Exploration of the High Entropy Alloy Space as a Constraint Satisfaction Problem

High Entropy Alloys (HEAs), Multi-principal Component Alloys (MCA), or Compositionally Complex Alloys (CCAs) are alloys that contain multiple principal alloying elements. While many HEAs have been shown to have unique properties, their discovery has been largely done through costly and time-consuming trial-and-error approaches, with only an infinitesimally small fraction of the entire possible composition space having been explored. In this work, the exploration of the HEA composition space is framed as a Continuous Constraint Satisfaction Problem (CCSP) and solved using a novel Constraint Satisfaction Algorithm (CSA) for the rapid and robust exploration of alloy thermodynamic spaces. The algorithm is used to discover regions in the HEA Composition-Temperature space that satisfy desired phase constitution requirements. The algorithm is demonstrated against a new (TCHEA1) CALPHAD HEA thermodynamic database. The database is first validated by comparing phase stability predictions against experiments and then the CSA is deployed and tested against design tasks consisting of identifying not only single phase solid solution regions in ternary, quaternary and quinary composition spaces but also the identification of regions that are likely to yield precipitation-strengthened HEAs.Comment: 14 pages, 13 figure

Creative User-Centered Visualization Design for Energy Analysts and Modelers

We enhance a user-centered design process with techniques that deliberately promote creativity to identify opportunities for the visualization of data generated by a major energy supplier. Visualization prototypes developed in this way prove effective in a situation whereby data sets are largely unknown and requirements open – enabling successful exploration of possibilities for visualization in Smart Home data analysis. The process gives rise to novel designs and design metaphors including data sculpting. It suggests: that the deliberate use of creativity techniques with data stakeholders is likely to contribute to successful, novel and effective solutions; that being explicit about creativity may contribute to designers developing creative solutions; that using creativity techniques early in the design process may result in a creative approach persisting throughout the process. The work constitutes the first systematic visualization design for a data rich source that will be increasingly important to energy suppliers and consumers as Smart Meter technology is widely deployed. It is novel in explicitly employing creativity techniques at the requirements stage of visualization design and development, paving the way for further use and study of creativity methods in visualization design

Bibliometric Mapping of the Computational Intelligence Field

In this paper, a bibliometric study of the computational intelligence field is presented. Bibliometric maps showing the associations between the main concepts in the field are provided for the periods 1996–2000 and 2001–2005. Both the current structure of the field and the evolution of the field over the last decade are analyzed. In addition, a number of emerging areas in the field are identified. It turns out that computational intelligence can best be seen as a field that is structured around four important types of problems, namely control problems, classification problems, regression problems, and optimization problems. Within the computational intelligence field, the neural networks and fuzzy systems subfields are fairly intertwined, whereas the evolutionary computation subfield has a relatively independent position.neural networks;bibliometric mapping;fuzzy systems;bibliometrics;computational intelligence;evolutionary computation

Quasiconvex Programming

We define quasiconvex programming, a form of generalized linear programming in which one seeks the point minimizing the pointwise maximum of a collection of quasiconvex functions. We survey algorithms for solving quasiconvex programs either numerically or via generalizations of the dual simplex method from linear programming, and describe varied applications of this geometric optimization technique in meshing, scientific computation, information visualization, automated algorithm analysis, and robust statistics.Comment: 33 pages, 14 figure

Arguing satisfaction of security requirements

This chapter presents a process for security requirements elicitation and analysis, based around the construction of a satisfaction argument for the security of a system. The process starts with the enumeration of security goals based on assets in the system, then uses these goals to derive security requirements in the form of constraints. Next, a satisfaction argument for the system is constructed, using a problem-centered representation, a formal proof to analyze properties that can be demonstrated, and structured informal argumentation of the assumptions exposed during construction of the argument. Constructing the satisfaction argument can expose missing and inconsistent assumptions about system context and behavior that effect security, and a completed argument provides assurances that a system can respect its security requirements

A Visibility and Spatial Constraint-Based Approach for Geopositioning

Over the past decade, automated systems dedicated to geopositioning have been the object of considerable development. Despite the success of these systems for many applications, they cannot be directly applied to qualitative descriptions of space. The research presented in this paper introduces a visibility and constraintbased approach whose objective is to locate an observer from the verbal description of his/her surroundings. The geopositioning process is formally supported by a constraint-satisfaction algorithm. Preliminary experiments are applied to the description of environmental scenes