Search results for "Casting"
showing 10 items of 500 documents
Aspects of Improving the Cost-Performance Ratio for the Manufacturing of Small-Size Cast Parts
2010
Abstract The paper analyses some of the commercially available solutions for casting small-size metallic parts and outlines possible solutions that would reduce the cost of such systems while maintaining a high performance level. high performance level. One typical application area for small-size parts is human prosthetics, so the commercial casting machines analysed here are used mainly in this application domain. The paper shows how high-cost systems added to the machines for providing protective atmospheres can be replaced by low-cost solutions with the same results in terms of part quality. Moreover, a solution is described that avoids the typical drawbacks of the currently used casting…
Atomic resolution imaging of beryl: an investigation of the nano-channel occupation
2016
(C) 2016 The Authors. Journal of Microscopy published by JohnWiley & Sons, Ltd on behalf of the Royal Microscopical Society. This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.
Multi-step ahead electricity price forecasting using a hybrid model based on two-layer decomposition technique and BP neural network optimized by fir…
2017
In the deregulated competitive electricity market, the price which reflects the relationship between electricity supply and demand is one of the most important elements, making it crucial for all market participants to precisely forecast the electricity price. However, electricity price series usually has complex features such as non-linearity, non-stationarity and volatility, which makes the price forecasting turn out to be very difficult. In order to improve the accuracy of electricity price forecasting, this paper first proposes a two-layer decomposition technique and then develops a hybrid model based on fast ensemble empirical mode decomposition (FEEMD), variational mode decomposition …
GA-ANN for Short-Term Wind Energy Prediction
2011
Wind turbine power output is totally intermittent in the nature. For grid connected wind turbine generators, power system operators (transmission system operators) need reliable and robust wind power forecasting system. Rapid changes in the wind generation relative to the load require proper energy management system to maintain the power system stability and of course to balance the power generation, frequency, voltage regulation within the statutory limits. Accurate wind energy forecasting helps the power system transmission system operators in anticipating rapid changes in wind turbine power output with respect to load and helps in making decision not only for optimum energy management bu…
The Berkeley Innovation Index: A Quantitative Approach to Measure, Track and Forecast Innovation Capability Within Individuals and Organizations
2018
Innovation and entrepreneurship are essential processes for human development, market growth, and technological breakthroughs, and it is vital for economic growth. Despite its importance, innovation is inherently difficult to measure and hence it is almost impossible for an individual or organization to know how they can improve their innovation output or claim that they are great at innovation. This paper presents a novel approach to measure and quantify innovation, called the Berkeley Innovation Index (BII). The BII characterizes and measures innovation capability of an individual or an organization. It builds on the hypothesis that innovation performance depends on the people, culture, a…
A novel ensemble computational intelligence approach for the spatial prediction of land subsidence susceptibility.
2020
Land subsidence (LS) is a significant problem that can cause loss of life, damage property, and disrupt local economies. The Semnan Plain is an important part of Iran, where LS is a major problem for sustainable development and management. The plain represents the changes occurring in 40% of the country. We introduce a novel-ensemble intelligence approach (called ANN-bagging) that uses bagging as a meta- or ensemble-classifier of an artificial neural network (ANN) to predict LS spatially on the Semnan Plain in Semnan Province, Iran. The ensemble model's goodness-of-fit (to training data) and prediction accuracy (of the validation data) are compared to benchmarks set by ANN-bagging. A total …
Chromatographic retention–activity relationships for prediction of the toxicity pH-dependence of phenols
2007
Abstract An investigation of the use of the chromatographic retention (log k ) as an in vitro approach for modeling the pH-dependence of the toxicity to Guppy of phenols is developed. A data set of 19 phenols with available experimental toxicity–pH data was used. The importance of the mechanism of toxic action (MOA) of phenols was studied. log k data at three pH values were used for the phenols classification and two groups or ‘MODEs’ were identified. For one ‘MODE’ a quantitative retention–activity relationship (QRAR) model was calculated. Finally, the model was used to assess the toxicity to Guppy of phenols at different pH values. The results of this investigation suggest that chromato…
Performance assessment of individual and ensemble data-mining techniques for gully erosion modeling
2017
Gully erosion is identified as an important sediment source in a range of environments and plays a conclusive role in redistribution of eroded soils on a slope. Hence, addressing spatial occurrence pattern of this phenomenon is very important. Different ensemble models and their single counterparts, mostly data mining methods, have been used for gully erosion susceptibility mapping; however, their calibration and validation procedures need to be thoroughly addressed. The current study presents a series of individual and ensemble data mining methods including artificial neural network (ANN), support vector machine (SVM), maximum entropy (ME), ANN-SVM, ANN-ME, and SVM-ME to map gully erosion …
Non-parametric probabilistic forecasting of academic performance in Spanish high school using an epidemiological modelling approach
2013
Academic underachievement is a concern of paramount importance in Europe, and particularly in Spain, where around of 30% of the students in the last two courses in high school do not achieve the minimum knowledge academic requirement. In order to analyse this problem, we propose a mathematical model via a system of ordinary differential equations to study the dynamics of the academic performance in Spain. Our approach is based on the idea that both, good and bad study habits, are a mixture of personal decisions and influence of classmates. Moreover, in order to consider the uncertainty in the estimation of model parameters, a bootstrapping approach is employed. This technique permits to for…
CorCast: A Distributed Architecture for Bayesian Epidemic Nowcasting and its Application to District-Level SARS-CoV-2 Infection Numbers in Germany
2021
Timely information on current infection numbers during an epidemic is of crucial importance for decision makers in politics, medicine, and businesses. As information about local infection risk can guide public policy as well as individual behavior, such as the wearing of personal protective equipment or voluntary social distancing, statistical models providing such insights should be transparent and reproducible as well as accurate. Fulfilling these requirements is drastically complicated by the large amounts of data generated during exponential growth of infection numbers, and by the complexity of common inference pipelines. Here, we present CorCast – a stable and scalable distributed arch…