Multi-scale Young measures

We introduce multi-scale Young measures to deal with problems where multi-scale phenomena are relevant. We prove some interesting representation results that allow the use of these families of measures in practice, and illustrate its applicability by treating, from this perspective, multi-scale convergence and homogenization of multiple integrals.

     

Dispersion of silica nano-particles in sol-gel hybrid resins for fabrication of multi-scale hybrid nanocomposite

Multi-scale hybrid nanocomposites containing both ∼15 nm silica colloids and ∼2 nm oligosiloxanes in a methacryl polymer matrix were newly designed and fabricated. Colloidal silica sols were dispersed in methacryl oligosiloxanes nano-hybrid resins synthesized by sol-gel reaction of methacryloxypropylmethoxysilane and diphenylsilanediol. On the basis of TEM and SANS analyses, it was confirmed that the silica colloids were compatibly dispersed and different sizes of colloidal silica and oligosiloxanes co-exist in the solutions. Multi-scale hybrid nanocomposites fabricated by UV and thermal curing with incorporation of silica colloids in the nano-hybrid materials show enhanced mechanical and thermal characteristics.     

Viscoelastic response and interlaminar delamination resistance of epoxy/glass fiber/functionalized graphene oxide multi-scale composites

Graphene oxide (GO) was functionalized using three different diamines, namely ethylenediamine (EDA), 4,4′-diaminodiphenyl sulfone (DDS) and p-phenylenediamine (PPD) to reinforce an epoxy/glass fiber (EP/GF) composite laminate, with the aim of improving the overall composite mechanical performance. Different mechanical characterization techniques were used to determine the mechanical performance, including: tensile stress strain, double cantilever beam (DCB) mode-I fracture toughness and dynamic mechanical thermal analysis (DMTA). Scanning electron microscopy (SEM) was used to support the results and conclusions. The results demonstrated remarkable enhancements in the mechanical performance of EP/GF composite laminates by incorporation of functionalized graphene oxide (FGO) nanofiller, whilst the mechanical performance of the GO reinforced composite only improved marginally. Finally, the mechanical performance of the EP/GF/FGO multi-scale composites was found to be dependent on the type of FGO functional groups; of which EDA exhibited the highest performance. These investigations confirmed that the EDA-FGO-reinforced EP/GF composites possess excellent potential to be used as multifunctional engineering materials in industrial applications.     

Identification,characterization and evolution of non-local quasi-Lagrangian structures in turbulence

The recent progress on non-local Lagrangian and quasi-Lagrangian structures in turbulence is reviewed. The quasi-Lagrangian structures, e.g., vortex surfaces in vis-cous flow, gas-liquid interfaces in multi-phase flow, and flame fronts in premixed combustion, can show essential Lagrangian following properties, but they are able to have topological changes in the temporal evolution. In addition, they can represent or influence the turbulent flow field. The challenges for the investigation of the non-local structures include their identification, characterization, and evolution. The improving understanding of the quasi-Lagrangian struc-tures is expected to be helpful to elucidate crucial dynamics and develop structure-based predictive models in turbulence.     

Two-scale image fusion of visible and infrared images using saliency detection

Military, navigation and concealed weapon detection need different imaging modalities such as visible and infrared to monitor a targeted scene. These modalities provide complementary information. For better situation awareness, complementary information of these images has to be integrated into a single image. Image fusion is the process of integrating complementary source information into a composite image. In this paper, we propose a new image fusion method based on saliency detection and two-scale image decomposition. This method is beneficial because the visual saliency extraction process introduced in this paper can highlight the saliency information of source images very well. A new weight map construction process based on visual saliency is proposed. This process is able to integrate the visually significant information of source images into the fused image. In contrast to most of the multi-scale image fusion techniques, proposed technique uses only two-scale image decomposition. So it is fast and efficient. Our method is tested on several image pairs and is evaluated qualitatively by visual inspection and quantitatively using objective fusion metrics. Outcomes of the proposed method are compared with the state-of-art multi-scale fusion techniques. Results reveal that the proposed method performance is comparable or superior to the existing methods.     

Good match exploration for thermal infrared face recognition based on YWF-SIFT with multi-scale fusion

Stable local feature detection is a critical prerequisite in the problem of infrared (IR) face recognition. Recently, Scale Invariant Feature Transform (SIFT) is introduced for feature detection in an infrared face frame, which is achieved by applying a simple and effective averaging window with SIFT termed as Y-styled Window Filter (YWF). However, the thermal IR face frame has an intrinsic characteristic such as lack of feature points (keypoints); therefore, the performance of the YWF-SIFT method will be inevitably influenced when it was used for IR face recognition. In this paper, we propose a novel method combining multi-scale fusion with YWF-SIFT to explore more good feature matches. The multi-scale fusion is performed on a thermal IR frame and a corresponding auxiliary visual frame generated from an off-the-shelf low-cost visual camera. The fused image is more informative, and typically contains much more stable features. Besides, the use of YWF-SIFT method enables us to establish feature correspondences more accurately. Quantitative experimental results demonstrate that our algorithm is able to significantly improve the quantity of feature points by approximately 38%. As a result, the performance of YWF-SIFT with multi-scale fusion is enhanced about 12% in infrared human face recognition.     

