Membrane-inspired quantum bee colony optimization and its applications for decision engine

In order to effectively solve combinatorial optimization problems,a membrane-inspired quantum bee colony optimization(MQBCO)is proposed for scientific computing and engineering applications.The proposed MQBCO algorithm applies the membrane computing theory to quantum bee colony optimization(QBCO),which is an effective discrete optimization algorithm.The global convergence performance of MQBCO is proved by Markov theory,and the validity of MQBCO is verified by testing the classical benchmark functions.Then the proposed MQBCO algorithm is used to solve decision engine problems of cognitive radio system.By hybridizing the QBCO and membrane computing theory,the quantum state and observation state of the quantum bees can be well evolved within the membrane structure.Simulation results for cognitive radio system show that the proposed decision engine method is superior to the traditional intelligent decision engine algorithms in terms of convergence,precision and stability.Simulation experiments under different communication scenarios illustrate that the balance between three objective functions and the adapted parameter configuration is consistent with the weights of three normalized objective functions.

Multi-factors decision-making entropy method and its application in engineering management

In order to meet the strict requirements for information in engineering management, the positive interval （0, 1 ] in Shannon information entropy is extended to the real number interval [ - 1, 1 ]. The information theory and the decision theory are combined effectively, and the deficiencies that the traditional Bayes decision-making methods only consider a single factor are made up for. The multi-factors engineering decision-making methods are proposed, and some critical problems are solved in the practical engineering management decision-making process.

Intervention decision-making in MAV/UAV cooperative engagement based on human factors engineering

Aiming at the intervention decision-making problem in manned/unmanned aerial vehicle（MAV/UAV） cooperative engagement, this paper carries out a research on allocation strategy of emergency discretion based on human factors engineering（HFE）.Firstly, based on the brief review of research status of HFE, it gives structural description to emergency in the process of cooperative engagement and analyzes intervention of commanders. After that,constraint conditions of intervention decision-making of commanders based on HFE（IDMCBHFE） are given, and the mathematical model, which takes the overall efficiency value of handling emergencies as the objective function, is established. Then, through combining K-best and variable neighborhood search（VNS） algorithm, a K-best optimization variable neighborhood search mixed algorithm（KBOVNSMA） is designed to solve the model. Finally,through three groups of simulation experiments, effectiveness and superiority of the proposed algorithm are verified.

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.

Forecasting and Decision-Making of Systematic Theories for Engineering Geology in Environmental Geoscience

The paper discusses the problems of engineering geology in environmental geoscience from several aspects. For natural sciences and social sciences, it deduces essential theory from logistic cycle model, logic mapping and Verhulst model. It had been discovered that these aspects are equal. However, these were the studies of normal effects. We must establish mathematical model to check from contrary course for gray forecasting and decision-making and answer several questions satisfactorily.

ChatGPT in transforming communication in seismic engineering: Case studies, implications, key challenges and future directions

Seismic engineering,a critical field with significant societal implications,often presents communication challenges due to the complexity of its concepts.This paper explores the role of Artificial Intelligence(AI),specifically OpenAI’s ChatGPT,in bridging these communication gaps.The study delves into how AI can simplify intricate seismic engineering terminologies and concepts,fostering enhanced understanding among students,professionals,and policymakers.It also presents several intuitive case studies to demonstrate the practical application of ChatGPT in seismic engineering.Further,the study contemplates the potential implications of AI,highlighting its potential to transform decision-making processes,augment education,and increase public engagement.While acknowledging the promising future of AI in seismic engineering,the study also considers the inherent challenges and limitations,including data privacy and potential oversimplification of content.It advocates for the collaborative efforts of AI researchers and seismic experts in overcoming these obstacles and enhancing the utility of AI in the field.This exploration provides an insightful perspective on the future of seismic engineering,which could be closely intertwined with the evolution of AI.

基于ArcGIS Engine的风暴潮淹没适应气候变化策略辅助决策系统开发

本研究构建了基于ArcGIS Engine的风暴潮淹没适应气候变化策略辅助决策系统,将风暴潮淹没适应气候变化策略研究过程流程化和简单化,并提供7个面向用户开放的决策端口,辅助决策者做出科学的风暴潮淹没适应气候变化策略选择。以青岛市风暴潮淹没为例的系统应用,进一步验证了该系统能够切实提高研究过程的分析效率,为中国沿海地区的适应风暴潮灾害提供技术支持。

