Self-Learning Production Control using Algorithms of Artificial Intelligence

Manufacturing companies are facing an increasingly turbulent market - a market defined by products growing in complexity and shrinking product life cycles. This leads to a boost in planning complexity accompanied by higher error sensitivity. In practice, IT systems and sensors integrated into the shop floor in the context of Industry 4.0 are used to deal with these challenges. However, while existing research provides solutions in the field of pattern recognition or recommended actions, a combination of the two approaches is neglected. This leads to an overwhelming amount of data without contributing to an improvement of processes. To address this problem, this study presents a new platform-based concept to collect and analyze the high-resolution data with the use of self-learning algorithms. Herby, patterns can be identified and reproduced, allowing an exact prediction of the future system behavior. Artificial intelligence maximizes the automation of the reduction and compensation of disruptive factors

Insights from an OTTR-centric Ontology Engineering Methodology

OTTR is a language for representing ontology modeling patterns, which enables to build ontologies or knowledge bases by instantiating templates. Thereby, particularities of the ontological representation language are hidden from the domain experts, and it enables ontology engineers to, to some extent, separate the processes of deciding about what information to model from deciding about how to model the information, e.g., which design patterns to use. Certain decisions can thus be postponed for the benefit of focusing on one of these processes. To date, only few works on ontology engineering where ontology templates are applied are described in the literature. In this paper, we outline our methodology and report findings from our ontology engineering activities in the domain of Material Science. In these activities, OTTR templates play a key role. Our ontology engineering process is bottom-up, as we begin modeling activities from existing data that is then, via templates, fed into a knowledge graph, and it is top-down, as we first focus on which data to model and postpone the decision of how to model the data. We find, among other things, that OTTR templates are especially useful as a means of communication with domain experts. Furthermore, we find that because OTTR templates encapsulate modeling decisions, the engineering process becomes flexible, meaning that design decisions can be changed at little cost.Comment: Paper accepted at the 14th Workshop on Ontology Design and Patterns (WOP 2023

Über die Differenzierung verschiedener Herzinsuffizienzentitäten mittels neuer Bildgebungsparameter der kardialen Magnetresonanztomographie

