Does intelligent CAD exist?

Intelligent CAD is a term which has grown in popularity in recent years as the results of research into Artificial Intelligence (AI) get applied to design problems. But there is confusion as to what the term means; e.g. whether current generations of expert systems for design are Intelligent CAD, or whether Intelligent CAD is simply an unachievable goal.This paper argues that Intelligent CAD represents a vision which is almost identical to the earliest visions of Computer Aided Design (CAD). The major difference now is that we have the opportunity through our improved knowledge of AI and cognitive science to take important strides towards delivering CAD systems close to these visions. However, the opportunity may be missed if we concentrate on the wrong issues.It is also argued that developments towards Intelligent CAD will be successful only if they accept the following tenets:

• 1.design is considered as an intellectual knowledge-based process,
• 2.systems do not need to replicate human intelligence; they are required only to exhibit behaviour regarded as intelligent, and
• 3.necessary components of such systems are knowledge-rich models of designs, the capacity for tacit knowledge, and the ability to learn.

     

What do we owe to intelligent robots?

AI & SOCIETY - Great technological advances in such areas as computer science, artificial intelligence, and robotics have brought the advent of artificially intelligent robots within our reach...     

An active intelligent decision support system — Architecture and simulation

Development and implementation of decision support systems to support intelligent decision making is an area of research that has gained in importance in recent years. Due to the increased complexity of decision making, active involvement of the user and the computer in an intelligent way is necessary in the decision process. This paper presents issues in the design of an active intelligent decision support system (IDSS), develops an architectural model based on cooperative distributed problem solving, and performs a simulation of the system using object oriented programming for an example application in airfleet control.     

Method and system for adaptive network security using intelligent packet analysis

<美国专利>6,499,107Gleichauf, et a1.December 24,2002<发明人>Gleichauf;Robert E.(San Antonio,TX);TealDaniel M.(San Antonio,TX);Wiley;Kevin L(Elgin,TX)<代理人>Cisco Technology,Inc.(San Jose,CA)<公开号>223071<公开日>December 29,1998<美国分类>713/20]<国际分类>G06F 01l/30<检索号>713/200,201 709/223,224,229,225,100,102,103,104,226 705/8,9<摘要>A method and system for adaptlye net-work security using intelligent packet analysisare provided.The method comprises monitoringnetwork data traff…     

Designer®—Intelligent expert system for intelligent manufacturing systems design

This survey on expert systems activities and trends in Yugoslavia offers some results already obtained in the domain of manufacturing science and technology. In the scope of a long-term research project “Intelligent Manufacturing Systems (IMS)—Theory and Application” a Designer® Intelligent Expert System for mafacturing engineering has been proposed and partially developed. Designer® IES is based on new developed knowledge automata theory enhanced with cellular automata concept. Induction learning by analogy and Quasimorphism knowledge mapping from real world to model world is used to generate a reasoning structure. The Intelligent Expert System is divided into three main subsystems, with a very large knowledge base:

• •Product designer
• •Process Designer, and
• •Production Planning and Control Designer.

All these segments were developed in pilot versions of expert systems for specific groups of activities inside each of these three domains.     

Application of networked discrete event system theory on intelligent transportation systems

The responses of vehicles to the changes in traffic situations inevitably have delays in observing an event and implementing a control command, which often causes fatal accidents. So far, the methods for handling delays are empirical and cannot be mathematically proven. To eliminate the accidents caused by such delays, in this paper, we develop mathematically provable methods to handle these delays. Specifically, we use networked discrete event systems to model the process of driving vehicles and present a supervisory controller for handling delay situations. The method developed in this paper could serve as a new start for modeling and controlling the responsive behaviors of self-driving vehicles in the future.     

Seeking intelligent product development—an integrator environment based on STEP

Rapid Prototyping (RP) is an emerging technology with different applications in the cycle of product and process development used during the conceptual and specification phases. To support it, the usage of a Data Base Management System (DBMS) and an Expert Systems (ES) are important to manage the data, support the identification of errors and check product conformance and production viability during this cycle, seeking intelligent product development. The underlying concept of RP and ES is very straight forward, but still exists a major problem that concerns the integration in the CAD/CAM environment. CAD data can have a variety of formats depending on the model used in the CAD system, as well as on ES' data and knowledge structure, that usually relies on the expert system shell's where it was developed. To support and speed up the integration process of these tools in a manufacturing environment, is essential to have an integrator environment where a standard is adopted for the data and knowledge exchange process, providing the software applications with a neutral format interface, becoming these tools open and standard compliant, ready to be used in any other standard environment. UNINOVA, in the scope of European and National projects, developed a STEP-based Platform for Integration of applications (SIP) to support and aid the software integration in industrial environments using the standard ISO10303-STEP, already used in different industrial domains. This paper addresses the integration aspects using STEP within industrial environments from the technical point of view, and proposes an architecture and a toolkit for the integrator environment, highlighting the implementation efforts required to have RP technology, ES and DBMS focused towards a STEP-based intelligent product development.     

