624 research outputs found

    LIPIcs, Volume 251, ITCS 2023, Complete Volume

    Get PDF
    LIPIcs, Volume 251, ITCS 2023, Complete Volum

    Exploration autonome et efficiente de chantiers miniers souterrains inconnus avec un drone filaire

    Get PDF
    Abstract: Underground mining stopes are often mapped using a sensor located at the end of a pole that the operator introduces into the stope from a secure area. The sensor emits laser beams that provide the distance to a detected wall, thus creating a 3D map. This produces shadow zones and a low point density on the distant walls. To address these challenges, a research team from the UniversitĂ© de Sherbrooke is designing a tethered drone equipped with a rotating LiDAR for this mission, thus benefiting from several points of view. The wired transmission allows for unlimited flight time, shared computing, and real-time communication. For compatibility with the movement of the drone after tether entanglements, the excess length is integrated into an onboard spool, contributing to the drone payload. During manual piloting, the human factor causes problems in the perception and comprehension of a virtual 3D environment, as well as the execution of an optimal mission. This thesis focuses on autonomous navigation in two aspects: path planning and exploration. The system must compute a trajectory that maps the entire environment, minimizing the mission time and respecting the maximum onboard tether length. Path planning using a Rapidly-exploring Random Tree (RRT) quickly finds a feasible path, but the optimization is computationally expensive and the performance is variable and unpredictable. Exploration by the frontier method is representative of the space to be explored and the path can be optimized by solving a Traveling Salesman Problem (TSP) but existing techniques for a tethered drone only consider the 2D case and do not optimize the global path. To meet these challenges, this thesis presents two new algorithms. The first one, RRT-Rope, produces an equal or shorter path than existing algorithms in a significantly shorter computation time, up to 70% faster than the next best algorithm in a representative environment. A modified version of RRT-connect computes a feasible path, shortened with a deterministic technique that takes advantage of previously added intermediate nodes. The second algorithm, TAPE, is the first 3D cavity exploration method that focuses on minimizing mission time and unwound tether length. On average, the overall path is 4% longer than the method that solves the TSP, but the tether remains under the allowed length in 100% of the simulated cases, compared to 53% with the initial method. The approach uses a 2-level hierarchical architecture: global planning solves a TSP after frontier extraction, and local planning minimizes the path cost and tether length via a decision function. The integration of these two tools in the NetherDrone produces an intelligent system for autonomous exploration, with semi-autonomous features for operator interaction. This work opens the door to new navigation approaches in the field of inspection, mapping, and Search and Rescue missions.La cartographie des chantiers miniers souterrains est souvent rĂ©alisĂ©e Ă  l’aide d’un capteur situĂ© au bout d’une perche que l’opĂ©rateur introduit dans le chantier, depuis une zone sĂ©curisĂ©e. Le capteur Ă©met des faisceaux laser qui fournissent la distance Ă  un mur dĂ©tectĂ©, crĂ©ant ainsi une carte en 3D. Ceci produit des zones d’ombres et une faible densitĂ© de points sur les parois Ă©loignĂ©es. Pour relever ces dĂ©fis, une Ă©quipe de recherche de l’UniversitĂ© de Sherbrooke conçoit un drone filaire Ă©quipĂ© d’un LiDAR rotatif pour cette mission, bĂ©nĂ©ficiant ainsi de plusieurs points de vue. La transmission filaire permet un temps de vol illimitĂ©, un partage de calcul et une communication en temps rĂ©el. Pour une compatibilitĂ© avec le mouvement du drone lors des coincements du fil, la longueur excĂ©dante est intĂ©grĂ©e dans une bobine embarquĂ©e, qui contribue Ă  la charge utile du drone. Lors d’un pilotage manuel, le facteur humain entraĂźne des problĂšmes de perception et comprĂ©hension d’un environnement 3D virtuel, et d’exĂ©cution d’une mission optimale. Cette thĂšse se concentre sur la navigation autonome sous deux aspects : la planification de trajectoire et l’exploration. Le systĂšme doit calculer une trajectoire qui cartographie l’environnement complet, en minimisant le temps de mission et en respectant la longueur maximale de fil embarquĂ©e. La planification de trajectoire Ă  l’aide d’un Rapidly-exploring Random Tree (RRT) trouve rapidement un chemin rĂ©alisable, mais l’optimisation est coĂ»teuse en calcul et la performance est variable et imprĂ©visible. L’exploration par la mĂ©thode des frontiĂšres est reprĂ©sentative de l’espace Ă  explorer et le chemin peut ĂȘtre optimisĂ© en rĂ©solvant un Traveling Salesman Problem (TSP), mais les techniques existantes pour un drone filaire ne considĂšrent que le cas 2D et n’optimisent pas le chemin global. Pour relever ces dĂ©fis, cette thĂšse prĂ©sente deux nouveaux algorithmes. Le premier, RRT-Rope, produit un chemin Ă©gal ou plus court que les algorithmes existants en un temps de calcul jusqu’à 70% plus court que le deuxiĂšme meilleur algorithme dans un environnement reprĂ©sentatif. Une version modifiĂ©e de RRT-connect calcule un chemin rĂ©alisable, raccourci avec une technique dĂ©terministe qui tire profit des noeuds intermĂ©diaires prĂ©alablement ajoutĂ©s. Le deuxiĂšme algorithme, TAPE, est la premiĂšre mĂ©thode d’exploration de cavitĂ©s en 3D qui minimise le temps de mission et la longueur du fil dĂ©roulĂ©. En moyenne, le trajet global est 4% plus long que la mĂ©thode qui rĂ©sout le TSP, mais le fil reste sous la longueur autorisĂ©e dans 100% des cas simulĂ©s, contre 53% avec la mĂ©thode initiale. L’approche utilise une architecture hiĂ©rarchique Ă  2 niveaux : la planification globale rĂ©sout un TSP aprĂšs extraction des frontiĂšres, et la planification locale minimise le coĂ»t du chemin et la longueur de fil via une fonction de dĂ©cision. L’intĂ©gration de ces deux outils dans le NetherDrone produit un systĂšme intelligent pour l’exploration autonome, dotĂ© de fonctionnalitĂ©s semi-autonomes pour une interaction avec l’opĂ©rateur. Les travaux rĂ©alisĂ©s ouvrent la porte Ă  de nouvelles approches de navigation dans le domaine des missions d’inspection, de cartographie et de recherche et sauvetage

