水泥企业信息系统体系结构研究

对水泥企业信息系统建设的现状以及水泥生产流程的特点做了阐述及分析,并在吸收Industrial IT、ERP及MES等先进思想与技术的基础上,提出更易于实施的基于三层结构(ERP/MES/PCS)的水泥企业信息系统体系结构,最终给出适合我国水泥企业特点的信息系统解决方案,并且在工程实践中证明方案的可行性以及有效性.     

Asynchronous decentralized event-triggered control

In this paper we propose an approach to the implementation of controllers with decentralized strategies triggering controller updates. We consider set-ups with a central node in charge of the computation of the control commands, and a set of not co-located sensors providing measurements to the controller node. The solution we propose does not require measurements from the sensors to be synchronized in time. The sensors in our proposal provide measurements in an aperiodic way triggered by local conditions. Furthermore, in the proposed implementation (most of) the communication between nodes requires only the exchange of one bit of information (per controller update), which could aid in reducing transmission delays and as a secondary effect result in fewer transmissions being triggered.     

Static output feedback control via PDE boundary and ODE measurements in linear cascaded ODE–beam systems

This paper addresses the problem of feedback control design for a class of linear cascaded ordinary differential equation (ODE)–partial differential equation (PDE) systems via a boundary interconnection, where the ODE system is linear time-invariant and the PDE system is described by an Euler–Bernoulli beam (EBB) equation with variable coefficients. The objective of this paper is to design a static output feedback (SOF) controller via EBB boundary and ODE measurements such that the resulting closed-loop cascaded system is exponentially stable. The Lyapunov’s direct method is employed to derive the stabilization condition for the cascaded ODE–beam system, which is provided in terms of a set of bilinear matrix inequalities (BMIs). Furthermore, in order to compute the gain matrices of SOF controllers, a two-step procedure is presented to solve the BMI feasibility problem via the existing linear matrix inequality (LMI) optimization techniques. Finally, the numerical simulation is given to illustrate the effectiveness of the proposed design method.     

Stability of nonlinear 2D systems described by the continuous-time Roesser model

This paper considers systems with two-dimensional dynamics (2D systems) described by the continuous-time nonlinear state-space Roesser model. The sufficient conditions of exponential stability in terms of vector Lyapunov functions are established. These conditions are then applied to analysis of the absolute stability of a certain class of systems comprising a linear continuous-time plant in the form of the Roesser model with a nonlinear characteristic in the feedback loop, which satisfies quadratic constraints. The absolute stability conditions are reduced to computable expressions in the form of linear matrix inequalities. The obtained results are extended to the class of continuous-time systems governed by the Roesser model with Markovian switching. The problems of absolute stability and stabilization via state- and output-feedback are solved for linear systems of the above class. The solution procedures for these problems are in the form of algorithms based on linear matrix inequalities.     

Fabrication of polymer derived ceramic parts by selective laser curing

Polymer derived ceramic parts of complex shape were fabricated by selective laser curing (SLC). The ceramic parts were built similar to the selective laser sintering process by sequentially irradiating layers consisting of SiC loaded polysiloxane powder with a CO₂-laser beam (λ = 10.6 μm), which locally induces curing reaction of the polymer phase at moderate temperatures around 400 °C. The laser-cured bodies were converted to SiOC/SiC ceramic parts in a subsequent pyrolysis treatment at 1200 °C in argon atmosphere. The scanning speed and the power of the CO₂-laser beam were varied, leading to pronounced differences in material properties. Laser irradiating a powder mixture containing 50 vol.% polymer/50 vol.% SiC resulted in relative green densities between 38 and 60%. Relative densities decreased to 32–50% after pyrolysis due to the polymer shrinkage. A post-infiltration with liquid silicon was carried out in order to produce dense parts. While before infiltration the bending strength only attained 17 MPa as a result of both microcracks in the SiOC matrix and a pronounced porosity, an average value of 220 MPa was achieved after post-infiltrating with Si. In the case of 50 vol.% SiC content the linear shrinkage after pyrolysis was only 3%. Thus, the SLC approach can be considered as a near-net-shape forming process of ceramic components with complex geometries.     

Artificial neural network application for modeling the rail rolling process

Rail rolling process is one of the most complicated hot rolling processes. Evaluating the effects of parametric values on this complex process is only possible through modeling. In this study, the production parameters of different types of rails in the rail rolling processes were modeled with an artificial neural network (ANN), and it was aimed to obtain optimum parameter values for a different type of rail. For this purpose, the data from the Rail and Profile Rolling Mill in Kardemir Iron & Steel Works Co. (Karabük, Turkey) were used. BD1, BD2, and Tandem are three main parts of the rolling mill, and in order to obtain the force values of the 49 kg/m rail in each pass for the BD1 and BD2 sections, the force and torque values for the Tandem section, parameter values of 60, 54, 46, and 33 kg/m type rails were used. Comparing the results obtained from the ANN model and the actual field data demonstrated that force and torque values were obtained with acceptable error rates. The results of the present study demonstrated that ANN is an effective and reliable method to acquire data required for producing a new rail, and concerning the rail production process, it provides a productive way for accurate and fast decision making.     