Hintergrund: Innovationen in der kardialen Magnetresonanztomographie (CMR) ermöglichen heute die Messung von myokardialen Verformungsparametern (Strain) und Gewebeeigenschaften. Ziel dieser Studie war die Erprobung neuer CMR-Parameter zur Differenzierung von Herzinsuffizienz (HI) mit reduzierter, mittelgradiger und erhaltener Ejektionsfraktion (HFrEF, HFmrEF, HFpEF) und Herzgesunden. Methoden: PatientInnen mit etablierter HI-Diagnose sowie Herzgesunde wurden klinisch, labormedizinisch und mittels CMR untersucht. Ausgeschlossen wurden unter anderem Menschen mit CMR-Kontraindikationen und instabilem klinischem Zustand. Linksventrikulärer globaler longitudinaler Strain (LV GLS) wurde vor und während isometrischer Handgrip-Belastung (HG) mittels fast strain-encoded CMR (Fast-SENC) bestimmt. Mittels feature-tracking wurde der LV GLS für die subendokardiale, intra-myokardiale und subepikardial Myokardschicht bestimmt (Multilayer-Strain). Tissue-Mapping zur Bestimmung von nativer T1- und T2-Relaxationszeit sowie extrazellulärem Volumen (ECV) wurde durchgeführt. Zur statistischen Auswertung kamen unter anderem analysis of variance, Pearson-Regressionskoeffizienten und die Fläche unter der receiver operating characteristic-Kurve (AUC) zum Einsatz. Ergebnisse: Insgesamt wurden 72 TeilnehmerInnen (Kontrollgruppe: n=19; HFpEF = 17; HFmrEF: n= 18; HFrEF: n=18) in die Studie eingeschlossen. Die mittlere Änderung des LV GLS während HG betrug +1.2 ± 5.4% in der Kontrollgruppe, −0.6 ± 8.3% bei HFpEF, −1.7 ± 10.7% bei HFmrEF und −3.1 ± 19.4% bei HFrEF (p = 0.746). Der Betrag der LV GLS Änderung unabhängig vom Vorzeichen unterschied sich signifikant zwischen den Subgruppen (Kontrollgruppe: 4.4 ± 3.2%; HFpEF: 5.9 ± 5.7%; HFmrEF: 6.8 ± 8.3%; HFrEF 14.1 ± 13.3%; p = 0.005) und korrelierte mit NTproBNP und Lebensqualitätsmetriken. In der Multilayer-Strain-Analyse unterschied sich LV GLS sowohl subendokardial (−20.8 ± 4.0 vs. −23.2 ± 3.4, p = 0.046) als auch intra-myokardial (−18.0 ± 3.0 vs. −21.0 ± 2.5, p = 0.002) und subepikardial (−12.2 ± 2.0 vs. −16.2 ± 2.5, p < 0.001) signifikant zwischen HFpEF und Herzgesunden. Insbesondere subepikardialer LV GLS differenzierte hervorragend zwischen HFpEF und Herzgesunden (AUC 0.90, 95% Confidence Interval 0.81-1). Die per Tissue-Mapping bestimmte native T1-Relaxationszeit war bei HFrEF (1033 ± 54 ms) und HFmrEF (1027 ± 40 ms) im Vergleich zu HFpEF (985 ± 32 ms) und Kontrollgruppe (972 ± 31 ms) angehoben, ebenso die T2 Relaxationszeit (Kontrollgruppe: 50.6 ± 2.1 ms; HFpEF: 52.6 ± 3.6 ms; HFmrEF: 55.4 ± 3.4 ms; HFrEF 56.0 ± 6.0 ms). ECV unterschied sich hingegen nicht signifikant. Fazit: Fast-SENC Strainmessung während HG liefert nur begrenzt diagnostisch verwertbare Informationen. Tissue-Mapping lässt strukturelle Ähnlichkeit von HFmrEF und HFrEF erkennen. Subepikardialer LV GLS ist ein vielversprechender diagnostischer Parameter zur Differenzierung von HFpEF und Herzgesunden.Background: Novel developments in cardiac magnetic resonance imaging (CMR) allow for quantification of myocardial strain and tissue characteristics. In this study we sought to evaluate the diagnostic utility of novel CMR parameters in heart failure (HF) with reduced, mid-range and preserved ejection fraction (HFrEF, HFmrEF, HFpEF) and healthy controls. Methods: Patients with an established diagnosis of HF and controls underwent physical examination, lab work and CMR. Exclusion criteria included CMR contraindications and unstable clinical status. Left ventricular global longitudinal strain (LV GLS) was measured before and during isometric handgrip (HG) using fast strain-encoded CMR. LV GLS was quantified on a subendocardial, mid-myocardial and subepicardial level employing feature tracking (multilayer strain). Using tissue mapping, native T1 and T2 relaxation times and extracellular volume (ECV) were quantified. Statistical methods included analysis of variance, Pearson’s coefficients and the area under the receiver operating characteristic curve (AUC). Results: The study comprised 72 subjects (Controls: n=19; HFpEF = 17; HFmrEF: n= 18; HFrEF: n=18). Mean change of LV GLS during HG was +1.2 ± 5.4%, −0.6 ± 8.3%, −1.7 ± 10.7% and −3.1 ± 19.4% in controls, HFpEF, HFmrEF and HFrEF, respectively (p = 0.746). The absolute value of LV GLS change differed significantly between subgroups. (Controls: 4.4 ± 3.2%; HFpEF: 5.9 ± 5.7%; HFmrEF: 6.8 ± 8.3%; HFrEF 14.1 ± 13.3%; p = 0.005) and correlated with NTproBNP and quality-of-life scores. Multilayer strain analysis showed significant differences in LV GLS between HFpEF and controls on subendocardial (−20.8 ± 4.0 vs. −23.2 ± 3.4, p = 0.046), mid-myocardial (−18.0 ± 3.0 vs. −21.0 ± 2.5, p = 0.002) and subepicardial levels (−12.2 ± 2.0 vs. −16.2 ± 2.5, p < 0.001). Subepicardial LV GLS in particular facilitated excellent discrimination between HFpEF and controls (AUC 0.90, 95% Confidence Interval 0.81-1). Tissue mapping showed elevated native T1 relaxation times in HFrEF (1033 ± 54 ms) and HFmrEF (1027 ± 40 ms) compared to HFpEF (985 ± 32 ms) und controls (972 ± 31 ms) and a similar pattern regarding T2 relaxation times (controls: 50.6 ± 2.1 ms; HFpEF: 52.6 ± 3.6 ms; HFmrEF: 55.4 ± 3.4 ms; HFrEF 56.0 ± 6.0 ms). ECV did not differ significantly between subgroups. Conclusion: The diagnostic utility of measuring strain during HG appears to be limited. Tissue mapping reveals structural similarities of HFmrEF and HFrEF. Subepicardial LV GLS is a promising diagnostic parameter discriminating between HFpEF and healthy subjects

