Soft Matter Lab

Jesper Boberg and Anders Segerlund will defend their Master thesis on 14 June 2022

Jesper Boberg and Anders Segerlund will defend their Master thesis in MPCAS at the Chalmers University of Technology on 14 June 2022 at 16:00.

Title: Early detection of rare evens: Predicting battery cell deviations.

Abstract:
Despite rigorous quality control in battery cell production, the production process is still subject to quality deviations. These quality deviations; known as “rare events” initially act as inherent passive quality deviations and may not affect the performance of the battery. Yet, a passive quality deviation can transition into an active quality deviation that give rise to behavioral deviations in the battery cell at some point during the battery’s lifetime. An active quality deviation may cause the entire battery to misbehave and eventually fail. This thesis investigates the possibility of predicting these cell deviations in car batteries. Better predictions of these events would avoid expensive and troublesome car failures and instead enable preventive car maintenance to solve the problem.

In this thesis different models have been created and evaluated with the aim of preventing these deviations. The dataset is supplied by Volvo Cars and contains a large amount of data collected from BEV cars where the arguably largest challenge comes from the imbalance of the dataset. In addition to the modelling, the thesis will include a thorough data analysis with the aim of improving both the dataset itself and the data collection process at Volvo Cars.

These deviations occur extremely rarely which also makes a relatively large amount of false positives difficult to avoid. The results show that a quite simple time series model can catch these deviations well but also brings along a large amount of false positives. A recurrent neural network was able to improve this significantly, still being able to catch the deviations while producing a lot fewer false positives.

Name of the master programme: MPALG – Computer Science: Algorithms, Languages, and Logic, MPCAS – Complex Adaptive Systems
Examiner: Giovanni Volpe
Supervisor: Herman Johnsson (Volvo Cars)
Opponent: Jonathan Stålberg and Josef Gullholm5

Place: Nexus
Time: 14 June, 2022, 16:00

Deep learning in light–matter interactions published in Nanophotonics

Artificial neurons can be combined in a dense neural network (DNN), where the input layer is connected to the output layer via a set of hidden layers. *(Image by the Authors.)*

Deep learning in light–matter interactions
Daniel Midtvedt, Vasilii Mylnikov, Alexander Stilgoe, Mikael Käll, Halina Rubinsztein-Dunlop and Giovanni Volpe
Nanophotonics, 11(14), 3189-3214 (2022)
doi: 10.1515/nanoph-2022-0197

The deep-learning revolution is providing enticing new opportunities to manipulate and harness light at all scales. By building models of light–matter interactions from large experimental or simulated datasets, deep learning has already improved the design of nanophotonic devices and the acquisition and analysis of experimental data, even in situations where the underlying theory is not sufficiently established or too complex to be of practical use. Beyond these early success stories, deep learning also poses several challenges. Most importantly, deep learning works as a black box, making it difficult to understand and interpret its results and reliability, especially when training on incomplete datasets or dealing with data generated by adversarial approaches. Here, after an overview of how deep learning is currently employed in photonics, we discuss the emerging opportunities and challenges, shining light on how deep learning advances photonics.

David Rinman will defended his Master thesis on 13 June 2022

David Rinman will defended his Master thesis in MPCAS at the Chalmers University of Technology on 13 June 2022 at 13:00.

Title:
Monitoring Monitors; ML-based Anomaly Detection in Loudspeakers

Abstract:
Measuring the input voltage and current passing through a loudspeker and comparing the results to a parametric model is a way to monitor the condition of a loudspeaker in amplifiers. Prior research has shown that this can be done using music as input signal and can therefore work in commercial audio applications during normal operation. However, this solution requires modelling the specific loudspeaker setup which can be impractical in real-world scenarios. The aim of this project is to attempt to overcome these limitations by applying machine learning to the problem of anomaly detection in loudspeakers. Data is collected while playing music through two real functioning speakers where anomalies are simulated by disturbing the movement of their diaphragms. Three models are proposed and evaluated, two of which are based on deep neural networks. The results show that all three models are capable of learning a representation of one of the loudspeakers and detect deviances in these representations, however not for all of the simulated anomalies. Furthermore, the robustness of the models in prescence of nonlinear loudspeaker behavior is examined, and the limitations and benefits of the models are discussed. Finally, suggestions for future research directions are proposed.

