Thermal and visual performance of real and theoretical thermochromic glazing solutions for office buildings

Thermochromic windows are able to modulate their transmittance in both the visible and the near-infrared field as a function of their temperature. As a consequence, they allow to control the solar gains in summer, thus reducing the energy needs for space cooling. However, they may also yield a reduction in the daylight availability, which results in the energy consumption for indoor artificial lighting being increased. This paper investigates, by means of dynamic simulations, the application of thermochromic windows to an existing office building in terms of energy savings on an annual basis, while also focusing on the effects in terms of daylighting and thermal comfort. In particular, due attention is paid to daylight availability, described through illuminance maps and by the calculation of the daylight factor, which in several countries is subject thresholds. The study considers both a commercially available thermochromic pane and a series of theoretical thermochromic glazing. The expected performance is compared to static clear and reflective insulating glass units. The simulations are repeated in different climatic conditions, showing that the overall energy savings compared to clear glazing can range from around 5% for cold climates to around 20% in warm climates, while not compromising daylight availability. Moreover the role played by the transition temperature of the pane is examined, pointing out an optimal transition temperatures that is irrespective of the climatic conditions.

An incremental space to visualize dynamic data sets

In Information Visualization, adding and removing data elements can strongly impact the underlying visual space. We have developed an inherently incremental technique (incBoard) that maintains a coherent disposition of elements from a dynamic multidimensional data set on a 2D grid as the set changes. Here, we introduce a novel layout that uses pairwise similarity from grid neighbors, as defined in incBoard, to reposition elements on the visual space, free from constraints imposed by the grid. The board continues to be updated and can be displayed alongside the new space. As similar items are placed together, while dissimilar neighbors are moved apart, it supports users in the identification of clusters and subsets of related elements. Densely populated areas identified in the incSpace can be efficiently explored with the corresponding incBoard visualization, which is not susceptible to occlusion. The solution remains inherently incremental and maintains a coherent disposition of elements, even for fully renewed sets. The algorithm considers relative positions for the initial placement of elements, and raw dissimilarity to fine tune the visualization. It has low computational cost, with complexity depending only on the size of the currently viewed subset, V. Thus, a data set of size N can be sequentially displayed in O(N) time, reaching O(N (2)) only if the complete set is simultaneously displayed.

Dynamic Changes in the Mental Rotation Network Revealed by Pattern Recognition Analysis of fMRI Data

We investigated the temporal dynamics and changes in connectivity in the mental rotation network through the application of spatio-temporal support vector machines (SVMs). The spatio-temporal SVM [Mourao-Miranda, J., Friston, K. J., et al. (2007). Dynamic discrimination analysis: A spatial-temporal SVM. Neuroimage, 36, 88-99] is a pattern recognition approach that is suitable for investigating dynamic changes in the brain network during a complex mental task. It does not require a model describing each component of the task and the precise shape of the BOLD impulse response. By defining a time window including a cognitive event, one can use spatio-temporal fMRI observations from two cognitive states to train the SVM. During the training, the SVM finds the discriminating pattern between the two states and produces a discriminating weight vector encompassing both voxels and time (i.e., spatio-temporal maps). We showed that by applying spatio-temporal SVM to an event-related mental rotation experiment, it is possible to discriminate between different degrees of angular disparity (0 degrees vs. 20 degrees, 0 degrees vs. 60 degrees, and 0 degrees vs. 100 degrees), and the discrimination accuracy is correlated with the difference in angular disparity between the conditions. For the comparison with highest accuracy (08 vs. 1008), we evaluated how the most discriminating areas (visual regions, parietal regions, supplementary, and premotor areas) change their behavior over time. The frontal premotor regions became highly discriminating earlier than the superior parietal cortex. There seems to be a parcellation of the parietal regions with an earlier discrimination of the inferior parietal lobe in the mental rotation in relation to the superior parietal. The SVM also identified a network of regions that had a decrease in BOLD responses during the 100 degrees condition in relation to the 0 degrees condition (posterior cingulate, frontal, and superior temporal gyrus). This network was also highly discriminating between the two conditions. In addition, we investigated changes in functional connectivity between the most discriminating areas identified by the spatio-temporal SVM. We observed an increase in functional connectivity between almost all areas activated during the 100 degrees condition (bilateral inferior and superior parietal lobe, bilateral premotor area, and SMA) but not between the areas that showed a decrease in BOLD response during the 100 degrees condition.

