Extension Modeling Strategy of Intelligent Detection in D.huoshanense Photosynthesis Process

Aiming at the limitations of the existing knowledge representations in intelligent detection, a new method of Extension-based Knowledge Representation (EKR) was proposed. The definitions, grammar rules, and storage structure of EKR were presented. An Extension Solving Model (ESM) based on EKR was discussed in detail, including creation of the extension constraint graph, extended inference, calculation of relevant functions and generation of extension set. A knowledge base system based on EKR and ESM was developed, which was applied in extension repository system intelligent design of detection in photosynthesis process of D.huoshanense. More reasonable results were obtained than traditional rule-based system. EKR was feasible in intelligent design to solve the problem of intelligent detection knowledge representations.

Research on virtual entity decision model for LVC tactical confrontation of army units

According to the requirements of the live-virtual-constructive(LVC)tactical confrontation(TC)on the virtual entity(VE)decision model of graded combat capability,diversified actions,real-time decision-making,and generalization for the enemy,the confrontation process is modeled as a zero-sum stochastic game(ZSG).By introducing the theory of dynamic relative power potential field,the problem of reward sparsity in the model can be solved.By reward shaping,the problem of credit assignment between agents can be solved.Based on the idea of meta-learning,an extensible multi-agent deep reinforcement learning(EMADRL)framework and solving method is proposed to improve the effectiveness and efficiency of model solving.Experiments show that the model meets the requirements well and the algorithm learning efficiency is high.

An Approach for Integrating Quantitative Decision Model with Qualitative Judgment

In this paper, decision making in complex environment is considered and an approach integrating quantitative decision model with qualitative judgment is proposed. The concept of belief degree for quantitative decision model in a complex environment is presented. The integration in formulation and reasoning of quantitative model with qualitative judgment is studied. The combination of various belief degree generated by quantitative model and qualitative judgment is discussed. A decision rule of tradeoff between optimality and belief degree of optimality is proposed.

An Intelligent Approach to Model Manipulation in DSS

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A New Approach to Intelligent Model Based Predictive Control Scheme

This paper describes a new approach to intelligent model based predictive control scheme for deriving a complex system. In the control scheme presented, the main problem of the linear model based predictive control theory in dealing with severe nonlinear and time variant systems is thoroughly solved. In fact, this theory could appropriately be improved to a perfect approach for handling all complex systems, provided that they are firstly taken into consideration in line with the outcomes presented. This control scheme is organized based on a multi-fuzzy-based predictive control approach as well as a multi-fuzzy-based predictive model approach, while an intelligent decision mechanism system (IDMS) is used to identify the best fuzzy-based predictive model approach and the corresponding fuzzy-based predictive control approach, at each instant of time. In order to demonstrate the validity of the proposed control scheme, the single linear model based generalized predictive control scheme is used as a benchmark approach. At last, the appropriate tracking performance of the proposed control scheme is easily outperformed in comparison with previous one.

Mobile and Context-Aware GeoBI Applications: A Multilevel Model for Structuring and Sharing of Contextual Information

With the requirements for high performance results in the today’s mobile, global, highly competitive, and technology-based business world, business professionals have to get supported by convenient mobile decision support systems (DSS). To give an improved support to mobile business professionals, it is necessary to go further than just allowing a simple remote access to a Business Intelligence platform. In this paper, the need for actual context-aware mobile Geospatial Business Intelligence (GeoBI) systems that can help capture, filter, organize and structure the user mobile context is exposed and justified. Furthermore, since capturing, structuring, and modeling mobile contextual information is still a research issue, a wide inventory of existing research work on context and mobile context is provided. Then, step by step, we methodologically identify relevant contextual information to capture for mobility purposes as well as for BI needs, organize them into context-dimensions, and build a hierarchical mobile GeoBI context model which (1) is geo-spatial-extended, (2) fits with human perception of mobility, (3) takes into account the local context interactions and information-sharing with remote contexts, and (4) matches with the usual hierarchical aggregated structure of BI data.

