Dynamic modeling and analysis of the closed-circuit grinding-classification process

Mathematical models of the grinding process are the basis of analysis, simulation and control. Most existent models including theoretical models and identification models are, however, inconvenient for direct analysis. In addition, many models pay much attention to the local details in the closed-circuit grinding process while overlooking the systematic behavior of the process as a whole. From the systematic perspective, the dynamic behavior of the whole closed-circuit grinding-classification process is considered and a first-order transfer function model describing the dynamic relation between the raw material and the product is established. The model proves that the time constant of the closed-circuit process is lager than that of the open-circuit process and reveals how physical parameters affect the process dynamic behavior. These are very helpful to understand, design and control the closed-circuit grinding-classification process.     

Review on the study of metallurgical process engineering

After nearly one hundred years of research, metallurgy(metallurgical science and engineering) has gradually become a system with three levels of knowledge:(1) micro metallurgy at the atomic/molecular scale,(2) process metallurgy at the procedure/device, and(3) macrodynamic metallurgy at the full process/process group. Macro-dynamic metallurgy development must eliminate the concept of an "isolated system" and establish concepts of "flow," "process network," and "operating program" to study the "structure–function–efficiency" in the macrodynamic operation of metallurgical manufacturing processes. It means considering "flow" as the ontology and observing dynamic change by"flow" to solve the green and intelligent potential of metallurgical enterprises. Metallurgical process engineering is integrated metallurgy, toplevel designed metallurgy, macro-dynamic operated metallurgy, and engineering science level metallurgy. Metallurgical process engineering is a cross-level, comprehensive, and integrated study of the macro-dynamic operation of manufacturing processes. Metallurgical process engineering studies the physical nature and constitutive characteristics of the dynamic operation of steel manufacturing process, as well as the analysis-optimization of the set of procedure functions, coordination-optimization of the set of procedures' relations, and reconstruction-optimization of the set of procedures in the manufacturing process. The study establishes rules for the macro operation of the manufacturing process, as well as dynamic and precise objectives of engineering design and production operation.     

Resin Matrix／Fiber Reinforced Composite Material,Ⅲ ： Simulating Results

According to the mathematical model which describes the curing process of composites constructed from continuous fiber-reinforced, thermosetting resin matrix prepreg materials, and the consolidation of the composite, the computer code calculates the data of the process variables of the flat-plate composites cured by a specified cure cycle, provides the variation of temperature distribution, the cure degree process, the resin variation and fibers stress inside the composite, the void variation and the residual stress distribution. The mechanism of curing process is illustrated and the cure cycle of composite material is optimized.     

A Comparison Study between Suspension Casting Process and Low Superheat Casting Process

Taking GCr15 bearing steel as experiment material, the effects of suspension casting process and low superheat casting process on the solidification of ingot were studied comparehvely. The results show that both suspension casting process and low superheat casting process can improve the censeal segregation and crystal structure of ingot. As the acting mechanism is different between the two kinds of processes, it is found that suspension casting process is more effective than low superheat casting process in improving the quality of ingot.     

Relationship between Microcellular Foaming Injection Molding Process Parameters and Cell Size

In order to study the relationship between the main process parameters and the cell size, the mathematical model of cell growth of microcellular foaming injection process is built. Then numeric simulation is employed as experimental method, and the Taguchi method is used to analyze significance of effect of process parameters on the cell size. At last the process parameters are focused on melt temperature, injection time, mold temperature and pretidied volume. The significance order from big to small of the effect of each process parameters on cell size is melt temperature, pre-filled volume, injection time, and mold temperature. On the basis of above research, the effect of each process parameter on cell size is further researched. Appropriate reduction of the melt temperature and increase of the pre-filled volume can optimize the cell size effectively, while the effects of injection time and mold temperature on cell size are less significant.     

Precision cold extrusion for multi-pole parts

Material forming process of manufacturing the parts by directly acting on the materials is the most fundamental content of the advanced manufacturing technology (AMT) and the general summation of physical process of production. The advancement and development of forming process radically represent the level of manufacturing theory and technology of a country and/or a region.     

Momnts and limiting distribution of a portfolio of whole life annuity pollcies

A dual random model of a portfolio of variable amount whole life anunity is set with the mth moment of the present value of benefits.and the respective expressions of the monments under the assumption that the force of interest accumulation fuction is Wiener process of Omstein-Uhlenbeck process,Furthermore,the limiting dstribution of average cost of this protfolio is discussed with the expression of the limiting distribution under the assumption that the force of interest accumulation is an independent increment process.     

