00 Informatik, Wissen, Systeme
Filtern
Erscheinungsjahr
Dokumenttyp
Sprache
- Englisch (23)
Volltext vorhanden
- ja (23) (entfernen)
Gehört zur Bibliographie
- nein (23)
Schlagworte
- Maschinelles Lernen (5)
- machine learning (4)
- Convolutional Neural Network (3)
- Künstliche Intelligenz (3)
- Nachhaltigkeit (3)
- Alkoholmissbrauch (2)
- Anomalie <Medizin> (2)
- Anomalieerkennung (2)
- App <Programm> (2)
- Bildverarbeitung (2)
- Deep learning (2)
- Intervention <Medizin> (2)
- Jugend (2)
- Software (2)
- alcohol (2)
- anomaly detection (2)
- internet (2)
- young people (2)
- "Visual Knowledge Communication" (research project) (1)
- 3D-Mapping (1)
- Agent <Künstliche Intelligenz> (1)
- Algorithmus (1)
- ArcGIS (1)
- Autofahren (1)
- Autonomer Roboter (1)
- Big Data (1)
- Bild (1)
- Bildanalyse (1)
- Bilderkennung (1)
- Branch-and-Bound-Methode (1)
- CNN (1)
- Computeranimation (1)
- Crowdsourcing (1)
- DNN (1)
- Demontage (1)
- Diskriminanzanalyse (1)
- Docker (1)
- Dreidimensionales Modell (1)
- EHR (1)
- Edit operations (1)
- Edutainment (1)
- Elektronische Patientenakte (1)
- Energieverbrauch (1)
- Engagement (1)
- FDM@HAW.rlp (1)
- Fachhochschule (1)
- Fahrerassistenzsystem (1)
- Forschungsdaten (1)
- Forschungsprojekt (1)
- Fuzzy-Logik (1)
- GA-ANFIS (1)
- GA-FUZZY (1)
- GA-GA-FUZZY (1)
- Gehirn-Computer-Schnittstelle (1)
- Genetischer Algorithmus (1)
- Geologische Kartierung (1)
- Geotop (1)
- Geotopes (1)
- Gesundheitsökonomie (1)
- Green-IT (1)
- Handy (1)
- Hippocampus (1)
- ICU (1)
- Industrieroboter (1)
- Informatik (1)
- Intensivpflege (1)
- Intensivstation (1)
- Interdisziplinäre Forschung (1)
- Internet (1)
- Internet der Dinge (1)
- Internet of Things (1)
- Kehlkopf (1)
- Klassifikation (1)
- Konturfindung (1)
- Künstliche Beatmung (1)
- Laborparameter (1)
- Landscape Genesis (1)
- Landschaftsentwicklung (1)
- Management (1)
- Medizin (1)
- Medizinische Informatik (1)
- Mixed Reality (1)
- Model versioning (1)
- Model-driven engineering (1)
- Modellgetriebene Entwicklung (1)
- NIR-Spektroskopie (1)
- Neuro-Fuzzy-System (1)
- Neuronales Netz (1)
- Neurowissenschaften (1)
- Nichtlineare Diffusion (1)
- Online-Algorithmus (1)
- Peer Review (1)
- Programmierumgebung (1)
- Prozessmanagement (1)
- Psychologie (1)
- Rapid Prototyping (1)
- Raucherentwöhnung (1)
- Rekursives neuronales Netz (1)
- Rheinland-Pfalz (1)
- Robotertechnik (1)
- Saar-Lor-Lux (1)
- Saarschleife (1)
- Selbstorganisierende Karte (1)
- Sepsis (1)
- Social Media (1)
- Software product line engineering (1)
- Softwareentwicklung (1)
- Soziale Norm (1)
- Sozialökologie (1)
- Statistik (1)
- Stimmband (1)
- Student (1)
- Suchverfahren (1)
- Taxonomie (1)
- Teer (1)
- Universities of Applied Sciences (1)
- Unterstützungssystem <Informatik> (1)
- VentAI (1)
- Verteiltes System (1)
- Visuelle Kommunikation (1)
- Wissensvermittlung (1)
- Zigarettenrauch (1)
- adaptive neuro fuzzy system (1)
- animations (1)
- artificial intelligence (1)
- big data (1)
- brain-computer interface (1)
- branch and bound search algorithm (1)
- classification (1)
- cluster-management (1)
- containerization (1)
- convolutional neural network (1)
- convolutional neural networks (1)
- critical care (1)
- critically-ill patient (1)
- data-oriented business process (1)
- deep learning (1)
- deep learning-based image registration (DLIR) (1)
- disassembly plan (1)
- disassembly process (1)
- distributed computing (1)
- driver-assisting system (1)
- driving performance (1)
- edge detection (1)
- energy awareness (1)
- energy-aware software (1)
- engagement taxonomy (1)
- environmental criteria for software (1)
- environmental sustainability (1)
- fingerprint recognition (1)
- fuzzy logic (1)
- genetic algorithm (1)
- green software (1)
- human-robot-collaboration (1)
- image processing (1)
- image recognition (1)
- incremental learning (1)
- indoor localization (1)
- inferencing (1)
- informed software agent (1)
- institutional RDM (1)
- intelligent robot assistant (1)
- intensive care (1)
- internet of things (1)
- knowledge-intensive process (1)
- lab values (1)
