Demand forecasting plays an important role for every business and gives companies an opportunity to prepare for coming shifts in the market. The empirical findings of this study aim to support construction equipment manufacturers, distributors, and suppliers in apprehending the equipment market in more depth and foreseeing market demand to be able to adjust their business strategies and production capacities, allocate resources more efficiently, optimize the level of output and stock and, as a result, reduce associated costs, increase profitability and competitiveness. It is demonstrated that demand for construction equipment is heavily influenced by changes in economic conditions and country-specific economic indicators can serve as reliable input parameters to anticipate fluctuations in the construction equipment market. The Artificial Neural Networks (ANN) forecasting technique has been successfully employed to predict sales of construction equipment four quarters ahead in selected countries (Germany, The United Kingdom, France, Italy, Norway, Russia, Turkey and Saudi Arabia) with country related economic indicators used as an input.

Around the world bridges are ageing. In Europe approximately two thirds of all railway bridges are over 50 years old. As these structures age, it becomes increasingly important that they are properly maintained. If damage remains undetected this can lead to premature replacement which can have major financial and environmental costs. It is also imperative that bridges are kept safe for the people using them. Thus, it is necessary for damage to be detected as early as possible. This research investigates an unsupervised, model-free damage detection method which could be implemented for continuous structural health monitoring. The method was based on past research by Gonzalez and Karoumi (2015), Neves et al. (2017) and Chalouhi et al. (2017). An artificial neural network (ANN) was trained on accelerations from the healthy structural state. Damage sensitive features were defined as the root mean squared errors between the measured data and the ANN predictions. A baseline healthy state could then be established by presenting the trained ANN with more healthy data. Thereafter, new data could be compared with this reference state. Outliers from the reference data were taken as an indication of damage. Two outlier detection methods were used: Mahalanobis distance and the Kolmogorov-Smirnov test. A model steel bridge with a span of 5 m, width of 1 m and height of approximately 1.7 m was used to study the damage detection method. The use of an experimental model allowed damaged to be freely introduced to the structure. The structure was excited with a 12.7 kg rolling mass at a speed of approximately 2.1 m/s (corresponding to a 20.4 ton axle load moving at 47.8 km/h in full scale). Seven accelerometers were placed on the structure and their locations were determined using an optimal sensor placement algorithm. The objectives of the research were to: identify a number of single damage cases, distinguish between gradual damage cases and identify the location of damage. The proposed method showed promising results and most damage cases were detected by the algorithm. Sensor density and the method of excitation were found to impact the detection of damage. By training the ANN to predict correlations between accelerometers the sensor closest to the damage could be detected, thus successfully localising the damage. Finally, a gradual damage case was investigated. There was a general increase in the damage index for greater damage however, this did not progress smoothly and one case of ‘greater’ damage showed a decrease in the damage index.

Global sea surface temperature (SST) anomalies have a demonstrable effect on terrestrial climate dynamics throughout the continental U.S. SST variations have been correlated with greenness (vegetation densities) and precipitation via ocean-atmospheric interactions known as climate teleconnections. Prior research has demonstrated that teleconnections can be used for climate prediction across a wide region at sub-continental scales. Yet these studies tend to have large uncertainties in estimates by utilizing simple linear analyses to examine chaotic teleconnection relationships. Still, non-stationary signals exist, making teleconnection identification difficult at the local scale. Part 1 of this research establishes short-term (10-year), linear and non-stationary teleconnection signals between SST at the North Atlantic and North Pacific oceans and terrestrial responses of greenness and precipitation along multiple pristine sites in the northeastern U.S., including (1) White Mountain National Forest - Pemigewasset Wilderness, (2) Green Mountain National Forest - Lye Brook Wilderness and (3) Adirondack State Park - Siamese Ponds Wilderness. Each site was selected to avoid anthropogenic influences that may otherwise mask climate teleconnection signals. Lagged pixel-wise linear teleconnection patterns across anomalous datasets found significant correlation regions between SST and the terrestrial sites. Non-stationary signals also exhibit salient co-variations at biennial and triennial frequencies between terrestrial responses and SST anomalies across oceanic regions in agreement with the El Nino Southern Oscillation (ENSO) and North Atlantic Oscillation (NAO) signals. Multiple regression analysis of the combined ocean indices explained up to 50% of the greenness and 42% of the precipitation in the study sites. The identified short-term teleconnection signals improve the understanding and projection of climate change impacts at local scales, as well as harness the interannual periodicity information for future climate projections. Part 2 of this research paper builds upon the earlier short-term study by exploring a long-term (30-year) teleconnection signal investigation between SST at the North Atlantic and Pacific oceans and the precipitation within Adirondack State Park in upstate New York. Non-traditional teleconnection signals are identified using wavelet decomposition and teleconnection mapping specific to the Adirondack region. Unique SST indices are extracted and used as input variables in an artificial neural network (ANN) prediction model. The results show the importance of considering non-leading teleconnection patterns as well as the known teleconnection patterns. Additionally, the effects of the Pacific Ocean SST or the Atlantic Ocean SST on terrestrial precipitation in the study region were compared with each other to deepen the insight of sea-land interactions. Results demonstrate reasonable prediction skill at forecasting precipitation trends with a lead time of one month, with r values of 0.6. The results are compared against a statistical downscaling approach using the HadCM3 global circulation model output data and the SDSM statistical downscaling software, which demonstrate less predictive skill at forecasting precipitation within the Adirondacks.

