Ventilation systems are incorporated at intensive poultry farms to control environment conditions and thermal comfort of broilers. The ventilation system operates based on environmental data, particularly measured by sensors of temperature and relative humidity.Sensors are placed at different positions of the facility. Quality, number and positioning of the sensors are critical factors to achieve an efficient performance of the system. For this reason, a strategic positioning of the sensors associated to controllers could support the maintenance and management of the microclimate inside the facility. This research aims to identify the three most representative points for the positioning of sensors in order to support the ventilation system during the critical period from 12h00 to 15h00 on summer days. Temperature, relative humidity and wind speed were measured in four different tunnel ventilated barns at the final stage of the production cycle. The descriptive analysis was performed on these data. TheTemperature and Humidity Index (THI) was also calculated. Then, the geostatistical analysis of THI was performed by GS + and the position of sensors was determined by ordinary kriging. The methodology was able to detect the most representative points for the positioning of sensors in a case study (southeastern Brazil). The results suggested that this strategic positioning would help controllers to obtain a better inference of the microclimate during the studied period (the hottest microclimate), considered critical in Brazil. In addition, these results allow developing a future road map for a decision support system based on 24 h monitoring of the ventilation systems in broiler houses.
This paper describes the integral Knowledge Discovery (KDD) process, including both prior expert knowledge and interpretation oriented tools to extract the behavior of a real pilot wastewater treatment plant. Special emphasis is made on the interest of developing postprocessing tools for clustering methods which can help the expert to understand the meaning of the clusters and bridge the important existing gap between Data Mining and effective Decision Support. Traffic Lights Panel (TLP) is presented as a suitable visual interpretation oriented tool for clustering results. Based on this tool, four typical behaviours are identified in the pilot plant, which have been validated by the experts. Till now, the TLP is manually derived from the clustering results, but it has been well accepted by the domain experts of several real applications as a very helpful contribution to understand the classes meaning and improve reliable decision-making. Here, a proposal for automatic construction of TLP is presented trying to mimic the real process that the analyst performs to manually build them. A criterion based on conditional Median as a central trend statistics of the variables inside a class is introduced and refined to gain robustness towards outliers. Both criteria are tested and compared with the real target case study. A deep analysis of the advantages and drawbacks of the proposed criterion, permitted to better understand the analyst process when manually building TLPs, to identify the scope of the proposal, and to typify some of the situations in which additional conditions are required.
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