameters downstream. The optimization is performed in a 22-dimensional actuation space that comprises most known excitations. The plant benefits from a 22-dimensional actuation space that comprises most known excitations. This search space parameterizes distributed actuation imposed on the bulk flow and at the periphery of the nozzle in the streamwise and radial directions. The momentum flux measures the energy input of the actuation. The optimization quadruples the jet radius Req with a 7-armed blooming jet after around 570 evaluations. The control input requires 2% momentum flux of the main flow, which is one order of magnitude lower than an ad hoc dual-mode excitation. Intriguingly, a pronounced suboptimum in the search space is associated with a double-helix jet, a new flow pattern. This jet pattern results in a mixing improvement comparable to the blooming jet. A state-of-the-art Bayesian optimization converges towards this double helix solution. The learning is accelerated and converges to another better optimum by including surrogate model trained along the optimization. Persistent data topology extracts the global and many local minima in the actuation space. These minima can be identified with flow patterns beneficial to the mixing. © 2023, CC BY-NC-ND.

关键词： Flow patterns

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Navigational freespace detection for autonomous driving in fixed routes 28

Navigational freespace detection for autonomous driving in f...

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28th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2020

作者： Narayan, Aparajit Tuci, Elio Sachiti, William Parsons, Aaron Academy of Robotics 33 Cathedral Road Cardiff WalesCF11 9HB United Kingdom Faculty of Computer Science University of Namur Rue Grandgagnage 21 Namur5000 Belgium

ISBN: (纸本)9782875870742

Vision-based modules are largely exploited by autonomous driving vehicles to identify the road area and to avoid collisions with other vehicles, pedestrians and obstacles. This paper illustrates the results of a comparative study in which eight different vision-based modules are evaluated for detecting free navigational space in urban environments. All modules are implemented using Convolutions Neural Networks. The distinctive and innovative feature of these modules is the manner via which navigational freespace is identified from image inputs. The modules generate the coordinates of a triangle, whose area represents the navigation freespace. The relative position of the triangle top corner with respect to the image centre points toward the vehicle direction of motion. Thus, when trained on a fixed route, these modules are able to successfully detect the road-freepsace and to make appropriate decisions concerning where to go at roundabouts, intersections etc., in order to reach the final destination. © ESANN 2020 - Proceedings, 28th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning.

关键词： Autonomous vehicles

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Tools for affective computations in the management of energy facilities, considering the emotional state of operators

Tools for affective computations in the management of energy...

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International Conference on Electrotechnical Complexes and Systems (ICOECS)

作者： Nafisa Yusupova Diana Bogdanova Nadejda Komendantova Elena Sirotenko Albert Niyazgulov Faculty of Computer Science and Robotics Ufa State Aviation Technical University Ufa Russia Cooperation and Transformative Governance Group International Institute for Applied Systems Analysis (IIASA) Laxenburg Austria

ISBN: (纸本)9781665407281

The paper discusses the issues of using the tools of affective computations in the management of energy facilities, taking into account the emotional state of operators. The analysis of known approaches in this direction of research is carried out. A diagram of the decision support process and a complex of management mechanisms are proposed, taking into account the use of emotionally colored information. The results of a computational experiment for the problem of classifying emotionally colored information are considered. To assess the emotional state of the operator, the best results can be obtained using a neural convolutional network.

关键词： Support vector machines Decision support systems Machine learning Convolutional neural networks

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Intelligent analysis of medical and psychophysiological data (invited paper) 3

Intelligent analysis of medical and psychophysiological data...

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3rd International Workshop on Young Scientist's Trends in Information Processing, YSIP3 2019

作者： Yusupova, Nafisa Mironov, Konstantin Faculty of Computer Science and Robotics Ufa State Aviation Technical University Ufa Russia

The paper is dedicated to the application of Intelligent methods of data analysis on the examples of medical and psychophysiological tasks. Although there are pretty much research in this field, unified complex methodology of medical data analysis does not exist. In this paper we present the short overview of using various means of data analysis in medical applications: big data, machine learning, text mining, multi-agent systems. We present wo cases of intelligent data analysis performed by the researchers from Ufa State Aviation Technical University in collaboration with experts and researchers from the medical institutions in the city of Ufa. First case consist in analysis of weakstructured data about acute poisonings in the Republic of Bashkortostan. The second case was connected to analysis of the results of psychophysiological diagnostics of students in order to determine recommendations for their physical activity. © 2019 for this paper by its authors.