基于Google Earth Engine的前郭县春季农田覆膜提取

本文基于Google Earth Engine(GEE)云平台,综合考虑光学影像的波段反射率、光谱指数特征和雷达影像的极化、纹理特征,分别构建仅使用光学特征、仅使用雷达特征以及光学和雷达特征组合3种特征输入组合;根据精度确定最佳输入特征后,分别结合机器学习中的分类与回归树、支持向量机、最小距离分类法、梯度提升树和随机森林5种方法建立覆膜提取模型,依据结果精度评估不同方法的性能,并基于最优化模型提取出最终的覆膜农田面积。结果表明:①最佳输入特征为波段反射率特征+光谱指数特征+极化特征+纹理特征;②采用随机森林方法建立的模型精度最高,研究区I的总体精度达到了95.84%,Kappa系数为0.95,地物错分率为1.2%,明显优于其他4种方法(地物错分率较分类与回归树、支持向量机、最小距离和梯度提升树法降低0.8%、7.3%、38.0%和0.3%),研究区II的验证精度达到了87.84%,证明该模型在覆膜提取中可以取得更加准确的结果;③使用本文方法得到2022年研究区I覆膜农田面积为1302.48 km2,估算地膜使用量约为7585.62 t。本文综合考虑光学和雷达影像在地物识别中的特点建立模型,可以准确、高效的识别农田地膜,掌握地膜面积,对环境治理与防治具有重要意义。

基于ArcGIS Engine的农用地定级决策支持系统

针对当前农用地定级系统的计算过程复杂、通用性较差等问题,该文对农用地定级过程中涉及的自然、社会经济等多方面的图件和属性数据进行了分析,并对农用地定级相关模型进行了分析和归纳;在此基础上,采用地理信息系统技术,结合专家系统和决策支持系统理论,重点设计了系统空间数据库、知识库、模型库和推理机。在VS2005集成开发环境下,采用C#语言,结合ArcGIS Engine组件技术,研制开发出了农用地定级决策支持系统。系统实现了农用地定级的可视化、自动化和智能化,能较好地解决农用地定级中半结构和非结构化问题,具有较好的通用性和可推广性。

基于ArcGIS Engine的林火工程扑救辅助决策模块设计与实现

目前我国森林火灾多发,对森林的破坏性极大,危害极深,造成的经济损失十分严重。该研究基于地理信息系统二次开发工具ArcGIS Engine设计,开发了一套林火扑救辅助决策模块。该模块主要实现了林火扑救决策中的火点定位、扑火战场预测、扑火最短路径分析、扑火资源有效配置、阻火隔离带的正确设置等功能。这些功能的设计针对林火扑救中的各方面决策工作,结合3S技术和计算机技术,可以有效地提高决策人员对于林火扑救的决策能力。此模块可方便嵌入其他林火管理系统中,实现林火扑救辅助决策功能。

基于ArcGIS Engine的退耕还林决策支持系统研究

采用ArcGIS Engine组件式开发环境,开发退耕还林信息管理系统,并在此基础上结合退耕还林地区立地条件、退耕还林规程规划、造林知识库,实现了退耕还林工程中多源数据的集成管理、空间分析、专家知识推理和退耕还林规划合理性分析,为退耕还林工程的规划、实施和监测提供技术支撑和辅助决策。

Construction and realization of the knowledge base and inference engine of an IDSS model for air-conditioning cooling/heating sources selection

The knowledge representation mode and inference control strategy were analyzed according to the specialties of air-conditioning cooling/heating sources selection. The constructing idea and working procedure for knowledge base and inference engine were proposed while the realization technique of the C language was discussed. An intelligent decision support system (IDSS) model based on such knowledge representation and inference mechanism was developed by domain engineers. The model was verified to have a small kernel and powerful capability in list processing and data driving, which was successfully used in the design of a cooling/heating sources system for a large-sized office building.

基于组件式GIS——ArcGIS Engine的县域测土配方施肥专家决策支持系统开发和应用

利用组件式地理信息系统技术(Component Geographic Information System,ComGIS),在Visual Basic环境下嵌入ArcGIS Engine,结合数据库技术、专家系统技术,针对典型冀东山麓平原区——河北省唐山市丰南区开发了县域测土配方施肥专家决策支持系统。在管理丰南区基础地理、土壤、农业经济等信息的基础上,分析了丰南区土壤养分的空间变异,通过离散化技术生成了土壤养分三维空间数据,基于会员和非会员两种形式进行了县域尺度测土配方施肥指导。

基于ArcGIS Engine工程施工自动规划系统设计与实现

新疆地处内陆,幅员辽阔,人迹稀少,地质条件复杂,工程施工及野外踏勘难度大、效率低。针对这种情况,在深入了解ArcObject组件功能基础上,提出一种基于ArcGIS Engine的工程施工自动规划辅助决策系统的设计方案。系统实现对DEM遥感数据、地表介质分布数据、工程施工方案等多种数据处理融合,为规划者提供有效的规划决策预案。可实现从大面积戈壁荒漠地形中选取适合施工的区域,有效地减少野外踏勘次数,缩小踏勘范围,提高规划效率。

基于CityEngine的三维城市规划设计与研究

城市规划领域越来越需要三维建模技术的支持。CityEngine作为一种新型的三维建模软件,不仅可以批量生成规划模型,还可以用来辅助三维城市规划设计。本文基于CityEngine规则建模方法,实现了规划方案对比、建筑体量调整、规划指标计算等功能,通过对某小区进行实际建模验证,证明了CityEngine可以为城市规划者们提供新的思路,为城市设计和城市管理提供决策支持。

