2004아테네 올림픽대회출전 한국레슬링 대표선수의 무산소성 운동능력 및 등속성 근력

Integration of photovoltaics into power grids is difficult as solar energy is highly dependent on climate and geography; often fluctuating erratically. This causes penetrations and voltage surges, system instability, inefficient utilities planning and financial loss. Forecast models can help; however, time stamp, forecast horizon, input correlation analysis, data pre and post-processing, weather classification, network optimization, uncertainty quantification and performance evaluations need consideration. Thus, contemporary forecasting techniques are reviewed and evaluated. Input correlational analyses reveal that solar irradiance is most correlated with Photovoltaic output, and so, weather classification and cloud motion study are crucial. Moreover, the best data cleansing processes: normalization and wavelet transforms, and augmentation using generative adversarial network are recommended for network training and forecasting. Furthermore, optimization of inputs and network parameters, using genetic algorithm and particle swarm optimization, is emphasized. Next, established performance evaluation metrics MAE, RMSE and MAPE are discussed, with suggestions for including economic utility metrics. Subsequently, modelling approaches are critiqued, objectively compared and categorized into physical, statistical, artificial intelligence, ensemble and hybrid approaches. It is determined that ensembles of artificial neural networks are best for forecasting short term photovoltaic power forecast and online sequential extreme learning machine superb for adaptive networks; while Bootstrap technique optimum for estimating uncertainty. Additionally, convolutional neural network is found to excel in eliciting a model's deep underlying non-linear input-output relationships. The conclusions drawn impart fresh insights in photovoltaic power forecast initiatives, especially in the use of hybrid artificial neural networks and evolutionary algorithms.

A review and evaluation of the state-of-the-art in PV solar power forecasting:Techniques and optimization

A review on concrete surface treatment Part I: Types and mechanisms

Predicting the compressive strength of normal and High-Performance Concretes using ANN and ANFIS hybridized with Grey Wolf Optimizer

Predicting the compressive strength of silica fume concrete using hybrid artificial neural network with multi-objective grey wolves

Compressive strength of concrete has been predicted using evolutionary artificial neural networks (EANNs) as a combination of artificial neural network (ANN) and evolutionary search procedures, such as genetic algorithms (GA). In this paper for purpose of constructing models samples of cylindrical concrete parts with different characteristics have been used with 173 experimental data patterns. Water-cement ratio, maximum sand size, amount of gravel, cement, 3/4 sand, 3/8 sand, and coefficient of soft sand parameters were considered as inputs; and using the ANN models, the compressive strength of concrete is calculated. Moreover, using GA, the number of layers and nodes and weights are optimized in ANN models. In order to evaluate the accuracy of the model, the optimized ANN model is compared with the multiple linear regression (MLR) model. The results of simulation verify that the recommended ANN model enjoys more flexibility, capability, and accuracy in predicting the compressive strength of concrete.

/pdf/prediction-of-concrete-compressive-strength-by-evolutionary-2dp6ze5yva.pdf

Prediction of Concrete Compressive Strength by Evolutionary Artificial Neural Networks

�� � � � – � � � � � – � � – – – – – � � � � � – � � – � � � � � � – – – � � � – – � � – � – � – � � � � � – – � – � � � � � � � � – � � – � � � � � � � – � – � – � � � � � � – – � � � � – � – – – – – � � – � – – – � � – – – � � – � – – � � – � – – � � � � – – � � – � – – – � – � – – � � – � – – � � – � – – � – – – – – � � – � – – � – – – – – � – – – – – � � – – – � � – – – � – � – – � – � – – – – � – – – – – � � – – – – � – – – – – � – – – – – � – – – – – � – – – – � – – � – – – – – – � – – – – – � – – – – – � – – – – – � – – – – – � – – – – – � � – � – – � – � – –

https://journals.pan.pl/Content/84097/PDF/10_paper.pdf

Non-destructive and semi-destructive diagnostics of concrete structures in assessment of their durability

Usefulness of 3D surface roughness parameters for nondestructive evaluation of pull-off adhesion of concrete layers

Hybrid ultrasonic-neural prediction of the compressive strength of environmentally friendly concrete screeds with high volume of waste quartz mineral dust

Łukasz Sadowski

Papers

Prediction of Concrete Compressive Strength by Evolutionary Artificial Neural Networks

Non-destructive and semi-destructive diagnostics of concrete structures in assessment of their durability

Usefulness of 3D surface roughness parameters for nondestructive evaluation of pull-off adhesion of concrete layers

Hybrid ultrasonic-neural prediction of the compressive strength of environmentally friendly concrete screeds with high volume of waste quartz mineral dust

Evaluation of the height 3D roughness parameters of concrete substrate and the adhesion to epoxy resin