Name of the master programme: MPCAS – Complex Adaptive Systems
Examiner: Giovanni Volpe
Supervisor: Jesper Pedersen (MusicTribe)
Opponent: TBA

Place: van Bahr
Time: 13 June, 2022, 13:00

Presentation by Vide Ramsten, 10 June 2022

Observer, Target Generation and Control Design in Robotics
Vide Ramsten
10 June 2022, 15:00 CET

Abstract
In this presentation, three topics related to Control Theory will be discussed together with practical examples from my Bachelor and Master thesis projects. First, the concept of state observers will be presented, where internal system states are estimated based on the measurable outputs of the system. Second, target generation will be discussed, in which the particular output or state trajectory of the system that is desired, is created. Lastly, we consider controller design, where we specify how to create the input given the previously defined parts such as target reference, measurable output and estimated system states. The theory will be applied to two projects. One in which a wheeled robot is developed for guiding purposes, so that the robot can show users the way to certain locations specified by the user. The project gives examples of state observers by localization algorithms, as well as target generation by path planning algorithms. The other example is a robotic testing system for passive prosthesis, where target generation through a motion-capture system is used as a reference for robot motion. A control strategy has been implemented in order to track this reference signal.

Short Bio
Vide Ramsten got his Bachelor degree in Automation and Mechatronic at the Chalmers University of Technology. After that, he continued his studies in a master programme in Systems, Control and Mechatronics at Chalmers. During his master, he did a double degree exchange with the University of Stuttgart, Germany in Engineering Cybernetics. While in Germany, he did a six-month internship at the robotics company BEC Gmbh focused on applications of control in robotics, as well as his master thesis at the Fraunhofer Institute of Manufacturing Engineering and Automation IPA.

Date: 10 June 2022
Time: 11:00
Place: Faraday

Kasper Hall and Noell Hall defended their Master thesis on 10 June 2022. Congrats!

Kasper Hall and Noell Hall defended their Master thesis in MPCAS at the Chalmers University of Technology on 10 June 2022 at 14:00. Congrats!

Title: Interference Object Detection using TensorFlow Lite and Transfer Learning for Android Devices

With the rapid evolution of machine learning and artificial intelligence faster and more robust network architectures are developed. This is possible due to the increase in computational power, improved algorithms and the creation of large scale annotated datasets. Re-purposing these state of the art networks using transfer learning allows for customized models to be created and applied to niche problems. In this paper, we create an object detection application able to detect interference points in anechoic testing chambers. The application runs detection on a mobile device using networks created with TensorFlow Lite. Utilizing the detection result the application can give advice on how to improve the installation in the testing chamber and can thus enforce a baseline for how installations are conducted increasing the repeatability of tests.

Name of the master programme: MPCAS – Complex Adaptive Systems
Examiner: Giovanni Volpe
Supervisor: Giovanni Volpe and Christian Heina, Ericsson
Opponent: Angelo Barona Balda

Place: Origo 5.102
Time: 10 June, 2022, 14:00

Angelo Barona Balda defended his Master Thesis on 10 June 2022. Congrats!

Angelo Barona Balda defended his Master thesis in MPCAS at the Chalmers University of Technology on 10 June 2022 at 13:00. Congrats!

Title: Playful Experiments with Macroscopic Active Matter

Abstract:
Active matter is a substance or system composed of individual agents that consume energy. These agents use the energy to vibrate, self-propel, or apply force to their surroundings.

As it is such a strong theoretical resource, professors use active matter as a gateway to introduce students into non-equilibrium research. However, it is difficult to intuitively explain the behavior of microscopic particles. To better visualize it, academics frequently use simulations , both in classrooms and in research.

In this study, we use the HEXBUGS to replicate active matter simulations performed in previous research papers. We create experiments that are didactic, understandable, and easy to reproduce. Through this, we prove that HEXBUGS behave like active particles.

