A. Van Brecht

A computer vision method for on-line behavioral quantification of individually caged poultry

In addition to production, physiology, and health, behavior is an important issue with respect to animal welfare when evaluating novel housing systems. Behavioral characteristics are usually evaluated by audio-visual observation done by a human observer present on the scene. This method is time consuming, expensive, subjective, and prone to human error. Automated objective surveillance, by means of inexpensive cameras and image-processing techniques, has the ability to generate data that provide an objective measure of behavior, without disturbing the animals. The specific purpose of this study was to develop a fully automatic on-line image-processing technique to quantify the behavior of a single laying hen as opposed to the current human visual observation. The image-processing system is based on the principle that the classification of behavior can be translated into classification of time series of different postures of the hen. The hen’s postures can be recognized in the camera image. The classification of the hen’s behavior is performed by dynamic analysis of a set of measurable parameters, which are calculated from the images using image-processing techniques. The parameters were chosen based on their computational demands and analysis of their discriminative power regarding the different types of a specific behavior. A first implementation of the system allowed us to identify three different types of individual behavior (standing, walking, and scratching). The objective of further investigation will be the classification of up to 15 different types of behavior, such as pecking, eating, drinking, wing stretching, etc.

Control of the 3-D spatio-temporal distribution of air temperature

An on-line mathematical approach was used to model the 3-D spatio-temporal temperature distribution in an imperfectly mixed forced ventilated room. A second-order model proved to be a sufficiently good description of the temperature dynamics (R 2 = 0.929) of the system for control purposes. Furthermore, it was possible to fully understand the physical meaning of the second order model structure. Using this model, a model-based predictive controller (MBPC) was developed for a single input single output (SISO) system. The controller was able to accurately control the mean temperature level of four spatial points in the room, and to robustly react to a random local disturbance signal. The results presented in this paper show that MBPC using data-based mechanistic modelling can be of significant importance in the development of a new generation of climate controllers.

CLIMATE CONTROL BASED ON TEMPERATURE MEASUREMENT IN THE ANIMAL-OCCUPIED ZONE OF A PIG ROOM WITH GROUND CHANNEL VENTILATION

It is known that there can be a significant temperature difference between the position of the climate controller sensor (room temperature) and the animal-occupied zone (AOZ) in a pig room. This study explores the advantages of using AOZ temperature in climate control. The objectives were: (1) to evaluate a current climate control system in a practical room with ground channel ventilation for weaned piglets by comparing AOZ and room temperature, and (2) to determine advantages of control of the heating system based on AOZ temperature by a model-based predictive (MBP) controller. Comparison of AOZ and room temperature showed that during the first 10 days of the two experimental batches, AOZ temperature was lower and showed greater fluctuations than room temperature, most likely due to the switching of the heating system (on/off). Animals close to the sensor could disturb the AOZ measurement. This was not the case during colder nights, when animals moved away from the sensor and the measured AOZ temperature was a good indicator of the air temperature around the animals. The data for those periods were suitable for use in this climate control study, but when applying the system in practice the disturbing effect needs to be prevented by better protection of the AOZ sensor. For the second objective, the course of the AOZ temperature was modeled based on data for five nights when the heating switched on and off several times (goodness of fit Rt 2 = 0.77). One of the models was integrated in a simulated MBP controller that uses the model to predict future AOZ temperature; the controller switches the heating system on before the AOZ gets too cold and off before it gets too warm. The simulated AOZ temperature was more stable during an 11 h cold period; the standard deviation was reduced from 0.44°C to 0.18°C.

Model-based PIP control of the spatial temperature distribution in cars.

An on-line mathematical approach was used to model the spatio-temporal temperature distribution in the imperfectly mixed air inside a car. A second-order model proved to be a sufficiently good description of the temperature dynamics (R 2 = 0.985) of the system. Furthermore, it was possible to fully understand the physical meaning of the second-order model structure. Using this model, a proportional integral plus (PIP) climate controller was developed for a single input single output (SISO) system. The controller was able to follow a temperature level of 18–23–21°C at any desirable point, and to robustly react to a random local disturbance.

QUANTIFICATION OF THE SPATIO–TEMPORAL EGGSHELL TEMPERATURE AND CONVECTION COEFFICIENT BY INFRARED THERMOGRAPHY

In relation to the percentage of chicks that hatch out of eggs in an incubator, the maintenance of the optimum incubation temperature (37.2.C to 37.8.C) is a critical factor. The temperature of the embryo depends on three factors: (1) the air temperature of the incubator, (2) the exchange of heat between the egg and its micro–environment, and (3) the time–variable heat production of the embryo. In commercial incubators, the transport of heat between the egg and its environment is mainly the result of convection. The objective of this research is to quantify the gradients of the eggshell temperature and to quantify the heat transport between the eggshell and the surrounding air. The airflow pattern plays an important role in the spatio–temporal gradients of eggshells. Although the setpoint of the air temperature was 37.7.C, the spatio–temporal eggshell temperatures at day 17 varied between 36.51.C and 37.98.C. Despite these large eggshell temperature spatio–temporal gradients, 222 chicks hatched from the 300 incubated eggs. The fertility of the eggs was 78%. Using the eggshell surface temperatures, the mean convection coefficient at the beginning and at the end of the incubation process was determined.

