Team:NCTU Formosa/modeling

Modeling Introduction

In the modeling part, we make two models in our project to optimize our result and enhance the convenience of the device usage. In the first model, we demonstrate a model for our biobricks which is composed of Pcons, RBS, 9 PBAN, GFP, and terminator. And in the second model, we model our device with two kinds of natural factor which are temperature and the wavelength of light. Before introducing the model, we would like to make a brief introduction for our modeling method and the modeling tool we used.

The following contents we can devided into three parts:

(1)Modeling Software: First, we introduce the tool we use. ANFIS, a tool involved in MATLAB

(2)Modeling for PBAN: Second, we use ANFIS to build a PBAN model that can fit to a theoretical and real condition at the same time

(3)Modeling for Device: At last, a device model is also established. This model can let the user know the insect capture performance in any condition.

Modeling Software

MATLAB

MATLAB (matrix laboratory) is a numerical computing environment and fourth-generation programmingnk= language. It is developed by MathWorks, a company in United States. MATLAB allows matrix manipulations, plotting of functions and data, implementation of algorithms, creation of user interfaces, and interfacing with programs written in other languages, including C, C++, Java, and Fortran. Although MATLAB is intended primarily for numerical computing, an optional toolbox uses the MuPAD symbolic engine, allowing access to symbolic computing capabilities. An additional package, Simulink, adds graphical multi-domain simulation and Model-Based Design for dynamic and embedded systems.

ANFIS

Adaptive-Network-Based Fuzzy Inference System, in short ANFIS, is a power tool for constructing a set of fuzzy if-then rules to generate stipulated output and input pairs. Unlike system modeling using mathematical rules that lacks the ability to deal with ill-defined and uncertain system, ANFIS can transform human knowledge into rule base, and therefore, ANFIS can effectively tune membership functions, minimizing the output error. In this project, we use two main function of ANFIS, which is data adjusting and data simulating, in our PBAN model and device model. In PBAN model, we use a theoretical biobrick to adjust our experiment data, and in device model, we use the simulation function to find a prediction surface to predict the insect capture performance of our device. These result will describe in the next two part.

Modeling for PBAN

In this project, 9 kinds of PBAN are used to attract 9 different kinds of insect into our device. Even though these 9 PBAN(PBAN(BM), PBAN(MB), PBAN(AI), PBAN(LD), PBAN(HAH), PBAN(AS), PBAN(SI), PBAN(AA), PBAN(SL)) didn’t work the same in attracting different kinds of insect, 9 PBAN are all produced by E.coli, which should get same production rate. Thus, we use a “Pcons+ RBS + GFP+ Term” as the theoretical condition to simulate PBAN biobrick (Pcons+ RBS+ PBAN+ GFP+ Term) expression. By detecting the expression value from the theoretical biobrick, and modified by our PBAN biobrick expression, this modified model can not only fit a theoretical condition that prevents our model from operating bias, but also fit to a real condition. To make a brief introduction of our PBAN model, the following contents are divided into two parts: (1) Theoretical biobrick (2) 9 PBAN biobrick and modeling result.

Theoretical biobrick

9 different kinds of PBAN biobrick and modeling result

Modeling for Device

Not only building a model for biobricks, in this project, a customized model for our debug device is also useful for the device user such as farmers or engineers. However, to find a good condition for our device becomes a problem for the users due to the lack of information for the parameters we set. To make the users easily use our operation debug device, we make a device model to let the user input the condition value and they can know the performance of the device under such condition. Here, we make a briefly introduction for our debug device and the parameters we used for modeling. In this device modeling, the wavelength of light and the surrounding temperature are used for modeling. The following contents we devided into three parts: (1) Wave Length (2) Temperature (3) Experiment Data

Wave Length

According to the reference, Insect have chemotactic properties of light, and different degrees of light will have different attractive effect, so we use the different kinds of wave lengths for the same moths. To evaluate a best wave length for the insect. Variable Light－we divide the wave of visible light into five parts－475, 510, 570 and 650nm, hoping to modeling all of visible light condition.

Temperature

Temperature is key factor that can significantly influence the performance of a device, and it is hard to change the surrounding temperature if you put the device to the field. Thus, we take temperature into consider, and we select five temperature between the highest and lowest average temperature last year (17.03。C /30.1。C) of the major city in Taiwan, and want to modeling all of temperature condition.

Experiment Data

In the experiment part, we use CCW no. 1 that we introduce in "Result/Insect Aspects" to perform the attracting ability by changing the light wavelength and surrounding temperature. The insect will choose a bottle based on the favor light color in a consist temperature. Repeat the experiment by changing the temperature from 17 to 29 Celsius degree. And the following table is the result of the insect capture ability by using CCW no. 1.

After experiment, the modeling using these data can simulate the capture ability in all condition.

The device model are aim to let the user easily input the condition value and know the device performance by this simulating surface. And the user can also find the local optima between the light wavelength 475nm to 650nm and the temperature between 17 to 29 Celsius degree.

Reference