Forced landing technologies for unmanned aerial vehicles : towards safer operations

While using unmanned systems in combat is not new, what will be new in the foreseeable future is how such systems are used and integrated in the civilian space. The potential use of Unmanned Aerial Vehicles in civil and commercial applications is becoming a fact, and is receiving considerable attention by industry and the research community. The majority of Unmanned Aerial Vehicles performing civilian tasks are restricted to flying only in segregated space, and not within the National Airspace. The areas that UAVs are restricted to flying in are typically not above populated areas, which in turn are the areas most useful for civilian applications. The reasoning behind the current restrictions is mainly due to the fact that current UAV technologies are not able to demonstrate an Equivalent Level of Safety to manned aircraft, particularly in the case of an engine failure which would require an emergency or forced landing. This chapter will preset and guide the reader through a number of developments that would facilitate the integration of UAVs into the National Airspace. Algorithms for UAV Sense-and-Avoid and Force Landings are recognized as two major enabling technologies that will allow the integration of UAVs in the civilian airspace. The following sections will describe some of the techniques that are currently being tested at the Australian Research Centre for Aerospace Automation (ARCAA), which places emphasis on the detection of candidate landing sites using computer vision, the planning of the descent path trajectory for the UAV, and the decision making process behind the selection of the final landing site.

Fault diagnosis of induction motor based on decision trees and adaptive neuro-fuzzy inference

This paper presents a fault diagnosis method based on adaptive neuro-fuzzy inference system (ANFIS) in combination with decision trees. Classification and regression tree (CART) which is one of the decision tree methods is used as a feature selection procedure to select pertinent features from data set. The crisp rules obtained from the decision tree are then converted to fuzzy if-then rules that are employed to identify the structure of ANFIS classifier. The hybrid of back-propagation and least squares algorithm are utilized to tune the parameters of the membership functions. In order to evaluate the proposed algorithm, the data sets obtained from vibration signals and current signals of the induction motors are used. The results indicate that the CART–ANFIS model has potential for fault diagnosis of induction motors.

Resonance : a music director’s theomusicological study of ‘praise’ and ‘glory’ in a selection of four liturgical music excerpts

Some of my most powerful spiritual experiences have come from the splendorous and sublime sounding hymns performed by a choir and church organ at the traditional Anglican church I’ve attended since I was very young. In the later stage of my life, my pursuit of education in the field of engineering caused me to move to Australia where I regularly attended a contemporary evangelical church and subsequently became a music director in the faith community. This environmental and cultural shift altered my perception and musical experiences of Christian music and led me to enquire about the relationship between Christian liturgy and church music. Throughout history church musicians and composers have synthesised the theological, congregational, cultural and musical aspects of church liturgy. Many great composers have taken into account the conditions surrounding the process of sacred composition and arrangement of music to enhance the experience of religious ecstasy – they sought resonances with Christian values and beliefs to draw congregational participation into the light of praising and glorifying God. As a music director in an evangelical church this aspiration has become one I share. I hope to identify and define the qualities of these resonances that have been successful and apply them to my own practice. Introduction and Structure of the Thesis In this study I will examine four purposively selected excerpts of Christian church vocal music combining theomusicological and semiotic analysis to help identify guidelines that might be useful in my practice as a church music director. The four musical excerpts have been selected based upon their sustained musical and theological impact over time, and their ability to affect ecstatic responses from congregations. This thesis documents a personal journey through analysis of music and uses a context that draws upon ethno-musicological, theological and semiotic tools that lead to a preliminary framework and principles which can then be applied to the identified qualities of resonance in church music today. The thesis is comprised of four parts. Part 1 presents a literature study on the relationship between sacred music, the effects of religious ecstasy and the Christian church. Multiple lenses on this phenomenon are drawn from the viewpoints of prominent western church historians, Biblical theologians, and philosophers. The literature study continues in Part 2, where the role of embodiment is examined from the current perspective of cognitive learning environments. This study offers a platform for a critical reflection on two distinctive musical liturgical systems that have treated differently the notion of embodied understanding amidst a shifting church paradigm. This allows an in-depth theological and philosophical understanding of the liturgical conditions around sacred music-making that relates to the monistic and dualistic body/mind. Part 3 involves undertaking a theomusicological methodology that utilises creative case studies of four purposively selected spiritual pieces. A semiotic study focuses on specific sections of sacred vocal works that express the notions of ‘praise’ and ‘glorification’, particularly in relation to these effects,which combine an analysis of theological perspectives around religious ecstasy and particular spiritual themes. Part 4 presents the critiques and findings gathered from the study that incorporate theoretical and technological means to analyse the purposive selected musical artefact, particularly with the sonic narratives expressing notions of ‘Praise' and 'Glory’. The musical findings are further discussed in relation to the notion of resonance, and then a conceptual framework for the role of contemporary musicdirector is proposed. The musical and Christian terminologies used in the thesis are explained in the glossary, and the appendices includes tables illustrating the musical findings, conducted surveys, written musical analyses and audio examples of selected sacred pieces available on the enclosed compact disc.

