模式识别在过程控制系统中的应用

模式识别方法本质上是一种分类方法,它不需要过程的数学模型就可做出高级决策。模式识别方法已在过程控制中的许多领域得到了应用,如非线性和分布参数系统的结构辨识、在未知环境下运行系统的控制、自适应控制、智能控制、动态建模、状态估计、优化控制、故障诊断等。本文将概述模式识别方法在以上各方面(?)情况,并指出今后可能的研究方向。     

一个基于神经网络的专家系统

医疗专家系统主要使用基于知识的技术，其中的决策规则和策略来自于人类的专家。把这些知识和各种推理方法结合，可以建立一个模拟专家决策过程的系统。建立这样一个系统，需要经常与专家磋商，以获取专家的知识，因而需要大量的时间和精力。为此，本文提出直接从数据中提取有效的信息，即用神经网络提取隐含在大量数据中对医疗诊断有效的信息，继之与基于规则的知识，各种推理方法相结合，建立一个神经网络专家系统。     

Intelligent robotic control

A theoretical approach is developed to deal with man-machine interactive systems requiring advanced decision making in unpredictable environments. The hierarchical method consists of a three-layer control of "increasing intelligence and decreasing precision." The lowest level consists of several controllers designed for effective control with existing hardware using an approximation theory of optimal control. The next level is that of a coordinator which utilizes new computer architectures to effectively control the overall hardware system. The highest level is the organizer which supervises the performance of the overall system. Both highest levels are computer implemented and the research involved is in developing the appropriate architecture and software to accommodate others. The lowest level, aimed for end-point control tasks, is dominated by typical hardware control methods. The coexistence of the two approaches makes the method novel. Application of intelligent control techniques to robotics and manipulative systems is considered.     

用于复杂系统建模与仿真的面向智能体技术

在构造兼具数据处理和知识处理能力的新型面对象型形式化体系上,根据复杂系统仿真的需要,使系统组分“对象”具有自主决策、控制和通信功能,建成多Agent系统。给出多Agent系统实现群组决策的示例。     

基础信息层经验规则的一种表达方法

该文讨论在复杂的大型辅助决策系统中,构造智能决策规则模型的一种方法。这是一种基于决策表的知识表示方法。它在传统决策表的基础上,吸收了产生式规则、框架表示法、模糊理论、关系模型等多种方法的思想和技术,把传统决策表加以扩展,得到了一种结构性好、表达能力强、可操作性较好的智能决策表达工具,用来表示大型辅助决策系统中的复杂领域知识,将其中松散的经验规则形式化成智能决策规则模型,从而增强其结构性和可操作性,有效支持对其它信息的操作。     

A greenhouse control with feed-forward and recurrent neural networks

Greenhouses are classified as complex systems, so it is difficult to implement classical control methods for this kind of process. In our case we have chosen neural network techniques to drive the internal climate of a greenhouse. An Elman neural network has been used to emulate the direct dynamics of the greenhouse. Based on this model, a multilayer feed-forward neural network has been trained to learn the inverse dynamics of the process to be controlled. The inverse neural network has been placed in cascade with the neural model in order to drive the system outputs to desired values. Simulation results will be given to prove the performance of neural networks in control of the greenhouse.     

Quantitative risk-based requirements reasoning

At NASA we have been developing and applying a risk management framework, "Defect Detection and Prevention" (DDP). It is based on a simple quantitative model of risk and is supported by custom software. We have used it to aid in study and planning for systems that employ advanced technologies. The framework has proven successful at identifying problematic requirements (those which will be the most difficult to attain), at optimizing the allocation of resources so as to maximize requirements attainment, at identifying areas where research investments should be made, and at supporting tradeoff analyses among major alternatives. We describe the DDP model, the information that populates a model, how DDP is used, and its tool support. DDP has been designed to aid decision making early in development. Detailed information is lacking at this early stage. Accordingly, DDP exhibits a number of strategic compromises between fidelity and tractability. The net result is an approach that appears both feasible and useful during early requirements decision making.     

