Índice fuzzy de conforto térmico para frangos de corte

A estimativa de conforto térmico na avicultura moderna é importante para que sistemas de climatização possam ser acionados no tempo correto, diminuindo perdas e aumentando rendimentos. Embora a literatura corrente apresente alguns índices de conforto térmico, que são aplicados para essa estimativa, estes são baseados apenas em condições do ambiente térmico e não consideram fatores importantes inerentes aos animais, tais como genética e capacidade de aclimatação, provendo, geralmente, uma estimativa inadequada do conforto térmico das aves. Este trabalho desenvolveu o Índice Fuzzy de Conforto Térmico (IFCT), com o intuito de estimar o conforto térmico de frangos de corte, considerando que o mecanismo usado pelas aves para perda de calor em ambientes fora da zona termoneutra é a vasodilatação periférica, que aumenta a temperatura superficial, e que pode ser usada como indicador do estado de conforto. O IFCT foi desenvolvido a partir de dois experimentos, que proporcionaram 108 cenários ambientais diferentes. Foram usadas imagens termográficas infravermelhas, para o registro dos dados de temperaturas superficiais das penas e da pele, e o grau de empenamento das aves. Para os mesmos cenários de ambiente térmico observados nos experimentos, foram comparados os resultados obtidos usando o IFCT e o Índice de Temperatura e Umidade (ITU). Os resultados validaram o IFCT para a estimativa do conforto térmico de frangos de corte, sendo específico na estimativa de condições de perigo térmico, usual em alojamentos em países de clima tropical. Essa característica é desejável em modelos que estimem o bem-estar térmico de frangos de corte, pois situações classificadas como perigo acarretam no dispêndio de recursos para evitar perdas produtivas.

Application of fuzzy logic for the evaluation of livestock slaughtering

The fuzzy logic admits infinite intermediate logical values between false and true. With this principle, it developed in this study a system based on fuzzy rules, which indicates the body mass index of ruminant animals in order to obtain the best time to slaughter. The controller developed has as input the variables weight and height, and as output a new body mass index, called Fuzzy Body Mass Index (Fuzzy BMI), which may serve as a detection system at the time of livestock slaughtering, comparing one another by the linguistic variables "Very Low", "Low", "Average ", "High" and "Very High". For demonstrating the use application of this fuzzy system, an analysis was made with 147 Nellore beeves to determine Fuzzy BMI values for each animal and indicate the location of body mass of any herd. The performance validation of the system was based on a statistical analysis using the Pearson correlation coefficient of 0.923, representing a high positive correlation, indicating that the proposed method is appropriate. Thus, this method allows the evaluation of the herd comparing each animal within the group, thus providing a quantitative method of farmer decision. It was concluded that this study established a computational method based on fuzzy logic that mimics part of human reasoning and interprets the body mass index of any bovine species and in any region of the country.

Procedimento fuzzy aplicado à avaliação da insalubridade em atividades agrícolas

Diante do alto grau de mecanização a que as atividades agrícolas estão sendo submetidas, objetivou-se, com esta pesquisa, desenvolver um modelo fuzzy capaz de avaliar e classificar o nível de insalubridade em diversos ambientes de trabalho. O modelo desenvolvido tem como variáveis de entrada: o índice de bulbo úmido e temperatura de globo (IBUTG, °C), o nível de ruído (dBA), a taxa de metabolismo (W m-2) e o tempo de descanso (%) e, como variável de saída, o índice de bem-estar humano (IBEH). O método de inferência utilizado foi o de Mandani e, na defuzificacão, utilizou-se o método do centro de gravidade. O sistema de regras foi desenvolvido com base nas combinações das variáveis de entrada. Foram definidas 400 regras com pesos iguais a 1, sendo que, na elaboração das regras, um especialista da área foi consultado. Foram utilizados dados de campo visando a testar o sistema desenvolvido, e os resultados mostraram que a modelagem proposta é uma ferramenta promissora na determinação do IBEH, apresentando tempo de descanso ideal variando de 64,2% (motosserra, próximo ao ouvido do operador) até 25% (derriçadora, 20 m de distância do operador), sendo que, diante de um cenário predefinido do ambiente térmico e acústico, foi possível determinar o grau de bem-estar humano e o tempo de descanso ideal para cada equipamento avaliado.

