Chun Che Fung

How does desktop virtual reality enhance learning outcomes? A structural equation modeling approach

This study examined how desktop virtual reality (VR) enhances learning and not merely does desktop VR influence learning. Various relevant constructs and their measurement factors were identified to examine how desktop VR enhances learning and the fit of the hypothesized model was analyzed using structural equation modeling. The results supported the indirect effect of VR features to the learning outcomes, which was mediated by the interaction experience and the learning experience. Learning experience which was individually measured by the psychological factors, that is, presence, motivation, cognitive benefits, control and active learning, and reflective thinking took central stage in affecting the learning outcomes in the desktop VR-based learning environment. The moderating effect of student characteristics such as spatial ability and learning style was also examined. The results show instructional designers and VR software developers how to improve the learning effectiveness and further strengthen their desktop VR-based learning implementation. Through this research, an initial theoretical model of the determinants of learning effectiveness in a desktop VR-based learning environment is contributed.

Distinguishing games and simulation games from simulators

The advanced computational capabilities in modern personal computers have made it possible for consumers to experience simulations with a high degree of verisimilitude through simulation games (a.k.a. Sims). In recent years, the cross-boundary technology exchange between game and simulation technology, along with other factors, has contributed to the confusion as to what makes a simulation game and what makes a simulator. This article provides a users and designers perspective on a definitive comparison of the similarities and differences between games in general, simulation games, and simulators. It also introduces a method that can be easily used to distinguish games and simulation games from simulators by using observable design characteristics. On the other hand, the convergence of functionality and technology in simulation games and simulators has created new applications of simulation. One such application is in serious games. Serious games and simulation games are confusingly similar in many ways. However, they greatly differ in functionality. This article also provides a method to distinguish serious games from simulation games, to clarify the strict categorization between these two applications of simulation.

Modular artificial neural network for prediction of petrophysical properties from well log data

This paper reports the application of Kohonens Self-Organizing Map (SOM) and Learning Vector Quantization (LVQ) algorithms, and the commonly used Back Propagation Neural Network (BPNN) to the prediction of petrophysical properties from well log data. Recently, the use of artificial neural networks (ANN) in the field of petrophysical properties prediction has received increasing attentions. In this paper, a modular ANN comprises of a complex network made up of a number of sub-networks is introduced. In this approach, the SOM algorithm is first applied to classify the well log data into a pre-defined number of classes. This gives an indication of the lithology of the given well. The LVQ algorithm is then applied to train the network under supervised learning. A set of BPNN which corresponds to different classes is then developed for the prediction of petrophysical properties. Once the network is trained if is then used as the classification and prediction model for subsequent input data. Results obtained from example studies using this proposed method have shown to be fast and accurate as compared to a single BPNN network.

Classification of imbalanced data by combining the complementary neural network and SMOTE algorithm

In classification, when the distribution of the training data among classes is uneven, the learning algorithm is generally dominated by the feature of the majority classes. The features in the minority classes are normally difficult to be fully recognized. In this paper, a method is proposed to enhance the classification accuracy for the minority classes. The proposed method combines Synthetic Minority Over-sampling Technique (SMOTE) and Complementary Neural Network (CMTNN) to handle the problem of classifying imbalanced data. In order to demonstrate that the proposed technique can assist classification of imbalanced data, several classification algorithms have been used. They are Artificial Neural Network (ANN), k-Nearest Neighbor (k-NN) and Support Vector Machine (SVM). The benchmark data sets with various ratios between the minority class and the majority class are obtained from the University of California Irvine (UCI) machine learning repository. The results show that the proposed combination techniques can improve the performance for the class imbalance problem.