On Realization of Intelligent Decision Making in the Real World:A Foundation Decision Model Perspective

The pervasive uncertainty and dynamic nature of real-world environments present significant challenges for the widespread implementation of machine-driven Intelligent Decision-Making(IDM)systems.Consequently,IDM should possess the ability to continuously acquire new skills and effectively generalize across a broad range of applications.The advancement of Artificial General Intelligence(AGI)that transcends task and application boundaries is critical for enhancing IDM.Recent studies have extensively investigated the Transformer neural architecture as a foundational model for various tasks,including computer vision,natural language processing,and reinforcement learning.We propose that a Foundation Decision Model(FDM)can be developed by formulating diverse decision-making tasks as sequence decoding tasks using the Transformer architecture,offering a promising solution for expanding IDM applications in complex real-world situations.In this paper,we discuss the efficiency and generalization improvements offered by a foundation decision model for IDM and explore its potential applications in multi-agent game AI,production scheduling,and robotics tasks.Lastly,we present a case study demonstrating our FDM implementation,DigitalBrain(DB1)with 1.3 billion parameters,achieving human-level performance in 870 tasks,such as text generation,image captioning,video game playing,robotic control,and traveling salesman problems.As a foundation decision model,DB1 represents an initial step toward more autonomous and efficient real-world IDM applications.

A Framework for Intelligent Decision Support System for Traffic Congestion Management System

Traffic congestion problem is one of the major problems that face many transportation decision makers for urban areas. The problem has many impacts on social, economical and development aspects of urban areas. Hence the solution to this problem is not straight forward. It requires a lot of effort, expertise, time and cost that sometime are not available. Most of the existing transportation planning software, specially the most advanced ones, requires personnel with lots practical transportation planning experience and with high level of education and training. In this paper we propose a comprehensive framework for an Intelligent Decision Support System (IDSS) for Traffic Congestion Management System that utilizes a state of the art transportation network equilibrium modeling and providing an easy to use GIS-based interaction environment. The developed IDSS reduces the dependability on the expertise and level of education of the transportation planners, transportation engineers, or any transportation decision makers.

Intelligent prediction of RBC demand in trauma patients using decision tree methods

Background:The vital signs of trauma patients are complex and changeable,and the prediction of blood transfusion demand mainly depends on doctors'experience and trauma scoring system;therefore,it cannot be accurately predicted.In this study,a machine learning decision tree algorithm[classification and regression tree(CRT)and eXtreme gradient boosting(XGBoost)]was proposed for the demand prediction of traumatic blood transfusion to provide technical support for doctors.Methods:A total of 1371 trauma patients who were diverted to the Emergency Department of the First Medical Center of Chinese PLA General Hospital from January 2014 to January 2018 were collected from an emergency trauma database.The vital signs,laboratory examination parameters and blood transfusion volume were used as variables,and the non-invasive parameters and all(non-invasive+invasive)parameters were used to construct an intelligent prediction model for red blood cell(RBC)demand by logistic regression(LR),CRT and XGBoost.The prediction accuracy of the model was compared with the area under curve(AUC).Results:For non-invasive parameters,the LR method was the best,with an AUC of 0.72[95%confidence interval(CI)0.657–0.775],which was higher than the CRT(AUC 0.69,95%CI 0.633–0.751)and the XGBoost(AUC 0.71,95%CI 0.654–0.756)(P<0.05).The trauma location and shock index are important prediction parameters.For all the prediction parameters,XGBoost was the best,with an AUC of 0.94(95%CI 0.893–0.981),which was higher than the LR(AUC 0.80,95%CI 0.744–0.850)and the CRT(AUC 0.82,95%CI 0.779–0.853)(P<0.05).Haematocrit(Hct)is an important prediction parameter.Conclusions:The classification performance of the intelligent prediction model of red blood cell transfusion in trauma patients constructed by the decision tree algorithm is not inferior to that of the traditional LR method.It can be used as a technical support to assist doctors to make rapid and accurate blood transfusion decisions in emergency rescue environment,so as to improve the success rate of patient treatment.

Energy Efficient Cluster Based Clinical Decision Support System in IoT Environment

Internet of Things(IoT)has become a major technological development which offers smart infrastructure for the cloud-edge services by the interconnection of physical devices and virtual things among mobile applications and embedded devices.The e-healthcare application solely depends on the IoT and cloud computing environment,has provided several characteristics and applications.Prior research works reported that the energy consumption for transmission process is significantly higher compared to sensing and processing,which led to quick exhaustion of energy.In this view,this paper introduces a new energy efficient cluster enabled clinical decision support system(EEC-CDSS)for embedded IoT environment.The presented EECCDSS model aims to effectively transmit the medical data from IoT devices and perform accurate diagnostic process.The EEC-CDSS model incorporates particle swarm optimization with levy distribution(PSO-L)based clustering technique,which clusters the set of IoT devices and reduces the amount of data transmission.In addition,the IoT devices forward the data to the cloud where the actual classification procedure is performed.For classification process,variational autoencoder(VAE)is used to determine the existence of disease or not.In order to investigate the proficient results analysis of the EEC-CDSS model,a wide range of simulations was carried out on heart disease and diabetes dataset.The obtained simulation values pointed out the supremacy of the EEC-CDSS model interms of energy efficiency and classification accuracy.

