Application of fuzzy ARTMAP and fuzzy <Emphasis Type="Italic">c</Emphasis>-means clustering to pattern classification with incomplete data

In this paper, a hybrid neural network that is capable of incremental learning and classification of patterns with incomplete data is proposed. Fuzzy ARTMAP (FAM) is employed as the constituting network for pattern classification while fuzzy c-means (FCM) clustering is used as the underlying algorithm for processing training as well as test samples with missing features. To handle an incomplete training set, FAM is first trained using complete samples only. Missing features of the training samples are estimated and replaced using two FCM-based strategies. Then, network training is conducted using all the complete and estimated samples. To handle an incomplete test set, a non-substitution FCM-based strategy is employed so that a predicted output can be produced rapidly. The performance of the proposed hybrid network is evaluated using a benchmark problem, and its practical applicability is demonstrated using a medical diagnosis task. The results are compared, analysed and quantified statistically with the bootstrap method. Implications of the proposed network for pattern classification tasks with incomplete data are discussed.     

Design and development of an intelligent controller for a pole-balancing robot

In this paper, an intelligent controller capable of static balancing as well as dynamic balancing of a pole mounted on a motorized robot is designed and developed. The brain of the intelligent controller lies in the Fuzzy Inference System, which receives as its input displacement, velocity and acceleration information. An embedded instrumentation system onboard the robot measures the displacement of the robot and the angle of inclination of the pole from the vertical position. For static balancing, the controller needs to maintain the pole in an upright position while the robot is free to move on a flat surface. For dynamic balancing, the robot needs to balance the pole while performing transitions up and down a ramp. Furthermore, the robot needs to steer itself back to the center to prevent it from falling off the ramp.     

Application of genetic algorithm for fuzzy rules optimization on semi expert judgment automation using Pittsburg approach

Genetic algorithm is well-known of its best heuristic search method. Fuzzy logic unveils the advantage of interpretability. Genetic fuzzy system exploits potential of optimization with ease of understanding that facilitates rules optimization. This paper presents the optimization of fourteen fuzzy rules for semi expert judgment automation of early activity based duration estimation in software project management. The goal of the optimization is to reduce linguistic terms complexity and improve estimation accuracy of the fuzzy rule set while at the same time maintaining a similar degree of interpretability. The optimized numbers of linguistic terms in fuzzy rules by 27.76% using simplistic binary encoding mechanism managed to improve accuracy by 14.29% and reduce optimization execution time by 6.95% without compromising on interpretability in addition to promote improvement of knowledge base in fuzzy rule based systems.     

Disturbance decoupled fault reconstruction using cascaded sliding mode observers

This paper presents a disturbance decoupled fault reconstruction (DDFR) scheme using cascaded sliding mode observers (SMOs). The processed signals from a SMO are found to be the output of a fictitious system which treats the faults and disturbances as inputs; the ‘outputs’ are then fed into the next SMO. This process is repeated until the attainment of a fictitious system which satisfies the conditions that guarantee DDFR. It is found that this scheme is less restrictive and enables DDFR for a wider class of systems compared to previous work when only one or two SMOs were used. This paper also presents a systematic routine to check for the feasibility of the scheme and to calculate the required number of SMOs from the outset and also to design the DDFR scheme. A design example verifies its effectiveness.     

Characteristics and usage patterns of older people in a 3D online multi-user virtual environment

3D online multi-user virtual environments (MUVEs) have a lot of potential in supporting older people in their daily lives, yet little research has been conducted to explore how older people engage with this type of technology. This paper aims to investigate the characteristics, user groups and activity patterns (particularly social networks and gift giving behaviour) of older users within a 3D online multi-user virtual environment. Data from approximately 5000 online user profiles of older and younger users from a 3D MUVE, namely IMVU, was collected for analysis. Overall, we identified several distinct patterns of use (e.g. size of social ties, level of reciprocity, etc.) among older users when compared with younger users. We also found that despite the capabilities of 3D MUVEs to provide the users immersion in alternative realities, a feature well embraced by younger users in this study, older users seemed more interested in activities which serve as an extension to their physical life.     

Analog piezoelectric-driven tunable gratings with nanometer resolution

This work presents the design, fabrication, and characterization of a piezoelectrically actuated MEMS diffractive optical grating, whose spatial periodicity can be tuned in analog fashion to within a fraction of a nanometer. The fine control of the diffracted beams permits applications in dense wavelength-division multiplexing (DWDM) optical telecommunications and high-resolution miniaturized spectrometers. The design concept consists of a diffractive grating defined on a deformable membrane, strained in the direction perpendicular to the gratings grooves via thin-film piezoelectric actuators. The tunable angular range for the first diffracted order is up to 400 /spl mu/rad with 0.2% strain (/spl sim/8 nm change in grating periodicity) at 10 V actuation, as predicted by device modeling. The actuators demonstrate a piezoelectric d/sub 31/ coefficient of -100 pC/N and dielectric constant /spl epsiv//sub r/ of 1200. Uniformity across the tunable grating and the out-of-plane deflections are also characterized and discussed.     

New results in robust actuator fault reconstruction for linear uncertain systems using sliding mode observers

This paper presents a robust actuator fault reconstruction scheme for linear uncertain systems using sliding mode observers. In existing work, fault reconstruction via sliding mode observers is limited to either linear certain systems subject to unknown inputs, relative degree one systems or a specific class of relative degree two systems. This paper presents a new method that is applicable to a wider class of systems with relative degree higher than one, and can also be used for systems with more unknown inputs than outputs. The method uses two sliding mode observers in cascade. Signals from the first observer are processed and used to drive the second observer. Overall, this results in actuator fault reconstruction being feasible for a wider class of systems than using existing methods. A simulation example verifies the claims made in this paper. Copyright © 2007 John Wiley & Sons, Ltd.     

Subband Synthesis for Color Filter Array Demosaicking

This paper presents a new algorithm for demosaicking images captured through a color filter array (CFA). The objective of the CFA demosaicking is to render a full color image from the mosaicked image. This is commonly achieved by estimating the missing color information from the surrounding observed pixels. In this paper, we integrate the observation that color images have a strong intrachannel spatial correlation in low-frequency components and a dominant interchannel correlation in the high- frequency components. A new framework is proposed to utilize this information where the missing pixels in each color channel are estimated from the wavelet subbands. A modified median-filtering operation is then applied in the subband domain. The algorithm is adaptive and produces superior full-resolution images when compared with other methods.     

Knowledge construction and misconstruction: A case study approach in asynchronous discussion using Knowledge Construction – Message Map (KCMM) and Knowledge Construction – Message Graph (KCMG)

This paper introduces a methodology for analyzing the knowledge construction and misconstruction processes occurring in online asynchronous discussion for Secondary 2 (Grade 8) students. The use of purposive sampling targeting specific students in a single course forum could potentially advance understanding of these processes using Knowledge Construction – Message Map (KCMM) and Knowledge Construction – Message Graph (KCMG). This paper traced the communication patterns and knowledge construction as well as misconstruction processes of students working in groups to formulate scientific ideas. This methodology reveals the dynamics of asynchronous discussion through mapping and quantifying the electronic learning process. This will equip educational practitioners and researchers with a useful tool for describing online interaction through adoption of a measurement methodology more systematic and effective than anecdotally. The research findings suggest that self-directed learning through asynchronous discussion has to be monitored by facilitators as learners possessed misconceptions that could potentially mislead other participants.