人员聘用风险管理研究——基于信息不对称理论的解读

组织在雇佣员工的时候，招聘方和应聘方同时存在风险。从招聘方的视角，运用信息不对称理论，对聘用过程中常见的逆向选择和道德风险进行分析，并提出相应的风险管理策略。     

《航空学报》蝉联"百种中国杰出学术期刊奖"

2006年10月27日,第十四届中国科技论文统计结果发布会在北京国际会议中心举行。根据中国科学技术信息研究所的统计结果,《航空学报》再次荣获“百种中国杰出学术期刊奖”,至此,本刊已连续5次获此殊荣。“百种中国杰出学术期刊”评选活动是2002年起由中国科学技术信息研究所发起举办的,每年开展一次,今年已是第5届。中国科学技术信息研究所是国内从事科技期刊评价的权威机构,其出版的《中国科技期刊引证报告》选择了1652种中国出版的中英文科技期刊作为来源期刊,根据来源期刊的引文数据,如总被引频次、影响因子、即年指标、基金论文比和他引总引比等国际上通用的期刊评价指标,进行规范化处理和统计分析,建立起科技期刊综合评价指标体系,便于读者对科技期刊的学术水平、学科     

中国民航"禁折令"的"囚徒困境"

利用博弈论的方法，对中国民航业施行多年的“禁折令”进行分析，指出各航空公司在“禁折令”中的博弈属于完全信息静态博弈。通过对民航业差别产品价格竞争需求曲线模型的分析，证明了“禁折令”所形成的价格卡特尔具有非稳定性，各航空公司存在突破“禁折令”限制的违约冲动，因此，实施机票打折销售，是市场运作的客观要求和必然趋势。     

东航招聘“空嫂”对高校空中乘务专业的冲击和启示

2013年10月27日，东方航空公司在四川招聘乘务员。与以往不同，各大媒体对此次招聘的报道聚焦于“婚育女性”、“放宽至32岁”和“空嫂”这样的字眼，不啻于是对传统的“空姐”观念的颠覆，但对于国内民航业的服务理念转变却具有重要的意义，对高校空中乘务专业而言，既是一次冲击也是一个机遇。     

我国产业结构调整的重心选择——基于"优化"与"升级"视角

产业结构调整包括“升级”和“优化”两个基本方面，这是两个不同层面的问题，前者讲的主要是高度化问题，后者则主要指的是合理化问题。其中，“升级”是“优化”的前提，“优化”是“升级”的结果。世界各国实现产业结构升级的模式主要有两种，即平衡升级模式和非平衡升级模式。从动力机制上看，前者主要是通过市场机制来实现的，后者主要通过政府主导来实现的。目前我国产业结构虽然还存在诸多不协调方面，但总体说来是基本合理的。“十二五”时期我国产业结构调整的主攻方向和工作重心，应该放在如何促进产业结构的升级上，其机制应是市场基础上的政府主导。这就是中国的特色。     

试论获取审计信息

在人是“有限理性”的前提下 ,应当侧重程序理性 ,加强对行为过程的考核与控制 ,而不应该刻意追求结果理性 ;社会公众对审计信息的获取 ,仅仅关注审计人员最终出具的审计报告结果和意见类型 ,而不关注产生这种结果和意见的程序或过程是否合理。获取审计信息 ,不仅要关注审计结果和意见类型 ,更要关注审计程序或过程。     

与"阿帕奇"竞争的"虎"

“虎”武装直升机是由法国航字公司和德国MBB公司联合组成的欧洲直升机公司研制的。从1975年11月德国和法国的国防部长交换共同研制反坦克直升机的信件算起，到1989年11月正式授予研制合同，并把研制的武装直升机取名为“虎”时止，前后花了14年的时间进行谈判和论证。在此后的研制过程中，由于经费不足，致使计划一拖再拖，     

