工资智能决策支持系统SIDSS的设计与实现

根据工资政策的特点,在此提出一种三库结构的智能工资决策支持系统,即规则库、方法库和数据库。与四库结构相比,它具有独立性较好,易于实现的特点。本文介绍工资智能决策支持系统SIDSS系统结构和主要功能,讨论了事件处理器、规则处理器及方法库与数据库等关键技术。     

探讨工资智能决策支持系统的设计

在各单位人事部门的日常事务中,工资的调整无疑是一项令人头疼的工作。在简介决策支持系统及人工智能的基础上,对工资智能决策支持系统进行概要设计,并对其中的正常晋升及职务晋升功能的流程作设计分析。研究设计更为理想地满足实际决策工作中的工资调整需要。     

智能决策支持系统中基于粗糙集的知识推理

智能决镱支持系统已成为众多学科领域的研究热点,特别是伴随人工智能的发展,不断有新的理论和方法用于智能决策支持系统的实现.在智能决策支持系统中的知识表达的基础上,讨论根据粗糙集理论分析海量信息中的有用特征,通过分析、推理产生最小决策规则集.     

工程项目评标智能决策支持系统的研究与实现

评标是工程项目招投标的重要阶段,也是决定招投标工作能否成功的关键因素。工程项目评标智能决策支持系统的研究与开发,为评委的评标工作提供了辅助决策。使招投标活动更加公开、公正、公平。     

汽车维修故障诊断智能决策支持系统的研究

本文主要从智能决策支持系统的概念出发,研究其在汽车维修故障诊断中的应用,希望能给汽车维修的发展提供借鉴。     

基于3M特征的设计知识表达模型

针对目前支持创新设计的知识多是分类表达和管理,缺乏统一的知识表达和管理模型的问题,提出了一种基于3M（multi-attribute multi-dimensional multi-hierarchy,多属性多维度多层次）特征的知识统一表达与管理方法。根据支持创新设计的知识需求,将知识属性分为创新属性和基本属性两大类：创新属性是对知识内容的抽象化提取并实现知识间的多属性关联,包括知识的功能、输入输出流、TRIZ原理、领域属性;基本属性是对多学科知识物理属性的具体表述,包含背景、功能、原理、结构属性。通过建立基于知识创新属性和基本属性统一架构的知识表达模板,实现对不同类型知识的综合表达与交叉迁移,并根据该方法构建了相应知识库原型系统。以核反应堆堆下结构改进设计为例,说明了知识库的应用流程。结果表明此知识模型能够实现对设计知识的统一表达和管理,有利于知识的查找和应用,能有效地辅助设计者进行创新设计。     

暖通空调设计智能决策支持系统的探讨

随着计算机技术的发展及暖通空调领域研究的深入,智能决策支持系统(IDSS)已进入空调冷热源选择、设备选型及系统方案选择等暖通空调设计研究领域.对暖通空调设计IDSS的效益、结构和集成等重要的理论问题进行了积极的探讨,以促进其更深入、持久的研究和发展.     

TIDSS问题处理器的研究与实现

本文阐述了TIDSS(Test Intelligent Decision Support System-TIDSS)系统中核心部件问题处理器(PPS)的设计方法,提出了具有广义的智能问题处理器(IPPS的系统结构和一种问题求解的方法,并且经过实践检验,证明是正确的。     

智能决策支持系统的研究与实现

文章介绍了一个用于作业调度的ＩＤＳＳ的新型的机智能决策支持系统结构。该系统实现了Ｂｏｎｃｚｅｋ的基于知识的决策支持系统结构．并把黑板机构引进ＩＤＳＳ。该ＩＤＳＳ使用多类知识表示方法．并为模型与对话的操纵知识及决策数据提供了统一的管理模式，研究者应用机器学习技术建立了一个评价函数．用来获取和精化模型操纵知识。这个ＩＤＳＳ能动态地构造模型并智能地求解问题．从而具备了结构紧凑，控制灵活，适应性强等突出优点。     

公式类知识管理系统的研究与开发

介绍了现今的知识管理思想及企业知识资源分类方法，分析了工程知识库管理系统的主要特点以及存在的不足。针对产品设计活动中应用知识的特点，采用面向对象方法来描述公式类型的知识，开发了一个通用的公式类知识管理系统。该系统不仅能够方便用户直接对公式类型知识进行操作、管理和维护，同时也可以为不同的智能系统提供统一的接口。     

