Identification of Flash floods using Soil Flux and CO2: An implementation of Neural Network with Less False Alarm Rate

Flash floods are very sudden and abrupt and are the major root cause of casualties and loss of infrastructure. Flash floods can be regarded as the topmost natural disasters in many countries. Usually floods are due to high precipitation, wind velocity, water wave current and melting of ice bergs. Diversified strategies have been designed and applied to identify the flash floods. Mainly dozen of sensors have been utilized to detect the flash floods like upstream level, rainfall intensity, run-off magnitude, run-off speed, color of the water, precipitation velocity, pressure, temperature, wind speed, wave current pattern and cloud to ground (CG flashes). Ultrasonic and passive infrared (PIR) sensors have also been utilized for this purpose. Sensors generate high amount of fake alerts due to the incompetent algorithms. In our research we have proposed a novel approach analysis of soil flux depicting atmospheric carbon dioxide level as the plants take smaller amount of water from the soil due to the heightened levels of carbon dioxide. Due to this newly discovered research the soil is saturated abruptly causes more floods and run-offs. In our research we have reduced the false alarms and reduced the false alarms by using scaled conjugate gradient back propagation. Simulation results showed that scaled conjugate gradient propagation performed better than the other previous methods

Assessment of goal-directed closed-loop management in intensive care medicine

Given an aging population, shortage of nursing staff and a continuously increasing workload, automation in the medical sector is an important aspect of future intensive care. Although automation and machine learning are current research topics, progress is still very limited in comparison to other application areas. Probably one of the most serious problems is data shortage in a heterogeneous landscape of medical devices with limited interfaces and various protocols. In addition, the recording of data or, even more so, the evaluation of automation is limited by a complex legal framework. Given these complications and the sensitive legal nature of medical records, only very limited data is accessible for further analysis and development of automated systems. For this reason, within the context of this thesis various solutions for data acquisition and automation were developed and evaluated concomitant to two clinical studies utilizing a large animal model in a realistic intensive care setting at the University Hospital Tübingen. Foremost, to overcome the problems of data availability and interconnection of medical devices, a software framework for data collection and remote control using a client-server architecture was developed and significant amounts of research data could be collected in a central database. Furthermore, a closed-loop controller based on fuzzy logic was developed and used for management of end-tital CO2, glucose, and other parameters to stabilize the animal subjects during therapy and reduce caregivers’ workload. In addition to the fuzzy controller, closed-loop management for temperature and anticoagulation could be established by developing hardware interfaces for a forced-air warming unit and a point-of-care analysis device, respectively. Besides further reduction of caregivers’ workload, such systems can provide additional patient safety and allow management in settings where human supervision may not be present at all times. One general and encountered problem for closed-loop control in a medical setting is limited availability of measurements, especially if manual blood withdrawals are required. As an initial step to address this problem, measured parameters from other devices as potential surrogates were evaluated in a comparison between different regression approaches. The required training data, a matched set of blood gas and monitoring parameters, was obtained by utilizing a developed algorithm for automated detection of withdrawal events. Yet, besides any specific implementations and analysis, many general aspects regarding the physical implementation of such a system and interaction with caregivers could be evaluated in the experimental setting and might guide further development of clinical automation.Angesichts der alternden Bevölkerung, des Mangels an Pflegekräften und der ständig steigenden Arbeitsbelastung ist Automatisierung ein wichtiger Aspekt zukünftiger Intensivmedizin. Obwohl Automatisierung und maschinelles Lernen aktuelle Forschungsthemen sind, ist der Fortschritt im Vergleich zu anderen Anwendungsbereichen jedoch noch sehr begrenzt. Eines der größten Probleme ist wohl die Datenknappheit in einer heterogenen Medizinproduktelandschaft mit begrenzten Schnittstellen und zahlreichen unterschiedlichen Protokollen. Darüber hinaus sind die Datenerfassung und erst recht die Erprobung einer Automatisierung durch ein komplexes rechtliches Rahmenwerk eingeschränkt. Aufgrund dieser Komplikationen und der sensiblen Rechtslage für Patientendaten sind diese nur sehr begrenzt für weitere Analysen und die Entwicklung automatisierter Systeme zugänglich. Im Rahmen dieser Dissertation wurden daher verschiedene Lösungen zur Datenerfassung und Automatisierung begleitend zu zwei klinischen Studien des Universitätsklinikums Tübingen am Großtiermodell in einer realitätsnahen Intensivstation entwickelt und evaluiert. Um die Probleme der Datenverfügbarkeit und Vernetzung medizinischer Geräte zu lösen, wurde vorrangig ein Software-Framework für die Datenerfassung und Steuerung mittels einer Client-Server-Architektur entwickelt und umfangreiche Forschungsdaten in einer zentralen Datenbank gesammelt. Darüber hinaus wurde ein auf Fuzzy-Logik basierender Regler entwickelt, welcher zur Stabilisierung des endtitalen CO2, Glukose und anderen Parametern verwendet wurde und damit die Arbeitsbelastung der Pflegekräfte reduzieren konnte. Zusätzlich zum Fuzzy-Regler konnten durch die Entwicklung von Hardware-Schnittstellen für Geräte zum Temperaturmanagement mittels luftbasierter Wärmedecken und zur Messung der Blutgerinnung geschlossene Regelkreise aufgebaut werden. Neben einer weiteren Arbeitserleichterung für die Pflegekräfte können solche Systeme zusätzliche Sicherheit für den Patienten bieten und die Anwendung in nicht ständig überwachten Bereichen ermöglichen. Ein allgemeines und auch beobachtetes Problem für Regelkreise im medizinischen Bereich ist die begrenzte Verfügbarkeit von Messwerten, insbesondere bei manuellen Blutentnahmen. Als erster Schritt zur Lösung dieses Problems wurden Messparameter anderer Geräte als potentielle Ersatzparameter mit verschiedenen Regressionsansätzen analysiert und verglichen. Die dazu erforderlichen Trainingsdaten, Paare von Blutgas- und weiteren Vitaldaten, wurden mit Hilfe eines entwickelten Algorithmus zur automatisierten Erkennung von Blutentnahmen erzeugt. Abgesehen von diesen konkreten Anwendungen und Analysen konnten in der experimentellen Evaluation auch viele generelle Aspekte der realen Implementierung eines solchen Systems und die Interaktion mit Ärzten und Pflegekräften untersucht werden und damit der Entwicklung weiterer klinischen Automatisierung dienen

