23103906
Modeling of Fermentation Process Based on QDPSO-SVM
Utilizing the ability simple in application and quick in convergence of quantum delta-potential-well-based particle swarm optimization (QDPSO) algorithm and the high generalization ability of support vector machine (SVM), selecting the appropriate state variables, a dynamic time-varying model has been built. Using the model and algorithm to per-estimate some biochemical state variables which can not be measured on-line, and to optimise some operational variables. It is proved that the method is efficiency through the practical application of penicillin fermentation process.
2696556
Dynamic Modeling of Biotechnical Process Based on Online Support Vector Machine
Due to the complexity and high non-linearity of biotechnical process, most simple mathematical models cannot describe the behavior of biochemistry systems very well. Therefore, dynamic modeling of biotechnical process is indispensable. Support vector machine (SVM) is a novel machine learning method, which is powerful for the problem characterized by small sample, non-linearity, high dimension and local minima, and has high generalization. But currently most support vector machine regression (SVR) training algorithms are offline, which could not be suit for time-variant system. So an improved SVM called online support vector machine was presented to modeling for the dynamic feature of fermentation process. The model based on the modified SVM was developed and demonstrated using simulation experiments. Some models based on SVM were also presented. The result shows that the modeling based online SVM is superior to modeling based on SVW.
20260133
Temporal aggregates for Web services on the semantic Web
In this paper we describe how we encode our temporal aggregates ontology in OWL for Web services on the semantic Web. We also present one example to show how to use the ontology to represent temporal aggregates information.
24069960
Study of Improved Hierarchy Genetic Algorithm Based on Adaptive Niches
Canonical genetic algorithms have the defects of pre-maturity and stagnation when applied in optimizing problems. In order to avoid the shortcomings, an adaptive niche hierarchy genetic algorithm (ANHGA) is proposed. The algorithm is based on the adaptive mutation operator and crossover operator to adjust the crossover rate and probability of mutation of each individual, whose mutation values are decided using individual gradient. This approach is applied in Percy and Shubert function optimization. Comparisons of niche genetic algorithm (NGA), hierarchy genetic algorithm (HGA) and ANHGA have been done by establishing a simulation model and the results of mathematics model and actual industrial model show that ANHGA is feasible and efficient in the design of multi-extremum.
39269573
An adaptive edge-preserving artifacts removal filter for video post-processing
We propose an adaptive edge-preserving artifacts removal filter (EPARF) for post-processing video compressed using block-based motion compensation so as to improve visual quality. The proposed filter is based on half-quadratic regularization, and it can adaptively remove both blocking and ringing artifacts while preserving object edges. The proposed algorithm has been applied to video sequences compressed by the MSRA scalable video coding (SVC) codec with block-based motion-compensated temporal filtering (MCTF). Experimental results demonstrate that EPARF can significantly improve both subjective and objective quality.
7351167
Fast intermode decision in H.264/AVC video coding
The new video coding standard, H.264/MPEG-4 AVC, uses variable block sizes ranging from 4/spl times/4 to 16/spl times/16 in interframe coding. This new feature has achieved significant coding gain compared to coding a macroblock (MB) using fixed block size. However, this feature results in extremely high computational complexity when brute force rate distortion optimization (RDO) algorithm is used. This paper proposes a fast intermode decision algorithm to decide the best mode in intercoding. It makes use of the spatial homogeneity and the temporal stationarity characteristics of video objects. Specifically, spatial homogeneity of a MB is decided based on the MB's edge intensity, and temporal stationarity is decided by the difference of the current MB and it colocated counterpart in the reference frame. Based on the homogeneity and stationarity of the video objects, only a small number of intermodes are selected in the RDO process. The experimental results show that the fast intermode decision algorithm is able to reduce on the average 30% encoding time, with a negligible peak signal-to-noise ratio loss of 0.03 dB or, equivalently, a bit rate increment of 0.6%.
108674
Sample Selection for Maximal Diversity
The problem of selecting a sample subset sufficient to preserve diversity arises in many applications. One example is in the design of recombinant inbred lines (RIL) for genetic association studies. In this context, genetic diversity is measured by how many alleles are retained in the resulting inbred strains. RIL panels that are derived from more than two parental strains, such as the collaborative cross (Churchill et al., 2004), present a particular challenge with regard to which of the many existing lab mouse strains should be included in the initial breeding funnel in order to maximize allele retention. A similar problem occurs in the study of customer reviews when selecting a subset of products with a maximal diversity in reviews. Diversity in this case implies the presence of a set of products having both positive and negative ranks for each customer. In this paper, we demonstrate that selecting an optimal diversity subset is an NP-complete problem via reduction to set cover. This reduction is sufficiently tight that greedy approximations to the set cover problem directly apply to maximizing diversity. We then suggest a slightly modified subset selection problem in which an initial greedy diversity solution is used to effectively prune an exhaustive search for all diversity subsets bounded from below by a specified coverage threshold. Extensive experiments on real datasets are performed to demonstrate the effectiveness and efficiency of our approach.