IMDIS — An integrated approach to Gaussian dispersion modeling

Gaussian dispersion models are well established tools for the estimation of ground level concentrations and regulatory purposes. These models are attractive for microcomputers, since the corresponding programs are fast and the input source and meteorological data are usually available. The purpose of this paper is to present a multisource bi-Gaussian dispersion computer program (IMDIS) applicable to stationary point sources, specifically written for the Apple Macintosh microcomputer. The program takes advantage of a user-friendly graphical interface for interactive data entry and retrieval and for displaying concentration isopleths. The model may be adapted to different situations, since it provides several alternatives for the estimation of plume rise, dispersion coefficients, wind profile power law, mean transport velocity and time averaging methods. Powerful numerical non-linear optimization procedures allow for ‘worst-case’ or actual maxima to be determined, independently of the formulas employed within the different submodels. The program was tested with literature data and compared with some other Gaussian models available, and preliminary results suggest its adequacy as a flexible tool for dispersion modeling.     

Behavior modeling in mechanical engineering — A modular approach

Many engineering activities are confronted with the relation between shape and behavior. Modern state-of-the-art CAD systems can support a dynamic design process with flexible and umambiguous geometric modeling of artifacts. The Finite Element (FE) method is a general method to model and simulate physical behavior. CAD and FE integration enables numerical prediction of the physical behavior of artifacts. To deal with the complexity of modern products and the dynamic character of the design process, the integration must be addressed from both a systems and a process point of view. The models must thus be flexible, scalable, and reusable. A modularized modeling approach based on four stages is proposed, and exemplified with behavior modeling of a turn-key grinding machine.     

Interactive reconstructive student modeling: A machine‐learning approach

Reconstructive bug modeling is a well‐known approach to student modeling in intelligent tutoring systems, suitable for modeling procedural tasks. Domain knowledge is decomposed into the set of primitive operators and the set of conditions of their applicability. Reconstructive modeling is capable of describing errors that come from irregular application of correct operators. The main obstacle to successfulness of this approach is such decomposition of domain knowledge to primitive operators with a very low level of abstraction so that bugs could never occur within them. The other drawback of this modeling scheme is its efficiency because it is usually done offline, due to vast search spaces involved.

This article reports a novel approach to reconstructive modeling based on machine‐learning techniques for inducing procedures from traces. The approach overcomes the problems of reconstructive modeling by its interactive nature. It allows online model generation by using domain knowledge and knowledge about the student to focus the search on the portion of the problem space the student is likely to traverse while solving the problem. Furthermore, the approach is not only incremental, but also truly interactive because it involves the student in explicit dialogs about his or her goals. In such a way, it is possible to determine whether the student knows the operator he or she is trying to apply. Pedagogical actions and the student model are generated interchangeably, thus allowing for dynamic adaptation of instruction, problem generation, and immediate feedback on student's errors. The approach presented is examined in the context of the symbolic integration tutoring system (SINT), an intelligent tutoring system (ITS) for the domain of symbolic integration.     

A review and analysis of “graph theoretical-matrix permanent” approach to decision making with example applications

In this paper, a multiple attribute decision making technique is explained in detail and its existing applications are reviewed/analyzed first time in the literature. The technique was originated from combinatorial mathematics and it is based on the graph theory and matrix algebra and has some desirable properties like “ability to model criteria interactions”, “ability to generate hierarchical models etc.” for modeling and solving complex decision making problems. In order to enable a better understanding of the technique, two illustrative examples (new graduates’ industry sector preferences and supermarket location selection) with crisp and fuzzy values are also modeled and solved in the present study.     

An adjustable subdivision surface modeling scheme

Based on triangle and quadrilateral meshes, this paper presents an adjustable subdivision surface scheme. The scheme can produce subdivision surface of Cl continuity of limit surface Since an adjustable parameter is introduced to the scheme, the surface modeling is flexible. Depended on given initial data, the limited surface shape can be adjusted and controlled through selecting appropriate parameters. The method is effective in generating smooth surfaces.     

