An Integrated Model of the Toyota Production System with Total Quality Management and People Factors

The Toyota production system (TPS), or lean production, has been associated with many benefits for manufacturing firms that implement the system. However, to implement the TPS successfully, it is necessary to integrate the so‐called “hard side” of the system (that is, the technical aspects of material handling) with the “soft side” of the program (that is, the aspects associated with human factors). The present study makes a contribution to such a holistic view of the TPS by proposing an integrated model that consists of the technical aspects of the TPS, together with elements associated with total quality management (TQM), human resources management (HRM), and certain identified “people factors.” The study then uses structural equation modeling (SEM) to test this holistic model using data obtained from a questionnaire survey of 153 Taiwanese manufacturing firms. The empirical study confirms the appropriateness of the causal model, which is evaluated by several goodness‐of‐fit methods. The study confirms the proposition that the inclusion of the “people factors” together with TQM and HRM significantly improves the realization of benefits associated with the TPS practices of “just‐in‐time” (JIT) and “autonomation.” The conclusion is that such an integrated model provides a much more effective “lean system” and has the potential to produce significantly enhanced benefits for firms that implement it. © 2012 Wiley Periodicals, Inc.     

From big smartphone data to worldwide research: The Mobile Data Challenge

This paper presents an overview of the Mobile Data Challenge (MDC), a large-scale research initiative aimed at generating innovations around smartphone-based research, as well as community-based evaluation of mobile data analysis methodologies. First, we review the Lausanne Data Collection Campaign (LDCC), an initiative to collect unique longitudinal smartphone dataset for the MDC. Then, we introduce the Open and Dedicated Tracks of the MDC, describe the specific datasets used in each of them, discuss the key design and implementation aspects introduced in order to generate privacy-preserving and scientifically relevant mobile data resources for wider use by the research community, and summarize the main research trends found among the 100+ challenge submissions. We finalize by discussing the main lessons learned from the participation of several hundred researchers worldwide in the MDC Tracks.     

基于SVM的人脸检测研究

支持向量机（SVM）是在统计学习理论基础上发展起来的一种新的机器学习算法。依据SVM良好识别和泛化能力,实现了一种基于支持向量机的图像人脸识别方法。利用Opencv提取样本类的低层特征,训练具有径向基核函数的SVM分类器,在VS2008和Qt平台下实现识别软件开发。运行结果表明,软件具有良好的图像人脸检测能力。     

Human eye sclera detection and tracking using a modified time-adaptive self-organizing map

This paper investigates the use of time-adaptive self-organizing map (TASOM)-based active contour models (ACMs) for detecting the boundaries of the human eye sclera and tracking its movements in a sequence of images. The task begins with extracting the head boundary based on a skin-color model. Then the eye strip is located with an acceptable accuracy using a morphological method. Eye features such as the iris center or eye corners are detected through the iris edge information. TASOM-based ACM is used to extract the inner boundary of the eye. Finally, by tracking the changes in the neighborhood characteristics of the eye-boundary estimating neurons, the eyes are tracked effectively. The original TASOM algorithm is found to have some weaknesses in this application. These include formation of undesired twists in the neuron chain and holes in the boundary, lengthy chain of neurons, and low speed of the algorithm. These weaknesses are overcome by introducing a new method for finding the winning neuron, a new definition for unused neurons, and a new method of feature selection and application to the network. Experimental results show a very good performance for the proposed method in general and a better performance than that of the gradient vector field (GVF) snake-based method.     

VICUR: A human-vision-based algorithm for curve reconstruction

In this paper, a new algorithm, named VICUR, is presented for curve reconstruction problem. From a set of unorganized points, the proposed algorithm can construct curves that look natural to human vision. The VICUR algorithm is based on two connectivity criteria: proximity and good continuation from the prominent Gestalt principles of perception. Experimental results are presented to show the effectiveness of VICUR.     

Human tracking and silhouette extraction for human–robot interaction systems

In this study, we propose a new integrated computer vision system designed to track multiple human beings and extract their silhouette with a pan-tilt stereo camera, so that it can assist in gesture and gait recognition in the field of Human–Robot Interaction (HRI). The proposed system consists of three modules: detection, tracking and silhouette extraction. These modules are robust to camera movements, and they work interactively in near real-time. Detection was performed by camera ego-motion compensation and disparity segmentation. For tracking, we present an efficient mean shift-based tracking method in which the tracking objects are characterized as disparity weighted color histograms. The silhouette was obtained by two-step segmentation. A trimap was estimated in advance and then effectively incorporated into the graph-cut framework for fine segmentation. The proposed system was evaluated with respect to ground truth data, and it was shown to detect and track multiple people very well and also produce high-quality silhouettes.

Synaptic proteome changes in the superior frontal gyrus and occipital cortex of the alcoholic brain

Cognitive deficits and behavioral changes that result from chronic alcohol abuse are a consequence of neuropathological changes that alter signal transmission through the neural network. To focus on the changes that occur at the point of connection between the neural network cells, synaptosomal preparations from post‐mortem human brain of six chronic alcoholics and six non‐alcoholic controls were compared using 2‐D differential in‐gel electrophoresis (DIGE). Functionally affected and spared regions (superior frontal gyrus, SFG, and occipital cortex, OC, respectively) were analyzed from both groups to further investigate the specific pathological response that alcoholism has on the brain. Forty‐nine proteins were differentially regulated between the SFG of alcoholics and the SFG of controls and 94 proteins were regulated in the OC with an overlap of 23 proteins. Additionally, the SFG was compared to the OC within each group (alcoholics or controls) to identify region‐specific differences. A selection was identified by MALDI‐TOF mass spectrometry revealing proteins involved in vesicle transport, metabolism, folding and trafficking, and signal transduction, all of which have the potential to influence synaptic activity. A number of proteins identified in this study have been previously related to alcoholism; however, the focus on synaptic proteins has also uncovered novel alcoholism‐affected proteins. Further exploration of these proteins will illuminate the mechanisms altering synaptic plasticity, and thus neuronal signaling and response, in the alcoholic brain.     

基于PERT技术的人员调度研究

人员成本占软件项目总成本的大部分,但是现有的Pert技术未能体现人员计划管理这重要的一环,不合理的活动指派时间可能导致人员闲置或者项目延迟.本文提出一种解决方法,基于Pert技术,结合人员闲置、项目延迟和缓冲区计算活动负责人的指派时间,以此指导项目管理,可有效减少总开销.     

基于小波系数块能量分析和混沌映射的水印算法

小波变换具有时频局部化特性和与人眼视觉特性相符的变换机制,而混沌序列具有确定、似随机、不可预测等特性.在对小波系数块能量分析的基础上,提出了一种基于小波系数块能量分析和混沌映射的水印算法.该算法具有一定的鲁棒性,并且抗破译性强.