The wildspace™ decision support system

A system architecture was developed for the wildspace decision support system (DSS) to provide a better understanding of complex wildlife and habitat problems. The system makes use of two key concepts, SPECIES and SPACES, to define the study domain. wildspace DSS’s flexible user interface allows users to select SPECIES through a number of different approaches, including direct selection and selection using information such as avian life history and project metadata. On the SPACES side, the system uses the raison™ object system (ROS) for mapping functions and spatial analysis. The key element in wildspace DSS is its knowledge-based database manager that provides intelligent support to various components of the system. It keeps track of all the legitimate databases, provides intelligence within the SPECIES and SPACES selection process and, more importantly, interfaces with the knowledge templates which are sets of operations implementing pre-defined analysis routines used for integrated analysis. This integrated decision support approach allows users to combine a diverse set of tools within a common framework. wildspace DSS is used to study complex wildlife problems involving multiple projects and data that are temporally and spatially heterogeneous. A case study about a relevant wildlife conservation question is presented using a series of queries and analyses performed within wildspace DSS. The system also serves as the repository for all past, current and future wildlife data collected by the Canadian Wildlife Service—Ontario Region.     

Architecture and operating system support for two-dimensional runtime partial reconfiguration

Reconfigurable machines based on field programmable gate array (FPGA) chips adapt to applications’ needs through hardware reconfiguration. Partial reconfiguration allows the configuration of a portion of a chip while the rest of the chip is busy working on tasks. This paper considers a two-dimensional partially reconfigurable FPGA chip that allows the dynamic swap in and out of circuit modules. Such a chip supports the concurrent execution of multiple applications or an application that is otherwise too large to fit. A challenging issue for 2-D runtime partial reconfiguration is how to support the efficient connection, or routing, between circuit modules or between modules and I/O pins, when those modules may be placed on any area of a chip. Because commercial chips are not efficient in 2-D runtime routing, a new FPGA architecture is proposed based on an array of clusters of configurable logic blocks and a mesh of segmented buses. To evaluate the runtime performance of the architecture, an operating system is specified and implemented which takes care of the scheduling, placement, and routing of circuits on the architecture. Simulation is used to evaluate the efficiency of the OS kernel and to determine the optimal cluster size of the architecture.     

System analysis and simulation—electric energy system

This paper deals with the application of cybernetical tools to different aspects of electric power industry. The effectiveness of the recursive least square instrument variables algorithm has been shown for on-line flow simulation of a hydro-electric power plant. The growth model of electric energy consumption has been developed with multi-layer group method of data handling algorithm. A polynomial model of optimum complexity shows the trends of installed plant capacity and electric energy consumption in India up to 2000 A.D. A model of electric energy utilisation factor is also obtained.     

MISMIS – A comprehensive decision support system for stock market investment

Nowadays, stock market is becoming a popular investment platform for both institutional and individual investors. The current financial information systems serve to provide latest information. However, they lack sophisticated analytical tools. This paper proposes a new architecture for financial information systems. The developed prototype is entitled as the Multi-level and Interactive Stock Market Investment System (MISMIS). It is specially designed for investors to build their financial models to forecast stock price and index. The performance of the financial models can be evaluated on a virtual trading platform. There are other features in MISMIS that are tailor-made to handle financial data; these include synchronized time frame, time series prediction techniques, preprocessing and transformation functions, multi-level modeling and interactive user interface. To illustrate the capability of MISMIS, we have evaluated strategies of trading the future options of Hang Seng Index (HSI). We find that historical HSI, Dow Jones Index, property price index, retailing sales figure, prime lending rate, and consumer price index in Hong Kong are essential factors affecting the performance of the trading of HSI’s future option. Also there are some feedbacks from the in-depth interviews of six financial consultant upon how they perceived the prototype MISMIS.     

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.     

An analysis of students�� gaming behaviors in an intelligent tutoring system: predictors and impacts

Students who exploit properties of an instructional system to make progress while avoiding learning are said to be ??gaming?? the system. In order to investigate what causes gaming and how it impacts students, we analyzed log data from two Intelligent Tutoring Systems (ITS). The primary analyses focused on six college physics classes using the Andes ITS for homework and test preparation, starting with the research question: What is a better predictor of gaming, problem or student? To address this question, we developed a computational gaming detector for automatically labeling the Andes data, and applied several data mining techniques, including machine learning of Bayesian network parameters. Contrary to some prior findings, the analyses indicated that student was a better predictor of gaming than problem. This result was surprising, so we tested and confirmed it with log data from a second ITS (the Algebra Cognitive Tutor) and population (high school students). Given that student was more predictive of gaming than problem, subsequent analyses focused on how students gamed and in turn benefited (or not) from instructional features of the environment, as well as how gaming in general influenced problem solving and learning outcomes.     

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.     

Improving enterprise system support—a case-based approach

Many organisations have implemented or are implementing large enterprise systems, like ERP and PDM, for integrating their business functions and streamlining the flow of information. Implementing such systems is very complex, however. In many companies the results envisioned have not or have only partly been achieved. In the literature many explanations can be found for insufficient or failing implementation efforts. A large part of the problems encountered appears to be human and organisational in nature, while it has been stressed that implementing an enterprise system requires and involves organisational change.Success and failure factors and do's and don'ts, as can be found in the literature, provide necessary, but not sufficient, preconditions for starting an implementation project. The complexity of such a project makes full anticipation and control of potential problems impossible. An organisation needs to be prepared to encounter disturbances and take corresponding actions. Knowledge on the dynamics of enterprise system implementation processes is, however, scarce and scattered. Moreover, to what extent the context in which an enterprise system is implemented influences implementation is not yet fully known.In this article, an approach is presented to gather knowledge on implementation process dynamics. The approach builds on theoretical and practical contributions in search for a structured human and organisational approach. The knowledge gathered has been analysed by means of a comprehensive reference framework. After this step a tool has been developed to support a consultant in assessing the maturity of a company to start an enterprise system implementation project. The knowledge, used directly in the tool, makes it possible to suggest improvement actions sensitive to the situation and context. Through statistical analysis, interesting context differences have been identified, which may also help in further refining the advice. Initial validation results are promising.     