Sample stacking in CZE using dynamic thermal junctions I. Analytes with low dpK(a)/dT crossing a single thermally induced pH junction in a BGE with high dpH/dT

The possibility to compress analyte bands at the beginning of CE runs has many advantages. Analytes at low concentration can be analyzed with high signal-to-noise ratios by using the so-called sample stacking methods. Moreover, sample injections with very narrow initial band widths (small initial standard deviations) are sometimes useful, especially if high resolutions among the bands are required in the shortest run time. In the present work, a method of sample stacking is proposed and demonstrated. It is based on BGEs with high thermal sensitive pHs (high dpH/dT) and analytes with low dpK(a)/dT. High thermal sensitivity means that the working pK(a) of the BGE has a high dpK(a)/dT in modulus. For instance, Tris and Ethanolamine have dpH/dT = -0.028/degrees C and -0.029/degrees C, respectively, whereas carboxylic acids have low dpK(a)/dT values, i.e. in the -0.002/degrees C to+0.002/degrees C range. The action of cooling and heating sections along the capillary during the runs affects also the local viscosity, conductivity, and electric field strength. The effect of these variables on electrophoretic velocity and band compression is theoretically calculated using a simple model. Finally, this stacking method was demonstrated for amino acids derivatized with naphthalene-2,3-dicarboxaldehyde and fluorescamine using a temperature difference of 70 degrees C between two neighbor sections and Tris as separation buffer. In this case, the BGE has a high pH thermal coefficient whereas the carboxylic groups of the analytes have low pK(a) thermal coefficients. The application of these dynamic thermal gradients increased peak height by a factor of two (and decreased the standard deviations of peaks by a factor of two) of aspartic acid and glutamic acid derivatized with naphthalene-2,3-dicarboxaldehyde and serine derivatized with fluorescamine. The effect of thermal compression of bands was not observed when runs were accomplished using phosphate buffer at pH 7 (negative control). Phosphate has a low dpH/dT in this pH range, similar to the dK(a)/dT of analytes. It is shown that vertical bar dK(a)/dT-dpH/dT vertical bar >> 0 is one determinant factor to have significant stacking produced by dynamic thermal junctions.

Measurement of the economic impact of visual impairment from age-related macular degeneration in Australia

The purpose of this report was to: (i) outline the potential value of health economic studies into age-related macular degeneration (AMD); (ii) provide an overview of health economic studies pertinent to AMD; and (iii) outline the basic frame work of cost-of-illness studies (a useful first step in applying economic methods). The detection and management of sensory loss in the elderly plays a key role in the Australian Government's Healthy Ageing Strategy. Age-related macular degeneration is currently the leading cause of blindness in elderly Australians. Although a large proportion of AMD cases remain untreatable, the introduction of photo­dynamic therapy provides a relatively expensive and possibly cost-effective innovation for others. Antioxidant therapy has also been proven effective in reducing progression of early to late disease. The discipline of economics can contribute to an understanding of AMD prevention and treatment through: (i) describing the current burden of disease; (ii) predicting the changes in the burden of disease over time, and (iii) evaluating the efficiency of different interventions. Cost-of-illness studies have been performed in many fields of medicine. Little work, however, has been done on describing the economic impact from AMD. A number of different economic evaluation methods can be used in judging the efficiency of possible interventions to reduce the disease burden of AMD. Although complementary in nature, each has specific uses and limitations. Studies of the economic impact of eye diseases are both feasible and necessary for informed health care decision-making.

An evolutionary computing approach to minimize dynamic hedging error

The objective of our present paper is to derive a computationally efficient genetic pattern learning algorithm to evolutionarily derive the optimal rebalancing weights (i.e. dynamic hedge ratios) to engineer a structured financial product out of a multiasset, best-of option. The stochastic target function is formulated as an expected squared cost of hedging (tracking) error which is assumed to be partly dependent on the governing Markovian process underlying the individual asset returns and partly on
randomness i.e. pure white noise. A simple haploid genetic algorithm is advanced as an alternative numerical scheme, which is deemed to be
computationally more efficient than numerically deriving an explicit solution to the formulated optimization model. An extension to our proposed scheme is suggested by means of adapting the Genetic Algorithm parameters based on fuzzy logic controllers.