基于中印认识论的譬喻研究

譬喻是一种比较常见的文化现象，在文化典籍中随处可见。目前学术界已从多个角度对譬喻进行了探讨研究。在汲取前人研究成果的基础上，从中印认识论的角度，利用儒家、墨家及佛教典籍中的丰富材料，对譬喻进行再研究。通过研究，文章认为：(一)譬喻是建立在“直告之不明”“义理难晓”的基础上，是不得已而为之的明意手段；(二)为了实现明意目的，人们往往是引类以譬喻，即在“类同”事物中，由先前已认识的事物类推到尚未认识的事物；(三)譬喻表达的过程是将理性认识转化为感性认识的过程，而譬喻认识的过程则正好与之相反。     

回归人本管理——百年管理史从"科学"到"人文"的发展趋势

管理学诞生百年来，随着社会的进步，“人”的价值日益突显。早期泰勒的“科学管理”是产生于机器大工业特殊历史背景下的产物，其基本信条是工人如牛马，效率高于一切，管理就是监控。20世纪20年代至20世纪30年代，管理学开始关注“人”，其中，人事管理学派主张在管理中“善待”其雇员，但其追求的最高境界只是雇主与雇员利益关系的整合。20世纪中后期，特别是20世纪90年代以来，由于新技术革命的推动，人力资源，特别是知识工人和专业化的人力资本越来越成为决定企业市场竞争成败的战略性要素，于是，以“战略性激励”为核心理念的人力资源管理学逐渐成为管理学主流，但人力资源管理的实质是要求从战略高度将人作为资财（资源、资产、资本）来运营。21世纪的管理要进一步超越人力资源管理，真正树立“以人为本”的理念，从总体上、动态上、“人本”精神层面去挖掘员工的“群体精神创造力”。这在本质上符合马克思关于“人的全面发展”的思想。     

“众里寻他千百度”——中国民航价格改革的设想

民航价格改革从“犹抱琵琶半遮面”，终于快到“千呼万唤始出来”的时刻。民航总局不因1998年的挫折而“杯弓蛇影”，远离井绳，正在研究新的改革方案。业内同行更应该痛定思痛，由国内外航空公司在价格竞争的连年混战中，悟出几分真谛来。改革既要凭“摸着石头过河”的勇气，也需要科学的态度，及对产业具体情况与特点的洞察力。航空公司是最早引入计算机管理的产业，机票价格之复杂，几近数学上的“哥德巴赫猜想”，令无数行家里手竞折腰。由管制价到市场价的改革过程，孕育着机会，亦隐藏着风险。须广证博议，谋断而行。国内航空运价改革…     

A Gaussian Mixture PHD Filter for Jump Markov System Models

The probability hypothesis density (PHD) filter is an attractive approach to tracking an unknown and time-varying number of targets in the presence of data association uncertainty, clutter, noise, and detection uncertainty. The PHD filter admits a closed-form solution for a linear Gaussian multi-target model. However, this model is not general enough to accommodate maneuvering targets that switch between several models. In this paper, we generalize the notion of linear jump Markov systems to the multiple target case to accommodate births, deaths, and switching dynamics. We then derive a closed-form solution to the PHD recursion for the proposed linear Gaussian jump Markov multi-target model. Based on this an efficient method for tracking multiple maneuvering targets that switch between a set of linear Gaussian models is developed. An analytic implementation of the PHD filter using statistical linear regression technique is also proposed for targets that switch between a set of nonlinear models. We demonstrate through simulations that the proposed PHD filters are effective in tracking multiple maneuvering targets.     

Track labeling and PHD filter for multitarget tracking

Multiple target tracking requires data association that operates in conjunction with filtering. When multiple targets are closely spaced, the conventional approaches (as, e.g., MHT/assignment) may not give satisfactory results. This is mainly because of the difficulty in deciding what the number of targets is. Recently, the probability hypothesis density (PHD) filter has been proposed and particle filtering techniques have been developed to implement the PHD filter. In the particle PHD filter, the track labeling problem is not considered, i.e., the PHD is obtained only for a frame at a time, and it is very difficult to perform the multipeak extraction, particularly in high clutter environments. A track labeling method combined with the PHD approach, as well as considering the finite resolution, is proposed here for multitarget tracking, i.e., we keep a separate tracker for each target, use the PHD in the resolution cell to get the estimated number and locations of the targets at each time step, and then perform the track labeling ("peak-to-track" association), whose results can provide information for PHD peak extraction at the next time step. Besides, by keeping a separate tracker for each target, our approach provides more information than the standard particle PHD filter. For example, in group target tracking, if we are interested in the motion of a specific target, we can track this target, which is not possible for the standard particle PHD filter, since the standard particle PHD filter does not keep track labels. Using our approach, multitarget tracking can be performed with automatic track initiation, maintenance, spawning, merging, and termination     