A hybrid approach to interactive global illumination and soft shadows

We present a hybrid approach to simulate global illumination and soft shadows at interactive frame rates. The strengths of hardware-accelerated GPU techniques are combined with CPU methods to achieve physically consistent results while maintaining reasonable performance. The process of image synthesis is subdivided into multiple passes accounting for the different illumination effects. While direct lighting is rendered efficiently by rasterization, soft shadows are simulated using a novel approach combining the speed of shadow mapping and the accuracy of visibility ray tracing. A shadow refinement mask is derived from the result of the direct lighting pass and from a small number of shadow maps to identify the penumbral region of an area light source. This region is accurately rendered by ray tracing. For diffuse indirect illumination, we introduce radiosity photons to profit from the flexibility of a point-based sampling while maintaining the benefits of interpolation over scattered data approximation or density estimation. A sparse sampling of the scene is generated by particle tracing. An area is approximated for each point sample to compute the radiosity solution using a relaxation approach. The indirect illumination is interpolated between neighboring radiosity photons, stored in a multidimensional search tree. We compare different neighborhood search algorithms in terms of image quality and performance. Our method yields interactive frame rates and results consistent with path tracing reference solutions.     

A response to “Genetic programming and emergence”

Banzhaf (Genet Program Evol Mach, 2013) raises some interesting points about emergence in the context of genetic programming. However, his central tenet, that genetic programming is an example of top-down emergence, is invalidated by the fact that the evolutionary framework and the system being evolved are two separate structures. Rather, genetic programming is an instance of one emergent system designing a second one. Biological evolution provides a better example of what could be considered top-down emergence.     

A Gauss–Newton approach to joint image registration and intensity correction

We develop a new efficient numerical methodology for automated simultaneous registration and intensity correction of images. The approach separates the intensity correction term from the images being registered in a regularized expression. Our formulation is consistent with the existing non-parametric image registration techniques, however, an extra additive intensity correction term is carried throughout. An objective functional is formed for which the corresponding Hessian and Jacobian is computed and employed in a multi-level Gauss–Newton minimization approach. In this paper, our experiments are based on elastic regularization on the transformation and total variation on the intensity correction. Validations on dynamic contrast enhanced MR abdominal images for both real and simulated data verified the efficacy of the model.     

Human–Robot co-manipulation during surface tooling: A general framework based on impedance control,haptic rendering and discrete geometry

Despite the advancements in machine learning and artificial intelligence, there are many tooling tasks with cognitive aspects that are rather challenging for robots to handle in full autonomy, thus still requiring a certain degree of interaction with a human operator. In this paper, we propose a theoretical framework for both planning and execution of robot-surface contact tasks whereby interaction with a human operator can be accommodated to a variable degree.The starting point is the geometry of surface, which we assume known and available in a discretized format, e.g. through scanning technologies. To allow for realtime computation, rather than interacting with thousands of vertices, the robot only interacts with a single proxy, i.e. a massless virtual object constrained to ‘live on’ the surface and subject to first order viscous dynamics. The proxy and an impedance-controlled robot are then connected through tuneable and possibly viscoelastic coupling, i.e. (virtual) springs and dampers. On the one hand, the proxy slides along discrete geodesics of the surface in response to both viscoelastic coupling with the robot and to a possible external force (a virtual force which can be used to induce autonomous behaviours). On the other hand, the robot is free to move in 3D in reaction to the same viscoelastic coupling as well as to a possible external force, which includes an actual force exerted by a human operator. The proposed approach is multi-objective in the sense that different operational (autonomous/collaborative) and interactive (for contact/non-contact tasks) modalities can be realized by simply modulating the viscoelastic coupling as well as virtual and physical external forces. We believe that our proposed framework might lead to a more intuitive interfacing to robot programming, as opposed to standard coding. To this end, we also present numerical and experimental studies demonstrating path planning as well as autonomous and collaborative interaction for contact tasks with a free-form surface.     

