Index

Development of an AI-based ring detection algorithm for CT image quality control

Diffusion-based Super Resolution for X-ray Microscopy

Diffusion Models for Generating Offline Handwritten Text Images

Unsupervised Contextual Anomaly Detection in Frequency Converter Data

Thesis Description

Anomaly detection is a widely researched topic and is already extensively used in many different domains such as fraud detection for credit cards, intrusion detection for cyber-security or fault detection in safety critical systems [1]. Various anomaly detection techniques are also applied in the industry where electric motors are one of the most important components which is underlined by the fact that they account for approximately 40% of global electricity consumption. Being able to detect anomalies in their sensor data can help predicting errors and therefore avoiding expensive reactive maintenances which will save a lot of cost for unplanned downtime [2].

As it is difficult to come up with general anomaly detection techniques and since different domains have different requirements regarding performance and other limitations, a wide range of methods such as Neural Networks, Rule-based techniques, Clustering, Nearest Neighbor, Statistical models and more exist. The different techniques have certain limitations what kind of anomaly they can detect (such as point, contextual or collective anomaly) and what labels need to be available. To give an example, a nearest neighbor anomaly detection would be able to detect point anomalies without needing labeled data, which makes it an unsupervised algorithm. In general it is exceptionally hard to get labeled data in some domains and therefore in such cases an unsupervised solution is preferable [1].

In this thesis the focus is on unlabeled multivariate timeseries data coming from a SINAMIC frequency converter, that is used to convert the mains voltage to a suitable signal powering an electric motor [3]. A partial timeframe of this data is declared as reference measurement and assumed to be mostly regular without anomalies. The frequency converters have a set of sensor parameters that can be extracted and processed using the Siemens Industrial Edge Device [4]. State of the art methods for this problem include calculating the distance metric between timeseries using Dynamic Time Warping and applying the Local Outlier Factor algorithm as shown in [5], using a neural network consisting of stacked autoencoders that will classify sequences based on whether they can be reconstructed by the autoencoder [2] or using a Generative Adversarial Network (GAN) including LSTMs (Long Short Term Memory) to capture temporal correlation as demonstrated in [6].

Especially Autoencoders, that encode an input into a compact hidden representation that is then decoded with the aim of reconstructing the original input, are commonly used [7]. The idea is that since the hidden representation is reduced, it will only be able to represent regular data patterns and not the patterns in anomalies. If an anomaly is fed through the autoencoder it is expected to generate a higher reconstruction error than usual. However, the approach suffers, if the training data contains anomalies, since their patterns might be learned in this case. And indeed, this applies to many real-world applications [8].

In this thesis the unlabeled data of industry motors will be analyzed and different solutions for anomaly detection in multivariate timeseries data will be tested. Since the computation power of the edge device is limited, the algorithms will be evaluated regarding their performance. Classification of anomalies would be desirable but highly depend on the application domain and is therefore not feasible in a general anomaly detection approach.

For summarization the thesis will deal with the following points:

  1. Data analysis
    (a) Statistics
    (b) (manual) identification of anomalies
  2. Development of various models for anomaly detection
  3. Model evaluation regarding to different metrics at least including the following
    (a) Accuracy
    (b) Training performance
    (c) Runtime performance
  4. Optional: Classification

