Using analytic network process to analyze problems for implementing turn-key construction projects in Taiwan

The turn-key construction project is implemented in Taiwan not by a single company but by a make-shift group of several companies. Hence,problems to coordinate the professional construction management (PCM) and the supervising architectural company often occur for the lack of long-term experience to work together. The various factors that affect the implementation of turn-key projects currently practiced in Taiwan are analyzed using the analytic network process (ANP). The objective is to study how the twelve key factors in the four layers of "Role assignment","Signing contract","Operational procedures" and "Losing capital investment" affect the progress of implementing the turn-key project in Taiwan. The results reveal that "Delay in payment" has the most negative influence with 15.62% weighing factor; "Latent risk" comes next with 11.14% weighing factor,and "Responsibility of construction company for project quality" is the third with 10.79% weighing factor.

Application of fuzzy analytic hierarchy process and neural network in power transformer risk assessment

In operation,risk arising from power transformer faults is of much uncertainty and complicacy.To timely and objectively control the risks,a transformer risk assessment method based on fuzzy analytic hierarchy process(FAHP) and artificial neural network(ANN) from the perspective of accuracy and quickness is proposed.An analytic hierarchy process model for the transformer risk assessment is built by analysis of the risk factors affecting the transformer risk level and the weight relation of each risk factor in transformer risk calculation is analyzed by application of fuzzy consistency judgment matrix;with utilization of adaptive ability and nonlinear mapping ability of the ANN,the risk factors with large weights are used as input of neutral network,and thus intelligent quantitative assessment of transformer risk is realized.The simulation result shows that the proposed method increases the speed and accuracy of the risk assessment and can provide feasible decision basis for the transformer risk management and maintenance decisions.

Using Extension Theory and Fuzzy Analytic Hierarchy Process on Heterogeneous Wireless Network Selection Algorithm

In order to solve the problem that determining decision factors weights is of subjectivity in heterogeneous wireless network selection algorithm, a network selection algorithm based on extension theory and fuzzy analytic hierarchy process (FAHP) is proposed in this paper. In addition, user and operator codetermine the optimal network using the proposed algorithm, which can give consideration to user and operator benefits. The fuzzy judgment matrix is constructed by membership degree of decision factors which is calculated according to extension theory. The comprehensive weight of each decision factor is obtained using FAHP. Finally, the optimal network is selected through total property value ranking of each candidate network under user preference and operator preference. The simulation results show that the proposed algorithm can select the optimal network efficiently and accurately, satisfy user preference, and implement load balance between networks.

The Selection of Dry Port Location by Analytic Network Process Model: A Case Study of Dosso-Niger

The aim of this paper is to select the best location for the construction of a dry port in Niger which is a land locked country (LLC). Niger is located in the Sahel and has a land area of 1,267,000 square kilometers [1], with the closest port being port of Cotonou in Benin. The transport corridor from Niamey to Cotonou is approximately 1036 km long [2]. It is estimated that this corridor carries about 40 percent of Niger’s overseas trade traffic [3]. In this work, the Analytic Network Process (ANP) model is used to determine the optimal location of the dry port, among three major cities: Niamey (capital city), Dosso and Gaya. From the application of this selection model, Dosso was selected as the best location for the location of the dry port, while Gaya and Niamey were placed second and third respectively. The results obtained in this work strongly confirm the decision of the government of Niger to construct a dry port in Dosso, a project that commenced in 2010 and is still in progress.

