Fuzzy Logic Inference System for Managing Intensive Care Unit Resources Based on Knowledge Graph

With the rapid growth in the availability of digital health-related data,there is a great demand for the utilization of intelligent information systems within the healthcare sector.These systems can manage and manipulate this massive amount of health-related data and encourage different decision-making tasks.They can also provide various sustainable health services such as medical error reduction,diagnosis acceleration,and clinical services quality improvement.The intensive care unit(ICU)is one of the most important hospital units.However,there are limited rooms and resources in most hospitals.During times of seasonal diseases and pandemics,ICUs face high admission demand.In line with this increasing number of admissions,determining health risk levels has become an essential and imperative task.It creates a heightened demand for the implementation of an expert decision support system,enabling doctors to accurately and swiftly determine the risk level of patients.Therefore,this study proposes a fuzzy logic inference system built on domain-specific knowledge graphs,as a proof-of-concept,for tackling this healthcare-related issue.The system employs a combination of two sets of fuzzy input parameters to classify health risk levels of new admissions to hospitals.The proposed system implemented utilizes MATLAB Fuzzy Logic Toolbox via several experiments showing the validity of the proposed system.

UNCERTAIN KNOWLEDGE MANAGEMENT IN EXPERT SYSTEMS USING FUZZY METAGRAPHS

This paper presented a new graph theoretic construct——fuzzy metagraphs and discussed their applications in constructing fuzzy knowledge base. Fuzzy metagraphs describe the relationships between sets of fuzzy elements but not single fuzzy element and offer some distinct advantages both for visualization of systems, as well as for formal analysis of system structure. In rule based system, a fuzzy metagraph is a unity of the knowledge base and the reasoning engine. Based on the closure of the adjacency matrix of fuzzy metagraphs, this paper presented an optimized inferential mechanism working mainly by an off line approach. It can greatly increase the efficiency of inference. Finally, it was applied in a daignostic expert system and satisfactory results were obtained.

Knowledge Representation and Fuzzy Reasoning of an Agricultural Expert System

The design scheme of an agricultural expert system based on longan and cauliflower planting techniques is presented. Using an object-oriented design and a combination of the techniques in multimedia, database, expert system and artificial intelligence, an in-depth analysis and summary are made of the knowledge features of die agricultural multimedia expert system and data models involved. According to the practical problems in agricultural field, the architectures and functions of the system are designed, and some design ideas about the hybrid knowledge representation and fuzzy reasoning are proposed.

FUZZY KNOWLEDGE ACQUIREMENT IN GATE DESIGN

In order to reduce time and cost in engineering design, such as gate design, integral field knowledge must be obtained efficiently. After studying ID3 and its improved methods, a new algorithm(Fuzzy ID3 ) and its main concepts are presented to overcome some disadvantage in classical ID3 algorithms. In its application of gate design, fuzzy knowledge gotten by the new algorithm is more efficient than the knowledge by other ID3 algorithms, the set of fuzzy knowledge is much smaller than that of knowledge by classical ID3 algorithms. On the other hand, fuzzy knowledge represents field data more naturally to the way of bar thinking and more robust in tolerating imprecise, conflicting and missing information

Knowledge Representation Methods in Expert System for Earthquake Prediction ESEP 3.0

Knowledge representation is a key to the building of expert systems. The performance of knowledge representation methods directly affects the intelligence level and the problem-solving ability of the system. There are various kinds of knowledge representation methods in ESEP3.0. In this paper, the authors introduce the knowledge representation methods, such as structure knowledge, seismological and precursory forecast knowledge, machine learning knowledge, synthetic prediction knowledge, knowledge to validate and verify certainty factors of anomalous evidence and support knowledge, etc. and propose a model for validation of certainty factors of anomalous evidence. The knowledge representation methods represent all kinds of earthquake prediction knowledge well.

Integrated knowledge-based modeling and its application for classification problems

Knowledge discovery from data directly can hardly avoid the fact that it is biased towards the collected experimental data, whereas, expert systems are always baffled with the manual knowledge acquisition bottleneck. So it is believable that integrating the knowledge embedded in data and those possessed by experts can lead to a superior modeling approach. Aiming at the classification problems, a novel integrated knowledge-based modeling methodology, oriented by experts and driven by data, is proposed. It starts from experts identifying modeling parameters, and then the input space is partitioned followed by fuzzification. Afterwards, single rules are generated and then aggregated to form a rule base, on which a fuzzy inference mechanism is proposed. The experts are allowed to make necessary changes on the rule base to improve the model accuracy. A real-world application, welding fault diagnosis, is presented to demonstrate the effectiveness of the methodology.

