E?cient Video Cutout by Paint Selection

Video cutout refers to extracting moving objects from videos, which is an important step in many video editing tasks. Recent algorithms have limitations in terms of e?ciency, interaction style, and rob...     

Mathematical representation of radiality constraint in distribution system reconfiguration problem

Distribution systems are most commonly operated in a radial configuration for a number of reasons. In order to impose radiality constraint in the optimal network reconfiguration problem, an efficient algorithm is introduced in this paper based on graph theory. The paper shows that the normally followed methods of imposing radiality constraint within a mixed-integer programming formulation of the reconfiguration problem may not be sufficient. The minimum-loss network reconfiguration problem is formulated using different ways to impose radiality constraint. It is shown, through simulations, that the formulated problem using the proposed method for representing radiality constraint can be solved more efficiently, as opposed to the previously proposed formulations. This results in up to 30% reduction in CPU time for the test systems used in this study.     

Optimal Pricing Algorithm in the Electricity Market with Battery and Accumulator and Demand–Supply Balancing

This paper considers the day‐ahead market with batteries and accumulators to level power generation. First, we model consumers with batteries, generators with batteries, and accumulators, all of whom strive to maximize their own profit. An optimal pricing algorithm based on dual decomposition and the steepest descent method is derived and shown to be stable. Finally, the effectiveness of the algorithm is demonstrated by means of numerical simulations which indicate that it achieves demand–supply balance.     

Human mobility synthesis using matrix and tensor factorizations

Human mobility prediction is of great advantage in route planning and schedule management. However, mobility data is a high-dimensional dataset in which multi-context prediction is difficult in a single model. Mobility data can usually be expressed as a home event, a work event, a shopping event and a traveling event. Previous works have only been able to learn and predict one type of mobility event and then integrate them. As the tensor model has a strong ability to describe high-dimensional information, we propose an algorithm to predict human mobility in tensors of location context data. Using the tensor decomposition method, we extract human mobility patterns with multiple expressions and then synthesize the future mobility event based on mobility patterns. The experiment is based on real-world location data and the results show that the tensor decomposition method has the highest accuracy in terms of prediction error among the three methods. The results also prove the feasibility of our multi-context prediction model.     

Evolutionary collective behavior decomposition model for time series data mining

In this research, we propose a novel framework referred to as collective game behavior decomposition where complex collective behavior is assumed to be generated by aggregation of several groups of agents following different strategies and complexity emerges from collaboration and competition of individuals. The strategy of an agent is modeled by certain simple game theory models with limited information. Genetic algorithms are used to obtain the optimal collective behavior decomposition based on history data. The trained model can be used for collective behavior prediction. For modeling individual behavior, two simple games, the minority game and mixed game are investigated in experiments on the real-world stock prices and foreign-exchange rate. Experimental results are presented to show the effectiveness of the new proposed model.     

Robust skin detection in real-world images

Human skin detection in images is desirable in many practical applications, e.g., human–computer interaction and adult-content filtering. However, existing methods are mainly suffer from confusing backgrounds in real-world images. In this paper, we try to address this issue by exploring and combining several human skin properties, i.e. color property, texture property and region property. First, images are divided into superpixels, and robust skin seeds and background seeds are acquired through color property and texture property of skin. Then we combining color, region and texture properties of skin by proposing a novel skin color and texture based graph cuts (SCTGC) to acquire the final skin detection results. Comprehensive and comparative experiments show that the proposed method achieves promising performance and outperforms many state-of-the-art methods over publicly available challenging datasets with a great part of hard images.     

Data-driven model reduction-based nonlinear MPC for large-scale distributed parameter systems

Model predictive control (MPC) has been effectively applied in process industries since the 1990s. Models in the form of closed equation sets are normally needed for MPC, but it is often difficult to obtain such formulations for large nonlinear systems. To extend nonlinear MPC (NMPC) application to nonlinear distributed parameter systems (DPS) with unknown dynamics, a data-driven model reduction-based approach is followed. The proper orthogonal decomposition (POD) method is first applied off-line to compute a set of basis functions. Then a series of artificial neural networks (ANNs) are trained to effectively compute POD time coefficients. NMPC, using sequential quadratic programming is then applied. The novelty of our methodology lies in the application of POD's highly efficient linear decomposition for the consequent conversion of any distributed multi-dimensional space-state model to a reduced 1-dimensional model, dependent only on time, which can be handled effectively as a black-box through ANNs. Hence we construct a paradigm, which allows the application of NMPC to complex nonlinear high-dimensional systems, even input/output systems, handled by black-box solvers, with significant computational efficiency. This paradigm combines elements of gain scheduling, NMPC, model reduction and ANN for effective control of nonlinear DPS. The stabilization/destabilization of a tubular reactor with recycle is used as an illustrative example to demonstrate the efficiency of our methodology. Case studies with inequality constraints are also presented.     

Locally one-dimensional-alternating segment explicit–implicit and locally one-dimensional-alternating segment Crank–Nicolson methods for two-dimension parabolic equations

In the paper two new locally one-dimensional alternating segment schemes for solving the 2-D parabolic problems are developed. The parabolic problems can be solved efficiently over decomposed sub-domains. Two schemes are proved to be unconditionally stable and numerical experiments show the validity of the schemes.     

Controllability Analysis of Process Alternatives for Biobutanol Purification

Biobutanol has characteristics similar to petroleum fuel and is considered as a superior biofuel compared to ethanol. The development of technologies for biobutanol production by fermentation has resulted in higher final biobutanol concentrations together with less energy‐intensive separation and purification techniques. These new technological developments have the potential to provide a production process for biobutanol that is economically viable in comparison to the petrochemical pathway for its production. The control properties of four different possible process designs for biobutanol purification are analyzed. The results, using the singular value decomposition technique, indicated that the scheme where only biobutanol flow is purified, and both ethanol and acetone leaving the purification process mixed with water and biobutanol traces, showed the best control properties.     

Cobalt doping of α-Fe/Al2O3 catalysts for the production of hydrogen and high-quality carbon nanotubes by thermal decomposition of methane

Fe–Co/Al₂O₃ catalysts were developed and tested in the catalytic decomposition of methane (CDM) for the synthesis of multi-wall carbon nanotubes (MWCNT) and the CO₂-free hydrogen production. While Fe (54.5–66.7 mol.%) is the main active phase for the carbon formation on the catalyst, Co acts as dopant aiming to improve its overall catalytic behaviour. Catalysts with Co contents of up to 18.2 M% showed the presence of α-Fe and Fe–Co crystallites with different size and lattice parameter. Fe_1-xCo_x alloy with bcc crystal system was identified only for Co contents of 14.0% and above, and presented a lattice constant lower than α-Fe, which would modify the carbon diffusion of the metal particle during the MWCNT growth. Co inhibited the Fe₃C formation during CDM resulting in higher carbon formations and longer activity times. This phase, shown in undoped catalysts, favored the presence of bamboo-type carbon nanotubes.