A decision-making module for aiding ship system automation design: A knowledge-based approach

The use of elements of artificial intelligence, including knowledge-based systems, becomes more and more widespread in aiding design problem solutions. The authors have been working on problems of control systems for many years. A design process involves many decision problems connected with, for example, a choice of a subsystem structure, subunits or particular elements selection. Because of such regards, it was decided to extend knowledge-based system with a module for support of such decision making.In this paper, an elaborated module for decision-making support is considered. The basic theoretical assumptions concerning the accepted method of multiattribute decision making based on pairwise comparison in categories of hierarchical decision process (AHP) is presented. Accepted knowledge representation in AHP method and pairwise comparison method and methods of expert knowledge acquisition are discussed. The module functioning is illustrated by an example of choice of temperature sensors in a system of fuel transport to Diesel engine of a main propulsion unit of a ship.     

Fingerprint image enhancement and recognition algorithms: a survey

Fingerprint systems have received a great deal of research and attracted many researchers’ effort since they provide a powerful tool for access control and security and for practical applications. A literature review of the techniques used to extract the features of fingerprint as well as recognition techniques is given in this paper. Some of the reviewed research articles have used traditional methods such as recognition techniques, whereas the other articles have used neural networks methods. In addition, fingerprint techniques of enhancement are introduced.     

A fuzzy stochastic multi-criteria model for the selection of urban pervious pavements

Multi-criteria decision making methods (MCDM) have been widely used throughout the last years to assist project contractors in selection processes related to the construction field. Sustainable urban drainage systems (SUDS) are an especially suitable discipline to implement these techniques, since they involve important impacts on each branch of sustainability: economy, environment and society. Considering that pervious pavements constitute an efficient solution to manage urban stormwater runoff as a source control system, this paper presents a multi-criteria approach based on the Integrated Value Model for Sustainable Assessments (MIVES) method to facilitate their proper selection. Given the lack of accurate information to shape the behavior of the alternatives regarding some of the criteria defining the decision-making environment, a series of variables are modeled by executing stochastic simulations based on the Monte Carlo methods. Additionally, a group of ten experts from various sectors related to water management was requested to provide their opinions about the importance of the set of selected criteria, according to the comparison levels of the Analytic Hierarchy Process (AHP). These judgments are converted into triangular fuzzy numbers, in order to capture the vagueness that human attitude entails when making judgments. A case of study in which the three major types of pervious pavements (porous asphalt, porous concrete and interlocking concrete pavers) are evaluated is presented to demonstrate the potential of the model.     

Practical verification of decision-making in agent-based autonomous systems

We present a verification methodology for analysing the decision-making component in agent-based hybrid systems. Traditionally hybrid automata have been used to both implement and verify such systems, but hybrid automata based modelling, programming and verification techniques scale poorly as the complexity of discrete decision-making increases making them unattractive in situations where complex logical reasoning is required. In the programming of complex systems it has, therefore, become common to separate out logical decision-making into a separate, discrete, component. However, verification techniques have failed to keep pace with this development. We are exploring agent-based logical components and have developed a model checking technique for such components which can then be composed with a separate analysis of the continuous part of the hybrid system. Among other things this allows program model checkers to be used to verify the actual implementation of the decision-making in hybrid autonomous systems.     

Hybrid MCDM based methodology for selecting the optimum maintenance strategy for ship machinery systems

The key to achieving optimum ship system reliability and safety is to have a sound maintenance management system in place for mitigating or eliminating equipment/component failures. Maintenance has three key elements; risk assessment, maintenance strategy selection and the process of determining the optimal interval for the maintenance task. The optimisation of these three main elements of maintenance is what constitute a sound maintenance management system. One of the challenges that marine maintenance practitioners are faced with is the problem of maintenance selection for each equipment item of the ship machinery system. The decision making process involves utilising different conflicting decision criteria in selecting the optimum maintenance strategy from among multiple maintenance alternatives. In tackling such decision making problems the application of a multi-criteria decision making (MCDM) method is appropriate. Hence in this paper two hybrid MCDM methods; Delphi-AHP and Delphi-AHP-PROMETHEE, are presented for the selection of appropriate maintenance strategies for ship machinery systems and other related ship systems. A case study of a ship machinery system maintenance strategy selection problem is used to demonstrate the suitability of the proposed methods.     