A Decision Support System for Spatial Analysis of Agricultural Production in Madagascar

In this article, our research aims to set up a geo-decisional system, more precisely we are particularly interested in the spatial analysis system of agricultural production in Madagascar. For this, we used the spatial data warehouse technique based on the SOLAP spatial analysis tool. After having defined the concepts underlying these systems, we propose to address the research issues related to them from four points of view: needs study of the Malagasy Ministry of Agriculture, modeling of a multidimensional conceptual model according to the MultiDim model and the implementation of the system studied using GeoKettle, PostGIS, GeoServer, SPAGO BI and Géomondrian technologies. This new system helps improve the decision-making process for agricultural production in Madagascar.

Online Dispatching Decision Support System for Smart Grid Reservoir Group

Based on the full use of historical reservoir dispatching information, artificial intelligence is applied to grid reservoir group dispatching. A knowledge representation method, which combines dispatching rules and intelligence models, is put forward. The intelligent dispatching system is established and the system architecture is presented. Additionally, the acquisition, representation and reasoning mechanism of reservoir dispatching knowledge are designed in detail.

MECHANICAL PRODUCT EXTENSIVE INTELLIGENT CONCEPTUAL DESIGN

Based on extenics, an extensive functional information model(function-behavioral action-structure-environmental constraint) of the mechanical productintelligent conceptual design is developed, and the mechanism of theoretic structure solutions isproduced, the mapping relations between function-behavior and behavior-structure are analyzed. Themodel is applied to the filling material system's conceptual design to verify validity.

Heap Based Optimization with Deep Quantum Neural Network Based Decision Making on Smart Healthcare Applications

The concept of smart healthcare has seen a gradual increase with the expansion of information technology.Smart healthcare will use a new generation of information technologies,like artificial intelligence,the Internet of Things(IoT),cloud computing,and big data,to transformthe conventional medical system in an all-around way,making healthcare highly effective,more personalized,and more convenient.This work designs a new Heap Based Optimization with Deep Quantum Neural Network(HBO-DQNN)model for decision-making in smart healthcare applications.The presented HBO-DQNN modelmajorly focuses on identifying and classifying healthcare data.In the presented HBO-DQNN model,three stages of operations were performed.Data normalization is applied to pre-process the input data at the initial stage.Next,the HBO algorithm is used in the second stage to choose an optimal set of features from the healthcare data.At last,the DQNN model is exploited for healthcare data classification.A series of experiments were carried out to portray the promising classifier results of the HBO-DQNN model.The extensive comparative study reported the improvements of the HBO-DQNN method over other existing models with maximum accuracy of 97.05%and 95.72%under the colon cancer and lymphoma dataset.

Digital Twin-Driven Intelligent Construction:Features and Trends

Digital twin(DT)can achieve real-time information fusion and interactive feedback between virtual space and physical space.This technology involves a digital model,real-time information management,comprehensive intelligent perception networks,etc.,and it can drive the rapid conceptual development of intelligent construction(IC)such as smart factories,smart cities,and smart medical care.Nevertheless,the actual use of DT in IC is partially pending,with numerous scientific factors still not clarified.An overall survey on pending issues and unsolved scientific factors is needed for the development of DT-driven IC.To this end,this study aims to provide a comprehensive review of the state of the art and state of the use of DT-driven IC.The use of DT in planning,design,manufacturing,operation,and maintenance management of IC is demonstrated and analyzed,following which the driving functions of DT in IC are detailed from four aspects:information perception and analysis,data mining and modeling,state assessment and prediction,intelligent optimization and decision-making.Furthermore,the future direction of research,using DT in IC,is presented with some comments and suggestions.This work will help researchers gain in-depth and systematic understanding of the use of DT,and help practitioners to better promote its implementation in IC.

