A Component-Based Debugging Approach for Detecting Structural Inconsistencies in Declarative Equation Based Models

Object-oriented modeling with declarative equation based languages often unconsciously leads to structural inconsistencies. Component-based debugging is a new structural analysis approach that addresses this problem by analyzing the structure of each component in a model to separately locate faulty components. The analysis procedure is performed recursively based on the depth-first rule. It first generates fictitious equations for a component to establish a debugging environment, and then detects structural defects by using graph theoretical approaches to analyzing the structure of the system of equations resulting from the component. The proposed method can automatically locate components that cause the structural inconsistencies, and show the user detailed error messages. This information can be a great help in finding and localizing structural inconsistencies, and in some cases pinpoints them immediately.     

A structured modeling based methodology to design decision support systems

Many Decision Support Systems (DSS) support the decision making process through the use of mathematical models and data. DSS design involves modeling data as well as mathematical relationships in a domain. The process of model formulation and subsequent integration of model with data in a DSS is a complex and ill-structured process. This paper proposes a methodology based on Structured Modeling (SM), originally introduced by Geoffrion together with the modeling language SML, to model and design the DSS. The methodology includes rigorous and step by step procedures to design and integrate data and modelbases. The main contribution of our approach lies in the integration of research in database design, and mathematical model formulation within the structured modeling framework. The resultant procedures can be easily automated and taught to students in DSS courses. The motivation for our research stemmed from our constant frustrations in teaching DSS courses over the last five years. In the last two years, when we used our methodology, the performance of the students improved significantly. The average score in the DSS project went up to 85 from 60. Our positive experience in using our methodology in classes over the past two years suggests that the methodology imposes structure into the analysis of decision problems, and as a result students produce better DSS designs for classroom cases.     

基于工作流的知识流建模与控制

知识在多个参与者之间的产生、传播与应用称为知识流.在知识密集型组织中,对业务过程的控制和对知识资产的管理具有紧密的依赖关系.工作流管理是实现业务过程控制的重要技术.当前的工作流过程元模型不支持对知识管理机制的表示.为此,提出了一个扩展的工作流过程元模型,以支持业务过程控制与知识管理的集成.在此基础上,对知识流的建模与控制进行了深入的研究.提出了一种知识流建模方法,通过5类知识流单元对知识传递与重用、人员协作与交流进行表示.针对知识流中的动态因素,研究了基于资源约束、知识需求变化和时间约束的知识流控制方法,以实现自适应的知识流控制,并给出了有关算法.为工作流技术与知识管理技术的有效结合提供了一个有益的途径.     

Process structure-based recurrent neural network modeling for model predictive control of nonlinear processes

In this work, physics-based recurrent neural network (RNN) modeling approaches are proposed for a general class of nonlinear dynamic process systems to improve prediction accuracy by incorporating a priori process knowledge. Specifically, a hybrid modeling method is first introduced to integrate first-principles models and RNN models. Subsequently, a partially-connected RNN modeling method that designs the RNN structure based on a priori structural process knowledge, and a weight-constrained RNN modeling method that employs weight constraints in the optimization problem of the RNN training process are developed. The proposed physics-based RNN models are utilized in model predictive controllers and applied to a chemical process network example to demonstrate their improved approximation performance compared to the fully-connected RNN model that is developed as a black box model.     

A case-based reasoning approach for building a decision model

A methodology based on case-based reasoning is proposed to build a topological-level influence diagram. It is then applied to a project proposal review process. The formulation of decision problems requires much time and effort, and the resulting model, such as an influence diagram, is applicable only to one specific problem. However, some prior knowledge from the experience in modeling influence diagrams can be utilized to resolve other similar decision problems. The basic idea of case-based reasoning is that humans reuse the problem-solving experience to solve new problems.
In this paper, we suggest case-based decision class analysis (CB-DCA), a methodology based on case-based reasoning, to build an influence diagram. CB-DCA is composed of a case retrieval procedure and an adaptation procedure. Two measures are suggested for the retrieval procedure, one a fitting ratio and the other a garbage ratio. The adaptation procedure is based on decision-analytic knowledge and decision participants' domain-specific knowledge. Our proposed methodology has been applied to an environmental review process in which decision-makers need decision models to decide whether a project proposal is accepted or not. Experimental results show that our methodology for decision class analysis provides decision-makers with robust knowledge-based support.     

