Simulating California reservoir operation using the classification and regression-tree algorithm combined with a shuffled cross-validation scheme

The controlled outflows from a reservoir or dam are highly dependent on the decisions made by the reservoir operators, instead of a natural hydrological process. Difference exists between the natural upstream inflows to reservoirs and the controlled outflows from reservoirs that supply the downstream users. With the decision maker's awareness of changing climate, reservoir management requires adaptable means to incorporate more information into decision making, such as water delivery requirement, environmental constraints, dry/wet conditions, etc. In this paper, a robust reservoir outflow simulation model is presented, which incorporates one of the well-developed data-mining models (Classification and Regression Tree) to predict the complicated human-controlled reservoir outflows and extract the reservoir operation patterns. A shuffled cross-validation approach is further implemented to improve CART's predictive performance. An application study of nine major reservoirs in California is carried out. Results produced by the enhanced CART, original CART, and random forest are compared with observation. The statistical measurements show that the enhanced CART and random forest overperform the CART control run in general, and the enhanced CART algorithm gives a better predictive performance over random forest in simulating the peak flows. The results also show that the proposed model is able to consistently and reasonably predict the expert release decisions. Experiments indicate that the release operation in the Oroville Lake is significantly dominated by SWP allocation amount and reservoirs with low elevation are more sensitive to inflow amount than others

Improving the multi-objective evolutionary optimization algorithm for hydropower reservoir operations in the California Oroville-Thermalito complex

This study demonstrates the application of an improved Evolutionary optimization Algorithm (EA), titled Multi-Objective Complex Evolution Global Optimization Method with Principal Component Analysis and Crowding Distance Operator (MOSPD), for the hydropower reservoir operation of the Oroville-Thermalito Complex (OTC) - a crucial head-water resource for the California State Water Project (SWP). In the OTC's water-hydropower joint management study, the nonlinearity of hydropower generation and the reservoir's water elevation-storage relationship are explicitly formulated by polynomial function in order to closely match realistic situations and reduce linearization approximation errors. Comparison among different curve-fitting methods is conducted to understand the impact of the simplification of reservoir topography. In the optimization algorithm development, techniques of crowding distance and principal component analysis are implemented to improve the diversity and convergence of the optimal solutions towards and along the Pareto optimal set in the objective space. A comparative evaluation among the new algorithm MOSPD, the original Multi-Objective Complex Evolution Global Optimization Method (MOCOM), the Multi-Objective Differential Evolution method (MODE), the Multi-Objective Genetic Algorithm (MOGA), the Multi-Objective Simulated Annealing approach (MOSA), and the Multi-Objective Particle Swarm Optimization scheme (MOPSO) is conducted using the benchmark functions. The results show that best the MOSPD algorithm demonstrated the best and most consistent performance when compared with other algorithms on the test problems. The newly developed algorithm (MOSPD) is further applied to the OTC reservoir releasing problem during the snow melting season in 1998 (wet year), 2000 (normal year) and 2001 (dry year), in which the more spreading and converged non-dominated solutions of MOSPD provide decision makers with better operational alternatives for effectively and efficiently managing the OTC reservoirs in response to the different climates, especially drought, which has become more and more severe and frequent in California

Probabilistic teleportation of unknown two-particle state via POVM

We propose a scheme for probabilistic teleportation of unknown two-particle state with partly entangled four-particle state via POVM. In this scheme the teleportation of unknown two-particle state can be realized with certain probability by performing two Bell state measurements, a proper POVM and a unitary transformation.Comment: 5 pages, no figur

Critical exponents of the driven elastic string in a disordered medium

We analyze the harmonic elastic string driven through a continuous random potential above the depinning threshold. The velocity exponent beta = 0.33(2) is calculated. We observe a crossover in the roughness exponent zeta from the critical value 1.26 to the asymptotic (large force) value of 0.5. We calculate directly the velocity correlation function and the corresponding correlation length exponent nu = 1.29(5), which obeys the scaling relation nu = 1/(2-zeta), and agrees with the finite-size-scaling exponent of fluctuations in the critical force. The velocity correlation function is non-universal at short distances.Comment: 4 pages, 3 figures. corrected references and typo

Molecular gas in extreme star-forming environments: the starbursts Arp220 and NGC6240 as case studies

