Search results for "NEURAL NETWORK"

Artificial neural network applied to the discrimination of antibacterial activity by topological methods

2000

Abstract A new topological method that makes it possible to discriminate the active and inactive molecules on the basis of their chemical structures is applied in the present study to the antibacterial agents. This method uses neural networks in which training algorithms are used as well as different concepts and methods of artificial intelligence with a suitable set of topological descriptors. It is possible to obtain a QSAR interpretation of the information contained in the network after the training has been carried out.

Application of adaptive models for the determination of the thermal behaviour of a photovoltaic panel

2013

The use of reliable forecasting models for the PV temperature is necessary for a more correct evaluation of energy and economic performances. Climatic conditions certainly have a remarkable influence on thermo-electric behaviour of the PV panel but the physical system is too complex for an analytical representation. A neural-network-based approach for solar panel temperature modelling is here presented. The models were trained using a set of data collected from a test facility. Simulation results of the trained neural networks are presented and compared with those obtained with an empirical correlation.

Extraction of Endmembers from Spectral Mixtures

1999

Abstract Linear spectral mixture modeling (LSMM) divides each ground resolution element into its constituent materials using endmembers which represent the spectral characteristics of the cover types. However, it is difficult to identify and estimate the spectral signature of pure components or endmembers which form the scene, since they vary with the scale and purpose of the study. We propose three different methods to estimate the spectra of pure components from a set of unknown mixture spectra. Two of the methods consist in different optimization procedures based on objective functions defined from the coordinate axes of the dominant factors. The third one consists in the design of a neu…

Predicting Real-Time Roadside CO and NO2 Concentrations using Neural Networks

2008

Prediction of concrete materials compressive strength using surrogate models

2022

Using soft computing methods could be of great interest in predicting the compressive strength of Ultra-High-Performance Fibre Reinforced Concrete (UHPFRC). Therefore, this study developed four soft computing techniques. The models are the Linear- relationship (LR), pure quadratic, M5P-tree (M5P), and artificial neural network (ANN). The models were trained and developed using 306 datasets comprising 11 input parameters, including the curing temperature (T), the water-to-cement ratio (w/c), silica fume (SF), cement content (C), fiber content (Fb), water (W), sand content (S), superplasticizer (SP), fiber aspect ratio (AR) and curing time (t). Experimental results were used and compared to t…

SURFACE ROUGHNESS PREDICTION OF ELECTRO-DISCHARGE MACHINED COMPONENTS USING ARTIFICIAL NEURAL NETWORKS

2016

Electro-Discharge machining (EDM) is a thermal process comprising a complex metal removal mechanism, which involves the formation of a plasma channel between the tool and the workpiece electrodes leading to the melting and evaporation of the material to be removed. EDM is considered especially suitable for machining complex contours with high accuracy, as well as for materials that are not amenable to conventional removal methods. However, several phenomena negatively affecting the surface integrity of EDMed workpieces need to be taken into account and studied in order to achieve the optimization of the process. Recently, artificial neural networks (ANN) have emerged as a novel modeling tec…

Revealing the nature of metakaolin-based concrete materials using artificial intelligence techniques

2022

In this study, a model for the estimation of the compressive strength of concretes incorporating metakaolin is developed and parametrically evaluated, using soft computing techniques. Metakaolin is a component extensively employed in recent decades as a means to reduce the requirement for cement in concrete. For the proposed models, six parameters are accounted for as input data. These are the age at testing, the metakaolin percentage in relation to the total binder, the water-to-binder ratio, the percentage of superplasticizer, the binder to sand ratio and the coarse to fine aggregate ratio. For training and verification of the developed models a database of 867 experimental specimens has …

An intelligent way to predict the building thermal needs: ANNs and optimization

2022

The evaluation of the energy performance of existing or new buildings is a fundamental action to guarantee the feasibility of a project and the achievement of the minimum efficiency requirements. In general, the determination of the thermal loads of a building is carried out via software but their use requires adequate knowledge of physical phenomena and therefore the presence of an expert user. Furthermore, the resolution can be difficult to implement and can require high computational costs; all conditions that can influence the success of a project. Based on these considerations, this work proposes an alternative solution to traditional calculation tools, which in a simple and effective …

Assessment of indoor illuminance and study on best photosensors' position for design and commissioning of Daylight Linked Control Systems. A new meth…

2018

Abstract Artificial lighting systems have to ensure appropriate illuminance with high energy efficiency according to best design practice and technical standards. These aims can be tackled, by incorporating a Daylight linked control system. However, the system behaviour is strongly influenced by several factors and, in particular, by the sensors' position. Indeed, very often the illuminance on work-plane is not fully correlated with illuminance measured by the photo-sensor used to control the luminaires. This fact leads to wrong information for the Daylight linked control systems affecting its efficacy. The artificial intelligence of Neural Networks can be exploited to provide a method for …

Forecasting noise levels by means of neural networks for assessing urban traffic policies

2005

Within the assessment of the sustainability of plans and actions concerning the built environment, the transportation sector plays an increasing role, due to its importance in the economic and social life of countries. As that, the analysis of the sustainability concerning the transportation sector is now often embodied into the so called Strategic Environmental Analyses (SEA), that should provide local administrators with easy criteria for ranking the environmental suitability of designing policies, and that would seem to encounter the needed features for a correct evaluation of the urban masterplan. The urban noise forecast is very useful for local administrations, which have presently to…