Application of advanced control methods in the pulp and paper industry — A survey

Digital computers have been used for process control in the pulp and paper industry for more than 20 years. This paper reviews the applications of advanced control methods to pulp and paper unit process control reported during the last decade. Modern control theory and adaptive control theory are most often applied to paper machine and headboxes. However a large number of reports deal only with simulated or laboratory scale processes. Indeed very few industrial applications are reported. Some of the reasons for this are analyzed and future trends briefly discussed.     

Process control in conventional oil and gas fields—Challenges and opportunities

This paper presents some key operational challenges in the upstream petroleum industries in which advanced control and model-based optimization has or may have a significant impact. A broad array of distinct applications are discussed and opportunities for process control methods are identified. This includes stabilizing control on an individual well level, model-based optimization for production allocation and long term recovery control.     

Failure analysis expert system for onshore pipelines. Part – I: Structured database and knowledge acquisition

In this article is described a knowledge-based system or expert system for failures identification in onshore pipelines. This expert system is called Failure Analysis Expert System (FAES). The FAES development has been split in two parts. In the present part I, the database structure and knowledge acquisition process are described, while in second part, the End-User interface and learning algorithm will be described.The proposed FAES includes a structured database with document processing of typical failures of pipeline collected from failure analysis reports and which were supported by expertise of failure analysis experts. A total de 854 cases of onshore pipeline failures were considered for FAES development; 683 cases for training and 171 cases for testing. Several failure mechanisms were identified with the following frequency order: external corrosion, internal corrosion, third parties, erosion, material failure, and vandalism. For machine learning, an inductive learning algorithm through Artificial Neural Network (ANN) was used.     

A hierarchical—multilevel approach to the development and ranking of systems in the tanning industry

Large-scale, complex systems arise in the design, modeling and planning of many resource allocation problems in the areas of water resources management, energy production and distribution, industrial production, health delivery and landscaping of new cities, to mention a few. This paper applies a hierarchical—multilevel approach to the design and modeling of plant operations in the tanning industry; the decomposition of the overall system into “independent” subsystems that address plant operations, hide collection itineraries, and waste water treatment options; the development of alternative regional plant systems and the selection of a preferred system. Each system is evaluated with regards to specified multiple criteria and compared with the other candidate systems as the preferences of a decision-maker are brought into the analysis.     

Bidirectional edges problem: Part I—A simple algorithm

The bidirectional edges problem is to find an edge-labeling of an undirected network,G=(V, E), with a source and a sink, such that an edge [u, v] E is labeled (u, v) or (v, u) (or both) depending on the existence of a (simple) path from the source to sink that visits the verticesu andv, in the orderu, v orv, u, respectively. We provide several algorithms for this problem in the current paper and the sequel. In this paper we show the relation between this problem and the classical two-vertex-disjoint-paths problem and then devise a simple algorithm with a time complexity ofO(|E|·|V|²). In the sequel we improve the time complexity toO(|E|·|V|). The main technique exploits aclever partition of the graph into a set of paths and bridges which are then analyzed recursively.The bidirectional edges problem arises naturally in the context of the simulation of an MOS transistor network, in which a transistor may operate as a unilateral or a bilateral device, depending on the voltages at its source and drain nodes. For efficient simulation, it is required to detect the set of transistors that may operate as bilateral devices. Also, sometimes it is intended to propagate information in one direction only, and propagation in the wrong direction (resulting in asneak path) can cause functional error. Our algorithms can be used to detect all the sneak paths.This research was supported in part by National Science Foundation Grants DMS-8703458 and CCR-9002819.     

A system of processes related by cause and effect Part I. The model†

This paper develops a mathematical model for a system composed of product producing processes. The system is divided into subsets of processes called cycles. In each cycle the processes are linearly ordered and related by a cause and effect relationship where a change in any one process propagates from process to process throughout the entire cycle. This cause and effect relationship is a structure on processes of a cycle which asserts that each process of the cycle performs a step in a sequence of steps that finally load to the main product for which the cycle is identified. It is shown here that this model exhibits many of the empirical properties we would expect. It obeys a conservation law. Also in its state of equilibrium reserves are unchanged and it is neither efficient nor inefficient. There are also additional empirically expected phenomena which it exhibits. With this model the system is also shown to be able to insulate itself from environmental stimuli which could be regarded as a state of sleep or hibernation. Clearly such a model has application to many disciplines including biology, economics and ecology.     