Concurrent Material Selection of Natural Fibre Filament for Fused Deposition Modeling Using Integration of Analytic Hierarchy Process/Analytic Network Process

The employment of natural fibres in fused deposition modeling has raised much attention from researchers in finding a suitable formulation for the natural fibre composite filaments.Moreover,selection of suitable natural fibres for fused deposition modeling should be performed before the development of the composites.It could not be performed without identifying selection criteria that comprehend both materials and fused deposition modeling process requirements.Therefore,in this study,integration of the Analytic Hierarchy Process(AHP)/Analytic Network Process(ANP)has been introduced in selecting the natural fibres based in different clusters of selection concurrently.The selection process has been performed based on the interdependency among the selection criteria.Pairwise comparison matrices are constructed based on AHP’s hierarchical model and super matrices are constructed based on the ANP’s network model.As a result,flax fibre has ranked at the top of the selection by scored 19.5%from the overall evaluation.Flax fibre has excellent material properties and been found in various natural fibre composite applications.Further investigation is needed to study the compatibility of this fibre to be reinforced with a thermoplastic polymer matrix to develop a resultant natural fibre composite filament for fused deposition modeling.

IoT data analytic algorithms on edge-cloud infrastructure:A review

The adoption of Internet of Things(IoT)sensing devices is growing rapidly due to their ability to provide realtime services.However,it is constrained by limited data storage and processing power.It offloads its massive data stream to edge devices and the cloud for adequate storage and processing.This further leads to the challenges of data outliers,data redundancies,and cloud resource load balancing that would affect the execution and outcome of data streams.This paper presents a review of existing analytics algorithms deployed on IoT-enabled edge cloud infrastructure that resolved the challenges of data outliers,data redundancies,and cloud resource load balancing.The review highlights the problems solved,the results,the weaknesses of the existing algorithms,and the physical and virtual cloud storage servers for resource load balancing.In addition,it discusses the adoption of network protocols that govern the interaction between the three-layer architecture of IoT sensing devices enabled edge cloud and its prevailing challenges.A total of 72 algorithms covering the categories of classification,regression,clustering,deep learning,and optimization have been reviewed.The classification approach has been widely adopted to solve the problem of redundant data,while clustering and optimization approaches are more used for outlier detection and cloud resource allocation.

Visual analytics tool for the interpretation of hidden states in recurrent neural networks

In this paper,we introduce a visual analytics approach aimed at helping machine learning experts analyze the hidden states of layers in recurrent neural networks.Our technique allows the user to interactively inspect how hidden states store and process information throughout the feeding of an input sequence into the network.The technique can help answer questions,such as which parts of the input data have a higher impact on the prediction and how the model correlates each hidden state configuration with a certain output.Our visual analytics approach comprises several components:First,our input visualization shows the input sequence and how it relates to the output(using color coding).In addition,hidden states are visualized through a nonlinear projection into a 2-D visualization space using t-distributed stochastic neighbor embedding to understand the shape of the space of the hidden states.Trajectories are also employed to show the details of the evolution of the hidden state configurations.Finally,a time-multi-class heatmap matrix visualizes the evolution of the expected predictions for multi-class classifiers,and a histogram indicates the distances between the hidden states within the original space.The different visualizations are shown simultaneously in multiple views and support brushing-and-linking to facilitate the analysis of the classifications and debugging for misclassified input sequences.To demonstrate the capability of our approach,we discuss two typical use cases for long short-term memory models applied to two widely used natural language processing datasets.

基于ANP-FCE的高校教学实验室使用效益评估体系构建与实践

针对指标权重计算方法不合理、定性指标评价难以量化的问题,将网络分析(analytic network process,ANP)法与模糊综合评价(fuzzy comprehensive evaluation,FCE)法相结合,借助专业辅助软件yaanp和yafce,构建基于ANP-FCE的高校教学实验室使用效益评估体系。效益评估体系提高了指标权重排序的科学性和合理性,以及评估结果的精准性和真实性,并在实际应用中取得了良好的效果,371名服务对象对评估体系的整体满意度达90.58%。效益评估体系对于提升高校实验室建设与管理水平,提高实验室使用效益,发挥实验室在人才培养、科学研究、社会服务等方面的作用具有现实意义和推广价值。

Analytical solutions for the slow neutron capture process of heavy element nucleosynthesis

In this paper, the network equation for the slow neutron capture process （s-process） of heavy element nucleosynthesis is investigated. Dividing the s-process network reaction chains into two standard forms and using the technique of matrix decomposition, a group of analytical solutions for the network equation are obtained. With the analytical solutions, a calculation for heavy element abundance of the solar system is carried out and the results are in good agreement with the astrophysical measurements.