A neuromorphic controller for a robotic vehicle equipped with a dynamic vision sensor

Neuromorphic electronic systems exhibit advantageous characteristics, in terms of low energy consumption and low response latency, which can be useful in robotic applications that require compact and low power embedded computing resources. However, these neuromorphic circuits still face significant limitations that make their usage challenging: these include low precision, variability of components, sensitivity to noise and temperature drifts, as well as the currently limited number of neurons and synapses that are typically emulated on a single chip. In this paper, we show how it is possible to achieve functional robot control strategies using a mixed signal analog/digital neuromorphic processor interfaced to a mobile robotic platform equipped with an event-based dynamic vision sensor. We provide a proof of concept implementation of obstacle avoidance and target acquisition using biologically plausible spiking neural networks directly emulated by the neuromorphic hardware. To our knowledge, this is the first demonstration of a working spike-based neuromorphic robotic controller in this type of hardware which illustrates the feasibility, as well as limitations, of this approach

A neuromorphic controller for a robotic vehicle equipped with a dynamic vision sensor

Neuromorphic electronic systems exhibit advantageous characteristics, in terms of low energy consumption and low response latency, which can be useful in robotic applications that require compact and low power embedded computing resources. However, these neuromorphic circuits still face significant limitations that make their usage challenging: these include low precision, variability of components, sensitivity to noise and temperature drifts, as well as the currently limited number of neurons and synapses that are typically emulated on a single chip. In this paper, we show how it is possible to achieve functional robot control strategies using a mixed signal analog/digital neuromorphic processor interfaced to a mobile robotic platform equipped with an event-based dynamic vision sensor. We provide a proof of concept implementation of obstacle avoidance and target acquisition using biologically plausible spiking neural networks directly emulated by the neuromorphic hardware. To our knowledge, this is the first demonstration of a working spike-based neuromorphic robotic controller in this type of hardware which illustrates the feasibility, as well as limitations, of this approach

Large Language Models on Wikipedia-Style Survey Generation: an Evaluation in NLP Concepts

Large Language Models (LLMs) have achieved significant success across various natural language processing (NLP) tasks, encompassing question-answering, summarization, and machine translation, among others. While LLMs excel in general tasks, their efficacy in domain-specific applications remains under exploration. Additionally, LLM-generated text sometimes exhibits issues like hallucination and disinformation. In this study, we assess LLMs' capability of producing concise survey articles within the computer science-NLP domain, focusing on 20 chosen topics. Automated evaluations indicate that GPT-4 outperforms GPT-3.5 when benchmarked against the ground truth. Furthermore, four human evaluators provide insights from six perspectives across four model configurations. Through case studies, we demonstrate that while GPT often yields commendable results, there are instances of shortcomings, such as incomplete information and the exhibition of lapses in factual accuracy