Customized query auto-completion and suggestion — A review

Nowadays, with the widespread use of the internet, users meet their information needs with the help of search engines. Users tend to retrieve the most relevant results by entering short phrases in the search engines. Customizing the retrieved results helps attain this goal. In this study, research works in the fields of query suggestion, particularly query auto-completion have been studied with special attention to customization. First, the sophisticated customizing features were classified into four dimensions: time, location, context, and demographic features. Then, related works were investigated regarding algorithm, dataset and evaluation measures. Regarding the literature, we found that the research works employing context or time as sophisticated features for customization are more than those using location or demographic features. While the location dimension has been recently taken into consideration, using other dimensions has a long background. Moreover, in the related works, the AOL dataset and Mean Reciprocal Rank (MRR) are known as the most frequent dataset and evaluation measure, respectively.     

Exact optimal grillage layouts — Part I: Combinations of free and simply supported edges

In the first instalment of this three-part study, a comprehensive treatment of analytically derived, exact optimal grillage layouts for combinations of simply supported and free edges is given. In part two, grillages with combinations of simply supported, clamped and free edges will be considered.Notation k constant in specific cost function - M beam bending moment - r radius of circular edge - R ⁺,R ^–,S ⁺,S ^–,T optimal regions - x, x _j coordinate along a beam (j) - slope of the adjoint deflection at pointD in directionDA - t, v coordinates along the free edge - adjoint deflection - angle between long beams and free edge - angle between free and simply supported edges - curvature of the adjoint deflection - , angles for layouts with circular edge - total weight (cost) of grillage - coordinate along a beam in anR ⁺ region - distance defined in Fig. 3     

SADDLE: A computer-aided structural analysis and dynamic design language—Part I. design system

The SADDLE system is a tool for computer-aided optimal design of structural and mechanical systems. The system is divided into four parts—the preprocessor, the ‘analyzer’, the ‘synthesizer’, and the postprocessor. The structural model and the design data are generated by the preprocessor. The analyzer uses the finite-element method to compute deflections and stresses in the structure. The synthesizer solves the design problem that is cast into a nonlinear programming format. The postprocessor is used to examine the analysis and design results. Different parts of the system interact via a global database. This study presents the design system configuration. In a companion study, the data management part is presented in detail. The user-interface for design is examined first. The design problem formulation, in the nonlinear programming problem context, is presented. The use of utility libraries to achieve this objective with a minimum programming effort is also explained. The article concludes with two numerical examples that belong to two different classes of design problems—a minimum weight design problem and a min-max problem.     

A posteriori optimization of parameters in stabilized methods for convection–diffusion problems – Part I

Stabilized finite element methods for convection-dominated problems require the choice of appropriate stabilization parameters. From numerical analysis, often only their asymptotic values are known. This paper presents a general framework for optimizing stabilization parameters with respect to the minimization of a target functional. Exemplarily, this framework is applied to the SUPG finite element method and the minimization of a residual-based error estimator, an error indicator, and a functional including the crosswind derivative of the computed solution. Benefits of the basic approach are demonstrated by means of numerical results.     

Guidelines for applied machine learning in construction industry—A case of profit margins estimation

