Prediction of compressive strength of SCC and HPC with high volume fly ash using ANN

An artificial neural network (ANN) is presented to predict a 28-day compressive strength of a normal and high strength self compacting concrete (SCC) and high performance concrete (HPC) with high volume fly ash. The ANN is trained by the data available in literature on normal volume fly ash because data on SCC with high volume fly ash is not available in sufficient quantity. Further, while predicting the strength of HPC the same data meant for SCC has been used to train in order to economise on computational effort. The compressive strengths of SCC and HPC as well as slump flow of SCC estimated by the proposed neural network are validated by experimental results.

Generalizability of established prostate cancer risk variants in men of African ancestry

Genome-wide association studies have identified more than 80 risk variants for prostate cancer, mainly in European or Asian populations. The generalizability of these variants in other racial/ethnic populations needs to be understood before the loci can be used widely in risk modeling. In our study, we examined 82 previously reported risk variants in 4,853 prostate cancer cases and 4,678 controls of African ancestry. We performed association testing for each variant using logistic regression adjusted for age, study and global ancestry. Of the 82 known risk variants, 68 (83%) had effects that were directionally consistent in their association with prostate cancer risk and 30 (37%) were significantly associated with risk at p < 0.05, with the most statistically significant variants being rs116041037 (p = 3.7 × 10(-26) ) and rs6983561 (p = 1.1 × 10(-16) ) at 8q24, as well as rs7210100 (p = 5.4 × 10(-8) ) at 17q21. By exploring each locus in search of better markers, the number of variants that captured risk in men of African ancestry (p < 0.05) increased from 30 (37%) to 44 (54%). An aggregate score comprised of these 44 markers was strongly associated with prostate cancer risk [per-allele odds ratio (OR) = 1.12, p = 7.3 × 10(-98) ]. In summary, the consistent directions of effects for the vast majority of variants in men of African ancestry indicate common functional alleles that are shared across populations. Further exploration of these susceptibility loci is needed to identify the underlying biologically relevant variants to improve prostate cancer risk modeling in populations of African ancestry.

Ann and Grete Unger seated outdoors with Dick and Gitta Meier with their infant daughter Karen.

Digital Image

Characterization of Failure Modes in CFRP Composites - An ANN Approach

Carbon fiber reinforced polymer (CFRP) composite specimens with different thickness, geometry, and stacking sequences were subjected to fatigue spectrum loading in stages. Another set of specimens was subjected to static compression load. On-line acoustic Emission (AE) monitoring was carried out during these tests. Two artificial neural networks, Kohonen-self organizing feature map (KSOM), and multi-layer perceptron (MLP) have been developed for AE signal analysis. AE signals from specimens were clustered using the unsupervised learning KSOM. These clusters were correlated to the failure modes using available a priori information such as AE signal amplitude distributions, time of occurrence of signals, ultrasonic imaging, design of the laminates (stacking sequences, orientation of fibers), and AE parametric plots. Thereafter, AE signals generated from the rest of the specimens were classified by supervised learning MLP. The network developed is made suitable for on-line monitoring of AE signals in the presence of noise, which can be used for detection and identification of failure modes and their growth. The results indicate that the characteristics of AE signals from different failure modes in CFRP remain largely unaffected by the type of load, fiber orientation, and stacking sequences, they being representatives of the type of failure phenomena. The type of loading can have effect only on the extent of damage allowed before the specimens fail and hence on the number of AE signals during the test. The artificial neural networks (ANN) developed and the methods and procedures adopted show significant success in AE signal characterization under noisy environment (detection and identification of failure modes and their growth).

Comparison of ANN models for predicting water quality in distribution systems

Deterministic models have been widely used to predict water quality in distribution systems, but their calibration requires extensive and accurate data sets for numerous parameters. In this study, alternative data-driven modeling approaches based on artificial neural networks (ANNs) were used to predict temporal variations of two important characteristics of water quality chlorine residual and biomass concentrations. The authors considered three types of ANN algorithms. Of these, the Levenberg-Marquardt algorithm provided the best results in predicting residual chlorine and biomass with error-free and ``noisy'' data. The ANN models developed here can generate water quality scenarios of piped systems in real time to help utilities determine weak points of low chlorine residual and high biomass concentration and select optimum remedial strategies.