Development of a knowledge library for automated watershed modeling

In this work, we develop a library of components for building semi-distributed watershed models. The library incorporates basic modeling knowledge that allows us to adequately model different water fluxes and nutrient loadings on a watershed scale. It is written in a formalism compliant with the equation discovery tool ProBMoT, which can automatically construct watershed models from the components in the library, given a conceptual model specification and measured data. We apply the proposed modeling methodology to the Ribeira da Foupana catchment to extract a set of viable hydrological models. By specifying the conceptual model and using the knowledge library, two different hydrological models are generated. Both models are automatically calibrated against measurements and the model with the lower root mean squared error (RMSE) value is selected as an appropriate hydrological model for the selected study area.     

Load sensitivity analyses of elastic structures by differential and boundary integral equation formulations

A direct unification of the boundary integral equations (BIE) of elasticity and load sensitivity analysis procedures is proposed in a novel approach. In the so-called BIE formulation, it is found that more than one type of adjoint functions are necessary, which are defined by different integral equations at complementary points of the structural boundary. The presence of double volume/surface integrals and the switching of the source and field points in the fundamental solutions are further interesting characteristics of the proposed formulation. An analytical example is solved by using the standard differential equation formulation, as well as the new BIE formulation of load sensitivity analysis.     

Kinematic modeling of wheeled mobile robots with slip

This work presents a kinematic modeling method for wheeled mobile robots with slip based on physical principles. First, we present the kinematic modeling of a mobile robot with no-slip considering four types of wheels: fixed, centered orientable, off-centered orientable (castor) and Swedish (also called Mecanum, Ilon or universal). Then, the dynamics of a wheeled mobile robot based on Lagrange formulation are derived and discussed. Next, a quasi-static motion is considered to obtain the kinematic conditions that provide the slip modeling equations. Several types of traction models for the slip between the wheel and the floor are indicated. In particular, for a frictional force linearly dependent on the sliding velocity, the no-slip kinematic equation of the wheeled mobile robot is related, through the weighted least-squares algorithm, with the slip modeling equations. To illustrate the applications of the proposed approach a tricycle vehicle is considered in a real situation. The experimental results obtained for the slip kinematic model are compared with the ones obtained for the well-known Kalman filter.     

基于整体判断的多层风险决策模型

针对现有研究难以解决具有一般性的多层风险决策问题,在分析给出多层决策中知识表达具有整体判断特征命题的基础上,基于整体判断思想提出了多层风险决策的建模思路及相关原理,并针对其中的关键问题—–决策信息的整合优化与方案前景价值的有机综合,构建了相应的决策模型和决策定理.数值模拟验证结果表明,所提出的决策模型具有科学合理性和应用可行性.     

REASONING FOR AUTOMATED MODEL INTEGRATION

This paper investigates some reasoning issues involved in developing an integrated modeling environment called a model management system. A model management system is a computer-based environment designed to support effective development and utilization of quantitative decision models. Since the construction of decision models is a knowledge-intensive process, reasoning plays a critical role. Reasoning is particularly important when automated model integration is needed in a large-scale system. In this case, heuristics are required to reduce the complexity of the process. This paper examines the planning and automated formulation of complex models from smaller building blocks. First, a hierarchy of abstractions that integrates previous contributions in model management is presented. Then, a modeling process is formulated as a planning process by which a set of operators are scheduled to achieve a specific goal. This process involves searches for alternatives that can eliminate the difference between the initial stale and the goal state. Various reasoning strategies and heuristic evaluation Junctions that can be used to improve the efficiency of developing a master plan for model integration are discussed.     

STRUCTURED FUZZY MODELS

Abstract

The paper deals with a problem of modeling of fuzzy systems in random environments. A model is proposed that is capable of handling two distinct forms of imprecision, viz. randomness and fuzziness. The model is required to cope with both of them while modeling a variety of problems in management, medical diagnosis, and unsupervised pattern recognition. The models proposed in the paper are constructed and evaluated in a formal framework established by fuzzy relation equations. Randomness is introduced as additional constraints imposed on the structure of the fuzzy relation equation (hence: structured fuzzy models). The forecasting (prediction) problem is studied in detail.     