Control–structural design optimization for vibration of piezoelectric intelligent truss structures

Based on the design sensitivity analysis for structural dynamics in time domain, an integrated control–structural design optimization method is proposed to the vibration control of piezoelectric intelligent truss structure. In this investigation, the objective function and constraint functions include not only the conventional design indexes of structure but also the vibration control indexes and the feedback control variables. The structural design variables are optimized simultaneously with the vibration control system. The sensitivity relations for the control–structure optimization model are derived by using a new method, and the sequential linear programming algorithm is used to solve this kind of optimization problem. The numerical examples given in the paper demonstrate the effectiveness of methods and the program.     

Method of intelligent recommendation of Web serviceresources towards users balanced requirements

研究了满足用户在服务功能、服务属性和服务质量上的多层次均衡需求的Web服务资源智能推荐方法, 解决了服务信息过载问题。现有服务推荐技术存在未考虑用户对服务多层次均衡需求的不足, 结合Web服务资源的特点, 综合考虑用户服务需求与候选服务群之间的多层次相似度, 设计了综合Web服务资源的功能、属性和质量三个层次的适合度匹配算法, 以及用户主观兴趣度匹配算法。通过这两个算法建立了面向用户均衡需求的个性化Web服务资源智能推荐方法, 能将最大匹配度的Web服务资源推荐给目标用户。     

An intelligent forecasting model based on robust wavelet ν-support vector machine

Aiming at the problem of small samples, season character, nonlinearity, randomicity and fuzziness in product demand series, the existing support vector kernel does not approach the random curve of the demands time series in the L²(Rⁿ) space (quadratic continuous integral space). The robust loss function is also proposed to solve the shortcoming of ε-insensitive loss function during handling hybrid noises. A novel robust wavelet support vector machine (RW ν-SVM) is proposed based on wavelet theory and the modified support vector machine. Particle swarm optimization algorithm is designed to select the optimal parameters of RW ν-SVM model in the scope of constraint permission. The results of application in car demand forecasts show that the forecasting approach based on the RW ν-SVM model is effective and feasible, the comparison between the method proposed in this paper and other ones is also given which proves this method is better than RW ν-SVM and other traditional methods.     

An ‘intelligent’ extraction and aggregation tool for company data bases

To ease the access to data bases for the casual user, a system called SMARTY has been developed as an intelligent front end to the relational data base management system SQL/DS. A user only needs to specify the data attributes and possibly their value ranges which he wants to be displayed. SMARTY will test the meaningfulness of this request for information and subsequently generate and execute the SQL statements for deriving the information from SQL relations. SMARTY is capable of extracting or aggregating detailed information in a company database. It can be regarded as an expert system on top of a relational DBMS to improve the user interface, or as a query system to a universal relation. SMARTY was written in Prolog. Both Prolog and SQL/DS are running under the IBM operating system VM/SP 3.     

Hybrid credit ranking intelligent system using expert system and artificial neural networks

The main goal of all commercial banks is to collect the savings of legal and real persons and allocate them as credit to industrial, services and production companies. Non repayment of such credits cause many problems to the banks such as incapability to repay the central bank’s loans, increasing the amount of credit allocations comparing to credit repayment and incapability to allocate more credits to customers. The importance of credit allocation in banking industry and it’s important role in economic growth and employment creation leads the development of many models to evaluate the credit risk of applicants. But many of these models are classic and are incapable to do credit evaluation completely and efficiently. Therefore the demand to use artificial intelligence in this field has grown up. In this paper after providing appropriate credit ranking model and collecting expert’s knowledge, we design a hybrid intelligent system for credit ranking using reasoning-transformational models. Expert system as symbolic module and artificial neural network as non-symbolic module are components of this hybrid system. Such models provide the unique features of each components, the reasoning and explanation of expert system and the generalization and adaptability of artificial neural networks. The results of this system demonstrate hybrid intelligence system is more accurate and powerful in credit ranking comparing to expert systems and traditional banking models.     

Interactive design of intelligent machine vision based on human–computer interaction mode

The intelligent machine vision technology based on man-machine interaction mode has the advantages of weak intrusion, low adhesion and no device binding. With the development and progress of science and technology, the intelligent machine vision technology has become one of the most important directions in the field of human–computer interaction. Compared with the traditional interactive mode, the intelligent machine vision interaction technology is quite convenient, and the existence of these problems can also influence the vision of the intelligent machine to a certain extent. And the wide application of the sense interaction. At present, the operation precision is the main basis of the machine vision interaction design. Therefore, this paper studies the precision of the visual interaction of the intelligent machine. In this paper, three point-to-click experiments will be carried out on the basis of the Fitts' Law. The accuracy of the machine vision algorithm is experimentally studied from the operating direction, the mapping scale equation and the machine vision algorithm, and a reasonable suggestion is made for the intelligent machine vision interactive design of the human interaction mode.     