基于标签多伯努利滤波的衍生目标跟踪算法

针对现有随机有限集(RFS)滤波器在低信噪比环境下对衍生目标跟踪性能严重下降的问题,提出了一种基于Delta扩展标签多伯努利(δ-GLMB)滤波器的改进算法。基于随机集理论和伯努利衍生模型,推导了新的预测方程,并采用了假设裁剪及分组手段和多伯努利近似技术以降低算法的计算量。针对假设增多引起的虚警问题,将多帧平滑思想和算法相结合,利用标签信息对新目标进行回溯处理。仿真结果表明,所提算法能对目标数目进行无偏估计,在低探测概率和强杂波环境下性能明显优于概率假设密度(PHD)算法,计算开销在衍生初始阶段增长快于PHD,目标较分散时低于PHD。     

新生目标强度未知的双门限粒子PHD滤波器

传统粒子概率假设密度(PHD)滤波器假定新生目标强度已知,当新生目标在整个观测区域随机出现时不再适用。为解决新生目标强度未知时的多目标跟踪问题,提出了一种基于量测信息的双门限粒子PHD(PHD-DT)滤波器。首先基于似然函数设定门限对存活目标量测进行粗提取,利用上一时刻的目标估计值构建圆形波门进行精细提取,并对门限设定方法进行分析,然后根据提取结果对目标PHD进行分解,得到存活目标和新生目标的PHD预测及更新表达式,最后给出了滤波器的实现方法并同基于量测驱动的PHD(PHD-M)滤波器和Logic+联合概率数据互联(JPDA)方法进行了仿真对比。仿真结果表明,在新生目标强度未知时,PHD-DT可有效避免Logic+JPDA在杂波背景下因航迹起始错误带来的估计误差,并较好地解决了PHD-M的目标数目过估问题,多目标估计性能更优,且杂波越强性能优势越明显。     

Multiple-Model Probability Hypothesis Density Filter for Tracking Maneuvering Targets

Tracking multiple targets with uncertain target dynamics is a difficult problem, especially with nonlinear state and/or measurement equations. With multiple targets, representing the full posterior distribution over target states is not practical. The problem becomes even more complicated when the number of targets varies, in which case the dimensionality of the state space itself becomes a discrete random variable. The probability hypothesis density (PHD) filter, which propagates only the first-order statistical moment (the PHD) of the full target posterior, has been shown to be a computationally efficient solution to multitarget tracking problems with a varying number of targets. The integral of PHD in any region of the state space gives the expected number of targets in that region. With maneuvering targets, detecting and tracking the changes in the target motion model also become important. The target dynamic model uncertainty can be resolved by assuming multiple models for possible motion modes and then combining the mode-dependent estimates in a manner similar to the one used in the interacting multiple model (IMM) estimator. This paper propose a multiple-model implementation of the PHD filter, which approximates the PHD by a set of weighted random samples propagated over time using sequential Monte Carlo (SMC) methods. The resulting filter can handle nonlinear, non-Gaussian dynamics with uncertain model parameters in multisensor-multitarget tracking scenarios. Simulation results are presented to show the effectiveness of the proposed filter over single-model PHD filters.     

幅度及多普勒信息辅助的多目标跟踪算法

针对现有的多目标跟踪（MTT）算法在强杂波环境下会出现严重的性能衰退,利用目标的幅度及多普勒特征,基于随机有限集（RFS）理论,提出了一种幅度及多普勒信息辅助的δ广义标签多伯努利（ADI-δ-GLMB）滤波器。首先,利用幅度及多普勒信息对目标状态进行扩展,在此基础上建立了新的量测似然函数。然后,基于δ-GLMB滤波器框架推导了新的更新方程。最后,进行仿真验证,结果表明：强杂波下,ADI-δ-GLMB滤波器跟踪性能明显优于δ-GLMB滤波器,估计精度及稳定性更高,且计算量更低;同信息辅助的概率假设密度（PHD）、多目标多伯努利（MeMBer）滤波器相比,ADI-δ-GLMB滤波器状态估计精度更高。     