An ambitious long-term goal for neuroevolution, which studies how artificial evolutionary processes can be driven to produce brain-like structures, is to evolve neurocontrollers with a high density of neurons and connections that can adapt and learn from past experience. Yet while neuroevolution has produced successful results in a variety of domains, the scale of natural brains remains far beyond reach. In this dissertation two extensions to the recently introduced Hypercube-based NeuroEvolution of Augmenting Topologies (HyperNEAT) approach are presented that are a step towards more brain-like artificial neural networks (ANNs). First, HyperNEAT is extended to evolve plastic ANNs that can learn from past experience. This new approach, called adaptive HyperNEAT, allows not only patterns of weights across the connectivity of an ANN to be generated by a function of its geometry, but also patterns of arbitrary local learning rules. Second, evolvable-substrate HyperNEAT (ES-HyperNEAT) is introduced, which relieves the user from deciding where the hidden nodes should be placed in a geometry that is potentially infinitely dense. This approach not only can evolve the location of every neuron in the network, but also can represent regions of varying density, which means resolution can increase holistically over evolution. The combined approach, adaptive ES-HyperNEAT, unifies for the first time in neuroevolution the abilities to indirectly encode connectivity through geometry, generate patterns of heterogeneous plasticity, and simultaneously encode the density and placement of nodes in space. The dissertation culminates in a major application domain that takes a step towards the general goal of adaptive neurocontrollers for legged locomotion.

No description available.

Electrocardiogram (ECG) signal processing has been the subject of intense research in the past years, due to its strategic place in the detection of several cardiac pathologies. However, ECG signal is frequently corrupted with different types of noises such as 60Hz power line interference, baseline drift, electrode movement and motion artifact, etc. In this thesis, a hybrid two-stage model based on the combination of wavelet decomposition and artificial neural network is proposed for ECG noise reduction based on excellent localization features: wavelet transform and the adaptive learning ability of neural network. Results from the simulations validate the effectiveness of this proposed method. Simulation results on actual ECG signals from MIT-BIH arrhythmia database [30] show this approach yields improvement over the un-filtered signal in terms of signal-to-noise ratio (SNR).

Electrochemical Energy Storage Systems are a viable and popular solution to fulfill energy storage requirements for energy generated through sustainable energy resources. With the increasing demand for Electrical Vehicles (EVs), Lithium-ion batteries (LIB) are being widely and getting popular compared to other battery technologies due to their energy storage capacity. However, LIBs suffer from disadvantages such as battery life and the degradation of electrode material with time, that can be improved by understanding these mechanisms using experimental and computational techniques. Further, it has been experimentally observed and numerically determined that lithium-ion intercalation induced stress and thermal loading can cause capacity fading and local fractures in the electrode materials. These fractures are one of the major degradation mechanisms in Lithium-ion batteries. With LixMn2O4 as a cathode material, stress values differ widely especially for intermediate State Of Charge (SOC), and very few attempts have been made to understand the stress distribution as a function of SOC at molecular level. Therefore, the estimates of mechanical properties such as Young’s modulus, diffusion coefficient etc. differ, especially for partially charged states. Further, the effect of temperature, particularly elevated temperatures, have not been taken into the consideration. Studying these parameters at the atomic scale can provide insight information and help in improving these materials lifetime. Hence, molecular/atomic level mathematical modelling has been used to understand capacity fade due to Lithium-ion intercalation/de-intercalation induced stress. Large-scale Atomic/Molecular Massively Parallel Simulator (LAMMPS) [1], that is widely used for atomic simulations, has been used for the simulation studies of this dissertation. Thus, the objective of this study is to understand the fracture mechanisms in the Lithium Manganese Oxide (LiMn2O4) electrode at the molecular level by studying mechanical properties of the material at different SOC values using the principles of molecular dynamics (MD). As part of the model validation, the lattice parameter and volume changes of LixMn2O4 as a function of SOC (0 < x < 1) has been studied and validated with respect to the experimental data. This validated model has been used for a parametric study involving the SOC value, strain-rate (charge and discharge rate), and temperature. Based on the validated MD setup, doping and co-doping studies have been undertaken to design and develop new and novel cathode materials with enhanced properties. In the absence of experimental data for the new engineered structures, validation with Quantum Mechanics generated lattice structures has been done. The results suggest that lattice constant values obtained from both MD and QM simulations are in good agreement (∼ 99%) with experimental values. Further, Single Particle Model (SPM) based macro scale Computational Fluid Dynamics findings show that co-doping has improved the material properties especially for Yttrium and Sulfur doped structures which can improve the cycle life anywhere between 600-7000 cycles. Further in order to reduce the required computational time to obtain minimum potential energy ionic configuration out of millions of scenario, Artificial Neural Network (ANN) technique is being used. It improved the processing time by more than 88%. / Thesis / Doctor of Philosophy (PhD)

