This paper presents an empirical model and a three-layer (7:11:1) artificial neural network (ANN) approach for the determination of completely mixed activated sludge reactor volume (CMASRV). CMASRV values were estimated by a new mathematical formulation and a three-layer ANN model for 1,000 different artificial scenarios given in a wide range of seven biological variables. The predicted results obtained from each stochastic approach were compared with the well-known steady state volume model based on mass balance equations. The computational analysis showed that the proposed empirical model and ANN outputs were obviously in agreement with the steady-state volume model and all the predictions proved to be satisfactory with a correlation coefficient of about 0.9989 and 1, respectively. The maximum volume deviations from the steady-state volume equation were recorded as only 7.17% and 6.89% for the proposed model and ANN outputs respectively. In addition to volume comparison, waste sludge mass flow rates (PX), food to mass ratios (F/M), hydraulic retention times (HRTs), volumetric organic loads (LV) and oxygen requirements (ORs) were also compared for each model, and significant points of proposed approaches were evaluated.
The study presents a modification to conventional finite element method under plane strain conditions to address the problem of successive excavation of linear parts of tunnels. Although the successive excavation is a three-dimensional mechanical problem, the designers often prefer 2D analysis owing to considerably simple and transparent geometric model and fast computations when compared to a 3D solution. The main idea behind the suggested method referred to as, 2D3D model, is to express the influence of excavation of a single stroke of soil not only in the particular cross section but in the entire soil body in front of and behind the examined profile. This is achieved by introducing special finite elements which have common triangular cross-section but are of infinite length in the longitudinal direction. The longitudinal approximations of the displacement field adopt the evolution of convergence measurements, while standard linear shape functions are kept in the element triangular cross-section. A profile corresponding to the city road tunnel Blanka in Prague with available convergence measurements was examined to verify the method. The results show that the method provides reasonably accurate results when compared to the convergence confinement method without the need to subjectively determine the lambda parameter. It also significantly reduces the computational time of a more versatile but complex 3D analysis., Tomáš Janda, Michal Šejnoha and Jiří Šejnoha., and Obsahuje bibliografii
In the present study, bainite fraction results of continuous cooling of high strength low alloy steels have been modeled by artificial neural networks. The artificial neural network models were constructed by 16 input parameters including chemical compositions (C, Mn, Nb, Mo, Ti, N, Cu, P, S, Si, Al, V), Nb in solution, austenitizing temperature, initial austenite grain size and cooling rate over the temperature range of the occurrence of phase transformations. The value for the output layer was the bainite fraction. According to the input parameters in feed-forward back-propagation algorithm, the constructed networks were trained, validated and tested. To make a decision on the completion of the training processes, two termination states are declared: state 1 (ANN-I model) means that the training of neural network was ended when the maximum epoch of process reached (1000) while state 2 (ANN-II model) means the training ended when minimum error norm of network gained. The entire statistical evaluators of ANN-II model has higher performance than those of ANN-I. However, both of the models exhibit valuable results and the entire statistical values show that the proposed ANN-I and ANN-II models are suitably trained and can predict the bainite fraction values very close to the experimental ones.
Experimental studies have shown a symmetry-to-asymmetry transition of the spike-timing dependent plasticity (STDP) curve exists in the proximal stratum radiatum (SR) dendrite of the hippocampal CA1 pyramidal neuron, which is probably due to the presence of GABAergic inhibition [2, 3, 4]. A recent computational model predicted that symmetry-to-asymmetry transition is strongly dependent on the frequency and conductance value of GABA inhibition and that the largest long term potentiation (LTP) value and the two distinct long-term depression (LTD) tails of the symmetrical STDP curve are centred at +10 ms, +40 ms and -10 ms, respectively [8, 9]. In the present paper, we continue to investigate even further via computer simulations the effects of gamma frequency inhibition and its conductance value to the symmetry-to-asymmetry transition of the STDP profile in the SR dendrite and predict that the transition is even more robust when there is a temporal offset between the onsets of the pre-post excitatory stimulation and the GABAergic inhibition. The largest LTP value and the two distinct LTD tails are inversely proportional to the increase of GABA conductance.
Evidence from behavioral studies demonstrates that spoken language guides attention in a related visual scene and that attended scene information can influence the comprehension process. Here we model sentence comprehension within visual contexts. A recurrent neural network is trained to associate the linguistic input with the visual scene and to produce the interpretation of the described event which is part of the visual scene. A feedback mechanism is investigated, which enables explicit utterance-mediated attention shifts to the relevant part of the scene. We compare four models - a simple recurrent network (SRN) and three models with specific types of additional feedback - in order to explore the role of the attention mechanism in the comprehension process. The results show that all networks learn not only successfully to produce the interpretation at the sentence end, but also demonstrate predictive behavior reflected by the ability to anticipate upcoming constituents. The SRN performs expectedly very well, but demonstrates that adding an explicit attentional mechanism does not lead to loss of performance, and even results in a slight improvement in one of the models.
