Tax Evasion: Models with Self-Audit

Different from traditional tax audit, supervision with self-audit is a combination of audit by the taxpayer himself and audit by the tax authority. This paper mainly studies a taxpayer＇s optimal policy of tax evasion under supervision with self-audit and its related properties, in order to deduce some effective suggestions and theoretical bases to restrain tax evasion. Assuming that only a certain proportion of evaded tax can be discovered when the audit is executed, the authors first formulate a static model with self-audit. This model is divided into two stages. At the first stage, taxpayers declare their taxes, then the tax authority chooses audit objects, based on a known probability, and announces the result; if the taxpayer is chosen, he will enter the second stage, during which he has a chance to pay the evaded tax and the corresponding late fees and then is audited by the tax authority. The authors show the existence and uniqueness of the optimal amount of tax evasion at the first stage and the optimal proportion to self-expose at the second stage. The authors also discuss the related properties of the interior solution, and do elasticity analyses on some parameters. Besides, the authors extend the static model into the corresponding two-period model, and study the existence and uniqueness of the solution of the extended model. Finally, under the assumption that tax evasion can only be discovered with a certain probability when the audit is executed, the authors formulate another static model with self-audit and investigate its properties.     

Process capability analysis and estimation scheme for autocorrelated data

<正>Autocorrelation is prevalent in continuous production processes,such as the processes in the chemical and pharmaceutical industries.With the development of measurement technology and data acquisition technology,sampling frequency is getting higher and the existence of autocorrelation cannot be ignored.This paper analyzes five estimation schemes of process capability for autocorrelated data.Comparisons among these schemes are discussed for small sample and large sample.In conclusion,this paper gives a procedure of process capability analysis for autocorrelated data.     

Clustering patents using non-exhaustive overlaps

Patent documents are unique external sources of information that reveal the core technology underlying new inventions.Patents also serve as a strategic data source that can be mined to discover state-of-the-art technical development and subsequently help guide R&D investments.This research incorporates an ontology schema to extract and represent patent concepts.A clustering algorithm with non-exhaustive overlaps is proposed to overcome deficiencies with exhaustive clustering methods used in patent mining and technology discovery.The non-exhaustive clustering approach allows for the clustering of patent documents with overlapping technical findings and claims,a feature that enables the grouping of patents that define related key innovations.Legal advisors can use this approach to study potential cases of patent infringement or devise strategies to avoid litigation.The case study demonstrates the use of non-exhaustive overlaps algorithm by clustering US and Japan radio frequency identification (RFID) patents and by analyzing the legal implications of automated discovery of patent infringement.     

Connecting informal networks to management of tacit knowledge

The relationship between tacit knowledge and informal networks has not been fully explored.Tacit knowledge cannot be well managed directly,because it is deeply embedded in individual actions and experiences.This study proposes an approach to managing informal networks as a new middle way to facilitate the sharing and transferring of tacit knowledge,owing to the fact that tacit knowledge is mostly shared and transferred through informal networks.To support the idea of the approach,an empirical case study of applying the approach to management of a large scientific project is also included.Our findings suggest that informal networks play essential roles for management of tacit knowledge,and thus good management of informal networks can lead to efficient sharing and transferring of tacit knowledge.     

基于虚拟现实技术的援潜救生仿真系统研究

援潜救生是一项必要而又复杂的系统工程,存在着很多不确定因素,为了对救援方案进行评估、验证,提高救援能力,应用虚拟现实技术,建立了援潜救生仿真系统。系统采用离散调度方法获取初始调度方案,通过人因工程分析、动力学及流体力学计算等反映实体的物理行为,利用人机接口与系统进行交互,验证与完善初始救援方案。构建了数据输入、场景建模与场景驱动的系统框架,并对系统的关键技术进行了详细描述。该仿真系统的实现为海上救援提供了必要的理论依据及直观的视觉表现。     

基于Simulink的广义执行机构仿真模型库建模

为了提高方案生成后仿真模型的建模效率,建立了基于Simulink的广义执行机构仿真模型库。该模型库将广义执行机构分为执行机构和驱动元件两类,分别建立了支持广义执行机构仿真的执行机构库和以驱动输入分类的驱动元件模型。其中执行机构库使用屏蔽实现了模型接口的封装,同时将每个模型的基本参数定义为变量来表示。设计者可通过直接搜索调用方案解对应的仿真原型或分解和手工合并相关模型文件来快速构建仿真模型。以某船载装置移动机构和惯性振动筛机构的方案设计为例说明了仿真模型库的有效性。     

基于活动轮廓模型的人脸特征提取方法的研究

根据活动轮廓模型理论 ,提出了提取人脸面部特征的系列算法 ,特征是精确的轮廓描述而非简单的数字表达.     

投资组合有效集问题和它的一种算法

本文构造了一种二次参数规划转轴方法,并在此基础上提出了Ｍａｒｋｏｗｉｔｚ投资组合有效集的一个解法。该方法的优点在于能够处理各投资项目之间的协方差距阵为半正定的情形。     

A DEA-TOPSIS method for multiple criteria decision analysis in emergency management

A hybrid approach of DEA (data envelopment analysis) and TOPSIS (technique for order performance (preference) by similarity to ideal solution) is proposed for multiple criteria decision analysis in emergency management. Two DEA-based optimization models are constructed to facilitate identifying parameter information regarding criterion weights and quantifying qualitative criteria in TOPSIS. An emergency management case study utilizing data from the Emergency Management Australia (EMA) Disasters Database is provided to demonstrate the feasibility of the proposed analysis procedure.     

基于图切割的快速运动分割方法

基于图切割的图像分割是近几年发展的一项新技术,但随着图像大小及空间维数的增加,图切割的运算量成为负担。针对运动分割提出了一种快速图切割方法,利用运动分割的特点将差分图像中不变的背景映射为一点或几点,然后参与网络图的构造和图切割的求解,以减少图切割运算量。通过实验将这种快速方法应用于2D图切割和3D图切割,对图像序列的运动目标分别进行了自动分割。实验结果表明,这种快速图切割方法不仅大大地降低了存储消耗,提高了运算速度,而且也获得了良好的分割结果。结果证明这种方法在运动目标的分割中是行之有效的。