Search our Publications

Results

Jan Goepfert, Heiko Wersing, Barbara Hammer, Lukas Hindemith, "Intuitiveness in Active Teaching", IEEE Transactions on Human-Machine Systems, vol. 52, no. 3, pp. 458 - 467, 2022.

Abstract

Machine learning is a double-edged sword: it gives rise to astonishing results in automated systems, but at the cost of tremendously large data requirements. This makes many successful algorithms from machine learn- ing unsuitable for human-machine interaction, where the machine must learn from a small number of training samples that can be provided by a user within a reasonable time frame. Fortunately, the user can tailor the training data...



Download Bibtex file Download PDF

Ernest Hutapea, Nivesh Dommaraju, Mariusz Bujny, Fabian Duddeck, "Clustering Topologically-Optimized Designs based on Structural Deformation", Munich Symposium on Lightweight Design 2021, 2022.

Abstract

Topology optimization can be used to generate a large set of lightweight structural solutions either by changing the constraints or the weights for different objectives in multi-objective optimization. Engineers must analyze and review the designs to select solutions according to their preference towards objectives such as structural compliance and crash performance. However, the sheer number of solutions challenge the engineers' decision-making ...



Download Bibtex file Per Mail Request

Duc Anh Nguyen, Anna Kononova, Stefan Menzel, Bernhard Sendhoff, Thomas Bäck, "An Efficient Contesting Procedure for AutoML Optimization", IEEE Access, 2022.

Abstract

Automated Machine Learning (AutoML) frameworks are designed to select the optimal combination of operators and hyperparameters. Classical AutoML-based Bayesian Optimization (BO) approaches often integrate all operator search spaces into a single search space. However, a disadvantage of this history-based strategy is that it can be less robust when initialized randomly than optimizing each operator algorithm combination independently. To overcome ...



Download Bibtex file Download PDF

Stephen Friess, Peter Tino, Stefan Menzel, Zhao Xu, Bernhard Sendhoff, Xin Yao, "Spatio-Temporal Activity Recognition for Evolutionary Search Behavior Prediction", International Joint Conference on Neural Networks, 2022.

Abstract

Traditional methods for solving problems within computer science rely mostly upon the application of handcrafted algorithms. As however manual engineering of them can be considered to be a tedious process, it is interesting to consider how far internal mechanisms can be directly learned in an end-to-end manner instead. This is especially tempting when considering metaheuristic and evolutionary optimization routines which rely inherently upon stoc...



Download Bibtex file Per Mail Request

Hao Tong, Leandro Minku, Stefan Menzel, Bernhard Sendhoff, Xin Yao, "Benchmarking Dynamic Capacitated Arc Routing Algorithms Using Real-World Traffic Simulation", IEEE Congress on Evolutionary Computation, 2022.

Abstract

The combinatorial optimization of the dynamic capacitated arc routing problem (DCARP) targets to re-schedule the service plans of agents, such as vehicles in a city scenario, when dynamic events deteriorate the quality of the current schedule. Various algorithms have been proposed to solve DCARP instances in different dynamic scenarios. However, most existing work in literature developed algorithms and evaluated their performance based on artifi ...



Download Bibtex file Per Mail Request

Jiawen Kong, Wojtek Kowalczyk, Kees Jonkers, Stefan Menzel, Thomas Bäck, "Improved Sample Type Identification for Multi-Class Imbalanced Classification with Real-World Applications", 18th International Conference on Data Science (ICDATA22), 2022.

Abstract

Driven by studying the nature of imbalanced data, researchers proposed to consider different types of samples (safe, borderline, rare samples and outliers) in the minority class. The idea was first proposed and evaluated on binary imbalanced classification problems and then extended to multi-class scenarios. However, simply extending the identification rule in binary scenarios to multi-class scenarios results in several problems, for example, a h...



Download Bibtex file Per Mail Request

Chao Wang, "Design for Collaborative Intelligence: from Connected Vehicle, Autonomous Driving to Robotics. ", Jiangnan University (Wuxi, China) online Forum, 2022.

Abstract

The aim of the intelligent system should be enhancing human’s ability instead of replacing them. There is a much larger space for humans and AI to complement each other than compete, because their advantages are located in different aspects. This complementarity can be called Collaborative Intelligence(CI), which enables machines to achieve goals in complex environments together with human. CI requires mutual understanding and seamless communic...



Download Bibtex file Per Mail Request

Viktor Losing and Martina Hasenjäger, "A Multi-Modal Gait Database of Natural Everyday-Walk in an Urban Environment", Scientific Data, vol. 9, no. 473, 2022.

Abstract

Human gait data have traditionally been recorded in controlled laboratory environments focusing on single aspects in isolation. In contrast, the database presented here provides recordings of everyday walk scenarios in a natural urban environment, including synchronized IMU−, FSR−, and gaze data. Twenty healthy participants (five females, fifteen males, between 18 and 69 years old, 178.5 ± 7.64 cm, 72.9 ± 8.7 kg) wore a full-body Lycra suit...



Download Bibtex file Per Mail Request

Linda Spaa, van der, Jens Kober, Michael Gienger, "Disagreement-Aware Control for Online Learning Personal Solutions to Intention-Aware Physical Human-Robot Cooperation on Real Hardware", IEEE 2022 ICRA full day workshop: Shared Autonomy in Physical Human-Robot Interaction: Adaptability and Trust, 2022.

Abstract

In this paper we present a control methodology that allows the robot to be controlled and learn from its observations in a high-level abstract state-action space, without the human having to pay special attention to the robot’s kinematics/ dynamics or limits. A small number of data efficient steps allow quick setup and easy transfer to different setups in similarly structured contexts. The controller is tested on a 7-DoF Franka Emika Pan...



Download Bibtex file Per Mail Request

Felix Lanfermann and Sebastian Schmitt, "Concept identification for complex engineering datasets", Advanced Engineering Informatics, 2022.

Abstract

Finding meaningful concepts in engineering application datasets which allow for a sensible grouping of designs is very helpful in many contexts. It allows for determining different groups of designs with similar properties and provides useful knowledge in the engineering decision making process. Also, it opens the route for further refinements of specific design candidates which exhibit certain characteristic features. In this work, an approach t...



Download Bibtex file Download PDF

1 2 3 4 ... 134

Search