浅析大型铸锻件质量体系

本文主要从质量体系的产生和发展、质量体系分析的资料、质量体系的环境分析及结构型式分析几个方面探讨分析了大型铸锻件的质量体系 ,可供大型铸锻件生产厂按 ISO90 0 0系列标准分析、设计、建立和完善大型铸锻件质量体系时参考。     

国内轴承钢的生产现状及发展

介绍了2002年国内高碳铬轴承钢的产量及品种结构，评述了国内轴承钢生产工艺装备、质量状况和发展方向。     

基于Web构架的软件质量评估工具的设计和实现*

设计并实现了一个基于Web的软件质量评估工具,该工具使用层次分析法和模糊综合评价定量地对软件质量进行评价。讨论了该工具的软件构架选择、系统分析、系统设计,包括存储结构、数据库结构,以及XML文件格式等,给出了工具实现的关键算法。     

无损检测的质量控制技术

以控制无损检测中的误判为例,介绍了质量控制技术在无损检测质量管理工作中的应用.首先利用流程图发现出现误判问题的产生环节,即缺陷的定位或定量错误.然后利用两图一表(因果图、排列图和检查表)分析引起缺陷定量或定位错误的原因.最后制定了防止误判产生的对策表.采用质量控制技术可以实现无损检测质量的持续改进.     

ISO9000与塑料企业卓越质量经营的实践

分析了以ISO9000为基础平台对质量经营的核心要素进行整合与再造，使塑料企业的质量经营体系卓越有效；以达到增强塑料企业核心竞争力的。     

Customer satisfaction and competitiveness in the Chinese E-retailing: Structural equation modeling (SEM) approach to identify the role of quality factors

This paper attempts to study the impact of customer satisfaction and the Chinese electronic retailers (E-retailers) competitiveness using quality factors. Two conceptual models based on asset-process-performance (APP) competitive theoretical framework have been proposed. The proposed models include E-retailers strategic factors such as logistics, quality and customer satisfaction equivalent to asset, process and performance entities of the APP framework. Using an empirical survey from a young population, this study, with the support of structural equation modeling (SEM) identifies reliability in service quality and purchasing experience in e-service quality as dominant customer satisfaction factors. This study uses both exploratory and confirmatory factor analysis and suggests that to be competitive Chinese E-retailers have to focus more on the delivery of products (logistics) compared to other intangible service quality factors. On theoretical front, this study is a novel attempt to validate the APP framework for E-retailers’ competitiveness. On the practical front, the outcome of the study would be highly beneficial to the Chinese E-retailers to fine tune their strategy to satisfy the growing demand. Furthermore, this study can supplement government policy makers to regulate the growing volatile market.     

A knowledge and collaboration-based CBR process to improve network performance-related support activities

In a context characterized by a growing demand for networked services, users of advanced applications sometimes face network performance troubles that may actually prevent them from completing their tasks. Therefore, providing assistance for user communities that have difficulties using the network has been identified as one of the major issues of performance-related support activities. Despite the advances network management has made over the last years, there is a lack of guidance services to provide users with information that goes beyond merely presenting network properties. In this light, the research community has been highlighting the importance of User-Perceived Quality (UPQ) scores during the evaluation of network services for network applications, such as Quality of Experience (QoE) and Mean Opinion Score (MOS). However, despite their potential to assist end-users to deal with network performance troubles, only few types of network applications have well established UPQ scores. Besides that, they are defined through experiments essentially conducted in laboratory, rather than actual usage. This paper thus presents a knowledge and Collaboration-based Network Users’ Support (CNUS) Case-Based Reasoning (CBR) Process that predicts UPQ scores to assist users by focusing on the collaboration among them through the sharing of their experiences in using network applications. It builds (i) a knowledge base that includes not only information about network performance problems, but also applications’ characteristics, (ii) a case base that contains users’ opinions, and (iii) a user database that stores users’ profiles. By processing them, CNUS benefits users through the indication of the degree of satisfaction they may achieve based on the general opinion from members of their communities in similar contexts. In order to evaluate the suitability of CNUS, a CBR system was built and validated through an experimental study conducted in laboratory with a multi-agent system that simulated scenarios where users request for assistance. The simulation was supported by an ontology of network services and applications and reputation scheme implemented through the PageRank algorithm. The results of the study pointed to the effectiveness of CNUS, and its resilience to users’ collusive and incoherent behaviors. Besides that, they showed the influence of the knowledge about network characteristics, users’ profiles and application features on computer-based support activities.     

Time-optimized contextual information forwarding in mobile sensor networks

We study on the forwarding of quality contextual information in mobile sensor networks (MSNs). Mobile nodes form ad-hoc distributed processing networks that produce accessible and quality-stamped information about the surrounding environment. Due to the dynamic network topology of such networks the context quality indicators seen by the nodes vary over time. A node delays the context forwarding decision until context of better quality is attained. Moreover, nodes have limited resources, thus, they have to balance between energy conservation and quality of context. We propose a time-optimized, distributed decision making model for forwarding context in a MSN based on the theory of optimal stopping. We compare our findings with certain context forwarding schemes found in the literature and pinpoint the advantages of the proposed model.     

Cloud based intelligent system for delivering health care as a service

The promising potential of cloud computing and its convergence with technologies such as mobile computing, wireless networks, sensor technologies allows for creation and delivery of newer type of cloud services. In this paper, we advocate the use of cloud computing for the creation and management of cloud based health care services. As a representative case study, we design a Cloud Based Intelligent Health Care Service (CBIHCS) that performs real time monitoring of user health data for diagnosis of chronic illness such as diabetes. Advance body sensor components are utilized to gather user specific health data and store in cloud based storage repositories for subsequent analysis and classification. In addition, infrastructure level mechanisms are proposed to provide dynamic resource elasticity for CBIHCS. Experimental results demonstrate that classification accuracy of 92.59% is achieved with our prototype system and the predicted patterns of CPU usage offer better opportunities for adaptive resource elasticity.