Determination of aquifer units using vertical electrical sounding technique: a case study of federal low cost housing estate, Okeho, SW Nigeria

The groundwater development of Federal Low Cost Housing Estate Okeho involved the use of schlumberger vertical electrical sounding technique. The result of the survey showed that qualitatively three major curve types H, QH and KH were observed. The geoelectric layers range from 3 to 4 while the quantitative interpretation resulted in deducing layer parameters of 190-1103Ώm and 0.70-1.10m for the topsoil. The intermediate layer has layer resistivity of 93 -1590Ώm and thickness of 0.9-4.70m while the weathered basement has resistivity and thickness of 18-50Ώm and 6.0-10.4m. The bedrock resistivity range from 426-6284Ώm with an infinite thickness; the bedrock resistivity of less than 1000Ώm in this area and the weathered layer constitute the aquifer. Keywords: aquifer occurrences, geoelectric parameters, sounding curves Nigeria Journal of Pure and Applied Physics Vol. 4(1) 2005: 60-6

Design And Construction Of A Die Casting Mould Suitable For Production Of Various Sizes Of Key Blanks

Efforts have been made to group the various types of key that are most commonly used by the public at large. A suitable die casting method is used taking into account such limitations as the sizes of the keys, their serviceability, rate of production, as they affect the method of producing the unit price of the key blanks and the selection of materials. The new design and construction is an improvement over the existing one in use, which utilizes only one space in the mould for the production of the blank key. If the technique is put into practice, it will save the country's foreign exchange earnings and could also discourage the importation of blank keys from abroad. Key words: Die-Casting, Mould, Design, Production, Key Blanks Nig. J. of Pure & Appl. Physics Vol.3 2004: 16-2

Feature extraction and genetic algorithm enhanced adaptive deep neural network for energy consumption prediction in buildings

The Department for Business, Energy & Industrial Strateg

Genetic algorithm-determined deep feedforward neural network architecture for predicting electricity consumption in real buildings

© 2020 The Author(s). A genetic algorithm-determined deep feedforward neural network architecture (GA-DFNN) is proposed for both day-ahead hourly and week-ahead daily electricity consumption of a real-world campus building in the United Kingdom. Due to the comprehensive relationship between affecting factors and real-world building electricity consumption, the adoption of multiple hidden layers in the deep neural network (DFNN) algorithm would improve its prediction accuracy. The architecture of a DFNN model mainly refers to its quantity of hidden layers, quantity of neurons in the hidden layers, activation function in each layer and learning process to obtain the connecting weights. The optimal architecture of DFNN model was generally determined through a trial-and-error process, which is an exponential combinatorial problem and a tedious task. To address this problem, genetic algorithm (GA) is adopted to automatically design an optimal architecture with improved generalization ability. One year and six months of measurement data from a campus building is used for training and testing the proposed GA- DFNN model, respectively. To demonstrate the effectiveness of the proposed GA-DFNN prediction model, its prediction performance, including mean absolute percentage error, coefficient of determination, root mean square error and mean absolute error, was compared to the reference feedforward neural network models with single hidden layer, DFNN models with other architecture, random search determined DFNN model, long-short-term- memory model and temporal convolutional network model. The comparison results show that the proposed GA-DFNN predictive model has superior performance than all the reference prediction models, demonstrating the optimization effectiveness of GA and the prediction effectiveness of DFNN model with multiple hidden layers and optimal architecture.The Department for Business, Energy & Industrial Strategy (BEIS) grant project number TEIF-101-7025