Lance Chun Che Fung

The present study evaluates the potential use of graphene oxide (GO) and reduced graphene oxide (RGO) additives to improve the photothermal response and evaporation rates of basin water used in solar thermal stills. The prepared GO-based and RGO-based test solutions were dilute, well dispersed, and stable. Improvements in photothermal response and evaporation rates were found to be significant. The best-performing fluid was the 30% RGO stock solution-based water solution, which achieved a temperature enhancement of 5.2% and a significant evaporation rate improvement of 30.5% compared to pure water samples. Importantly, the evaporation rates achieved were at relatively lower solution temperatures that were typically between 39 and 41 °C, thus highlighting the advantage of adding either GO or RGO to basin water to improve evaporation rates for solar thermal stills operating at lower temperatures. All GO- and RGO-based solutions displayed excellent dispersion stability over the investigated temperature range.

Information overloading leads to the need for an efficient search tool to eliminate a considerable amount of irrelevant or unimportant data and present the contents in an easy-browsing form. Personalized faceted search has been one of the potential tools to provide a hierarchical list of facets or categories that helps searchers to organize the information of the search results. Facet selection is one of the important steps to pursue a good faceted search. Collaborative-based personalization was introduced to facet selection. Previous studies have been performed on the use of Collaborative Filtering techniques for personalized facet selection. However, none of the study has investigated Artificial neural network techniques on personalized facet selection. Therefore, this study aims to investigate the possible use of deep Autoencoder on the prediction of facet interests. Autoencoder model was applied to address the association of collaborative interest in facets. The experiments were conducted on 100K and 1M rating records of Movielen dataset. Rating score was used to represent the explicit feedback on facet interests. The performance was reported by comparing the proposed technique and the state-of-the-art model-based Collaborative Filtering techniques in terms of prediction accuracy and computational time. The results showed that the proposed Autoencoder-based model achieved better performance and it was able to significantly improve the prediction of personal facet interests.

Search engine plays an important role in information retrieval as being the preferred tool by users to locate and manage their desired information. The volume of online data has dramatically increased and this phenomenon of impressive growth leads to the need of efficient systems to deal with issues associated with storage and retrieval. Keyword search is the most popular search paradigm which prompts user to search the entire repository based on a few keywords. From research article collection, using keyword search only may not be enough for researchers to explore academic documents related to their interests from the entire repository. Knowledge discovery tool has recently received much attention in order to compensate the weakness of keyword search usage for academic collection. This paper presents the practical system design and implementation of a knowledge discovery tool in terms of faceted search. This study focused on Thai research articles for use by Thai scholars. The proposed faceted search system was constructed based on the Apache Solr search platform. The methodology of data preparation, knowledge extraction and implementation are discussed in the paper.

n this paper, a Complementary Fuzzy Support Vector Machine (CMTFSVM) technique is proposed to handle outlier and noise in classification problems. Fuzzy membership values are applied for each input point to reflect the degree of importance of the instances. Datasets from the UCI and KEEL are used for the comparison. In order to confirm the proposed methodology, 40 % random noise is added to the datasets. The experiment results of CMTFSVM are analysed and compared with the Complementary Neural Network (CMTNN). The outcome indicated that the combined CMTFSVM outperformed the CMTNN approach.

Recently, recommendation systems have become an important tool to support and improve decision making for educational purposes. However, developing recommendation systems is far from trivial and there are specific issues associated with individual problems. Low-correlated input features is a problem that influences the overall accuracy of decision tree models. Weak relationship between input features can cause decision trees work inefficiently. This paper reports the use of features grouping method to improve the classification accuracy of decision trees. Such method groups related input features together based on their ontologies. The new inherited features are then used instead as new features to the decision trees. The proposed method was tested with five decision tree models. The dataset used in this study were collected from schools in Nakhonratchasima province, Thailand. The experimental results indicated that the proposed method can improve the classification accuracy of all decision tree models. Furthermore, such method can significantly decrease the computational time in the training period.

