Autonomous Drones for Trail Navigation using DNNs

Στην παρούσα διπλωματική εργασία, προτείνεται ο σχεδιασμός και η υλοποίηση ενός πρότυπου drone που έχει τη δυνατότητα αυτόνομης πλοήγησης σε δασικό μονοπάτι χωρίς πρότερη γνώση του περιβάλλοντα χώρου. Χρησιμοποιεί σύστημα τεχνητής όρασης τριών επιπέδων: (i) ένα νευρωνικό δίκτυο βάθους (DNN) για εκτίμηση πλευρικής μετατόπισης και προσανατολισμού ως προς το κέντρο του μονοπατιού, (ii) ένα DNN για αναγνώριση αντικειμένων, και (iii) ένα σύστημα αποφυγής εμποδίων. Η σύνθεση του μικρού εναέριου σκάφους (MAV) έγινε από διαθέσιμα εξαρτήματα (hardware) του εργαστηρίου. Για τον αλγόριθμο ακολουθίας δασικών μονοπατιών, ως βάση νευρωνικού δικτύου χρησιμοποιήθηκε το TrailNet. Στη συνέχεια επανεκπαιδεύτηκε και εμπλουτίστηκε με σύνολο δεδομένων που δημιουργήθηκε από την δασική περιοχή της Πανεπιστημιούπολης Ιλισίων, προσαρμόζοντάς το στην τοπική βλάστηση. Για την επιλογή των βέλτιστων αλγορίθμων αναγνώρισης αντικειμένων, έγινε δοκιμή και αξιολόγηση από αντίστοιχους της τελευταίας γενιάς στην πλακέτα επεξεργασίας Jetson TX2 της NVIDIA. Τέλος δίνεται πρόταση πειραματικής πτήσης με συγκεκριμένες παραμέτρους για την αξιολόγηση της ορθής λειτουργίας.This thesis proposes the design and implementation of a prototype drone stack that is able to autonomously navigate through a forest trail path without having prior knowledge of the surrounding area. It uses a 3 level vision system: (i) a deep neural network (DNN) for estimating the view orientation and lateral offset of the vehicle with respect to the trail center, (ii) a DNN for object detection and (iii) a Guidance system for obstacle avoidance. Hardware synthesis of the Micro Aerial Vehicle (MAV) was built upon hardware parts, available from the lab. Trail following algorithm makes use of TrailNet’s neural network. It was also retrained and enriched by a newly created dataset, formed with footage from the nearby forest canopy of Ilisia Univesity Campus. This also made the model more adaptive to local vegetation characteristics. For object detection service, a comparison between well-known algorithms was made and an evaluation was done in terms of accuracy and efficiency. These were tested on NVIDIA’s Jetson TX2 Dev Kit board. At last, a suggestion of an experimental flight is given with particular parameters, for the evaluation of the proper operation

Study of plastic zone at the end transverse fissure by the method of coustics

161 σ.Στην παρούσα διπλωματική εργασία παρουσιάζεται η μέθοδος των καυστικών και πιο συγκεκριμένα μελετάται η πλαστική ζώνη σε ένα δοκίμιο που έχει υποστεί τεχνητά εγκάρσια ρωγμή.In this dissertation the scientific method of coustics was presented and more specified was studied the plastic zone at the end of transverse fissure.Γεώργιος Σ. Καλαμπόκη

