Multi-scale patch-based sparse appearance model for robust object tracking

When objects undergo large pose change, illumination variation or partial occlusion, most existing visual tracking algorithms tend to drift away from targets and even fail to track them. To address the issue, in this paper we propose a multi-scale patch-based appearance model with sparse representation and provide an efficient scheme involving the collaboration between multi-scale patches encoded by sparse coefficients. The key idea of our method is to model the appearance of an object by different scale patches, which are represented by sparse coefficients with different scale dictionaries. The model exploits both partial and spatial information of targets based on multi-scale patches. Afterwards, a similarity score of one candidate target is input into a particle filter framework to estimate the target state sequentially over time in visual tracking. Additionally, to decrease the visual drift caused by frequently updating model, we present a novel two-step object tracking method which exploits both the ground truth information of the target labeled in the first frame and the target obtained online with the multi-scale patch information. Experiments on some publicly available benchmarks of video sequences showed that the similarity involving complementary information can locate targets more accurately and the proposed tracker is more robust and effective than others.     

PARAMETER ESTIMATION IN EXPONENTIAL MODELS

Abstract. In this paper the problems of parameter estimation and order determination of an exponential (EX) model are studied in the time domain. In order to estimate the parameters, the parameter equations of an EX model are given in terms of the autocorrelation function, which is similar to the Yule-Walker equations of an autoregressive moving-average model. Estimates of parameters are obtained with the aid of the parameter equations and theorems are proved relating the convergence rate and asymptotic distribution of the estimates. We present two kinds of methods for estimating the order and prove that the estimates of the order are consistent.     

Lithium-ion battery remaining useful life estimation based on fusion nonlinear degradation AR model and RPF algorithm

The lithium-ion battery cycle life prediction with particle filter (PF) depends on the physical or empirical model. However, in observation equation based on model, the adaptability and accuracy for individual battery under different operating conditions are not fully considered. Therefore, a novel fusion prognostic framework is proposed, in which the data-driven time series prediction model is adopted as observation equation, and combined to PF algorithm for lithium-ion battery cycle life prediction. Firstly, the nonlinear degradation feature of the lithium-ion battery capacity degradation is analyzed, and then, the nonlinear accelerated degradation factor is extracted to improve prediction ability of linear AR model. So an optimized nonlinear degradation autoregressive (ND–AR) time series model for remaining useful life (RUL) estimation of lithium-ion batteries is introduced. Then, the ND–AR model is used to realize multi-step prediction of the battery capacity degradation states. Finally, to improve the uncertainty representation ability of the standard PF algorithm, the regularized particle filter is applied to design a fusion RUL estimation framework of lithium-ion battery. Experimental results with the lithium-ion battery test data from NASA and CALCE (The Center for Advanced Life Cycle Engineering, the University of Maryland) show that the proposed fusion prognostic approach can effectively predict the battery RUL with more accurate forecasting result and uncertainty representation of probability density distribution (pdf).     

A self-adaptive intelligent single-particle optimizer compression algorithm

This paper presents a self-adaptive intelligent single-particle optimizer (AdpISPO) for DNA sequence data compression codebook design. Featured with the crucial self-adaptive optimization process, AdpISPO is capable of attaining better fitness value than most existing particle swarm optimization variants with no specific parameters required. A novel DNA sequence data compression algorithm, namely BioSqueezer, is proposed in this paper. Introducing all the unique data features in constructing the compression codebook, BioSqueezer compresses DNA sequences by replacing similar fragments with the index of its corresponding code vector. For attaining higher compression ratio, the AdpISPO is employed in BioSqueezer for the codebook design. Experimental results on benchmark DNA sequences demonstrate that BioSqueezer attains better performance than other state-of-the-art DNA compression algorithms.     

C-19 diacid-based polyamides

The preparation and properties of polyamide hot-melt adhesives derived from Koch C-19 diacid are described. They are comparable to those of commercial C-36 dimer acid-based polyamides for bonding leather and metals and somewhat superior to those of polyamides made from C-19 diacid derived from a hydroformylation process. Presented at the AOCS annual meeting, Toronto, May 1982.     

生物质焦油裂解催化剂制备及其催化裂解性能

以萘为生物质焦油模型化合物 ,在以白云石为载体制备的 Ni基催化剂上进行了催化裂解实验研究 ,对催化剂的制备技术、活性、积炭失活性能和再生方式进行了实验分析。结果表明 :在 70 0℃的裂解温度、重量空速 0 .8L· h-1的反应条件下 ,萘的单程转化率可达 95 %,以饱和湿空气为再生气流 ,在程序升温的条件下烧碳 ,催化剂再生时间短 (<0 .5 h) ,与同温度的热裂解相比催化裂解更有利于萘的深度裂解 .     

复杂制品注塑成型模拟分析

应用CAE软件MoldflowPlasticsInsight4.1的充模、保压、冷却、翘曲等分析模块,对汽车尾门饰件注塑成型过程进行CAE分析,研究了浇口位置、工艺参数等对制品性能的影响。最后,基于模拟结果分析了制品翘曲的主要原因,为模具设计及注塑工艺的确定提供参考。     

耐碱玻璃纤维砖骨料混凝土性能正交试验研究

以水胶比、粉煤灰掺量、砖骨料取代率、耐碱玻璃纤维体积掺量和耐碱玻璃纤维长度作为试验因素,通过设置五因素三水平正交试验,研究耐碱玻璃纤维二次破碎砖骨料混凝土28 d的立方体抗压强度以及28 d劈裂抗拉强度两种评价指标的变化规律.结果 表明:两种评价指标受水胶比、粉煤灰掺量、耐碱玻璃纤维体积掺量影响较大,二次破碎砖骨料取代率对劈裂抗拉强度影响较小,耐碱玻璃纤维长度对二者影响均不显著,综合考虑配置耐碱玻璃纤维砖骨料混凝土的最佳因素水平方案为水胶比0.4、砖骨料50％、粉煤灰10％、耐碱玻璃纤维体积掺量和长度分别为0.1％和16 mm.此结论可为耐碱玻璃纤维再生砖骨料新型混凝土工程使用提供试验依据.     

基于计算机测控技术的转矩流变仪的研制

介绍了基于计算机测控技术的聚合物转矩流变仪的研制，该仪器具有优良的性能指标，强大的数据文件处理能力、测试功能扩展能力，是高分子材料研究和开发的理想工具。