Sirindhorn International Institute of Technology

About: Sirindhorn International Institute of Technology is a based out in . It is known for research contribution in the topics: Supply chain & Combustion. The organization has 1048 authors who have published 1678 publications receiving 30067 citations.

Application of geographical information system to site selection of small run-of-river hydropower project by considering engineering/economic/environmental criteria and social impact

Abstract: In the process of site selection of a small run-of-river hydropower project in Thailand, some problems are addressed as follows: the accessibility of the possible sites which are mostly located in rural and mountainous areas, the large amount of data required, and the lack of participation of the local people living nearby. In order to cope with these problems, this study proposes a new method to select feasible sites of small run-of-river hydropower projects by using Geographic Information System (GIS) technology. A combination of engineering, economic, and environmental criteria, as well as social impact is employed in this study. The selected study area is the upper Nan river basin situated in the north of Thailand. For the engineering criteria, the project locations are found by GIS in visual basic platform, and then economic evaluations of the selected projects are performed. Next, the environmental parameters are used to rank the projects by total weighted scores. Finally, a social impact study at the potential sites is conducted based on the public participation process, i.e. questionnaire survey and focus group discussions. The applicability of the proposed method is verified by the results of site selection of the small hydropower projects located on the Nan river basin in Thailand. This case study can be the model for the process of site selection of similar projects.

Machine learning approach to automatic exudate detection in retinal images from diabetic patients

Abstract: Exudates are among the preliminary signs of diabetic retinopathy, a major cause of vision loss in diabetic patients. Early detection of exudates could improve patients’ chances to avoid blindness. In this paper, we present a series of experiments on feature selection and exudates classification using naive Bayes and support vector machine (SVM) classifiers. We first fit the naive Bayes model to a training set consisting of 15 features extracted from each of 115,867 positive examples of exudate pixels and an equal number of negative examples. We then perform feature selection on the naive Bayes model, repeatedly removing features from the classifier, one by one, until classification performance stops improving. To find the best SVM, we begin with the best feature set from the naive Bayes classifier, and repeatedly add the previously-removed features to the classifier. For each combination of features, we perform a grid search to determine the best combination of hyperparameters ν (tolerance for training er...