Consequences and conjectures in preordered sets

In a preordered set, or preset, consequence operators in the sense of Tarski, defined on families of subsets, are introduced. From them, the corresponding sets of conjectures, hypotheses, speculations and refutations are considered, studying the relationships between these sets and those previously defined on ortholattices. All the concepts introduced are illustrated with three particular consequence operators, whose behavior is studied in detail. The results obtained are applied to the case of fuzzy sets endowed with the usual pointwise ordering.     

A particular approach for the Three-dimensional Packing Problem with additional constraints

This paper focuses on a restrained concretization of the general NP-hard Container Loading Problem that arises from a real world application. This particular problem can be informally described as: given different sets of bins and boxes, find the packing of the boxes into the smallest number of bins obeying some additional restrictions on the placement. The mathematical programming formulation that appears to better model this application is the Three-dimensional Bin-Packing Problem (3D-BPP) which is no more than an extension of the classic Bin-Packing Problem to the orthogonal packing of solid objects.We present an alternative non-linear formulation for the 3D-BPP version as well as new designed heuristic algorithms (CPBOX and BOXCP) for the approximation of the optimal solution using boxes and bins of heterogeneous dimensions. Moreover, we also present a heuristic specifically designed to work under very particular storage application restrictions. The numerical results indicate that all of these new heuristics have good performance standards especially when applied to replications of real input data.     

Control of absorption columns in the bioethanol process: Influence of measurement uncertainties

The alcohol lost by evaporation during the bioethanol fermentation process may be collected and recovered using an absorption column. This equipment is also used in the carbonic gas treatment, a by-product from the sugar cane fermentation. In the present work, the development of nonlinear feedforward–feedback controllers, based on neural network inverse models, was proposed and tested to manipulate the absorbent flow rates. The control purposes are: to keep low ethanol concentration in the effluent gas phase from the first absorption column (ethanol recovery column); and to reduce the residual water concentration in the CO₂ gas effluent from the second tower (CO₂ treatment column).Based on simulation studies, the neural network (ANN) controller performance was compared with the conventional PID control scheme application. The best ANN architecture was set up according to the Foresse and Hagan (1997) criterion, while the PID parameters were found from the well-known Cohen–Coon Equations and trial-and-error fine tuning.Initially, performances were evaluated for the system without concentration measurement uncertainties. From these tests, the ANN controller presented the smallest response time and overshoot for regulator and servo problems. Three uncertainty levels were applied afterwards: 5%, 10%, and 15%.The ANN controller outperformed the PID for all uncertainty levels tested for the ethanol recovery column. For the CO₂ treatment column, the ANN controller proceeded successfully under uncertainties of 5% and 10%, while the PID did not deal properly with uncertainties above 5%. The statistical F-test, besides the ITAE, ISE, and IAE performance criteria, were calculated for both controllers applications and then compared. They proved the superiority of the ANN control scheme.Using appropriately the proposed well-controlled absorption columns increases the efficiency of the bioethanol production plant and can also provide carbon credits by avoiding CO₂ emission into the atmosphere.     

BOO: Behavior-oriented ontology to describe participant dynamics in collocated design meetings

Facilitating meetings is not an easy task. To assist the facilitator, we have been designing intelligent support systems, which can help contextual sensemaking, decision making and action. However, these systems are constructed based on behavioral models that provide guidelines to understand participant behaviors. This paper presents an ontology to describe participants’ behaviors in collaborative design meetings and rules that correlate them with the group’s acceptance of the final product. This ontology describes the group dynamics at collocated meetings, using verbal and non-verbal cues of attention shifts and attention maintenance as its basic constructs. The objective of creating this ontology was to better understand face-to-face meetings to eventually help meeting facilitators identify issues that may lead to dissatisfaction with the final product through behavioral cues. The ontology was derived through extensive analysis of a series of engineering design session videos. The design group was composed of experts with similar backgrounds, but working in different divisions of the same company. Different points of view were argued and decisions were made at the end of each meeting. After each meeting, participants were asked to asynchronously commit to the decisions made in the group. Our ontology can be used to identify the factors that lead to an undesired outcome, and now serves as a basis for a new project, which uses rules to support design meetings, improve final artifact acceptance and reduce rework. Our conclusions point out correlations between designers’ behaviors and future artifact acceptance and actions that interrupt or bring back group attention. The ontology was validated through application to other meeting situations. These findings may guide software developers in the creation of tools to support group design, and may be applied by an intelligent system.     

