Knowledge discOvery And daTa minINg inteGrated (KOATING) Moderators for collaborative projects

A major issue in any multidiscipline collaborative project is how to best share and simultaneously exploit different types of expertise, without duplicating efforts or inadvertently causing conflicts or loss of efficiency through misunderstanding of individual or shared goals. Moderators are knowledge based systems designed to support collaborative teams by raising awareness of potential problems or conflicts. However, the functioning of a Moderator is limited by the knowledge it has about the team members. Knowledge acquisition, learning and updating of knowledge are the major challenges for a Moderator's implementation. To address these challenges a Knowledge discOvery And daTa minINg inteGrated (KOATING) framework is presented for Moderators to enable them to continuously learn from the operational databases of the company and semi-automatically update their knowledge about team members. This enables the reuse of discovered knowledge from operational databases within collaborative projects. The integration of knowledge discovery in database (KDD) techniques into the existing Knowledge Acquisition Module of a moderator enables hidden data dependencies and relationships to be utilised to facilitate the moderation process. The architecture for the Universal Knowledge Moderator (UKM) shows how Moderators can be extended to incorporate a learning element which enables them to provide better support for virtual enterprises. Unified Modelling Language diagrams were used to specify the ways to design and develop the proposed system. The functioning of a UKM is presented using an illustrative example.     

Bayesian-network-based safety risk assessment for steel construction projects

There are four primary accident types at steel building construction (SC) projects: falls (tumbles), object falls, object collapse, and electrocution. Several systematic safety risk assessment approaches, such as fault tree analysis (FTA) and failure mode and effect criticality analysis (FMECA), have been used to evaluate safety risks at SC projects. However, these traditional methods ineffectively address dependencies among safety factors at various levels that fail to provide early warnings to prevent occupational accidents. To overcome the limitations of traditional approaches, this study addresses the development of a safety risk-assessment model for SC projects by establishing the Bayesian networks (BN) based on fault tree (FT) transformation. The BN-based safety risk-assessment model was validated against the safety inspection records of six SC building projects and nine projects in which site accidents occurred. The ranks of posterior probabilities from the BN model were highly consistent with the accidents that occurred at each project site. The model accurately provides site safety-management abilities by calculating the probabilities of safety risks and further analyzing the causes of accidents based on their relationships in BNs. In practice, based on the analysis of accident risks and significant safety factors, proper preventive safety management strategies can be established to reduce the occurrence of accidents on SC sites.     

An interactive risk visualisation tool for large-scale and complex engineering and construction projects under uncertainty and interdependence

Implementation of the megaprojects with large-scale engineering and construction projects are risky in nature and evaluating the associated risks of those large projects is a critical success factor. The systematic approaches and empirical studies related to the visualisation and communicating risks of these projects remain missing. This paper aims to develop a systematic approach to managing and visualising the risk of these mega-projects using joint application of fuzzy group decision-making, analytic network process and mapping the resulting network of dependencies together with proximity information, graph theory, and mutual information theory. We have applied the model in a real case study of megaprojects in the oil and gas industry. The methodology proposed in this study could be used in the other large-scale engineering and construction projects considering the contracts features and the contextual factors.     

决策知识结构的全方格诱发与分析

本研究以决策者的信息加工模式和决策机制作为系统设计的理论依据，获得了富有意义的结果。在研究中，通过全方格技术对具体和抽象的概念在不同标准下的知识结构诱发，探讨全方格技术在决策知识结构中的具体运用，得出了一些有关决策知识结构的新的规律和特点。研究表明，不论是抽象概念组还是具体概念组，当标准清晰时，其结构就相对整合得有规则，更符合人们的认知规律。反之，结构就比较混乱和模糊。另外，当进行区分的维度标准都比较清晰时，同一组概念的认知结构就比较接近，因素间的区别也小，结构较明确，而当标准比较模糊时，认知结构就缺乏规则。本文讨论了研究的理论意义和应用价值。     

风电项目中的土木工程问题及有关研究的思考和发展展望

从配套工程建设、风况数据的搜集、整理及分析、预测等角度,探讨了促进我国风电事业的发展方式;指出关注风电建设中的土木工程问题,编制有关规范或指导手册等文件,做好风资源的普查、评估和风电场发电量预测,并在这些技术领域做到专业化、规范化,形成可靠的、成熟的、具有自主知识产权的技术体系对我国的风电发展至关重要。最后探讨了需要进行研究的方向,并结合中国国情对风电的发展做了展望。     