SEDRET—an intelligent system for the diagnosis and prediction of events in power plants

Artificial Intelligence applications in large-scale industry, such as fossil power plants, require the ability to manage uncertainty and time. In this paper, we present an intelligent system to assist an operator of a power plant. This system, called SEDRET, is based on a novel knowledge representation of uncertainty and time, called Temporal Nodes Bayesian Networks (TNBN), a type of Probabilistic Temporal Network. A set of temporal nodes and a set of edge define a TNBN, each temporal node is defined by a value of a variable and a time interval associate to the change of variable value. A TNBN generates a formal and systematic structure for modeling the temporal evolution of a process under uncertainty. The inference mechanism is based on probabilistic reasoning. A TNBN can be used to recognize events and state variables with respect to current plant conditions and predict the future propagation of disturbances. SEDRET was validated with the diagnosis and prediction of events in a steam generator with a power plant training simulator. The results performed in this work indicate that SEDRET can potentially improve plant availability through early diagnosis and prediction of disturbances that could lead to plant shutdown.     

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.     

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.     

DISPECER-H — A decision supporting system in water resources dispatching

DISPECER-H is a software package designed to assist the operational control of a water system (WS). Based on a model of WS dynamics and also on optimal water resources distribution proposed by the DISPECER-H, the human dispatcher can decide quickly and correctly on water distribution. The paper briefly approaches a WS within a decision support system. The model and the decision-making problem are presented The functions and performances of the system are also described.     

A new deterministic approach in a decision support system for ship’s trajectory planning

The paper introduces a Decision Support System for ships, developed to solve a problem of collision avoidance with static and dynamic obstacles. The system maps the decision making capability of a human (navigation) expert to solve the path planning problem for a ship in a complex navigation environment. It can be further developed to provide automatic control of a ship. It utilizes a new, fast and effective, deterministic method, called the Trajectory Base Algorithm, to calculate a safe, optimal path for a ship. The system structure, a detailed explanation of a new method, followed by results of simulation tests are all presented in the paper. The results proof a successful application of the method to solve a path planning problem for ships with the consideration of both static and dynamic obstacles in the environment, marine traffic regulations and dynamic properties of a ship, what makes this approach applicable in commercial systems. The approach can also be adapted for application in mobile robots path planning. The experimental results and ability of the system to achieve a new functionality of full autonomy show significance of this contribution to the development of Expert and Intelligent Systems domain. The author believes that autonomous systems constitute the future of Expert and Intelligent Systems.     

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.     

IMDAS—An integrated manufacturing data administration system

A typical manufacturing facility uses a set of heterogeneous computer systems to support various design, planning, and manufacturing activities. These systems usually consist of different hardware (computers, NC machines, robots and workstations), software (operating systems, programming languages) and application systems (analysis packages, simulators, database management systems, file management systems). The databases of these systems are often different in their logical and physical structures since they are established using different data management facilities and their structures are tailored to support the various design and manufacturing activities. In Computer Integrated Manufacturing (CIM), these systems need to be interconnected to allow sharing of hardware and software resources and the timely delivery of data to these component systems. This paper describes the architecture of an Integrated Manufacturing Data Administration System (IMDAS) which provides the users and application software with a unified view of the global database that is physically distributed among dissimilar component systems. The IMDAS provides a high-level semantic view of the global database using a semantic association model OSAM¹ and a high-level language for global data manipulation to enable the user or the application software to access the global database easily and meaningfully.     

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     

Physics-based simulation for manual robot guidance—An eRobotics approach

Manual robot guidance is an intuitive approach to teach robots with human's skills in the loop. It is particularly useful to manufacturers because of its high flexibility and low programming effort. However, manual robot guidance requires compliance control that is generally not available in position-controlled industrial robots. We address this issue from a simulation-driven approach. We systematically capture the interactive dynamic behavior of intelligent robot manipulators within physics-based virtual testbeds, regardless of the type of application. On this basis, we develop structures to equip and employ simulated robots with motion control capabilities that include soft physical interaction control driven in real-time with real external guidance forces. We then transfer the virtual compliant behavior of the simulated robots to their physical counterparts to enable manual guidance. The simulator provides assistance to operators through timely and insightful robot monitoring, as well as meaningful performance indexes. The testbed allows us to swiftly assess guidance within numerous interaction scenarios. Experimental case studies illustrate the practical usefulness of the symbiotic transition between 3D simulation and reality, as pursued by the eRobotics framework to address challenging issues in industrial automation.     

Performance analysis and simulation of TH-PPM UWB system in image transmission

TH-PPM-UWB system of image transmission is presented in this paper. And then the performance analysis over an ideal AWGN （Additive White Gaussian Noise） channel is given. The studies indicate that the selection of parameters has a great influence on BER （Bit Error Rate） of the system, especially the number of pulses per information bit. The simulation results of image transmission demonstrate that a larger number of pulses per information bit will lead to better BER performance.