    Advances and Applications of DSmT for Information Fusion. Collected Works, Volume 5

    Get PDF
    This ïŹfth volume on Advances and Applications of DSmT for Information Fusion collects theoretical and applied contributions of researchers working in different ïŹelds of applications and in mathematics, and is available in open-access. The collected contributions of this volume have either been published or presented after disseminating the fourth volume in 2015 in international conferences, seminars, workshops and journals, or they are new. The contributions of each part of this volume are chronologically ordered. First Part of this book presents some theoretical advances on DSmT, dealing mainly with modiïŹed Proportional ConïŹ‚ict Redistribution Rules (PCR) of combination with degree of intersection, coarsening techniques, interval calculus for PCR thanks to set inversion via interval analysis (SIVIA), rough set classiïŹers, canonical decomposition of dichotomous belief functions, fast PCR fusion, fast inter-criteria analysis with PCR, and improved PCR5 and PCR6 rules preserving the (quasi-)neutrality of (quasi-)vacuous belief assignment in the fusion of sources of evidence with their Matlab codes. Because more applications of DSmT have emerged in the past years since the apparition of the fourth book of DSmT in 2015, the second part of this volume is about selected applications of DSmT mainly in building change detection, object recognition, quality of data association in tracking, perception in robotics, risk assessment for torrent protection and multi-criteria decision-making, multi-modal image fusion, coarsening techniques, recommender system, levee characterization and assessment, human heading perception, trust assessment, robotics, biometrics, failure detection, GPS systems, inter-criteria analysis, group decision, human activity recognition, storm prediction, data association for autonomous vehicles, identiïŹcation of maritime vessels, fusion of support vector machines (SVM), Silx-Furtif RUST code library for information fusion including PCR rules, and network for ship classiïŹcation. Finally, the third part presents interesting contributions related to belief functions in general published or presented along the years since 2015. These contributions are related with decision-making under uncertainty, belief approximations, probability transformations, new distances between belief functions, non-classical multi-criteria decision-making problems with belief functions, generalization of Bayes theorem, image processing, data association, entropy and cross-entropy measures, fuzzy evidence numbers, negator of belief mass, human activity recognition, information fusion for breast cancer therapy, imbalanced data classiïŹcation, and hybrid techniques mixing deep learning with belief functions as well