I2QFD: A blackboard-based multiagent system for supporting concurrent engineering projects

Although Concurrent Engineering has been accredited as a superior approach to product development, its intrinsic complexity in coordinating the multidisciplinary project team is further augmented when team members are remotely distributed. Assuming the teamwork is supported by an Intranet, this paper proposes the framework of a blackboard-based multiagent system, called I 2 QFD, to facilitate the communication and coordination of the design projects. Configured within a hierarchical structure, this system is defined with IDEF0 and high-level Petri nets to depict the operations of Intelligent Agencies and their affiliated agents. Based on temporal logic, the operation of the project team with I 2 QFD is simulated to manifest the dynamics of the design process as well as to generate the project's schedule that ensures the intended concurrency.     

人事决策专家与新手的知识诱发和多维空间结构分析

本研究运用认知工程学实验方法，对21名人事管理干部(专家)和大学生(新手)进行人事决策知识的诱发与获取实验，并采用多维量表法作出多维空间结构分析。结果表明，两类知识结构存在较大差异，等家的知识结构表现出“人一职”元素之间的依存关系．知识元素分化程度较高，并具网络特征；而新手的知识结构则缺乏分化和明确的维度特征，较少结构化。本研究为多层次决策支持与辅助提供了新的实验依据。     

Improving expert forecasts in reliability: Application and evidence for structured elicitation protocols

Quantitative expert judgements are used in reliability assessments to inform critically important decisions. Structured elicitation protocols have been advocated to improve expert judgements, yet their application in reliability is challenged by a lack of examples or evidence that they improve judgements. This paper aims to overcome these barriers. We present a case study where two world-leading protocols, the IDEA protocol and the Classical Model, were combined and applied by the Australian Department of Defence for a reliability assessment. We assess the practicality of the methods and the extent to which they improve judgements. The average expert was extremely overconfident, with 90% credible intervals containing the true realisation 36% of the time. However, steps contained in the protocols substantially improved judgements. In particular, an equal weighted aggregation of individual judgements and the inclusion of a discussion phase and revised estimate helped to improve calibration, statistical accuracy, and the Classical Model score. Further improvements in precision and information were made via performance weighted aggregation. This paper provides useful insights into the application of structured elicitation protocols for reliability and the extent to which judgements are improved. The findings raise concerns about existing practices for utilising experts in reliability assessments and suggest greater adoption of structured protocols is warranted. We encourage the reliability community to further develop examples and insights.     

气候变化与青藏高原工程设计

最近半个世纪,青藏高原地面气候发生了一定变化,主要表现在地面平均气温明显上升,冬季、夜间和城镇区域气温上升尤其显著,多数地区降水量呈现不同程度增加。气候变暖对高原地区自然和人类系统产生了一定影响。预计未来青藏高原气候总体将继续趋向变暖,这可能对冰冻圈、河湖系统、陆地植被、农业自然条件、能源气候资源、交通和水利设施、城镇人居环境等产生明显影响。与生态保护和经济社会发展有关的各类大型工程的规划、设计和维护,需要考虑今后气候变化的可能影响,及早制定可行的适应性措施。     

Expert elicitation approach for performing ATHEANA quantification

An expert elicitation approach has been developed to estimate probabilities for unsafe human actions (UAs) based on error-forcing contexts (EFCs) identified through the ATHEANA (A Technique for Human Event Analysis) search process. The expert elicitation approach integrates the knowledge of informed analysts to quantify UAs and treat uncertainty (‘quantification-including-uncertainty’). The analysis focuses on (a) the probabilistic risk assessment (PRA) sequence EFCs for which the UAs are being assessed, (b) the knowledge and experience of analysts (who should include trainers, operations staff, and PRA/human reliability analysis experts), and (c) facilitated translation of information into probabilities useful for PRA purposes. Rather than simply asking the analysts their opinion about failure probabilities, the approach emphasizes asking the analysts what experience and information they have that is relevant to the probability of failure. The facilitator then leads the group in combining the different kinds of information into a consensus probability distribution. This paper describes the expert elicitation process, presents its technical basis, and discusses the controls that are exercised to use it appropriately. The paper also points out the strengths and weaknesses of the approach and how it can be improved. Specifically, it describes how generalized contextually anchored probabilities (GCAPs) can be developed to serve as reference points for estimates of the likelihood of UAs and their distributions.     