Effect of hot-dip galvanizing processes on the microstructure and mechanical properties of 600-MPa hot-dip galvanized dual-phase steel

A C-Mn dual-phase steel was soaked at 800℃ for 90 s and then either rapidly cooled to 450℃ and held for 30 s (process A) or rapidly cooled to 350℃ and then reheated to 450℃ (process B) to simulate the hot-dip galvanizing process. The influence of the hot-dip galvanizing process on the microstructure and mechanical properties of 600-MPa hot-dip galvanized dual-phase steel (DP600) was investigated using optical microscopy, scanning electron microscopy (SEM), transmission electron microscopy (TEM), and tensile tests. The results showed that, in the case of process A, the microstructure of DP600 was composed of ferrite, martensite, and a small amount of bainite. The granular bainite was formed in the hot-dip galvanizing stage, and martensite islands were formed in the final cooling stage after hot-dip galvanizing. By contrast, in the case of process B, the microstructure of the DP600 was composed of ferrite, martensite, bainite, and cementite. In addition, compared with the yield strength (YS) of the DP600 annealed by process A, that for the DP600 annealed by process B increased by approximately 50 MPa because of the tempering of the martensite formed during rapid cooling. The work-hardening coefficient (n value) of the DP600 steel annealed by process B clearly decreased because the increase of the YS affected the computation result for the n value. However, the ultimate tensile strength (UTS) and elongation (A₈₀) of the DP600 annealed by process B exhibited less variation compared with those of the DP600 annealed by process A. Therefore, DP600 with excellent comprehensive mechanical properties (YS=362 MPa, UTS=638 MPa, A₈₀=24.3%, n=0.17) was obtained via process A.     

A template-based approach for automatic mapping between business process and BPEL process

In order to support just-in-time reconfiguration of business process execution language （BPEL） process, a template-based mapping method is proposed to map business level process to BPEL process. The method builds up mapping relationships between the key points of business level process and BPEL process, which helps users to adjust the business level process with simplified operations, and automatically maps the changes to BPEL. In addition, a case of the application of the method in a clothing enterprise was illustrated.     

Study on Professional Process of Bra Design

The process of bra design in the underwear industry is studied. Several important aspects of the process were identified: sizing, fabric selection, pattern development and grading, the use of CAD system, fitting and wear trials. Although the design process relies heavily on the expertise and experience of designers, the modern technology such as CAD can facilitate and optimize the design process, and the fitting process on life models is essential for the underwear design. The differences between domestic underwear companies and foreign major ones mainly lie at the lack of dress form specially used for underwear design, lack of CAD/CAM or not making full use of them, and most of all, lack of the professional bra designers or even skillful pattern designers. The prospects and future model of bra design process were also elaborated in this paper.     

Visual categorization shapes feature selectivity in the primate temporal cortex.

The way that we perceive and interact with objects depends on our previous experience with them. For example, a bird expert is more likely to recognize a bird as a sparrow, a sandpiper or a cockatiel than a non-expert. Neurons in the inferior temporal cortex have been shown to be important in the representation of visual objects; however, it is unknown which object features are represented and how these representations are affected by categorization training. Here we show that feature selectivity in the macaque inferior temporal cortex is shaped by categorization of objects on the basis of their visual features. Specifically, we recorded from single neurons while monkeys performed a categorization task with two sets of parametric stimuli. Each stimulus set consisted of four varying features, but only two of the four were important for the categorization task (diagnostic features). We found enhanced neuronal representation of the diagnostic features relative to the non-diagnostic ones. These findings demonstrate that stimulus features important for categorization are instantiated in the activity of single units (neurons) in the primate inferior temporal cortex.     

基于稀疏转稠密机制的仿射不变特征匹配算法

提出一种基于稀疏、稠密特征转换的仿射不变特征匹配算法,其中稀疏特征包括坐标,尺度,仿射模拟参数等,稠密特征指基于图像局部区域内光学属性的局部描述符.本文算法在Affine-SIFT算法基础之上,针对在特征提取阶段仅使用稀疏特征提取的缺陷做出了改进.由于稠密信息只有在稀疏参数满一定足检测条件时才能提取到特征,导致本可以匹配到的特征(包括稀疏、稠密参数)无法提取,将通过使用稀疏特征构造新的模拟图像,通过将稀疏特征重新稠密化,并在模拟图像基础上进一步提取稀疏特征,同时可检测到原始图像中检测不到的可匹配特征,最终达到增大特征建立匹配的概率,提升正确匹配数量的目标.经实验验证,本文提出的稀密特征转换算法相比于ASIFT算法能大量增加特征匹配的数量.除针对ASIFT方法提供扩展外,该方法也可用于扩展具有充分稀疏特征参数的其它特征提取和匹配方法,并适用于目标识别、目标分类和三维重建等问题.     

