Indiana University-Purdue University Indianapolis (IUPUI) / Heating, ventilation and air conditioning systems (HVAC) are the single largest consumer of energy in commercial and residential sectors. Minimizing its energy consumption without compromising indoor air quality (IAQ) and thermal comfort would result in environmental and financial benefits. Currently, most buildings still utilize constant air volume (CAV) systems with on/off control to meet the thermal loads. Such systems, without any consideration of occupancy, may ventilate a zone excessively and result in energy waste. Previous studies showed that CO2-based demand-controlled ventilation (DCV) methods are the most widely used strategies to determine the optimal level of supply air volume. However, conventional CO2 mass balanced models do not yield an optimal estimation accuracy. In this study, feed-forward neural network algorithm (FFNN) was proposed to estimate the zone occupancy using CO2 concentrations, observed occupancy data and the zone schedule. The occupancy prediction result was then utilized to optimize supply fan operation of the air handling unit (AHU) associated with the zone. IAQ and thermal comfort standards were also taken into consideration as the active constraints of this optimization. As for the validation, the experiment was carried out in an auditorium located on a university campus. The results revealed that utilizing neural network occupancy estimation model can reduce the daily ventilation energy by 74.2% when compared to the current on/off control.

Lakes present in arid regions of Central Anatolia need further attention with regard to water quality. In most cases, mathematical modeling is a helpful tool that might be used to predict the DO concentration of a lake. Deterministic models are frequently used to describe the system behavior. However most ecological systems are so complex and unstable. In case, the deterministic models have high chance of failure due to absence of priori information. For such cases black box models might be essential. In this study DO in Eymir Lake located in Ankara was modeled by using both Artificial Neural Networks (ANN) and Adaptive Neuro Fuzzy Inference System (ANFIS). Phosphate, Orthophospate, pH, Chlorophyll-a, Temperature, Alkalinity, Nitrate, Total Kjeldahl Nitrogen, Wind, Precipitation, Air Temperature were the input parameters of ANN and ANFIS. The aims of these modeling studies were: to develop models with ANN to predict DO concentration in Lake Eymir with high fidelity to actual DO data, to compare the success (prediction capacity) of ANN and ANFIS on DO modeling, to determine the degree of dependence of different parameters on DO. For modeling studies &ldquo / Matlab R 2007b&rdquo / software was used. The results indicated that ANN has high prediction capacity of DO and ANFIS has low with respect to ANN. Failure of ANFIS was due to low functionality of Matlab ANFIS Graphical User Interface. For ANN Modeling effect of meteorological data on DO data on surface of the lake was successfully described and summer month super saturation DO concentrations were successfully predicted.

This thesis presents a preliminary design of a fully articulated five-fingered anthropomorphic human hand prosthesis with particular emphasis on the controller and actuator design. The proposed controller is a modified artificial neural network PID-based controller with application to the nonlinear and highly coupled dynamics of the hand prosthesis. The new solid state actuator has been designed based on electroactive polymers, which are a type of material that exhibit electromechanical behavior and a liquid metal alloy acts as the electrode. The solid state actuators reduce the overall mechanical complexity, risk failure and required maintenance of the prosthesis. / Graduate

Current activated sludge models are deterministic in character and are constructed by basing on the fundamental biokinetics. However, calibrating these models are extremely time consuming and laborious. An easy-to-calibrate and user friendly computer model, one of the artificial intelligence techniques, Artificial Neural Networks (ANNs) were used in this study. These models can be used not only directly as a substitute for deterministic models but also can be plugged into the system as error predictors. Three systems were modeled by using ANN models. Initially, a hypothetical wastewater treatment plant constructed in Simulation of Single-Sludge Processes for Carbon Oxidation, Nitrification &amp / Denitrification (SSSP) program, which is an implementation of Activated Sludge Model No 1 (ASM1), was used as the source of input and output data. The other systems were actual treatment plants, Ankara Central Wastewater Treatment Plant, ACWTP and iskenderun Wastewater Treatment Plant (IskWTP). A sensitivity analysis was applied for the hypothetical plant for both of the model simulation results obtained by the SSSP program and the developed ANN model. Sensitivity tests carried out by comparing the responses of the two models indicated parallel sensitivities. In hypothetical WWTP modeling, the highest correlation coefficient obtained with ANN model versus SSSP was about 0.980. By using actual data from IskWTP the best fit obtained by the ANN model yielded R value of 0.795 can be considered very high with such a noisy data. Similarly, ACWTP the R value obtained was 0.688, where accuracy of fit is debatable.

In this thesis, three methods are presented to model the decision process of whether or not to bid for a tender in offshore petroleum platform fabrication. A sample data and the assessment based on this data are gathered from an offshore petroleum platform fabrication company and this information is analyzed to understand the significant parameters in the industry. The alternative methods, &ldquo / Regression Analysis&rdquo / , &ldquo / Neural Network Method&rdquo / and &ldquo / Fuzzy Neural Network Method&rdquo / , are used for modeling of the bidding decision process. The regression analysis examines the data statistically where the neural network method and fuzzy neural network method are based on artificial intelligence. The models are developed using the bidding data compiled from the offshore petroleum platform fabrication projects. In order to compare the prediction performance of these methods &ldquo / Cross Validation Method&rdquo / is utilized. The models developed in this study are compared with the bidding decision method used by the company. The results of the analyses show that regression analysis and neural network method manage to have a prediction performance of 80% and fuzzy neural network has a prediction performance of 77,5% whereas the method used by the company has a prediction performance of 47,5%. The results reveal that the suggested models achieve significant improvement over the existing method for making the correct bidding decision.

