A Practical Decision Making on Design of Fixed Offshore Wind Turbine Support Structure Considering Socio-economic Impact

Wind energy is considered one of the most promising alternative energy sources against the conventional fossil fuels.However,the deployment of these structures in deep-water for better power production is considered as a complex task.This also has raised the issue regarding selection of appropriate support structures for various sea conditions by considering environmental impact and carbon footprint.This paper considers a jacket like support structure as a case study for an intermediate water depth(50m).The jacket is considered to be located in North of Dutch Sea,and 100-extreme wave is applied as load condition.Here,the presented methodology provides an insight towards environmental/social impact made by the optimized designs in comparison with reference design.

Brain areas activated by uncertain reward-based decision-making in healthy volunteers

Reward-based decision-making has been found to activate several brain areas, including the ven- trolateral prefronta~ lobe, orbitofrontal cortex, anterior cingulate cortex, ventral striatum, and mesolimbic dopaminergic system. In this study, we observed brain areas activated under three de- grees of uncertainty in a reward-based decision-making task （certain, risky, and ambiguous）. The tasks were presented using a brain function audiovisual stimulation system. We conducted brain scans of 15 healthy volunteers using a 3.0T magnetic resonance scanner. We used SPM8 to ana- lyze the location and intensity of activation during the reward-based decision-making task, with re- spect to the three conditions. We found that the orbitofrontal cortex was activated in the certain reward condition, while the prefrontal cortex, precentral gyrus, occipital visual cortex, inferior parietal lobe, cerebellar posterior lobe, middle temporal gyrus, inferior temporal gyrus, limbic lobe, and midbrain were activated during the ＇risk＇ condition. The prefrontal cortex, temporal pole, inferior temporal gyrus, occipital visual cortex, and cerebellar posterior lobe were activated during am- biguous decision-making. The ventrolateral prefrontal lobe, frontal pole of the prefrontal lobe, orbi- tofrontal cortex, precentral gyrus, inferior temporal gyrus, fusiform gyrus, supramarginal gyrus, infe- rior parietal Iobule, and cerebellar posterior lobe exhibited greater activation in the ＇risk＇ than in the ＇certain＇ condition （P 〈 0.05）. The frontal pole and dorsolateral region of the prefrontal lobe, as well as the cerebellar posterior lobe, showed significantly greater activation in the ＇ambiguous＇ condition compared to the ＇risk＇ condition （P 〈 0.05）. The prefrontal lobe, occipital lobe, parietal lobe, temporal lobe, limbic lobe, midbrain, and posterior lobe of the cerebellum were activated during deci- sion-making about uncertain rewards. Thus, we observed different levels and regions of activation for different types of reward processing during decision-making. Specifically, when the degree of reward uncertainty increased, the number of activated brain areas increased, including greater ac- tivation of brain areas associated with loss.

Integration of Decision-Support and Knowledge-Based Techniques in a Problem-Solving Strategy for Selection Problems

The limitations of traditional approaches to selection problems are examined. A problemsolving strategy is presented in which decision-support and knowledge-based techniques play complementary roles. An approach to the representation of knowledge to support the problem-solving strategy is presented which avoids commitment to a specific programming language or implementation environment. The problem of choosing a home is used to illustrate the representation of knowledge in a specific problem domain. Techniques for implementation of the problem-solving strategy are described. Knowledge elicitation techniques and their implementation in a development shell for application of the problem-solving strategy to any selection problem are also described.

Neural mechanism of proposer's decision-making in the ultimatum and dictator games

Previous studies have demonstrated that reactions to unfair offers in the ultimatum game are correlated with negative emotion. However, little is known about the difference in neural activity between a proposer＇s decision-making in the ultimatum game compared with the dictator game. The present functional magnetic resonance imaging study revealed that proposing fair offers in the dictator game elicited greater activation in the right supramarginal gyrus, right medial frontal gyrus and left anterior cingulate cortex compared with proposing fair offers in the ultimatum game in 23 Chinese undergraduate and graduate students from Beijing Normal University in China. However, greater activation was found in the right superior temporal gyrus and left cingulate gyrus for the reverse contrast. ＂The results indicate that proposing fair offers in the dictator game is more strongly associated with cognitive control and conflicting information processing compared with proposing fair offers in the ultimatum game.