Development of a crash records system in Greece

177 σ.Ένας από τους βασικότερους παράγοντες, ίσως και ο πιο σημαντικός, που αποτελεί βασική προϋπόθεση για τη βελτίωση της οδικής ασφάλειας είναι η καταγραφή και η ανάλυση των αιτιών και των συνεπειών των οδικών ατυχημάτων και συμβάντων. Σκοπός αυτής της διπλωματικής εργασίας είναι η δημιουργία ενός εργαλείου, κατάλληλου για την συλλογή στοιχείων από τροχαία ατυχήματα, για τα ελληνικά δεδομένα. Αρχικά λοιπόν, μελετάται η υφιστάμενη κατάσταση στην Ελλάδα σχετικά με τις μεθόδους καταγραφής, κωδικοποίησης και αποθήκευσης δεδομένων τροχαίων ατυχημάτων. Παρουσιάζονται εν συντομία οι βάσεις δεδομένων που χρησιμοποιούνται από τους αρμόδιους φορείς της οδικής ασφάλειας: Ε.Σ.Υ.Ε., Διεύθυνση Τροχαίας Υ.Δ.Τ., Γ.Ο.Α. του Υ.ΠΕ.ΧΩ.Δ.Ε., Αρχείο Νοσοκομείων, και Υ.Σ.Α.Ε. Στη συνέχεια δίνεται μια γενική επισκόπηση αντίστοιχων συστημάτων καταγραφής δεδομένων τροχαίων ατυχημάτων που χρησιμοποιούνται σε χώρες της Ευρωπαϊκής Ένωσης, αλλά και της Αμερικής. Μελετώντας τα εξ’ ολοκλήρου από κάθε οπτική γωνία, εξάγονται κάποια συμπεράσματα σχετικά με τα δυνατά σημεία και τις αδυναμίες που αυτά παρουσιάζουν, κατά την λειτουργία τους με το πέρασμα του χρόνου. Συγκεκριμένα γίνεται περιγραφή των αντίστοιχων συστημάτων της Ολλανδίας, της Σουηδίας (STRADA), της Μεγάλης Βρετανίας, και μερικών που χρησιμοποιούνται από ορισμένες πολιτείες των Η.Π.Α. (TraCS, FARS, CODES, κλπ.). Στην προσπάθεια δημιουργίας κοινών στοιχείων καταγραφής τροχαίων ατυχημάτων, έχοντας ως στόχο την ποιοτική και ποσοτική βελτίωση δεδομένων, αναπτύχθηκαν μερικά πρότυπα ορισμού και ανάλυσης των στοιχείων περιγραφής των οδικών ατυχημάτων για την ελληνική πραγματικότητα. Πιο συγκεκριμένα έγινε μια διαλογή στοιχείων μεταξύ των προϋπαρχόντων στο Δ.Ο.Τ.Α. της Ε.Σ.Υ.Ε., καθώς προτάθηκαν κάποια επιπλέον στοιχεία βάση του Αμερικανικού οδηγού προτύπων MMUCC, και του αντίστοιχου Αμερικανικού οδηγού ανάλυσης θανατηφόρων ατυχημάτων FARS. Στη συνέχεια, αφού προτάθηκαν όλα τα στοιχεία καταγραφής οδικών ατυχημάτων, δημιουργήσαμε ένα πρόγραμμα βάσης δεδομένων το οποίο βοηθά στην κωδικοποίηση και αποθήκευση των στοιχείων αυτών. Το πρόγραμμα αυτό δημιουργήθηκε πειραματικά, με σκοπό τη χρήση του τοπικά σε κάθε τμήμα τροχαίας, δίνοντας μελλοντικά ιδέες ανάπτυξής για ένα ολοκληρωμένο πληροφοριακό σύστημα οδικής ασφάλειας. Η υλοποίηση της βάσης έγινε σε Microsoft Access με την βοήθεια της γλώσσας προγραμματισμού Visual Basic for Applications. Συμπερασματικά θα λέγαμε ότι η εργασία αυτή περιορίζεται περισσότερο στο επίπεδο της ανάπτυξης της εφαρμογής, και λιγότερο στην απόδοση της. Η αξιολόγηση αυτής απαιτεί μακροχρόνια λειτουργία και σύγκριση των αποτελεσμάτων με άλλες τεχνολογίες που υλοποιούν παρόμοιες εφαρμογές απ’ την πλευρά του εξυπηρετητή. Τελικώς δίνονται κάποιες προτάσεις για την πιθανή βελτίωση της εφαρμογής.One of the main factors, perhaps the most important, which is also a basic premise for improving road safety is data collection, processing and management that referring to the causes and effects of road accidents and facts. The current diploma thesis aims to create an appropriate method for crash data collection, depicting the existing situation in Greece. Initially, the existing situation in Greece is depicted referring to the methods used for crash data collection, codification, and recording. Briefly, crash records systems, exploited by the public authorities: Ε.Σ.Υ.Ε., Department of Υ.Δ.Τ., Γ.Ο.Α. of Υ.ΠΕ.ΧΩ.Δ.Ε., Hospital Records, and Υ.Σ.Α.Ε., are presented. Subsequently, an overall review is provided concerning the corresponding crash records systems, used in countries of the European Union (EU), as well as in the most states of America. Taking into consideration the systems mentioned above various conclusions are drawn concerning the strengths and weaknesses presented in such systems, throughout time. In particular there is a description of the corresponding systems in Netherlands, Sweden, Great Britain, as well as several systems used in some states of America (TraCS, FARS, CODES, etc.). In our attempt to create common data for crash data collection, targeting at improving qualitative and quantitative data, several definition and analysis models of road accident data elements are developed for the existing situation in Greece. More specifically, a data selection among the pre-existing data has been completed in Δ.Ο.Τ.Α. of Ε.Σ.Υ.Ε., as several additional data were suggested on the basis of American model guide MMUCC and the equivalent American fatalities analysis guide FARS. Furthermore, after suggesting the needed recording crash data, we created a data collection program which helps in data codification and storage. This research program was created to be used in every local road accident office, providing ideas for further development in order to consist a completed informational road safety system. The program was developed in Microsoft Access 2003 by the aid of Visual Basic for Application. In conclusion, this thesis centers mainly on delineating the level of program development and less on its performance. The program evaluation requires long lasting usage and comparison of its results using other technologies which have similar applications on behalf of the user. Finally, some suggestions are presented for further improvement of the application.Γεώργιος A. Καλαμπόκη