Name of the master programme: MPCAS – Complex Adaptive Systems
Examiner: Giovanni Volpe
Supervisor: Giovanni Volpe, Aykut Argun
Opponent: Noel Hall, Kasper Hall

Place: Origo 5.102
Time: 10 June, 2022, 13:00

Yanuar Rizki Pahlevi defended his Master thesis on 9 June 2022. Congrats!

Yanuar Rizki Pahlevi defended his Master thesis in MPCAS at the Chalmers University of Technology on 9 June 2022 at 17:00. Congrats!

Title: Deep Learning for Optical Tweezers. DeepCalib Implementation for Brownian Motion with Delayed Feedback

Brownian motion with delayed feedback theoretically studied to take control of Brownian particle movement’s direction. One can use optical tweezers to implement delayed feedback. Calibrating optical tweezers with delay implemented is not an easy job. In this study, Deep learning technique using Long Short Term Memory (LSTM) layer as main composition of the model to calibrate the trap stiffness and to measure the delayed feedback employed, using the trapped particle trajectory as an input. We demonstrate that this approach is outperforming variance methods in order to calibrate stiffness, also outperforming approximation method to measure the delay in harmonic trap case.

Name of the master programme: MPCAS – Complex Adaptive Systems
Examiner: Giovanni Volpe
Supervisor: Aykut Argun
Opponent: Ivan Gentile Japiassu

Place: Nexus
Time: 9 June, 2022, 17:00

Invited Talk by G. Volpe at International Workshop On Active Systems, IIT Madras, India, 9 June 2022.

Emergent Complex Behaviors in Active Matter
Giovanni Volpe
9 June 2022, 14:30 (IST)
Online for MNBF Workshop: International Workshop On Active Systems
IIT Madras, India, 8-9 June 2022

Presentation by Lun Li, 7 June 2022

Robots in real-world scenarios
Lun Li
7 June 2022, 15:00 CET

Abstract
In this presentation, I will demonstrate how robots can work in the real-world and dynamic environments assisting or replacing humans and present examples from my previous work experiences. I will also explain the basic knowledge about robots, the challenges to design a robust robot system for business, and the current state of the robotics industry.

Short Bio
Lun Li is a robotics engineer. His work focuses on artificial intelligence and robot designing in the areas of robot navigation, manipulation, and cooperation. In the past three years, he has served as the CTO of robot startups in China. He has led two robot projects, one is an agricultural robotic jasmine tea harvester, and the other is an industrial unmanned forklift. The latter has been successfully launched in the market. Before entering the workplace, he got his two bachelor’s degrees from Beihang University in China and a master’s degree from Texas A&M University in United States.

Date: 7 June 2022
Time: 15:00
Place: Faraday

Santhosh Shivan Gurumurthy defendeded his Master thesis on 2 June 2022. Congrats!

Santhosh Shivan Gurumurthy defendeded his Master thesis in MPCAS at the Chalmers University of Technology on 2 June 2022 at 19:00. Congrats!

Title: Digital video microscopy using deep learning

Abstract:
Particle tracking is essential in various fields of science, from study of cells in biology to studying particle dynamics in physics. The standard classical methods are algorithmic in nature, requiring heavy manual work to tweak the parameter to various conditions and tasks. In this project, we try to apply four such methods to different particle tracking tasks and show that these are better alternatives to standard methods. We use Deeptrack as the base framework for all our tasks. We show that CNN can track a single Janus particle suspended in a liquid and outperforms a standard method called radial center method especially under noisy and unsymmetrical particle conditions. We then demonstrate how the LodeSTAR algorithm outperforms the YOLO algorithm in tracking the centers of multiple particles in an image. Subsequently, we apply a method called MAGIK (Motion Analysis through GNN Inductive Knowledge) to predict the trajectories of the particles in a video.

Name of the master programme: MPCAS – Complex Adaptive Systems
Examiner: Giovanni Volpe
Supervisor: Jesús Pineda
Opponent: Emil Jansson

Place: Nexus
Time: 2 June, 2022, 19:00