Measuring and analyzing agents’ uncertainty in argumentation-based negotiation dialogue games

Nowadays, multiagent systems have become a widely used technology in everyday life, and many authors have adopted the view of communication or interaction between agents as a joint activity regulated by means of dialogue games. Dialogue games are a set of communication rules that agents can combine in their complex interactions. In these games, uncertainty is an important problem that each agent faces when making decisions, especially in the absence of enough information. This paper focuses on the uncertainty in a particular type of dialogue games, namely argumentation-based negotiation. There exist several proposals on this type of dialogue games in the literature, and most of them are concerned with proposing protocols to show how agents can communicate with each other, and how arguments and offers can be generated, evaluated and exchanged. Nevertheless, none of them is directly targeting the agents’ uncertainty about the exchanged arguments and how this uncertainty could be measured at each dialogue step to assist those agents make better decisions. The aim of this paper is to tackle this problem by defining a new set of uncertainty measures in negotiation dialogue games from an external agent’s point of view. In particular, we introduce two types of uncertainty: Type I and Type II. Type I is about the uncertainty index of playing the right move. For this, we use Shannon entropy to measure: (i) the uncertainty index of the agent that he is selecting the right move at each dialogue step; and (ii) the uncertainty index of participating agents in the negotiation about the whole dialogue. This is done in two different ways; the first is by taking the average of the uncertainty index of all moves, and the second is by determining all possible dialogues and applying the general formula of Shannon entropy. Type II is about the uncertainty degree of the agent that the move will be accepted by the addressee. In this context, we introduce a new classification for the arguments based on their certainty to be accepted by the addressee.     

Time series forecasting by neural networks: A knee point-based multiobjective evolutionary algorithm approach

In this paper, we investigate the problem of time series forecasting using single hidden layer feedforward neural networks (SLFNs), which is optimized via multiobjective evolutionary algorithms. By utilizing the adaptive differential evolution (JADE) and the knee point strategy, a nondominated sorting adaptive differential evolution (NSJADE) and its improved version knee point-based NSJADE (KP-NSJADE) are developed for optimizing SLFNs. JADE aiming at refining the search area is introduced in nondominated sorting genetic algorithm II (NSGA-II). The presented NSJADE shows superiority on multimodal problems when compared with NSGA-II. Then NSJADE is applied to train SLFNs for time series forecasting. It is revealed that individuals with better forecasting performance in the whole population gather around the knee point. Therefore, KP-NSJADE is proposed to explore the neighborhood of the knee point in the objective space. And the simulation results of eight popular time series databases illustrate the effectiveness of our proposed algorithm in comparison with several popular algorithms.     

Robust spatial flood vulnerability assessment for Han River using fuzzy TOPSIS with α-cut level set

This study aims to improve the general flood vulnerability approach using fuzzy TOPSIS based on α-cut level sets which can reduce the uncertainty inherent in even fuzzy multi-criteria decision making process. Since fuzzy TOPSIS leads to a crisp closeness for each alternative, it is frequently argued that fuzzy weights and fuzzy ratings should be in fuzzy relative closeness. Therefore, this study used a modified α-cut level set based fuzzy TOPSIS to develop a spatial flood vulnerability approach for Han River in Korea, considering various uncertainties in weights derivation and crisp data aggregation. Two results from fuzzy TOPSIS and modified fuzzy TOPSIS were compared. Some regions which showed no or small ranking changes have their centro-symmetric distributions, while other regions whose rankings varied dynamically, have biased (anti-symmetric) distributions. It can be concluded that α-cut level set based fuzzy TOPSIS produce more robust prioritization since more uncertainties can be considered. This method can be applied to robust spatial vulnerability or decision making in water resources management.     

An architectural design for effective information retrieval in semantic web

The current web IR system retrieves relevant information only based on the keywords which is inadequate for that vast amount of data. It provides limited capabilities to capture the concepts of the user needs and the relation between the keywords. These limitations lead to the idea of the user conceptual search which includes concepts and meanings. This study deals with the Semantic Based Information Retrieval System for a semantic web search and presented with an improved algorithm to retrieve the information in a more efficient way.This architecture takes as input a list of plain keywords provided by the user and the query is converted into semantic query. This conversion is carried out with the help of the domain concepts of the pre-existing domain ontologies and a third party thesaurus and discover semantic relationship between them in runtime. The relevant information for the semantic query is retrieved and ranked according to the relevancy with the help of an improved algorithm. The performance analysis shows that the proposed system can improve the accuracy and effectiveness for retrieving relevant web documents compared to the existing systems.