Enhancing Palliative Care for Patients With Advanced Heart Failure Through Simple Prognostication Tools: A Comparison of the Surprise Question, the Number of Previous Heart Failure Hospitalizations, and the Seattle Heart Failure Model for Predicting 1-Year Survival

Background: Score-based survival prediction in patients with advanced heart failure (HF) is complicated. Easy-to-use prognostication tools could inform clinical decision-making and palliative care delivery. Objective: To compare the prognostic utility of the Seattle HF model (SHFM), the surprise question (SQ), and the number of HF hospitalizations (NoH) within the last 12 months for predicting 1-year survival in patients with advanced HF. Methods: We retrospectively analyzed data from a cluster-randomized controlled trial of advanced HF patients, predominantly with reduced ejection fraction. Primary outcome was the prognostic discrimination of SHFM, SQ (“Would you be surprised if this patient were to die within 1 year?”) answered by HF cardiologists, and NoH, assessed by receiver operating characteristic (ROC) curve analysis. Optimal cut-offs were calculated using Youden’s index (SHFM: <86% predicted 1-year survival; NoH ≥ 2). Results: Of 535 subjects, 82 (15.3%) had died after 1-year of follow-up. SHFM, SQ, and NoH yielded a similar area under the ROC curve [SHFM: 0.65 (0.60–0.71 95% CI); SQ: 0.58 (0.54–0.63 95% CI); NoH: 0.56 (0.50–0.62 95% CI)] and similar sensitivity [SHFM: 0.76 (0.65–0.84 95% CI); SQ: 0.84 (0.74–0.91 95% CI); NoH: 0.56 (0.45–0.67 95% CI)]. As compared to SHFM, SQ had lower specificity [SQ: 0.33 (0.28–0.37 95% CI) vs. SHFM: 0.55 (0.50–0.60 95% CI)] while NoH had similar specificity [0.56 (0.51–0.61 95% CI)]. SQ combined with NoH showed significantly higher specificity [0.68 (0.64–0.73 95% CI)]. Conclusion: SQ and NoH yielded comparable utility to SHFM for 1-year survival prediction among advanced HF patients, are easy-to-use and could inform bedside decision-making

Don't break a leg: Running birds from quail to ostrich prioritise leg safety and economy in uneven terrain

Cursorial ground birds are paragons of bipedal running that span a 500-fold mass range from quail to ostrich. Here we investigate the task-level control priorities of cursorial birds by analysing how they negotiate single-step obstacles that create a conflict between body stability (attenuating deviations in body motion) and consistent leg force–length dynamics (for economy and leg safety). We also test the hypothesis that control priorities shift between body stability and leg safety with increasing body size, reflecting use of active control to overcome size-related challenges. Weight-support demands lead to a shift towards straighter legs and stiffer steady gait with increasing body size, but it remains unknown whether non-steady locomotor priorities diverge with size. We found that all measured species used a consistent obstacle negotiation strategy, involving unsteady body dynamics to minimise fluctuations in leg posture and loading across multiple steps, not directly prioritising body stability. Peak leg forces remained remarkably consistent across obstacle terrain, within 0.35 body weights of level running for obstacle heights from 0.1 to 0.5 times leg length. All species used similar stance leg actuation patterns, involving asymmetric force–length trajectories and posture-dependent actuation to add or remove energy depending on landing conditions. We present a simple stance leg model that explains key features of avian bipedal locomotion, and suggests economy as a key priority on both level and uneven terrain. We suggest that running ground birds target the closely coupled priorities of economy and leg safety as the direct imperatives of control, with adequate stability achieved through appropriately tuned intrinsic dynamics