Modelling, Optimisation and Explicit Model Predictive Control of Anaesthesia Drug Delivery Systems

The contributions of this thesis are organised in two parts. Part I presents a mathematical model for drug distribution and drug effect of volatile anaesthesia. Part II presents model predictive control strategies for depth of anaesthesia control based on the derived model. Closed-loop model predictive control strategies for anaesthesia are aiming to improve patient's safety and to fine-tune drug delivery, routinely performed by the anaesthetist. The framework presented in this thesis highlights the advantages of extensive modelling and model analysis, which are contributing to a detailed understanding of the system, when aiming for the optimal control of such system. As part of the presented framework, the model uncertainty originated from patient-variability is analysed and the designed control strategy is tested against the identified uncertainty. An individualised physiologically based model of drug distribution and uptake, pharmacokinetics, and drug effect, pharmacodynamics, of volatile anaesthesia is presented, where the pharmacokinetic model is adjusted to the weight, height, gender and age of the patient. The pharmacodynamic model links the hypnotic depth measured by the Bispectral index (BIS), to the arterial concentration by an artificial effect site compartment and the Hill equation. The individualised pharmacokinetic and pharmacodynamic variables and parameters are analysed with respect to their influence on the measurable outputs, the end-tidal concentration and the BIS. The validation of the model, performed with clinical data for isoflurane and desflurane based anaesthesia, shows a good prediction of the drug uptake, while the pharmacodynamic parameters are individually estimated for each patient. The derived control design consists of a linear multi-parametric model predictive controller and a state estimator. The non-measurable tissue and blood concentrations are estimated based on the end-tidal concentration of the volatile anaesthetic. The designed controller adapts to the individual patient's dynamics based on measured data. In an alternative approach, the individual patient's sensitivity is estimated on-line by solving a least squares parameter estimation problem.Open Acces