A fuzzy rule-based decision support system for Duodopa treatment in Parkinson

A decision support system (DSS) was implemented based on a fuzzy logic inference system (FIS) to provide assistance in dose alteration of Duodopa infusion in patients with advanced Parkinson’s disease, using data from motor state assessments and dosage. Three-tier architecture with an object oriented approach was used. The DSS has a web enabled graphical user interface that presents alerts indicating non optimal dosage and states, new recommendations, namely typical advice with typical dose and statistical measurements. One data set was used for design and tuning of the FIS and another data set was used for evaluating performance compared with actual given dose. Overall goodness-of-fit for the new patients (design data) was 0.65 and for the ongoing patients (evaluation data) 0.98. User evaluation is now ongoing. The system could work as an assistant to clinical staff for Duodopa treatment in advanced Parkinson’s disease.

A web based decision support system for status assessment in advanced parkinson

The purpose of this work is to develop a web based decision support system, based onfuzzy logic, to assess the motor state of Parkinson patients on their performance in onscreenmotor tests in a test battery on a hand computer. A set of well defined rules, basedon an expert’s knowledge, were made to diagnose the current state of the patient. At theend of a period, an overall score is calculated which represents the overall state of thepatient during the period. Acceptability of the rules is based on the absolute differencebetween patient’s own assessment of his condition and the diagnosed state. Anyinconsistency can be tracked by highlighted as an alert in the system. Graphicalpresentation of data aims at enhanced analysis of patient’s state and performancemonitoring by the clinic staff. In general, the system is beneficial for the clinic staff,patients, project managers and researchers.

ANFIS BASED MODELS FOR ACCESSING QUALITY OF WIKIPEDIA ARTICLES

Wikipedia is a free, web-based, collaborative, multilingual encyclopedia project supported by the non-profit Wikimedia Foundation. Due to the free nature of Wikipedia and allowing open access to everyone to edit articles the quality of articles may be affected. As all people don’t have equal level of knowledge and also different people have different opinions about a topic so there may be difference between the contributions made by different authors. To overcome this situation it is very important to classify the articles so that the articles of good quality can be separated from the poor quality articles and should be removed from the database. The aim of this study is to classify the articles of Wikipedia into two classes class 0 (poor quality) and class 1(good quality) using the Adaptive Neuro Fuzzy Inference System (ANFIS) and data mining techniques. Two ANFIS are built using the Fuzzy Logic Toolbox [1] available in Matlab. The first ANFIS is based on the rules obtained from J48 classifier in WEKA while the other one was built by using the expert’s knowledge. The data used for this research work contains 226 article’s records taken from the German version of Wikipedia. The dataset consists of 19 inputs and one output. The data was preprocessed to remove any similar attributes. The input variables are related to the editors, contributors, length of articles and the lifecycle of articles. In the end analysis of different methods implemented in this research is made to analyze the performance of each classification method used.

Hand Gesture Detection & Recognition System

The project introduces an application using computer vision for Hand gesture recognition. A camera records a live video stream, from which a snapshot is taken with the help of interface. The system is trained for each type of count hand gestures (one, two, three, four, and five) at least once. After that a test gesture is given to it and the system tries to recognize it.A research was carried out on a number of algorithms that could best differentiate a hand gesture. It was found that the diagonal sum algorithm gave the highest accuracy rate. In the preprocessing phase, a self-developed algorithm removes the background of each training gesture. After that the image is converted into a binary image and the sums of all diagonal elements of the picture are taken. This sum helps us in differentiating and classifying different hand gestures.Previous systems have used data gloves or markers for input in the system. I have no such constraints for using the system. The user can give hand gestures in view of the camera naturally. A completely robust hand gesture recognition system is still under heavy research and development; the implemented system serves as an extendible foundation for future work.

Speed prediction for triggering vehicle activated signs

Accurate speed prediction is a crucial step in the development of a dynamic vehcile activated sign (VAS). A previous study showed that the optimal trigger speed of such signs will need to be pre-determined according to the nature of the site and to the traffic conditions. The objective of this paper is to find an accurate predictive model based on historical traffic speed data to derive the optimal trigger speed for such signs. Adaptive neuro fuzzy (ANFIS), classification and regression tree (CART) and random forest (RF) were developed to predict one step ahead speed during all times of the day. The developed models were evaluated and compared to the results obtained from artificial neural network (ANN), multiple linear regression (MLR) and naïve prediction using traffic speed data collected at four sites located in Sweden. The data were aggregated into two periods, a short term period (5-min) and a long term period (1-hour). The results of this study showed that using RF is a promising method for predicting mean speed in the two proposed periods.. It is concluded that in terms of performance and computational complexity, a simplistic input features to the predicitive model gave a marked increase in the response time of the model whilse still delivering a low prediction error.

