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Solar irradiance prediction has a significant impact on various aspects of power system generation. The predictive models can be deployed to improve the planning and operation of renewable systems and can improve the power purchase process and bring several advantages to the power utilities. The irradiance is affected by several factors, such as clouds and dust, and it becomes challenging for physical...
ESN load forecasting model has high stability, and is able to learn fast and not easy to fall into local optimum, compared with standard recurrent neural network. In the process of constructing the typical ESN model, the choice of parameters is always empirical or random. The forecasting performance of ESN was analyzed on the basis of its key parameters. While the dynamic reserve pool has black box...
This paper discusses the elimination of C.I. Acid Yellow 23 (AY23) using UV/Ag-TiO2 process. To anticipate the photocatalytic elimination of AY23 with the existence of Ag-TiO2 nanoparticles processed under desired circumstances, two computational techniques namely neural network (NN) and particle swarm optimization (PSO) modeling are developed. A summed up of 100 data are used to establish the models,...
For processing purposes of silver colloidal suspensions in view of specific applications, this study evaluates the suitability of using alginate/lignosulfonate mixtures as an efficient dispersion matrix for the silver nanoparticles. The rheological behavior of the in situ obtained silver nanoparticle suspensions was investigated by rotational measurements performed using cone-plate geometry, considering...
Research results in neurophysiology show the predictive nature of vergence eye movement, vergence eye movement can persistently track a moving target which shifts in distance relative to the head. Few models have attempted to consider prediction of target motion in vergence models. Most models only considered static targets, their input are frozen driving signals. In this paper, a model with estimator...
In this paper, the problem of time series prediction is studied. A Bayesian procedure based on Gaussian Process (GP) model combined with Linear Discriminate Analysis (LDA) as dimensionality reduction method is proposed. To evaluate the proposed approach, its performance is assessed using three scenarios: long window (latest 50 variables), short window (latest 5 variables) and persistence. To evaluate...
This paper proposes a development's prediction model based on Artificial Neural Network. The methodology proposed consists in: i) countries selection; ii) selection and obtainment of indicators referring to the selected countries; iii) proposal prediction model; iv) Artificial Neural Network training and validation. The results indicated predicted values close to the real values of the Brazilian indicators...
As it is well-known, orange peel is used for making jam and oil. For this purpose, orange samples with high peel thickness are best. In order to predict peel thickness in orange fruit, we present a system based in image features, comprising: area, eccentricity, perimeter, length/area, blue value, green value, red value, wide, contrast, texture, wide/area, wide/length, roughness, and length. A novel...
In this paper, we present a model for rainfall rate prediction 30 seconds ahead of time using an artificial neural network. The resultant predicted rainfall rate can then be used in determining an appropriate fade counter-measure, for instance, digital modulation scheme ahead of time, to keep the bit error rate (BER) on the link within acceptable levels to allow constant flow of data on the link during...
The oil filled in the hydraulic pipe connected to the clutch piston influences the clutch engagement in the transmission of construction machinery. In order to improve shift control, we need to catch the precision time to fill the pipe with oil (hereinafter called filling time). However, oil dynamics have nonlinearity which makes it difficult to predict the filling time with high accuracy. Moreover,...
In this work, the potential application of Artificial Neural Network (ANN) was studied to predict the absorption of Carbon Dioxide (CO2) in Ionic Liquid (IL) solutions over wide-ranging operating conditions. A few physical properties had been chosen as input data which were temperature, partial pressure of CO2, molecular weight, acentric value, critical temperature and critical pressure of IL. A sample...
In this paper, we present artificial neural network (ANN) models to predict hard and soft-responses of three configurations of arbiter based physical unclonable functions (PUFs): standard, feed-forward (FF) and modified feed-forward (MFF). The models are trained using data extracted from 32-stage arbiter PUF circuits fabricated using IBM 32 nm HKMG process. The contributions of this paper are two-fold...
Everywhere in the world tax revenues are rolled back for the commonwealth to invest and finance goods and public services, such as: infrastructure, health, security and education. The predict income revenue (taxes) is one of the challenges that the Secretariat of the Federal Revenue of Brazil (RFB for its Portuguese acronym) has. This is an important challenge since the obtained information is valuable...
According to the characteristics of car ownership prediction influenced by multi-factor and non-linear, a combination forecasting model was proposed based on principal component analysis (PCA) and BP neural network for the purpose of car ownership prediction. Take the national car ownership as an example, the principal component analysis is carried out on the factors affecting the car ownership, and...
This paper illustrates the risk assessment on electricity price forecast uncertainty. The high-risk periods under different time have been indicated. Autoregressive integrated moving average (ARIMA) models and artificial neural network (ANN) techniques are introduced to forecast electricity prices in UK electricity market. Also, this paper investigates the risk index of electricity prices due to forecast...
The ELM is used to predict the delay, and combines with implicit generalized predictive control (IGPC) to compensate time delay in this paper. The random time-delay in the networked control system (NCS) can usually deteriorate the control performance and stability of networked control system. In order to solve this problem, this paper puts forward the networked time delay in a short time based on...
In recent years, although large volumes of data of health-related physical fitness (HRPF) have been collected, the exercise prescription for Chinese kids is still formulated manually by experts. It is necessary to develop an effective and efficient mechanism to recommend an automatic physical exercise prescription. Toward this purpose, this paper presents an experimental study of the framework for...
Short-term prediction of water demand provides basic guarantee of water supply system operation and management. In this study, an effective model for daily water demand forecasting is proposed. Firstly, principle component analysis (PCA) is utilized to simplify the complexity and reduce the correlation between influence variables, and the score values of selected principle components (PCs) turn into...
In recent years, the strong growth in solar power generation industries is requiring an increasing need to predict the profile of solar power production over the day, in order to develop high efficient and optimized stand-alone and grid connected photovoltaic systems. Moreover, the opportunities offered by battery energy storage systems coupled with PV systems, require the load power to be forecasted...
Short-term traffic prediction has a lot of potential for traffic management. However, most research has traditionally focused on either traffic models — which do not scale very well to large networks, computationally — or on data-driven methods for freeways, leaving out urban arterials completely. Urban arterials complicate traffic predictions, compared to freeways, because the non-linear effects...
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