Thomas Schmitt, Jens Engel, Tobias Rodemann,
"Regression-Based Model Error Compensation for a Hierarchical MPC Building Energy Management System",
7th IEEE Conference on Control Technology and Applications (CCTA) 2023, 2023.
Abstract
One of the major challenges in the development of energy
management systems (EMSs) for complex buildings is accurate
modeling. To address this, we propose an EMS, which
combines a Model Predictive Control (MPC) approach with
data-driven model error compensation. The hierarchical MPC
approach consists of two layers: An aggregator controls the
overall energy flows of the building in an aggregated
perspective, while a distributor distributes ...
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Anna Belardinelli, Chao Wang, Michael Gienger, Daniel Tanneberg, Stephan Hasler, Theodoros Stouraitis,
"AR-mediated explainability for teaching and cooperation",
Second TRR 318 Conference: Measuring Understanding, 2023.
Abstract
The current spread of social and assistive robotics applications is increasingly highlighting the need for robots that can be easily
taught and interacted with, even by users with no technical background. Still, it is often difficult to grasp what such robots know
or to assess if a correct representation of the task is being formed. Augmented Reality (AR) has the potential to bridge this gap. We
demonstrate three use cases where AR design elem...
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Sebastian Brulin,
"Multi-Objective Bi-level Optimization Approach for Network Design Problems",
MATSim User Meeting 2023, 2023.
Abstract
This paper presents a novel study in the field of Network Design Problems (NDPs) with a particular focus on multi-stakeholder situations in city planning, addressing the complexities of balancing profit, fairness, and system efficiency. A new multi-objective bi-level optimization framework is proposed, which expands upon traditional NDP methodologies by incorporating dynamic demand and supply-side information. Our method moves beyond standard NDP...
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Ahmed Sadik, Antonello Ceravola, Frank Joublin, Jibesh Patra,
"Analysis of ChatGPT on Source Code ",
ArXiv, 2023.
Abstract
This paper explores the use of Large Language Models (LLMs) and in particular ChatGPT in programming, source code analysis, and code generation. LLMs and ChatGPT are built using machine learning and artificial intelligence techniques, and they offer several benefits to developers and programmers. While these models can save time and provide highly accurate results, they are not yet advanced enough to replace human programmers entirely. The paper ...
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Thiago Rios, Stefan Menzel, Bernhard Sendhoff,
"Large Language and Text-to-3D Models for Engineering Design Optimization",
arXiv, 2023.
Abstract
The current advances in generative AI for learning large neural network models with the capability to produce essays, images, music and even 3D assets from text prompts create opportunities for a manifold of disciplines. In the present paper, we study the potential of deep text-to-3D models in the engineering domain, with focus on the chances and challenges when integrating and interacting with 3D assets in computational simulation-based design o...
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Johannes Varga, Emil Karlsson, Guenther Raidl, Elina Rönnberg, Fredrik Lindsten, Tobias Rodemann,
"Speeding up Logic-Based Benders Decomposition by Strengthening Cuts with Graph Neural Networks",
9th International Conference on Machine Learning, Optimization, and Data Science (LOD), 2023.
Abstract
Logic-based Benders decomposition is a technique to solve optimization problems to optimality. It works by splitting the problem into a master problem, which neglects some aspects of the problem, and a subproblem, which adds cuts to the master problem to account for those aspects. These cuts need to be refi ned to achieve good overall performance. In this paper we investigate the use of Machine Learning to learn a good refi nement procedure. We a...
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Angus Kenny, Tapabrata Ray, Steffen Limmer, Hemant Singh, Tobias Rodemann, Markus Olhofer,
"Hybridizing TPOT with Bayesian Optimization",
GECCO 2023, pp. 502-510, 2023.
Abstract
Tree-based pipeline optimization tool (TPOT) is used to automatically construct and optimize machine learning pipelines for classification or regression tasks. The pipelines are represented as trees comprising multiple data transformation and machine learning operators — each using discrete hyper-parameter spaces — and optimized with genetic programming. During the evolution process, TPOT evaluates numerous pipelines which can be challenging ...
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Muhammad Yousaf, Duane Detwiler, Fabian Duddeck, Stefan Menzel, Satchit Ramnath, Nate Zurbrugg, Mariusz Bujny,
"Similarity-driven Topology Optimization for Statics and Crash via Energy Scaling Method",
ASME Journal of Mechanical Design (JMD), 2023.
Abstract
Topology Optimization (TO) is used in the initial design phase to optimize certain objective functions under given boundary conditions by finding suitable material distributions in a specified design domain. Currently available methods in industry work very efficiently to get topologically-optimized design concepts under static and dynamic load cases. However, conventional methods do not address the designer’s preferences about the final materi...
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Hao Tong, Leandro Minku, Stefan Menzel, Bernhard Sendhoff, Xin Yao,
"A Novel Optimization Framework for Dynamic Capacitated Arc Routing Problems",
Genetic and Evolutionary Computation Conference Companion (GECCO Companion), 2023.
Abstract
The capacitated arc routing problem (CARP) aims at scheduling a fleet of vehicles with limited capacities to serve a set of tasks in a graph. The dynamic CARP (DCARP) optimization focuses on
updating the vehicles’ service routes when unpredicted dynamic events happen and deteriorate the current service plan. Due to the outside vehicles are still being in their service when dynamic
events happen and being located at different positions of the ...
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Chao Wang,
"Design for Collaborative Intelligence: From Connected Vehicle, Autonomous Driving, Robotics to AI ",
University of Nottingham Ningbo China Science and Technology Open Day, 2023.
Abstract
The goal of the intelligent system should be to enhance human capabilities, not replace them. There is much more scope for humans and AI to complement each other than to compete, as their advantages lie in different aspects. This complementarity can be called Collaborative Intelligence (CI), which enables machines to achieve goals together with humans in complex environments. CI requires mutual understanding and seamless communication between the...
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