IT外包中知识转移的风险规避对策研究1

本文对IT外包中的相关文献与IT外包、知识转移、风险规避等概念进行了阐述,提出了建立IT外包知识管理系统、加强IT外包双方的关系管理系统等对策,该些对策对IT外包双方知识转移中的风险进行控制具有一定的指导性意义。     

基于感性工学与知识工程的用户需求认知研究

目的提出基于感性工学与知识工程的用户需求认知模型以促进产品设计领域更好地满足用户需求。方法首先从基本理论、流程、相关技术等角度出发介绍感性工学与知识工程;然后依据其在产品设计领域具有相似性及互补性,将两者结合并提出基于感性工学与知识工程的用户需求认知模型;最后通过自行车设计案例进行模型应用的简单介绍。结果在基于感性工学与知识工程的用户需求认知模型中,知识工程模块能确保设计方案在物理层面满足用户功能需求,感性工学模块则能确保设计方案在心理层面满足用户感性需求,因此该模型生成的设计方案能够在物理层面和心理层面同时满足用户需求。结论在产品设计领域,感性工学与知识工程的结合能更高效、更合理地满足用户需求。     

Quality assessment of coarse models and surrogates for space mapping optimization

One of the central issues in space mapping optimization is the quality of the underlying coarse models and surrogates. Whether a coarse model is sufficiently similar to the fine model may be critical to the performance of the space mapping optimization algorithm and a poor coarse model may result in lack of convergence. Although similarity requirements can be expressed with proper analytical conditions, it is difficult to verify such conditions beforehand for real-world engineering optimization problems. In this paper, we provide methods of assessing the quality of coarse/surrogate models. These methods can be used to predict whether a given model might be successfully used in space mapping optimization, to compare the quality of different coarse models, or to choose the proper type of space mapping which would be suitable to a given engineering design problem. Our quality estimation methods are derived from convergence results for space mapping algorithms. We provide illustrations and several practical application examples. This work was supported in part by the Natural Sciences and Engineering Research Council of Canada under Grants RGPIN7239-06 and STPGP336760-06.     

An ethnographic study of design knowledge reuse in the architecture, engineering, and construction industry

This paper presents an ethnographic study of knowledge reuse in the architecture/engineering/construction industry. Reuse occurs largely through social knowledge networks. Even when reuse from an external repository occurs, a human expert is needed to provide input on what to reuse and contextual information about the designs being reused. This is attributed to the effectiveness of internal knowledge reuse, the reuse of knowledge from one’s personal experiences. Internal knowledge reuse is effective because the designer can find items to reuse, and can recall the context of these items and can therefore understand them. This ethnographic study was used to develop a corporate memory, a rich, detailed repository of knowledge in context. The corporate memory will support finding and understanding. Understanding can be brought about by enabling the designer to explore the item’s context. This helps the designer to manage the tradeoff between productivity and creativity in deciding what to reuse.     

Using mental mapping to unpack perceived cycling risk

Cycling is the most energy-efficient mode of transport and can bring extensive environmental, social and economic benefits. Research has highlighted negative perceptions of safety as a major barrier to the growth of cycling. Understanding these perceptions through the application of novel place-sensitive methodological tools such as mental mapping could inform measures to increase cyclist numbers and consequently improve cyclist safety. Key steps to achieving this include: (a) the design of infrastructure to reduce actual risks and (b) targeted work on improving safety perceptions among current and future cyclists.This study combines mental mapping, a stated-preference survey and a transport infrastructure inventory to unpack perceptions of cycling risk and to reveal both overlaps and discrepancies between perceived and actual characteristics of the physical environment. Participants translate mentally mapped cycle routes onto hard-copy base-maps, colour-coding road sections according to risk, while a transport infrastructure inventory captures the objective cycling environment. These qualitative and quantitative data are matched using Geographic Information Systems and exported to statistical analysis software to model the individual and (infra)structural determinants of perceived cycling risk.This method was applied to cycling conditions in Galway City (Ireland). Participants’ (n = 104) mental maps delivered data-rich perceived safety observations (n = 484) and initial comparison with locations of cycling collisions suggests some alignment between perception and reality, particularly relating to danger at roundabouts. Attributing individual and (infra)structural characteristics to each observation, a Generalised Linear Mixed Model statistical analysis identified segregated infrastructure, road width, the number of vehicles as well as gender and cycling experience as significant, and interactions were found between individual and infrastructural variables. The paper concludes that mental mapping is a highly useful tool for assessing perceptions of cycling risk with a strong visual aspect and significant potential for public participation. This distinguishes it from more traditional cycling safety assessment tools that focus solely on the technical assessment of cycling infrastructure. Further development of online mapping tools is recommended as part of bicycle suitability measures to engage cyclists and the general public and to inform ‘soft’ and ‘hard’ cycling policy responses.     