基于ANP-FCE的矿井风险防控系统研发

为加强煤矿安全风险防控能力,采用ANP-FCE相结合的方法构建矿井风险防控系统。首先,基于事故致因理论对矿井生产体系进行调研,并建立矿井风险防控指标体系;其次,运用ANP分析主要影响因素,并采用FCE建立矿井风险防控评价模型;最后,利用Java平台,依托JSP+SSM框架与MySQL数据库联合开发矿井风险防控系统,并在某煤矿试运行。结果表明:该系统可以确定矿井安全生产工作的重点,实现矿井生产数据实时显示,并具有重点作业监控和预警功能。系统的开发有利于企业各级领导及时掌握各种数据信息,提高企业自身的风险防控和预警能力,为煤矿企业的运营和发展提供安全保障。

基于ISM-ANP的绿色包装灰色评价模型研究

目的 对绿色包装设计方案进行多维度综合评价,提供具体的包装优化依据,解决过度包装、废弃包装带来的环境问题。方法 首先建立涵盖环境、产品性能、创新性、经济性、用户体验感五大属性的评价指标体系,以ISM分析指标之间的内在关联情况并利用ANP确定权重值;其次通过灰色模糊评价对包装等级进行划分;最后以综合评价方法求得包装的绿色度评分。结果 将指标权重值与传统AHP方法得出的结果进行比较,发现本研究模型更科学合理。以一项小麦壳制保鲜包装设计为例,运用该综合评价模型进行运算后得出该方案评价值为5.043,再根据指标的得分数据有针对性地提出包装优化方向。结论 研究模型适用于各类绿色包装设计方案的筛选与优化,有助于包装设计与生产相关人员进行决策,引导我国包装行业更好地向绿色模式转变。

基于ANP-SPA的装配式建筑施工安全风险评价

为加强装配式建筑施工的安全管理,有效降低施工过程的安全隐患,提出了用于评价装配式建筑施工安全风险的新方法。基于网络层次分析法(Analytic Network Process,ANP)对指标体系中各指标的权重进行了计算并确定,依据集对分析法(Set Pair Analysis,SPA)构建了装配式建筑施工安全风险评价模型。结合实际案例进行分析与评价,为实现装配式建筑施工安全管理的科学化、规范化提供了借鉴与参考,从而降低事故的发生率。

基于ANP动火作业事故应急处置影响因素研究

为提高企业对动火作业突发事件的应急处置能力,利用ANP网络层次分析法构建评价指标体系。根据法律法规、标准文件以及对事故案例分析,结合应急处置工作闭环原则确定4个一级指标和13个二级指标,借助yaanp辅助软件计算各影响因素权重,得到动火作业应急处置能力影响因素的重要度排序。结果表明,4个一级指标的权重值分别为0.22704、0.22704、0.42359、0.12232;13个二级指标中,动火作业应急处置能力影响因素最终排序为应急保障、处置情况评估与改进、应急救援,权重值分别为0.243885、0.131847、0.039.725。

基于模糊DANP-GRA的应急工程施工进度风险评价研究

为构建一套完整的应急工程施工进度风险综合评价体系以确定风险等级,进而明确导致应急工程施工进度延误的关键因素,首先,通过案例分析、文献研究对应急工程施工进度影响因素进行识别;其次,将模糊决策实验室和网络层次分析法相结合,研究指标间的相互关系并确定权重大小;再次,运用灰色关联度分析对应急工程施工进度风险进行综合评价;最后,以中山市应急救治医院为例进行实例计算,得出进度风险等级为中等,经验证符合工程建设实际情况。研究表明,该模型可用于应急工程进度风险综合评价,并可根据评价结果制定对应的风险控制措施,提升进度风险应对能力。

