Man has a limited ability to accurately and continuously analyse large amounts of data. Observers are typically required to monitor displays over extended periods and to execute overt detection responses to the appearance of low probability critical signals. The signals are usually clearly perceivable when observers are alerted to them, but they can be missed in the operating environment. The challenge is to develop a computer application that will accumulate information on a variable, or several variables, over time and identify when the trend in observations has changed. In recent years, there has been a rapid growth in patient monitoring and medical data analysis using decision support systems, smart alarm monitoring systems, expert systems and many other computer aided protocols. The expert systems have the potential to improve clinician performance by accurately executing repetitive tasks, to which humans are ill-suited. Anaesthetists working in the operating theatre are responsible for carrying out a multitude of tasks which requires constant vigilance and thus a need for a smart decision support system has arisen. The decision support tools capable of detecting pathological events can enhance the anaesthetist’s performance by providing alternative diagnostic information. The main goal of this research was to develop a clinically useful diagnostic alarm system using two different techniques for monitoring a pathological event during anaesthesia. Several techniques including fuzzy logic, artificial neural networks, control and monitoring techniques were explored. Firstly, an industrial monitoring system called Supervisory Control and Data Acquisition (SCADA) software is used and implemented in the form of a prototype system called SCADA monitoring system (SMS). The output of the system in detecting hypovolaemia was classified into three levels; mild, moderate and severe using SCADA’s InTouch software. In addition, a new GUI display was developed for direct interaction with the anaesthetists. Secondly, a fuzzy logic monitoring system (FLMS) was developed using the fuzzy logic technique. New diagnostic rules and membership functions (MF) were developed using MATLAB. In addition, fuzzy inference system FIS, adaptive neuro fuzzy inference system ANFIS and clustering techniques were explored for developing the FLMS’s diagnostic modules. The raw physiological patient data acquired from an S/5 monitor were converted to a readable format using the DOMonitor application. The data was filtered, preprocessed, and analysed for detecting anaesthesia related events like hypovolaemia. The accuracy of diagnoses generated by SMS and FLMS was validated by comparing their diagnostic information with the one provided by the anaesthetist for each patient. Kappa-analysis was used for measuring the level of agreement between the anaesthetist’s, SMS’s, and FLMS’s diagnoses. In offline analysis both systems were tested with data from 15 patients. The SMS and FLMS achieved an overall agreement level of 87 and 88 percent respectively. It implies substantial level of agreement between SMS or FLMS and the anaesthetists. These diagnostic alarm systems (SMS and FLMS) have shown that evidence-based expert diagnostic systems can diagnose hypovolaemia, with a substantial degree of accuracy, in anaesthetized patients and could be useful in providing decision support to anaesthetists.

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The main goal of the master thesis is design and implementation of information system for monitoring purposes. The implemented monitoring system will be able to automatically monitor devices of a certain company. This goal is preceded by strategic analysis using PEST and 7S methods for examination of current state of the company. Another part is the analysis of the efficiency of service processes using the HOS method together with the selection of a suitable monitoring system. An enhancement of service processes is designed by the Lewin model. Finally, the selected information system for monitoring was designed, implemented and tested. A deployment of the system should provide simplification of devices supervision, to save costs as well as to speed-up the response-time to unexpected situations.

Complete overview of energy use in a residential building is depends on many different factors. When analyzing proper and effective ways for energy reducing/conserving systems, often times only technological solutions for households appliances are considered. Human behavior has been shown to be an important factor affecting the overall energy use in the household. Many aspects of energy use are directly connected to user behavior and are affected by how the user utilizes available systems. This paper focuses on describing the mean influencing causes of human environmental psychology based on study on a Swedish suburb community, called Fårdala. User behaviors and actions affecting residential energy use are analyzed and presented in form of eleven (11) abstract triggers to households energy use. Finally an energy monitoring system based on the findings are purposed. What is found from study on human psychology, shows that human behavior is mainly controlled by three (3) key categories of behavior. Conscious/voluntary behavior, Socio-environmental/cultural based behavior and Systemic/learned behavior. Out of the three, while the last one poses as most influential on behavior related energy use, it is also the hardest to affect and change. To effectively counteract the negative effects of user behavior on residential energy use, energy saving devices should react more accordingly to the users and offer engagement. Such a system is an energy monitoring device, which allows for a “double-sided” communication with the user. The user is presented with relevant information about real-time energy use of all of the systems and is able to make changes on the fly. The system should also be able to learn and apply energy saving actions based on user behavior.

No description available.

