A paris-model approach to modular simulation

The development of complex models can be greatly facilitated by the utilization of libraries of reusable model components. In this paper we describe an object-oriented module specification formalism (MSF) for implementing archivable modules in support of continuous spatial modeling. This declarative formalism provides the high level of abstraction necessary for maximum generality, provides enough detail to allow a dynamic simulation to be generated automatically, and avoids the “hard-coded” implementation of space-time dynamics that makes procedural specifications of limited usefulness for specifying archivable modules. A set of these MSF modules can be hierarchically linked to create a parsimonious model specification, or “parsi-model”. The parsi-model exists within the context of a modeling environment (an integrated set of software tools which provide the computer services necessary for simulation development and execution), which can offer simulation services that are not possible in a loosely-coupled “federated” environment, such as graphical module development and configuration, automatic differentiation of model equations, run-time visualization of the data and dynamics of any variable in the simulation, transparent distributed computing within each module, and fully configurable space-time representations. We believe this approach has great potential for bringing the power of modular model development into the collaborative simulation arena.     

An intelligent approach to position control of a hard disk drive

A new self-tuning neuro-proportional+integral+derivative (PID) controller is proposed and applied to position control of the magnetic head in a hard disk drive. The proportional, integral, and derivative gains are tuned using two types of neural network, which reduces the burden of trial and error by human operators. Simulation results show the effectiveness of the proposed method over several conventional control methods. This work was presented in part at the Fifth International Symposium on Artificial Life and Robotics, Oita, Japan, January 26–28, 2000     

Supervisory control of a multi-echelon supply chain: A modular Petri net approach for inter-organizational control

Petri nets (PNs) are frequently utilized to model system dynamics due to their ability to handle concurrencies and sequential dependence. In this paper, a portion of the supply chain operations reference (SCOR) model has been extracted and modeled using PNs for the purpose of exerting supervisory control upon a multi-echelon supply chain (SC). The activities of source, make and deliver, inherent in the SCOR model form the basis of the representation of the PN model for each echelon considered in the SC model. Two control nets are utilized: one above the base model of each echelon to exert local constraints and an enterprise level supply chain manager (SCM). The local constraints are at the tactical and operational levels while the SCM enforces additional constraints consisting of long term planning goals at the strategic level. Place invariants are used to create the supervisors. Performance measures of the total SC are formulated to determine the effectiveness of any partnership. An efficient method for finding the current state of the system is developed which is used to determine the performance measures of each echelon. This paper presents a modular approach to the overall structure and PN modeling for a SC system. It is intended to extend the use of supervisory control from a shop-floor level to an inter-organizational facility and enterprise level.     

A modular and integratable approach to CIM

The factory of the future holds the promise of improved productivity and efficiency through the computerization of manufacturing applications. To fulfill this promise, computer technologies must provide superior results for each targeted manufacturing application. One approach to providing those superior results takes advantage of the natural evolution of manufacturing operations, a modular set of computeraided applications that can be integrated. Each module provides a “pocket of excellence” that can be integrated through networking as and when such communication becomes advantageous. Graphics workstation technology has provided dramatic improvements in performance, at price levels attractive to more segments of the industry than ever thought possible. Modular systems already exist that take advantage of this technological explosion through application software tailored to specific manufacturing functions. Communications between these modular applications can be achieved by networking but only at the discretion of the user, not at the discretion of the vendor. This paper will discuss how industry can increase productivity and efficiency by adopting a modular computer-integrated manufacturing (CIM) approach that is practical.     

New approach to intelligent control systems with self-exploring process

This paper proposes an intelligent control system called self-exploring-based intelligent control system (SEICS). The SEICS is comprised of three basic mechanisms, namely, controller, performance evaluator (PE), and adaptor. The controller is constructed by a fuzzy neural network (FNN) to carry out the control tasks. The PE is used to determine whether or not the controller's performance is satisfactory. The adaptor, comprised of two elements, action explorer (AE) and rule generator (RG), plays the main role in the system for generating new control behaviors in order to enhance the control performance. AE operates through a three-stage self-exploration process to explore new actions, which is realized by the multiobjective genetic algorithm (GA). The RG transforms control actions to fuzzy rules based on a numerical method. The application of the adaptor can make a control system more adaptive in various environments. A simulation of robotic path-planning is used to demonstrate the proposed model. The results show that the robot reaches the target point from the start point successfully in the lack-of-information and changeable environments.     

