Caustic stress corrosion cracking of alloys 600 and 690 with NaOH concentrations

In order to evaluate the stress corrosion cracking resistance for commercial alloys (C600MA, C600TT, C690TT) and Korean-made alloys (K600MA, K690TT), C-ring tests were performed in a caustic environment of 4, 10, 20, 30, and 50% NaOH solution at 315°C, for 480 h with an applied potential of 125 mV vs. OCP. Different stress corrosion cracking phenomena were observed according to the NaOH concentration. The rate of caustic IGSCC attack did not appear to increase monotonically with caustic concentrations, but peaked at a concentration between 4 and 50% caustic, or approximately 30% NaOH. Intergranular stress corrosion cracking was found for C600MA in 10, 20, and 30% NaOH solutions, while no cracking was observed in the 4 and 50% NaOH solutions. In 30% NaOH solution, transgrnular stress corrosion cracking was detected in C690TT, which may be related with the large amount of plastic strain (150% yield) and the applied potential (125 mV vs. OCP). The overall data clearly indicate that C600MA has the worst SCC resistance while K690TT offers the best resistance. There is also fairly good correlation between the caustic SCC susceptibility and some metallurgical parameters, particularly the grain size and the yield strength at room temperature. Specifically, materials having larger grain size and lower yield strength exhibited higher caustic SCC resistance.     

Single-view-based 3D facial reconstruction method robust against pose variations

The 3D Morphable Model (3DMM) and the Structure from Motion (SfM) methods are widely used for 3D facial reconstruction from 2D single-view or multiple-view images. However, model-based methods suffer from disadvantages such as high computational costs and vulnerability to local minima and head pose variations. The SfM-based methods require multiple facial images in various poses. To overcome these disadvantages, we propose a single-view-based 3D facial reconstruction method that is person-specific and robust to pose variations. Our proposed method combines the simplified 3DMM and the SfM methods. First, 2D initial frontal Facial Feature Points (FFPs) are estimated from a preliminary 3D facial image that is reconstructed by the simplified 3DMM. Second, a bilateral symmetric facial image and its corresponding FFPs are obtained from the original side-view image and corresponding FFPs by using the mirroring technique. Finally, a more accurate the 3D facial shape is reconstructed by the SfM using the frontal, original, and bilateral symmetric FFPs. We evaluated the proposed method using facial images in 35 different poses. The reconstructed facial images and the ground-truth 3D facial shapes obtained from the scanner were compared. The proposed method proved more robust to pose variations than 3DMM. The average 3D Root Mean Square Error (RMSE) between the reconstructed and ground-truth 3D faces was less than 2.6 mm when 2D FFPs were manually annotated, and less than 3.5 mm when automatically annotated.     

Object tracking based on an online learning network with total error rate minimization

This paper presents a visual object tracking system which is tolerant to external imaging factors such as illumination, scale, rotation, occlusion and background changes. Specifically, an integration of an online version of total-error-rate minimization based projection network with an observation model of particle filter is proposed to effectively distinguish between the target object and the background. A re-weighting technique is proposed to stabilize the sampling of particle filter for stochastic propagation. For self-adaptation, an automatic updating scheme and extraction of training samples are proposed to adjust to system changes online. Our qualitative and quantitative experiments on 16 public video sequences show convincing performances in terms of tracking accuracy and computational efficiency over competing state-of-the-art algorithms.     

A note on the optimal pricing and production decisions with price‐driven substitution

Recently, Kim and Bell ( 2011 ) developed a revenue managemnent pricing model with price‐driven substitution. The authors considered production decisions under unlimited production capacity and investigated the impact of price‐driven substitution on a firm's pricing and production decisions. The authors modeled the consumer demands for each market segment as linear additive demand function based on exogenous variables, where demand substitution occurred as a function of price differences between the two products. In this article, we extend this work to examine the impact of a production capacity constraint on the firm's joint pricing and inventory decisions. Based on this extended model, we investigate the impact of price‐driven substitution on a firm's pricing and production decisions where there is a limit on total capacity. We show how revenue managers should adjust prices and production levels to take into account price‐driven substitution under a capacity constraint setting. Both deterministic and stochastic models are developed, and the impact of price‐driven substitution and a capacity constraint on the optimal prices, production levels, and revenues is illustrated.