Rainfall prediction model using soft computing technique

Abstract Rainfall prediction in this paper is a spatial interpolation problem that makes use of the daily rainfall information to predict volume of rainfall at unknown locations within area covered by existing observations. This paper proposed the use of self-organising map (SOM), backpropagation neural networks (BPNN) and fuzzy rule systems to perform rainfall spatial interpolation based on local method. The SOM is first used to separate the whole data space into some local surface automatically without any knowledge from the analyst. In each sub-surface, the complexity of the whole data space is reduced to something more homogeneous. After classification, BPNNs are then use to learn the generalization characteristics from the data within each cluster. Fuzzy rules for each cluster are then extracted. The fuzzy rule base is then used for rainfall prediction. This method is used to compare with an established method, which uses radial basis function networks and orographic effect. Results show that this method could provide similar results from the established method. However, this method has the advantage of allowing analyst to understand and interact with the model using fuzzy rules.

Accuracy in position estimation of mobile robots based on coded infrared signal transmission

A system based on coded, infrared signal transmission for the estimation of position of mobile robots in a structured environment has been reported. Particular emphasis is given on the polar coordinate arrangement in which signals are sent from the transmitters situated at the corners of boundaries of operation. A multi-sensor system strategically situated on board the robot has been found to improve the accuracy of the position estimation substantially. The information detected by the sensors are processed suitably to calculate the central position of the robot geometrically. The algorithms for the position calculations and operational strategy are presented. This system forms the basis for the coordination and cooperation philosophy of multiple mobile robots sharing the same environment performing cooperative or competitive tasks

The Relationship between Game Genres, Learning Techniques and Learning Styles in Educational Computer Games

Educational computer game has many similar characteristics like any other genres of games. However its particular aspect is designed to teach, and in which main objective involves in learning a topic. To develop an effective educational computer game, different game genres, learning activities and techniques, and learning styles are important issues for consideration. This paper presents an analysis by comparing and establishing relationships between the game genres and learning techniques based on the types of learning and potential game style of Prensky [1] and learning styles based on the study of Chong et al. [2].

Operation of mobile robots in a structured infrared environment

A micro robot has been built and integrated into a structured infrared positioning sensing system. In the position sensing system the operation area is divided into sectors to allow multiple robots to operate simultaneously. The robot monitors its position whilst in motion and makes its own logical decisions. It can autonomously decide for the next action to execute. It is also able to avoid both dynamic or static obstacles by either sensing at possible collision or by anticipating obstacles from its position and position of others by means of acting on a set of given prioritised instructions. Errors in the system have been discussed and recommendations are made.

Binary classification using ensemble neural networks and interval neutrosophic sets

This paper presents an ensemble neural network and interval neutrosophic sets approach to the problem of binary classification. A bagging technique is applied to an ensemble of pairs of neural networks created to predict degree of truth membership, indeterminacy membership, and false membership values in the interval neutrosophic sets. In our approach, the error and vagueness are quantified in the classification process as well. A number of aggregation techniques are proposed in this paper. We applied our techniques to the classical benchmark problems including ionosphere, pima-Indians diabetes, and liver-disorders from the UCI machine learning repository. Our approaches improve the classification performance as compared to the existing techniques which applied only to the truth membership values. Furthermore, the proposed ensemble techniques also provide better results than those obtained from only a single pair of neural networks.

Comparing the Performance of Different Neural Networks Architectures for the Prediction of Mineral Prospectivity

In the mining industry, effective use of geographic information systems (GIS) to identify new geographic locations that are favorable for mineral exploration is very important. However, definitive prediction of such location is not an easy task. In this paper, four different neural networks, namely, the Polynomial Neural Network (PNN), General Regression Neural Network (GRNN), Probabilistic Neural Network (PrNN) and Back Propagation Neural Network (BPNN) have been used to classify data corresponding to cells in a map grid into deposit cells and barren cells. These approaches were tested on the GIS mineral exploration data from the Kalgoorlie region of Western Australia. The performance of individual neural networks is compared based on simulation results. The results demonstrate various degrees of success for the networks and suggestions on how to integrate the results are discussed.