References
[1] Varun Chandola, Arindam Banerjee, and Vipin Kumar. Anomaly detection: A survey. ACM Comput. Surv.,
41(3), jul 2009.
[2] Sean Givnan, Carl Chalmers, Paul Fergus, Sandra Ortega-Martorell, and Tom Whalley. Anomaly detection
using autoencoder reconstruction upon industrial motors. Sensors, 22(9), 2022.
[3] Siemens AG. Converter, 2022. Accessed on 14.12.2022.
[4] Siemens AG. Industrial edge / production machines, 2022. Accessed on 14.12.2022.
[5] Wang Yong, Mao Guiyun, Chen Xu, and Wei Zhengying. Anomaly detection of semiconductor processing
data based on dtw-lof algorithm. In 2022 China Semiconductor Technology International Conference
(CSTIC), pages 1–3, 2022.
[6] Alexander Geiger, Dongyu Liu, Sarah Alnegheimish, Alfredo Cuesta-Infante, and Kalyan Veeramachaneni.
Tadgan: Time series anomaly detection using generative adversarial networks. CoRR, abs/2009.07769, 2020.
[7] Ane Bl´azquez-Garc´ia, Angel Conde, Usue Mori, and Jos´e Antonio Lozano. A review on outlier/anomaly
detection in time series data. CoRR, abs/2002.04236, 2020.
[8] Tung Kieu, Bin Yang, Chenjuan Guo, Razvan-Gabriel Cirstea, Yan Zhao, Yale Song, and Christian S.
Jensen. Anomaly detection in time series with robust variational quasi-recurrent autoencoders. In 2022
IEEE 38th International Conference on Data Engineering (ICDE), pages 1342–1354, 2022.

Fetal Re-Identification: Deep Learning on Pregnancy Ultrasound Images

Project description

Accurate analysis of ultrasound images during pregnancy is important for monitoring fetal development and detecting abnormalities. For better accuracy and time convenience, the help of artificial intelligence or accordingly deep learning is useful [1]. However, currently there is less research in deep learning in the field of ultrasound imaging compared to MRI or CT images [2].

Considering its non-invasive nature, lower cost, and lower risk to patients compared to other modalities such as MRI or CT, ultrasound imaging is the most commonly used method to assess fetal development and maternal health [3]. Overall, the correct acquisition of fetal ultrasound data is difficult and time-consuming. Deep Learning can help to reduce examiner dependence, and improve analysis as well as maternal-fetal medicine in general [1].

Although literature on fetal ultrasound imaging in conjunction with deep learning exists [4-6], little previous work investigated fetal re-identification. In multiple pregnancies with fetuses of the same sex or early in pregnancy, the fetuses cannot be distinguished. Therefore, the fetuses are assigned an order at physician descretion (usually based on position in the mother’s womb), although it is not clear whether this order preserves during subsequent examinations. However, this information is important because the risk of fetal abnormalities is greater in multiple pregnancies than in singleton pregnancies [7]. In addition, a good representation of the fetus is also eminent for the emotional connect of the parents [8].

Consequently, the aim of this thesis is an early feasibility investigation of re-identification approaches in fetal ultrasound.

 

References

[1] J. Weichert, A. Rody, and M. Gembicki. Zukünftige Bildanalyse mit Hilfe automatisierter Algorithmen. Springer Medizin Verlag GmbH, 2020.

[2] Xavier P. Burgos-Artizzu, David Coronado-Guiérrez, Brenda Valenzuela-Alcaraz, Elisenda Bonet-Carne, Elisenda Eixarch, Fatima Crispi, and Eduard Gratacos. Evaluation of deep convolutional neural networks for automatic classification of common maternal fetal ultrasound planes. Nature Scientific Reports, 2020.

[3] D. Selvathi and R. Chandralekha. Fetal biometric based abnormality detection during prenatal development using deep learning techniques. Springer, 2021.

[4] Jan Weichert, Amrei Welp, Jann Lennard Scharf, Christoph Dracopoulos, Achim Rody, and Michael Gembicki. Künstliche Intelligenz in der pränatalen kardialen Diagnostik. page 10, 2021.

[5] Christian F. Baumgartner, Konstantinos Kamnitsas, Jacqueline Matthew, Tara P. Fletcher, Sandra Smith, Lisa M. Koch, Bernhard Kainz, and Daniel Rueckert. SonoNet: Real-Time Detection and Localisation of Fetal Standard Scan Planes in Freehand Ultrasound. page 12, 2017.