A Model-Based, Aspiration-Led Decision Support System NY-IEDSS

An AI-aided simulation system embedded in a model-based, aspiration-led decision support system NY-IEDSS is reported. The NY-IEDSS is designed for mid-term development strategic study of the Nanyang Region in Henan, China, and is getting beyond its prototype stage under the decision maker's (the end user) orientation. The integration of simulation model system, decision analysis and expert system for decision support in the system implementation was reviewed. The intent of the paper is to provide insight as to how system capability and acceptability can be enhanced by this integration. Moreover, emphasis is placed on problem orientation in applying the method.

Early Building Design:Informed decision-making by exploring multidimensional design space using sensitivity analysis

This paper describes a novel approach to explore a multidimensional design space and guide multi-actor decision making in the design of sustainable buildings.The aim is to provide proactive and holistic guidance of the design team.We propose to perform exhaustive Monte Carlo simulations in an iterative design approach that consists of tw o steps:1) preparation by the modeler,and 2) a multi-collaborator meeting.In the preparation phase,the simulation modeler performs Morris sensitivity analysis to fixate insignificant model inputs and to identify non-linearity and interaction effects.Next,a representation of the global design space is obtained from thousands of simulations using low-discrepancysequences(LPτ) for sampling.From these simulations,the modeler constructs fast metamodels and performs quantitative sensitivity analysis.During the meeting,the design team explores the global design space by filtering the thousands of simulations.Variable filter criteria are easily applied using an interactive parallel coordinate plot w hich provide immediate feedback on requirements and design choices.Sensitivity measures and metamodels show the combined effects of changing a single input and how to remedy unw anted output changes.The proposed methodology has been developed and tested through real building cases using a normative model to assess energy demand,thermal comfort,and daylight.

Decision Making Support System(DSS)——A Synthesized and Integrated Crystallization of Systems Engineering, Artificial Intelligence and Electronic Technologies

I firmly believe that of systems engineering is the requirement-driven force for the progress ofsoftware engineering, artificial intelligence and electronic technologies. The development ofsoftware engineering, artificial intelligence and electronic technologies is the technical supportfor the progress of systems engineering. INTEGRATION can be considered as "bridging" the ex-isting technologies and the People together into a coordinated SYSTEM.

DEVELOPING EXPERT DECISION SUPPORT SYSTEM WTH VP-EXPERT AND LOTUS 1-2-3 IN RE-BLENDING BY USE

An expert decision support system (EDSS) for multi-bins balance and contro1 of orequality in production ore bins of some large-scale open pit iron mine in China has been developed byexpert svitem tool software VP-EXPERT and integration software LOTUS 1-2-3 in this paper. Itis known by practicing that a medium-scale EDSS constructed on microcomputer is completcly, feaasible by means of VP-EXEPERT to construct knowledge base system (KBS), LOTUS 1-2-3 tomake decision support system (DSS) and link them with BAT.

Feed back Mechanism in Decision Support Systems

Feedback mechanism is a specific feature of control systems, however, the similarity has been found in decision support systems. Just because of this, the DSS can provide a flexible learning and thinking environment, and help decision maker to solve semistructured and unstructured problems actively and creatively. In this paper, the role and type of the feedback in decision making are discussed from different point of view. This is also true in DSS because it supports decision making. The feedback design, especially the feedback interface design, is described through a case of practice DSS. Based on these points, the feedback mechanism is an important feature of DSS, and it is one of the differences between DSS and MIS.

Computerized decision support in adult and pediatric critical care

Computerized decision support(CDS) is the most advanced form of clinical decision support available and has evolved with innovative technologies to provide meaningful assistance to medical professionals. Critical care clinicians are in unique environments where vast amounts of data are collected on individual patients, and where expedient and accurate decisions are paramount to the delivery of quality healthcare. Many CDS tools are in use today among adult and pediatric intensive care units as diagnostic aides, safety alerts, computerized protocols, and automated recommendations for management. Some CDS use have significantly decreased adverse events and improved costs when carefully implemented and properly operated. CDS tools integrated into electronic health records are also valuable to researchers providing rapid identification of eligible patients, streamlining data-gathering and analysis, and providing cohorts for study of rare and chronic diseases through data-warehousing. Although the need for human judgment in the daily care of critically ill patients has limited the study and realization ofmeaningful improvements in overall patient outcomes, CDS tools continue to evolve and integrate into the daily workflow of clinicians, and will likely provide advancements over time. Through novel technologies, CDS tools have vast potential for progression and will significantly impact the field of critical care and clinical research in the future.