- laryngeal high-speed video (1)
- learning vector quantization (1)
- lightGBM (1)
- linear discriminant analysis (LDA) (1)
- long short-term memory (1)
- maximal function (1)
- medical consumables (1)
- medical economics (1)
- medical informatics (1)
- mobile phone (1)
- model of software impacts (1)
- multi-atlas hippocampus segmentation (MAHS) (1)
- multi-scale complexity-aware module (MSCA-Module) (1)
- multi-scale complexity-aware registration network (MSCAReg-Net) (1)
- near-infrared spectroscopy (1)
- nonlinear diffusion (1)
- online learning (1)
- ontology (1)
- passive crowdsourcing (1)
- peer review process (1)
- problem drinking (1)
- product model (1)
- rapid prototyping (1)
- registration accuracy (1)
- reinforcement learning algorithm (1)
- resource efficiency (1)
- robot system (1)
- scalability (1)
- self organizing maps (SOM) (1)
- sharp function (1)
- signal processing (1)
- smoking cessation (1)
- social media (1)
- social norms (1)
- social-ecological systems (1)
- software energy measurements (1)
- spreading RDM (1)
- students (1)
- sustainability criteria (1)
- sustainability indicators (1)
- sustainability management (1)
- sustainable software (1)
- tar (1)
- text messaging (1)
- time series classification (1)
- tobacco (1)
- ventilation (1)
- visual programming environments (1)
- vocal fold vibration (1)
- web (1)
- web servers (1)
The Saarschleife geotope (SE-Germany) represents one of the most prominent geotopes of the SaarLorLux region and is known far beyond the borders of the Greater Region. Surprisingly, there is no visual representation of the relief history and genesis of this river meander, which is unique for Central Europe - as is common at places with comparable outstanding phenomena, such as e.g. the Rocher Saint-Michel d'Aiguilhe (France) or some national parks in the U.S. (e.g. Grand Canyon). The Saarschleife geotope therefore was choosen as a pilot object for the envisaged analysis of the landscape genesis but also regarding the 3D mapping and visualization. The visualisation presents the relief history and geological evolution of the last 300 million years in selected geological epochs, which are of fundamental importance for the understanding of today's geomorphological relief conditions, and is compiled into a summarized chronology.
Background: High numbers of consumable medical materials (eg, sterile needles and swabs) are used during the daily routine of intensive care units (ICUs) worldwide. Although medical consumables largely contribute to total ICU hospital expenditure, many hospitals do not track the individual use of materials. Current tracking solutions meeting the specific requirements of the medical environment, like barcodes or radio frequency identification, require specialized material preparation and high infrastructure investment. This impedes the accurate prediction of consumption, leads to high storage maintenance costs caused by large inventories, and hinders scientific work due to inaccurate documentation. Thus, new cost-effective and contactless methods for object detection are urgently needed.
Objective: The goal of this work was to develop and evaluate a contactless visual recognition system for tracking medical consumable materials in ICUs using a deep learning approach on a distributed client-server architecture.
Methods: We developed Consumabot, a novel client-server optical recognition system for medical consumables, based on the convolutional neural network model MobileNet implemented in Tensorflow. The software was designed to run on single-board computer platforms as a detection unit. The system was trained to recognize 20 different materials in the ICU, while 100 sample images of each consumable material were provided. We assessed the top-1 recognition rates in the context of different real-world ICU settings: materials presented to the system without visual obstruction, 50% covered materials, and scenarios of multiple items. We further performed an analysis of variance with repeated measures to quantify the effect of adverse real-world circumstances.
