1. Detection and localization of hyperfunctioning parathyroid glands on [18F]fluorocholine PET/CT using deep learning – model performance and comparison to human expertsLeon Jarabek, Jan Jamšek, Anka Cuderman, Sebastijan Rep, Marko Hočevar, Tomaž Kocjan, Mojca Jensterle Sever, Žiga Špiclin, Žiga Maček Ležaić, Filip Cvetko, Luka Ležaič, 2022, original scientific article Abstract: In the setting of primary hyperparathyroidism (PHPT), [18F]fluorocholine PET/CT (FCH-PET) has excellent diagnostic performance, with experienced practitioners achieving 97.7% accuracy in localising hyperfunctioning parathyroid tissue (HPTT). Due to the relative triviality of the task for human readers, we explored the performance of deep learning (DL) methods for HPTT detection and localisation on FCH-PET images in the setting of PHPT. Patients and methods. We used a dataset of 93 subjects with PHPT imaged using FCH-PET, of which 74 subjects had visible HPTT while 19 controls had no visible HPTT on FCH-PET. A conventional Resnet10 as well as a novel mPETResnet10 DL model were trained and tested to detect (present, not present) and localise (upper left, lower left, upper right or lower right) HPTT. Our mPETResnet10 architecture also contained a region-of-interest masking algorithm that we evaluated qualitatively in order to try to explain the model’s decision process. Results. The models detected the presence of HPTT with an accuracy of 83% and determined the quadrant of HPTT with an accuracy of 74%. The DL methods performed statistically worse (p < 0.001) in both tasks compared to human readers, who localise HPTT with the accuracy of 97.7%. The produced region-of-interest mask, while not showing a consistent added value in the qualitative evaluation of model’s decision process, had correctly identified the foreground PET signal. Conclusions. Our experiment is the first reported use of DL analysis of FCH-PET in PHPT. We have shown that it is possible to utilize DL methods with FCH-PET to detect and localize HPTT. Given our small dataset of 93 subjects, results are nevertheless promising for further research
Keywords: primary hyperparathyroidism, deep learning, nuclear medicine
Published in DiRROS: 25.07.2024; Views: 16; Downloads: 10
 Full text (810,33 KB)
This document has many files! More... |
2. A ǂFramework for applying data-driven AI/ML models in reliabilityRok Hribar, Margarita Antoniou, Gregor Papa, 2024, independent scientific component part or a chapter in a monograph Abstract: In this chapter, we present a framework for applying artificial intelligence (AI)/machine learning (ML) in reliability, in the context of the iRel40 project. Data-driven models are becoming an increasingly fruitful tool for detecting patterns in complex data and identifying the circumstances in which they occur. Using only data, gathered along the value chain, data-driven methods are now being used to detect indications of potential early failures, signs of wear out or degradation, and other unwanted events within the development, fabrication, or service phases of the electronic components and systems. We present general considerations that were found to be important during the iRel40 project, when designing pipelines that combine data processing with the AI/ML models for predicting or detecting reliability issues. This chapter serves as an introduction to the definitions and concepts used within the specific use cases that rely on the AI/ML methodology within the iRel40 project.
Keywords: machine learning, artificial intelligence, data-driven models
Published in DiRROS: 23.07.2024; Views: 14; Downloads: 4
 Link to file |
3. Reliability improvements for in-wheel motorGašper Petelin, Rok Hribar, Stane Ciglarič, Jernej Herman, Anton Biasizzo, Peter Korošec, Gregor Papa, 2024, independent scientific component part or a chapter in a monograph Abstract: Setting up a reliable electric propulsion system in the automotive sector requires an intelligent condition monitoring device capable of reliably assessing the state and the health of the electric motor. To allow for a massive integration of such monitoring devices, they must be inexpensive and small. These requirements limit their accuracy. However, we show in this chapter that these limitations can be significantly reduced by appropriate processing of the sensor data. We have used machine learning models (random forest and XGBoost) to transform very noisy motor winding insulation resistance measurements made by a low-cost device into a much more reliable value that can compete with measurements made by a high-priced state-of-the-art measurement system. The proposed method is an important building block for a future smart condition monitoring system and enables a cost-effective and accurate assessment of the condition of electric motor health in connection with the condition of their winding insulation.