基于ANP和SD的矿工不安全行为干预策略研究

为预防矿工不安全行为,采用网络层次分析法构建矿工不安全行为影响因素的指标体系,并计算指标权重;基于指标体系,制定矿工不安全行为干预策略,从个体、环境、群体、管理等方面建立矿工不安全行为系统动力学模型并通过Vensim软件进行模拟,探讨不同干预策略对不安全行为水平的影响。结果表明:一级指标的权重排序为:安全管理>个人因素>领导氛围>群体氛围>工作环境>工作性质;二级指标中权重较大的有安全规章制度、安全教育与培训、安全文化;干预策略使矿工不安全行为水平下降速度加快,且各方面干预中均有一项最优干预策略;最优干预策略的提出为煤矿加强安全管理、减少事故发生提供了新思路。

基于ANP和聚类思想的火箭炮模拟训练评估方法

火箭炮模拟训练的评估结果能够反映受训人员水平,是安排后续训练方案的重要参考。火箭炮模拟训练的评估大多依靠专家经验,各指标没有明确的权重。针对这一问题,提出了基于网络分析法(ANP)和聚类思想的评估方法,综合考虑了各指标之间的相互关系,并对各专家的结果进行聚类分析,最终确定了指标权重。通过对训练数据的计算,验证了该评估方法的有效性。

基于ANP与ArcGIS的城市火灾风险评估

城市火灾风险评估对于降低火灾损失具有重要作用,但建立简便高效的风险评估模型一直是难点与热点问题。在充分调查分析西安市临潼区火灾危险度、区域特征性、防灾减灾能力的基础上,建立西安市临潼区火灾风险评估指标体系。选出火灾载荷密度、重大危险源空间分布、重点消防单位面积、消防供水能力等10个评价指标,采用网络层次分析法(analytic network process,ANP)结构运算确定各评价指标的权重。以500 m×500 m的渔网作为评价单元,构建火灾风险评估模型,并利用ArcGIS空间分析功能进行指标量化、评价单元划分、评价模型运算,最终得到西安市临潼区火灾风险评估图。评估结果显示:西安市临潼区火灾风险整体成南高北低的趋势,高风险区域占1.81%,中风险区域占5.82%,次风险区域占16.85%,低风险区域占75.53%,火灾高风险区域主要集中在骊山、斜口、代王、新丰、北田五个街道办。据现场实际调查,这些街道办人口和建筑密集,重大危险源较多,说明所建风险评估模型准确可靠,简便高效,可为火灾孕灾环境类似的其它城市火灾风险评估提供有益借鉴。

SAM-FFTA-ANP在高校实验室中的应用

为了减少或避免实验室火灾事故带来的一系列损失,提出了一种基于相似度聚合法的模糊故障树分析方法(similarity aggregation method-fuzzy fault tree analysis, SAM-FFTA),并与网络层次分析法(analytic network process, ANP)相结合,完善了故障树中各原因事件相互依赖的关系,有效地弥补使用单一方法时的不足。首先基于危机生命周期理论分析得出高校实验室火灾演化过程;其次,根据SAM-FFTA合理聚合多位专家的不同意见,得到故障树中各基本事件的失效概率;最后,采用ANP建立了高校实验室火灾评价结构模型,进一步研究致因中影响性较大的事件。结果表明,电气故障是导致实验室火灾发生的最主要因素,同时,高温热源和化学品的管理也对实验室火灾影响显著。

基于ANP-物元可拓法的中小餐饮场所火灾风险评价

为有效预防中小餐饮场所火灾事故的发生,运用事故树分析法辨识中小餐饮场所火灾风险因素,并从人为因素、设备因素、环境因素、管理因素4个方面建立中小餐饮场所火灾风险评价指标体系;通过网络层次分析法分析指标间的影响关系并确定各评价指标的权重;构建基于ANP-物元可拓法的中小餐饮场所火灾风险评价模型,并选取某中小餐饮场所进行实例研究。评价结果显示:该餐饮场所的火灾风险等级为Ⅱ级,处于风险较低的状态,与该餐饮场所的实际相符合,验证了该方法的合理性和可靠性,为中小餐饮场所火灾风险评价提供一种新方法。