Multiobjective Optimization Techniques for Selecting Important Metrics in the Design of Ensemble Systems

Ensemble systems are classification structures that apply a two‐level decision‐making process, in which the first level produces the outputs of the individual classifiers and the second level produces the output of the combination method (final output). Although ensemble systems have been proven to be efficient for pattern recognition tasks, its efficient design is not an easy task. This article investigates the influence of two diversity measures when used explicitly to guide the design of ensemble systems. These diversity measures were proposed recently, and they proved to be very interesting for the diversity–accuracy dilemma. To perform this investigation, we will use two well‐known optimization techniques, genetic algorithms, and tabu search, in their mono‐objective and multiobjective versions. As objectives of the optimization techniques, we use error rate and two diversity measures as well as all possible combinations of these three objectives. In this article, we aim to analyze which set of objectives can generate more accurate ensembles. In addition, we aim to analyze whether or not the diversity measures (good and bad diversities) have a positive effect in the design of ensemble systems, mainly if they can replace the error rate as an optimization objective without incurring significant losses in the accuracy level of the generated ensembles.     

Multi-objective decision support system in numerical reliability optimization of modern electronic packaging

Nowadays, numerical prototyping methods in electronic packaging are widely used. This is mainly due to cost and time reduction and improved functionality and reliability of final products. Recently, there has been a lot of interest and work conducted on advanced numerical optimization, which can be directly applied to prototyping. So far, the optimization is focused on one criteria while neglecting problem of multi-objectivity, which is not the best approach from practical point of view. Nevertheless, such an approach is jusitified from the point of view of complex analysis, interdisciplinary issues and reduced accuracy of numerical models. In reality, there are usually many criteria which, in order to solve the problem, have to be taken into consideration. There are many multi-objective methods, of which the Pareto set approach is mostly cited in the literature. The “problem” of multi-objective optimization is that not a single optimal solution has resulted but the set of equivalent optimal solutions. This set of equivalent optimal solutions is referenced as “the Pareto set”. From the mathematical point of view, every value from this set can be treated as optimal for certain assumed constraints. However, there could be some additional conditions which cannot be applied to optimization process and some of the results from the Pareto set are more likely (i.e., the fabrication process will be more repeatable) then the others. So, the question is: which value from the Pareto set should be taken to further processing? There are two possibilities: asking an expert for the advice or use the decision making system. Decision making methods based on multi-objective optimization could be referenced as “Multiple criteria decision making” (MCDM) or “Multiple criterial decision aid” (MCDA) systems. There are several groups of these methods: (a) mathematical multi-objective programming, (b) artificial intelligence methods, (c) simple arithmetic methods, and (d) advanced mathematical methods. The current paper will focus on designing and application of the decision support system for multi-objective numerical reliability optimization of electronic packaging. The work will be based on the self developed numerical tool based on Python Scrippting language and will present its application to selected microelectronic packages based on its numerical model elaborated in ABAQUS.     

Speech-based E-mail and driver behavior: effects of an in-vehicle message system interface

As mobile office technology becomes more advanced, drivers have increased opportunity to process information "on the move." Although speech-based interfaces can minimize direct interference with driving, the cognitive demands associated with such systems may still cause distraction. We studied the effects on driving performance of an in-vehicle simulated "E-mail" message system; E-mails were either system controlled or driver controlled. A high-fidelity, fixed-base driving simulator was used to test 19 participants on a car-following task. Virtual traffic scenarios varying in driving demand. Drivers compensated for the secondary task by adopting longer headways but showed reduced anticipation of braking requirements and shorter time to collision. Drivers were also less reactive when processing E-mails, demonstrated by a reduction in steering wheel inputs. In most circumstances, there were advantages in providing drivers with control over when E-mails were opened. However, during periods without E-mail interaction in demanding traffic scenarios, drivers showed reduced braking anticipation. This may be a result of increased cognitive costs associated with the decision making process when using a driver-controlled interface when the task of scheduling E-mail acceptance is added to those of driving and E-mail response. Actual or potential applications of this research include the design of speech-based in-vehicle messaging systems.     