We report single-dish multi-transition measurements of the 12^CO, HCN, and HCO^+ molecular line emission as well as HNC J=1-0 and HNCO in the two ultraluminous infra-red galaxies Arp220 and NGC6240. Using this new molecular line inventory, in conjunction with existing data in the literature, we compiled the most extensive molecular line data sets to date for such galaxies. The many rotational transitions, with their different excitation requirements, allow the study of the molecular gas over a wide range of different densities and temperatures with significant redundancy, and thus allow good constraints on the properties of the dense gas in these two systems. The mass (~(1-2) x 10^10 Msun) of dense gas (>10^5-6 cm^-3) found accounts for the bulk of their molecular gas mass, and is consistent with most of their IR luminosities powered by intense star bursts while self-regulated by O,B star cluster radiative pressure onto the star-forming dense molecular gas. The highly excited HCN transitions trace a gas phase ~(10-100)x denser than that of the sub-thermally excited HCO^+ lines (for both galaxies). These two phases are consistent with an underlying density-size power law found for Galactic GMCs (but with a steeper exponent), with HCN lines tracing denser and more compact regions than HCO^+. Whether this is true in IR-luminous, star forming galaxies in general remains to be seen, and underlines the need for observations of molecular transitions with high critical densities for a sample of bright (U)LIRGs in the local Universe -- a task for which the HI-FI instrument on board Herschel is ideally suited to do.Comment: 38 pages (preprint ApJ style), 3 figures, accepted for Ap

An enhanced artificial neural network with a shuffled complex evolutionary global optimization with principal component analysis

The classical Back-Propagation (BP) scheme with gradient-based optimization in training Artificial Neural Networks (ANNs) suffers from many drawbacks, such as the premature convergence, and the tendency of being trapped in local optimums. Therefore, as an alternative for the BP and gradient-based optimization schemes, various Evolutionary Algorithms (EAs), i.e., Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Simulated Annealing (SA), and Differential Evolution (DE), have gained popularity in the field of ANN weight training. This study applied a new efficient and effective Shuffled Complex Evolutionary Global Optimization Algorithm with Principal Component Analysis – University of California Irvine (SP-UCI) to the weight training process of a three-layer feed-forward ANN. A large-scale numerical comparison is conducted among the SP-UCI-, PSO-, GA-, SA-, and DE-based ANNs on 17 benchmark, complex, and real-world datasets. Results show that SP-UCI-based ANN outperforms other EA-based ANNs in the context of convergence and generalization. Results suggest that the SP-UCI algorithm possesses good potential in support of the weight training of ANN in real-word problems. In addition, the suitability of different kinds of EAs on training ANN is discussed. The large-scale comparison experiments conducted in this paper are fundamental references for selecting proper ANN weight training algorithms in practice

Magnetic influence on the frequency of the soft-phonon mode in the incipient ferroelectric EuTiO3

The dielectric constant of the incipient ferroelectric EuTiO$_3$ exhibits a sharp decrease at about 5.5K, at which temperature antiferromagnetic ordering of the Eu spins simultaneously appears, indicating coupling between the magnetism and dielectric properties. This may be attributed to the modification of the soft-phonon mode, $T_{1\mu}$, which is the main contribution to the large dielectric constant, by the Eu spins(7$\mu_B$ per Eu). By adding the coupling term between the magnetic and electrical subsystems as $-g\sum\limits_l {\sum\limits_{< {i,j}} {q_l^2}} \overrightarrow {S_i} \cdot \overrightarrow {S_j}$ we show that the variation of the frequency of soft-phonon mode depends on the spin correlation between the nearest neighbors Eu spins and is substantially changed under a magnetic field.Comment: 13 pages, 4 figure

On the Triality Theory for a Quartic Polynomial Optimization Problem

This paper presents a detailed proof of the triality theorem for a class of fourth-order polynomial optimization problems. The method is based on linear algebra but it solves an open problem on the double-min duality left in 2003. Results show that the triality theory holds strongly in a tri-duality form if the primal problem and its canonical dual have the same dimension; otherwise, both the canonical min-max duality and the double-max duality still hold strongly, but the double-min duality holds weakly in a symmetrical form. Four numerical examples are presented to illustrate that this theory can be used to identify not only the global minimum, but also the largest local minimum and local maximum.Comment: 16 pages, 1 figure; J. Industrial and Management Optimization, 2011. arXiv admin note: substantial text overlap with arXiv:1104.297

Modeling the Optical Afterglow of GRB 030329

The best-sampled afterglow light curves are available for GRB 030329. A distinguishing feature of this event is the obvious rebrightening at around 1.6 days after the burst. Proposed explanations for the rebrightening mainly include the two-component jet model and the refreshed shock model, although a sudden density-jump in the circumburst environment is also a potential choice. Here we re-examine the optical afterglow of GRB 030329 numerically in light of the three models. In the density-jump model, no obvious rebrightening can be produced at the jump moment. Additionally, after the density jump, the predicted flux density decreases rapidly to a level that is significantly below observations. A simple density-jump model thus can be excluded. In the two-component jet model, although the observed late afterglow (after 1.6 days) can potentially be explained as emission from the wide-component, the emergence of this emission actually is too slow and it does not manifest as a rebrightening as previously expected. The energy-injection model seems to be the most preferred choice. By engaging a sequence of energy-injection events, it provides an acceptable fit to the rebrightening at $\sim 1.6$ d, as well as the whole observed light curve that extends to $\sim 80$ d. Further studies on these multiple energy-injection processes may provide a valuable insight into the nature of the central engines of gamma-ray bursts.Comment: 18 pages, 3 figures; a few references added and minor word changes; now accepted for publication in Ap