The evaluation of the soil spatial variability using a fast, robust and cheap tool is one of the key steps towards the implementation of Precision Agriculture (PA) successfully. Soil organic carbon (OC), soil total nitrogen (TN) and soil moisture content (MC) are needed to be monitored for both agriculture and environmental applications. The literature has proven that visible and near infrared (vis-NIR) spectroscopy to be a quick, cheap and robust tool to acquire information about key soil properties simultaneously with relatively high accuracy. The on-line vis-NIR measurement accuracy depends largely on the quality of calibration models. In order to establish robust calibration models for OC, TN and MC valid for few selected European farms, several factors affecting model accuracy have been studied. Nonlinear calibration techniques, e.g. artificial neural network (ANN) combined with partial least squares regression (PLSR) has provided better calibration accuracy than the linear PLSR or principal component regression analysis (PCR) alone. It was also found that effects of sample concentration statistics, including the range or standard derivation and the number of samples used for model calibration are substantial, which should be taking into account carefully. Soil MC, texture and their interaction effects are other principle factors affecting the in situ and on-line vis-NIR measurement accuracy. This study confirmed that MC is the main negative effect, whereas soil clay content plays a positive role. The general calibration models developed for soil OC, TN and MC for farms in European were validated using a previously developed vis-NIR on-line measurement system equipped with a wider vis-NIR spectrophotometer (305 – 2200 nm) than the previous version. The validation results showed this wider range on-line vis-NIR system can acquire larger than 1500 data point per ha with a very good measurement accuracy for TN and OC and excellent accuracy for MC. The validation also showed that spiking few target field samples into the general calibration models is an effective and efficient approach for upgrading the implementation of the on-line vis-NIR sensor for measurement in new fields in the selected European farms.

One of the major limiting factors in fuel cell production is the time and effort that are required in the ink coating process of the Nafion film that is at the heart of what makes a fuel cell work. The principle reason that this issued has not been tackled by the industry at large is the inherent difficulties that arise. First and foremost is the rapid and extensive expansion of the material upon contact with the liquid ink causing the Nafion film to wrinkle while being processed. In the drive to help mitigate this issue it must be understood by what conditions and severity that wrinkling occurs. The method chosen to detect this was to develop a laser profile scanner to record and output the severity of any wrinkles present in the film. This thesis showcases and explains the laser scanning system designed specifically for this problem and material. / Mechanical Engineering / text

BOD (Biochemical Oxygen Demand) is one of the important parameters in wastewater treatment technology,  it normally takes five days to get the result. Using MFC (Microbial Fuel Cell) to build up a small on-line system to measure BOD will reduce to operating time to 20 minutes. In this report, the MFC BOD on-line measurement was processed in order to test if this rapid way would work in practical usage. The experiment used secondary sedimentation sludge as the bacteria source on the MFC anode pattern with GGA (Glucose and Glutamic Acid solution) as an electron producer, and the 30 minutes voltage value changing curves was gotten by measuring the voltage. At the end the curve was analyzed and compared with the real BOD value in order to get the conclusion. The BOD on-line measurement is quite promising and efficient when multi-samples are needed to be tested.

Esta tese apresenta o desenvolvimento de uma técnica de processamento numérico capaz de reconstruir o sinal da temperatura do processo original a partir do sinal distorcido, atrasado e ruidoso, medido através de uma sonda intrusiva. Uma técnica de regularização foi adotada para contornar o mau condicionamento do modelo numérico inverso da equação de transdução para obter o sinal do processo, que considera o acúmulo térmico e as transferências de calor convectivo e radiativo entre o meio e o sensor térmico. O método dos mínimos quadrados simplificado foi implementado como técnica de regularização, por ser um método rápido e possuir um código computacional pequeno, permitindo, obter os dados em tempo real e desenvolver um sensor térmico inteligente. Testes numéricos demonstraram as discrepâncias introduzidas pela inércia térmica, convecção e radiação, bem como a extrema sensibilidade da presença de ruídos quando o problema inverso é resolvido. Testes experimentais foram conduzidos para validar o algoritmo de reconstrução sob condições práticas com sinais obtidos por um termopar encapsulado / This thesis presents the development of a numerical processing technique capable of reconstructing the original process temperature signal from distorted, late and noisy measured signal obtained from an intrusive probe. A regularization technique was adopted to palliate the ill conditioning of the inverse numerical model of the transduction equation to obtain the process signal, which considers thermal accumulation and convective and radiative heat transfers between the medium and the thermal sensor. The simplified least square method was implemented as the regularization technique, because it is a fast method and results in a small computational code, which produces on-line information and allow the development an intelligent temperature sensor. Numerical tests demonstrated the discrepancies introduced by thermal inertia, convection and radiation, as well as the extreme sensitivities to the presence of noise when solving the inverse problem. Experimental tests were carried out to validate the reconstruction algorithm under realistic experimental conditions with the signals obtained by the sheathed thermocouple