The Future of AI in Ovarian Cancer Research: The Large Language Models Perspective

Conversational large language model (LLM)-based chatbots utilize neural networks to process natural language. By generating highly sophisticated outputs from contextual input text, they revolutionize the access to further learning, leading to the development of new skills and personalized interactions. Although they are not developed to provide healthcare, their potential to address biomedical issues is rather unexplored. Healthcare digitalization and documentation of electronic health records is now developing into a standard practice. Developing tools to facilitate clinical review of unstructured data such as LLMs can derive clinical meaningful insights for ovarian cancer, a heterogeneous but devastating disease. Compared to standard approaches, they can host capacity to condense results and optimize analysis time. To help accelerate research in biomedical language processing and improve the validity of scientific writing, task-specific and domain-specific language models may be required. In turn, we propose a bespoke, proprietary ovarian cancer-specific natural language using solely in-domain text, whereas transfer learning drifts away from the pretrained language models to fine-tune task-specific models for all possible downstream applications. This venture will be fueled by the abundance of unstructured text information in the electronic health records resulting in ovarian cancer research ultimately reaching its linguistic home

A Large Language Model based legal assistant for governance applications

Large Language Models (LLMs) have gained significant traction, primarily due to their potential disruptive influence across industries reliant on natural language processing. Governance stands out as one such sector. Notably, there has been a surge in research activity surrounding the implications of LLMs in deciphering complex legal corpora. This research offers substantial assistance to various stakeholders, including decision-makers, administrators, and citizens. This article focuses on the design and implementation of an LLM-based legal assistant tailored for interacting with legal resources. To achieve this, a real-world scenario has been chosen, incorporating models GPT3.5 and GPT4 as the LLMs, a well-defined legal corpus comprising European Union (EU) legislation and case law concerning the General Data Protection Regulation (GDPR), alongside a series of reference legal queries of varying complexity. Retrieval Augmented Generation (RAG) as well as agent methodologies are employed to seamlessly integrate the LLMs' functionalities with the customized dataset. The results appear to be promising, as the system managed to correctly address the majority of the legal queries, though with variable precision. Expectantly, the complexity of the queries severely impacted the quality of the outcome

