Building envelope : performance optimisation processes for a daylight responsive architecture

Traditional shading design principles guide the vertical and horizontal orientation of fins, louvres and awnings being applied to orthogonal planar façades. Due to doubly curved envelopes characterising many contemporary designs, these rules of thumb are now not always applicable. Operable blinds attempt to regulate the fluctuating luminance of daylight and aid in shading direct sunlight. Mostly they remain closed, as workers are commonly too preoccupied to continually adjust them so a reliance on electrically powered lights remains a preference. To remedy these problems, the idea of what it is to sustainable enclose space is reconsidered through the geometric and kinetic optimisation of a parametric skin, with sunlight responsive modules that regulate interior light levels. This research concludes with an optimised design and also defines some unique metrics to gauge the design’s performance in terms of, the amount of exterior unobstructed view, its ability to shade direct sunlight and, its daylight glare probability.

Optimisation of performance in top level athletes : an action-based coping approach : a commentary

INTRODUCTION In their target article, Yuri Hanin and Muza Hanina outlined a novel multidisciplinary approach to performance optimisation for sport psychologists called the Identification-Control-Correction (ICC) programme. According to the authors, this empirically-verified, psycho-pedagogical strategy is designed to improve the quality of coaching and consistency of performance in highly skilled athletes and involves a number of steps including: (i) identifying and increasing self-awareness of ‘optimal’ and ‘non-optimal’ movement patterns for individual athletes; (ii) learning to deliberately control the process of task execution; and iii), correcting habitual and random errors and managing radical changes of movement patterns. Although no specific examples were provided, the ICC programme has apparently been successful in enhancing the performance of Olympic-level athletes. In this commentary, we address what we consider to be some important issues arising from the target article. We specifically focus attention on the contentious topic of optimization in neurobiological movement systems, the role of constraints in shaping emergent movement patterns and the functional role of movement variability in producing stable performance outcomes. In our view, the target article and, indeed, the proposed ICC programme, would benefit from a dynamical systems theoretical backdrop rather than the cognitive scientific approach that appears to be advocated. Although Hanin and Hanina made reference to, and attempted to integrate, constructs typically associated with dynamical systems theoretical accounts of motor control and learning (e.g., Bernstein’s problem, movement variability, etc.), these ideas required more detailed elaboration, which we provide in this commentary.

Characterisation of the cascode gate drive of power MOSFETs in clamped inductive switching applications

This thesis proposes a novel gate drive circuit to improve the switching performance of MOSFET power switches in power electronic converters. The proposed topology exploits the cascode configuration, allowing the minimisation of switching losses in the presence of practical circuit constraints, which enables efficiency and power density improvements. Switching characteristics of the new topology are investigated and key mechanisms that control the switching process are identified. Unique analysis tools and techniques are also developed to demonstrate the application of the cascode gate drive circuit for switching performance optimisation.

Deploying speaker verification techniques to vision-based RPA detect and avoid

Detect and Avoid (DAA) technology is widely acknowledged as a critical enabler for unsegregated Remote Piloted Aircraft (RPA) operations, particularly Beyond Visual Line of Sight (BVLOS). Image-based DAA, in the visible spectrum, is a promising technological option for addressing the challenges DAA presents. Two impediments to progress for this approach are the scarcity of available video footage to train and test algorithms, in conjunction with testing regimes and specifications which facilitate repeatable, statistically valid, performance assessment. This paper includes three key contributions undertaken to address these impediments. In the first instance, we detail our progress towards the creation of a large hybrid collision and near-collision encounter database. Second, we explore the suitability of techniques employed by the biometric research community (Speaker Verification and Language Identification), for DAA performance optimisation and assessment. These techniques include Detection Error Trade-off (DET) curves, Equal Error Rates (EER), and the Detection Cost Function (DCF). Finally, the hybrid database and the speech-based techniques are combined and employed in the assessment of a contemporary, image based DAA system. This system includes stabilisation, morphological filtering and a Hidden Markov Model (HMM) temporal filter.

