Effect of speculative prefetching on network load in distributed systems

Previous studies in speculative prefetching focus on building and evaluating access models for the purpose of access prediction. This paper on the other hand investigates the performance of speculative prefetching. When prefetching is performed speculatively, there is bound to be an increase in the network load. Furthermore, the prefetched items must compete for space with existing cache occupants. These two factors-increased load and eviction of potentially useful cache entries-are considered in the analysis. We obtain the following conclusion: to maximise the improvement in access time, prefetch exclusively all items with access probabilities exceeding a certain threshold.

Interval type-2 fuzzy logic systems for load forecasting : a comparative study

Accurate short term load forecasting (STLF) is essential for a variety of decision-making processes. However, forecasting accuracy can drop due to the presence of uncertainty in the operation of energy systems or unexpected behavior of exogenous variables. This paper proposes the application of Interval Type-2 Fuzzy Logic Systems (IT2 FLSs) for the problem of STLF. IT2 FLSs, with additional degrees of freedom, are an excellent tool for handling uncertainties and improving the prediction accuracy. Experiments conducted with real datasets show that IT2 FLS models precisely approximate future load demands with an acceptable accuracy. Furthermore, they demonstrate an encouraging degree of accuracy superior to feedforward neural networks and traditional type-1 Takagi-Sugeno-Kang (TSK) FLSs.

Short term load forecasting using a hybrid neural network

Short Term Load Forecasting (STLF) is very important from the power systems grid operation point of view. STLF involves forecasting load demand in a short term time frame. The short term time frame may consist of half hourly prediction up to weekly prediction. Accurate forecasting would benefit the utility in terms of reliability and stability of the grid ensuring adequate supply is present to meet with the load demand. Apart from that it would also affect the financial performance of the utility company. An accurate forecast would result in better savings while maintaining the security of the grid. This paper outlines the STLF using a novel hybrid online learning neural network, known as the Gaussian Regression (GR). This new hybrid neural network is a combination of two existing online learning neural networks which are the Gaussian Adaptive Resonance Theory (GA) and the Generalized Regression Neural Network (GRNN). Both GA and GRNN implemented online learning, but each of them suffers from limitation. Originally GA is used for unsupervised clustering by compressing the training samples into several categories. A supervised version of GA is available, namely Gaussian ARTMAP (GAM). However, the GAM is still not capable on solving regression problem. On the other hand, GRNN is designed for solving real value estimation (regression) problem, but the learning process would involve of memorizing all training samples, hence high computational cost. The hybrid GR is considered an enhanced version of GRNN with compression ability while still maintains online learning properties. Simulation results show that GR has comparable prediction accuracy and has less prototype as compared to the original GRNN as well as the Support Vector Regression.

Quantification of physical load in elite junior Australian football players

Results from this thesis provide insights into the physical loads experienced by the elite junior Australian footballer. The information presented can assist in the facilitation of best practice advice for player management and training prescription through the use of training diaries and GPS TMA and HR device technologies.

It can be quite difficult to have your creativity on tap : balancing client expectations and artistic practice in the tattoo industry

Tattoo artists work in a commission structure. Their artistic practice, possibly more than in any other creative career, requires the complete approval of the client prior to the creation of the final work. An unsatisfactory tattoo cannot be on-sold, discarded or easily replaced. Rarely can a tattooer practice their art without external participation. Therefore, tattoo artists come up with a number of strategies to manage their client base to ensure that the art they are asked to create satisfies both the client and their own artistic skills and preferences. Drawing on phenomenological research conducted for my PhD investigating artistic persona, this paper will explore the strategies tattoo artists use to construct their portfolios, manage the tattoo consultation and design process, and develop their own artistic skills, in order to build a successful and rewarding career in the tattoo industry.

Improving load forecasting accuracy through combination of best forecasts

Neural network (NN) models have been widely used in the literature for short-term load forecasting. Their popularity is mainly due to their excellent learning and approximation capability. However, their forecasting performance significantly depends on several factors including initializing parameters, training algorithm, and NN structure. To minimize negative effects of these factors, this paper proposes a practically simple, yet effective and an efficient method to combine forecasts generated by NN models. The proposed method includes three main phases: (i) training NNs with different structures, (ii) selecting best NN models based on their forecasting performance for a validation set, and (iii) combination of forecasts for selected best NNs. Forecast combination is performed through calculating the mean of forecasts generated by best NN models. The performance of the proposed method is examined using real world data set. Comparative studies demonstrate that the accuracy of combined forecasts is significantly superior to those obtained from individual NN models.

Propensity score matching and variations on the balancing test

Balancing tests are diagnostics designed for use with propensity score methods, a widely used non-experimental approach in the evaluation literature. Such tests provide useful information on whether plausible counterfactuals have been created. Currently, multiple balancing tests exist in the literature but it is unclear which is the most useful. This article highlights the poor size properties of commonly employed balancing tests and attempts to shed some light on the link between the results of balancing tests and bias of the evaluation estimator. The simulation results suggest that in scenarios where the conditional independence assumption holds, a permutation version of the balancing test described in Dehejia and Wahba (Rev Econ Stat 84:151–161, 2002) can be useful in applied study. The proposed test has good size properties. In addition, the test appears to have good power for detecting a misspecification in the link function and some power for detecting an omission of relevant non-linear terms involving variables that are included at a lower order.

Prediction intervals for electricity load forecasting using neural networks

Most of the research in time series is concerned with point forecasting. In this paper we focus on interval forecasting and its application for electricity load prediction. We extend the LUBE method, a neural network-based method for computing prediction intervals. The extended method, called LUBEX, includes an advanced feature selector and an ensemble of neural networks. Its performance is evaluated using Australian electricity load data for one year. The results showed that LUBEX is able to generate high quality prediction intervals, using a very small number of previous lag variables and having acceptable training time requirements. The use of ensemble is shown to be critical for the accuracy of the results.

Effects of mechanical deformation on electric performance of rechargeable batteries embedded in load carrying composite structures

Integrating rechargeable battery cells with fibre reinforced polymer matrix composites is a promising technology to enable composite structures to concurrently carry load and store electric energy, thus significantly reducing weight at the system level. To develop a design criterion for structural battery composites, rechargeable lithium polymer battery cells were embedded into carbon fibre/epoxy matrix composite laminates, which were then subjected to tensile, flexural and compressive loading. The electric charging/discharging properties were measured at varying levels of applied loads. The results showed that degradation in battery performance, such as voltagea and energy storage capacity, correlated well with the applied strain under three different loading conditions. Under compressive loading, battery cells, due to their multilayer construction, were unable to prevent buckling of composite face sheets due to the low lateral stiffness, leading to lower compressive strength that sandwich panels with foam core.