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A Simulation of 6R Industrial Articulated Robot Arm Using Backpropagation Neural Network
Supachoke Manigpan,Supaporn Kiattisin,Adisorn Leelasantitham 제어로봇시스템학회 2010 제어로봇시스템학회 국제학술대회 논문집 Vol.2010 No.10
This paper presents a simulation of a 6 degrees-of-freedom (6R) articulated robot arm using backpropagation neural network to solve the problem regarding inverse kinematics for the industrial articulated robot. The Denavit ? Hartenberg model is used to analyze the robot arm movement. Next, the forward kinematics is used to identify the relationships for each joint of the robot arm and to determine various parameters for learning system of random neural network for 5,000 data points. The simulation results show that the robot arm can move to target positions with precision, and the average error for the entire 6 joints is at approximately 4.03 degrees.
Construction of 3D Thai Monument Models Used for Google Earth
Adisorn Leelasantitham,Supaporn Kiattisin,Prawat Chaiprapa 제어로봇시스템학회 2009 제어로봇시스템학회 국제학술대회 논문집 Vol.2009 No.8
This paper presents a construction of 3D Thai monument models used for Google Earth (GE) through the useof a commercial program e.g. 3DS Max program. In this method, information of democracy monument and giantswingare brought from measurements and are drawn into ratios of structures. They are made for realistic models, adjusted for suitable models, taken in colors for the realistic models and rendered to the beautiful models. These models areconverted to files which are used in GE and arranged into suitable and realistic positions of GE map. And then such files are saved into GE program and uploaded on website for a download of other persons. For testing the download ofmodels on the GE program, the result shows that these models can be located on the real sites of the world and can beseen from any view. Twenty students are assigned to test the GE program. The most students reveal that it is easy to usethe GE program and to understand the models.
A Detection of Defect in Diamond Images Using 2-D Haar Wavelet Transform
Puttipong Markchai,Supaporn Kiattisin,Adisorn Leelasantitham 제어로봇시스템학회 2010 제어로봇시스템학회 국제학술대회 논문집 Vol.2010 No.10
This paper presents a detection of defect in diamond images using 2-D wavelet transform for helping to solve problems in diamond quality examination by photographs .The wavelet is able to examine images in a form of texture, graph and histogram which this method is cable to take these values to analyze the differences between defective and flawless diamond because the image of each diamond have different textures. There are more or less the texture depending on many factors such as square cut and flaw. With this approach, the examining process of diamond detection is reduced then it can be done faster. As a result of 2-D wavelet transform, there are 30 diamond images for testing which all images can be verified for the flaw in diamond images.
A Real-Time GPS Vehicle Tracking System Displayed on a Google-Map-Based Website
Prawat Chaiprapa,Supaporn Kiattisin,Adisorn Leelasantitham 대한전자공학회 2009 ITC-CSCC :International Technical Conference on Ci Vol.2009 No.7
This paper proposes a real-time vehicle tracking system using a global positioning system (GPS) technology module to receive the location of the vehicle, to forward into microcontroller and to connect internet by a general packet radio service (GPRS) technology for displaying a real time on the website map developed by Google Map which allows inspection of vehicles at all times. There are 3 parts of this project. The first part is a program developed in C language for controlling the hardware. Employing PHP and AJAX language will be developed for the Google Map API to help a map construction on the website. The second part is the hardware, there are the GPS and GPRS modules, the GPS module will locate the vehicles via the satellite, and the GPRS module will assemble all data and send it to the website by the microcontroller. The final part is the interface using RS232 for connecting between the GPS and GPRS modules. With the Google Map on a real-time website, vehicles can be monitored and located very effectively. This includes paths and/or vehicles directions. However, the small error is at approximately 5 meters in the wrong location due to the limitation of hardware and the ratio of map reference.
Veerachai GOSASANG,Watcharavee CHANDRAPRAKAIKUL,Supaporn KIATTISIN 한국해운물류학회 2011 The Asian journal of shipping and Logistics Vol.27 No.3
However, forecasts of container throughput growth and development of Bangkok Port, the significant port of Thailand, have been scant and the findings are divergence. Moreover, the existing literature emphasizes only two forecasting methods, namely time series and regression analysis. The aim of this paper is to explore Multilayer Perceptron (MLP) and Linear Regression for predicting future container throughput at Bangkok Port. Factors affecting cargo throughput at Bangkok Port were identified and then collected from Bank of Thailand, Office of the National Economic and Social Development Board, World Bank, Ministry of Interior, and Energy Policy and Planning Office. These factors were entered into MLP and Linear Regression forecasting models that generated a projection of cargo throughput. Subsequently, the results were measured by root mean squared error (RMSE) and mean absolute error (MAE). Based on the results, this research provides the best application of forecasting technique which is Neural Network – Multilayer Perceptron technique for predicting container throughput at Bangkok Port.