基于Google Earth Engine的中老缅交界区橡胶林分布遥感提取

天然橡胶林作为东南亚地区主要的经济林和关系国计民生的一种重要战略物资,对当地社会经济发展和环境保护发挥着重要作用。目前对于中、老、缅交界区的橡胶林空间分布情况、分布面积和演变特征等方面相关信息缺乏相应的研究,严重限制了中国农业上关于橡胶产量消费、贸易以及储备。此外,针对橡胶林的时空大尺度的监测主要是采用长时间序列的中低分辨率影像,但此类影像存在大量的混合像元,严重限制了橡胶提取精度。为解决这些问题,该研究基于Google Earth Engine(GEE)云计算平台,利用2015-2019年Landsat OLI的多时相遥感影像数据,通过分析橡胶的物候特征,构建分类参数和模型,应用专家知识决策树的分类方法,并结合2015-2019年间每年12月份实地调查数据对橡胶的分类结果进行验证。结果表明基于GEE平台利用橡胶物候信息计算参数的方法提取较大范围研究区内的橡胶林的准确性较高。总体精度为90.32%,Kappa系数为0.87,可满足一般生产需求。截至2019年中老缅交界区橡胶林总面积达126.29万hm^2,其中,西双版纳区域橡胶林面积有52.37万hm^2,缅甸区域橡胶林面积有56.93万hm^2,老挝北部5省橡胶林面积有16.99万hm^2。分析发现在替代政策发展过程中,由于老挝、缅甸实际情况不同而出现政策差异性,导致区域政策发展不均衡。该研究结果表明应用云计算技术可以克服时空大尺度橡胶监测运算能力不足的问题,可为中老缅甸交界地区橡胶合理布局与区域可持续发展提供科学依据和决策支持。

Solution to multiple attribute group decision making problems with two decision makers

A kind of multiple attribute group decision making （MAGDM） problem is discussed from the perspective of statistic decision-making. Firstly, on the basis of the stability theory, a new idea is proposed to solve this kind of problem. Secondly, a con- crete method corresponding to this kind of problem is proposed. The main tool of our research is the technique o~ the jackknife method. The main advantage of the new method is that it can identify and determine the reliability degree of the existed decision making information. Finally, a traffic engineering example is given to show the effectiveness of the new method.

Intelligent Decisions Modeling for Energy Saving in Lifts:An Application for Kleemann Hellas Elevators

The present work proposes a methodological approach for modeling decisions regarding energy reduction in an elevator. This is achieved with the integration of existing as well as acquired knowledge, in a decision module implemented in the electronics of an elevator. So far, elevators do not exploit information regarding their recent usage. In the developed system decisions are driven based on information arising from monitoring the use of the elevator. Monitoring provides various records of usage which consequently are used to predict elevator’s future usage and to adapt accordingly its functioning. Till now, there are only elevators that encompass in their electronics algorithms with if then rules in order to control elevator’s functioning. However, these if then rules are based only on good practice knowledge of similar elevators installed in similar buildings. Even this knowledge which unavoidably is associated with uncertainty is not encoded in a mathematically consisted way in the algorithms. The design, the implementation and a first pilot evaluation study of an elevator’s intelligent decision module are presented. The study concludes that the presented application sufficiently reduces energy consumption through properly controlled functioning.

A conceptual evolutionary aseismic decision support framework for hospitals

In this paper, aconceptual evolutionary framework for aseismic decision support for hospitalsthat attempts to integrate a range of engineering and sociotechnical models is presented. Genetic algorithms are applied to find the optimal decision sets. A case study is completed to demonstrate how the frameworkmay applytoa specific hospital.The simulations show that the proposed evolutionary decision support framework is able to discover robust policy sets in either uncertain or fixed environments. The framework also qualitatively identifies some of the characteristicbehavior of the critical care organization. Thus, by utilizing the proposedframework, the decision makers are able to make more informed decisions, especially toenhance the seismic safety of the hospitals.

Importance Analysis of a Multi-state System Based on Direct Partial Logic Derivatives and Multi-valued Decision Diagrams

Importance analysis quantifies the critical degree of individual component. Compared with the traditional binary state system,importance analysis of the multi-state system is more aligned with the practice. Because the multi-valued decision diagram( MDD) can reflect the relationship between the components and the system state bilaterally, it was introduced into the reliability calculation of the multi-state system( MSS). The building method,simplified criteria,and path search and probability algorithm of MSS structure function MDD were given,and the reliability of the system was calculated. The computing methods of importance based on MDD and direct partial logic derivatives( DPLD) were presented. The diesel engine fuel supply system was taken as an example to illustrate the proposed method. The results show that not only the probability of the system in each state can be easily obtained,but also the influence degree of each component and its state on the system reliability can be obtained,which is conducive to the condition monitoring and structure optimization of the system.