全自动腹膜透析机的设计

腹膜透析机是治疗急性肾功能衰竭、慢性肾功能衰竭和尿毒症的医疗设备。文中介绍了基于LPC1768处理器和μC／OS-Ⅱ实时操作系统的智能腹膜透析机。该系统实现了全自动控制定时定量输液、定时排液、引导用户设置透析参数、过温过重、流速不足自动报警。透析流程设计更加合理安全,实时性良好。特别适用于家庭透析,该腹透机在自动化和智能化方向优势明显。     

A data mining approach to classify serum creatinine values in patients undergoing continuous ambulatory peritoneal dialysis

Wireless Networks - Continuous ambulatory peritoneal dialysis (CAPD) is a treatment used by patients in the end-stage of chronic kidney diseases. Those patients need to be monitored using blood...     

装备维修智能决策支持系统研究

从系统的功能需求入手介绍传统的智能决策支持系统的结构模型,分析装备维修智能决策支持系统的功能实现,按照智能维修的目标设计了系统的总体构架。数据仓库的建立和分析采用传统的方法;对装备维修资料进行分析组建相应的知识库;分析建立模型条件结合知识库建立模型库;用数据挖掘方法处理数据仓库、知识库和模型库中的知识实现系统的智能决策。结果证明该方法在装备维修智能决策支持系统中得到很好的应用。     

A new concept toward computer-aided medical diagnosis - a prototypeimplementation addressing pulmonary diseases

The original concept that led to the structuring of a computer-based medical decision support system (DSS) that is able to support a physician's diagnosis is introduced in this paper. The concept's implementation modeled a generic DSS, the core of which are an integrated knowledge/information base (KIB) along with the inference properties of a data evaluator. The KIB encapsulates the necessary medical knowledge and experience in the form of rules and constraints, preemptive tasks and actual patients' clinical data. The data evaluator handles approved medical subjective and objective criteria for assessing the KIB's data. The data evaluator incorporates a medical standard data gathering and decision process, structured upon the principles of the clinical differential diagnosis methodology, and has been integrated in the system by means of both algorithmic and artificial intelligence techniques. The novel model and the resulted computer-based package have been extensively tested at the Pulmonary Department of the University Regional Hospital Patras, Greece     

粗糙集理论在步战车故障诊断专家系统中知识自动获取的应用

根据粗糙集理论,实现步战车故障诊断专家系统知识自动获取。首先根据历史故障数据建立决策表,利用粗糙集数据挖掘算法对决策表进行属性约简,对得到的决策表消除冗余,再利用属性值约简算法对决策表进行属性值约简,最后得到决策规则。知识库由条件表、结论表和事实表组成,保存在Access2003中。把条件属性保存到知识库的条件表中,把决策属性保存到知识库的结论表中,这样就很方便的建立了知识库。     

关于粗糙集属性约简的进化算法研究和应用

粗糙集理论是一种新的处理模糊和不确定知识的数学工具．属性约简是粗糙集理论研究中的重要内容之一。本文提出了用其构建科学、合理、简洁有效的科研项目评审指标体系，为科研智能管理提供新的解决方法。本文中提出一种基于进化算法的知识相对约简算法。通过在知识表达系统中引入决策属性支持度的概念．来描述由条件属性所提供的知识对整体决策的支持程度．并通过决策属性支持度定义条件属性对决策属性的相对重要性。以此作为启发式信息求出相对核。并将相对核加入进化算法的初始种群中以加快算法的收敛。同时．在适应值函数中引入惩罚函数。可以保证所求约筒既含较少的属性又有较强的支持度，能够获得最佳的搜索效果。     

Estimating Reliability Growth

Test programs are conducted to identify system failure modes and to estimate reliability. If the system can be changed so that some of the identified failure modes are eliminated and new failure modes are not introduced, the reliability of the system is improved. This paper examines small-sample techniques for estimating the change in reliability without the benefit of test data from the improved system. A decision to make the improvements can be based on the estimated increase in reliability. The procedures used for estimating changes can also be adapted to estimating reliability using data from a test program conducted in stages. For this case, a sample is taken at each stage and changes are made so that all newly identified failure modes which are correctable are eliminated from the system. Simulation is used to study reliability estimators both when just one sample is taken and when sampling is conducted in stages.     

