Predicting the distribution of discrete spatial events using artificial neural networks

Although the development of geographic information system (GIS) technology and digital data manipulation techniques has enabled practitioners in the geographical and geophysical sciences to make more efficient use of resource information, many of the methods used in forming spatial prediction models are still inherently based on traditional techniques of map stacking in which layers of data are combined under the guidance of a theoretical domain model. This paper describes a data-driven approach by which Artificial Neural Networks (ANNs) can be trained to represent a function characterising the probability that an instance of a discrete event, such as the presence of a mineral deposit or the sighting of an endangered animal species, will occur over some grid element of the spatial area under consideration. A case study describes the application of the technique to the task of mineral prospectivity mapping in the Castlemaine region of Victoria using a range of geological, geophysical and geochemical input variables. Comparison of the maps produced using neural networks with maps produced using a density estimation-based technique demonstrates that the maps can reliably be interpreted as representing probabilities. However, while the neural network model and the density estimation-based model yield similar results under an appropriate choice of values for the respective parameters, the neural network approach has several advantages, especially in high dimensional input spaces.

Direct methods for distinction between endogenous and exogenous erythropoietin

Since the commercialization of the first recombinant human erythropoietin (rhEPO) product (epoetin-a) in 1989 as a treatment for acute anemia, rhEPO detection has represented a continuous challenge for the anti-doping fight. Indeed, it appeared rapidly that this ergogenic hormone would be abused by athletes looking for an artificial performance enhancer. Hemoglobin is one of the principal modulators of aerobic power [1, 2] and, consequently, of performance in endurance sports [3]. By stimulating the red blood cells production, EPO is known to raise hemoglobin concentration in a dose-dependant and predictable way. Therefore, this hormone soon became one of the athletes most popular doping agent. Since 1984, all forms of blood doping in sport have been officially banned. In 1990, the IOC medical commission, which was in charge of the anti-doping regulations, added rhEPO to the list of the prohibited drugs in sports, even if a direct test allowing to detect the molecule became available a decade after only.

Use of respiration rates of scyphozoan jellyfish to estimate their effects on the food web

One of the main objectives of research on jellyfish is to determine their effects on the food web. They are voracious consumers that have similar diets to those of zooplanktivorous fish, as well as eating microplankton and ichthyoplankton. Respiration rates (RRs) can be used to estimate the amount of food needed to balance metabolism, and thereby estimate minimum ingestion. We compiled RRs for scyphozoan medusae in three suborders (Semeaostomeae, Rhizostomeae, and Coronatae) to determine if a single regression could relate RRs to mass for diverse scyphomedusan species. Temperature (7–30°C) was not a significant factor. RRs versus wet weight (WW) regressions differed significantly for semeaostome and rhizostome medusae; however, RRs versus carbon mass over five-orders of magnitude did not differ significantly among suborders. RRs were isometric against medusa carbon mass, with data for all species scaling to the power 0.94. The scyphomedusa respiration rate (SRR) regression enables estimation of RR for any scyphomedusa from its carbon mass. The error of the SRR regression was ±72%, which is small in comparison with the 1,000-fold variation in field sampling. This predictive equation (RR in ml O2 d−1 = 83.37 * g C0.940) can be used to estimate minimum ingestion by scyphomedusae without exhaustive collection of feeding data. In addition, effects of confinement on RRs during incubation of medusae were tested. Large medusae incubated in small container volumes (CV) relative to their size (ratios of CV:WW < 50) had RRs ~one-tenth those of medusae in relatively larger containers. Depleted oxygen during incubation did not depress RRs of the medusae; however, swimming may have been restricted and respiration reduced in consequence. We briefly review other problems with RR experiments and suggest protocols and limitations for estimating ingestion rates of jellyfish from metabolic rates.

Predicting house damage class using artificial intelligence method

The unsatisfactory performance of light structures founded on expansive soils subject to seasonal movements is frequently reported since the early 1950's in Australia. Excessive movements have caused damage to numerous structures that have not been adequately designed to accommodate soil volume changes. However, the sole presence of expansive soil is not necessarily the main cause of damage. Other factors such as vegetation, climate factors, types of construction materials and geology type may also contribute. This paper presents a model which predicts the damage class by analyzing combinations of the contributing factors using artificial intelligence methods. This model can help to identify if any serious and urgent repairs are necessary and immediate actions could be initiated without delay.

Respiration rate and breathing patterns from doppler radar measurements

This paper presents an evaluation of microwave Doppler radar used for capturing different types of breathing patterns in addition to the respiration rate. Finding therespiration rate is equally important as identifying abnormal breathing patterns which it could be used to gain a better insight into respiratory disorders. Various known breathing disorders were role played and captured using a non-contactmicrowave Doppler radar which further supports the feasibility of Doppler radar in obtaining an accurate detection of different types of breathing patterns. The results obtained for all the experiments were compared with a standard measurementapparatus, respiration strap, yielding a good correlations with the Doppler radar signals. In a nutshell, Doppler radar can be potentially used as an alternative approach, not only for finding the respiration rates, but also for identifying respiration patterns replacing the conventional contact methods.