Analysis of detection performance in spectrum sensing optimisation for long sensing periods

Spectrum sensing optimisation techniques maximise the efficiency of spectrum sensing while satisfying a number of constraints. Many optimisation models consider the possibility of the primary user changing activity state during the secondary user's transmission period. However, most ignore the possibility of activity change during the sensing period. The observed primary user signal during sensing can exhibit a duty cycle which has been shown to severely degrade detection performance. This paper shows that (a) the probability of state change during sensing cannot be neglected and (b) the true detection performance obtained when incorporating the duty cycle of the primary user signal can deviate significantly from the results expected with the assumption of no such duty cycle.

Improved GrabCut segmentation via GMM optimisation

Semi-automatic segmentation of still images has vast and varied practical applications. Recently, an approach "GrabCut" has managed to successfully build upon earlier approaches based on colour and gradient information in order to address the problem of efficient extraction of a foreground object in a complex environment. In this paper, we extend the GrabCut algorithm further by applying an unsupervised algorithm for modelling the Gaussian Mixtures that are used to define the foreground and background in the segmentation algorithm. We show examples where the optimisation of the GrabCut framework leads to further improvements in performance.

Fit optimisation of a distal medial tibia plate

An iterative method for the fit optimisation of a pre-contoured fracture fixation plate for a given bone data set is presented. Both plate shape optimisation and plate fit quantification are conducted in a virtual environment utilising computer graphical methods and 3D bone and plate models. Two optimised shapes of the undersurface of an existing distal medial tibia plate were generated based on a dataset of 45 3D bone models reconstructed from computed tomography image data of Japanese tibiae. The existing plate shape achieved an anatomical fit on 13% of tibiae from the dataset. Modified plate 1 achieved an anatomical fit for 42% and modified plate 2 a fit for 67% of the bones. If either modified plate 1 or plate 2 is used, then the anatomical fit can be increased to 82% for the same dataset. Issues pertaining to any further improvement in plate fit/shape are discussed.

Design Optimisation of UAVs Systems achieved on a Framework Environment via Evolution and Game Theory

Over recent years, Unmanned Air Vehicles or UAVs have become a powerful tool for reconnaissance and surveillance tasks. These vehicles are now available in a broad size and capability range and are intended to fly in regions where the presence of onboard human pilots is either too risky or unnecessary. This paper describes the formulation and application of a design framework that supports the complex task of multidisciplinary design optimisation of UAVs systems via evolutionary computation. The framework includes a Graphical User Interface (GUI), a robust Evolutionary Algorithm optimiser named HAPEA, several design modules, mesh generators and post-processing capabilities in an integrated platform. These population –based algorithms such as EAs are good for cases problems where the search space can be multi-modal, non-convex or discontinuous, with multiple local minima and with noise, and also problems where we look for multiple solutions via Game Theory, namely a Nash equilibrium point or a Pareto set of non-dominated solutions. The application of the methodology is illustrated on conceptual and detailed multi-criteria and multidisciplinary shape design problems. Results indicate the practicality and robustness of the framework to find optimal shapes and trade—offs between the disciplinary analyses and to produce a set of non dominated solutions of an optimal Pareto front to the designer.

The design and testing of a daylighting device : optimising the energy and optical performance of Australian commercial buildings

