LEGClust- a clustering algorithm based on layered entropic subgraphs.

Hierarchical clustering is a stepwise clustering method usually based on proximity measures between objects or sets of objects from a given data set. The most common proximity measures are distance measures. The derived proximity matrices can be used to build graphs, which provide the basic structure for some clustering methods. We present here a new proximity matrix based on an entropic measure and also a clustering algorithm (LEGClust) that builds layers of subgraphs based on this matrix, and uses them and a hierarchical agglomerative clustering technique to form the clusters. Our approach capitalizes on both a graph structure and a hierarchical construction. Moreover, by using entropy as a proximity measure we are able, with no assumption about the cluster shapes, to capture the local structure of the data, forcing the clustering method to reflect this structure. We present several experiments on artificial and real data sets that provide evidence on the superior performance of this new algorithm when compared with competing ones.     

Design of a decision support system for machine tool selection based on machine characteristics and performance tests

Economic globalization, together with heightened market competition and increasingly short product life cycles are motivating companies to use advanced manufacturing technologies. Use of high speed machining is increasingly widespread; however, as the technology is relatively new, it lacks a deep-rooted knowledge base which would facilitate implementation. One of the most frequent problems facing companies wishing to adopt this technology is selecting the most appropriate machine tool for the product in question and own enterprise characteristics. This paper presents a decision support system for high speed milling machine tool selection based on machine characteristics and performance tests. Profile machining tests are designed and conducted in participating machining centers. The decision support system is based on product dimension accuracy, process parameters such as feed rate and interpolation scheme used by CNC and machine characteristics such as machine accuracy and cost. Experimental data for process error and cycle operation time are obtained from profile machining tests with different geometrical feature zones that are often used in manufacturing of discrete parts or die/moulds. All those input parameters have direct impact on productivity and manufacturing cost. Artificial neural network models are utilized for decision support system with reasonable prediction capability.     

An haptic-based immersive environment for shape analysis and modelling

Currently, the design of aesthetic products is a process that requires a set of activities where digital models and physical mockups play a key role. Typically, these are modified (and built) several times before reaching the desired design, increasing the development time and, consequently, the final product cost. In this paper, we present an innovative design environment for computer-aided design (CAD) surface analysis. Our system relies on a direct visuo-haptic display system, which enables users to visualize models using a stereoscopic view, and allows the evaluation of sectional curves using touch. Profile curves are rendered using an haptic device that deforms a plastic strip, thanks to a set of actuators, to reproduce the curvature of the shape co-located with the virtual model. By touching the strip, users are able to evaluate shape characteristics, such as curvature or discontinuities (rendered using sound), and to assess the surface quality. We believe that future computer-aided systems (CAS)/CAD systems based on our approach will contribute in improving the design process at industrial level. Moreover, these will allow companies to reduce the product development time by reducing the number of physical mockups necessary for the product design evaluation and by increasing the quality of the final product, allowing a wider exploration and comparative evaluation of alternatives in the given time.     

Consistent triangulation for mobile robot localization using discontinuous angular measurements

Localization is a fundamental operation for the navigation of mobile robots. The standard localization algorithms fuse external measurements of the environment with the odometric evolution of the robot pose to obtain its optimal estimation. In this work, we present a different approach to determine the pose using angular measurements discontinuously obtained in time. The presented method is based on an Extended Kalman Filter (EKF) with a state-vector composed of the external angular measurements. This algorithm keeps track of the angles between actual measurements from robot odometric information. This continuous angular estimation allows the consistent use of the triangulation methods to determine the robot pose at any time during its motion. The article reports experimental results that show the localization accuracy obtained by means of the presented approach. These results are compared to the ones obtained applying the EKF algorithm with the standard pose state-vector. For the experiments, an omnidirectional robotic platform with omnidirectional wheels is used.     

Aero-structural optimization using adjoint coupled post-optimality sensitivities

A new subspace optimization method for performing aero-structural design is introduced. The method relies on a semi-analytic adjoint approach to the sensitivity analysis that includes post-optimality sensitivity information from the structural optimization subproblem. The resulting coupled post-optimality sensitivity approach is used to guide a gradient-based optimization algorithm. The new approach simplifies the system-level problem, thereby reducing the number of calls to a potentially costly aerodynamics solver. The aero-structural optimization of an aircraft wing is performed using linear aerodynamic and structural analyses, and a performance comparison is made between the new approach and the conventional multidisciplinary feasible method. The new asymmetric suboptimization method is found to be the more efficient approach when it adequately reduces the number of system evaluations or when there is a large enough discrepancy between disciplinary solution times.     

Locally perceiving hard global constraints in multi-agent scheduling

We propose how to model enterprise facilities (like factories, warehouses, etc.) in a multi-product production/distribution network, capacity management at those facilities, and scheduling agents which act as enterprise managers, taking decisions that affect the available capacity. A coordination mechanism through which scheduling agents can locally perceive hard global temporal constraints is also proposed.     

Decision support system to divide a large network into suitable District Metered Areas

This paper presents a new approach to divide large Water Distribution Networks (WDN) into suitable District Metered Areas (DMAs). It uses a hydraulic simulator and two operational models to identify the optimal number of DMAs, their entry points and boundary valves, and the network reinforcement/replacement needs throughout the project plan. The first model divides the WDN into suitable DMAs based on graph theory concepts and some user-defined criteria. The second model uses a simulated annealing algorithm to identify the optimal number and location of entry points and boundary valves, and the pipes reinforcement/replacement, necessary to meet the velocity and pressure requirements. The objective function is the difference between the economic benefits in terms of water loss reduction (arising from the average pressure reduction) and the cost of implementing the DMAs. To illustrate the proposed methodology, the results from a hypothetical case study are presented and discussed.     

Scientific productivity paradox: The case of China's S&T system

Summary In 1985 China began the reform of its Science & Technology (S&T) sector inherited from the planned economy. To disclose the impact of the drawn-out reform on the efficiency of the whole sector, we measure the scientific productivity of China's S&T institutes. The analysis is based on R&D input and output data at the country aggregate and provincial level. We utilize Polynomial Distributed Lag model to uncover the structure of the lag between R&D input and output. The findings reveal that the growth rate of scientific productivity of China's S&T institutes has been negative since the 1990s.