Sistema fuzzy para predição do desempenho produtivo de frangos de corte de 1 a 21 dias de idade

Um sistema de inferência fuzzy foi desenvolvido baseado em dados da literatura para predição do consumo de ração, ganho de peso e conversão alimentar de frangos de corte com idade variando de 1 a 21, dias submetidos a diferentes condições térmicas. O sistema fuzzy foi estruturado com base em três variáveis de entrada: idade das aves (semanas), temperatura (°C) e umidade relativa (%) ambientes, sendo que as variáveis de saída consideradas foram: ganho de peso, consumo de ração e conversão alimentar. A inferência foi realizada por meio do método de Mamdani, que consistiu na elaboração de 45 regras e a defuzzificação por meio do método do Centro de Gravidade. Com base nos resultados, ao se compararem os dados da literatura com os obtidos pelo sistema fuzzy proposto, verificou-se desempenho satisfatório na predição das variáveis respostas, com R² da ordem de 0,995; 0,998 e 0,976, respectivamente. O ganho de peso predito pela lógica fuzzy foi validado com dados experimentais de campo, no qual se obteve R² = 0,975, apresentando grande potencial de uso em sistemas de climatização automatizado.

Prediction of free-stall occupancy rate in dairycattle barns through fuzzy sets

The goal of this study was to develop a fuzzy model to predict the occupancy rate of free-stalls facilities of dairy cattle, aiding to optimize the design of projects. The following input variables were defined for the development of the fuzzy system: dry bulb temperature (Tdb, °C), wet bulb temperature (Twb, °C) and black globe temperature (Tbg, °C). Based on the input variables, the fuzzy system predicts the occupancy rate (OR, %) of dairy cattle in free-stall barns. For the model validation, data collecting were conducted on the facilities of the Intensive System of Milk Production (SIPL), in the Dairy Cattle National Research Center (CNPGL) of Embrapa. The OR values, estimated by the fuzzy system, presented values of average standard deviation of 3.93%, indicating low rate of errors in the simulation. Simulated and measured results were statistically equal (P>0.05, t Test). After validating the proposed model, the average percentage of correct answers for the simulated data was 89.7%. Therefore, the fuzzy system developed for the occupancy rate prediction of free-stalls facilities for dairy cattle allowed a realistic prediction of stalls occupancy rate, allowing the planning and design of free-stall barns.

Electrically driven fertilizer applicator controlled by fuzzy logic

The present study shows the development, simulation and actual implementation of a closed-loop controller based on fuzzy logic that is able to regulate and standardize the mass flow of a helical fertilizer applicator. The control algorithm was developed using MATLAB's Fuzzy Logic Toolbox. Both open and closed-loop simulations of the controller were performed in MATLAB's Simulink environment. The instantaneous deviation of the mass flow from the set point (SP), its derivative, the equipment´s translation velocity and acceleration were all used as input signals for the controller, whereas the voltage of the applicator's DC electric motor (DCEM) was driven by the controller as output signal. Calibration and validation of the rules and membership functions of the fuzzy logic were accomplished in the computer simulation phase, taking into account the system's response to SP changes. The mass flow variation coefficient, measured in experimental tests, ranged from 6.32 to 13.18%. The steady state error fell between -0.72 and 0.13g s-1 and the recorded average rise time of the system was 0.38 s. The implemented controller was able to both damp the oscillations in mass flow that are characteristic of helical fertilizer applicators, and to effectively respond to SP variations.