Esta tese apresenta o desenvolvimento de uma técnica de processamento numérico capaz de reconstruir o sinal da temperatura do processo original a partir do sinal distorcido, atrasado e ruidoso, medido através de uma sonda intrusiva. Uma técnica de regularização foi adotada para contornar o mau condicionamento do modelo numérico inverso da equação de transdução para obter o sinal do processo, que considera o acúmulo térmico e as transferências de calor convectivo e radiativo entre o meio e o sensor térmico. O método dos mínimos quadrados simplificado foi implementado como técnica de regularização, por ser um método rápido e possuir um código computacional pequeno, permitindo, obter os dados em tempo real e desenvolver um sensor térmico inteligente. Testes numéricos demonstraram as discrepâncias introduzidas pela inércia térmica, convecção e radiação, bem como a extrema sensibilidade da presença de ruídos quando o problema inverso é resolvido. Testes experimentais foram conduzidos para validar o algoritmo de reconstrução sob condições práticas com sinais obtidos por um termopar encapsulado / This thesis presents the development of a numerical processing technique capable of reconstructing the original process temperature signal from distorted, late and noisy measured signal obtained from an intrusive probe. A regularization technique was adopted to palliate the ill conditioning of the inverse numerical model of the transduction equation to obtain the process signal, which considers thermal accumulation and convective and radiative heat transfers between the medium and the thermal sensor. The simplified least square method was implemented as the regularization technique, because it is a fast method and results in a small computational code, which produces on-line information and allow the development an intelligent temperature sensor. Numerical tests demonstrated the discrepancies introduced by thermal inertia, convection and radiation, as well as the extreme sensitivities to the presence of noise when solving the inverse problem. Experimental tests were carried out to validate the reconstruction algorithm under realistic experimental conditions with the signals obtained by the sheathed thermocouple

Submitted by Claudinei Pracidelli (cpracide@ipen.br) on 2016-11-11T09:45:23Z No. of bitstreams: 0 / Made available in DSpace on 2016-11-11T09:45:23Z (GMT). No. of bitstreams: 0 / Um desafio recorrente em processos produtivos é o desenvolvimento de sistemas de monitoração e diagnóstico. Esses sistemas ajudam na detecção de mudanças inesperadas e interrupções, prevenindo perdas e mitigando riscos. Redes Neurais Artificiais (RNA) têm sido largamente utilizadas na criação de sistemas de monitoração. Normalmente as RNA utilizadas para resolver este tipo de problema são criadas levando-se em conta apenas parâmetros como o número de entradas, saídas e quantidade de neurônios nas camadas escondidas. Assim, as redes resultantes geralmente possuem uma configuração onde há uma total conexão entre os neurônios de uma camada e os da camada seguinte, sem que haja melhorias em sua topologia. Este trabalho utiliza o algoritmo de Otimização por Colônia de Formigas (OCF) para criar redes neurais otimizadas. O algoritmo de busca OCF utiliza a técnica de retropropagação de erros para otimizar a topologia da rede neural sugerindo as melhores conexões entre os neurônios. A RNA resultante foi aplicada para monitorar variáveis do reator de pesquisas IEA-R1 do IPEN. Os resultados obtidos mostram que o algoritmo desenvolvido é capaz de melhorar o desempenho do modelo que estima o valor de variáveis do reator. Em testes com diferentes números de neurônios na camada escondida, utilizando como comparativos o erro quadrático médio, o erro absoluto médio e o coeficiente de correlação, o desempenho da RNA otimizada foi igual ou superior ao da tradicional. / Dissertação (Mestrado em Tecnologia Nuclear) / IPEN/D / Instituto de Pesquisas Energeticas e Nucleares - IPEN-CNEN/SP

The field of computer vision offers tools that allow machines to derive meaningful infor-mation from video and images and consequently make decisions based on visual inputs. In the paper industry, implementation of machine vision (MV) can be used to automate and speed up processes that require visual inspection, particularly certain segments of quality control – one such application being detection and measurement of paper roll core eccentricity. Core eccentricity is a roll build error in which the roll core is offset from the geometric roll center, potentially causing runnability issues. This particular project aims to improve the detection of paper roll core eccentricity at the Mondi Dynäs integrated pulp and paper mill through creation, calibration and evaluation of a machine-vision-based tool for on-line core eccentricity measurement. The tool utilizes the Hough Transform (HT), since HT is a simple yet fast and robust algorithm when it comes to identification of basic shapes such as lines and circles. The proposed solution was evaluated in two ways; firstly by determining at what level of accuracy the measurements could be provided, accounting for how well the solution deals with correction of systematic error caused by environmental factors, and secondly by analyzing how well characteristic roll features could be accurately identified in large sets of data, necessary to consistently perform measurements. The evaluation of the proposed solution showed a 99.9% detection rate for characteristic paper roll features, and a 98.1% detection rate of laser lines used for correction of position and orientation induced error. Assessment of the measurement accuracy following successful detection was on par with the current optical measurement method, and the proposed solution was able to classify distinctive features with a 96.8% accuracy. Lastly, several improvement actions to address faulty detection were identified, and factors to be considered for future installment were highlighted.

This diploma thesis is focused on use of measuring interface myDAQ by National Instruments company in teaching of electrotechnical measurement at vocational school. The aim of this work is the design of set of suitable measuring tasks covering the curriculum in electrotechnical measurements for the 3rd and 4th class of study branch of 26 41 M/01 Electrotechnics. Description of the properties of the measuring interface myDAQ and the respective utility programme National Instruments Multisim is a part of this work. The practical part contains technological and photographic documentation of the various products that can be connected to the interface myDAQ. This thesis is built on the author's bachelor thesis.