An ANN based approach for voltage stability assessment

Artificial Neural Networks (ANNs) have recently been proposed as an alterative method for salving certain traditional problems in power systems where conventional techniques have not achieved the desired speed, accuracy or efficiency. This paper presents application of ANN where the aim is to achieve fast voltage stability margin assessment of power network in an energy control centre (ECC), with reduced number of appropriate inputs. L-index has been used for assessing voltage stability margin. Investigations are carried out on the influence of information encompassed in input vector and target out put vector, on the learning time and test performance of multi layer perceptron (MLP) based ANN model. LP based algorithm for voltage stability improvement, is used for generating meaningful training patterns in the normal operating range of the system. From the generated set of training patterns, appropriate training patterns are selected based on statistical correlation process, sensitivity matrix approach, contingency ranking approach and concentric relaxation method. Simulation results on a 24 bus EHV system, 30 bus modified IEEE system, and a 82 bus Indian power network are presented for illustration purposes.

Intelligent prediction of ground vibration in coal formations using ANN

The present paper details the prediction of blast induced ground vibration, using artificial neural network. The data was generated from five different coal mines. Twenty one different parameters involving rock mass parameters, explosive parameters and blast design parameters, were used to develop the one comprehensive ANN model for five different coal bearing formations. A total of 131 datasets was used to develop the ANN model and 44 datasets was used to test the model. The developed ANN model was compared with the USBM model. The prediction capability to predict blast induced ground vibration, of the comprehensive ANN model was found to be superior.

Prediction of Land Surface Temperature under Cloudy Conditions using Microwave Remote Sensing and ANN

Land surface temperature (LST) is an important variable in climate, hydrologic, ecological, biophysical and biochemical studies (Mildrexler et al., 2011). The most effective way to obtain LST measurements is through satellites. Presently, LST from moderate resolution imaging spectroradiometer (MODIS) sensor is applied in various fields due to its high spatial and temporal availability over the globe, but quite difficult to provide observations in cloudy conditions. This study evolves of prediction of LST under clear and cloudy conditions using microwave vegetation indices (MVIs), elevation, latitude, longitude and Julian day as inputs employing an artificial neural network (ANN) model. MVIs can be obtained even under cloudy condition, since microwave radiation has an ability to penetrate through clouds. In this study LST and MVIs data of the year 2010 for the Cauvery basin on a daily basis were obtained from MODIS and advanced microwave scanning radiometer (AMSR-E) sensors of aqua satellite respectively. Separate ANN models were trained and tested for the grid cells for which both LST and MVI were available. The performance of the models was evaluated based on standard evaluation measures. The best performing model was used to predict LST where MVIs were available. Results revealed that predictions of LST using ANN are in good agreement with the observed values. The ANN approach presented in this study promises to be useful for predicting LST using satellite observations even in cloudy conditions. (C) 2015 The Authors. Published by Elsevier B.V.

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Evaluating approaches and technologies for monitoring organic contaminants in the aquatic environment, Ann Arbor, MI, July 21-23, 2006: workshop proceedings