产品设计方案的多目标决策分析及其智能支持系统

用模糊多目标决策方法对产品设计方案进行决策分析并设计出相应的智能计算机支持系统，实例证明这一方法在实际应用中是有效的和可靠的。     

项目投标决策支持系统研究

据投标的实际流程，从投标的全过程出发，利用预测理论、决策理论、对策理论和计算机技术等开发了项目投标决策支持系统PBDSS，并系统介绍了PBDSS的结构、功能、实现技术和应用。PBDSS可在施工企业投标工作的信息咨询、项目选择、总价制定、报价内部调整及标书生成等方面发挥辅助决策的作用。     

节水灌溉管理智能决策支持系统研究

应用人工智能中的专家系统技术,开发并集成了3个不同层次的节水灌溉管理决策支持系统：灌溉预报与节水灌溉决策子系统、灌区计划用水与水量调配管理系统、灌区管理体制改革子系统;介绍了节水灌溉管理智能决策支持系统的理论基础,系统结构,实际应用以及陕西省旱情决策子系统等。     

基于粗集方法的智能专家系统

文章主要介绍基于粗集方法的智能专家系统的基本构成、知识表达方式及学习推理方法。提出了利用不可分辨性或根据算法相容性进行知识简化,导出决策规则的方法,通过研究导师的知识与学习者的知识之间的依赖程度,找出每一概念的有用特征,进行知识简化和系统简化,导出决策规则的学习推理方法。     

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 EEC-CDSS 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.     

河北省可持续发展决策支持系统研究

阐述了河北省可持续发展评价指标体系及其评价方法，分析了建立河北省可持续发展决策支持系统（HBSDDSS）的意义、系统目标及其设计原则；最后给出了HBSDDSS的总体框架。     

Intelligent Decision Support System for COVID-19 Empowered with Deep Learning

The prompt spread of Coronavirus (COVID-19) subsequently adorns a big threat to the people around the globe. The evolving and the perpetually diagnosis of coronavirus has become a critical challenge for the healthcare sector. Drastically increase of COVID-19 has rendered the necessity to detect the people who are more likely to get infected. Lately, the testing kits for COVID-19 are not available to deal it with required proficiency, along with-it countries have been widely hit by the COVID-19 disruption. To keep in view the need of hour asks for an automatic diagnosis system for early detection of COVID-19. It would be a feather in the cap if the early diagnosis of COVID-19 could reveal that how it has been affecting the masses immensely. According to the apparent clinical research, it has unleashed that most of the COVID-19 cases are more likely to fall for a lung infection. The abrupt changes do require a solution so the technology is out there to pace up, Chest X-ray and Computer tomography (CT) scan images could significantly identify the preliminaries of COVID-19 like lungs infection. CT scan and X-ray images could flourish the cause of detecting at an early stage and it has proved to be helpful to radiologists and the medical practitioners. The unbearable circumstances compel us to flatten the curve of the sufferers so a need to develop is obvious, a quick and highly responsive automatic system based on Artificial Intelligence (AI) is always there to aid against the masses to be prone to COVID-19. The proposed Intelligent decision support system for COVID-19 empowered with deep learning (ID2S-COVID19-DL) study suggests Deep learning (DL) based Convolutional neural network (CNN) approaches for effective and accurate detection to the maximum extent it could be, detection of coronavirus is assisted by using X-ray and CT-scan images. The primary experimental results here have depicted the maximum accuracy for training and is around 98.11 percent and for validation it comes out to be approximately 95.5 percent while statistical parameters like sensitivity and specificity for training is 98.03 percent and 98.20 percent respectively, and for validation 94.38 percent and 97.06 percent respectively. The suggested Deep Learning-based CNN model unleashed here opts for a comparable performance with medical experts and it is helpful to enhance the working productivity of radiologists. It could take the curve down with the downright contribution of radiologists, rapid detection of COVID-19, and to overcome this current pandemic with the proven efficacy.     

工程移民决策支持研究

工程移民指工程建设引起的非自愿的搬迁安置活动，工程移民决策包括规划决策和管理决策，这在国内外都是一个新课题，基于GIS的工程移民DSS是实施科学决策的有效手段，章对工程移民决策支持的理论、方法进行了探讨，并结合万家寨工程移民决策支持进行了试验。     

A Project Management and Decision Support System for Non-standard Equipment''''s Eexecuting Work

1 Introduction Non-standard Mechanical Equipment (NSE) is usually a large critical one of power, pyrology, material haulage, storage and processing, and is directly related to the daily production of enterprises. The NSE executing work includes the installation, disassembly, haulage, maintenance and reconstruction. Many enterprises, especially some large ones in aviation or astronautics, often possess thousands of NSE, so the executing work quality, performing efficiency, maintenance level…