A novel pose and illumination robust face recognition with a single training image per person algorithm

In the real-world application of face recognition system, owing to the difficulties of collecting samples or storage space of systems, only one sample image per person is stored in the system, which is so-called one sample per person problem. Moreover, pose and illumination have impact on recognition performance. We propose a novel pose and illumination robust algorithm for face recognition with a single training image per person to solve the above limitations. Experimental results show that the proposed algorithm is an efficient and practical approach for face recognition.     

认可机制下实验室人员培训工作的组织与开展

针对部分实验室在人员培训工作中存在的培训模式不清、培训机制不健全、培训质量较低的问题,系统地分析了计量实验室人员培训所涉及的主要类别、内容及要求等。对培训工作的流程逐个进行详尽地描述,分内部培训和外部培训两个方面对培训中应注意的问题进行重点说明,为广大计量实验室管理人员开展人员培训工作提供借鉴。     

A rehabilitation training system with double-CCD camera and automatic spatial positioning technique

This study aimed to develop a computer game for machine vision integrated rehabilitation training system. The main function of the system is to allow users to conduct hand grasp-and-place movement through machine vision integration. Images are captured by a double-CCD camera, and then positioned on a large screen. After defining the right, left, upper, and lower boundaries of the captured images, an automatic spatial positioning technique is employed to obtain their correlation functions, and lookup tables are defined for cameras. This system can provide rehabilitation courses and games that allow users to exercise grasp-and-place movements, in order to improve their upper limb movement control, trigger trunk control, and balance training.     

外企与国企培训战略对比

人才培训战略是企业人力资源战略的基础。本通过外资企业与国内企业的培训战略对比，并对国内企业中人才培训存在的不足与缺点进行了分析与探讨。     

Multilevel-in-width training for deep neural network regression

A common challenge in regression is that for many problems, the degrees of freedom required for a high-quality solution also allows for overfitting. Regularization is a class of strategies that seek to restrict the range of possible solutions so as to discourage overfitting while still enabling good solutions, and different regularization strategies impose different types of restrictions. In this paper, we present a multilevel regularization strategy that constructs and trains a hierarchy of neural networks, each of which has layers that are wider versions of the previous network's layers. We draw intuition and techniques from the field of Algebraic Multigrid (AMG), traditionally used for solving linear and nonlinear systems of equations, and specifically adapt the Full Approximation Scheme (FAS) for nonlinear systems of equations to the problem of deep learning. Training through V-cycles then encourage the neural networks to build a hierarchical understanding of the problem. We refer to this approach as multilevel-in-width to distinguish from prior multilevel works which hierarchically alter the depth of neural networks. The resulting approach is a highly flexible framework that can be applied to a variety of layer types, which we demonstrate with both fully connected and convolutional layers. We experimentally show with PDE regression problems that our multilevel training approach is an effective regularizer, improving the generalize performance of the neural networks studied.     

Maneuver analysis methodology to predict vehicle impacts on training lands

Tactical mobility analysis techniques were merged with land management strategies to assess potential impacts of vehicle operations on training areas for rangeland planning and management. A vehicle mobility analysis was performed for a suite of vehicle types using the NATO Reference Mobility Model (NRMM II). Input parameters include terrain information (soil type, slope, vegetation, surface roughness, soil strength), terrain surface condition based on climate (terrain strength, freeze–thaw, moisture content, snow cover), and vehicle specifications (tire, power train, weight on each axle, ground clearance, dimensions, ride). The vehicle performance was spatially mapped over the terrain for different seasons of the year and used to calculate the maneuverable acreage, which was compared to acreage needed for training requirements. This can be related to land capability based on expected training impact (Maneuver Impact Miles, MIM) and Land Condition Curves which link training density to land condition. This methodology can be used to determine the suitability of training lands and the degree of land management or rehabilitation expected. The methodology was applied to the transformation of the Alaska training lands to support a new brigade unit called the Stryker Brigade Combat Team (SBCT3), but is equally useful for other training areas and military units. For summer use, Alaska training lands are capable of supporting four times the projected training requirements. For winter, when the ground is frozen, more than 10 times the area needed was available.     

模糊AHP在电网企业员工岗前培训评估中的应用

电网企业技术密集、人员众多、架构庞大,员工岗前培训与企业健康持续发展密不可分.培训工作中最突出的问题在于对培训效果缺乏合理的评估,从而无法了解企业投资的效果,以及培训对组织的贡献.以柯氏培训四级评估模型为基础,采用模糊AHP规范化整个评估过程.最后以电力企业进行的培训项目为例验证分析,证明所提算法的有效性.     

Recent developments in Dual Resource Constrained (DRC) system research

Real world manufacturing systems are usually constrained by both machine and human resources. Human operators are often the constraining resource and transfer between workstations to process jobs when required. This kind of system is known as a Dual Resource Constrained (DRC) system and presents additional technical challenges which must be considered during planning and scheduling. These technical challenges can be categorised into the five main dimensions of job release mechanisms, job dispatching, worker flexibility, worker assignment and transfer costs. This paper aims to provide an overview of recent developments in DRC research concerned with each of these areas and also discusses some possible approaches to solving the resource scheduling problem in a DRC system. The focus is on materials published after 1995 and up to 2009. Previous reviews on DRC systems are commented on and followed by a review of recent works associated with each of the five dimensions of DRC system research. Advancements made and new methodologies proposed are discussed and future research directions are identified.     

商科型高校理工类研究生创新能力的培养

目前,研究生教育处于我国高等教育的最高层次,肩负着为国家发展培养高技术专业型人才的重要任务.尤其是在各国竞争愈发激烈的今天,理工类研究生更是在未来国家现代化建设、科技创新中发挥着重要作用.但是,由于近几年研究生招生规模的逐渐扩大,研究生的培养模式、培养质量等方面出现了许多亟待解决的问题.从商科型高校理工类研究生的培养现状出发,通过运用文献法和归纳法,本文对当前研究生培养过程中存在的问题进行分析,并针对学生创新能力的培养,从学校、导师和学生自身三方面给出了相应的解决措施,着重强调了数学建模在理工类研究生创新能力培养过程中所发挥的重要作用.最后,以湖南工商大学近几年在理工类研究生创新能力培养实践中所采取的主要举措以及取得的成效来进一步论证本文所提出措施的有效性.     

模糊传感器训练算法

本文提出了模糊传感器的一种分段式训练调参方法。对温度模糊传感器的仿真结果表明，该方法能在不同的测量结构下，较好地解决人类感知温度的模糊语言描述问题。