Knowledge-Based Systems for the Assessment and Management of Bridge Structures: A Review

It is becoming an important social problem to make maintenance and rehabilitation of existing infrastructures such as bridges, buildings, etc. in the world. The kernel of such structure management is to develop a method of safety assessment on items which include remaining life and load carrying capacity. The purpose of this paper is to summarize the finding of up-to-date research articles concerning the application of knowledge-based systems to assessment and management of structures and to illustrate the potential of such systems in the structural engineering. In here, knowledge-based systems include knowledge-based expert systems incorporation with artificial neural networks, fuzzy reasoning and genetic or immune algorithms. Specifically, two modern bridge management systems (BMS’s) are presented in the paper. The first is a BMS to assess the performance and derive optimal strategies for inspection and maintenance of concrete bridge structures using reliability based and knowledge-based systems. The second is the concrete bridge rating expert system (J-BMS BREX) to evaluate the performance of existing bridges by incorporating with artificial neural networks and fuzzy reasoning.

Knowledge modeling based on interval-valued fuzzy rough set and similarity inference: prediction of welding distortion

Knowledge-based modeling is a trend in complex system modeling technology. To extract the process knowledge from an information system, an approach of knowledge modeling based on interval-valued fuzzy rough set is presented in this paper, in which attribute reduction is a key to obtain the simplified knowledge model. Through defining dependency and inclusion functions, algorithms for attribute reduction and rule extraction are obtained. The approximation inference plays an important role in the development of the fuzzy system. To improve the inference mechanism, we provide a method of similaritybased inference in an interval-valued fuzzy environment. Combining the conventional compositional rule of inference with similarity based approximate reasoning, an inference result is deduced via rule translation, similarity matching, relation modification, and projection operation. This approach is applied to the problem of predicting welding distortion in marine structures, and the experimental results validate the effectiveness of the proposed methods of knowledge modeling and similarity-based inference.

Educational Advisor System Implemented by Web-Based Fuzzy Expert Systems

Using of the Internet technology and the field of Fuzzy expert systems has proposed new branches of sharing and distributing knowledge. However, there has been a general lack of investigation in the area of web-based Fuzzy expert systems (FES). In this paper the issues associated with the design, development, and use of web-based FES from a standpoint of the benefits and challenges of developing and using them. The original theory and concepts in conventional FES were reviewed and a knowledge engineering framework for developing them was revisited. Student in an educational place need an educational advisor for solve problems. Some of educational circulars order changing because advisor must update information away. The student's request is linguistic and crisp Expert System cannot solve problems completely. In my approach we build Web-Based Fuzzy Expert System for Student Education Advisor (FES-SEA) and stays in university portal. This system implemented with ASP.NET, SQL-SERVER 2008.

A FUZZY MATCH BASED EXPERT SYSTEM FOR SYNTHESIS OF SEPARATION PROCESSES

A predictive parallel search algorithm,the fuzzy match inference strategy,is implemented ina prototype expert system.Selection of separation technologies and sequencing of separators are beingapproached in an integrated manner.The fuzzy match mechanism results in a relatively smaller subsetof favored schemes,constituting a hyperstructure for further quantitative evaluation and combinationoptimization.An industrial application example of aromatics extraction separation is presented.

A NEURAL NETWORK BASED FAULT FUZZY DIAGNOSTIC SYSTEM

A fault fuzzy diagnostic system(FFDS) based on neural network and fuzzy logic hybrid is proposed. FFDS consists of two modes: a fuzzy inference mode and a rule learning mode. The fuzzy inference rules are stored in the memory layer. The excitation levels of the memory neurons reflect the matching degrees between the input vectors and the prototype rules. In the rule learning mode, the rules can be produced automatically through the cluster process. As an application case of this diagnostic system, the fault diagnosis experiment of the rotating axis is simulated.

Development Expert System

The expert system is a high-level technology.It is a sub-field of artificial intelligence.We demonstrated the character and software evaluation,carrying out an initial study of expert system.A good development expert system was developed.

A PRELIMINARY STUDY OF EXPERT SYSTEM FOR MINING UNDER STRUCTURES

The expert system MUST (Mining Under Structures) shown in this paper and established by the authors is a preliminary expert system to solve the policy-making problems for mining under structures by means of computers instead of humanbeing. Based on the experience of relative experts,the authors established a knowledge base about the minings under structures,researched into reasonable method to simulate thinking processes of human experts when they are solving the problems, established the network of an expert system and named it ' MUST system' . MUST system uses the method of the structural system analysis approach. A kind of methods of Turbo Prolog and Fortran 77 language alternations is designed to meet the needs of exchange information within the MUST system. Based on this kind of methods MUST system has been constructed and realised on IBM-PC computer. For verifying the correctness, suitability and reliablity of MUST system,some practical examples of minings under structures were tentatively solved using MUST system,whose results are satisfactory.