关键词： Medical applications

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AI-perspectives: the Turing option

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AI Perspectives 2020年第1期2卷 1-12页

作者： Kirchner, Frank Faculty of Mathematics and Computer Science University of Bremen Robotics Group Bremen Germany DFKI GmbH (German Research Center for Artificial Intelligence) Robotics Innovation Center Bremen Germany

This paper presents a perspective on AI that starts with going back to early work on this topic originating in theoretical work of Alan Turing. The argument is made that the core idea - that leads to the title of this paper - of these early thoughts are still relevant today and may actually provide a starting point to make the transition from today functional AI solutions towards integrative or general AI.

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Cancer Breakpoint Hotspots Versus Individual Breakpoints Prediction by Machine Learning Models 1

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16th International Symposium on Bioinformatics Research and Applications, ISBRA 2020

作者： Cheloshkina, Kseniia Bzhikhatlov, Islam Poptsova, Maria Laboratory of Bioinformatics Faculty of Computer Science National Research University Higher School of Economics 11 Pokrovsky boulvar Moscow101000 Russia Faculty of Control Systems and Robotics ITMO University 49 Kronverksky Pr. St. Petersburg197101 Russia

ISBN: (数字)9783030578213

ISBN: (纸本)9783030578206

Genome rearrangement is a hallmark of all cancers. Cancer breakpoint prediction appeared to be a difficult task, and various machine learning models did not achieve high prediction power. We investigated the power of machine learning models to predict breakpoint hotspots selected with different density thresholds and also compared prediction of hotspots versus individual breakpoints. We found that hotspots are considerably better predicted than individual breakpoints. While choosing a selection criterion, the test ROC AUC only is not enough to choose the best model, the lift of recall and lift of precision should be taken into consideration. Investigation of the lift of recall and lift of precision showed that it is impossible to select one criterion of hotspot selection for all cancer types but there are three to four distinct groups of cancer with similar properties. Overall the presented results point to the necessity to choose different hotspots selection criteria for different types of cancer. © 2020, Springer Nature Switzerland AG.

关键词： Machine learning

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Distributed formation control of networked mechanical systems

arXiv

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arXiv 2022年

作者： Javanmardi, Najmeh Borja, P. Yazdanpanah, M.J. Scherpen, J.M.A. Control and Intelligent Processing Center of Excellence School of Electrical and Computer Engineering University of Tehran Tehran Iran Department of Cognitive Robotics Delft University of Technology Delft Netherlands Jan C. Wilems Center for Systems and Control ENTEG Faculty of Science and Engineering University of Groningen Groningen Netherlands

This paper investigates a distributed formation tracking control law for large-scale networks of mechanical systems. In particular, the formation network is represented by a directed communication graph with leaders and followers, where each agent is described as a port-Hamiltonian system with a constant mass matrix. Moreover, we adopt a distributed parameter approach to prove the scalable asymptotic stability of the network formation, i.e., the scalability with respect to the network size and the specific formation preservation. A simulation case illustrates the effectiveness of the proposed control approach. © 2022, CC BY.

关键词： Networked control systems

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Towards continuous control for mobile robot navigation: A reinforcement learning and slam based approach 4

Towards continuous control for mobile robot navigation: A re...

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4th ISPRS Geospatial Week 2019

作者： Mustafa, K.A.A. Botteghi, N. Sirmacek, B. Poel, M. Stramigioli, S. Robotics and Mechatronics Faculty of Electrical Engineering Mathematics and Computer Science University of Twente Netherlands Data Science Faculty of Electric Engineering Mathematics and Computer Science University of Twente Netherlands

We introduce a new autonomous path planning algorithm for mobile robots for reaching target locations in an unknown environment where the robot relies on its on-board sensors. In particular, we describe the design and evaluation of a deep reinforcement learning motion planner with continuous linear and angular velocities to navigate to a desired target location based on deep deterministic policy gradient (DDPG). Additionally, the algorithm is enhanced by making use of the available knowledge of the environment provided by a grid-based SLAM with Rao-Blackwellized particle filter algorithm in order to shape the reward function in an attempt to improve the convergence rate, escape local optima and reduce the number of collisions with the obstacles. A comparison is made between a reward function shaped based on the map provided by the SLAM algorithm and a reward function when no knowledge of the map is available. Results show that the required learning time has been decreased in terms of number of episodes required to converge, which is 560 episodes compared to 1450 episodes in the standard RL algorithm, after adopting the proposed approach and the number of obstacle collision is reduced as well with a success ratio of 83% compared to 56% in the standard RL algorithm. The results are validated in a simulated experiment on a skid-steering mobile robot. © Authors 2019.

关键词： Reinforcement learning

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