[6] Juan C. Prieto, Hina Shah, Alan J. Rosenbaum, Xiaoning Jiang, Patrick Musonda, Joan T. Price, Elizabeth M. Stringer, Bellington Vwalika, David M. Stamilio, and Jeffrey S. A. Stringer. An automated framework for image classification and segmentation of fetal ultrasound images for gestational age estimation. page 11, 2021.

[7] R Townsend and A Khalil. Ultrasound surveillance in twin pregnancy: An update for practitioners. Ultrasound, 2018.

[8] Tejal Singh, Srinivas Rao Kudavelly, and Venkata Suryanarayana. Deep Learning Based Fetal Face Detection And Visualization In Prenatal Ultrasound. 2021.

Machine Learning Based Analysis of EEG Data Recorded during Stimulation with Continuous Speech

Motif analysis of resting-state and stimulus driven fMRI networks with special focus on functional role of the motifs

Adaptive Training of Heat Demand Prediction using Continual Learning

One of the primal challenges faced by utility companies is ensuring efficient supply with minimal greenhouse gas emissions. On the track of facilitating the energy transition and mitigating the anthropomorphic climate change, framework for heat demand forecast poses the basis for different applications, impacting the operational as well as the economical efficiency of a utility company. More concretely, VK Energy [1] optimize combined heat and power (CHP) generation systems to increase their overall efficiency and flexibility. In order to operate the CHP system in heating networks with heat storage adaptively to the demand of the electricity system, a real-time, accurate, robust and user friendly (i.e., ideally without extensive hyperparameter tuning) forecast of the heat demand in the respective heating network is indispensable.

As heat demand forecast is an on-going research topic, numerous methods [2-5] have been proposed in the recent years. Although advanced machine learning (ML) and deep learning (DL) methods proposed in the state-of-the-arts demonstrate tremendous capability to predict the heat demand accurately, their performance is limited by the training data. As the heat consumption strongly depends on the weather condition, which is a dynamic environment, ML/DL models trained initially may not be valid anymore with changing consumption behavior and climate. Therefore, continual learning paradigm [6] should be considered to improve the applicability of ML/DL algorithms for heat demand forecast.

In this thesis, following aspects need to be considered:

  • Literature review of heat consumption prediction and continual learning.
  • Development and implementation of a continual learning framework for heat demand prediction comprising different forecasting methos and retraining mechanism.
  • Comprehensive evaluation of the performance of the implemented framework w.r.t. accuracy, robustness, run-time, and potentially usability.

[1] https://www.vk-energie.de/

[2] Y. Zhao, Y. Shen, Y. Zhu and J. Yao, “Forecasting Wavelet Transformed Time Series with Attentive Neural Networks,” 2018 IEEE International Conference on Data Mining (ICDM), 2018, pp. 1452-1457, doi: 10.1109/ICDM.2018.00201.

[3] Chatterjee, Satyaki and Bayer, Siming and Maier, Andreas K “Prediction of Household-level Heat-Consumption using PSO enhanced SVR Model”, NeurIPS 2021 Workshop on Tackling Climate Change with Machine Learning, 2021, https://www.climatechange.ai/papers/neurips2021/42

[4] Kováč, Szabolcs and Micha’čonok, German and Halenár, Igor and Važan, Pavel,” Comparison of Heat Demand Prediction Using Wavelet Analysis and Neural Network for a District Heating Network”, Special Issue “Artificial Intelligence in the Energy Industry”, https://www.mdpi.com/1996-1073/14/6/1545

[5] Chatterjee S., Ramachandran A., Neergaard TF., Maier A., Bayer S., Heat Demand Forecasting with Multi-Resolutional Representation of Heterogeneous Temporal Ensemble. In: NeurIPS 2022 Workshop Tackling Climate Change with Machine Learning, 2022

[6] https://towardsdatascience.com/how-to-apply-continual-learning-to-your-machine-learning-models-4754adcd7f7f

Unsupervised detection of small hyperreflective features in ultrahigh resolution optical coherence tomography

Optical Character Recognition on Technical Drawings using Deep Learning