Incorporation of a Regularization Term to Control Negative Correlation in Mixture of Experts

Combining accurate neural networks (NN) in the ensemble with negative error correlation greatly improves the generalization ability. Mixture of experts (ME) is a popular combining method which employs special error function for the simultaneous training of NN experts to produce negatively correlated NN experts. Although ME can produce negatively correlated experts, it does not include a control parameter like negative correlation learning (NCL) method to adjust this parameter explicitly. In this study, an approach is proposed to introduce this advantage of NCL into the training algorithm of ME, i.e., mixture of negatively correlated experts (MNCE). In this proposed method, the capability of a control parameter for NCL is incorporated in the error function of ME, which enables its training algorithm to establish better balance in bias-variance-covariance trade-off and thus improves the generalization ability. The proposed hybrid ensemble method, MNCE, is compared with their constituent methods, ME and NCL, in solving several benchmark problems. The experimental results show that our proposed ensemble method significantly improves the performance over the original ensemble methods.     

A robust critical path in an environment with hybrid uncertainty

In this article, we consider the project critical path problem in an environment with hybrid uncertainty. In this environment, the duration of activities are considered as random fuzzy variables that have probability and fuzzy natures, simultaneously. To obtain a robust critical path with this kind of uncertainty a chance constraints programming model is used. This model is converted to a deterministic model in two stages. In the first stage, the uncertain model is converted to a model with interval parameters by alpha-cut method and distribution function concepts. In the second stage, the interval model is converted to a deterministic model by robust optimization and min-max regret criterion and ultimately a genetic algorithm with a proposed exact algorithm are applied to solve the final model. Finally, some numerical examples are given to show the efficiency of the solution procedure.     

Prediction of scouring around an arch-shaped bed sill using Neuro-Fuzzy model

In this study, Adaptive Neuro-Fuzzy Inference System (ANFIS) has been used to model local scouring depth and pattern scouring around concave and convex arch shaped circular bed sills. The experimental part of this research study includes seven sets of laboratory test cases which were performed in an experimental flume under different flow conditions. A data set consists of 2754 data points of scouring depth were collected to use in the ANFIS model. The ratio of arch diameter, D, to flume width, W, is used as a non dimensional variables in all test cases. The results from ANFIS model were compared with the results of ANN model obtained by Homayoon et al. [24] and previously presented models. The results indicated that for D/W equal to 1 and 1.2, the ANFIS models produced a good performance for convex and concave bed sills. As a result, the ANFIS models can be used as an alternative to ANN for estimation of scour depth and scour pattern around a concave bed sill installed with a bridge pier.     

Parameter identification and design of a robust attitude controller using <Emphasis Type="Italic">H</Emphasis><Subscript>∞</Subscript> methodology for the raptor E620 small-scale helicopter

Rigorous control synthesis for an unmanned aerial vehicle necessitates the availability of a good, reasonable model for such a vehicle. The work reported in this paper focuses on the modeling of a rotary unmanned aerial vehicle (RUAV) and the development of a robust controller that can handle parameter uncertainties and disturbances. The parameters of the plant model are obtained through the use of the prediction error method with real flight data. The response of the identified linear model shows a good match with the measured flight data. The H _∞ control scheme is applied to obtain a robustly stable controller using the identified model. The proposed controller is analyzed using frequency-domain analysis and time-domain simulations. The performance of the proposed H _∞ controller is better than that of the conventional proportional derivative controller in that the proposed controller has a shorter settling time and less overshoot. Furthermore, the degradation of the proposed controller performance is negligible and stability is maintained when the input gains to the plant are doubled, which demonstrates the good performance and robustness of the controller.     