XML-Based Modeling of Corporate Memory

Analytical knowledge is distributed among domain experts, analysts, and data-storage systems. Extracting such knowledge from databases is of interest to corporations. The traditional top-down development of corporate memory is not appropriate for modern organizations because of the distributed nature of information. This paper proposes models of analytical knowledge and new ways of developing corporate memory by using an extensible markup language (XML). It aims at efficient exploration of useful knowledge by mining the Web. The proposed approach of modeling analytical knowledge is explicit and sharable. The concepts introduced in the paper have been demonstrated with a manufacturing case study.     

A cognitivist model for decision support: COGITA project, a problem formulation assistant

The formulation of a problem may be defined as a process of acquisition and organization of knowledge related to a given situation, on which a decision maker projects some action. The assistance in the problem formulation that we may expect within decision support systems is difficult to design and to implement. This is mainly due to the frequent lack of attention to a sufficiently formalized conceptual framework which would consider the decision with a more cognition sciences oriented approach. In the first part, we will present an instrumental model for the study of decision processes as an attempt to simulate the cognitive process of knowledge acquisition and organization carried out by a decision maker facing a problematic situation. Considering its epistemological foundations, this model can be named “cognitivist model”. Within this model, the decision is defined as a cognitive construction which we call “decisional construct”. It consists of the elaboration of one or several abstract representations of the problematic situation (formulation phase), and the design of operational models (solving phase). In the second part, we will present the COGITA project, which consists of the design and realization of an environment for the development of problem formulation assistance systems. The modelization and simulation of cognitive processes call for relevant techniques originating either in artificial intelligence or in connectionism. We will show which are the main characteristics, potentials, limits and complementarity of these techniques and why their integration is fundamental and necessary to the simulation of the cognitive process associated with the formulation. COGITA is a hybrid system currently under development which tends to integrate symbolic artificial intelligence techniques and connectionist models in a cooperative hybridation the general architecture of which is presented.     

Context-aware sequence labeling for condition information extraction from historical bridge inspection reports

Effective upkeep of aging infrastructure systems with limited funding and resources calls for efficient bridge management systems. Although data-driven models have been extensively studied in the last decade for extracting knowledge from past experience to guide future maintenance decision making, their performance and usefulness have been limited by the level of detail and accuracy of currently available bridge condition databases. This paper leverages an untapped resource for bridge condition data and proposes a new method to extract condition information from it at a high level of detail. To that end, a natural language processing approach was developed to formalize structural condition knowledge by formulating a sequence labeling task and modeling inspection narratives as a combination of words representing defects, their severity and location, while accounting for the context of each word. The proposed framework employs a deep-learning-based approach and incorporates context-aware components including a bi-directional Long Short Term Memory (LSTM) neural network architecture and a Conditional Random Field (CRF) classifier to account for the context of words when assigning labels. A dependency-based word embedding model was also used to represent the raw text while incorporating both semantic and contextual information. The sequence labeling model was trained using bridge inspection reports collected from the Virginia Department of Transportation bridge inspection database and achieved an F1 score of 94.12% during testing. The proposed model also demonstrated improvements compared with baseline sequence labeling models, and was further used to demonstrate the capability of detecting condition changes with respect to previous inspection records. Results of this study show that the proposed method can be used to extract and create a condition information database that can further assist in developing data-driven bridge management and condition forecasting models, as well as automated bridge inspection systems.     

A geometric modelling framework for conceptual structural design from early digital architectural models

Computer support for conceptual structural design is still ineffective. This is due, in part, to the fact that current computer applications do not recognize that structural design and architectural design are highly interdependent processes, particularly at the early stages. The goal of this research is to assist structural engineers at the conceptual stage with early digital architectural models. This paper presents a geometric modeling framework for facilitating the engineers’ interactions with architectural models in order to detect potential structural problems, uncover opportunities, respect constraints, and ultimately synthesize structural solutions interactively with architectural models. It consists of a process model, a representation model and synthesis algorithms to assist the engineer on demand at different stages of the design process. The process model follows a top-down approach for design refinements. The representation model describes the structural system as a hierarchy of entities with architectural counterparts. The algorithms rely on geometric and topologic relationships between entities in the architectural model and a partial structural model to help advance the synthesis process. A prototype system called StAr (Structure-Architecture) implements this framework. A case study illustrates how the framework can be used to support the conceptual structural design process.     

