An FCM–FAHP approach for managing readiness-relevant activities for ERP implementation

Any organization which plans to introduce a new enterprise resource planning (ERP) system will carry out a range of activities to improve its readiness for the new system. This paper develops a new approach for managing these interrelated activities using fuzzy cognitive maps (FCMs) and the fuzzy analytical hierarchy process (FAHP). This approach enables the organization to (1) identify the readiness-relevant activities, (2) determine how these activities influence each other, (3) assess how these activities will contribute to the overall readiness and (4) prioritize these activities according to their causal interrelationships to allocate management effort for the overall readiness improvement. The approach first uses FCMs and a fuzzy connection matrix to represent all possible causal relationships between activities. It then uses FAHP to determine the contribution weights and uses FCM inference to include the effects of feedback between the activities. Based on the contribution and interrelationships between activities, a management matrix is developed to categorize them into four management zones for effective allocation of limited management efforts. An empirical study is conducted to demonstrate how the approach works.     

Managing readiness-relevant activities for the organizational dimension of ERP implementation

An organization requires performing readiness-relevant activities to ensure successful implementation of an enterprise resource planning (ERP) system. This paper develops a novel approach to managing these interrelated activities to get ready for implementing an ERP system. The approach enables an organization to evaluate its ERP implementation readiness by assessing the degree to which it can achieve the interrelated readiness relevant activities using fuzzy cognitive maps. Based on the interrelationship degrees among the activities, the approach clusters the activities into manageable groups and prioritizes them. To help work out a readiness improvement plan, scenario analysis is conducted.     

铝业废阴极炭块资源化利用技术研究

为实现铝业废阴极炭块在钢铁冶金流程中资源化利用，通过深入研究废阴极炭块的相关物性，结合炼钢工艺对炭素及氟化物的物质需求，明确了废阴极炭块的无害化资源利用原理，工业试验结果表明废阴极炭块在炼钢转炉内可较好地实现无害化资源利用。     

水利信息网络与信息资源整合共享的必要性研究

长期以来,我国在水利资源管理上存在整体性不足、各自为战的特点,因而在未来发展的过程中,要想提高水利管理的现代化程度,就要重视网络与信息安全结合的整体性,并不断提高研究的层级。基于此,文章主要分析水利信息网络与资源整合共享的必要性,并探讨系统框架分析建设路径,以提高水利网络与信息安全资源共享的实际效果。     

我国现状能源与水纽带关系定量识别

以省级行政区为研究对象,以2017年为研究时段,分析我国社会水循环过程耗能及电力生产耗水。结果显示:2017年我国社会水循环过程耗电总量为10 828.1亿kW·h,占当年我国全社会用电总量的17.2%,终端用水是最大的耗能环节;2017年我国电力生产过程耗水量为65.7亿m~3,占当年全社会耗水总量的2%;火电是我国最耗水的电源,其耗水量占全国电力开发耗水总量的78%。基于计算结果,提出了实现能源-水协同安全的相关建议。     

某钒钛磁铁矿尾矿资源化利用

承德市某钒钛磁铁矿尾矿钛含量较高,潜在回收价值可观。采用化学分析、XRD衍射分析、粒度分析、矿物解离分析仪(MLA)等手段研究了该尾矿的矿物学特性及钛的赋存状态。在工艺矿物学研究基础上,通过试验研究确立了重选短流程回收钛、选钛尾矿差异化分级利用的综合利用技术工艺,获得含TiO234.60%、回收率53.43%的钛铁矿精矿以及产率66.22%的建筑用商品砂,实现了铁尾矿的资源化综合回收利用,有助于矿山与社会环境和谐发展。     

改进卷积输入的端到端普通话语音识别

主流神经网络训练的交叉熵准则是对声学数据的每个帧进行分类优化，而连续语音识别是以序列级转录准确性为性能度量。针对这个不同，构建基于序列级转录的端到端语音识别系统。针对低资源语料条件下系统性能不佳的问题，其中模型使用卷积神经网络对输入特征进行处理，选取最佳的网络结构，在时域和频域进行二维卷积，从而改善输入空间中因不同环境和说话人产生的小扰动影响。同时神经网络使用批量归一化技术来减少泛化误差，加速训练。基于大型的语言模型，优化解码过程中的超参数，提高模型建模效果。实验结果表明系统性能提升约24%，优于主流语音识别系统。     

A methodology to benchmark flexible payload architectures in a megaconstellation use case

This paper proposes a methodology to benchmark satellite payload architectures and find the optimal trade‐offs between high flexibility and low complexity. High flexibility would enable the satellite to adapt to various distributions of user terminals on the ground and fulfill the data rate demand of these users. Besides, low complexity is required to keep satellite networks competitive in the context of emerging 5G networks. To estimate the flexibility of a payload, an indicator to characterize the non‐uniformity of user distributions is proposed. Each benchmarked payload may be characterized by a graph relating the throughput to this parameter further denoted . The payload provides the same throughput trends for different scenarios of user distributions with the same parameter. As a consequence, the average capacity of the system may be estimated by (a) calculating the probability distribution of over the orbit and (b) integrating the throughput based on this payload response. It thus results in a straightforward way for benchmarking payloads directly on an estimation of the averaged capacity, accounting for the user distribution over the earth. A simulation platform has been developed to characterize the payload throughput including the implementation of a resource allocation algorithm that accounts for constraints of various payloads. Using this definition and the developed tool, we benchmark a bent‐pipe architecture, a beam hopping architecture and a hybrid beam‐steering architecture for a LEO megaconstellation use case. The methodology showcases the interest for investigating different payload architectures depending on realistic traffic scenario analysis.     

云制造平台资源需求的高效匹配策略研究

为快速、精确地从云制造平台资源池中搜索到满足用户需求的资源,实现资源与需求的高效匹配,提出了一种资源需求高效匹配策略。首先,建立了资源与需求的形式化描述模型,在此基础上,采用改进的K-means聚类算法按基本信息进行聚类,对云服务池中的资源进行预处理,形成多个资源簇;其次,计算用户需求与各资源簇聚类中心基本信息的相似度,确立备选资源簇;最后,再分别从资源的状态信息、功能信息和服务信息3个方面对备选资源簇中的备选资源进行筛选匹配。实例分析和研究结果表明:与已有的匹配方法相比,该方法在保持较高匹配精度的同时具有更高的匹配效率。     

个性化学习路径推荐综述

近年来，伴随着现代信息技术的迅猛发展，以人工智能为代表的新兴技术在教育领域得到了广泛应用，引发了学习理念和方式的深刻变革.在这种大背景下，在线学习超越了时空的限制，为学习者“随时随地”学习提供了更多的可能性，从而得到了蓬勃发展.然而，在线学习中师生时间、空间分离的特征，导致教师无法及时掌握学生的学习状态，一定程度上制约了在线学习中教学质量的提升.面对多元化的学习需求及海量学习资源，如何迅速完成学习目标、降低学习成本、合理分配学习资源等问题成为限制个人和时代发展的重大问题.然而，传统的“一刀切”的教育模式已经不能满足人们获取知识的需求了，需要一个更高效、更科学的个性化教育模式，以帮助学习者以最小的学习成本最大限度地完成学习目标.基于以上背景，如何自动高效识别学习者特征，高效地组织和分配学习资源，为每一位学习者规划个性化路径，成为面向个体的精准化教育资源匹配机制研究中亟待解决的问题.系统地综述并分析了当前个性化学习路径推荐的研究现状，并从多学科领域的角度分析了对于同一问题的不同研究思路，同时也归纳总结了当前研究中最为主流的核心推荐算法.最后，强调当前研究存在的主要不足之处.