Fuzzy neural networks for control of dynamic systems

This thesis provides a unified and comprehensive treatment of the fuzzy neural networks as the intelligent controllers. This work has been motivated by a need to develop the solid control methodologies capable of coping with the complexity, the nonlinearity, the interactions, and the time variance of the processes under control. In addition, the dynamic behavior of such processes is strongly influenced by the disturbances and the noise, and such processes are characterized by a large degree of uncertainty. Therefore, it is important to integrate an intelligent component to increase the control system ability to extract the functional relationships from the process and to change such relationships to improve the control precision, that is, to display the learning and the reasoning abilities. The objective of this thesis was to develop a self-organizing learning controller for above processes by using a combination of the fuzzy logic and the neural networks. An on-line, direct fuzzy neural controller using the process input-output measurement data and the reference model with both structural and parameter tuning has been developed to fulfill the above objective. A number of practical issues were considered. This includes the dynamic construction of the controller in order to alleviate the bias/variance dilemma, the universal approximation property, and the requirements of the locality and the linearity in the parameters. Several important issues in the intelligent control were also considered such as the overall control scheme, the requirement of the persistency of excitation and the bounded learning rates of the controller for the overall closed loop stability. Other important issues considered in this thesis include the dependence of the generalization ability and the optimization methods on the data distribution, and the requirements for the on-line learning and the feedback structure of the controller. Fuzzy inference specific issues such as the influence of the choice of the defuzzification method, T-norm operator and the membership function on the overall performance of the controller were also discussed. In addition, the e-completeness requirement and the use of the fuzzy similarity measure were also investigated. Main emphasis of the thesis has been on the applications to the real-world problems such as the industrial process control. The applicability of the proposed method has been demonstrated through the empirical studies on several real-world control problems of industrial complexity. This includes the temperature and the number-average molecular weight control in the continuous stirred tank polymerization reactor, and the torsional vibration, the eccentricity, the hardness and the thickness control in the cold rolling mills. Compared to the traditional linear controllers and the dynamically constructed neural network, the proposed fuzzy neural controller shows the highest promise as an effective approach to such nonlinear multi-variable control problems with the strong influence of the disturbances and the noise on the dynamic process behavior. In addition, the applicability of the proposed method beyond the strictly control area has also been investigated, in particular to the data mining and the knowledge elicitation. When compared to the decision tree method and the pruned neural network method for the data mining, the proposed fuzzy neural network is able to achieve a comparable accuracy with a more compact set of rules. In addition, the performance of the proposed fuzzy neural network is much better for the classes with the low occurrences in the data set compared to the decision tree method. Thus, the proposed fuzzy neural network may be very useful in situations where the important information is contained in a small fraction of the available data.

Ecologically sustainable noise barrier – further developments

This paper reports on the second phase of a research project aimed at the development of an environmentally friendly noise barrier for urban freeways, also known as KMAK (Krezel and McManus, 2007). The concrete barrier, which has some unique capabilities to mitigate transportation noise, is made from recycled concrete (RC) aggregate and industrial by-products such as fly ash and reclaimed water. The current developmental work expands on a research project that resulted in a two-layer (2L) concrete barrier. Two prototypes of the 2L barrier were produced, followed by extensive acoustic testing and a number of simulations where standard timber and/or concrete barriers were substituted with KMAK barrier (Krezel et al, 2004). Current research investigates a variety of architectural finishes applied to the original KMAK barrier with the aim of improving its visual appearance and also fine-tuning its acoustic performance. The new three-layer (3L) barrier optimises sound absorption in a frequency range characteristic to that of transportation noise, especially road traffic noise. Three major aspects related to the development of architectural finishes were considered; environmentally responsible materials, surface features and production methods. The light-weight material used in the architectural finish is based on ordinary Portland cement (OPC) and uses very fine fraction of RC aggregate. The manufacturing process of the 3L barrier was tested in a commercial setting and two sets of prototype barrier were cast. An innovative, cost effective method of applying pattern and perforation to the surface of architectural finish was also developed and tested. The findings of the current investigation demonstrate that there is a positive correlation between surface features, percentage of perforation as well as depth of the architectural layer and increased potential of the 3L barrier to mitigate transportation noise. On average, the addition of perforated architectural finish contributes to 20% increase in sound absorption. The preliminary results also show that the sound absorbency of the 3L barrier can be better controlled and tuned to specific noise frequency. The visual appearance has been significantly improved with the addition of the architectural finish, which makes the barrier an attractive, feasible and viable alternative to road barriers made from standard concrete or timber.

Environmentally friendly sound absorbing noise barrier made from concrete waste - further developments