基于案例推理的隧道洞门智能设计方法研究

为将以往隧道案例的经验应用到新建隧道设计中,以解决传统设计方法工作量大且效率低下的问题,提出基于案例推理的隧道洞门智能设计方法。结合隧道洞门工程特点,选取影响洞门设计的主要因素,将影响因素划分为12个属性单元来表征工程案例,运用三角模糊数的层次赋权计算方法对各属性单元权重进行赋值,借助相似度理论建立隧道洞门设计方案相似决策模型。基于GIS技术开发了隧道洞门工程案例库,实现对既有工程的存储管理,同时利用SQL查询语言及Python计算脚本实现案例的属性特征值计算与最相似匹配,进一步修正后可实现隧道洞门修建方案的设计决策。以某山区隧道为例对智能设计方法进行验证,结果表明:根据属性单元特征值计算结果,在案例库中比选得出与拟建隧道相似度最高的既有工程为凤凰山隧道,且相似度为0.707。因此,设定阈值为0.7时能加快案例库的检索速率与匹配准确率,该方法吸取了既有工程案例的经验,能应用于隧道洞门施工方案的决策设计,可供今后隧道洞门方案的智能化设计参考。

水利大模型的建设思路、构建框架与应用场景初探

发展新质生产力是推动高质量发展的内在要求和重要着力点。推进数字孪生水利建设是推动新阶段水利高质量发展的显著标志和重要路径,模型和知识是数字孪生水利建设的关键所在。因此,按照“需求牵引、应用至上、数字赋能、提升能力”要求,针对水利业务需求融合大模型、水利专业模型和知识而形成“水利大模型”,是发展水利新质生产力的重要引擎。阐述了水利大模型的概念和构建的重要意义,介绍了构建的思路与总体框架,分析了需突破的关键技术,介绍了行业应用场景。研究成果可为人工智能大模型在水利行业落地提供引导和借鉴。

遥感卫星任务智能决策的机器学习方法研究

基于遥感卫星任务决策的特点,研究如何采用机器学习方法对执行任务时产生的大量动作、指令和遥测数据进行分析和训练。为了给遥感卫星任务建立机器学习方法,探索机器学习辅助遥感卫星任务智能决策的可行性,并探讨机器学习模型对卫星任务数据的适应性和处理效率。借鉴地面相关人工智能系统成熟的机器学习架构,研究建立遥感卫星任务相关智能决策的机器学习方法,并给出了机器学习的样例。研究结果表明:机器学习方法的适应性很强,初步实现了遥感卫星自主任务决策,并达到一定的准确率,对卫星任务智能决策技术进行了有益探索。

结合STPA和DEMATEL-ISM的民机起落架收放系统风险研究

为从系统整体角度完成对起落架收放系统的风险辨识和影响分析,将系统理论过程分析(Systematic Theory Process Analysis,STPA)与决策实验室分析-解释结构模型(Decision Making Trial and Evaluation Laboratory Interpretive Structural Modeling,DEMATEL-ISM)相结合来开展分析。首先,定义事故和系统级危险,以民机进近阶段放下起落架为例,运用STPA完成对风险因素的系统化辨识;其次,基于最大平均熵减(Maximum Mean De-entropy,MMDE)算法帮助DEMATEL-ISM模型确定阈值,完成对风险因素影响的重要性分析并识别可能引发系统级危险的风险传递路径,据此挖掘关键致因场景,以给出风险预防建议。结果显示:线路性能退化或失效、位置作动控制组件(Position Action Control Unit,PACU)核心处理器故障为关键原因因素,收放作动筒作动异常、机组成员操作不当、起落架指示灯显示异常、起落架液压选择阀作动异常、PACU信息接收有误为关键结果因素,这些因素均涉及多条可能引发系统级危险的风险传递路径,应予以重点控制。

基于前因因素的建筑工人安全行为评价模型与应用

建筑项目中的事故主要与建筑工人的不安全行为有关,因此对建筑工人安全行为进行事前评价是预防事故发生的关键。首先通过文献可视化分析初步筛选建筑工人安全行为前因因素,结合专家意见构建建筑工人安全行为评价的指标体系;然后采用群决策改进的层次分析法确定各评价指标的综合权重,并建立可拓物元评价模型;最后对某施工项目建筑工人安全行为进行评价与分析,以验证模型的合理性和可靠性。结果表明:建筑工人安全行为评价指标中安全意识所占权重较高,是事故预防的重要因素;实例应用结果表明该项目建筑工人总体安全行为等级处于“Ⅲ级”为一般安全,说明该项目施工作业人员整体处于比较安全的水平;建筑工人个体安全行为的维度指标评价结果为正态分布或偏正态分布,表明该评价模型能够有效识别建筑工人的安全行为水平,可为建筑安全管理的研究和实践提供参考。