The progress in the field of Machine Learning (ML) has enabled the automation of tasks that were considered impossible to program until recently. These advancements today have incited firms to seek intelligent solutions as part of their enterprise software stack. Even governments across the globe are motivating firms through policies to tape into ML arena as it promises opportunities for growth, productivity and efficiency. In reflex, many firms embark on ML without knowing what it entails. The outcomes so far are not as expected because the ML, as hyped by tech firms, is not the silver bullet. However, whatever ML offers, firms urge to capitalise it for their competitive advantage. Applying ML to real-life construction industry problems goes beyond just prototyping predictive models. It entails intensive activities which, in addition to training robust ML models, provides a comprehensive framework for answering questions asked by construction folks when intelligent solutions are getting deployed at their premises to substitute or facilitate their decision-making tasks. Existing ML guidelines used in the IT industry are vastly restricted to training ML models. This paper presents guidelines for Applied Machine Learning (AML) in the construction industry from training to operationalising models, which are drawn from our experience of working with construction folks to deliver Construction Simulation Tool (CST). The unique aspect of these guidelines lies not only in providing a novel framework for training models but also answering critical questions related to model confidence, trust, interpretability, bias, feature importance and model extrapolation capabilities. Generally, ML models are presumed black boxes; hence argued that nobody knows what a model learns and how it generates predictions. Even very few ML folks barely know approaches to answer questions asked by the end users. Without explaining the competence of ML, the broader adoption of intelligent solutions in the construction industry cannot be attained. This paper proposed a detailed process for AML to develop intelligent solutions in the construction industry. Most discussions in the study are elaborated in the context of profit margin estimation for new projects.     

Application of SVM in Feather and Down Category Recognition

Currently, feather and down category recognition is often done by man with a microscope, but this method has some disadvantages. Applying SVM （Support Vector Machine） to feather and down category recognition is proposed in this paper, feather and down category recognition is done by computer automatically. After image processing, the triangle nodes of two-value image of feather are to be recognized with SVM, then the triangle nodes which have been recognized will be matched and the distance between the matched triangle nodes is calculated. In the end, the feather and down category is recognized. In order to improve recognition rate, we retrain it when training SVM.     

A Common Reasoning Model and Its Application in Knowledge-Based System

To use reasoning knowledge accurately and efficiently,many reasoning methods have been proposed.However,the differences in form among the methods may obstruct the systematical analysis and harmonious integration of them.In this paper,a common reasoning model JUM(Judgement Model)is introduced.According to JUM,a common knowledge representation form is abstracted from different reasoning methods and its limitation is reduced.We also propose an algorithm for transforming one type of JUMs into another.In some cases,the algorithm can be used to resolve the key problem of integrating different types of JUM in one system.It is possible that a new architecture of knowledge-based system can be realized under JUM.     

The use of machine learning and deep learning algorithms in functional magnetic resonance imaging—A systematic review

Functional Magnetic Resonance Imaging (fMRI) is presently one of the most popular techniques for analysing the dynamic states in brain images using various kinds of algorithms. From the last decade, there is an exponential rise in the use of the machine and deep learning algorithms of artificial intelligence for analysing fMRI data. However, it is a big challenge for every researcher to choose a suitable machine or deep learning algorithm for analysing fMRI data due to the availability of a large number of algorithms in the literature. It takes much time for each researcher to know about the various approaches and algorithms which are in use for fMRI data. This paper provides a review in a systematic manner for the present literature of fMRI data that makes use of the machine and deep learning algorithms. The major goals of this review paper are to (a) identify machine learning and deep learning research trends for the implementation of fMRI; (b) identify usage of Machine Learning Algorithms and deep learning in fMRI, and (c) help new researchers based on fMRI to put their new findings appropriately in existing domain of fMRI research. The results of this systematic review identified various fMRI studies and classified them based on fMRI types, mental diseases, use of machine learning and deep learning algorithms. The authors have provided the studies with the best performance of machine learning and deep learning algorithms used in fMRI. The authors believe that this systematic review will help incoming researchers on fMRI in their future works.     

The integration of ergonomics into design—A review

Abstract

This paper examines the role of ergonomics in product and systems design. Market requirements as well as legislation have increased the use of ergonomics in design. This has increased the demands upon both ergonomists and designers. The ergonomist must learn to participate in the product development team. In return the design team must find the time and resources necessary for the inclusion of ergonomics in the development programme. This paper discusses the different roles played by the ergonomist in each stage of product and systems design. The need to give designers, engineers and management a fundamental education in ergonomics is highlighted as is the need for the ergonomist to consider the financial consequences of his work.