Evolving Fuzzy Neural Networks for Extracting Rules

ＥｖｏｌｖｉｎｇＦｕｚｚｙＮｅｕｒａｌＮｅｔｗｏｒｋｓｆｏｒＥｘｔｒａｃｔｉｎｇＲｕｌｅｓ＊＊ＴｈｉｓｗｏｒｋｗａｓｓｕｐｐｏｒｔｅｄｂｙｔｈｅＣｌｉｍｂｉｎｇＰｒｏｇｒａｍｍｅ┐ＮａｔｉｏｎａｌＫｅｙＰｒｏｊｅｃｔｆｏｒＦｕｎｄａｍｅｎｔａｌＲｅｓ...

基于GIS、模糊逻辑和专家知识的土壤制图及其在中国应用前景

详细的土壤空间与属性的信息已成为环境模型和土地管理的基本参数,传统的以类别多边形和手工编制为基础的传统土壤制图效率低精度也较差。本文基于GIS、模糊逻辑和专家知识,建立了土壤一环境推理模型(SoLIM),通过基于土壤一环境关系模型的土壤相似度模型与对该模型进行赋值的推理技术来编制土壤图,从而克服了传统土壤制图中的简化。通过两个小区的研究表明,与传统土壤制图相比,通过SoLIM得出的土壤信息在空间详细度和属性精确度都有较大的提高,也能够大量减少调查的时问和经费,从而大大提高土壤调查的效率。SoLIM方法在我国推广十分必要且具有一定的条件,但仍需要进一步完善。

烧结法生产氧化铝生料浆的配料专家系统设计

基于烧结法生产氧化铝生料浆的配料机理和长期积累的专家经验知识,建立了生料浆质量预测模型,开发了适应于生料浆配料生产特点的专家优化配料系统。根据实际生产和配料机理,提出了产生式规则的知识表示形式,结合规则集分类原理,设计了生料浆配料的专家知识库。通过规则集之间的有知识搜索、规则集内部的前向推理、规则前提和结论之间的哈希算法的有机结合来实现推理机制,从而实现专家系统的优化配比计算。实际结果表明:该专家系统优化配比计算速度快、效率高,既提高了生料浆质量,又稳定了生产。

汽车车轮侧滑检测与诊断专家系统

以专家系统基本原理为基础,结合汽车车轮侧滑检测的特点,应用Foxpro和VisualC++语言进行了汽车车轮侧滑检测与诊断专家系统的开发研究,并从知识库的设计、推理策略、解释机制和人机接口等方面进行了论述。结果表明,系统在汽车车轮侧滑检测的应用中,不仅能给出侧滑量,而且能给出相应的车轮定位参数的调整范围以及维修建议。

基于模糊专家系统的评估诊断方法

针对设备故障诊断中存在的不确定性问题,提出一种将模糊逻辑和专家系统结合的评估诊断方法·模糊专家系统中采用模糊产生式规则表示知识,通过采用模糊匹配的不确定性推理方法进行,并采用以置信度高低为标准的冲突消解策略来实现基于知识的诊断方法·文中介绍了专家系统功能组成和实现方法,同时在电力变压器的气相色谱分析故障诊断方法中应用了该模糊专家系统·通过现场近百次实际诊断,准确率可以达到95 7%,验证了将模糊专家系统应用到变压器的故障诊断中是可行的·

基于模糊Petri网的知识表示方法在变压器故障诊断专家系统中的应用

本文给出了用模糊 Petri网模型来表示变压器故障诊断专家系统中模糊产生式知识库的方法 ,在此模型基础上 ,给出了有效的推理算法 。

模糊逻辑在物理栖息模拟中的应用

为了克服已有物理栖息地模型仅能在具有较完善监测资料河流使用的局限性,介绍了一种应用模糊逻辑的物理栖息地模拟方法。该方法以河道平面二维流场模拟结果为基础,将鱼类学和生态学专家的知识经验融入物理栖息地模型中,运用模糊逻辑推理计算栖息地各单元适宜度,最后根据加权可用面积、高适宜度面积比例与流量的关系曲线确定生态需水量。运用该方法对葛洲坝坝下中华鲟产卵栖息地进行模拟。研究结果表明,中华鲟产卵期适宜生态需水量为10 000～17 000 m3/s。分析认为该方法对监测资料依赖程度低,使用专家知识经验能够弥补数据不足造成的不利影响,具有一定的可行性及实用性。该研究有助于监测资料不足的河流开展生态保护及河流管理工作,并且能够为模糊数学理论在水生态学中的应用提供参考。