In this work solar erythemal ultraviolet radiation (EUV) which affects human skin and skin cancer was investigated at Songkhla province (7.2°N 100.6°E). EUV was converted to UV index then the data was used for model development and forecasting of hourly UV index using Artificial Neural Network (ANN). The ANN model has one input layer two hidden layers and one output layer. This input layer consists of extraterrestrial erythemal ultraviolet radiation solar zenith angle aerosol optical depth and cloud index which affect ultraviolet radiation and the output layer is hourly UV index. The results show that hourly UV index obtained from ANN and that from the measurement are in reasonable agreement with root mean square difference of 12.8% and mean bias difference of -2.4%. For forecasting of the hourly UV index the data for 7 days earlier was used for forecasting the next UV index for one day or nine-hour (08:00 am.-16:00 pm.). Multi-layer perceptron and back propagation algorithm were used in the model forecasting. The results show that the UV index from the model forecasting is reasonable agrees with UV index from measurement with root mean square difference of 17.0% and mean bias difference of 0.3%.

he monthly rainfall forecasts are very important in Thailand because the most of people has a career in agriculture. The monthly rainfall forecasts can be analyzed for manage water resources. In addition the monthly rainfall forecasting is used to manage water for consume in Thailand. This research is forecasting monthly rainfall under the global warming using a multilayer neural network. It is a network with one hidden layer. The data from the Meteorological Department is use. Imported variables include temperature humidity and wind. The forecast from January to December 2018. The mean square error (MSE) to measure forecasting accurate and found that the value is suitable error (10.25). The neural network can provide accurate monthly rainfall forecasts from January to December. It was found that the southern region had the highest rainfall and the eastern region northeastern region northern region central and western regions respectively. Therefore neural networks can be forecast rainfall accurately and efficiently. It can be used to analyze and manage water resources effective.

This paper is a presentation of the Load-Frequency Control (LFC) of Interconnected Power System by using Fuzzy Logic and Artificial Neural Network (ANN) Controller. The both of controller have been widely applied for handling the system control of Industries and Engineering. Since the Load-Frequency behaviors of power system of uncontrolled and PI- Controller or Fuzzy Logic Controller (FLC) wanted to the stability at first of energize and load in a short time. The control system and controller then be significant for to go forward reform stability of interconnected energize power system a short time. In this research we have the Load-Frequency Controller of Interconnected Power System by using Artificial Neural Network Controller the improved can compared with conventional Fuzzy Logic Controller at power total 47.55 MW and load changing of power plan No.3 is 0.02 p.u. (1.75 MW)

In present there are widely use robot in many fields. Mobile robot can help us in several task such as moving to toxic area risk area or explosion area mine. Movement control can be used in many ways. This paper proposed mobile robot that movc by wheels. It move to previous defined picture. By using small camera process an images and check it that is the previous one by using neural network. If it is robot will move toward to that picture. The movement is in flat plane. If that defined picture is not in center robot will move to left or right depend on its position until it is on center. This paper proposed image processing only.

In present there are widely use robot in many fields. Mobile robot can help us in several task such as moving to toxic area risk area or explosion area mine. Movement control can be used in many ways. This paper proposed mobile robot that move by wheels. It move to previous defined picture. By using small camera process an images and check it that is the previous one by using neural network. If it is robot will move toward to that picture. The movement is in flat plane. If that defined picture is not in center robot will move to left or right depend on its position until it is on center. This paper proposed image processing only.