A modular neural network approach to fault diagnosis

Certain real-world applications present serious challenges to conventional neural-network design procedures. Blindly trying to train huge networks may lead to unsatisfactory results and wrong conclusions about the type of problems that can be tackled using that technology. In this paper a modular solution to power systems alarm handling and fault diagnosis is described that overcomes the limitations of "toy" alternatives constrained to small and fixed-topology electrical networks. In contrast to monolithic diagnosis systems, the neural-network-based approach presented here accomplishes the scalability and dynamic adaptability requirements of the application. Mapping the power grid onto a set of interconnected modules that model the functional behavior of electrical equipment provides the flexibility and speed demanded by the problem. After a preliminary generation of candidate fault locations, competition among hypotheses results in a fully justified diagnosis that may include simultaneous faults. The way in which the neural system is conceived allows for a natural parallel implementation.     

A modular approach to computerized material balance calculations

The problem of determining material requirements is an important one in any integrated manufacturing organization. This paper presents an approach to the computer calculation of material requirements and balances. The approach is particularly useful in planning applications where it is desired to evaluate numerous alternatives. It also forms a means of generating matrices for linear programming optimizations. The approach identifies particular types of calculations which can be written as subroutines in a computer program. Each subroutine can be represented by a particular name and symbol, thus becoming a module which can be used as a building block in a material balance calculation system. To model a particular material flow complex, an information flow diagram is drawn in which each calculation module is represented by a block. Directed lines connecting the blocks represent streams of information flow. The information flow diagram can be readily coded for computer case-study simulations using a unique application-oriented language.     

TETROBOT: a modular approach to parallel robotics

The TETROBOT is an actuated robotic structure which may be reassembled into many different configurations while still being controlled by the same hardware and software architecture. The TETROBOT system addresses the needs of application domains, such as space, undersea, mining, and construction, where adaptation to unstructured and changing environments and custom design for rapid implementation are required     

An approach to multi-agent interactive control in an intelligent space

International Journal of Control, Automation and Systems - A new type of multi-agent interactive control is proposed in an intelligent space system, which is based on heterogeneous multiple vision...     

An intelligent approach to monitor and control the blanking process

Sensory fusion can be used to infer and analyse complex phenomena and detect changes in a process from a set of measurements. This paper proposes to use Neural Network modelling to monitor product quality of blanking by assessing changes in tool geometry, material quality and tool configuration. The approach may also be used to detect internal and external fractures in the products of blanking. The neural network model is fed with representative parameters, extracted from acoustic signals emitted during fracture, the corresponding waveform and force/displacement data. The neural network model is used, after training to correlate the extracted features of various signals to changes in blanking process parameters such as tool geometry, material hardness and tools clearance. A computerised data-acquisition system, using specialised software and hardware is used to draw from several transducers to record data of the experiments. A total of 192 experiments were performed, using different blanking configurations and materials. This data is used to train the neural network model, which may be integrated with other elements of the control system, to provide a fully automated, real-time supervisory system for blanking. The system could be used to sort components, shutdown the press or alert operators in the event of manufacturing-related defects in the operating cycle.     

智能办公环境温度控制方法

针对变频空调技术参数固定不能适应智能办公环境变化的问题,为提高环境温度的舒适度,提出一种新的变频空调温度控制方法。该方法引入多智能体(Agent)技术设计温度模糊控制结构,确定输入输出变量及其模糊集,然后引入动作回报值改进模糊Q学习算法,由推理Agent执行算法学习手动调节空调的动作、修改模糊规则。将得到的优化模糊规则用于环境温度的控制。实验结果表明,与常规模糊温度控制方法相比,该控制方法缩短了空调的响应时间,减少了超调量。     

A modular approach to the dynamics of complex multirobot systems

This work presents a modular approach for the dynamic modeling and efficient simulation of complex robot systems composed of multiple robots constrained by multiple concurrent contacts. The modular nature of the algorithm enables existing open-chain models for individual robots and other mechanisms to be incorporated without significant reprogramming, while a general contact model allows both holonomic and nonholonomic constraints in the system. An example is provided to illustrate the algorithm's modularity and demonstrate its application. In addition to the development of the dynamic equations, this paper will discuss the implementation of the simulation algorithm in detail, including issues of computational complexity.     

A modular approach to defining and characterising notions of simulation

We propose a modular approach to defining notions of simulation, and modal logics which characterise them. We use coalgebras to model state-based systems, relators to define notions of simulation for such systems, and inductive techniques to define the syntax and semantics of modal logics for coalgebras. We show that the expressiveness of an inductively defined logic for coalgebras w.r.t. a notion of simulation follows from an expressivity condition involving one step in the definition of the logic, and the relator inducing that notion of simulation. Moreover, we show that notions of simulation and associated characterising logics for increasingly complex system types can be derived by lifting the operations used to combine system types, to a relational level as well as to a logical level. We use these results to obtain Baltag’s logic for coalgebraic simulation, as well as notions of simulation and associated logics for a large class of non-deterministic and probabilistic systems.     