The purpose of this study was to investigate the effect of varying optimization parameters on the proposed optimum of a tablet coating formulation requiring minimization of crack velocity and maximization of film opacity. An artificial neural network (ANN) comprising six input and two output nodes separated by a single hidden layer of five nodes was trained using 100 pseudo-randomly distributed records and optimized by guided evolutionary simulated annealing (GESA). GESA was unable to identify a formulation that satisfied both a crack velocity of 0 m s¿1 and a film opacity of 100% due to conflict centred on the response of the properties to variation in pigment particle size. Constraining film thickness exacerbated the property conflict. By adjusting property weights (i.e. the relative importance of each property), GESA was able to propose formulations that were either crack resistant or that were fully opaque. Reducing the stringency of the performance criteria (crack velocity >0 m s¿1, film opacity <100%) enabled GESA to propose optima that met or exceeded the looser targets. Under these conditions, starting GESA from different locations within model space resulted in the proposal of different optima. Therefore, application of loose targets resulted in the identification of an optimal zone within which all formulations satisfied these less stringent performance criteria. It is concluded that application of the most stringent performance criteria and selection of appropriate property weights is necessary for unequivocal identification of the true optimum. A strategy for optimization experiments is proposed.

No / Drug vehicles are chemical carriers that aid a drug's passage through an organism. Whilst they possess no intrinsic efficacy they are designed to achieve desirable characteristics which can include improving a drug's permeability and or solubility, targeting a drug to a specific site or reducing a drug's toxicity. All of which are ideally achieved without compromising the efficacy of the drug. Whilst the majority of drug vehicle research is focused on the solubility and permeability issues of a drug, significant progress has been made on using vehicles for toxicity reduction. Achieving this can enable safer and more effective use of a potent drug against diseases such as cancer. From a molecular perspective, drugs activate or deactivate biochemical pathways through interactions with cellular macromolecules resulting in toxicity. For newly developed drugs such pathways are not always clearly understood but toxicity endpoints are still required as part of a drug's registration. An understanding of which vehicles could be used to ameliorate the unwanted toxicities of newly developed drugs would be highly desirable to the pharmaceutical industry. In this paper we demonstrate the use of different classifiers as a means to select vehicles best suited to avert a drug's toxic effects when no other information about a drug's characteristics is known. Through analysis of data acquired from the Developmental Therapeutics Program (DTP) we are able to establish a link between a drug's toxicity and vehicle used. We demonstrate that classification and selection of the appropriate vehicle can be made based on the similarity of drug choice.

Yes / The current UK rail network is managed by Network Rail, which requires an investment of £5.2bn per year to cover operational costs [1]. These expenses include the maintenance and repairs of the railway rails. This paper aims to create a proof of concept for an autonomous health monitoring system of the rails using an integrated finite element analysis (FEA) and artificial neural network (ANN) approach. The FEA is used to model worn profiles of a standard rail and predict the stress field considering the material of the rail and the loading condition representing a train travelling on a straight line. The generated FEA data is used to train an ANN model which is utilised to predict the stress field of a worn rail using optically scanned data. The results showed that the stress levels in a rail predicted with the ANN model are in an agreement with the FEA predictions for a worn rail profile. These initial results indicate that the ANN can be used for the rapid prediction of stresses in worn rails and the FEA-ANN based approach has the potential to be applied to autonomous health monitoring of rails using fast scanners and validated ANN models. However, further development of this technology would be required before it could be used in the railway industry, including: real time data processing of scanned rails; improved scanning rates to enhance the inspection efficiency; development of fast computational methods for the ANN model; and training the ANN model with a large set of representative data representing application specific scenarios.