Implementation of Business Game Activity Support System

Business game can be used not only as an educational tool for the development of decision making ability, but also can be used for supporting the knowledge creation activity in organizations. In this paper, some conceptual considerations to meanings of the business game in the knowledge creation activity by using the knowledge creation theory and other related theories are given, and business game activity concept which refers to game play and development is proposed. Then focusing on the business game activity as an instantiation of the knowledge creation activity, and a Web based gaming activity support system based on the former system called YBG that enables us to play and develop many business games through the standard web browser is proposed. This system also provides us a lot of opportunities to play and develop the business games over business game communities.     

A decision support system using judgmental modeling: a case ofR&D in the pharmaceutical industry

The development of a computerized decision support system for R&D project ranking, monitoring and control in the pharmaceutical industry is described. Using a series of techniques based on a judgmental modeling approach, an overall system has been developed that covers several different aspects of the whole decision making process. Illustrative examples are given to show how the components of the model are used in practice, indicating their place in the overall management process. The results provide evidence supporting the value of such models which help reduce ambiguity by using structured approaches     

The use of visual search for knowledge gathering in image decision support

This paper presents a new method of knowledge gathering for decision support in image understanding based on information extracted from the dynamics of saccadic eye movements. The framework involves the construction of a generic image feature extraction library, from which the feature extractors that are most relevant to the visual assessment by domain experts are determined automatically through factor analysis. The dynamics of the visual search are analyzed by using the Markov model for providing training information to novices on how and where to look for image features. The validity of the framework has been evaluated in a clinical scenario whereby the pulmonary vascular distribution on Computed Tomography images was assessed by experienced radiologists as a potential indicator of heart failure. The performance of the system has been demonstrated by training four novices to follow the visual assessment behavior of two experienced observers. In all cases, the accuracy of the students improved from near random decision making (33%) to accuracies ranging from 50% to 68%.     

Dynamic Decision Elements for 3-Unit Systems

Simple triplication is widely analyzed either as a static redundancy by itself or as an active core for dynamic redundancy. Its practical value was tested when it was used to protect the hardcore test and repair processor (TARP) of JPL STAR computer and the CPU of Guidance Computer for SATURN V. Two current techniques for using triplicated modules are TMR and TMR/Simplex. In this paper a set of dynamic decision techniques is proposed for better use of triplication, assuming Pr {system is in a specified failure mode | system has failed} is known for all failure modes of concern.     

Decision theory in maintenance strategy for a 2-unit redundantstandby system

Obtaining a good maintenance strategy for a standby system is discussed. The problem is analyzed via decision theory to determine the waiting time to call the repair facility (for a two-unit standby system) when the first piece of equipment fails. Previous research into this kind of system is briefly described, and a need for constructing a decision model is explained. The uncertainty of the parameters is accounted for in a Bayes approach in order to consider expert prior knowledge. The failure and repair rates are assumed to be constant and are elicited from an expert's prior beliefs. When no data are available, expert guesses are used. A method is presented for solving the conflicting requirements of system availability and cost through a multiattribute utility function which can express cardinal values for the decision maker's preferences over the objective variables. The decision model derives the appropriate maintenance strategy; it corresponds to a set of actions, procedures, and resources, giving a consequent waiting time before calling the repair facility. The use of the model is demonstrated for a telecommunication system     

The Bayes predictive approach in reliability theory

A strong motivation for reliability analyses is to support decision-making relative to the construction and operation of systems involving an economic/environmental risk. The Bayes approach to making decisions in face of uncertainty about mission survival is presented step by step. The authors show how the decision maker defines his own predictive probability distribution on the system time to failure and ranks the couples [decision taken, observed value of system failure time] by means of a loss function. They also introduce the minimum-expected-loss principle as a leading criterion for decision making. Finally, they address the more general case in which the final decision can be delayed in favor of collecting more information and derive the optimal termination procedure for life testing. For selecting the best course of action in a Bayes reliability frame there is neither need nor room for estimation of probability distribution parameters     

飞机战伤评估与修理决策支持系统研究

研究和探讨了建立飞机战伤评估与修理决策支持系统的可行性、有益性，以及飞机战伤评估与修理决策支持系统的总体结构和功能，知识表示方法，推理机制和过程等方面的问题。     

Knowledge structure representation and automated updates in intelligent information management systems

Work reported in this paper is part of a continuing effort to apply rapid prototyping and Artificial Intelligence techniques to problems associated with projected Space Station-era information management systems. In particular, timely updating of the various databases and knowledge structures within our proposed intelligent information management system (IIMS) is critical to support decision making processes. Because of the significantly large amounts of data entering the IIMS on a daily basis, information updates will need to be automatically performed with some systems requiring that data be incorporated and made available to users within a few hours. Meeting these demands depends first, on the design and implementation of information structures that are easily modified and expanded, and second, on the incorporation of intelligent automated update techniques that will allow meaningful information relationships to be established. This paper examines potential techniques for developing such an automated update capability and examines IIMS update requirements in light of results obtained from our IIMS prototyping effort.     

