Construction of the Inference Engine of Blast Furnace Expert System

The expert system for judging blast furnace condition is built to realize automatic judgment of blast furnace conditions and standardization of blast furnace operation. This paper discusses the design and implementation of the inference engine of blast furnace expert system. The satisfactory simulation results have been obtained.

On-Line Real Time Realization and Application of Adaptive Fuzzy Inference Neural Network

In this paper, a modeling algorithm developed by transferring the adaptive fuzzy inference neural network into an on-line real time algorithm, combining the algorithm with conventional system identification method and applying them to separate identification of nonlinear multi-variable systems is introduced and discussed.

Design of Fine Life Cycle Prediction System for Failure of Medical Equipment

The inquiry process of traditional medical equipment maintenance management is complex,which has a negative impact on the efficiency and accuracy of medical equipment maintenance management and results in a significant amount of wasted time and resources.To properly predict the failure of medical equipment,a method for failure life cycle prediction of medical equipment was developed.The system is divided into four modules:the whole life cycle management module constructs the life cycle data set of medical devices from the three parts of the management in the early stage,the middle stage,and the later stage;the status detection module monitors the main operation data of the medical device components through the normal value of the relevant sensitive data in the whole life cycle management module;and the main function of the fault diagnosis module is based on the normal value of the relevant sensitive data in the whole life cycle management module.The inference machine diagnoses the operation data of the equipment;the fault prediction module constructs a fine prediction system based on the least square support vector machine algorithm and uses the AFS-ABC algorithm to optimize the model to obtain the optimal model with the regularized parameters and width parameters;the optimal model is then used to predict the failure of medical equipment.Comparative experiments are designed to determine whether or not the design system is effective.The results demonstrate that the suggested system accurately predicts the breakdown of ECG diagnostic equipment and incubators and has a high level of support and dependability.The design system has the minimum prediction error and the quickest program execution time compared to the comparison system.Hence,the design system is able to accurately predict the numerous causes and types of medical device failure.

Modeling and inferring 2.1D sketch with mixed Markov random field

This paper presents a method of computing a 2.1D sketch (i.e., layered image representation) from a single image with mixed Markov random field (MRF) under the Bayesian framework. Our model consists of three layers: the input image layer, the graphical representation layer of the computed 2D atomic regions and 3-degree junctions (such as T or arrow junctions), and the 2.1D sketch layer. There are two types of vertices in the graphical representation of the 2D entities: (i) regions, which act as the vertices found in traditional MRF, and (ii) address variables assigned to the terminators decomposed from the 3-degree junctions, which are a new type of vertices for the mixed MRF. We formulate the inference problem as computing the 2.1D sketch from the 2D graphical representation under the Bayesian framework, which consists of two components: (i) region layering/coloring based on the Swendsen-Wang cuts algorithm, which infers partial occluding order of regions, and (ii) address variable assignments based on Gibbs sampling, which completes the open bonds of the terminators of the 3-degree junctions. The proposed method is tested on the D-Order dataset, the Berkeley segmentation dataset and the Stanford 3D dataset. The experimental results show the efficiency and robustness of our approach. ? 2017 Beijing Institute of Aerospace Information.

A Motivation Framework to Promote Knowledge Translation in Healthcare

Globally,there is a great gulf between medical knowledge and clinical practice.Translating knowledge into clinical decision support(CDS) application has become the biggest challenge faced by evidence based medicine.This paper proposed a comprehensive motivation framework to facilitate knowledge translation in healthcare.Based on a unified medical knowledge ontology and knowledge base,the framework provides an infrastructure of fundamental services,such as inference service and data acquisition,to support development of knowledge-driven CDS applications and integration into clinical workflow.The framework has been implemented in a 2600-bed Chinese hospital,and is able to reduce the time and cost of developing typical CDS applications.

Research on Intelligent Decision Support System of Soybean

Based on agriculture production knowledge and computer technology, this paper applied database, artificial intelligent, management information system, decision support system, network technology and information integration technology to the field of soybean production, and offered decision support to users in imitating the curse during which experts solved the problems, therefore, guided production practice, introduced structure and function of decision support system of soybean, and then we analyzed the design of the knowledge base and the realization of inference engine in details as well.

An IPC-based Prolog design pattern for integrating backward chaining inference into applications or embedded systems

Prolog is one of the most important candidates to build expert systems and AI-related programs and has potential applications in embedded systems. However, Prolog is not suitable to develop many kinds of components, such as data acquisition and task scheduling, which are also crucial. To make the best use of the advantages and bypass the disadvantages, it is attractive to integrate Prolog with programs developed by other languages. In this paper, an IPC-based method is used to integrate backward chaining inference implemented by Prolog into applications or embedded systems. A Prolog design pattern is derived from the method for reuse, whose principle and definition are provided in detail. Additionally, the design pattern is applied to a target system, which is free software, to verify its feasibility. The detailed implementation of the application is given to clarify the design pattern. The design pattern can be further applied to wide range applications and embedded systems and the method described in this paper can also be adopted for other logic programming languages.

Fuzzy Logic Control ASIC Chip

A fuzzy logic colltrol VLSI chip, F100, for industry process real-time colltrol has been designed and fabricated with 0.8pm CMOS technology. The chip has the features of simplicity flealbility and generality. This paper presents the fuzzy control inference method of the chip, its VLSI implementation, and testing design consideration.