The Alliance for Coastal Technologies (ACT) convened a workshop on Evaluating Approaches and Technologies for Monitoring Organic Contaminants in the Aquatic Environment in Ann Arbor, MI on July 21-23, 2006. The primary objectives of this workshop were to: 1) identify the priority management information needs relative to organic contaminant loading; 2) explore the most appropriate approaches to estimating mass loading; and 3) evaluate the current status of the sensor technology. To meet these objectives, a mixture of leading research scientists, resource managers, and industry representatives were brought together for a focused two-day workshop. The workshop featured four plenary talks followed by breakout sessions in which arranged groups of participants where charged to respond to a series of focused discussion questions. At present, there are major concerns about the inadequacies in approaches and technologies for quantifying mass emissions and detection of organic contaminants for protecting municipal water supplies and receiving waters. Managers use estimates of land-based contaminant loadings to rivers, lakes, and oceans to assess relative risk among various contaminant sources, determine compliance with regulatory standards, and define progress in source reduction. However, accurately quantifying contaminant loading remains a major challenge. Loading occurs over a range of hydrologic conditions, requiring measurement technologies that can accommodate a broad range of ambient conditions. In addition, in situ chemical sensors that provide a means for acquiring continuous concentration measurements are still under development, particularly for organic contaminants that typically occur at low concentrations. Better approaches and strategies for estimating contaminant loading, including evaluations of both sampling design and sensor technologies, need to be identified. The following general recommendations were made in an effort to advance future organic contaminant monitoring: 1. Improve the understanding of material balance in aquatic systems and the relationship between potential surrogate measures (e.g., DOC, chlorophyll, particle size distribution) and target constituents. 2. Develop continuous real-time sensors to be used by managers as screening measures and triggers for more intensive monitoring. 3. Pursue surrogate measures and indicators of organic pollutant contamination, such as CDOM, turbidity, or non-equilibrium partitioning. 4. Develop continuous field-deployable sensors for PCBs, PAHs, pyrethroids, and emerging contaminants of concern and develop strategies that couple sampling approaches with tools that incorporate sensor synergy (i.e., measure appropriate surrogates along with the dissolved organics to allow full mass emission estimation).[PDF contains 20 pages]

Application of Drifting Buoy Technologies for Coastal Watershed and Ecosystem, Ann Arbor, Michigan, June 5-7,2005: workshop proceedings

The Alliance for Coastal Technologies (ACT) Partner University of Michigan convened a workshop on the Applications of Drifting Buoy Technologies for Coastal Watershed and Ecosystem Modeling in Ann Arbor, Michigan on June 5 to 7,2005. The objectives of the workshop were to: (1) educate potential users (managers and scientists) about the current capabilities and uses of drifting buoy technologies; (2) provide an opportunity for users (managers and scientists) to experience first hand the deployment and retrieval of various drifting buoys, as well as experience the capabilities of the buoys' technologies; (3) engage manufacturers with scientists and managers in discussions on drifting buoys' capabilities and their requirements to promote further applications of these systems; (4) promote a dialogue about realistic advantages and limitations of current drifting buoy technologies; and (5) develop a set of key recommendations for advancing both the capabilities and uses of drifting buoy technologies for coastal watershed and ecosystem modeling. To achieve these goals, representatives from research, academia, industry, and resource management were invited to participate in this workshop. Attendees obtained "hands on" experience as they participated in the deployment and retrieval of various drifting buoy systems on Big Portage Lake, a 644 acre lake northwest of Ann Arbor. Working groups then convened for discussions on current commercial usages and environmental monitoring approaches including; user requirements for drifting buoys, current status of drifting buoy systems and enabling technologies, and the challenges and strategies for bringing new drifting buoys "on-line". The following general recommendations were made to: 1). organize a testing program of drifting buoys for marketing their capabilities to resource managers and users. 2). develop a fact sheet to highlight the utility of drifting buoys. 3). facilitate technology transfer for advancements in drifter buoys that may be occurring through military funding and development in order to enhance their technical capability for environmental applications. (pdf contains 18 pages)

A novel FOCAL technique based on BP-ANN

In this paper, the feed-forward back-propagation artificial neural network (BP-ANN) algorithm is introduced in the traditional Focus Calibration using Alignment procedure (FOCAL) technique, and a novel FOCAL technique based on BP-ANN is proposed. The effects of the parameters, such as the number of neurons on the hidden-layer and the number of training epochs, on the measurement accuracy are analyzed in detail. It is proved that the novel FOCAL technique based on BP-ANN is more reliable and it is a better choice for measurement of the image quality parameters. (c) 2005 Elsevier GmbH. All rights reserved.

A new development on ANN in China - Biomimetic pattern recognition and multi weight vector neurons

A new model of pattern recognition principles-Biomimetic Pattern Recognition, which is based on "matter cognition" instead of "matter classification", has been proposed. As a important means realizing Biomimetic Pattern Recognition, the mathematical model and analyzing method of ANN get breakthrough: a novel all-purpose mathematical model has been advanced, which can simulate all kinds of neuron architecture, including RBF and BP models. As the same time this model has been realized using hardware; the high-dimension space geometry method, a new means to analyzing ANN, has been researched.