WSDL and UDDI extensions for version support in web services

Versioning is an important aspect of web service development, which has not been adequately addressed so far. In this article, we propose extensions to WSDL and UDDI to support versioning of web service interfaces at development-time and run-time. We address service-level and operation-level versioning, service endpoint mapping, and version sequencing. We also propose annotation extensions for developing versioned web services in Java. We have tested the proposed solution for versioning in two real-world environments and identified considerable improvements in service development and maintenance efficiency, improved service reuse, and simplified governance.     

Hybrid crossover operators with multiple descendents for real‐coded genetic algorithms: Combining neighborhood‐based crossover operators

Most real‐coded genetic algorithm research has focused on developing effective crossover operators, and as a result, many different types of crossover operators have been proposed. Some forms of crossover operators are more suitable to tackle certain problems than others, even at the different stages of the genetic process in the same problem. For this reason, techniques that combine multiple crossovers, called hybrid crossover operators, have been suggested as alternative schemes to the common practice of applying only one crossover model to all the elements in the population. On the other hand, there are operators with multiple offsprings, more than two descendants from two parents, which present a better behavior than the operators with only two descendants, and achieve a good balance between exploration and exploitation. © 2009 Wiley Periodicals, Inc.     

Reinforcement learning of pedagogical policies in adaptive and intelligent educational systems

In an adaptive and intelligent educational system (AIES), the process of learning pedagogical policies according the students needs fits as a Reinforcement Learning (RL) problem. Previous works have demonstrated that a great amount of experience is needed in order for the system to learn to teach properly, so applying RL to the AIES from scratch is unfeasible. Other works have previously demonstrated in a theoretical way that seeding the AIES with an initial value function learned with simulated students reduce the experience required to learn an accurate pedagogical policy. In this paper we present empirical results demonstrating that a value function learned with simulated students can provide the AIES with a very accurate initial pedagogical policy. The evaluation is based on the interaction of more than 70 Computer Science undergraduate students, and demonstrates that an efficient and useful guide through the contents of the educational system is obtained.     

Convolutional Sparse Coding for High Dynamic Range Imaging

Current HDR acquisition techniques are based on either (i) fusing multibracketed, low dynamic range (LDR) images, (ii) modifying existing hardware and capturing different exposures simultaneously with multiple sensors, or (iii) reconstructing a single image with spatially‐varying pixel exposures. In this paper, we propose a novel algorithm to recover high‐quality HDRI images from a single, coded exposure. The proposed reconstruction method builds on recently‐introduced ideas of convolutional sparse coding (CSC); this paper demonstrates how to make CSC practical for HDR imaging. We demonstrate that the proposed algorithm achieves higher‐quality reconstructions than alternative methods, we evaluate optical coding schemes, analyze algorithmic parameters, and build a prototype coded HDR camera that demonstrates the utility of convolutional sparse HDRI coding with a custom hardware platform.     

InSAR validation based on GNSS measurements in Bucharest

This research aims to complex validate radar displacement products over Bucharest based on Global Navigation Satellite System or GNSS. Validation was represented by the comparison of the recent TerraSAR-X (TSX) displacement results with field GNSS data. For this purpose, 24 and 27 high-resolution images acquired from the TSX satellite between 2011 and 2014 were processed by Permanent Scatterer Inteferometry (PSI) and Small BASeline Subset (SBAS) – interferometric techniques using synthetic aperture radar (SAR) images. The images were selected from a larger pool of SAR images, after consulting weather databases, which helped eliminate the images acquired under adverse weather conditions. GNSS locations were cross-checked on high-resolution optical images and 21 ground points (including three permanent stations) were selected. All these points were (1) characterized by high coherence on radar images; (2) were on the ground; (3) were situated in large vacant spaces; and (4) far away from radar obstacles such as trees. A major challenge in achieving the research goal was to overcome the lack of permanent stations at the ground level within the area of Bucharest. Instead of permanent stations, our team designed and installed forced centring systems that remained stable over time, eliminating the instalment errors of GNSS receivers. The line-of-site (LOS) components derived from GNSS data have been compared with those of the SAR scatterers closest to the GNSS markers (never coinciding). For each location, the vertical reading was projected in the LOS of the SAR sensor. These readings were compared with the displacement values determined at the locations of permanent scatterers (PSs) within 100 m from the GNSS nodes, by eliminating all scatterers on buildings. The comparison has shown a good agreement.