Object segmentation and labeling by learning from examples

We propose a system that employs low-level image segmentation followed by color and two-dimensional (2-D) shape matching to automatically group those low-level segments into objects based on their similarity to a set of example object templates presented by the user. A hierarchical content tree data structure is used for each database image to store matching combinations of low-level regions as objects. The system automatically initializes the content tree with only "elementary nodes" representing homogeneous low-level regions. The "learning" phase refers to labeling of combinations of low-level regions that have resulted in successful color and/or 2-D shape matches with the example template(s). These combinations are labeled as "object nodes" in the hierarchical content tree. Once learning is performed, the speed of second-time retrieval of learned objects in the database increases significantly. The learning step can be performed off-line provided that example objects are given in the form of user interest profiles. Experimental results are presented to demonstrate the effectiveness of the proposed system with hierarchical content tree representation and learning by color and 2-D shape matching on collections of car and face images.     

On an evolutionary approach for constrained optimization problem solving

Over the last few decades, many different evolutionary algorithms have been introduced for solving constrained optimization problems. However, due to the variability of problem characteristics, no single algorithm performs consistently over a range of problems. In this paper, instead of introducing another such algorithm, we propose an evolutionary framework that utilizes existing knowledge to make logical changes for better performance. The algorithmic aspects considered here are: the way of using search operators, dealing with feasibility, setting parameters, and refining solutions. The combined impact of such modifications is significant as has been shown by solving two sets of test problems: (i) a set of 24 test problems that were used for the CEC2006 constrained optimization competition and (ii) a second set of 36 test instances introduced for the CEC2010 constrained optimization competition. The results demonstrate that the proposed algorithm shows better performance in comparison to the state-of-the-art algorithms.     

Effect of particle dimension on biocompatibility of carbon nanomaterials

With various emerging applications ranging from medicine to materials and electronics, the risk of exposure to nanomaterials is rapidly increasing. Several routes of exposure to nanomaterials exist; the most important being dermal contact and inhalation. In this dermal toxicity study, the cellular effects of carbon-based materials with diameters ranging from micro- to nano-dimension were investigated using mouse keratinocytes (HEL-30). The carbon materials tested included carbon fibers (CF; 10 μm diameter), carbon nanofibers (CNF; 100 nm diameter), multi-walled carbon nanotubes (MWCNT; 10 nm diameter), and single-walled carbon nanotubes (SWCNT; 1 nm diameter). CF and CNF did not significantly affect cell viability; however, MWCNT and SWCNT reduced cell viability in a time-dependent manner up to 48 h, with full recovery of mitochondrial function by the 72 h time point. After a 24 h exposure, cells exposed to MWCNT produced up to 3-fold higher increase in reactive oxygen species than those exposed to SWCNT. The results of this study suggest that high-aspect ratio carbon material toxicity is dependent on dimension and composition.     

Radiation crosslinking of linear polyethyleneimine-based nanoparticles grafted to poly(glycidyl methacrylate) via trimethylolpropane triacrylate as potential electronic packaging material

Linear polyethyleneimine (L-PEI)-based nanoparticles were synthesized via hydrolysis of poly-2-methyl-2-oxazoline (PMeOx), which was prepared by cationic ring-opening polymerization of the oxazoline five-membered ring. Herein, a kinetic study of the ring-opening polymerization reaction is discussed. The nuclear magnetic resonance spectrum of PMeOx verified the presence of repeating units and terminal groups in the polymer's structure. Molar ratios of PEI and PMeOx were characterized using size exclusion chromatography with low-polydispersity polymer chains as the controlled polymerization reaction. PEI and PMeOx exhibited narrow particle size distribution with hydrodynamic radii of 89 and 67 nm, respectively, as determined via dynamic light scattering analysis. In addition, atomic forces and scanning electron microscopy were used to investigate the topography of the PEI thin films. Poly(glycidyl methacrylate) P(GMA) was grafted onto a PEI chain in the presence of trimethylolpropane triacrylate (TMPTA) as the crosslinking agent to synthesize the P(GMA–PEI–TMPTA) tripolymer via free radical polymerization using gamma irradiation. The thermal characterization of the P(GMA–PEI–TMPTA) tripolymer was conducted using thermogravimetric analysis and differential scanning calorimeter. Generally, the thermal stability of the P(GMA–PEI–TMPTA) tripolymer was improved at low-glycidyl methacrylate concentrations. The prepared tripolymer could be used as effective packaging materials for electronics industries.     