一种噪声环境下的语音识别方法(线性预测误差法)的研究

介绍一种平稳噪声环境下语音识别的新的方法。该方法利用噪声的LPC系数去预测语音信号,从而得到LPC预测序列,然后把它代替原语音序列来进行语音端点的检测、语音特征的提取和在合适的匹配方式下的识别。实验结果表明：该法在噪声环境下自动检测语音端点和提取语音信号的特征是可行的,获得了很满意的识别率。     

Mel 频率下基于 LPC 的语音信号深度特征提取算法

针对传统语音信号二次特征提取方法在保证识别率的前提下,实时性较差的问题,提出一种Mel频率下基于线性预测系数(linear predictive coefficient,LPC)的改进的语音信号深度特征提取算法.该方法根据人耳的听觉特性把LPC在Mel频率下进行非线性变换,再进行微分、高阶微分和按比例重组等步骤,得到一种既考虑声道激励又兼顾人耳听觉的新特征参数,从而大大减少传统语音信号深度特征提取的计算量,在不影响识别效率的情况下,极大提高系统的实时性.最后,将该算法在智能轮椅平台进行有效性验证,大量实验表明,语音控制系统实时性差的问题在使用该算法后能够得到明显改善,该算法既保证了特征提取识别率,也有效地改善了系统的实时性.在一定程度上使语音控制智能轮椅更具实用性.     

基于特征概念自动提取系统的研究

文本分类与文本信息特征概念的提取是当前智能信息服务研究的重点,为自动获取新的特征概念,提出了一种基于特征概念的自动提取系统,该系统包括分词、综合文本词权处理、类别归属和特征概念提取等部分,能有效地从概念上提取文本类特征,提高文本自动分类的准确性．     

一种新的复杂细胞启发下的特征提取方法

鉴于V1区复杂细胞具有提取外界刺激不变本质的能力,设计了一个提取目标图像局部不变特征的方法.该方法首先使用提出的无监督算法(PCICA),从图像中学习出类似于复杂细胞感受野的滤波器集合.然后利用这些滤波器组成的复杂细胞描述子,提取目标图像各个位置的不变特征.最后对图像特征图进行分块统计,将各区域的直方图序列作为图像的最终描述.测试结果表明,PCICA具有类似于快速独立分量分析算法(FastICA)三阶收敛的特点,从图像中学习出的滤波器集合,表现出复杂细胞感受野的拓扑结构.这些滤波器对于局部图像的微小变化并不敏感,对于检测不变特征十分有利,并在MNIST手写体数据库上取得0.84%的识别错误率.     

A New Approach of Feature Selection for Text Categorization

This paper proposes a new approach of feature selection based on the independent measure between features for text categorization. A fundamental hypothesis that occurrence of the terms in documents is independent of each other, widely used in the probabilistic models for text categorization （TC）, is discussed. However, the basic hypothesis is incom plete for independence of feature set. From the view of feature selection, a new independent measure between features is designed, by which a feature selection algorithm is given to ob rain a feature subset. The selected subset is high in relevance with category and strong in independence between features, satisfies the basic hypothesis at maximum degree. Compared with other traditional feature selection method in TC （which is only taken into the relevance account）, the performance of feature subset selected by our method is prior to others with experiments on the benchmark dataset of 20 Newsgroups.     

一种融合点线特征的视觉里程计架构设计与定位实现

基于视觉的同时定位与地图构建（SLAM）技术是实现移动机器人自主导航的关键.当机器人处在陌生环境中时,通常会利用周围目标的点特征来估计导航相机的位姿,并利用光束法平差来估计相机位姿和特征空间位置.但如果环境中的特征信息不丰富,则无法准确估计相机轨迹,且欧式坐标与反深度信息下的光束法平差部分条件下不收敛.为此,提出了一种在缺少特征点的环境下通过收集深度相机信息,同时利用点特征与线特征融合的视觉里程计,构建了融合视差角光束法平差与基于线特征的光束法平差的策略,从而使重投影误差达到最小化.最后与其他基于特征的SLAM系统进行比较,实验结果表明,在缺少特征点的真实环境中,系统位姿估计的性能与准确度得到提升.      

基于云计算的大数据关键特征高效提取方法研究

传统特征提取方法不适于大数据特征提取,提取效率低;为此,提出一种新的基于云计算的大数据关键特征高效提取方法。介绍了云计算中的MapReduce编程模型,给出Map与Reduce两个阶段的任务执行过程,通过MapReduce编程模型对大数据关键特征提取方法进行编程和处理,在云计算中实现高效运算。通过样本局部特征对特征集合进行评价,选择大数据关键特征。通过相空间重建保证大数据特征的不变性,在此基础上,引入关联维实现大数据关键特征的提取。实验结果表明,采用所提方法对大数据关键特征进行提取,可行性和效率高,与其他方法相比有更好的性能。     

基于视觉显著性的彩色图像分割

为了提高图像显著性检测的准确性,从数学模型上探索显著性的多特征空间.利用多尺度特征提取算法获得低层视觉特征,对特征矩阵用低秩矩阵恢复理论提取显著图,并在自底向上模型基础上融合了高层视觉特征,由高层视觉特征构成一幅权重的显著图.提高了显著度和显著目标的检测性能.通过自适应阈值算法对视觉显著目标进行分割.实验结果表明,该模型比传统的模型提取的显著目标更完整、更准确.