Results: Consumabot reached a >99% reliability of recognition after about 60 steps of training and 150 steps of validation. A desirable low cross entropy of <0.03 was reached for the training set after about 100 iteration steps and after 170 steps for the validation set. The system showed a high top-1 mean recognition accuracy in a real-world scenario of 0.85 (SD 0.11) for objects presented to the system without visual obstruction. Recognition accuracy was lower, but still acceptable, in scenarios where the objects were 50% covered (P<.001; mean recognition accuracy 0.71; SD 0.13) or multiple objects of the target group were present (P=.01; mean recognition accuracy 0.78; SD 0.11), compared to a nonobstructed view. The approach met the criteria of absence of explicit labeling (eg, barcodes, radio frequency labeling) while maintaining a high standard for quality and hygiene with minimal consumption of resources (eg, cost, time, training, and computational power).
Conclusions: Using a convolutional neural network architecture, Consumabot consistently achieved good results in the classification of consumables and thus is a feasible way to recognize and register medical consumables directly to a hospital’s electronic health record. The system shows limitations when the materials are partially covered, therefore identifying characteristics of the consumables are not presented to the system. Further development of the assessment in different medical circumstances is needed.
Numerous research methods have been developed to detect anomalies in the areas of security and risk analysis. In healthcare, there are numerous use cases where anomaly detection is relevant. For example, early detection of sepsis is one such use case. Early treatment of sepsis is cost effective and reduces the number of hospital days of patients in the ICU. There is no single procedure that is sufficient for sepsis diagnosis, and combinations of approaches are needed. Detecting anomalies in patient time series data could help speed the development of some decisions. However, our algorithm must be viewed as complementary to other approaches based on laboratory values and physician judgments. The focus of this work is to develop a hybrid method for detecting anomalies that occur, for example, in multidimensional medical signals, sensor signals, or other time series in business and nature. The novelty of our approach lies in the extension and combination of existing approaches: Statistics, Self Organizing Maps and Linear Discriminant Analysis in a unique and unprecedented way with the goal of identifying different types of anomalies in real-time measurement data and defining the point where the anomaly occurs. The proposed algorithm not only has the full potential to detect anomalies, but also to find real points where an anomaly starts.
Containerization is one of the most important topics for modern data centers and web developers. Since the number of containers on one- and multi-node systems is growing, knowledge about the energy consumption behavior of single web-service containers is essential in order to save energy and, of course, money. In this article, we are going to show how the energy consumption behavior of single containerized web services/web apps changes while creating replicas of the service in order to scale and balance the web service.
One key for successful and fluent human-robot-collaboration in disassembly processes is equipping the robot system with higher autonomy and intelligence. In this paper, we present an informed software agent that controls the robot behavior to form an intelligent robot assistant for disassembly purposes. While the disassembly process first depends on the product structure, we inform the agent using a generic approach through product models. The product model is then transformed to a directed graph and used to build, share and define a coarse disassembly plan. To refine the workflow, we formulate "the problem of loosening a connection and the distribution of the work" as a search problem. The created detailed plan consists of a sequence of actions that are used to call, parametrize and execute robot programs for the fulfillment of the assistance. The aim of this research is to equip robot systems with knowledge and skills to allow them to be autonomous in the performance of their assistance to finally improve the ergonomics of disassembly workstations.
Companies have made considerable progress in assessing the sustainability of their processes and products, including the information and communication technology (ICT) sector. However, it is surprising that little attention has been given to the sustainability performance of software products. For this article, we chose a case study approach to explore the extent, to which software manufacturers have considered sustainability criteria for their products. We selected a manufacturer of sustainability management software on the assumption that they would be more likely to integrate elements of sustainability performance in their products. In the case study, we applied a previously developed set of criteria for sustainable software (SCSS) using a questionnaire and experiments, to assess a web-based sustainability management software product regarding its sustainability performance. The assessment finds that despite a sustainability conscious manufacturer, a systematic assessment of sustainability regarding software products is missing in the case study. This implies that sustainability assessment for software products is still novel, corresponding knowledge is missing and suitable tools are not yet being widely applied in the industry. The SCSS presents a suitable approach to close this gap, but it does require further refinement, for example regarding its applicability to web-based software on external servers.
The aim of this work was to develop and evaluate the reinforcement learning algorithm VentAI, which is able to suggest a dynamically optimized mechanical ventilation regime for critically-ill patients. We built, validated and tested its performance on 11,943 events of volume-controlled mechanical ventilation derived from 61,532 distinct ICU admissions and tested it on an independent, secondary dataset (200,859 ICU stays; 25,086 mechanical ventilation events). A patient “data fingerprint” of 44 features was extracted as multidimensional time series in 4-hour time steps. We used a Markov decision process, including a reward system and a Q-learning approach, to find the optimized settings for positive end-expiratory pressure (PEEP), fraction of inspired oxygen (FiO2) and ideal body weight-adjusted tidal volume (Vt). The observed outcome was in-hospital or 90-day mortality. VentAI reached a significantly increased estimated performance return of 83.3 (primary dataset) and 84.1 (secondary dataset) compared to physicians’ standard clinical care (51.1). The number of recommended action changes per mechanically ventilated patient constantly exceeded those of the clinicians. VentAI chose 202.9% more frequently ventilation regimes with lower Vt (5–7.5 mL/kg), but 50.8% less for regimes with higher Vt (7.5–10 mL/kg). VentAI recommended 29.3% more frequently PEEP levels of 5–7 cm H2O and 53.6% more frequently PEEP levels of 7–9 cmH2O. VentAI avoided high (>55%) FiO2 values (59.8% decrease), while preferring the range of 50–55% (140.3% increase). In conclusion, VentAI provides reproducible high performance by dynamically choosing an optimized, individualized ventilation strategy and thus might be of benefit for critically ill patients.