用大容量内存，注意主板的内存Bank限制

我最近购买了一块华硕P5QL-EM主板,该主板的规格上写有最大支持8GB内存容量,但我插上了6GB和8GB容量的内存时却出现了无法开机的情况,一旦将总内存容量降低到4GB就没有问题。难道主板也有4GB内存容量的限制吗？     

The Modeling of Magnetorheological Elastomers: A State-of-the-Art Review

The magnetorheological elastomers (MREs) are novel multifunctional materials wherein their viscoelastic properties can be varied instantly under an application of applied magnetic field. Due to their field-dependent stiffness and damping properties, MREs are widely used in the development and design of MRE-based adaptive vibration isolators and absorbers and also biomedical engineering. Moreover, MREs due to their inherent magnetostriction effect have enormous potential for the development of soft actuators. The dynamic behavior of MREs is affected by various material parameters (e.g., matrix and particle types, particle concentration, additives) as well as mechanical and magnetic loading parameters (e.g., frequency, amplitude, temperature, magnetic flux density). Understanding and predicting the effect of materials and loading parameters on the response behavior of MREs are of paramount importance for the design of MRE-based adaptive structures and systems. This review paper mainly aims to provide a comprehensive study of material constitutive models to predict the nonlinear magnetomechanical behavior of MREs. Particular emphasis is paid to physics-based models including continuum- and microstructure-based models. Moreover, phenomenological models describing the dynamic magnetoviscoelastic behavior of MREs as well as the effect of temperature on the magnetomechanical behavior of such materials are properly addressed.     

A robust optimization approach for imprecise data envelopment analysis

Crisp input and output data are fundamentally indispensable in traditional data envelopment analysis (DEA). However, the input and output data in real-world problems are often imprecise or ambiguous. Some researchers have proposed interval DEA (IDEA) and fuzzy DEA (FDEA) to deal with imprecise and ambiguous data in DEA. Nevertheless, many real-life problems use linguistic data that cannot be used as interval data and a large number of input variables in fuzzy logic could result in a significant number of rules that are needed to specify a dynamic model. In this paper, we propose an adaptation of the standard DEA under conditions of uncertainty. The proposed approach is based on a robust optimization model in which the input and output parameters are constrained to be within an uncertainty set with additional constraints based on the worst case solution with respect to the uncertainty set. Our robust DEA (RDEA) model seeks to maximize efficiency (similar to standard DEA) but under the assumption of a worst case efficiency defied by the uncertainty set and it’s supporting constraint. A Monte-Carlo simulation is used to compute the conformity of the rankings in the RDEA model. The contribution of this paper is fourfold: (1) we consider ambiguous, uncertain and imprecise input and output data in DEA; (2) we address the gap in the imprecise DEA literature for problems not suitable or difficult to model with interval or fuzzy representations; (3) we propose a robust optimization model in which the input and output parameters are constrained to be within an uncertainty set with additional constraints based on the worst case solution with respect to the uncertainty set; and (4) we use Monte-Carlo simulation to specify a range of Gamma in which the rankings of the DMUs occur with high probability.     

Topology optimization and seismic collapse assessment of shape memory alloy (SMA)-braced frames: Effectiveness of Fe-based SMAs

This paper presents a seismic topology optimization study of steel braced frames with shape memory alloy (SMA) braces. Optimal SMA-braced frames (SMA-BFs) with either Fe-based SMA or NiTi braces are determined in a performance-based seismic design context. The topology optimization is performed on 5- and 10-story SMA-BFs considering the placement, length, and cross-sectional area of SMA bracing members. Geometric, strength, and performance-based design constraints are considered in the optimization. The seismic response and collapse safety of topologically optimal SMA-BFs are assessed according to the FEMA P695 methodology. A comparative study on the optimal SMA-BFs is also presented in terms of total relative cost, collapse capacity, and peak and residual story drift. The results demonstrate that Fe-based SMA-BFs exhibit higher collapse capacity and more uniform distribution of lateral displacement over the frame height while being more cost-effective than NiTi braced frames. In addition to a lower unit price compared to NiTi, Fe-based SMAs reduce SMA material usage. In frames with Fe-based SMA braces, the SMA usage is reduced by up to 80%. The results highlight the need for using SMAs with larger recoverable strains.