Fuzzy rule-based system for evaluation of uncertainty transaction in cassava chain

A fuzzy ruled-based system was developed in this study and resulted in an index indicating the level of uncertainty related to commercial transactions between cassava growers and their dealers. The fuzzy system was developed based on Transaction Cost Economics approach. The fuzzy system was developed from input variables regarding information sharing between grower and dealer on “Demand/purchase Forecasting”, “Production Forecasting” and “Production Innovation”. The output variable is the level of uncertainty regarding the transaction between seller and buyer agent, which may serve as a system for detecting inefficiencies. Evidences from 27 cassava growers registered in the Regional Development Offices of Tupa and Assis, São Paulo, Brazil, and 48 of their dealers supported the development of the system. The mathematical model indicated that 55% of the growers present a Very High level of uncertainty, 33% present Medium or High. The others present Low or Very Low level of uncertainty. From the model, simulations of external interferences can be implemented in order to improve the degree of uncertainty and, thus, lower transaction costs.

Management zones using fuzzy clustering based on spatial-temporal variability of soil and corn yield

Clustering soil and crop data can be used as a basis for the definition of management zones because the data are grouped into clusters based on the similar interaction of these variables. Therefore, the objective of this study was to identify management zones using fuzzy c-means clustering analysis based on the spatial and temporal variability of soil attributes and corn yield. The study site (18 by 250-m in size) was located in Jaboticabal, São Paulo/Brazil. Corn yield was measured in one hundred 4.5 by 10-m cells along four parallel transects (25 observations per transect) over five growing seasons between 2001 and 2010. Soil chemical and physical attributes were measured. SAS procedure MIXED was used to identify which variable(s) most influenced the spatial variability of corn yield over the five study years. Basis saturation (BS) was the variable that better related to corn yield, thus, semivariograms models were fitted for BS and corn yield and then, data values were krigged. Management Zone Analyst software was used to carry out the fuzzy c-means clustering algorithm. The optimum number of management zones can change over time, as well as the degree of agreement between the BS and corn yield management zone maps. Thus, it is very important take into account the temporal variability of crop yield and soil attributes to delineate management zones accurately.

Fuzzy modeling of broiler performance, raised from 1 to 21 days, subject to heat stress

ABSTRACT Given the need to obtain systems to better control broiler production environment, we performed an experiment with broilers from 1 to 21 days, which were submitted to different intensities and air temperature durations in conditioned wind tunnels and the results were used for validation of afuzzy model. The model was developed using as input variables: duration of heat stress (days), dry bulb air temperature (°C) and as output variable: feed intake (g) weight gain (g) and feed conversion (g.g-1). The inference method used was Mamdani, 20 rules have been prepared and the defuzzification technique used was the Center of Gravity. A satisfactory efficiency in determining productive responses is evidenced in the results obtained in the model simulation, when compared with the experimental data, where R2 values ​​calculated for feed intake, weight gain and feed conversion were 0.998, 0.981 and 0.980, respectively.

SOFTWARE TO ASSESS BEEF CATTLE BODY MASS THROUGH THE FUZZY BODY MASS INDEX

ABSTRACT The Body Mass Index (BMI) can be used by farmers to help determine the time of evaluation of the body mass gain of the animal. However, the calculation of this index does not reveal immediately whether the animal is ready for slaughter or if it needs special care fattening. The aim of this study was to develop a software using the Fuzzy Logic to compare the bovine body mass among themselves and identify the groups for slaughter and those that requires more intensive feeding, using "mass" and "height" variables, and the output Fuzzy BMI. For the development of the software, it was used a fuzzy system with applications in a herd of 147 Nellore cows, located in a city of Santa Rita do Pardo city – Mato Grosso do Sul (MS) state, in Brazil, and a database generated by Matlab software.