Evaluation of a thermally driven heat pump for solar heating and cooling applications

Exploiting solar energy technology for both heating and cooling purposes has the potential of meeting an appreciable portion of the energy demand in buildings throughout the year. By developing an integrated, multi-purpose solar energy system, that can operate all twelve months of the year, a high utilisation factor can be achieved which translates to more economical systems. However, there are still some techno-economic barriers to the general commercialisation and market penetration of such technologies. These are associated with high system and installation costs, significant system complexity, and lack of knowledge of system implementation and expected performance. A sorption heat pump module that can be integrated directly into a solar thermal collector has thus been developed in order to tackle the aforementioned market barriers. This has been designed for the development of cost-effective pre-engineered solar energy system kits that can provide both heating and cooling. This thesis summarises the characterisation studies of the operation of individual sorption modules, sorption module integrated solar collectors and a full solar heating and cooling system employing sorption module integrated collectors. Key performance indicators for the individual sorption modules showed cooling delivery for 6 hours at an average power of 40 W and a temperature lift of 21°C. Upon integration of the sorption modules into a solar collector, measured solar radiation energy to cooling energy conversion efficiencies (solar cooling COP) were between 0.10 and 0.25 with average cooling powers between 90 and 200 W/m2 collector aperture area. Further investigations of the sorption module integrated collectors implementation in a full solar heating and cooling system yielded electrical cooling COP ranging from 1.7 to 12.6 with an average of 10.6 for the test period. Additionally, simulations were performed to determine system energy and cost saving potential for various system sizes over a full year of operation for a 140 m2 single-family dwelling located in Madrid, Spain. Simulations yielded an annual solar fraction of 42% and potential cost savings of €386 per annum for a solar heating and cooling installation employing 20m2 of sorption integrated collectors.

Solcellsdrivet FTX-system för miljonprogramshus : Tjärna Ängar, Borlänge

Projektet omfattade undersökning och framtagande av ett solcellssystem med förmåga att försörja ett FTX-system i ett flerbostadshus från miljonprogrammet med el. För att kunna bedöma storlek och utformning av komponenter har information tagits genom: Informationssökning via databaser, kurslitteratur och intervjuer Simuleringar av solceller i datorprogrammet PVSYST Modulering av ventilationskanaler i datorprogrammet MagiCAD Syftet var främst att undersöka om det gick att få fram ett teoretiskt fungerande system med avseende på både solceller och ventilation. Beroende på vad resultatet blev skulle även ekonomin i projektet undersökas. Undersökningen visade att det teoretiskt ska gå att installera solceller för elframställning som klarar av att täcka FTX-systemets elbehov på årsbasis. Solcellerna bedöms även producera tillräckligt med el för viss övrig elkrävande utrustning under stora delar av året. Det visade sig även att det skulle gå att få solcellerna ekonomiskt lönsamma om en kalkyltid på 14 år används. Metoden som använts för dessa resultat är noga beskriven och är med små förändringar tillämpbar för ett stort antal byggnader i det svenska byggnadsbeståndet. En viktig slutsats är att om fastighetsägarna kan se 15 år fram i tiden för en investering i solenergi, skulle det innebära inte bara miljömässiga utan även ekonomiska vinster. Det finns redan idag kunnande, teknik och produkter för att utvinna en stor del av fastigheternas elbehov genom solens energi.

Intelligent Algorithms for a Hybrid FuelCell/Photovoltaic Standalone System : Simulation Of Hybrid FuelCell/Photovoltaic Standalone System

The Intelligent Algorithm is designed for theusing a Battery source. The main function is to automate the Hybrid System through anintelligent Algorithm so that it takes the decision according to the environmental conditionsfor utilizing the Photovoltaic/Solar Energy and in the absence of this, Fuel Cell energy isused. To enhance the performance of the Fuel Cell and Photovoltaic Cell we used batterybank which acts like a buffer and supply the current continuous to the load. To develop the main System whlogic based controller was used. Fuzzy Logic based controller used to develop this system,because they are chosen to be feasible for both controlling the decision process and predictingthe availability of the available energy on the basis of current Photovoltaic and Battery conditions. The Intelligent Algorithm is designed to optimize the performance of the system and to selectthe best available energy source(s) in regard of the input parameters. The enhance function of these Intelligent Controller is to predict the use of available energy resources and turn on thatparticular source for efficient energy utilization. A fuzzy controller was chosen to take thedecisions for the efficient energy utilization from the given resources. The fuzzy logic basedcontroller is designed in the Matlab-Simulink environment. Initially, the fuzzy based ruleswere built. Then MATLAB based simulation system was designed and implemented. Thenthis whole proposed model is simulated and tested for the accuracy of design and performanceof the system.