    On the Utility of Representation Learning Algorithms for Myoelectric Interfacing

    Get PDF
    Electrical activity produced by muscles during voluntary movement is a reflection of the firing patterns of relevant motor neurons and, by extension, the latent motor intent driving the movement. Once transduced via electromyography (EMG) and converted into digital form, this activity can be processed to provide an estimate of the original motor intent and is as such a feasible basis for non-invasive efferent neural interfacing. EMG-based motor intent decoding has so far received the most attention in the field of upper-limb prosthetics, where alternative means of interfacing are scarce and the utility of better control apparent. Whereas myoelectric prostheses have been available since the 1960s, available EMG control interfaces still lag behind the mechanical capabilities of the artificial limbs they are intended to steer—a gap at least partially due to limitations in current methods for translating EMG into appropriate motion commands. As the relationship between EMG signals and concurrent effector kinematics is highly non-linear and apparently stochastic, finding ways to accurately extract and combine relevant information from across electrode sites is still an active area of inquiry.This dissertation comprises an introduction and eight papers that explore issues afflicting the status quo of myoelectric decoding and possible solutions, all related through their use of learning algorithms and deep Artificial Neural Network (ANN) models. Paper I presents a Convolutional Neural Network (CNN) for multi-label movement decoding of high-density surface EMG (HD-sEMG) signals. Inspired by the successful use of CNNs in Paper I and the work of others, Paper II presents a method for automatic design of CNN architectures for use in myocontrol. Paper III introduces an ANN architecture with an appertaining training framework from which simultaneous and proportional control emerges. Paper Iv introduce a dataset of HD-sEMG signals for use with learning algorithms. Paper v applies a Recurrent Neural Network (RNN) model to decode finger forces from intramuscular EMG. Paper vI introduces a Transformer model for myoelectric interfacing that do not need additional training data to function with previously unseen users. Paper vII compares the performance of a Long Short-Term Memory (LSTM) network to that of classical pattern recognition algorithms. Lastly, paper vIII describes a framework for synthesizing EMG from multi-articulate gestures intended to reduce training burden

    Using machine learning to predict pathogenicity of genomic variants throughout the human genome

    Get PDF
    GeschĂ€tzt mehr als 6.000 Erkrankungen werden durch VerĂ€nderungen im Genom verursacht. Ursachen gibt es viele: Eine genomische Variante kann die Translation eines Proteins stoppen, die Genregulation stören oder das Spleißen der mRNA in eine andere Isoform begĂŒnstigen. All diese Prozesse mĂŒssen ĂŒberprĂŒft werden, um die zum beschriebenen PhĂ€notyp passende Variante zu ermitteln. Eine Automatisierung dieses Prozesses sind Varianteneffektmodelle. Mittels maschinellem Lernen und Annotationen aus verschiedenen Quellen bewerten diese Modelle genomische Varianten hinsichtlich ihrer PathogenitĂ€t. Die Entwicklung eines Varianteneffektmodells erfordert eine Reihe von Schritten: Annotation der Trainingsdaten, Auswahl von Features, Training verschiedener Modelle und Selektion eines Modells. Hier prĂ€sentiere ich ein allgemeines Workflow dieses Prozesses. Dieses ermöglicht es den Prozess zu konfigurieren, Modellmerkmale zu bearbeiten, und verschiedene Annotationen zu testen. Der Workflow umfasst außerdem die Optimierung von Hyperparametern, Validierung und letztlich die Anwendung des Modells durch genomweites Berechnen von Varianten-Scores. Der Workflow wird in der Entwicklung von Combined Annotation Dependent Depletion (CADD), einem Varianteneffektmodell zur genomweiten Bewertung von SNVs und InDels, verwendet. Durch Etablierung des ersten Varianteneffektmodells fĂŒr das humane Referenzgenome GRCh38 demonstriere ich die gewonnenen Möglichkeiten Annotationen aufzugreifen und neue Modelle zu trainieren. Außerdem zeige ich, wie Deep-Learning-Scores als Feature in einem CADD-Modell die Vorhersage von RNA-Spleißing verbessern. Außerdem werden Varianteneffektmodelle aufgrund eines neuen, auf AllelhĂ€ufigkeit basierten, Trainingsdatensatz entwickelt. Diese Ergebnisse zeigen, dass der entwickelte Workflow eine skalierbare und flexible Möglichkeit ist, um Varianteneffektmodelle zu entwickeln. Alle entstandenen Scores sind unter cadd.gs.washington.edu und cadd.bihealth.org frei verfĂŒgbar.More than 6,000 diseases are estimated to be caused by genomic variants. This can happen in many possible ways: a variant may stop the translation of a protein, interfere with gene regulation, or alter splicing of the transcribed mRNA into an unwanted isoform. It is necessary to investigate all of these processes in order to evaluate which variant may be causal for the deleterious phenotype. A great help in this regard are variant effect scores. Implemented as machine learning classifiers, they integrate annotations from different resources to rank genomic variants in terms of pathogenicity. Developing a variant effect score requires multiple steps: annotation of the training data, feature selection, model training, benchmarking, and finally deployment for the model's application. Here, I present a generalized workflow of this process. It makes it simple to configure how information is converted into model features, enabling the rapid exploration of different annotations. The workflow further implements hyperparameter optimization, model validation and ultimately deployment of a selected model via genome-wide scoring of genomic variants. The workflow is applied to train Combined Annotation Dependent Depletion (CADD), a variant effect model that is scoring SNVs and InDels genome-wide. I show that the workflow can be quickly adapted to novel annotations by porting CADD to the genome reference GRCh38. Further, I demonstrate the integration of deep-neural network scores as features into a new CADD model, improving the annotation of RNA splicing events. Finally, I apply the workflow to train multiple variant effect models from training data that is based on variants selected by allele frequency. In conclusion, the developed workflow presents a flexible and scalable method to train variant effect scores. All software and developed scores are freely available from cadd.gs.washington.edu and cadd.bihealth.org