TAGUS — A user and learner modeling workbench

In this paper we will describe, outline and exemplify the functionalities and architecture of a User and Learner Modeling System called TAGUS (within the project Theory and Applications for General User/Learner-modeling Systems).TAGUS was developed with two main goals: (1) to develop a framework to represent models of users and learners where the meta-cognitive activities of learners were taken into account; and (2) to try to capture in a system some general mechanisms and techniques for user and learner modeling in the form of services.The basic idea of TAGUS is to achieve a kind of workbench where some techniques and approaches for user and learner modeling are implemented and applied. TAGUS provides a set of services, to be used by people testing methods or by applications using user models. These services, provided to external agents, embed some mechanisms for maintaining models of the users and learners. Thus, TAGUS plays a role of a user and learner modeling server.To achieve this goal, we first identified some basic mechanisms in user and learner modeling, and based on them we established a general modeling cycle. This cycle involves two main stages: the acquisition and the maintenance of the model. The different strategies and techniques are specified in separate modules or knowledge sources in TAGUS, which uses them to execute parts of that cycle. The architecture of TAGUS is composed of: a User or Learner Model (ULM); a set of maintenance functions; an acquisition engine; a reason maintenance system; a meta-reasoner and two interfaces.At the same time, TAGUS provides a language for defining the models of the users and learners, which can be used with different techniques, in order to test the models and simulate them in the system. This language is used not only to represent the models, but also as a way of establishing the communication between TAGUS and its environment.TAGUS was built incrementally around a set of core functions for the manipulation of the User or Learner Model (ULM). Other layers of this set were built where the last layer corresponds to the services TAGUS supplies.     

Savitzky–Golay filter energy features-based approach to face recognition using symbolic modeling

Pattern Analysis and Applications - Face recognition is a well-researched domain however many issues for instance expression changes, illumination variations, and presence of occlusion in the face...     

Const—A constructive approach to information modelling

An approach to information modelling is presented which is based on the principles of a constructive theory and employs the formalism of production rules. In the paper a constructive approach is taken concerning the nature and representation of abstract objects (here referred to as quasi objects). The cognitive, formal and metaphysical advantages are presented and discussed, and examples employing the constructive approach are presented with comments.     

A “poor man’s” approach to topology optimization of natural convection problems

Structural and Multidisciplinary Optimization - Topology optimization of natural convection problems is computationally expensive, due to the large number of degrees of freedom (DOFs) in the model...     

A dual–polarimetric approach to earthquake damage assessment

In this study, a novel physical approach is proposed to detect damages due to earthquakes using dual polarimetric (DP) coherent Synthetic Aperture Radar (SAR) imagery. An optimization method, aimed at enhancing scattering basis differences between measurements collected before and after the event, is designed exploiting Lagrange optimization of the difference between two polarimetric covariance matrices. A meaningful showcase is presented to demonstrate the soundness of the proposed approach that consists of processing Sentinel–1 C–band scenes related to 2016 Central Italy Earthquake. The proposed approach, which is contrasted with the conventional coherence based single– and dual–polarization approaches, results in the best sensitivity to damages.     

A fractional approach to identify Wiener–Hammerstein systems

Block-oriented nonlinear models are appealing due to their simplicity and parsimony. Existing methods to identify the Wiener–Hammerstein model suffer from one or several drawbacks. This paper shows that it is possible to generate initial estimates in an alternative way. A fractional model parameterization is the key to the success of this approach. Advantages are that no more than two iterative optimizations are needed and that large model orders can be handled. As illustrated through a simulation example and experimental benchmark data, it gives superior initial estimates and comparable optimized results.     

A co-classification approach to learning from multilingual corpora

We address the problem of learning text categorization from a corpus of multilingual documents. We propose a multiview learning, co-regularization approach, in which we consider each language as a separate source, and minimize a joint loss that combines monolingual classification losses in each language while ensuring consistency of the categorization across languages. We derive training algorithms for logistic regression and boosting, and show that the resulting categorizers outperform models trained independently on each language, and even, most of the times, models trained on the joint bilingual data. Experiments are carried out on a multilingual extension of the RCV2 corpus, which is available for benchmarking.     

How does desktop virtual reality enhance learning outcomes? A structural equation modeling approach

This study examined how desktop virtual reality (VR) enhances learning and not merely does desktop VR influence learning. Various relevant constructs and their measurement factors were identified to examine how desktop VR enhances learning and the fit of the hypothesized model was analyzed using structural equation modeling. The results supported the indirect effect of VR features to the learning outcomes, which was mediated by the interaction experience and the learning experience. Learning experience which was individually measured by the psychological factors, that is, presence, motivation, cognitive benefits, control and active learning, and reflective thinking took central stage in affecting the learning outcomes in the desktop VR-based learning environment. The moderating effect of student characteristics such as spatial ability and learning style was also examined. The results show instructional designers and VR software developers how to improve the learning effectiveness and further strengthen their desktop VR-based learning implementation. Through this research, an initial theoretical model of the determinants of learning effectiveness in a desktop VR-based learning environment is contributed.