SCALABLE AND QUANTITATIVE DECISION SUPPORT FOR THE INITIAL BUILDING DESIGN STAGES OF REFURBISHMENT

Decision-making within the building industry often involves various criteria of very different natures.Some are qualitative,others quantitative,some are objective,others subjective,but ultimately,they should all be aggregated and handled holistically in order to support decision-making.This process can also be referred to as multi-criteria decision-making(MCDM).Some aspects of MCDM are often conducted unconsciously and non-transparently.By implementing mathematical methods that have been proved applicable for MCDM,multi-criteria decision-making processes can be handled more consciously and transparently and thus be made reproducible.The calculation method presented allows quantitative sustainability and qualitative indicator values to be accounted for with the level of importance desired.The MCDM method used is Technique for Order of Preference by Similarity to Ideal Solution(TOPSIS).As the model has already been described well in the scientific literature and is used frequently,the aim is to illustrate how TOPSIS can be applied for transparent decision-making within the building industry in the context of urban renewal and refurbishment schemes through case studies of various scales and descriptions.The case studies focused on in this paper incorporate a variety of specific pre-chosen criteria,including environmental performance,functional parameters and technical parameters.The case studies cover different parameters of refurbishment in a major hospital compound in Denmark due to be taken out of operation.One central design decision is whether to refurbish or demolish the old hospital buildings.The results reveal that decision support is first of all dependent on how the decision-making tool is applied and what choices are made in relation to the actual calculations.However,by implementing a mathematically based MCDM method like that being assessed in the case studies presented,the decisions and their argu-ments become transparent and are easily communicated within a project group.As a result,the tool is considered to be universally applicable across most decision-making contexts within the building industry.

An Approach to Continuous Approximation of Pareto Front Using Geometric Support Vector Regression for Multi-objective Optimization of Fermentation Process

The approaches to discrete approximation of Pareto front using multi-objective evolutionary algorithms have the problems of heavy computation burden, long running time and missing Pareto optimal points. In order to overcome these problems, an approach to continuous approximation of Pareto front using geometric support vector regression is presented. The regression model of the small size approximate discrete Pareto front is constructed by geometric support vector regression modeling and is described as the approximate continuous Pareto front. In the process of geometric support vector regression modeling, considering the distribution characteristic of Pareto optimal points, the separable augmented training sample sets are constructed by shifting original training sample points along multiple coordinated axes. Besides, an interactive decision-making(DM)procedure, in which the continuous approximation of Pareto front and decision-making is performed interactively, is designed for improving the accuracy of the preferred Pareto optimal point. The correctness of the continuous approximation of Pareto front is demonstrated with a typical multi-objective optimization problem. In addition,combined with the interactive decision-making procedure, the continuous approximation of Pareto front is applied in the multi-objective optimization for an industrial fed-batch yeast fermentation process. The experimental results show that the generated approximate continuous Pareto front has good accuracy and completeness. Compared with the multi-objective evolutionary algorithm with large size population, a more accurate preferred Pareto optimal point can be obtained from the approximate continuous Pareto front with less computation and shorter running time. The operation strategy corresponding to the final preferred Pareto optimal point generated by the interactive DM procedure can improve the production indexes of the fermentation process effectively.

基于群决策-云理论的装备保障能力评估

针对装备保障效能评估的复杂性、随机性和模糊性问题,建立了基于群决策-云理论的装备保障能力评估模型。首先,基于装备保障任务的需求,构建了装备保障能力评估指标体系;再利用序关系-群决策方法计算各级指标权重,计算过程简单快捷、更能体现群体决策;最后,将云理论引入装备保障能力评估中,能够实现定性概念与定量数据间的合理转换,清晰准确地表达了层级指标的聚合过程,有效解决了评估过程中的模糊性和随机性。实例表明:该方法计算简单、逻辑关系清楚、计算结果与客观实际相符,可为装备保障效能评估提供一种科学、客观的方法,为保障资源配置提供科学依据。