通用OCR开发工具的设计

提出了一个通用OCR开发工具的设想,用于各种文字的OCR软件的开发,它能够在使用者的干预下自动完成识别器的设计,大大减少文字识别软件开发的工作量。系统以决策树作为基本的判别器,并用多个决策树组成多方案识别系统。提出设计树和分类器设计器的概念,分别用于决策树设计过程的控制和决策树节点中的分类器的设计。最后实现一个实验系统,验证了该文的设想和设计方案的可行性。     

Complementary case-based reasoning and competitive fuzzy cognitive maps for advanced medical decisions

This paper presents a new hybrid modeling methodology suitable for complex decision making processes. It extends previous work on competitive fuzzy cognitive maps for medical decision support systems by complementing them with case based reasoning methods. The synergy of these methodologies is accomplished by a new proposed algorithm that leads to more dependable advanced medical decision support systems that are suitable to handle situations where the decisions are not clearly distinct. The methodology developed here is applied successfully to model and test two decision support systems, one a differential diagnosis problem from the speech pathology area for the diagnosis of language impairments and the other for decision making choices in external beam radiation therapy.     

A bio-inspired approach for self-correcting compliant assembly systems

Statistical process monitoring and control has been popularized in manufacturing as well as various other industries interested in improving product quality and reducing costs. Advances in this field have focused primarily on more efficient ways for diagnosing faults, reducing variation, developing robust design techniques, and increasing sensor capabilities. However, statistical process monitoring cannot address the need for instant variation reduction during assembly operations. This paper presents a unique dimensional error-compensation approach for compliant sheet metal assembly processes. The resulting autonomous self-correction system integrates rapidly advancing data mining methods, physical models, assembly modeling techniques, sensor capabilities, and actuator networks to implement part-by-part dimensional error compensation. Inspired by biological systems, the proposed quality control approach utilizes immunological principles as a means of developing the required mathematical framework behind the self-correcting methodology. The resulting assembly system obtained through this bio-mimicking approach will be used for autonomous monitoring, detection, diagnosis, and control of station and system level faults, contrary to traditional systems that largely rely on final product measurements and expert analysis to eliminate process faults.     

Extracting knowledge of customers’ preferences in massively multiplayer online role playing games

Due to fierce competition in game markets, to identify customers’ true needs is one of the crucial factors in online game industry. Traditionally, game producers heavily rely on game testers, who are primarily responsible for analyzing computer games, finding software defects and being a part of quality control process, to achieve this goal. But, it is not often reliable. To ensure the investment can be returned, game producers need an effective approach to discover frequently shifted customer preferences in time. Recently, Kano model and data mining techniques have been successfully applied to recognize customers’ preferences and implement customer relationship management tasks, respectively. However, in traditional Kano analysis, only basically statistical analysis techniques are used, and they are insufficient to provide advanced knowledge to enterprisers. Therefore, in order to discover the relationship between/among quality elements in Kano model and to extract knowledge related to customer preferences, this study proposes a knowledge acquisition scheme that integrates several data mining techniques including association rule discovery, decision tree, and self-organizing map neural network, into traditional Kano model. An actual case of customer satisfaction survey regarding massively multiplayer online role playing game has been provided to demonstrate the effectiveness of our proposed scheme.     

The effect of reasoning logics on real-time decision making

The advent of satellite tracking and communications technology has motivated organizations to investigate centralized real-time support for remote and mobile units and operations. Transportation companies have begun to use satellite tracking systems, production and energy plants are employing intelligent monitoring systems, and on-site emergency managers are being supported by headquarters in their assessment and decision making process. Although different reasoning logics have been proposed for real-time decision making, little attention has been given to the comparative assessment of these approaches. This paper presents empirical evidence on the impact of alternative reasoning logics on individual decision making in real-time. Following the definition of the decision tasks for hazardous operations, commonly used reasoning logics are discussed. Then, results of an empirical comparison of different reasoning approaches are presented. The data were gathered during an experiment with experienced operators. The conclusions of this research are that the selection of the appropriate reasoning logic in support of real-time decision making is crucial