A model experimental investigation of the accuracy of ground station coordinates, determined by Doppler satellite observations is made, depending on the number of passes, satellite altitude, orbit inclination, frequency of Doppler transmitter, intervals of Doppler counts, etc. Using satellite pases at altitudes of 1000, 3000 a 5000 km, optimization of the Doppler observations is also achieved.
Problems, methods and results are discussed to model finestructures of sunspots, plages and prominences from observed data. The very small scales of those structures prevents so far their full resolution in a spectrum. Thus, twocomponent models of other indirect methods are used to deduce model atmosphere, magnetic and velocity field within these finestructures.
The objective of this study is to make a conceptual and numerical model of the groundwater flow system which will improve the understanding of the groundwater cycle in the area of the Čenkov Valley, Slovakia. Extreme deficits of atmospheric precipitation and thereof resulting periods of low water flows and discharges could very negative impact the water management. Increasing water consumption in the future will be the most critical in strong and intensive dry periods. Almost every climatic zone could suffer from drought, although its features could considerably vary from region to region. The study is handling with creating, calibration and verification of numerical model of groundwater flow in the reparian alluvial aquifer of the Čenkov Valley in south-east part of the Danubian lowland for minimal anthropogenic disrupted natural conditions in the past and quasi-steady deficit water regime of the area. The conceptual model is based on data from earlier studies in the area complemented with data collected in the field. Results of model solutions are presented in the study - groundwater level, filtration velocity vectors, groundwater paths by particle tracking and water budget of study area. Created numerical model could be used for simulation of underground dam function, which belongs to the types of artificial recharge of reparian alluvial aquifer management, and also for creating prognostic scenarios concerning expected climatic changes. Additional future work may include adding a solute transport model to the flow model. and Cieľom predloženej štúdie je vytvorenie koncepčného a numerického modelu systému prúdenia podzemnej vody na území Čenkovskej nivy na Slovensku. Extrémne deficity atmosférických zrážok a z toho vyplývajúce obdobia nízkych vodných stavov a prietokov môžu vplývať na vodné hospodárstvo veľmi negatívne. Zvýšená spotreba vody bude v budúcnosti najkritickejšia práve počas drasticky suchých periód. Takmer každá klimatická zóna môže trpieť suchom, hoci jeho charakteristiky sa môžu od regiónu po región značne líšiť. Štúdia sa zaoberá tvorbou, kalibráciou a verifikáciou numerického modelu prúdenia podzemnej vody v pririečnom hydrogeologickom kolektore Čenkovskej nivy v juhovýchodnej časti Podunajskej roviny, za minimálne antropogénne narušených prírodných podmienok v minulosti a kvázi ustáleného deficitného vodného režimu územia. Koncepčný model je založený na údajoch z predošlých štúdií doplnených o údaje zhromaždené v teréne. V štúdii sú prezentované výsledky modelového riešenia - poloha hladiny podzemnej vody, vektory filtračnej rýchlosti, smery prúdenia podzemnej vody prostredníctvom trasovania pohybu častíc a vodná bilancia územia. Vytvorený numerický model môže byť využitý na simuláciu funkcie podzemnej priehrady, ktorá patrí medzi typy umelého nasycovania pririečneho aluviálneho kolektora a tiež pri tvorbe prognostických scenárov, zaoberajúcich sa klimatickými zmenami. Doplnková budúca štúdia sa môže venovať pripojeniu transportného modelu chemických látok k prezentovanému tokovému modelu.
This paper is concerned with the finite and infinite horizon optimal control issue for a class of networked control systems with stochastic communication protocols. Due to the limitation of networked bandwidth, only the limited number of sensors and actuators are allowed to get access to network mediums according to stochastic access protocols. A discrete-time Markov chain with a known transition probability matrix is employed to describe the scheduling behaviors of the stochastic access protocols, and the networked systems are modeled as a Markov jump system based on the augmenting technique. In such a framework, both the approaches of stochastic analysis and dynamic programming are utilized to derive the optimal control sequences satisfying the quadratic performance index. Moreover, the optimal controller gains are characterized by solving the solutions to coupled algebraic Riccati equations. Finally, a numerical example is provided to demonstrate the correctness and effectiveness of the proposed results.
This paper investigates the soybean-oil "crush" spread, that is the profit margin gained by processing soybeans into soyoil. Soybeans form a large proportion (over 1/5th of the agricultural output of US farmers and the profit margins gained will therefore have a wide impact on the US economy in general.
The paper uses a number of techniques to forecast and trade the soybean crush spread. A traditional regression analysis is used as a benchmark against more sophisticated models such as a MultiLayer Perceptron (MLP), Recurrent Neural Networks and Higher Order Neural Networks. These are then used to trade the spread, the implementation of a number of filtering techniques as used in the literature are utilised to further refine the trading statistics of the models.
The results show that the best model before transactions costs both in- and out-of-sample is the Recurrent Network generating a superior risk adjusted return to all other models investigated. However in the case of most of the models investigated the cost of trading the spread all but eliminates any profit potential.