The research topics presented in this dissertation focus on validation of customer-level voltage synchrophasor data for transmission system analysis, detection and categorization of power system events as measured by phasor measurement units (PMUs), and identification of the influence of power system conditions (wind power, daily and seasonal load variation) on low-frequency oscillations. Synchrophasor data can provide information across entire power systems but obtaining the data, handling the large dataset and developing tools to extract useful information from it is a challenge. To overcome the challenge of obtaining data, an independent synchrophasor network was created by taking synchrophasor measurements at customer-level voltage. The first objective is to determine if synchrophasor data taken at customer-level voltage is an accurate representation of power system behavior. The validation process was started by installing a transmission level (69 kV) PMU. The customer-level voltage measurements were validated by comparison of long term trends and low-frequency oscillations estimates. The techniques best suited for synchrophasor data analysis were identified after a detailed study and comparison. The same techniques were also applied to detect power system events resulting in the creation of novel categories for numerous events based on shared characteristics. The numerical characteristics for each category and the ranges of each numerical characteristic for each event category are identified. The final objective is to identify trends in power system behavior related to wind power and daily and seasonal variations by utilizing signal processing and statistical techniques. / text

Submitted by Kilvya Braga (kilvyabraga@hotmail.com) on 2018-05-15T12:49:12Z No. of bitstreams: 1 LEONARDO MELO DE MEDEIROS - TESE (PPGCC) 2016.pdf: 2301452 bytes, checksum: e08b1ce490fde57cff30bef5cb8e5b0e (MD5) / Made available in DSpace on 2018-05-15T12:49:12Z (GMT). No. of bitstreams: 1 LEONARDO MELO DE MEDEIROS - TESE (PPGCC) 2016.pdf: 2301452 bytes, checksum: e08b1ce490fde57cff30bef5cb8e5b0e (MD5) Previous issue date: 2016-06-03 / Capes / Os Sistemas de Monitoramento da Saúde (SMS) possibilitam aos médicos obterem informações sobre o estado de saúde de seus pacientes. Além disso, as identificações dos sintomas das doenças podem auxiliar no diagnóstico precoce e prevenir a ocorrência de situações críticas. Para acompanhar e avaliar a saúde motora de um paciente, é necessário realizar uma avaliação motora por meio de movimentos específicos. Isto dificulta a concepção de um SMS de dados motores não-invasivo e engajados na rotina diária dos pacientes. A abordagem apresentada nesta tese, utiliza os jogos eletrônicos como fator motivacional para o fornecimento dos dados motores. Durante o jogo, o usuário é induzido a executar movimentos relevantes, de modo que um sensor de movimento possa adquiri-los e quantificá-los. Este ambiente lúdico, de jogo eletrônico, abstrai o usuário do contexto de tratamento da saúde e incentiva a execução dos movimentos de uma maneira mais natural do que a imposta por um exame clínico. Para avaliar esta abordagem, foi desenvolvido um jogo com a arquitetura proposta para identificar sintomas motores relacionadas com a Doença de Parkinson. Num estudo de caso controle, foram avaliados os movimentos angulares dos braços para quantificar as habilidades motoras desses grupos. Os dados coletados foram processados e aplicados a uma Máquina de Vetor de Suporte(SVM) para classificar a ocorrência do sintoma da bradicinesia do Parkinson. Obteve-se uma classificação com uma acurácia de 86,67% e falsos positivos de6,67%. Além disso, em um experimento para avaliação da aceitação dos usuários, 90% ficaram motivados com o jogo desenvolvido e afirmaram que integrariam o SMS em sua rotina diária. Estes resultados demonstram que a abordagem de monitoramento baseado em jogos, apresentada nesta tese, tem potencial para ser um SMS para monitoramentos dos sintomas motores. / Health Monitoring Systems (HMS) allow doctors to gain a better picture of their patient’s health status. An early identification of symptoms can support the disease’s diagnostic and prevent critical situations. In order to monitor a patient’s motor abilities, it is necessary to record and evaluate specific movements. This makes it difficult to design a HMS that non-obtrusively integrates into the patient’s daily routine. The approach presented in this thesis makes use of the motivational power of electronic games. While playing the game, the user is incited to make the relevant movements, so that an optical sensor can detect and measure them. The playful situation distracts the user from thinking about health issues and therefore encourages more natural movements with improved validity for a health examination. To evaluate this approach, a game has been developed and employed to detect Parkinson related motor symptoms. In a study with patients diagnosed as affected by the Parkinson Disease and a healthy control group, the angular movements of the arms were used to measure motor abilities. The data was then processed and applied to a Support Vector Machine (SVM) to predict, based on the detected movements, whether a subject shows Parkinson related symptoms or should be classified as healthy. The system classified the subjects with an accuracy of 86.67% and arate of 6.67% false positives. Furthermore, the use racceptance of the game-based approach was studied and showed that 90% of the users felt motivated to play the game as part of their daily routine. These results demonstrate that the game-based approach presented in this thesis has the potential to become a base for HMS that monitor motor symptoms.