Knowledge management performance measurement based on World-Class Competitive Advantages to develop strategic-oriented projects: Case of Iranian oil industry

Knowledge Management (KM) is one of the main sources for achieving World-Class Competitive Advantages (WCCAs), and is also an incentive for the development of strategic-oriented projects. Because subsidiaries of the oil industry are always looking for WCCAs to implement their strategic-oriented projects, it is important to measure WCCAs-based KM performance. We offered a WCCAs-based Knowledge Management Performance Measurement (KMPM) instrument to develop strategic-oriented projects and validated it in the Iranian oil industry. Based on an in-depth review of previous studies, the indicators of KMPM that could lead to WCCAs were extracted. Then, an exploratory factor analysis and a confirmatory factor analysis were used to identify and confirm the criteria of the proposed instrument. Based on the proposed instrument, the KM performance of the subsidiaries of the Iranian Ministry of Petroleum (MoP) was assessed, and finally, strategic-oriented projects were suggested. Our proposed instrument includes four main criteria, namely “knowledge quality”, “knowledge utility”, “knowledge innovation”, and “business results”, all of which are valid in the Iranian oil industry. Our findings also present 17 strategic-oriented projects. The policymakers and the top managers of the surveyed companies could obtain more knowledge regarding measuring the WCCAs-based KM performance, and utilize it to develop their strategic-oriented projects. The present research is one of the first studies of its kind that provides a WCCAs-based KMPM instrument and expands the literature on KMPM and WCCAs.     

支持并行工程的造船分段作业计划系统

针对传统造船模式下,车间作业计划与工艺设计串行工作方式的缺点,基于并行工程的原理,提出了分段作业计划与工艺设计的集成运行模式,为实现造船CAPP系统与PPC系统的集成化和并行化提供了实现的基础。针对集成模式的特点,建立了分段作业计划系统资源优化的数学模型,应用遗传算法解决了针对任意分段装配工艺方案的多资源平衡优化问题,可以得到每项作业最优的开工时间,同时能够给出多种资源的最优分布结果,满足了多工艺方案之间资源利用率的比较。最后,给出了计算实例,计算机模拟结果说明了这一方法的有效性。     

Robust multiattribute decision making under risk and uncertainty in engineering design

In this article, the problem of choosing from a set of design alternatives based upon multiple, conflicting, and uncertain criteria is investigated. The problem of selection over multiple attributes becomes harder when risky alternatives exist. The overlap measure method developed in this article models two sources of uncertainties—imprecise or risky attribute values provided to the decision maker and inabilities of the decision-maker to specify an exact desirable attribute value. Effects of these uncertainties are mitigated using the overlap measure metric. A subroutine to this method, called the robust alternative selection method, ensures that the winning alternative is insensitive to changes in the relative importance of the different design attributes. The overlap measure method can be used to model and handle various sources of uncertainties and can be applied to any number of multiattribute decision-making methods. In this article, it is applied to the hypothetical equivalents and inequivalents method, which is a multiattribute selection method under certainty.     

A Practical Approach to Constructing a Knowledge Graph for Cybersecurity

Cyberattack forms are complex and varied, and the detection and prediction of dynamic types of attack are always challenging tasks. Research on knowledge graphs is becoming increasingly mature in many fields. At present, it is very significant that certain scholars have combined the concept of the knowledge graph with cybersecurity in order to construct a cybersecurity knowledge base. This paper presents a cybersecurity knowledge base and deduction rules based on a quintuple model. Using machine learning, we extract entities and build ontology to obtain a cybersecurity knowledge base. New rules are then deduced by calculating formulas and using the path-ranking algorithm. The Stanford named entity recognizer (NER) is also used to train an extractor to extract useful information. Experimental results show that the Stanford NER provides many features and the useGazettes parameter may be used to train a recognizer in the cybersecurity domain in preparation for future work.