A learning-based visual saliency prediction model for stereoscopic 3D video (LBVS-3D)

Saliency prediction models provide a probabilistic map of relative likelihood of an image or video region to attract the attention of the human visual system. Over the past decade, many computational saliency prediction models have been proposed for 2D images and videos. Considering that the human visual system has evolved in a natural 3D environment, it is only natural to want to design visual attention models for 3D content. Existing monocular saliency models are not able to accurately predict the attentive regions when applied to 3D image/video content, as they do not incorporate depth information. This paper explores stereoscopic video saliency prediction by exploiting both low-level attributes such as brightness, color, texture, orientation, motion, and depth, as well as high-level cues such as face, person, vehicle, animal, text, and horizon. Our model starts with a rough segmentation and quantifies several intuitive observations such as the effects of visual discomfort level, depth abruptness, motion acceleration, elements of surprise, size and compactness of the salient regions, and emphasizing only a few salient objects in a scene. A new fovea-based model of spatial distance between the image regions is adopted for considering local and global feature calculations. To efficiently fuse the conspicuity maps generated by our method to one single saliency map that is highly correlated with the eye-fixation data, a random forest based algorithm is utilized. The performance of the proposed saliency model is evaluated against the results of an eye-tracking experiment, which involved 24 subjects and an in-house database of 61 captured stereoscopic videos. Our stereo video database as well as the eye-tracking data are publicly available along with this paper. Experiment results show that the proposed saliency prediction method achieves competitive performance compared to the state-of-the-art approaches.

     

An Efficient Finite Difference Method for The Time‐Delay Optimal Control Problems With Time‐Varying Delay

In this paper, an efficient finite difference method is presented for the solution of time‐delay optimal control problems with time‐varying delay in the state. By using the Pontryagin's maximum principle, the original time‐delay optimal control problem is first transformed into a system of coupled two‐point boundary value problems involving both delay and advance terms. Then the derived system is converted into a system of linear algebraic equations by using a second‐order finite difference formula and a Hermite interpolation polynomial for the first‐order derivatives and delay terms, respectively. The convergence analysis of the proposed approach is provided. The new scheme is also successful for the optimal control of time‐delay systems affected by external persistent disturbances. Numerical examples are included to demonstrate the validity and applicability of the new technique. Some comparative results are included to illustrate the effectiveness of the proposed method.     

Selection of regression models for predicting strength and deformability properties of rocks using GA

Recently, many regression models have been presented for prediction of mechanical parameters of rocks regarding to rock index properties. Although statistical analysis is a common method for developing regression models, but still selection of suitable transformation of the independent variables in a regression model is difficult. In this paper, a genetic algorithm (GA) has been employed as a heuristic search method for selection of best transformation of the independent variables (some index properties of rocks) in regression models for prediction of uniaxial compressive strength (UCS) and modulus of elasticity (E). Firstly, multiple linear regression (MLR) analysis was performed on a data set to establish predictive models. Then, two GA models were developed in which root mean squared error (RMSE) was defined as fitness function. Results have shown that GA models are more precise than MLR models and are able to explain the relation between the intrinsic strength/elasticity properties and index properties of rocks by simple formulation and accepted accuracy.     

Domestic Water and Sanitation in Cyclones and Floods in Bangladesh and a Proposed Research Agenda

ABSTRACT

Water is one of the top priority item s i relief activities but the appropriateness and effectiveness of this activity may be questioned. Water and sanitation (WS) are inseparable and together they play major roles in the transmission of the pathogen of diarrhoeal disease. Thousands of people die or suffer from WS-related diseases during postdisaster periods. Here we present WS-related experiences gained after Bangladesh's 1991 cyclone and the views of participants in a regional and a national (local) workshop on this matter held in Bangladesh. About 63 per cent of the water purifying tablets (WPTs) distributed after the 1991 cyclone were found to have lost potency Relief personnel lack basic knowledge about WS practices and the management of WS provision. Participants in the two workshops documented serious problems i this field at both regional and national levels. Major issues for research may include: an appropriate environmental preparedness plan; appropriate waste disposal technologies; appropriate water treatment methods; self- help activities during vulnerable periods; and health education related to WS in disaster situations.     