Factors Predicting Surgical Effort Using Explainable Artificial Intelligence in Advanced Stage Epithelial Ovarian Cancer

(1) Background: Surgical cytoreduction for epithelial ovarian cancer (EOC) is a complex procedure. Encompassed within the performance skills to achieve surgical precision, intra-operative surgical decision-making remains a core feature. The use of eXplainable Artificial Intelligence (XAI) could potentially interpret the influence of human factors on the surgical effort for the cytoreductive outcome in question; (2) Methods: The retrospective cohort study evaluated 560 consecutive EOC patients who underwent cytoreductive surgery between January 2014 and December 2019 in a single public institution. The eXtreme Gradient Boosting (XGBoost) and Deep Neural Network (DNN) algorithms were employed to develop the predictive model, including patient- and operation-specific features, and novel features reflecting human factors in surgical heuristics. The precision, recall, F1 score, and area under curve (AUC) were compared between both training algorithms. The SHapley Additive exPlanations (SHAP) framework was used to provide global and local explainability for the predictive model; (3) Results: A surgical complexity score (SCS) cut-off value of five was calculated using a Receiver Operator Characteristic (ROC) curve, above which the probability of incomplete cytoreduction was more likely (area under the curve [AUC] = 0.644; 95% confidence interval [CI] = 0.598–0.69; sensitivity and specificity 34.1%, 86.5%, respectively; p = 0.000). The XGBoost outperformed the DNN assessment for the prediction of the above threshold surgical effort outcome (AUC = 0.77; 95% [CI] 0.69–0.85; p p 4, and a Peritoneal Carcinomatosis Index >7, in a surgical environment with the optimization of infrastructural support. (4) Conclusions: Using XAI, we explain how intra-operative decisions may consider human factors during EOC cytoreduction alongside factual knowledge, to maximize the magnitude of the selected trade-off in effort. XAI techniques are critical for a better understanding of Artificial Intelligence frameworks, and to enhance their incorporation in medical applications

Stratification of Length of Stay Prediction following Surgical Cytoreduction in Advanced High-Grade Serous Ovarian Cancer Patients Using Artificial Intelligence; the Leeds L-AI-OS Score

(1) Background: Length of stay (LOS) has been suggested as a marker of the effectiveness of short-term care. Artificial Intelligence (AI) technologies could help monitor hospital stays. We developed an AI-based novel predictive LOS score for advanced-stage high-grade serous ovarian cancer (HGSOC) patients following cytoreductive surgery and refined factors significantly affecting LOS. (2) Methods: Machine learning and deep learning methods using artificial neural networks (ANN) were used together with conventional logistic regression to predict continuous and binary LOS outcomes for HGSOC patients. The models were evaluated in a post-hoc internal validation set and a Graphical User Interface (GUI) was developed to demonstrate the clinical feasibility of sophisticated LOS predictions. (3) Results: For binary LOS predictions at differential time points, the accuracy ranged between 70–98%. Feature selection identified surgical complexity, pre-surgery albumin, blood loss, operative time, bowel resection with stoma formation, and severe postoperative complications (CD3–5) as independent LOS predictors. For the GUI numerical LOS score, the ANN model was a good estimator for the standard deviation of the LOS distribution by ± two days. (4) Conclusions: We demonstrated the development and application of both quantitative and qualitative AI models to predict LOS in advanced-stage EOC patients following their cytoreduction. Accurate identification of potentially modifiable factors delaying hospital discharge can further inform services performing root cause analysis of LOS