Keywords: machine learning models, low-cost device, electric motor
Published in DiRROS: 23.07.2024; Views: 13; Downloads: 6
Link to file |
4. Comparison of in-situ chlorophyll-a time series and sentinel-3 ocean and land color instrument data in Slovenian national waters (Gulf of Trieste, Adriatic Sea)El Khalil Cherif, Patricija Mozetič, Janja Francé, Vesna Flander-Putrle, Jana Faganeli Pucer, Martin Vodopivec, 2021, original scientific article Abstract: While satellite remote sensing of ocean color is a viable tool for estimating large-scale patterns of chlorophyll-a (Chl-a) and global ocean primary production, its application in coastal waters is limited by the complex optical properties. An exploratory study was conducted in the Gulf of Trieste (Adriatic Sea) to assess the usefulness of Sentinel-3 satellite data in the Slovenian national waters. OLCI (Ocean and Land Colour Instrument) Chl-a level 2 products (OC4Me and NN) were compared to monthly Chl-a in-situ measurements at fixed sites from 2017 to 2019. In addition, eight other methods for estimating Chl-a concentration based on reflectance in different spectral bands were tested (OC3M, OC4E, MedOC4, ADOC4, AD4, 3B-OLCI, 2B-OLCI and G2B). For some of these methods, calibration was performed on in-situ data to achieve a better agreement. Finally, L1-regularized regression and random forest were trained on the available dataset to test the capabilities of the machine learning approach. The results show rather poor performance of the two originally available products. The same is true for the other eight methods and the fits to the measured values also show only marginal improvement. The best results are obtained with the blue-green methods (OC3, OC4 and AD4), especially the AD4SI (a designated fit of AD4) with R = 0.56 and RMSE = 0.4 mg/m³, while the near infrared (NIR) methods show underwhelming performance. The machine learning approach can only explain 30% of the variability and the RMSE is of the same order as for the blue-green methods. We conclude that due to the low Chl-a concentration and the moderate turbidity of the seawater, the reflectance provided by the Sentinel-3 OLCI spectrometer carries little information about Chl-a in the Slovenian national waters within the Gulf of Trieste and is therefore of limited use for our purposes. This requires that we continue to improve satellite products for use in those marine waters that have not yet proven suitable. In this way, satellite data could be effectively integrated into a comprehensive network that would allow a reliable assessment of ecological status, taking into account environmental regulations.
Keywords: hydrobiology, coastal waters, Gulf of Trieste, chlorophyll-a, Sentinel-3, OLCI, machine learning
Published in DiRROS: 19.07.2024; Views: 43; Downloads: 26
Full text (5,77 MB)
This document has many files! More... |
5. Learning deep representations of enzyme thermal adaptationGang Li, Filip Buric, Jan Zrimec, Sandra Viknander, Jens Nielsen, Aleksej Zelezniak, Martin K. M. Engqvist, 2022, original scientific article Abstract: Temperature is a fundamental environmental factor that shapes the evolution of organisms. Learning thermal determinants of protein sequences in evolution thus has profound significance for basic biology, drug discovery, and protein engineering. Here, we use a data set of over 3 million BRENDA enzymes labeled with optimal growth temperatures (OGTs) of their source organisms to train a deep neural network model (DeepET). The protein-temperature representations learned by DeepET provide a temperature-related statistical summary of protein sequences and capture structural properties that affect thermal stability. For prediction of enzyme optimal catalytic temperatures and protein melting temperatures via a transfer learning approach, our DeepET model outperforms classical regression models trained on rationally designed features and other deep-learning-based representations. DeepET thus holds promise for understanding enzyme thermal adaptation and guiding the engineering of thermostable enzymes.
Keywords: bioinformatics, deep neural networks, enzyme catalytic temperatures, optimal growth temperatures, protein thermostability, transfer learning
Published in DiRROS: 17.07.2024; Views: 79; Downloads: 55
Full text (2,61 MB)
This document has many files! More... |
6. Adaptive visual quality inspection based on defect prediction from production parametersZvezdan Lončarević, Simon Reberšek, Samo Šela, Jure Skvarč, Aleš Ude, Andrej Gams, 2024, original scientific article Abstract: At the end of a production process, the manufactured products must usually be visually inspected to ensure their quality. Often, it is necessary to inspect the final product from several viewpoints. However, the inspection of all possible aspects might take too long and thus create a bottleneck in the production process. In this paper we propose and evaluate a methodology for adaptive, robot-aided visual quality inspection. With the proposed method, the most probable defects are first predicted based on the production process parameters. A suitable classifier for defect prediction is learnt in an unsupervised manner from a database that includes the produced parts and the associated parameters.Arobot then steers the camera only towards viewpoints associated with predicted defects, which implies that the trajectories of robot motion for the inspection might be different for every product. To enable dynamic planning of camera trajectories, we describe a methodology for evaluation and selection of the most appropriate autonomous motion planner. The proposed defect prediction approach was compared to other methods and evaluated on the products from a real-world production line for injection moulding, which was implemented for a producer of parts in the automotive industry.