Background: Problem drinking, particularly risky single-occasion drinking is widespread among adolescents and young adults in most Western countries. Mobile phone text messaging allows a proactive and cost-effective delivery of short messages at any time and place and allows the delivery of individualised information at times when young people typically drink alcohol. The main objective of the planned study is to test the efficacy of a combined web- and text messaging-based intervention to reduce problem drinking in young people with heterogeneous educational level.
Methods/Design: A two-arm cluster-randomised controlled trial with one follow-up assessment after 6 months will be conducted to test the efficacy of the intervention in comparison to assessment only. The fully-automated intervention program will provide an online feedback based on the social norms approach as well as individually tailored mobile phone text messages to stimulate (1) positive outcome expectations to drink within low-risk limits, (2) self-efficacy to resist alcohol and (3) planning processes to translate intentions to resist alcohol into action. Program participants will receive up to two weekly text messages over a time period of 3 months. Study participants will be 934 students from approximately 93 upper secondary and vocational schools in Switzerland. Main outcome criterion will be risky single-occasion drinking in the past 30 days preceding the follow-up assessment.
Discussion: This is the first study testing the efficacy of a combined web- and text messaging-based intervention to reduce problem drinking in young people. Given that this intervention approach proves to be effective, it could be easily implemented in various settings, and it could reach large numbers of young people in a cost-effective way.
Background: Tobacco smoking prevalence continues to be high, particularly among adolescents and young adults with lower educational levels, and is therefore a serious public health problem. Tobacco smoking and problem drinking often co-occur and relapses after successful smoking cessation are often associated with alcohol use. This study aims at testing the efficacy of an integrated smoking cessation and alcohol intervention by comparing it to a smoking cessation only intervention for young people, delivered via the Internet and mobile phone.
Methods/Design: A two-arm cluster-randomised controlled trial with one follow-up assessment after 6 months will be conducted. Participants in the integrated intervention group will: (1) receive individually tailored web-based feedback on their drinking behaviour based on age and gender norms, (2) receive individually tailored mobile phone text messages to promote drinking within low-risk limits over a 3-month period, (3) receive individually tailored mobile phone text messages to support smoking cessation for 3 months, and (4) be offered the option of registering for a more intensive program that provides strategies for smoking cessation centred around a self-defined quit date. Participants in the smoking cessation only intervention group will only receive components (3) and (4). Study participants will be 1350 students who smoke tobacco daily/occasionally, from vocational schools in Switzerland. Main outcome criteria are 7-day point prevalence smoking abstinence and cigarette consumption assessed at the 6-month follow up.
Discussion: This is the first study testing a fully automated intervention for smoking cessation that simultaneously addresses alcohol use and interrelations between tobacco and alcohol use. The integrated intervention can be easily implemented in various settings and could be used with large groups of young people in a cost-effective way.
Modeling and executing knowledge-intensive processes (KiPs) are challenging with state-of-the-art approaches, and the specific demands of KiPs are the subject of ongoing research. In this context, little attention has been paid to the ontology-driven combination of data-centric and semantic business process modeling, which finds additional motivation by enabling the division of labor between humans and artificial intelligence. Such approaches have characteristics that could allow support for KiPs based on the inferencing capabilities of reasoners. We confirm this as we show that reasoners can infer the executability of tasks based on a currently researched ontology- and data-driven business process model (ODD-BP model). Further support for KiPs by the proposed inference mechanism results from its ability to infer the relevance of tasks, depending on the extent to which their execution would contribute to process progress. Besides these contributions along with the execution perspective (start-to-end direction), we will also show how our approach can help to reach specific process goals by inferring the relevance of process elements regarding their support to achieve such goals (end-to-start direction). The elements with the most valuable process progress can be identified in the intersection of both, the execution and goal perspective. This paper will introduce this new approach and verifies its practicability with an evaluation of a KiP in the field of emergency call centers.