Infrared/laser multi-sensor fusion and tracking based on the multi-scale model

The state estimation problem of targets detected by infrared/laser composite detection system with different sampling rates was studied in this paper. An effective state estimation algorithm based on data fusion is presented. Because sampling rate of infrared detection system is much higher than that of the laser detection system, the theory of multi-scale analysis is used to establish multi-scale model in this algorithm. At the fine scale, angle information provided by infrared detection system is used to estimate the target state through the unscented Kalman filter. It makes full use of the high frequency characteristic of infrared detection system to improve target state estimation accuracy. At the coarse scale, due to the sampling ratio of infrared and laser detection systems is an integer multiple, the angle information can be fused directly with the distance information of laser detection system to determine the target location. The fused information is served as observation, while the converted measurement Kalman filter (CMKF) is used to estimate the target state, which greatly reduces the complexity of filtering process and gets the optimal fusion estimation. The simulation results of tracking a target in 3-D space by infrared and laser detection systems demonstrate that the proposed algorithm in this paper is efficient and can obtain better performance than traditional algorithm.     

Simultaneous studies of pressure effect on charge transport and photophysical properties in organic semiconductors: A theoretical investigation

High-mobility and strong luminescent materials are essential as an important component of organic photodiodes, having received extensive attention in the field of organic optoelectronics. Beyond the conventional chemical synthesis of new molecules, pressure technology, as a flexible and efficient method, can tune the electronic and optical properties reversibly. However, the mechanism in organic materials has not been systematically revealed. Here, we theoretically predicted the pressure-depended luminescence and charge transport properties of high-performance organic optoelectronic semiconductors, 2,6-diphenylanthracene (DPA), by first-principle and multi-scale theoretical calculation methods. The dispersion-corrected density functional theory (DFT-D) and hybrid quantum mechanics/molecular mechanics (QM/MM) method were used to get the electronic structures and vibration properties under pressure. Furthermore, the charge transport and luminescence properties were calculated with the quantum tunneling method and thermal vibration correlation function. We found that the pressure could significantly improve the charge transport performance of the DPA single crystal. When the applied pressure increased to 1.86 GPa, the hole mobility could be doubled. At the same time, due to the weak exciton coupling effect and the rigid flat structure, there is neither fluorescence quenching nor obvious emission enhancement phenomenon. The DPA single crystal possesses a slightly higher fluorescence quantum yield ～ 0.47 under pressure. Our work systematically explored the pressure-dependence photoelectric properties and explained the inside mechanism. Also, we proposed that the external pressure would be an effective way to improve the photoelectric performance of organic semiconductors.     

NUMERICAL SIMULATION WITH A COMPREHENSIVE CHEMICAL TRANSPORT MODEL OF NITRATE, SULFATE, AND AMMONIUM AEROSOL DISTRIBUTIONS OVER EAST ASIA

The transport and chemical production processes of nitrate, sulfate, and ammonium aerosols over East Asia were investigated by use of the Models-3 Community Multi-scale Air Quality （CMAQ） modeling system coupled with the Regional Atmospheric Modeling System （RAMS）. For the evaluation of the model＇s ability in depicting their 3-dimensional concentration distributions and temporal variations, modeled concentrations of nitrate, sulfate, and ammonium aerosols are compared with the observations obtained at a ground station in Japan in March 2001 and onboard of an aircraft DC-8 on 18 and 21 March 2001 during the Transport and Chemical Evolution over the Pacific （TRACE-P） field campaign. Comparison shows that simulated values of nitrate, sulfate, and ammonium aerosols are generally in good agreement with their observed data, and the model captures most important observed features, and reproduces temporal and spatial variations of nitrate, sulfate, and ammonium aerosol concentrations reasonably well, e.g., the timing and locations of the concentration spikes of nitrate, sulfate, and ammonium aerosols are well reproduced, but large discrepancies between observed and simulated values are also clearly seen at some points and some times due to the coarse grid resolution and uncertainties of the emissions used in this study. This comparison results indicate that CMAQ is able to simulate the distributions of nitrate, sulfate, and ammonium aerosols and their related species in the troposphere over East Asia reasonably well.     

Infrared image enhancement through saliency feature analysis based on multi-scale decomposition

To improve contrast between dim target region and background in infrared (IR) long-range surveillance, this paper proposes a fast image enhancement approach using saliency feature extraction based on multi-scale decomposition. Firstly, a smooth based multi-scale decomposition is designed and applied to original infrared image, generating sub-images with various frequency components at different decomposition levels. The dim target regions of sub-images are extracted by a local frequency-tuned based saliency feature detection method, secondly. With saliency maps created by saliency extraction using multi-scale local windows with different sizes, the sub-images are enhanced at different decomposition scales. Finally, the enhanced result is reconstructed by synthesizing the all sub-images with adjustable synthetic weights. Since salient areas are analyzed based on fast multi-scale image decomposition, IR image can be s enhanced with good contrast successfully and rapidly. Compared with other algorithms, the experimental results prove that the proposed method is robust and efficient for IR image enhancement.