Data Association and Track Management for the Gaussian Mixture Probability Hypothesis Density Filter

The Gaussian mixture probability hypothesis density (GM-PHD) recursion is a closed-form solution to the probability hypothesis density (PHD) recursion, which was proposed for jointly estimating the time-varying number of targets and their states from a sequence of noisy measurement sets in the presence of data association uncertainty, clutter, and miss-detection. However the GM-PHD filter does not provide identities of individual target state estimates, that are needed to construct tracks of individual targets. In this paper, we propose a new multi-target tracker based on the GM-PHD filter, which gives the association amongst state estimates of targets over time and provides track labels. Various issues regarding initiating, propagating and terminating tracks are discussed. Furthermore, we also propose a technique for resolving identities of targets in close proximity, which the PHD filter is unable to do on its own.     

Multitarget Bayes filtering via first-order multitarget moments

The theoretically optimal approach to multisensor-multitarget detection, tracking, and identification is a suitable generalization of the recursive Bayes nonlinear filter. Even in single-target problems, this optimal filter is so computationally challenging that it must usually be approximated. Consequently, multitarget Bayes filtering will never be of practical interest without the development of drastic but principled approximation strategies. In single-target problems, the computationally fastest approximate filtering approach is the constant-gain Kalman filter. This filter propagates a first-order statistical moment - the posterior expectation - in the place of the posterior distribution. The purpose of this paper is to propose an analogous strategy for multitarget systems: propagation of a first-order statistical moment of the multitarget posterior. This moment, the probability hypothesis density (PHD), is the function whose integral in any region of state space is the expected number of targets in that region. We derive recursive Bayes filter equations for the PHD that account for multiple sensors, nonconstant probability of detection, Poisson false alarms, and appearance, spawning, and disappearance of targets. We also show that the PHD is a best-fit approximation of the multitarget posterior in an information-theoretic sense.     

Epistemic uncertainty quantification in flutter analysis using evidence theory

Aimed at evaluating the structural stability and flutter risk of the system, this paper manages to quantify epistemic uncertainty in flutter analysis using evidence theory, including both parametric uncertainty and method selection uncertainty, on the basis of information from limited experimental data of uncertain parameters. Two uncertain variables of the actuator coupling system with unknown probability distributions, that is bending and torsional stiffness, which are both described with multiple intervals and the basic belief assignment(BBA) extricated from the modal test of actuator coupling systems, are taken into account. Considering the difference in dealing with experimental data by different persons and the reliability of various information sources, a new combination rule of evidence––the generalized lower triangular matrices method is formed to acquire the combined BBA. Finally the parametric uncertainty and the epistemic uncertainty of flutter analysis method selection are considered in the same system to realize quantification. A typical rudder of missile is selected to examine the present method, and the dangerous range of velocity as well as relevant belief and plausibility functions is obtained. The results suggest that the present method is effective in obtaining the lower and upper bounds of flutter probability and assessing flutter risk of structures with limited experimental data of uncertain parameters and the belief of different methods.     

Uncertain multiobjective redundancy allocation problem of repairable systems based on artificial bee colony algorithm

Based on the uncertainty theory, this paper is devoted to the redundancy allocation problem in repairable parallel-series systems with uncertain factors, where the failure rate, repair rate and other relative coefficients involved are considered as uncertain variables. The availability of the system and the corresponding designing cost are considered as two optimization objectives. A crisp multiobjective optimization formulation is presented on the basis of uncertainty theory to solve this resultant problem. For solving this problem efficiently, a new multiobjective artificial bee colony algorithm is proposed to search the Pareto efficient set, which introduces rank value and crowding distance in the greedy selection strategy, applies fast non-dominated sort procedure in the exploitation search and inserts tournament selection in the onlooker bee phase. It shows that the proposed algorithm outperforms NSGA-II greatly and can solve multiobjective redundancy allocation problem efficiently. Finally, a numerical example is provided to illustrate this approach.