Bankruptcy prediction using fuzzy methods

In this thesis, a classi cation problem in predicting credit worthiness of a customer is tackled. This is done by proposing a reliable classi cation procedure on a given data set. The aim of this thesis is to design a model that gives the best classi cation accuracy to e ectively predict bankruptcy. FRPCA techniques proposed by Yang and Wang have been preferred since they are tolerant to certain type of noise in the data. These include FRPCA1, FRPCA2 and FRPCA3 from which the best method is chosen. Two di erent approaches are used at the classi cation stage: Similarity classi er and FKNN classi er. Algorithms are tested with Australian credit card screening data set. Results obtained indicate a mean classi cation accuracy of 83.22% using FRPCA1 with similarity classi- er. The FKNN approach yields a mean classi cation accuracy of 85.93% when used with FRPCA2, making it a better method for the suitable choices of the number of nearest neighbors and fuzziness parameters. Details on the calibration of the fuzziness parameter and other parameters associated with the similarity classi er are discussed.

Feature selection using Fuzzy Entropy measures with Yu's Similarity measure

In this study, feature selection in classification based problems is highlighted. The role of feature selection methods is to select important features by discarding redundant and irrelevant features in the data set, we investigated this case by using fuzzy entropy measures. We developed fuzzy entropy based feature selection method using Yu's similarity and test this using similarity classifier. As the similarity classifier we used Yu's similarity, we tested our similarity on the real world data set which is dermatological data set. By performing feature selection based on fuzzy entropy measures before classification on our data set the empirical results were very promising, the highest classification accuracy of 98.83% was achieved when testing our similarity measure to the data set. The achieved results were then compared with some other results previously obtained using different similarity classifiers, the obtained results show better accuracy than the one achieved before. The used methods helped to reduce the dimensionality of the used data set, to speed up the computation time of a learning algorithm and therefore have simplified the classification task

"Fuzzy front end" ja sen menestyksekäs johtaminen teollisuusyrityksessä

Työssä käsitellään innovaatioprosessin ensimmäistä ”fuzzy front end” -vaihetta, jota työssä kutsutaan front end -vaiheeksi. Front end -vaihe on innovaatioprosessin alustava tutkimus ja suunnittelu vaihe ennen teknistä kehittämisvaihetta. Front end -vaihetta on tutkittu innovaatioprosessin osista vähiten, sekä se on useimmille yrityksillä sumea ja vaikeasti käsitettävä. Tutkimusten mukaan front end -vaiheen osaaminen on kuitenkin erittäin merkittävä tekijä yrityksen innovatiivisuudelle. Työssä avataan innovaatioprosessin sisältöä ja tavoitteita, sekä vertaillaan käytössä olevia malleja front end -vaiheen rakenteesta. Työssä selvitetään avaintekijöitä front end -vaiheen menestykseen ja tehokkuuteen. Lisäksi käsitellään johtamisen tekijöitä, jotka edesauttavat onnistumaan front end -vaiheessa.

Application of a Pid+fuzzy controller on the motion control system in machine tools

This work deals with an hybrid PID+fuzzy logic controller applied to control the machine tool biaxial table motions. The non-linear model includes backlash and the axis elasticity. Two PID controllers do the primary table control. A third PID+fuzzy controller has a cross coupled structure whose function is to minimise the trajectory contour errors. Once with the three PID controllers tuned, the system is simulated with and without the third controller. The responses results are plotted and compared to analyse the effectiveness of this hybrid controller over the system. They show that the proposed methodology reduces the contour error in a proportion of 70:1.

Speed Control of an Autonomous Mobile Robot: Comparison between a PID Control and a Control Using Fuzzy Logic

An Autonomous Mobile Robot battery driven, with two traction wheels and a steering wheel is being developed. This Robot central control is regulated by an IPC, which controls every function of security, steering, positioning localization and driving. Each traction wheel is operated by a DC motor with independent control system. This system is made up of a chopper, an encoder and a microcomputer. The IPC transmits the velocity values and acceleration ramp references to the PIC microcontrollers. As each traction wheel control is independent, it's possible to obtain different speed values for each wheel. This process facilities the direction and drive changes. Two different strategies for speed velocity control were implemented; one works with PID, and the other with fuzzy logic. There were no changes in circuits and feedback control, except for the PIC microcontroller software. Comparing the two different speed control strategies the results were equivalent. However, in relation to the development and implementation of these strategies, the difficulties were bigger to implement the PID control.