基于EWM-MARCOS综合评价模型的巷道支护参数优化研究

为应对云南某地下矿山巷道支护返修频繁、成本高的严峻挑战,结合矿山现有支护条件,并全面考虑支护安全、成本和效率等多方面因素,对锚杆长度、钢筋网网度和喷射混凝土厚度的参数进行优化。通过构建的EWM-MARCOS综合评价模型优选出最佳支护参数方案:锚杆长度2.5 m,钢筋网网度150 mm×200 mm,喷射混凝土厚度200 mm。现场实践表明:采用优选的支护方案对巷道进行支护,巷道顶板下沉量降低了3.37 mm,两帮收敛变形降低了3.38 mm,一定程度提高了围岩的稳定性;从经济上来看,考虑到支护返修频率情况下,其支护成本比原方案降低了85.10元/m。因此,采用EWM-MARCOS综合评价模型对支护方案进行优选,可以一定程度优化该矿巷道支护返修频率高、支护成本大的问题。

“决胜”系统---构建战争决策综合集成研讨与模拟环境的实践与思考

针对战争高层决策问题,我们提出了研制综合集成研讨与模拟环境的思路,并以复杂性科学方法论,特别是以钱学森等人提出的“从定性到定量综合集成研讨厅”方法论为指导,开发了决胜系列战争决策综合集成研讨与模拟环境原型系统。本文简要介绍了我们在系统体系结构、关键技术及应用等方面取的进展,对构建战争决策综合集成研讨与模拟环境的相关问题进行了总结与讨论。

多特征SVM-DS融合决策的缺陷识别

分析超声检测缺陷信号模式识别中存在的问题。提出一种将支持向量机(Support vector machine,SVM)和DS(Shafer-Dempster)证据理论相结合的多特征融合决策识别方法。阐述支持向量机解决分类问题的原理以及证据理论中的Dempster合成规则。将证据理论中的识别框架引入到缺陷类型识别,设计多缺陷类型的多特征SVM-DS融合决策规则。介绍4种不同空间域的特征提取方法以用于多特征融合决策识别。分别将单特SVM识别和SVM-DS融合决策识别应用于石油套管4种典型缺陷的识别。对比试验表明:SVM-DS融合决策识别方法能有效识别上述典型缺陷,其在识别率和泛化性方面都比单特征的SVM识别有优势。

基于3S的湿地监测与空间决策支持系统研究--以扎龙国家级湿地保护区为例

湿地具有重要的保护价值,传统的湿地管理和决策方法面临挑战。要提高湿地的保护和管理水平,不仅需要先进的管理模式,更需要先进的技术手段。建立高效实用的扎龙湿地监测与决策支持系统,将湿地管理和决策过程实时化和科学化,以加强对湿地的监控和管理,成为亟待解决的问题。以扎龙湿地为研究对象,以RS、GIS、GPS和Web技术为技术支撑,开展湿地监测与决策支持系统研究与开发,为扎龙湿地的保护提供及时、有效的辅助决策支持,并为其它湿地的管理提供借鉴。

基于多属性决策的PCAS-SNIF关键节点分析

预先计划的近距空中支援(PCAS)是一种典型的空军进攻作战样式,研究PCAS信息流转过程对于提高PCAS的作战效能具有重要意义。针对PCAS作战过程难以建模分析的问题,引入超网络理论,通过研究PCAS作战的基本流程以及作战网络中各节点之间的交互关系,构建了"两层五网"的PCAS信息流转超网络模型,然后,仿真分析了PCAS信息流转超网络节点的度中心性、介数、聚类系数和接近中心性4个方面的拓扑特性,通过多属性决策节点重要性综合评价方法得出网络关键节点;最后,通过网络攻击实验验证了超网络模型和多属性决策算法的合理性和有效性,为研究作战信息流转问题提供了新的思路。

基于SAS-WEBLOGIC的数据挖掘模型及应用

提出了一种基于SAS-Weblogic的数据挖掘模型,SAS作为数据挖掘语言处于模型的核心,Weblogic作为中间构件,负责SAS数据集联系前端浏览器和后台数据库,起着监控和管理作用。该模型适用于大中型数据挖掘应用,为现代企业提供了一种重要的决策支持系统模型。最后通过实验证明了模型的有效性。

基于数据仓库的E-MIS柔性设计与开发

从扬子石化公司能源管理的现状出发,遵循软件工程的原则,开发了扬子石化能源信息管理系统,包括异构平台的数据自动化采集、复杂的能源平衡处理及合理的能源优化分配这三个层面的内容。实现了对全公司范围内能源消耗的控制,进而实现了能源的优化调度和管理,为总公司及时准确地掌握全公司能源信息和进行能源管理提供决策支持。