Fuzzy cognitive map software tool for treatment management of uncomplicated urinary tract infection

Uncomplicated urinary tract infection (uUTI) is a bacterial infection that affects individuals with normal urinary tracts from both structural and functional perspective. The appropriate antibiotics and treatment suggestions to individuals suffer of uUTI is an important and complex task that demands a special attention. How to decrease the unsafely use of antibiotics and their consumption is an important issue in medical treatment. Aiming to model medical decision making for uUTI treatment, an innovative and flexible approach called fuzzy cognitive maps (FCMs) is proposed to handle with uncertainty and missing information. The FCM is a promising technique for modeling knowledge and/or medical guidelines/treatment suggestions and reasoning with it. A software tool, namely FCM-uUTI DSS, is investigated in this work to produce a decision support module for uUTI treatment management. The software tool was tested (evaluated) in a number of 38 patient cases, showing its functionality and demonstrating that the use of the FCMs as dynamic models is reliable and good. The results have shown that the suggested FCM-uUTI tool gives a front-end decision on antibiotics’ suggestion for uUTI treatment and are considered as helpful references for physicians and patients. Due to its easy graphical representation and simulation process the proposed FCM formalization could be used to make the medical knowledge widely available through computer consultation systems.     

Domain specific DSS tools for knowledge-based model building

The formulation of complex planning models, such as linear programming (LP) systems, is a difficult task that enjoys little support by current decision support systems. It is hypothesized that current artificial intelligence technology is insufficient to build generalized formulation tools that would be usable by OR-naive end users. As an alternative, this paper presents a domain-specific approach to knowledge-based model formulation which combines the use of ‘syntactic’ knowledge about linear programming with ‘semantic’ guidance by knowledge specific to some application domain. As a prototype of this approach, a model formulation tool for LP-based production management is being developed at New York University.     

Forming Equivalent Subsystem Components to Facilitate the Modelling of Mechatronic Multibody Systems

This paper presents an approach that allows groups of components containing internal degrees of freedom and/or internal constraints to be modelled as single components, which we call “equivalent subsystem components” (ESCs). This ability to formulate and store the equations for a portion of a system allows the governing equations for complicated multibody systems to be formulated in a piecewise fashion. First, the symbolic equations governing the identified subsystems are generated, followed by the generation of the equations for the overall system. Such an approach results in decreased formulation times when repeated subsystems or parallel processing facilities are present. As well, this approach makes the modelling process faster and more intuitive, since single objects representing groups of components may be used to construct complicated systems. Since the methodology is based on the standard linear graph component model, all of the advantages inherent in a graph-theoretic approach (multi-domain, coordinate selection, systematic) are achieved. In addition, it is shown how symbolic models of complex subsystems, obtained with the user’s preferred formulation method, may be incorporated within this approach. To demonstrate the proposed approach, formulation times for an electromechanical RRR-planar parallel manipulator are compared using both standard and subsystem approaches.     

Modeling and simulation of structural components in recursive closed-loop multibody systems

Passenger cars, transit buses, railroad vehicles, off-highway trucks, earth moving equipment and construction machinery contain structural and light-fabrications (SALF) components that are prone to excessive vibration due to rough terrains and work-cycle loads’ excitations. SALF components are typically modeled as flexible components in the multibody system allowing the analysts to predict elastic deformation and hence the stress levels under different loading conditions. Including SALF component in the multibody system typically generates closed-kinematic loops. This paper presents an approach for integrating SALF modeling capabilities as a flexible body in a general-purpose multibody dynamics solver that is based on joint-coordinates formulation with the ability to handle closed-kinematic loops. The spatial algebra notation is employed in deriving the spatial multibody dynamics equations of motion. The system kinematic topology matrix is used to project the Cartesian quantities into the joint subspace, leading to a condensed set of nonlinear equations with minimum number of generalized coordinates. The proposed flexible body formulation utilizes the component mode synthesis approach to reduce the large number of finite element degrees of freedom to a small set of generalized modal coordinates. The resulting reduced flexible body model has two main characteristics: the stiffness matrix is constant while the mass matrix depends on the elastic modal coordinates. A consistent set of pre-computed inertia shape integrals are identified and used to update the modal mass matrix at each time step. The implementation of the component mode synthesis approach in a closed-loop recursive multibody formulation is presented. The kinematic equations are modified to include the effect of the flexible body modal elastic coordinates. Also, modified constraint equations that include the effect of flexibility at the joint connections and the necessary details of the Jacobian matrix are presented. Baumgarte stabilization approach is used to stabilize the constraint equations without using iterative schemes. A sample results for flexible body impeded in a closed system will be presented to demonstrate the above mentioned approach.