Computerized radiographic mass detection--part II: Decision support by featured database visualization and modular neural networks

Based on the enhanced segmentation of suspicious mass areas, further development of computer-assisted mass detection may be decomposed into three distinctive machine learning tasks: 1) construction of the featured knowledge database; 2) mapping of the classified and/or unclassified data points in the database; and 3) development of an intelligent user interface. A decision support system may then be constructed as a complementary machine observer that should enhance the radiologists performance in mass detection. We adopt a mathematical feature extraction procedure to construct the featured knowledge database from all the suspicious mass sites localized by the enhanced segmentation. The optimal mapping of the data points is then obtained by learning the generalized normal mixtures and decision boundaries, where a is developed to carry out both soft and hard clustering. A visual explanation of the decision making is further invented as a decision support, based on an interactive visualization hierarchy through the probabilistic principal component projections of the knowledge database and the localized optimal displays of the retrieved raw data. A prototype system is developed and pilot tested to demonstrate the applicability of this framework to mammographic mass detection.     

Shared Content Management in Replicated Web Systems: A Design Framework Using Problem Decomposition, Controlled Simulation, and Feedback Learning

Replication is one of the primary techniques used to improve the quality of distributed content service. It generally reduces user latencies and increases a site's availability. However, to our knowledge, there is no systematic framework that combines the structure of both content and service components of a Web application to design effective replica hosting architectures. Recent advances in interconnected and multiple content distribution network (CDN) architectures render this problem even more complex. In this study, we develop a systematic framework for designing and evaluating large-scale, component-based replication architectures for Web systems that are driven by both the quality and effectiveness of service provisioning on the service network. The proposed framework employs a combination of problem decomposition, configuration evaluation through controlled system simulations, and a neural-network-based feedback learning mechanism in the exploration of the design space. A case study demonstrates the viability of the framework. The framework can be an effective decision support tool for a system designer to systematically explore design options and select an appropriate design configuration that best meets the desired design objectives.     

Building a Normative Decision Support System for Clinical and Operational Risk Management in Hemodialysis

This paper describes the design and implementation of a decision support system for risk management in hemodialysis (HD) departments. The proposed system exploits a domain ontology to formalize the problem as a Bayesian network. It also relies on a software tool, able to automatically collect HD data, to learn the network conditional probabilities. By merging prior knowledge and the available data, the system allows to estimate risk profiles both for patients and HD departments. The risk management process is completed by an influence diagram that enables scenario analysis to choose the optimal decisions that mitigate a patient's risk. The methods and design of the decision support tool are described in detail, and the derived decision model is presented. Examples and case studies are also shown. The tool is one of the few examples of normative system explicitly conceived to manage operational and clinical risks in health care environments.      

Pattern discovery: a data driven approach to decision support

Decision support nowadays is more and more targeted to large scale complicated systems and domains. The success of a decision support system relies mainly on its capability of processing large amounts of data and efficiently extracting useful knowledge from the data, especially knowledge which is previously unknown to the decision makers. With a large scale system, traditional knowledge acquisition models become inefficient and/or more biased, due to the subjectivity of the experts or the pre-assumptions of certain ideas or algorithmic procedures. Today, with the rapid development of computer technologies, the capability of collecting data has been greatly advanced. Data becomes the most valuable resource for an organization. We present a fundamental framework toward intelligent decision support by analyzing a large amount of mixed-mode data (data with a mixture of continuous and categorical values) in order to bridge the subjectivity and objectivity of a decision support process. By considering significant associations of artifacts (events) inherent in the data as patterns, we define patterns as statistically significant associations among feature values represented by joint events or hypercells in the feature space. We then present an algorithm which automatically discovers statistically significant hypercells (patterns) based on: 1) a residual analysis, which tests the significance of the deviation when the occurrence of a hypercell differs from its expectation, and 2) an optimization formulation to enable recursive discovery. By discovering patterns from data sets based on such an objective measure, the nature of the problem domain will be revealed. The patterns can then be applied to solve specific problems as being interpreted or inferred with.