Making intelligent business decisions by mining the implicit relation from bloggers’ posts

More and more people use blogs to write down their ideas, opinions and individual thoughts. Mining blogs will obtain useful information, which can support business policy and decision-making, especially in analyzing product popularity. In this study, we mine the features of persons as implicit relations between the content of the bloggers’ posts and the bloggers. These kinds of implicit relations, which are also semantic relations, is called the Blogger Role. To mine this semantic relation, Wordnet is used to extract the Blogger Role and the features of Blogger Role. To get an appropriate Blogger Role, we cluster all bloggers’ posts and use the clustering result to revise the Blogger Role obtained by single document analysis. To get more relevant retrieval results, we combine this implicit relation with the classical retrieval model by the Blogger Role-based model. The combination is performed by the explicit model and implicit model. Results of experiments on TREC corpus show that Blogger Role reveals bloggers’ characters and mining Blogger Roles is useful in analyzing the popularity of products.     

Outbound logistics exception monitoring: A multi-perspective ontologies’ approach with intelligent agents

Logistics consists of a complex network of organizations and processes where exception monitoring is critical for the success of logistics service. In order to detect exceptions effectively, exception monitoring requires proper understanding of the possible exceptions. However, the extant exception monitoring approaches or systems still lack sufficient emphasis in exceptions understanding. This paper presents a novel outbound logistics exception monitoring approach by incorporating multi-perspective ontologies and intelligent agents. Specially, the multi-perspective ontologies, involving static ontology, social ontology and dynamic ontology, are firstly employed to develop the taxonomy of the logistics exception, to reflect the situation dependencies of logistics exception and to represent the dynamic nature of business processes. From this point forwards, an outbound exception monitoring system is designed by introducing multi-intelligent agents, which can ensure autonomous, flexible, and collaborative exception monitor in logistics service. Finally, the presented approach and designed system are exhibited through a case study of two ubiquitous logistics exceptions, which indicates that the proposed multi-perspective ontologies provide better understanding of exceptions thereby enabling the designed outbound exception monitoring system to perform well.     

Toward an information-integrated control system—the control-theoretic view of intelligent control technology

Recent computing technology is providing alternative strategies for the functionality and implementation of controllers for practical complex systems. An important issue will be how to integrate all information about control problems. In this paper, we present the control-theoretic view of intelligent control technology, and discuss a paradigm for an information-integrated control system. This work was presented, in part, at the Third International Symposium on Artificial Life and Robotics, Oita, Japan, January 19–21, 1998     

SmartGantt – An intelligent system for real time rescheduling based on relational reinforcement learning

With the current trend towards cognitive manufacturing systems to deal with unforeseen events and disturbances that constantly demand real-time repair decisions, learning/reasoning skills and interactive capabilities are important functionalities for rescheduling a shop-floor on the fly taking into account several objectives and goal states. In this work, the automatic generation and update through learning of rescheduling knowledge using simulated transitions of abstract schedule states is proposed. Deictic representations of schedules based on focal points are used to define a repair policy which generates a goal-directed sequence of repair operators to face unplanned events and operational disturbances. An industrial example where rescheduling is needed due to the arrival of a new/rush order, or whenever raw material delay/shortage or machine breakdown events occur are discussed using the SmartGantt prototype for interactive rescheduling in real-time. SmartGantt demonstrates that due date compliance of orders-in-progress, negotiating delivery conditions of new orders and ensuring distributed production control can be dramatically improved by means of relational reinforcement learning and a deictic representation of rescheduling tasks.     

Design of intelligent power controller for DC–DC converters using CMAC neural network

DC–DC converters are the devices which can convert a certain electrical voltage to another level of electrical voltage. They are very popularly used because of the high efficiency and small size. This paper proposes an intelligent power controller for the DC–DC converters via cerebella model articulation controller (CMAC) neural network approach. The proposed intelligent power controller is composed of a CMAC neural controller and a robust controller. The CMAC neural controller uses a CMAC neural network to online mimic an ideal controller, and the robust controller is designed to achieve L ₂ tracking performance with desired attenuation level. Finally, a comparison among a PI control, adaptive neural control and the proposed intelligent power control is made. The experimental results are provided to demonstrate the proposed intelligent power controller can cope with the input voltage and load resistance variations to ensure the stability while providing fast transient response and simple computation.     

An intelligent system for automated breast cancer diagnosis and prognosis using SVM based classifiers

In recent years, computational diagnostic tools and artificial intelligence techniques provide automated procedures for objective judgments by making use of quantitative measures and machine learning techniques. In this paper we propose a Support Vector Machines (SVMs) based classifier in comparison with Bayesian classifiers and Artificial Neural Networks for the prognosis and diagnosis of breast cancer disease. The paper provides the implementation details along with the corresponding results for all the assessed classifiers. Several comparative studies have been carried out concerning both the prognosis and diagnosis problem demonstrating the superiority of the proposed SVM algorithm in terms of sensitivity, specificity and accuracy.