蓄电池制造业的MRP实施关键点分析与研究

目的是探究一种适合蓄电池制造企业运行MRP的方法,并且推广成功的经验。只有把MRP作为反映业务数据及时性和准确性的重要考量标准,才能从根本上做好蓄电池制造业的信息化。其中使用了MRP相关的理论：安全库存、订货点法、产能计算等等。在成功实施了MRP之后,大幅提高了企业的运作能力,降低了成本。这些都说明,使用MRP来协调企业内部供应链,的确能给企业带来很大的效益,并从根本上提高企业的竞争力。     

具有智能交互的网络教学系统

由于在网络教学中,缺乏传统教学的灵活性,因此在网络教学中引入agent,设计基于浏览器/Agent/服务器的网络教学系统,并提出了各个模块的主要功能,使得网络教学更加智能化和个性化。     

An information-based adaptive strategy for resource exploitation in competitive scenarios

Given an exploitation problem, in which a number of agents compete for a limited renewable resource, the optimal harvesting strategy depends on the ratio between resource availability and exploitation effort. For scarce resource a purely competitive, greedy strategy outperforms a more collaborative approach based on the Collective Intelligence, while for more abundant resource the opposite holds. The rationale for this behaviour lies in the amount of information each strategy is able to provide and a combined strategy is possible according to which agents choose dynamically the most informative strategy according to a minimum entropy criterion. This approach, which provides best performance for both under and over-exploited scenarios, can be used to monitor the resource status for management purposes and is effective in both centralised and decentralised decision making.     

中国企业跨国并购中的竞争情报保障研究

从竞争情报的角度分析了中国企业跨国并购中的成败得失，挖掘跨国并购行为中的情报需求，弄清竞争情报该如何为跨国并购行为发挥应有的保障作用．让中国企业成功实现“走出去”的目标。     

智能建筑工程建设有关问题的探讨

在分析智能建筑工程建设项目现状的基础上,对智能建筑项目建设中存在的主要问题作了深入分析,提出了加强政府管理、成立行业协会和扶持专业咨询公司等管理措施,为智能建筑工程建设的管理提供了新的方法.     

日本天基情报侦察系统发展分析

本文就日本利用本国多用途民用遥感卫星和借助别国力量获取卫星图像信息为其军事服务，直至建立日本独立的军事侦察卫星系统这一发展天基情报侦察系统的策略进行了评述。同时，对日本天基情报侦察系统装备情况及其可能达到的水平也作了简略的阐述。     

论公益性和商业性地质勘查活动的划分以及运行规则

公益性工作包括基础性工作和专题性工作两部分."公益性活动和商业性活动分体运行"的提法行不通,只应是"分制运行".借鉴土木建筑行业经验,建立业主、设计、施工、监理"四分天下"的运行机制.政府行业主管部门和政府业主的分工体现了"政资分离".     

智慧金融——Work Smarter

围绕金融业和信息产业的发展阶段的论述,通过银行业和信息系统最佳实践的角度,探索利用新思维和新技术构建＂智慧金融＂体系的最基本要素。以SEB银行为便,解析商业银行利用新技术和商业智能功能获取更多信息和洞察力,帮助客户利用最新电子交易渠道和电子服务产品。金融智能化必将是现代金融学理论、金融业界经验、科技发展与运用、社会进步与生活智能化的集大成者。     

Advances in forecasting with neural networks? Empirical evidence from the NN3 competition on time series prediction

This paper reports the results of the NN3 competition, which is a replication of the M3 competition with an extension of the competition towards neural network (NN) and computational intelligence (CI) methods, in order to assess what progress has been made in the 10 years since the M3 competition. Two masked subsets of the M3 monthly industry data, containing 111 and 11 empirical time series respectively, were chosen, controlling for multiple data conditions of time series length (short/long), data patterns (seasonal/non-seasonal) and forecasting horizons (short/medium/long). The relative forecasting accuracy was assessed using the metrics from the M3, together with later extensions of scaled measures, and non-parametric statistical tests. The NN3 competition attracted 59 submissions from NN, CI and statistics, making it the largest CI competition on time series data. Its main findings include: (a) only one NN outperformed the damped trend using the sMAPE, but more contenders outperformed the AutomatANN of the M3; (b) ensembles of CI approaches performed very well, better than combinations of statistical methods; (c) a novel, complex statistical method outperformed all statistical and CI benchmarks; and (d) for the most difficult subset of short and seasonal series, a methodology employing echo state neural networks outperformed all others. The NN3 results highlight the ability of NN to handle complex data, including short and seasonal time series, beyond prior expectations, and thus identify multiple avenues for future research.     

Forecast combinations of computational intelligence and linear models for the NN5 time series forecasting competition

In this work we introduce the forecasting model with which we participated in the NN5 forecasting competition (the forecasting of 111 time series representing daily cash withdrawal amounts at ATM machines). The main idea of this model is to utilize the concept of forecast combination, which has proven to be an effective methodology in the forecasting literature. In the proposed system we attempted to follow a principled approach, and make use of some of the guidelines and concepts that are known in the forecasting literature to lead to superior performance. For example, we considered various previous comparison studies and time series competitions as guidance in determining which individual forecasting models to test (for possible inclusion in the forecast combination system). The final model ended up consisting of neural networks, Gaussian process regression, and linear models, combined by simple average. We also paid extra attention to the seasonality aspect, decomposing the seasonality into weekly (which is the strongest one), day of the month, and month of the year seasonality.