Honeymoon with IWBs: A qualitative insight in primary students' views on instruction with interactive whiteboard

The main purpose of this study was to investigate the views of primary students about interactive whiteboard [IWB] use in their classes from attitudinal and pedagogical perspectives. Research was designed as an empirical approach to phenomenology. Data was collected from fifty primary students (fourth to eighth) through focus group interviews. Nvivo 9 qualitative data analysis software was used to analyze data. Results showed that students like instruction with IWB especially for such reasons/capabilities as practical and economical use, better visual presentation, and test-based use. Students were predominantly uncomfortable with the technical problems. They believed that instruction with IWB positively impacted their learning especially because of visualization and contextualization, effective presentation, test-based use, and motivational factors. Finally it was inferred that IWBs were not used to their full potential, and both technical problems and common practices indicated that teachers were still at an initial stage of transmission to instruction with IWB and they needed both technical and pedagogical training.     

Vision Based UAV Attitude Estimation: Progress and Insights

Unmanned aerial vehicles (UAVs) are increasingly replacing manned systems in situations that are dangerous, remote, or difficult for manned aircraft to access. Its control tasks are empowered by computer vision technology. Visual sensors are robustly used for stabilization as primary or at least secondary sensors. Hence, UAV stabilization by attitude estimation from visual sensors is a very active research area. Vision based techniques are proving their effectiveness and robustness in handling this problem. In this work a comprehensive review of UAV vision based attitude estimation approaches is covered, starting from horizon based methods and passing by vanishing points, optical flow, and stereoscopic based techniques. A novel segmentation approach for UAV attitude estimation based on polarization is proposed. Our future insightes for attitude estimation from uncalibrated catadioptric sensors are also discussed.     

Experimental investigation on the stability of bride girder against tsunami forces

Bridges are the most useful part in the transportation network. Any disruption of the bridge structures may hamper the whole transportation system. In the last recent tsunamis, numerous number of bridges were structurally damaged by the tsunami waves. Lack of proper provisions of tsunami forces in the design guidelines also contributes to the augmentations of the damage level. Therefore, proper evaluation of the tsunami forces on the bridge girder should be introduced in the design promptly. In this study, laboratory experiments were carried out to reveal the damage mechanism of the bridge girder by assessing the exerted tsunami forces. Both broken and unbroken waves were considered for the analysis. The results showed that measured forces were larger for broken waves than those of unbroken waves. Maximum force of the broken waves was 4.59 times as large as the hydrostatic pressure. Also, waves reached the peak value more rapidly for larger wave heights than those of smaller ones. Additionally, a girder that is placed at a higher position is much vulnerable to tsunami hazard.     

Topic Identification in Dynamical Text by Complexity Pursuit

The problem of analysing dynamically evolving textual data has arisen within the last few years. An example of such data is the discussion appearing in Internet chat lines. In this Letter a recently introduced source separation method, termed as complexity pursuit, is applied to the problem of finding topics in dynamical text and is compared against several blind separation algorithms for the problem considered. Complexity pursuit is a generalisation of projection pursuit to time series and it is able to use both higher-order statistical measures and temporal dependency information in separating the topics. Experimental results on chat line and newsgroup data demonstrate that the minimum complexity time series indeed do correspond to meaningful topics inherent in the dynamical text data, and also suggest the applicability of the method to query-based retrieval from a temporally changing text stream. This revised version was published online in June 2006 with corrections to the Cover Date.     

A novel direct torque control for interior permanent-magnet synchronous machine drive with low ripple in torque and flux-a speed-sensorless approach

A novel direct torque control (DTC) scheme for an interior permanent magnet synchronous machine (IPMSM) is proposed in this paper, which features low torque and flux ripple and almost fixed switching frequency. The torque and flux ripples have been significantly reduced compared with those of the basic DTC reported in the literature. A speed estimation scheme is integrated with the proposed DTC scheme in order to achieve a fully sensorless high-performance IPMSM drive.     

Adaptive sliding mode approach for learning in a feedforward neural network

An adaptive learning algorithm is proposed for a feedforward neural network. The design principle is based on the sliding mode concept. Unlike the existing algorithms, the adaptive learning algorithm developed does not require a prioriknowledge of upper bounds of bounded signals. The convergence of the algorithm is established and conditions given. Simulations are presented to show the effectiveness of the algorithm.     

Development and implementation of a hybrid intelligent controller for interior permanent-magnet synchronous motor drives

A hybrid neuro-fuzzy scheme for online tuning of a genetic-based proportional-integral (PI) controller for an interior permanent-magnet synchronous motor (IPMSM) drive is presented in this paper. The proposed controller is developed for accurate speed control of the IPMSM drive under various system disturbances. In this work, initially different operating conditions are obtained based on motor dynamics incorporating uncertainties. At each operating condition a genetic algorithm is used to optimize the PI controller parameters in a closed-loop vector control scheme. In the optimization procedure a performance index is developed to reflect the minimum speed deviation, minimum settling time and zero steady-state error. A fuzzy basis function network (FBFN) is utilized for online tuning of the PI controller parameters to ensure optimum drive performance under different disturbances. The proposed FBFN-based PI controller provides a natural framework for combining numerical and linguistic information in a uniform fashion. The proposed controller is successfully implemented in real time using a digital signal processor board DS 1102 for a laboratory 1-hp IPMSM. The effectiveness of the proposed controller is verified by simulation as well as experimental results at different dynamic operating conditions. The proposed controller is found to be robust for applications in an IPMSM drive.     

EFFECTS OF COOLANT SUPPLY METHODS AND CUTTING CONDITIONS ON TOOL LIFE IN END MILLING TITANIUM ALLOY

Titanium machining poses a great challenge to cutting tools due to its severe negative influence on tool life primarily due to high temperature generated and strong adhesion in the cutting area. Thus, various coolant supply methods are widely used to improve the machining process. On account of this, tool life and cutting force are investigated based on dry cutting, flood cooling, and minimum quantity lubrication (MQL) techniques. The experimental results show that MQL machining can remarkably and reliably improve tool life, and reduce cutting force due to the better lubrication and cooling effect.     

Product-cost modelling approach for the development of a decision support system for optimal roofing material selection

Selection of optimal roofing materials is very important but it is a complex and onerous task as varieties of materials are available for housing roof construction. In order to select suitable materials, an extensive range of criteria would need to be considered. This paper presents the framework and the development of a knowledge-based decision support system for material selection implemented in roofing material selection domain, called ‘Knowledge-based Decision Support system for roofing Material Selection and cost estimating’ (KDSMS). It was developed to facilitate the selection of optimal materials for different roof sub elements. The system consists of a database and knowledge base that is equipped with an inference engine. The former is used to store different types of roofing materials with assigned attribute values. The later is used to hold qualitative and quantitative knowledge which were collected from domain experts and other technical literatures such as building regulations, price guide book and product catalogues. The proposed system employs the TOPSIS (Technique of ranking Preferences by Similarity to the Ideal Solution) multiple criteria decision making method to solve materials selection and optimisation problem. This study utilised the available roofing materials in the UK housing market in developing the system reported. The main contribution of the developed system is that it provides a tool for the architects, quantity surveyors or self house builder to select optimal materials from a wide array of possibilities for different roof sub elements and also to estimate the conceptual cost for the roof element in the early stage of building design.