    The Impact of Additive Manufacturing on Supply Chains and Business Models: Qualitative Analyses of Supply Chain Design, Governance Structure, and Business Model Change

    Get PDF
    Recent global crises like the COVID-19 pandemic challenge traditional global supply chains (SCs). Their disaggregated, “fine-sliced” character comes with a high risk of disruption, and current supply bottlenecks (e.g., the chip shortage in the automotive industry) demonstrate that there is often no quick fix. Firms are increasingly under pressure to react and (re-)design their SCs to increase their resilience. Additive manufacturing (AM) technologies are acclaimed for their potential to foster the shift from global SCs to shorter, decentralized, and more resilient SCs. The key feature of AM technologies lies in their inherently digital and flexible nature. Their specific characteristics are envisioned to enable location-independent manufacturing close to or even at the point of demand and lead to a commoditization of manufacturing infrastructure for flexible outsourcing to local partners. Moreover, AM technologies are expected to revolutionize the way firms do business and put traditional business models at stake. This doctoral thesis is motivated by the outlined potential of AM and the resulting impact on firms’ supply chain design (SCD) and business model choices. The extant literature raises high expectations for AM. However, concrete and real-world insights from specific application domains are still scarce. This thesis seeks to fill the gap between high-level literature-based visions and currently emerging realistic business models and SCDs for AM. Thereby, AM is understood as a potential intervention emanating from outside firms and requiring them to react by realigning their business models and SC structures to maintain a fit. This thesis aims to build an in-depth understanding of these mechanisms and, hence, of the inner causal processes involved in the AM SCD and business model choices. This concentration on the rationales and underlying behavioral patterns is formalized with primarily exploratory (how and why) research questions that are addressed with qualitative research methodologies, mainly case study research and grounded theory. These methodological practices are applied in the industrial AM context, entailing an embedding of this thesis in challenging industries where AM applications have already started to create value (i.e., in the aerospace, rail, automotive, and machinery and equipment industries). The selected research approaches are mostly inductive and, hence, strongly driven by the data collected from this context (e.g., in interviews, by reviewing documents, and by analyzing websites). Additionally, this thesis relies on grand theories, namely transaction cost economics, the resource-based view, and configuration theory, to discuss the findings in their light and to interpret and distill nuances of these theories for their application in the industrial AM context. This thesis is cumulative, consisting of four studies that form its main body. These studies are organized in two parts, part A and part B, since two domains of strategic decisions are targeted jointly, the business model development (part A) and AM SCD choice (part B) for industrial AM. Different perspectives are associated with the two parts. Logistics service providers (LSPs) are in a critical position to develop AM business models. Based on the expected shift to decentralized, shorter SCs, the traditional business models of LSPs are at risk, and their inherent customer orientation puts them under pressure to adjust to their customers’ needs in AM. In part A, study A.1 applies a process-based perspective to build a broad understanding of how LSPs currently respond to AM and consumer-oriented polymer 3D printing with specific AM activities. It proposes six profiles of how LSPs leverage AM, both as users for their in-house operations and as developers of AM-specific services for external customers. A key finding is that the initiated AM activities are oftentimes strongly based on LSPs’ traditional resources. Only a few LSPs are found whose AM activities are detached from their traditional business models to focus on digital platform-based services for AM. In contrast to the process-based perspective and focus on business model dynamics in study A.1, study A.2 takes an output perspective to propose six generic business model configurations for industrial AM. Each configuration emerges from the perspective of LSPs and is reflected by their potential partners/competitors and industrial customers. Study A.2 explores how the six generic configurations fit specific types of LSPs and how they are embedded in a literature-based service SC for industrial AM. In combination, studies A.1 and A.2 provide a comprehensive understanding of how LSPs are currently reacting to AM and an empirically grounded perspective on “finished” AM business models to evaluate and refine literature-based visions. Part B of this thesis is devoted to the mechanism of (re-)designing SCs for AM, which is investigated from the perspective of focal manufacturing firms based on their dominant position in SCs. Two dimensions are used