An analytical framework for event mining in video data

With the high availability of digital video contents on the internet, users need more assistance to access digital videos. Various researches have been done about video summarization and semantic video analysis to help to satisfy these needs. These works are developing condensed versions of a full length video stream through the identification of the most important and pertinent content within the stream. Most of the existing works in these areas are mainly focused on event mining. Event mining from video streams improves the accessibility and reusability of large media collections, and it has been an active area of research with notable recent progress. Event mining includes a wide range of multimedia domains such as surveillance, meetings, broadcast, news, sports, documentary, and films, as well as personal and online media collections. Due to the variety and plenty of Event mining techniques, in this paper we suggest an analytical framework to classify event mining techniques and to evaluate them based on important functional measures. This framework could lead to empirical and technical comparison of event mining methods and development of more efficient structures at future.     

A game attention model for efficient bit rate allocation in cloud gaming

The widespread availability of broadband internet access and the growth in server-based processing have provided an opportunity to run games away from the player into the cloud and offer a new promising service known as cloud gaming. The concept of cloud gaming is to render a game in the cloud and stream the resulting game scenes to the player as a video sequence over a broadband connection. To meet the stringent network bandwidth requirements of cloud gaming and support more players, efficient bit rate reduction techniques are needed. In this paper, we introduce the concept of game attention model (GAM), which is basically a game context-based visual attention model, as a means for reducing the bit rate of the streaming video more efficiently. GAM estimates the importance of each macro-block in a game frame from the player’s perspective and allows encoding the less important macro-blocks with lower bit rate. We have evaluated nine game video sequences, covering a wide range of game genre and a spectrum of scene content in terms of details, motion and brightness. Our subjective assessment shows that by integrating this model into the cloud gaming framework, it is possible to decrease the required bit rate by nearly 25 % on average, while maintaining a relatively high user quality of experience. This clearly enables players with limited communication resources to benefit from cloud gaming with acceptable quality.     

Fabrication of SiC-Whisker-Reinforced MoSi2 Composites by Tape Casting

In this paper, we describe a procedure for the processing of SiC-whisker-reinforced MoSi₂ composites via tape casting. Based on the characteristics of SiC whiskers and MoSi₂ powder in aqueous and nonaqueous solvents, a slip formulation (solvent, dispersant, binder, etc.) has been developed. The formulation developed allows for a uniform distribution of SiC whiskers in the matrix, easy separation of the tapes from the polymeric carrier, convenient control of both tape thickness and orientation of SiC whiskers, and a low binder burnout temperature. The latter is important for the prevention of the oxidation of MoSi₂ powder during the binder burnout in an oxidizing atmosphere.     

Stabilization of positive switched systems with time-varying delays under asynchronous switching

This paper investigates the state feedback stabilization problem for a class of positive switched systems with time-varying delays under asynchronous switching in the frameworks of continuous-time and discrete-time dynamics. The so-called asynchronous switching means that the switches between the candidate controllers and system modes are asynchronous. By constructing an appropriate co-positive type Lyapunov-Krasovskii functional and further allowing the functional to increase during the running time of active subsystems, sufficient conditions are provided to guarantee the exponential stability of the resulting closed-loop systems, and the corresponding controller gain matrices and admissible switching signals are presented. Finally, two illustrative examples are given to show the effectiveness of the proposed methods.     

Statistical Product Selectivity Modeling and Optimization for γ-Al2O3-Supported Cobalt Catalysts-Based Fischer–Tropsch Synthesis

The statistical selectivity models were developed for four different Fischer–Tropsch synthesis product range, including methane (CH₄), light olefins (C₂=C₄), light paraffins (C₂–C₄), and long-chain hydrocarbons (C₅₊), based on the experimental data obtained over thirteen γ-Al₂O₃ supported cobalt-based catalysts with different cobalt particle and pore sizes. The input variables consist of cobalt metal particle size and catalyst pore size. The cubic and quadratic polynomial equations were fitted to the experimental data, however, the mathematical models were subjected to model reduction for the enhancement of model adequacy, which was investigated through ANOVA. The multi-objective optimization revealed that the maximum C₅₊?selectivity (84.150%) could be achieved at the cobalt particle size and pore sizes of 14.764 and 23.129 nm, respectively, while keeping the selectivity to other hydrocarbon products minimum.

Graphic Abstract