Employee selection research identifies and makes use of associations between individual differences, such as those measured by psychological testing, and individual differences in job performance. Artificial neural networks are computer simulations of biological nerve systems that can be used to model unspecified relationships between sets of numbers. Thirty-five neural networks were trained to estimate normalized annual revenue produced by telephone sales agents based on personality and biographic predictors using concurrent validation data (N=1085). Accuracy of the neural estimates was compared to OLS regression and a proprietary nonlinear model used by the participating company to select agents.

The dissertation is generally focused on the use of artificial intelligence tools in practice and with regard to the focus of study in the field of Management and Business Economics at using the tools of artificial intelligence in corporate practice, as a tool for decision support at the operational and tactical level management. In the narrower sense, the task deals with the proposal of the prediction sales model of selected food commodities. The proposed model is designed to serve as a substitute for a human expert in support decision-making process in the purchase of selected commodities, especially when training new staff and extend the currently used methods of managerial decision-making about artificial intelligence tools for company management and existing employees. The aim of this dissertation is the design prediction sales model of selected food commodities (apples and potatoes) for specific wholesale of fruit and vegetable operating in the Czech Republic. To become familiar with the behaviour of selected commodities were used primary and secondary research as well and knowledge gained from Czech and foreign literature sources and research. The resulting predictive model is developed using statistical analysis of time series and the sales prediction proceeds using the tools of artificial intelligence and is modeled by an artificial neural network. The dissertation in the practical part also contains proposals for the use of the prediction model and partial processing procedures for: • practice, • theory, • pedagogical activities.

Submitted by Johnny Rodrigues (johnnyrodrigues@ufcg.edu.br) on 2018-08-14T16:42:54Z No. of bitstreams: 1 EWALDO EDER CARVALHO SANTANA - TESE PPGEE 2009..pdf: 5646465 bytes, checksum: d83cd716193f68815a22b066836f3ae6 (MD5) / Made available in DSpace on 2018-08-14T16:42:54Z (GMT). No. of bitstreams: 1 EWALDO EDER CARVALHO SANTANA - TESE PPGEE 2009..pdf: 5646465 bytes, checksum: d83cd716193f68815a22b066836f3ae6 (MD5) Previous issue date: 2009-11-06 / Neste trabalho é proposto o desenvolvimento de uma rede neuronial com aprendizagem não supervisionada, para modelar a organização topográfica do córtex visual primário. Para isto, estuda-se o comportamento dos campos receptivos do córtex visual primário(V1), e, para o modelamento da rede utilizam-se os conceitos de divergência local e de interação entre neurônios vizinhos, bem como da característica de não linearidades dos neurônios. Para treinamento da rede desenvolveu-se um algoritmo de ponto fixo. / In this work it is proposed an unsupervised neural network model, which seems biologically plausible in modeling the primary visual cortex (V1). It is, also, studied de behavior of the receptive fields of V1. In order to modeling the net it was used the concepts of local discrepancy and interactions between neighbor neurons, as well the non-linearity characteristics of neurons. It was designed a fixed-point algorithm to train the neural network.

Ce travail s’inscrit dans un projet de recherche interdisciplinaire (Zone Atelier Bassin du Rhône – Agence de l’Eau Rhône Méditerranée Corse) portant sur la rivière Cèze, affluent du Rhône.Le terrain d’expérimentation se situe dans les formations karstiques du bassin de la Cèze (Gard, France). Cette zone touristique est exposée à une croissance démographique et de l’activité agricole, engendrant une augmentation de la demande en eau. La thèse se concentre sur la restitution des eaux karstiques à la rivière en période estivale pour en comprendre le fonctionnement de l’hydrosystème en période de basses eaux, quand la ressource est vulnérable.Ce travail a permis d’élaborer une méthodologie, pour analyser et quantifier les échanges entre la rivière et l’aquifère karstique, fondée sur : la géologie, l’hydrologie, la géochimie, la biologie, la radioactivité en radon, l’analyse d’images infrarouges thermiques et la modélisation. Les résultats obtenus avec ces approches sont confrontés pour interpréter les interactions karst/rivière d’un point de vue qualitatif et/ou quantitatif (localisation, périodicité, débits). La confrontation de ces résultats met en avant l’intérêt d’une méthodologie interdisciplinaire pour interpréter et quantifier les échanges karst/rivière. L’application de la méthode montre qu’en juin 2015, la Cèze est alimentée à 50 % par des eaux karstiques.L’analyse multi-métrique du système karstique a permis d’acquérir de nouvelles connaissances sur son fonctionnement nécessaires pour paramétrer le modèle par réseaux de neurones qui constitue la dernière étape de ce travail. / This work is part of an interdisciplinary research project (Rhone Basin Workshop Zone – the Rhone-Mediterranean and Corsica Water Agency) on the river Cèze, a tributary of the Rhône.The experimental field is located in the karstic formations of the Cèze basin (Gard, France). This tourist area is exposed to population growth and agricultural activity, causing an increase in water demand. The thesis focuses on the karstic water restitution to the river during summer, in order to understand the functioning of the hydrosystem in periods of low water levels, when the resource is vulnerable.This work led to the development of a methodology to analyze and quantify the exchanges between karstic aquifers and rivers. This methodology is based on geology, hydrology, geochemistry, biology, radon radioactivity, infrared thermal imaging analysis and modeling. The results obtained with these approaches are compared in order to understand the karst/river interactions from a qualitative and/or quantitative point of view (localization, frequency, flow rates). The comparison of these results highlights the advantages of an interdisciplinary methodology for understanding and quantifying the karst/river exchanges. The application of this method shows that in June 2015, 50 % of the river Cèze was fed by karstic waters.The multi-metric analysis of the karstic system has led to new knowledge about its functioning. This knowledge is necessary to set the model’s parameters using neural networks, which is the last stage of this work.