Identification and classification of ERP critical failure factors in Iranian industries

Implementation of Enterprise Resource Planning systems (ERPs) is a complex and costly process, which usually results in serious failures. The majority of previous studies and research projects have been conducted in identifying ERP Critical Success Factors (CSFs) rather than Critical Failure Factors (CFFs). On the other hand, most studies have been devoted to developed countries, while in developing countries, many companies have moved towards using such systems. The majority of IT/IS management standards and guidelines have been developed by technologically-leading countries. But developing countries, which mostly confront with especial challenges, have a different condition from the implicit assumptions of leading countries. Iran is one of these countries that many of its ERP implementation projects led to failure. There is no comprehensive study that identifies ERP CFFs in Iranian industries. The main purpose of this study is to identify such factors and classify them to help other industries, consultants and implementers to prevent failures in the implementation of ERP projects. So, at first, with the semi structured interviews and literature reviews, 47 failure factors were identified. After that a questionnaire developed and sent to ERP project team members in Iranian industries that failed in their ERP projects. Robust Exploratory Factor Analysis (EFA) has been used for data analysis, which finally classified critical failure factors in seven groups named as vendor and consultant, human resources, managerial, project management, processes, organizational and technical. The results of this study have provided a very useful reference for scholars and managers to identify the relevant issues of ERP projects failure in developing countries.     

Heat transfer in a turbulent jet impinging on a moving plate considering high plate-to-jet velocity ratios

In this paper, heat transfer characteristics of a turbulent slot jet impinging orthogonally on an isothermal moving hot plate is studied numerically. The governing equations were discretized using the finite volume method and the υ ² — f turbulence model was employed for turbulence modeling. The effect of the jet Reynolds number and the plate-to-jet velocity ratio (R) on the Nusselt were investigated. Despite of most previous studies, which have been restricted to R≤2, in the present research higher values of R, also were considered (0≤R≤6). Range of studied jet Reynolds number was between 3000 and 60000. The results indicate that at a fixed plate-to-jet velocity ratio increment of the Reynolds number leads to the enhancement of the average Nusselt number. For each Reynolds number, the average Nusselt number reduces with increasing the plate-to-jet velocity ratio until it becomes minimum at R = 1.25. For R>1.25 trend changes so that these parameters increase. In addition, it was found that only for R>2.5 the average Nusselt number is improved due to the plate motion in comparison with the stationary jet. The results are validated against available experimental data, showing good agreement.     

An integrated model for closed-loop supply chain configuration and supplier selection: Multi-objective approach

Reverse logistics consists of all operations related to the reuse of products. External suppliers are one of the important members of reverse logistics and closed loop supply chain (CLSC) networks. However in CLSC network configuration models, suppliers are assessed based on purchasing cost and other factors such as on-time delivery are ignored. In this research, a general closed loop supply chain network is examined that includes manufacturer, disassembly, refurbishing, and disposal sites. Meanwhile, it is managed by the manufacturer. We propose an integrated model which has two phases. In the first phase, a framework for supplier selection criteria in RL is proposed. Besides, a fuzzy method is designed to evaluate suppliers based on qualitative criteria. The output of this stage is the weight of each supplier according to each part. In the second phase, we propose a multi objective mixed-integer linear programming model to determine which suppliers and refurbishing sites should be selected (strategic decisions), and find out the optimal number of parts and products in CLSC network (tactical decisions). The objective functions maximize profit and weights of suppliers, and one of them minimizes defect rates. To our knowledge, this model is the first effort to consider supplier selection, order allocation, and CLSC network configuration, simultaneously. The mathematical programming model is validated through numerical analysis.     

Fault diagnosis and accommodation of nonlinear systems based on multiple-model adaptive unscented Kalman filter and switched MPC and H-infinity loop-shaping controller

In this paper, a new active fault tolerant control (AFTC) methodology is proposed based on a state estimation scheme for fault detection and identification (FDI) to deal with the potential problems due to possible fault scenarios. A bank of adaptive unscented Kalman filters (AUKFs) is used as a core of FDI module. The AUKF approach alleviates the inflexibility of the conventional UKF due to constant covariance set up, leading to probable divergence. A fuzzy-based decision making (FDM) algorithm is introduced to diagnose sensor and/or actuator faults. The proposed FDI approach is utilized to recursively correct the measurement vector and the model used for both state estimation and output prediction in a model predictive control (MPC) formulation. Robustness of the proposed FTC system, H_∞ optimal robust controller and MPC are combined via a fuzzy switch that is used for switching between MPC and robust controller such that FTC system is able to maintain the offset free behavior in the face of abrupt changes in model parameters and unmeasured disturbances. This methodology is applied on benchmark three-tank system; the proposed FTC approach facilitates recovery of the closed loop performance after the faults have been isolated leading to an offset free behavior in the presence of sensor/actuator faults that can be either abrupt or drift change in biases. Analysis of the simulation results reveals that the proposed approach provides an effective method for treating faults (biases/drifts in sensors/actuators, changes in model parameters and unmeasured disturbances) under the unified framework of robust fault tolerant control.