Adaptive Synchronization-Based Approach for Finite-Time Parameters Identification of Genetic Regulatory Networks

Neural Processing Letters - This paper presents an approach to identify the unknown parameters of genetic regulatory network (GRN) in finite-time. The adaptive synchronization-based method is used...     

A novel solution of deep learning for endoscopic ultrasound image segmentation: enhanced computer aided diagnosis of gastrointestinal stromal tumor

Multimedia Tools and Applications - Gastrointestinal stromal tumor is one of the critical tumors that doctors do not suggest to get frequent endoscopy, so there is a need for a diagnosis system...     

Multiple clusterings of heterogeneous information networks

Machine Learning - Traditional clustering algorithms focus on a single clustering result; as such, they cannot explore potential diverse patterns of complex real world data. To deal with this...     

Prediction of sediment,particulate nutrient and dissolved nutrient concentrations in a dry tropical river to provide input to a mechanistic coastal water quality model

A Generalised Additive Modelling (GAM) approach is applied to prediction of both particulate and dissolved nutrient concentrations in a wet-tropical river (the Fitzroy River, Queensland, Australia). In addition to covariant terms considered in previous work (i.e. flow, discounted flow and a rising-falling limb term), we considered several new potential covariates: meteorological and hydrological variables that are routinely monitored, available in near-real time, and were considered to have potential predictive power. Of the additional terms considered, only flows from three tributaries of the Fitzroy River (namely, the Nogoa, Comet and Isaac Rivers) were found to significantly improve the model. Inclusion of one or more of these additional flow terms greatly improved results for dissolved nitrogen and dissolved phosphorus concentrations, which were not otherwise amenable to prediction. In particular, the Nogoa sub-catchment, dominated by pasture for cattle, was found to be important in determining dissolved inorganic nitrogen and phosphorus concentrations reaching the river mouth. This insight may direct further research, including future refinement of processed-based catchment models. The GAMs described here are used to provide near real-time river boundary conditions for a complex coupled hydrodynamic and biogeochemical model of the Great Barrier Reef Lagoon, and can be coupled with a forecasting hydrological model to allow integrated forecasting simulations of the catchment to coast system.     

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.     

Hierarchical multi-reservoir optimization modeling for real-world complexity with application to the Three Gorges system

High dimensionality in real-world multi-reservoir systems greatly hinders the application and popularity of evolutionary algorithms, especially for systems with heterogeneous units. An efficient hierarchical optimization framework is presented for search space reduction, determining the best water distributions, not only between cascade reservoirs, but also among different types of hydropower units. The framework is applied to the Three Gorges Project (TGP) system and the results demonstrate that the difficulties of multi-reservoir optimization caused by high dimensionality can be effectively solved by the proposed hierarchical method. For the day studied, power output could be increased by 6.79 GWh using an optimal decision with the same amount of water actually used; while the same amount of power could be generated with 2.59 × 10⁷ m³ less water compared to the historical policy. The methodology proposed is general in that it can be used for other reservoir systems and other types of heterogeneous unit generators.     

Abrasive waterjet micro-machining of channels in metals: Comparison between machining in air and submerged in water

Abrasive water jet technology can be used for micro-milling using recently developed miniaturized nozzles. Abrasive water jet (AWJ) machining is often used with both the nozzle tip and workpiece submerged in water to reduce noise and contain debris. This paper compares the performance of submerged and unsubmerged abrasive water jet micro-milling of channels in 316L stainless steel and 6061-T6 aluminum at various nozzle angles and standoff distances. The effect of submergence on the diameter and effective footprint of AWJ erosion footprints was measured and compared. It was found that the centerline erosion rate decreased with channel depth due to the spreading of the jet as the effective standoff distance increased, and because of the growing effect of stagnation as the channel became deeper. The erosive jet spread over a larger effective footprint in air than in water, since particles on the jet periphery were slowed much more quickly in water due to increased drag. As a result, the width of a channel machined in air was wider than that in water. Moreover, it was observed that the instantaneous erosion rate decreased with channel depth, and that this decrease was a function only of the channel cross-sectional geometry, being independent of the type of metal, the jet angle, the standoff distance, and regardless of whether the jet was submerged or in air, in either the forward or backward directions. It is shown that submerged AWJM results in narrower features than those produced while machining in air, without a decrease in centerline etch rate.     

Inverted pendulum stabilization: Characterization of codimension-three triple zero bifurcation via multiple delayed proportional gains

The paper considers the problem of stabilization of systems possessing a multiple zero eigenvalue at the origin. The controller that we propose, uses multiple delayed measurements instead of derivative terms. Doing so, we increase the performances of the closed loop in presence of system uncertainties and/or noisy measurements. The problem formulation and the analysis is presented through a classical engineering problem which is the stabilization of an inverted pendulum on a cart moving horizontally. On one hand, we perform a nonlinear analysis of the center dynamics described by a three dimensional system of ordinary differential equations with a codimension-three triple zero bifurcation. On the other hand, we present the complementary stability analysis of the corresponding linear time invariant system with two delays describing the behavior around the equilibrium. The aim of this analysis is to characterize the possible local bifurcations. Finally, the proposed control scheme is numerically illustrated and discussed.     

Parameter estimation for a morphochemical reaction-diffusion model of electrochemical pattern formation

The process of electrodeposition can be described in terms of a reaction-diffusion partial differential equation (PDE) system that models the dynamics of the morphology profile and the chemical composition. Here we fit such a model to the different patterns present in a range of electrodeposited and electrochemically modified alloys using PDE constrained optimization. Experiments with simulated data show how the parameter space of the model can be divided into zones corresponding to the different physical patterns by examining the structure of an appropriate cost function. We then use real data to demonstrate how numerical optimization of the cost function can allow the model to fit the rich variety of patterns arising in experiments. The computational technique developed provides a potential tool for tuning experimental parameters to produce desired patterns.