The effective daylighting of multistorey commercial building interiors poses an interesting problem for designers in Australia’s tropical and subtropical context. Given that a building exterior receives adequate sun and skylight as dictated by location-specific factors such as weather, siting and external obstructions; then the availability of daylight throughout its interior is dependant on certain building characteristics: the distance from a window façade (room depth), ceiling or window head height, window size and the visible transmittance of daylighting apertures. The daylighting of general stock, multistorey commercial buildings is made difficult by their design limitations with respect to some of these characteristics. The admission of daylight to these interiors is usually exclusively by vertical windows. Using conventional glazing, such windows can only admit sun and skylight to a depth of approximately 2 times the window height. This penetration depth is typically much less than the depth of the office interiors, so that core areas of these buildings receive little or no daylight. This issue is particularly relevant where deep, open plan office layouts prevail. The resulting interior daylight pattern is a relatively narrow perimeter zone bathed in (sometimes too intense) light, contrasted with a poorly daylit core zone. The broad luminance range this may present to a building occupant’s visual field can be a source of discomfort glare. Furthermore, the need in most tropical and subtropical regions to restrict solar heat gains to building interiors for much of the year has resulted in the widespread use of heavily tinted or reflective glazing on commercial building façades. This strategy reduces the amount of solar radiation admitted to the interior, thereby decreasing daylight levels proportionately throughout. However this technique does little to improve the way light is distributed throughout the office space. Where clear skies dominate weather conditions, at different times of day or year direct sunlight may pass unobstructed through vertical windows causing disability or discomfort glare for building occupants and as such, its admission to an interior must be appropriately controlled. Any daylighting system to be applied to multistorey commercial buildings must consider these design obstacles, and attempt to improve the distribution of daylight throughout these deep, sidelit office spaces without causing glare conditions. The research described in this thesis delineates first the design optimisation and then the actual prototyping and manufacture process of a daylighting device to be applied to such multistorey buildings in tropical and subtropical environments.

Optimisation of a class of transportation problems using genetic algorithms

This paper presents a Genetic Algorithms (GA) approach to search the optimized path for a class of transportation problems. The formulation of the problems for suitable application of GA will be discussed. Exchanging genetic information in the sense of neighborhoods will be introduced for generation reproduction. The performance of the GA will be evaluated by computer simulation. The proposed algorithm use simple coding with population size 1 converged in reasonable optimality within several minutes.

Robust multidisciplinary design optimisation using CFD and advanced evolutionary algorithms

Computation Fluid Dynamics (CFD) has become an important tool in optimization and has seen successful in many real world applications. Most important among these is in the optimisation of aerodynamic surfaces which has become Multi-Objective (MO) and Multidisciplinary (MDO) in nature. Most of these have been carried out for a given set of input parameters such as free stream Mach number and angle of attack. One cannot ignore the fact that in aerospace engineering one frequently deals with situations where the design input parameters and flight/flow conditions have some amount of uncertainty attached to them. When the optimisation is carried out for fixed values of design variables and parameters however, one arrives at an optimised solution that results in good performance at design condition but poor drag or lift to drag ratio at slightly off-design conditions. The challenge is still to develop a robust design that accounts for uncertainty in the design in aerospace applications. In this paper this issue is taken up and an attempt is made to prevent the fluctuation of objective performance by using robust design technique or Uncertainty.

Reduction of environmental effects of civil aircraft through multi-objective flight plan optimisation

With rising environmental alarm, the reduction of critical aircraft emissions including carbon dioxides (CO2) and nitrogen oxides (NOx) is one of most important aeronautical problems. There can be many possible attempts to solve such problem by designing new wing/aircraft shape, new efficient engine, etc. The paper rather provides a set of acceptable flight plans as a first step besides replacing current aircrafts. The paper investigates a green aircraft design optimisation in terms of aircraft range, mission fuel weight (CO2) and NOx using advanced Evolutionary Algorithms coupled to flight optimisation system software. Two multi-objective design optimisations are conducted to find the best set of flight plans for current aircrafts considering discretised altitude and Mach numbers without designing aircraft shape and engine types. The objectives of first optimisation are to maximise range of aircraft while minimising NOx with constant mission fuel weight. The second optimisation considers minimisation of mission fuel weight and NOx with fixed aircraft range. Numerical results show that the method is able to capture a set of useful trade-offs that reduce NOx and CO2 (minimum mission fuel weight).