Comparison of thermal comfort performance of two different types of road vehicle climate control systems

The performance of climate control systems in vehicles becomes more and more important, especially against the background of the important relationship between compartment climate and driver mental condition and, thus, traffic safety. The performance of two different types of climate control systems, an un-air-conditioned heating/cooling device (VW) and an air-conditioning climate control unit (BMW), is compared using modern and practical evaluation techniques quantifying both the dynamic 3-D temperature distribution and the local air refreshment rate. Both systems suffer from considerable temperature gradients: temperature gradients in the U-AC (VW) car up to 8–9°C are encountered, while the AC (BMW) delivers clear improvement resulting in temperature gradients of 5–6°C. The experiments clearly demonstrate the effect of the presence of even a single passenger on the thermal regime, increasing the existing thermal discrepancies in the compartment with 15% independent of ventilation rate. Furthermore, in terms of air refreshment rates in the vehicle compartment, an air-conditioning unit halves the air refreshment time at all positions in the vehicle cabin, delivering a significant improvement in terms of human comfort. Similarly, extra air inlets in the back compartment of a car deliver progress in terms of cabin refreshment rate (93 s down to 50 s).

Numerical Simulation and Controller Development for Energy Transfer in Imperfectly Mixed Fluids

This paper describes the possibility of using Computational Fluid Dynamics (CFD) models for developing Model Predictive Controller (MPC) algorithms. In this work a complex CFD model is combined with a reduced order Data-Based Mechanistic (DBM) formulation so that the low-order model can be used for control applications. To demonstrate the methodology, a Single Input-Single Output (SISO) ventilated installation is used. Initially, a CFD model is used for the installation. At the inlet a step rise in air temperature is applied and temperature responses at 36 monitoring positions are extracted. Then a reduced order model is formulated for the test case and model parameters are identified using the data generated by the CFD model. Finally the reduced order CFD model is used to develop an MPC algorithm. The developed algorithm is found to be robust for disturbance effects and capable of tracking the desired reference temperature trajectory very well.

OPTICAL FLOW ALGORITHM TO QUANTIFY THE TWO-DIMENSIONAL VELOCITY COMPONENTS OF A VISUALIZED AIR JET

In a ventilated space, the incoming air jet and the resulting airflow pattern play key roles in the removal and distribution of heat, moisture, harmful gases, and particles from or around living organisms (man, animal, and plant). In this research, an optical flow algorithm was used to visualize and quantify the two-dimensional velocity components of a visualized air jet in a ventilated room. The airflow is visualized by adding smoke particles to the air. The results of the optical flow algorithm are in agreement with experimental measurements, and the algorithm is a low-cost alternative technique to measure the two-dimensional velocity components. This technique might be used to quantify the airflow pattern by image analysis. The maximum absolute error of the mean air speed calculated by the optical flow algorithm compared to the measured mean air speed distribution is 0.0162 m/s, which is a relative error of 16%.

Acoustic Hatch Monitor for Egg Incubation: Detection of Internal Pipping in an Industrial Incubator

During the final phase of incubation, continuous recording and analysis of embryo sounds allow estimations about the percentage of hatched chickens. The objective of this study was to predict the time at which all eggs in an industrial incubator had passed the stage of internal pipping (IP100%). Chicken vocalization starts around day 19 when lung respiration commences. The amount of chicken vocalization increases during the hatching process, resulting in an increase of sound energy in the frequency range of 2500 to 3300 Hz. Using a standard microphone mounted inside the incubator, continuous sound recordings were processed to monitor the sound intensity during the hatching process. The method discussed in this article was assessed in an industrial-scale hatch setter (19,200 eggs) and repeated five times. The results show that the sound intensity signal results in a specific pattern that holds information about the stage of hatching. It was shown for five trials that the time at which automatic IP100% detection occurred was within ±3 h of the manually observed time for IP100%. The results imply that real-time sound recording and analysis during incubation allow prediction of the hatch outcome in industrial incubators. Such an acoustic monitoring system might provide industrial users with valuable information for hatch management.

Model-based temperature measurement technique to quantify the dynamic three-dimensional air freshness flow in the interior of a car

A data-based mechanistic (DBM) approach was used to model the spatio-temporal temperature distribution in the imperfectly mixed fluid in a car. The first phase of DBM involves the identification of a mathematical model from experimental data. A second order model proves to deliver a sufficiently good description of the temperature dynamics of the system (R² = 0.985). Furthermore, the physical interpretation of this second order model provides a useful variable. The physical meaning of one of the model parameters is what is called the local volumetric concentration of fresh air flow inside the car. It thus becomes possible to quantify the local air freshness in a complex geometric space as the interior of a car, only using simple temperature measurements. This technique could become a valuable tool in evaluating the performance of for instance climate controllers in interior spaces.