微分中值定理证明中辅助函数的方法

本文给出了一种用辅助函数证明微分中值定理的新方法,并说明了它在其它数学问题中的应用。     

Distributed Speech Recognition Systems and Some Key Factors Affecting It''''s Performance

1　IntroductionWiththerapiddevelopmentofInternetandmobilecommunications,theresearchonwirelessmobilenetworkshasbecomethenewhotspotinin formationtechnologyfield .Asweknow ,mobileterminals (mobilephone,forexample)arethetooltohelppeopletoaccessanyinformationtheyneedbyanymediaatanytimeandanywhere .However,itissometimesdifficultformobileterminalstoplaysucharolebecauseofthelimitationssuchaslimitedca pacity ,lowpower,andinconvenienceinoperating .So,peoplehopetofindanotherwaytoaccessnet workinformatio…     

Graph-Based Dimensionality Reduction for Hyperspectral Imagery: A Review

Hyperspectral image(HSI) contains a wealth of spectral information, which makes fine classification of ground objects possible. In the meanwhile, overly redundant information in HSI brings many challenges. Specifically, the lack of training samples and the high computational cost are the inevitable obstacles in the design of classifier. In order to solve these problems, dimensionality reduction is usually adopted. Recently, graph-based dimensionality reduction has become a hot topic. In this paper, the graph-based methods for HSI dimensionality reduction are summarized from the following aspects. 1) The traditional graph-based methods employ Euclidean distance to explore the local information of samples in spectral feature space. 2) The dimensionality-reduction methods based on sparse or collaborative representation regard the sparse or collaborative coefficients as graph weights to effectively reduce reconstruction errors and represent most important information of HSI in the dictionary. 3) Improved methods based on sparse or collaborative graph have made great progress by considering global low-rank information, local intra-class information and spatial information. In order to compare typical techniques, three real HSI datasets were used to carry out relevant experiments, and then the experimental results were analysed and discussed.Finally, the future development of this research field is prospected.     

THE APPARENT VISCOSITY OF SEMI-SOLID AZ91D ALLOY AT STEADY STATE

The rheological behavior of semi-solid AZ91D at steady state is studied using a Couette type viscometer in the present paper. The results show that the apparent viscosity of semi-solid AZ91D at the steady state increases with the solid fraction increasing, and goes up sharply when the solid fraction reaches a certain value, which is called critical fraction. In addition, the apparent viscosity of semi-solid AZ91D at the steady state takes on a distinct downtrend with the shearing rate increasing, which indicates a strong shear thinning property. In addition, the critical solid fraction becomes higher under larger shearing rate, owing to the more globular shape of the solid particles. Based on the present experiment results, an empirical equation is built as that, relating the steady state apparent viscosity of semi-solid AZ91D with the solid fraction fs andshearing rate (?) at the same time: ηapp=10.74exp(6.95fs)(?)-0.86.     

Contrastive Meta-learning on Heterogeneous Information Networks for Cold-start Recommendation

In recommender systems, the cold-start issue is challenging due to the lack of interactions between users or items. Such an issue can be alleviated via data-level and model-level strategies. Traditional data-level methods employ auxiliary information like feature information to enhance the learning of user and item embeddings. Recently, Heterogeneous Information Networks (HINs) have been incorporated into the recommender system as they provide more fruitful auxiliary information and meaningful semantics. However, these models are unable to capture the structural and semantic information comprehensively and neglect the unlabeled information of HINs during training. Model-level methods propose to apply the meta-learning framework which naturally fits into the cold-start issue, as it learns the prior knowledge from similar tasks and adapts to new tasks quickly with few labeled samples. Therefore, we propose a contrastive meta-learning framework on HINs named CM-HIN, which addresses the cold-start issue at both data level and model level. In particular, we explore meta-path and network schema views to describe the higher-order and local structural information of HINs. Within meta-path and network schema views, contrastive learning is adopted to mine the unlabeled information of HINs and incorporate these two views. Extensive experiments on three benchmark datasets demonstrate that CM-HIN outperforms all state-of-the-art baselines in three cold-start scenarios.     

Distribution and release of 2,4,5-trichlorobiphenyl in ice

The distribution of persistent organic pollutants(POPs) in ice and the release of POPs from ice during ice melting have an important influence on the eco-environment and water quality of a river. Through laboratory simulation experiments, the distribution and release of 2,4,5-trichlorobiphenyl(PCB29) in ice and the partition coefficients of PCB29 in ice water at different temperatures, concentrations, and p H levels were studied. The results showed that, at different temperatures and concentrations, the concentration of PCB29 in ice increased progressively with depth. The modes of release of different concentrations of PCB29 from ice were obtained. A large amount of PCB29 was released rapidly in the first melting period, and then the remaining PCB29 was released uniformly. The p H value dominated both the distribution and late release of PCB29 in ice. In ice water, at different temperatures, concentrations, and p H levels, the majority of PCB29 entered the water, and a lesser amount remained in the ice. Finally, laboratory experiment results were verified with field investigations. A theoretical framework is provided by this research of the behavior of POPs in ice under different environmental conditions, but a more quantitative understanding of the behavior of POPs in ice will need to be developed through further laboratory studies combined with field investigations.     

A mathematical morphology-based multi-level filter of LiDAR data for generating DTMs

Light detecting and ranging(LiDAR)technology has become an efective way to generate highresolution digital terrain models(DTMs).To generate DTMs,point measurements from non-ground features,such as buildings,vegetation and vehicles,have to be identifed and removed while preserving the terrain points.This paper proposes an efcient mathematical morphology-based multi-level flter to generate DTMs from airborne LiDAR data.Preliminary non-ground points are frst identifed with the characteristics of the multiecho airborne LiDAR data.The localized mathematical morphology opening operations are then immediately applied to the remaining points.By gradually increasing the window size of the flter and using a dynamic critical gradient threshold,the non-ground points are removed,while the ground points are preserved.Eight samples were chosen from eight sites provided with the ISPRS Commission III,Working Group 3,to evaluate the accuracy of our algorithm.Both the qualitative and quantitative experiment analyses show that our morphologybased multi-level flter method achieves promising results,not only in flat urban areas but also in rural areas,especially in preserving complex terrain details,while non-ground spatial objects are removed.     

基于混沌伪随机序列的流密码的分割攻击

研究了文献[2]提出的基于混沌伪随机序列设计的流密码算法,分析了其信息泄漏规律.利用该算法所产生的乱数序列具有前几个比特对密钥的低位比特变化不够敏感的性质,提出了能够有效降低该流密码算法密钥熵的分割攻击方法.在密钥长度为128比特时,该分割攻击方法的成功率为0.9498,平均计算复杂性至多为279.     

基于嵌入式Linux的移动终端的软件设计

移动终端是车辆监控系统的重要组成部分,主要用来接收GPS信号并对其进行处理,然后通过GPRs网络发送给监控中心.本文介绍了一种嵌入式Linux系统平台下,移动终端软件的设计方案及其相关实现.