A symbolic solution to intelligent real-time control

Autonomous systems must operate in dynamic, unpredictable environments in real time. The task of flying a plane is an example of an environment in which the agent must respond quickly to unexpected events while pursuing goals at different levels of complexity and granularity. We present a system, Air-Soar, that achieves intelligent control through fully symbolic reasoning in a hierarchy of simultaneously active problem spaces. Achievement goals, changing to a new state, and homeostatic goals, continuously maintaining a constraint, are smoothly integrated within the system. The hierarchical approach and support for multiple, simultaneous goals give rise to multi-level reactive behavior, in which Air-Soar responds to unexpected events at the same granularity where they are first sensed.     

A simulated annealing approach to integrated production scheduling

This paper describes an approach to manufacturing planning that seeks to integrate both process planning and scheduling. We show that separating these two related tasks, as is the common practice, can impose constraints that substantially reduce the quality of the final schedule. These constraints arise from premature decisions regarding operation sequence and allocation of manufacturing resources. Having formulated an integrated process planning and scheduling problem, we describe a solution technique based on simulated annealing. We compare this approach with others reported in the literature, considering both their generality and performance. In particular, we perform a detailed empirical comparison between simulated annealing and the popular technique of dispatching rules. Our results, achieved with two distinct sets of example problems, show that simulated annealing can produce solutions of significantly higher quality than those achieved through a published dispatching rule approach.     

A simulated annealing approach to police district design

This paper considers the problem of redistricting or redrawing police command boundaries. We model this problem as a constrained graph-partitioning problem involving the partitioning of a police jurisdiction into command districts subject to constraints of contiguity, compactness, convexity and size. Since the districting affects urban emergency services, there also exist quality-of-service constraints, which limit the response time (queue time plus travel time) to calls for service. Confronted with the combinatorial challenge of the districting problem, we propose a simulated annealing algorithm to search for a “good” partitioning of the police jurisdiction. At each iteration of the algorithm, we employ a variant of the well-known PCAM model to optimally assign the patrol cars and assess the “goodness” of a particular district design with respect to some prescribed performance measures. This approach differs from the well-known Hypercube queuing model, which simply evaluates the performance of a user-specified district design and allocation. A computational case study using data from the Buffalo, New York, Police Department reveals the merits of this approach.Scope and purposeTwo of the primary concerns of urban police departments are the effective use of patrol cars and the workload balance between officers in different geographical districts. In recent years, a well known, public domain software package based on the Patrol Car Allocation Model (PCAM) has been developed. PCAM was designed to help police management specify the number of patrol cars that should be on duty at various times of the day on each day of the week in each district. For long-term planning, police management also faces the thorny problem of designing these districts. To address this problem, we employ a simulated annealing search method to determine the geographic boundaries between the police districts. PCAM is used to evaluate the “goodness” of each district design encountered in this search, where “goodness” involves minimizing the disparity between the maximum workload and the minimum workload of the police officers. Working with the Buffalo, New York, Police Department, we were able to significantly reduce officer workload disparity while maintaining current levels of response time.     

A modular approach to real-time programming using actors and Java

This paper describes an actor-based approach to real-time programming, which focuses on the separation of functional from timing behaviour. The approach favours modularity and time predictability. Clusters of actors, allocated on distinct processors, are orchestrated by a control machine which provides an event-driven and time-driven customisable scheduling framework. The approach can be hosted by Java, which fosters a clean and type-safe programming style. Temporal analysis can be formally assisted by Coloured Petri Nets.     

A modular approach to learning manipulation strategies from human demonstration

Object manipulation is a challenging task for robotics, as the physics involved in object interaction is complex and hard to express analytically. Here we introduce a modular approach for learning a manipulation strategy from human demonstration. Firstly we record a human performing a task that requires an adaptive control strategy in different conditions, i.e. different task contexts. We then perform modular decomposition of the control strategy, using phases of the recorded actions to guide segmentation. Each module represents a part of the strategy, encoded as a pair of forward and inverse models. All modules contribute to the final control policy; their recommendations are integrated via a system of weighting based on their own estimated error in the current task context. We validate our approach by demonstrating it, both in a simulation for clarity, and on a real robot platform to demonstrate robustness and capacity to generalise. The robot task is opening bottle caps. We show that our approach can modularize an adaptive control strategy and generate appropriate motor commands for the robot to accomplish the complete task, even for novel bottles.     

A multi-agent approach to intelligent monitoring in smart grids

In this paper, we propose a scalable multi-agent architecture to give support to smart grids, paying special attention to the intelligent monitoring of distribution substations. The data gathered by multiple sensors are used by software agents that are responsible for monitoring different aspects or events of interest, such as normal voltage values or unbalanced intensity values that can end up blowing fuses and decreasing the quality of service of end consumers. The knowledge bases of these agents have been built by means of a formal model for normality analysis that has been successfully used in other surveillance domains. The architecture facilitates the integration of new agents and can be easily configured and deployed to monitor different environments. The experiments have been conducted over a power distribution network.