to characterize AM SCDs, their horizontal scope (geographic dispersion) and vertical scope (governance structure). The combination of both dimensions is ideally suited to capture the literature-based vision of shorter, decentralized AM SCs (horizontal scope) with eased outsourcing to local partners (vertical scope). Study B.1 takes a firm-centric perspective to develop an in-depth understanding for AM make-or-buy decisions of manufacturing firms, the outcomes of which determine the SC governance structure. This study elaborates how the specific (digital and emerging) traits of industrial AM technologies modify arguments of grand theories that explain make-or-buy decisions in the “analog” age. In comparison, study B.2 shifts from a firm-centric to a network perspective to rely on both dimensions for investigating cohesive AM SCD configurations. More specifically, study B.2 explores four polar AM SCD configurations and reveals manufacturing firms’ rationales for selecting them. Thereby, it builds an understanding for why manufacturing firms currently have valid reasons to implement industrial AM in-house or distributed in a secure, firm-owned network. As a result, combining both studies provides an understanding of why manufacturing firms currently select specific governance structures for industrial AM and opt for SCDs that differ from the literature-based vision of decentralized, outsourced AM. Overall, this thesis positions itself as theory-oriented research that also aims at supporting managers of manufacturing firms and LSPs in making informed decisions when implementing AM in their SCs and developing AM-based business models. The three studies A.1, A.2, and B.2 contribute to initial theory building on how and why specific AM business models and SCDs emerge. With their focus on developing an understanding for the causal processes (how and why) and by assuming a process-based and output perspective, they can draw a line from firms’ current reactions to sound reflections on future-oriented, high-level expectations for AM. As a result, the studies significantly enrich and refine the current body of knowledge in the AM business model literature on LSPs and the operations and supply chain management literature on AM SCDs, focusing on their geographic dispersion and governance structure. This thesis further contributes with its context-specificity to building domain knowledge for industrial AM, which can serve as one “puzzle piece” for theorizing on how AM and other digitally dominated (manufacturing) technologies will shape the era of digital business models and SCs. In particular, study B.1 stands out by its focus on theory elaboration and the objective of developing contextual middle-range theory. It reveals that emerging digital AM is a setting where the argumentation of grand theories provides contradicting guidance on whether to develop AM in-house or outsource the manufacturing process. Such findings for industrial AM raise multiple opportunities for future research, among them are the comparison with other industry contexts with similar characteristics and the operationalization of the propositions developed in this thesis in follow-up quantitative decision-support models

    Behavior quantification as the missing link between fields: Tools for digital psychiatry and their role in the future of neurobiology

    Full text link
    The great behavioral heterogeneity observed between individuals with the same psychiatric disorder and even within one individual over time complicates both clinical practice and biomedical research. However, modern technologies are an exciting opportunity to improve behavioral characterization. Existing psychiatry methods that are qualitative or unscalable, such as patient surveys or clinical interviews, can now be collected at a greater capacity and analyzed to produce new quantitative measures. Furthermore, recent capabilities for continuous collection of passive sensor streams, such as phone GPS or smartwatch accelerometer, open avenues of novel questioning that were previously entirely unrealistic. Their temporally dense nature enables a cohesive study of real-time neural and behavioral signals. To develop comprehensive neurobiological models of psychiatric disease, it will be critical to first develop strong methods for behavioral quantification. There is huge potential in what can theoretically be captured by current technologies, but this in itself presents a large computational challenge -- one that will necessitate new data processing tools, new machine learning techniques, and ultimately a shift in how interdisciplinary work is conducted. In my thesis, I detail research projects that take different perspectives on digital psychiatry, subsequently tying ideas together with a concluding discussion on the future of the field. I also provide software infrastructure where relevant, with extensive documentation. Major contributions include scientific arguments and proof of concept results for daily free-form audio journals as an underappreciated psychiatry research datatype, as well as novel stability theorems and pilot empirical success for a proposed multi-area recurrent neural network architecture.Comment: PhD thesis cop

