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 ...
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Sadullah Canakci , Nikolay Matyunin, Kalman Graffi, Manuel Egele,
"TargetFuzz: Using DARTs to Guide Directed Greybox Fuzzers",
The 17th ACM ASIA Conference on Computer and Communications Security (ACM ASIACCS 2022), 2022.
Abstract
Software development is a continuous and incremental process. Developers continuously improve their software in small batches rather than in one large batch. The high frequency of small batches makes it essential to use effective testing methods that detect bugs under limited testing time. To this end, researchers propose directed greybox fuzzing (DGF) which aims to generate test cases towards stressing certain target sites. Different from the co...
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Tapabrata Ray, Hemant Singh, Kamrul Rahi, Tobias Rodemann, Markus Olhofer,
"Towards identification of solutions of interest for multi-objective problems considering both objective and variable space information",
Applied Soft Computing, vol. 119, 2022.
Abstract
In practical multi/many-objective optimization problems, a decision maker (DM) is often only interested in a handful of solutions of interest (SOI) instead of a large collection of trade-off solutions spanning the entire Pareto Front (PF). Optimization algorithms capable of searching for SOIs have so far considered selection measures based on objective space only, such as reflex angle, bend angle, expected marginal utility, etc. Depending on the ...
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Hao Tong, Leandro Minku, Stefan Menzel, Bernhard Sendhoff, Xin Yao,
"A Novel Generalised Meta-Heuristic Framework for Dynamic Capacitated Arc Routing Problems",
IEEE Transactions on Evolutionary Computation, 2022.
Abstract
The capacitated arc routing problem (CARP) is a challenging combinatorial optimisation problem abstracted from many real-world applications, such as waste collection, road gritting and mail delivery. However, few studies considered dynamic changes during the vehicles’ service, which can cause the original schedule infeasible or obsolete. The few existing studies are limited by the dynamic scenarios considered, and by overly complicated algorith...
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Patricia Wollstadt, Mariusz Bujny, Satchit Ramnath, Jami Shah, Duane Detwiler, Stefan Menzel,
"CarHoods10k: An Industry-grade Data Set for Representation Learning and Design Optimization in Engineering Applications",
IEEE Transactions on Evolutionary Computation Special Issue on Benchmarking Sampling-Based Optimization Heuristics: Methodology and Software (BENCH), 2022.
Abstract
Research and development of cutting-edge optimization frameworks that exploit the advances in novel machine learning methods is dependent on the availability of large data sets resembling the targeted application. Especially in the engineering domain such high quality data sets are rare due to confidentiality concerns and generation costs, be it computational or manual efforts. Here, we introduce the OSU-Honda Automobile Hood Dataset (CarHoods10k...
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Yali Wang,
"Multi-objective Evolutionary Algorithms for Optimal Scheduling",
Leiden University, 2022.
Abstract
The research topic of the thesis is the extension of evolutionary multi-objective optimization for real-world scheduling problems. Several novel algorithms are proposed: the diversity indicator-based multi-objective evolutionary algorithm (DI-MOEA) can achieve a uniformly distributed solution set; the preference-based MOEA can obtain preferred solutions; the edge-rotated cone can improve the performance of MOEAs for many-objective optimization; a...
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Satchit Ramnath, Jami Shah, Patricia Wollstadt, Mariusz Bujny, Stefan Menzel, Duane Detwiler,
"OSU-Honda automobile hood dataset (CarHoods10k)",
Dryad.org, 2022.
Abstract
The CarHoods10k data set comprises a set of over 10,000 3D mesh geometries for variants of car hood frames, generated through an automated, industry-grade Computer Aided Design (CAD) workflow described in Ramnath (2019). The data set provides realistic designs that were validated by experts with respect to realism, manufacturability, variability, and performance. Variations in geometries were generated by a feature-based approach that varies para...
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Jihong Zhu, Michael Gienger, Jens Kober,
"Learning Task-Parameterized Skills from Few Demonstrations",
ICRA / RA-L, 2022.
Abstract
This is a follow-up of pub-4739
Moving away from repetitive tasks, robots nowadays
demand versatile skills that adapt to different situations. Task-
parameterized approaches improve the generalization of motion
policy by encoding relevant contextual information in the task
parameters, hence enabling flexible task executions. However,
training such a policy often requires collecting multiple demon-
strations in different situations. To cr...
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Jihong Zhu, Andrea Cherubini, Claire Dune, David Navarro-Alarcon, Farshid Alambeigi, Dmitry Berenson, Fanny Ficuciello, Michael Gienger, Kensuke Harada, Jens Kober, Xiang Li, Jia Pan, Wenzhen Yuan,
"Challenges and Outlook in Robotic Manipulation of Deformable Objects",
Robotics and Automation Magazine (RAM), 2022.
Abstract
Deformable object manipulation (DOM) is an emerging research problem in robotics. The ability to manipulate deformable objects endows robots with higher autonomy and promises new applications in the industrial, services, and healthcare sectors. However, compared to rigid object manipulation, the manipulation of deformable objects is considerably more complex, and is still an open research problem. Addressing DOM challenges demands breakthroughs i...
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Shen Li, Theodoros Stouraitis, Michael Gienger, Sethu Vijayakumar, Julie Shah,
"Set-based State Estimation with Probabilistic Consistency Guarantee under Epistemic Uncertainty",
Research and Automation Letters, 2022.
Abstract
Consistent state estimation is challenging, especially when
both dynamic and observation models are nonlinear
and learned from data. In this work, we develop a set-based
estimation algorithm, that produces zonotopic state estimates
that respect the epistemic uncertainties in the learned mod-
els, in addition to the aleatoric uncertainties. Our algorithm
guarantees probabilistic consistency, in the sense that the
true state is always bound...
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