Monthly rainfall spatial interpolation is an important task in hydrological study to comprehensively observe the spatial distribution of the monthly rainfall variable in the study area. A number of spatial interpolation methods have been successfully applied to perform this task. However, those methods mainly aim at achieving satisfactory interpolation accuracy and they disregard the interpolation interpretability. Without interpretability, human analysts will not be able to gain insight of the model of the spatial data. This paper proposes an alternative approach to achieve both accuracy and interpretability of the monthly rainfall spatial interpolation solution. A combination of fuzzy clustering, fuzzy inference system, genetic algorithm and modular technique has been used. The accuracy of the proposed method has been compared to the most commonly-used methods in geographic information systems as well as previously proposed method. The experimental results showed that the proposed model provided satisfactory interpolation accuracy in comparison with other methods. Besides, the interpretability of the proposed model could be achieved in both global and local perspectives. Human analysts may therefore understand the model from the derived model’s parameters and fuzzy rules.

Spatial interpolation is a method to create spatial continuous surface from observed data points. Spatial interpolation is important to water management and planning because it could provide estimation of rainfall at unobserved area. This paper proposes a methodology to analyze and establish an integrated intelligent spatial interpolation model for monthly rainfall data. The proposed methodology starts with determining the optimal number of sub-regions by means of standard deviation analysis and artificial neural networks. Once the optimal number of sub-regions is determined, a Mamdani fuzzy inference system is generated by fuzzy c-means and then optimized by genetic algorithm. Four case studies were used to evaluate the accuracy of the established models and compared with trend surface analysis and artificial neural networks. The experimental results demonstrated that the proposed methodology provided reasonable interpolation accuracy and the methodology gave human understandable fuzzy rules to human analysts.

Ancient manuscripts have been preserved by many organizations so as to protect these documents and retrieve traditional knowledge. With the advanced computer technology, digitized media is now commonly used to record these documents. One objective of such work is to develop an efficient image processing system that could be used to retrieve knowledge and information automatically from these ancient manuscripts. Binarization is a preprocessing technique used to extract text and characters from the manuscripts. The output is then used for further processes such as character recognition and knowledge extraction. This paper compares different binarization techniques that could be used for processing of ancient manuscripts. The aim is to improve the binarization techniques with the main objective of developing an automated preprocessing technique for ancient manuscript recognition and knowledge extraction.

Development of humanoid and dance robots has improved greatly due to rapid advancement of electronics, computer, mechatronics and control technologies. While humanoid robots such as Honda ASIMO, Fujita HOAP-3 and Sony QRIO have dazed the public with their amazing capabilities, such robots are in very limited supply and they are also extremely expensive. On the other hand, the low cost toy robot, WowWee's Robosapien (RS), has become very popular. It has also expanded its functionalities in later models since its line of products were first launched in 2004. The most important aspect of such robot is its cost is only a fraction of the highly sophisticated robots. This study investigates the feasibility of using low cost robots such as RS for the purposes of entertainment and teaching Thai dances. Informal feedbacks and comments have shown a high degree of acceptance and keen interest. This demonstrates the potential of low cost robots for training, entertainment and edutainment purposes.

The availability of multiple media channels through the Internet has added new dimensions of communication between people or communities who are geographically separated. In the environment of informal communication on the Internet, chat applications are popular in which a user may be represented only by a nickname or an alias. This suggests that a person may be able to communicate more freely when his or her identity is concealed. Popular chatting or instant messaging (IM) systems such as Microsoft MSN Messenger, America Online's Instant Messenger, Yahoo! Messenger, and GoogleTalk have changed the way that a user may communicate with friends, acquaintances, and business colleagues. Once limited to desktop personal computers (PCs) or laptops, popular instant messaging systems are finding their way onto handheld devices and mobile phones. This allows a user to chat from virtually anywhere. Nowadays, IM is found on almost every personal PC connected to the Internet as well as on many corporate desktops.