    Pre-Trained Driving in Localized Surroundings with Semantic Radar Information and Machine Learning

    Get PDF
    Entlang der Signalverarbeitungskette von Radar Detektionen bis zur Fahrzeugansteuerung, diskutiert diese Arbeit eine semantischen Radar Segmentierung, einen darauf aufbauenden Radar SLAM, sowie eine im Verbund realisierte autonome Parkfunktion. Die Radarsegmentierung der (statischen) Umgebung wird durch ein Radar-spezifisches neuronales Netzwerk RadarNet erreicht. Diese Segmentierung ermöglicht die Entwicklung des semantischen Radar Graph-SLAM SERALOC. Auf der Grundlage der semantischen Radar SLAM Karte wird eine beispielhafte autonome ParkfunktionalitĂ€t in einem realen VersuchstrĂ€ger umgesetzt. Entlang eines aufgezeichneten Referenzfades parkt die Funktion ausschließlich auf Basis der Radar Wahrnehmung mit bisher unerreichter Positioniergenauigkeit. Im ersten Schritt wird ein Datensatz von 8.2 · 10^6 punktweise semantisch gelabelten Radarpunktwolken ĂŒber eine Strecke von 2507.35m generiert. Es sind keine vergleichbaren DatensĂ€tze dieser Annotationsebene und Radarspezifikation öffentlich verfĂŒgbar. Das ĂŒberwachte Training der semantischen Segmentierung RadarNet erreicht 28.97% mIoU auf sechs Klassen. Außerdem wird ein automatisiertes Radar-Labeling-Framework SeRaLF vorgestellt, welches das Radarlabeling multimodal mittels Referenzkameras und LiDAR unterstĂŒtzt. FĂŒr die kohĂ€rente Kartierung wird ein Radarsignal-Vorfilter auf der Grundlage einer Aktivierungskarte entworfen, welcher Rauschen und andere dynamische Mehrwegreflektionen unterdrĂŒckt. Ein speziell fĂŒr Radar angepasstes Graph-SLAM-Frontend mit Radar-Odometrie Kanten zwischen Teil-Karten und semantisch separater NDT Registrierung setzt die vorgefilterten semantischen Radarscans zu einer konsistenten metrischen Karte zusammen. Die Kartierungsgenauigkeit und die Datenassoziation werden somit erhöht und der erste semantische Radar Graph-SLAM fĂŒr beliebige statische Umgebungen realisiert. Integriert in ein reales Testfahrzeug, wird das Zusammenspiel der live RadarNet Segmentierung und des semantischen Radar Graph-SLAM anhand einer rein Radar-basierten autonomen ParkfunktionalitĂ€t evaluiert. Im Durchschnitt ĂŒber 42 autonome Parkmanöver (∅3.73 km/h) bei durchschnittlicher ManöverlĂ€nge von ∅172.75m wird ein Median absoluter Posenfehler von 0.235m und End-Posenfehler von 0.2443m erreicht, der vergleichbare Radar-Lokalisierungsergebnisse um ≈ 50% ĂŒbertrifft. Die Kartengenauigkeit von verĂ€nderlichen, neukartierten Orten ĂŒber eine Kartierungsdistanz von ∅165m ergibt eine ≈ 56%-ige Kartenkonsistenz bei einer Abweichung von ∅0.163m. FĂŒr das autonome Parken wurde ein gegebener Trajektorienplaner und Regleransatz verwendet

    Beyond Quantity: Research with Subsymbolic AI

    Get PDF
    How do artificial neural networks and other forms of artificial intelligence interfere with methods and practices in the sciences? Which interdisciplinary epistemological challenges arise when we think about the use of AI beyond its dependency on big data? Not only the natural sciences, but also the social sciences and the humanities seem to be increasingly affected by current approaches of subsymbolic AI, which master problems of quality (fuzziness, uncertainty) in a hitherto unknown way. But what are the conditions, implications, and effects of these (potential) epistemic transformations and how must research on AI be configured to address them adequately
    • 

    corecore