Effect of ECG-derived respiration (EDR) on modeling ventricular repolarization dynamics in different physiological and psychological conditions

Ventricular repolarization dynamics is an important predictor of the outcome in cardiovascular diseases. Mathematical modeling of the heart rate variability (RR interval variability) and ventricular repolarization variability (QT interval variability) is one of the popular methods to understand the dynamics of ventricular repolarization. Although ECG derived respiration (EDR) was previously suggested as a surrogate of respiration, but the effect of respiratory movement on ventricular repolarization dynamics was not studied. In this study, the importance of considering the effect of respiration and the validity of using EDR as a surrogate of respiration for linear parametric modeling of ventricular repolarization variability is studied in two cases with different physiological and psychological conditions. In the first case study, we used 20 young and 20 old healthy subjects’ ECG and respiration data from Fantasia database at Physionet to analyze a bivariate QT–RR and a trivariate QT–RR–RESP or QT–RR–EDR model structure to study the aging effect on cardiac repolarization variability. In the second study, we used 16 healthy subjects’ data from drivedb (stress detection for automobile drivers) database at Physionet to do the same analysis for different psychological condition (i.e., in stressed and no stress condition). The results of our study showed that model having respiratory information (QT–RR–RESP and QT–RR–EDR) gave significantly better fit value (p < 0.05) than that of found from the QT–RR model. EDR showed statistically similar (p > 0.05) performance as that of respiration as an exogenous model input in describing repolarization variability irrespective of age and different mental conditions. Another finding of our study is that both respiration and EDR-based models can significantly (p < 0.05) differentiate the ventricular repolarization dynamics between healthy subjects of different age groups and with different psychological conditions, whereas models without respiration or EDR cannot distinguish between the groups. These results established the importance of using respiration and the validity of using EDR as a surrogate of respiration in the absence of respiration signal recording in linear parametric modeling of ventricular repolarization variability in healthy subjects.

Artificial neural network modeling of flow stress in hot rolling

In this study, an artificial neural network model is proposed to predict the flow stress variations during the hot rolling process. Optimization of the proposed neural network with respect to number of neurons within the hidden layer, different training methods and transfer functions of the neural network is performed. The results of the optimal network were compared with those of the conventional analytic method and it is shown that using an optimal neural network the mean calculated error is drastically reduced.

A review on computational intelligence methods for controlling traffic signal timing

Urban traffic as one of the most important challenges in modern city life needs practically effective and efficient solutions. Artificial intelligence methods have gained popularity for optimal traffic light control. In this paper, a review of most important works in the field of controlling traffic signal timing, in particular studies focusing on Q-learning, neural network, and fuzzy logic system are presented. As per existing literature, the intelligent methods show a higher performance compared to traditional controlling methods. However, a study that compares the performance of different learning methods is not published yet. In this paper, the aforementioned computational intelligence methods and a fixed-time method are implemented to set signals times and minimize total delays for an isolated intersection. These methods are developed and compared on a same platform. The intersection is treated as an intelligent agent that learns to propose an appropriate green time for each phase. The appropriate green time for all the intelligent controllers are estimated based on the received traffic information. A comprehensive comparison is made between the performance of Q-learning, neural network, and fuzzy logic system controller for two different scenarios. The three intelligent learning controllers present close performances with multiple replication orders in two scenarios. On average Q-learning has 66%, neural network 71%, and fuzzy logic has 74% higher performance compared to the fixed-time controller.

Statistical modelling of artificial neural network for sorting temporally synchronous spikes

Artificial neural network (ANN) models are able to predict future events based on current data. The usefulness of an ANN lies in the capacity of the model to learn and adjust the weights following previous errors during training. In this study, we carefully analyse the existing methods in neuronal spike sorting algorithms. The current methods use clustering as a basis to establish the ground truths, which requires tedious procedures pertaining to feature selection and evaluation of the selected features. Even so, the accuracy of clusters is still questionable. Here, we develop an ANN model to specially address the present drawbacks and major challenges in neuronal spike sorting. New enhancements are introduced into the conventional backpropagation ANN for determining the network weights, input nodes, target node, and error calculation. Coiflet modelling of noise is employed to enhance the spike shape features and overshadow noise. The ANN is used in conjunction with a special spiking event detection technique to prioritize the targets. The proposed enhancements are able to bolster the training concept, and on the whole, contributing to sorting neuronal spikes with close approximations.

State of the art artificial intelligence-based MPPT techniques for mitigating partial shading effects on PV systems - a review

Given the considerable recent attention to distributed power generation and interest in sustainable energy, the integration of photovoltaic (PV) systems to grid-connected or isolated microgrids has become widespread. In order to maximize power output of PV system extensive research into control strategies for maximum power point tracking (MPPT) methods has been conducted. According to the robust, reliable, and fast performance of artificial intelligence-based MPPT methods, these approaches have been applied recently to various systems under different conditions. Given the diversity of recent advances to MPPT approaches a review focusing on the performance and reliability of these methods under diverse conditions is required. This paper reviews AI-based techniques proven to be effective and feasible to implement and very common in literature for MPPT, including their limitations and advantages. In order to support researchers in application of the reviewed techniques this study is not limited to reviewing the performance of recently adopted methods, rather discusses the background theory, application to MPPT systems, and important references relating to each method. It is envisioned that this review can be a valuable resource for researchers and engineers working with PV-based power systems to be able to access the basic theory behind each method, select the appropriate method according to project requirements, and implement MPPT systems to fulfill project objectives.

Application of artificial neural networks in linear profile monitoring

In many quality control applications the quality of process or product is characterized and summarized by a relation (profile) between a response variable and one or more explanatory variables. Such profiles can be modeled using linear or nonlinear regression models. In this paper we use artificial neural networks to detect and classify the shifts in linear profiles. Three monitoring methods based on artificial neural networks are developed to monitor linear profiles. Their efficacies are assessed using average run length criterion. © 2010 Elsevier Ltd. All rights reserved.