Kinetic study of Chinese biomass slow pyrolysis: Comparison of different kinetic models

The slow pyrolysis of six Chinese biomasses was studied by thermogravimetric experiments. Non-linear square fitting method is used to calculate DTG data. The analysis results show that it is not possible to exactly represent the biomass pyrolysis by a one-step model with different mechanisms. Thus, three-pseudocomponent models were used to simulate the biomass pyrolysis. It was found that the three-pseudocomponent model with n-order kinetics (model II) is more accurate than the model with first-order kinetics (model I). Activation energies of three-pseudocomponents in model II are bigger than the values in model I. It is shown that model II yields the best simulation results, especially with respect to describe accurately the pyrolysis of the first pseudocomponent (hemicellulose) and the last one (lignin). Nevertheless, with regard to a practical utilization, the three-pseudocomponent model with a reaction order of one could be used, because the accuracy to represent biomass pyrolysis is high enough. Unrealistic high values of the reaction order are avoided, and thus this model is more realistic with respect to the chemical interpretation of the reaction order.     

Time dependences of conduction and self‐heating in acetylene carbon black‐filled high‐density polyethylene composites

The time dependences of electrical conduction and self‐heating behaviors in high‐density polyethylene filled with acetylene carbon black of 0.082 in volume fraction are studied in relation to voltage and ambient temperature. The characteristic decay current constant τ_i, and the exponential growth time constant for self‐heating τ_g are determined for the samples under voltages U above the critical value U_c for the onset of self‐heating. The influences of voltage and ambient temperature on τ_i and τ_g as well as the amplitude of the low‐resistance to high‐resistance switching are discussed on the basis of the random resistor network (RRN) model and the relationship between U_c and the intrinsic resistivity. © 2005 Wiley Periodicals, Inc. J Appl Polym Sci 99: 1124–1131, 2006     

Holistic schedulability analysis for multipacket messages in AFDX networks

The ARINC-664, Part 7 (AFDX) standard defines a communication network based on Ethernet and the UDP/IP protocols. Contrary to general-purpose Ethernet, the timing behavior in AFDX is deterministic due to the use of special network switches and end systems with static routing tables and traffic policing at the sending end through mechanisms called virtual links. Even though the latencies in this network are bounded, there are scheduling and contention effects that need to be analyzed. In this paper we develop a response-time analysis for multipacket messages transmitted through an AFDX network including the scheduling of the virtual links and sub-virtual links, and also the contention in the end systems and in the switches. This analysis allows us to obtain worst-case latencies and output jitter for the network messages with a precise modeling of the sending and receiving ends. These results can be integrated in a holistic approach with the response time analysis of the threads in the processing nodes to obtain end-to-end response times in heterogeneous distributed systems.     

Automatic inpainting by removing fence-like structures in RGBD images

Recent inpainting techniques usually require human interactions which are labor intensive and dependent on the user experiences. In this paper, we introduce an automatic inpainting technique to remove undesired fence-like structures from images. Specifically, the proposed technique works on the RGBD images which have recently become cheaper and easier to obtain using the Microsoft Kinect. The basic idea is to segment and remove the undesired fence-like structures by using both depth and color information, and then adapt an existing inpainting algorithm to fill the holes resulting from the structure removal. We found that it is difficult to achieve a satisfactory segmentation of such structures by only using the depth channel. In this paper, we use the depth information to help identify a set of foreground and background strokes, with which we apply a graph-cut algorithm on the color channels to obtain a more accurate segmentation for inpainting. We demonstrate the effectiveness of the proposed technique by experiments on a set of Kinect images.     

Nonflat surface level pyramid: a high connectivity multidimensional interconnection network

Parallel machines are extensively used to increase computational speed in solving different scientific problems. Various topologies with different properties have been proposed so far and each one is suitable for specific applications. Pyramid interconnection networks have potentially powerful architecture for many applications such as image processing, visualization, and data mining. The major advantage of pyramids which is important for image processing systems is hierarchical abstracting and transferring the data toward the apex node, just like the human being vision system, which reach to an object from an image. There are rapidly growing applications in which the multidimensional datasets should be processed simultaneously. For such a system, we need a symmetric and expandable interconnection network to process data from different directions and forward them toward the apex. In this paper, a new type of pyramid interconnection network called Non-Flat Surface Level (NFSL) pyramid is proposed. NFSL pyramid interconnection networks constructed by L-level A-lateral-base pyramids that are named basic-pyramids. So, the apex node is surrounded by the level-one surfaces of NFSL that are the first nearest level of nodes to apex in the basic pyramids. Two topologies which are called NFSL-T and NFSL-Q originated from Trilateral-base and Quadrilateral-base basic-pyramids are studied to exemplify the proposed structure. To evaluate the proposed architecture, the most important properties of the networks are determined and compared with those of the standard pyramid networks and its variants.     

Robustness of globally exponential stability of delayed neural networks in the presence of random disturbances

This paper analyzes the robustness of globally exponential stability of time-varying delayed neural networks (NNs) subjected to random disturbances. Given a globally exponentially stable neural network, and in the presence of noise, we quantify how much noise intensity that the delayed neural network can remain to be globally exponentially stable. We characterize the upper bounds of the noise intensity for the delayed NNs to sustain globally exponential stability. The upper bounds of parameter uncertainty intensity are characterized by using transcendental equation. A numerical example is provided to illustrate the theoretical result.     

Dynamic watermarking scheme for quantum images based on Hadamard transform

In this paper, a novel watermarking scheme for quantum images based on Hadamard transform is proposed. In the new scheme, a unitary transform controlled by a classical binary key is implemented on quantum image. Then, we utilize a dynamic vector, instead of a fixed parameter as in other previous schemes, to control the embedding process. The dynamic embedding vector is decided by both the carrier quantum image and the watermark image, which is only known by the authorized owner. The proposed scheme is analyzed from visual quality, computational complexity, and payload capacity. Analysis and results show that the proposed scheme has better visual quality under a higher embedding capacity and lower complexity compared with other schemes proposed recently.     

Relative image similarity learning with contextual information for Internet cross-media retrieval

With the fast explosive rate of the amount of image data on the Internet, how to efficiently utilize them in the cross-media scenario becomes an urgent problem. Images are usually accompanied with contextual textual information. These two heterogeneous modalities are mutually reinforcing to make the Internet content more informative. In most cases, visual information can be regarded as an enhanced content of the textual document. To make image-to-image similarity being more consistent with document-to-document similarity, this paper proposes a method to learn image similarities according to the relations of the accompanied textual documents. More specifically, instead of using the static quantitative relations, rank-based learning procedure by employing structural SVM is adopted in this paper, and the ranking structure is established by comparing the relative relations of textual information. The learning results are in more accordance with the human’s recognition. The proposed method in this paper can be used not only for the image-to-image retrieval, but also for cross-modality multimedia, where a query expansion framework is proposed to get more satisfactory results. Extensive experimental evaluations on large scale Internet dataset validate the performance of the proposed methods.