Selection of solar energy for green building using superiority and inferiority multi-criteria ranking (SIR) method

Green energy is one of the key factors, driving down electricity bill and zero carbon emission generating electricity to green building. However, the climate change and environmental policies are accelerating people to use renewable energy instead of coal-fired (convention type) energy for green building that energy is not environmental friendly. Therefore, solar energy is one of the clean energy solving environmental impact and paying less in electricity fee. The method of solar energy is collecting sun from solar array and saves in battery from which provides necessary electricity to whole house with zero carbon emission. However, in the market a lot of solar arrays suppliers, the aims of this paper attempted to use superiority and inferiority multi-criteria ranking (SIR) method with 13 constraints establishing I-flows and S-flows matrices to evaluate four alternatives solar energies and determining which alternative is the best, providing power to sustainable building. Furthermore, SIR is well-known structured approach of multi-criteria decision support tools and gradually used in construction and building. The outcome of this paper significantly gives an indication to user selecting solar energy.

Facial expression recognition using facial movement features

Facial expression is an important channel for human communication and can be applied in many real applications. One critical step for facial expression recognition (FER) is to accurately extract emotional features. Current approaches on FER in static images have not fully considered and utilized the features of facial element and muscle movements, which represent static and dynamic, as well as geometric and appearance characteristics of facial expressions. This paper proposes an approach to solve this limitation using ‘salient’ distance features, which are obtained by extracting patch-based 3D Gabor features, selecting the ‘salient’ patches, and performing patch matching operations. The experimental results demonstrate high correct recognition rate (CRR), significant performance improvements due to the consideration of facial element and muscle movements, promising results under face registration errors, and fast processing time. The comparison with the state-of-the-art performance confirms that the proposed approach achieves the highest CRR on the JAFFE database and is among the top performers on the Cohn-Kanade (CK) database.

Learning the kernel matrix with semidefinite programming

Kernel-based learning algorithms work by embedding the data into a Euclidean space, and then searching for linear relations among the embedded data points. The embedding is performed implicitly, by specifying the inner products between each pair of points in the embedding space. This information is contained in the so-called kernel matrix, a symmetric and positive semidefinite matrix that encodes the relative positions of all points. Specifying this matrix amounts to specifying the geometry of the embedding space and inducing a notion of similarity in the input space - classical model selection problems in machine learning. In this paper we show how the kernel matrix can be learned from data via semidefinite programming (SDP) techniques. When applied to a kernel matrix associated with both training and test data this gives a powerful transductive algorithm -using the labeled part of the data one can learn an embedding also for the unlabeled part. The similarity between test points is inferred from training points and their labels. Importantly, these learning problems are convex, so we obtain a method for learning both the model class and the function without local minima. Furthermore, this approach leads directly to a convex method for learning the 2-norm soft margin parameter in support vector machines, solving an important open problem.

Online discovery of similarity mappings

We consider the problem of choosing, sequentially, a map which assigns elements of a set A to a few elements of a set B. On each round, the algorithm suffers some cost associated with the chosen assignment, and the goal is to minimize the cumulative loss of these choices relative to the best map on the entire sequence. Even though the offline problem of finding the best map is provably hard, we show that there is an equivalent online approximation algorithm, Randomized Map Prediction (RMP), that is efficient and performs nearly as well. While drawing upon results from the "Online Prediction with Expert Advice" setting, we show how RMP can be utilized as an online approach to several standard batch problems. We apply RMP to online clustering as well as online feature selection and, surprisingly, RMP often outperforms the standard batch algorithms on these problems.

Evaluation of texture and geometry for dimensional facial expression recognition

Facial expression recognition (FER) algorithms mainly focus on classification into a small discrete set of emotions or representation of emotions using facial action units (AUs). Dimensional representation of emotions as continuous values in an arousal-valence space is relatively less investigated. It is not fully known whether fusion of geometric and texture features will result in better dimensional representation of spontaneous emotions. Moreover, the performance of many previously proposed approaches to dimensional representation has not been evaluated thoroughly on publicly available databases. To address these limitations, this paper presents an evaluation framework for dimensional representation of spontaneous facial expressions using texture and geometric features. SIFT, Gabor and LBP features are extracted around facial fiducial points and fused with FAP distance features. The CFS algorithm is adopted for discriminative texture feature selection. Experimental results evaluated on the publicly accessible NVIE database demonstrate that fusion of texture and geometry does not lead to a much better performance than using texture alone, but does result in a significant performance improvement over geometry alone. LBP features perform the best when fused with geometric features. Distributions of arousal and valence for different emotions obtained via the feature extraction process are compared with those obtained from subjective ground truth values assigned by viewers. Predicted valence is found to have a more similar distribution to ground truth than arousal in terms of covariance or Bhattacharya distance, but it shows a greater distance between the means.