Keywords: robot learning, robotic quality inspection, visual quality inspection, injection moulding, production parameters, robot motion planning
Published in DiRROS: 15.07.2024; Views: 91; Downloads: 42
Full text (7,44 MB)
This document has many files! More... |
7. Exploiting image quality measure for automatic trajectory generation in robot-aided visual quality inspectionAtae Jafari-Tabrizi, Dieter P. Gruber, Andrej Gams, 2024, original scientific article Abstract: Currently, the standard method of programming industrial robots is to perform it manually, which is cumbersome and time-consuming. Thus, it can be a burden for the flexibility of inspection systems when a new component with a different design needs to be inspected. Therefore, developing a way to automate the task of generating a robotic trajectory offers a substantial improvement in the field of automated manufacturing and quality inspection. This paper proposes and evaluates a methodology for automatizing the process of scanning a 3D surface for the purpose of quality inspection using only visual feedback. The paper is divided into three sub-tasks in the same general setting: (1) autonomously finding the optimal distance of the camera on the robot’s end-effector from the surface, (2) autonomously generating a trajectory to scan an unknown surface, and (3) autonomous localization and scan of a surface with a known shape, but with an unknown position. The novelty of this work lies in the application that only uses visual feedback, through the image focus measure, for determination and optimization of the motion. This reduces the complexity and the cost of such a setup. The methods developed have been tested in simulation and in real-world experiments and it was possible to obtain a precision in the optimal pose of the robot under 1 mm in translational, and 0.1° in angular directions. It took less than 50 iterations to generate a trajectory for scanning an unknown free-form surface. Finally, with less than 30 iterations during the experiments it was possible to localize the position of the surface. Overall, the results of the proposed methodologies show that they can bring substantial improvement to the task of automatic motion generation for visual quality inspection.
Keywords: robot learning, eobotic quality inspection, visual quality inspection
Published in DiRROS: 09.05.2024; Views: 267; Downloads: 494
Full text (3,00 MB)
This document has many files! More... |
8. Outward Bound and outdoor adventure education : a scoping review, 1995-2019Timothy J. Mateer, Joshua Pighetti, Derrick Taff, Pete Allison, 2022, original scientific article Abstract: Outdoor adventure education (OAE) programming is often referenced as an ef-fective intervention that encourages a wide array of outcomes in participants such as increased confidence, independence, and communication skills. However, as outdoor adventure education continues to increase globally, what does the academic literature say about the outcomes related to these programs? Hattie, Marsh, Neill, and Richards (1997) conducted the last major review of program efficacy in this realm. This updated scoping review, largely following PRISMA guidelines (Tricco et al., 2018), aims to summarize the academic literature on one of the primary outdoor adventure education providers internationally, Outward Bound (OB). Fifty-four studies, published betwe-en 1995 and 2019, have been summarized in this review. Utilizing Outward Bound International’s (OBI) framework of “people”, “place”, and “process”, themes and gaps in the literature are explored. Specifically, the OB literature has progressed since 1995 in demonstrating social and emotional outcomes in a variety of settings, a better understanding of the nature of effective programming, and further documenting the role the instructor plays in the learning experience. Recommendations are provided on developing more rigorous methodologies for future research, understanding the role of the physical environment in the learning experience, and utilizing theoretical approa-ches to integrate outdoor adventure education into broader academic realms
Keywords: outdoor education, adventure education, Outward Bound, emotional learning, experiental learning, scoping review
Published in DiRROS: 15.04.2024; Views: 281; Downloads: 175
Full text (951,53 KB)
This document has many files! More... |
9. Sensitivity analysis of RF+clust for leave-one-problem-out performance predictionAna Nikolikj, Michal Pluhacek, Carola Doerr, Peter Korošec, Tome Eftimov, 2023, published scientific conference contribution Keywords: automated performance prediction, autoML, single-objective black-box optimization, zero-shot learning
Published in DiRROS: 13.11.2023; Views: 524; Downloads: 288
Full text (4,94 MB)
This document has many files! More... |
10. |
