面向业务问题求解的知识供应过程模型

为了满足企业在业务问题求解方面的实际需求,提出了一个面向业务问题求解的知识供应过程模型。该模型提出了知识供应过程中的问题空间、知识空间和流程空间的概念及其关联关系,并进一步给出了包含知识的获取、评价、存储和重用的知识供应过程的有关算法。该模型与算法将业务问题求解过程与知识供应过程进行了融合,通过关联关系实现了知识的主动供应,在优化了业务问题求解过程的同时,提高了知识积累的质量和知识重用的效率。基于该模型建立了知识管理问题求解的原型系统KMPSS,并得到了初步应用验证。     

基于LabVIEW 8.0测定纯液体蒸发焓的虚拟仪器

将常规的数字温度仪与压力计通过串行口和计算机连接,构建基于LabVIEW 8.0测定纯液体蒸发焓的虚拟仪器,实现实验数据的动态采集、实时显示、自动绘制p～T曲线并显示曲线方程、自动数据处理和写成结果报告等。应用结果表明:基于LabVIEW构建的测定纯液体蒸发焓的虚拟仪器,免除人工操作的繁琐和人为误差,测定乙醇和苯蒸发焓的相对误差分别为0.86%、-2.97%,提高了测量结果的准确度,且具有界面友好,操作简便和功能易扩展等诸多优点。     

A hybrid clustering and graph based algorithm for tagSNP selection

TagSNP selection, which aims to select a small subset of informative single nucleotide polymorphisms (SNPs) to represent the whole large SNP set, has played an important role in current genomic research. Not only can this cut down the cost of genotyping by filtering a large number of redundant SNPs, but also it can accelerate the study of genome-wide disease association. In this paper, we propose a new hybrid method called CMDStagger that combines the ideas of the clustering and the graph algorithm, to find the minimum set of tagSNPs. The proposed algorithm uses the information of the linkage disequilibrium association and the haplotype diversity to reduce the information loss in tagSNP selection, and has no limit of block partition. The approach is tested on eight benchmark datasets from Hapmap and chromosome 5q31. Experimental results show that the algorithm in this paper can reduce the selection time and obtain less tagSNPs with high prediction accuracy. It indicates that this method has better performance than previous ones.     

Control of frictional dynamics of a one-dimensional particle array

Control of frictional forces is required in many applications of tribology. While the problem is approached by chemical means traditionally, a recent approach was proposed to control the system mechanically to tune frictional responses. We design feedback control laws for a one-dimensional particle array sliding on a surface subject to friction. The Frenkel-Kontorova model describing the dynamics is a nonlinear interconnected system and the accessible control elements are average quantities only. We prove local stability of equilibrium points of the un-controlled system in the presence of linear and nonlinear particle interactions, respectively. We then formulate a tracking control problem, whose control objective is for the average system to reach a designated targeted velocity using accessible elements. Sufficient stabilization conditions are explicitly derived for the closed-loop error systems using the Lyapunov theory based methods. Simulation results show satisfactory performances. The results can be applied to other physical systems whose dynamics is described by the Frenkel-Kontorova model.     

A preliminary attempt to use radiomic features in the diagnosis of extra-articular long head biceps tendinitis

Magnetic Resonance Materials in Physics, Biology and Medicine - This study aims to present a radiomic application in diagnosing the long head of biceps (LHB) tendinitis. Moreover, we evaluated...     

Relational attention-based Markov logic network for visual navigation

The Journal of Supercomputing - We argue the agent’s low generalization problem for searching target object in challenging visual navigation could be solved by "how" and...     

Syntax–Aware graph convolutional network for the recognition of chinese implicit inter-sentence relations

The Journal of Supercomputing - In the literature, most previous studies on English implicit inter-sentence relation recognition only focused on semantic interactions, which could not exploit the...     

Joint Classification and Regression for Visual Tracking with Fully Convolutional Siamese Networks

International Journal of Computer Vision - Visual tracking of generic objects is one of the fundamental but challenging problems in computer vision. Here, we propose a novel fully convolutional...     

A Survey on Long-Tailed Visual Recognition

The heavy reliance on data is one of the major reasons that currently limit the development of deep learning. Data quality directly dominates the effect of deep learning models, and the long-tailed distribution is one of the factors affecting data quality. The long-tailed phenomenon is prevalent due to the prevalence of power law in nature. In this case, the performance of deep learning models is often dominated by the head classes while the learning of the tail classes is severely underdeveloped. In order to learn adequately for all classes, many researchers have studied and preliminarily addressed the long-tailed problem. In this survey, we focus on the problems caused by long-tailed data distribution, sort out the representative long-tailed visual recognition datasets and summarize some mainstream long-tailed studies. Specifically, we summarize these studies into ten categories from the perspective of representation learning, and outline the highlights and limitations of each category. Besides, we have studied four quantitative metrics for evaluating the imbalance, and suggest using the Gini coefficient to evaluate the long-tailedness of a dataset. Based on the Gini coefficient, we quantitatively study 20 widely-used and large-scale visual datasets proposed in the last decade, and find that the long-tailed phenomenon is widespread and has not been fully studied. Finally, we provide several future directions for the development of long-tailed learning to provide more ideas for readers.