This paper reports on the second phase of a research project aimed at the development of an environmentally friendly noise barrier for urban freeways, also known as KMAK [1]. The concrete barrier, which has some unique capabilities to mitigate transportation noise, is made from recycled concrete (RC) aggregate and industrial by-products such as fly ash and reclaimed water. The current developmental work expands on a research project that resulted in a two-layer (2L) concrete barrier. Two prototypes of the 2L barrier were produced, followed by extensive acoustic testing and a number of simulations where standard timber and/or concrete barriers were substituted with KMAK barrier [2]. Current research investigates a variety of architectural finishes applied to the original KMAK barrier with the aim of improving its visual appearance and also fine-tuning its acoustic performance. The new three-layer (3L) barrier optimizes sound absorption in a frequency range characteristic similar to that of transportation noise, especially road traffic noise. Three major aspects related to the development of architectural finishes were considered; environmentally responsible materials, surface features, and production methods. The findings of the current investigation demonstrate that there is a positive correlation between surface features, percentage of perforation as well as depth of the architectural layer, and increased potential of the 3L barrier to mitigate transportation noise. On average, the addition of perforated architectural finish contributes to a 20% increase in sound absorption. The preliminary results also show that the sound absorbency of the 3L barrier can be better controlled and tuned to specific noise frequency than the 2L type. The visual appearance has been significantly improved with the addition of the architectural finish, which makes the barrier an attractive, feasible, and viable alternative to road barriers made from standard concrete or timber.

A visual mining system for theme development evolution analysis of scientific literature

Theme development evolution analysis of literature is a significant tool to help the scientific scholars find and study the frontier problems more efficiently. This paper designs and develops a visual mining system for theme development evolution analysis to deal with the large number of literature information. The analysis of related themes based on sub-themes, together with the dynamic threshold strategy are adopted for improving the accuracy of system. Experiments results prove that correlations of themes obtained from the system are accurate and achieve better practical effect in comparison with that of our early work.

Threshold dynamic time warping for spatial activity recognition

Non-invasive spatial activity recognition is a difficult task, complicated by variation in how the same activities are conducted and furthermore by noise introduced by video tracking procedures. In this paper we propose an algorithm based on dynamic time warping (DTW) as a viable method with which to quantify segmented spatial activity sequences from a video tracking system. DTW is a widely used technique for optimally aligning or warping temporal sequences through minimisation of the distance between their components. The proposed algorithm threshold DTW (TDTW) is capable of accurate spatial sequence distance quantification and is shown using a three class spatial data set to be more robust and accurate than DTW and the discrete hidden markov model (HMM). We also evaluate the application of a band dynamic programming (DP) constraint to TDTW in order to reduce extraneous warping between sequences and to reduce the computation complexity of the approach. Results show that application of a band DP constraint to TDTW improves runtime performance significantly, whilst still maintaining a high precision and recall.

Context aware privacy in visual surveillance

In this paper we present preliminary work implementing dynamic privacy in public surveillance. The aim is to maximise the privacy of those under surveillance, while giving an observer access to sufficient information to perform their duties. As these aspects are in conflict, a dynamic approach to privacy is required to balance the system's purpose with the system's privacy. Dynamic privacy is achieved by accounting for the situation, or context, within the environment. The context is determined by a number of visual features that are combined and then used to determine an appropriate level of privacy.

Microhabitat selection by sea turtles in a dynamic thermal marine environment

1Reproductive fitness is often compromised at the margins of a species’ range due to sub-optimal conditions.2Set against this backdrop, the Mediterranean's largest loggerhead sea turtle (Caretta caretta) rookery at Zakynthos (Greece) presents a conundrum, being at a very high latitude for this species, yet hosting a high concentration of nesting.3We used visual surveys combined with global positioning system (GPS) tracking to show that at the start of the breeding season, individuals showed microhabitat selection, with females residing in transient patches of warm water. As the sea warmed in the summer, this selection was no longer evident.4As loggerhead turtles are ectothermic, this early season warm-water selection presumably speeds up egg maturation rates before oviposition, thereby allowing more clutches to be incubated when sand conditions are optimal during the summer.5Active selection of warm waters may allow turtles to initiate nesting at an earlier date.

Development of a dynamic surface roughness monitoring system based on artificial neural networks (ANN) in milling operation

Dynamic surface roughness prediction during metal cutting operations plays an important role to enhance the productivity in manufacturing industries. Various machining parameters such as unwanted noises affect the surface roughness, whatever their effects have not been adequately quantified. In this study, a general dynamic surface roughness monitoring system in milling operations was developed. Based on the experimentally acquired data, the milling process of Al 7075 and St 52 parts was simulated. Cutting parameters (i.e., cutting speed, feed rate, and depth of cut), material type, coolant fluid, X and Z components of milling machine vibrations, and white noise were used as inputs. The original objective in the development of a dynamic monitoring system is to simulate wide ranges of machining conditions such as rough and finishing of several materials with and without cutting fluid. To achieve high accuracy of the resultant data, the full factorial design of experiment was used. To verify the accuracy of the proposed model, testing and recall/verification procedures have been carried out and results showed that the accuracy of 99.8 and 99.7 % were obtained for testing and recall processes.

Transfer function-noise modeling and spatial interpolation to evaluate the risk of extreme (shallow) water-table levels in the Brazilian Cerrados

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)