Learning large-scale spatial relationships in a maze and effects of MK-801 on retrieval in the rhesus monkey

Monkeys have strong abilities to remember the visual properties of potential food sources for survival in the nature. The present study demonstrated the first observations of rhesus monkeys learning to solve complex spatial mazes in which routes were guid

An investigation of the kinetics of CO2 uptake by a synthetic calcium based sorbent

This study examines the kinetics of carbonation by CO2 at temperatures of ca. 750 °C of a synthetic sorbent composed of 15 wt% mayenite (Ca12Al14O33) and CaO, designated HA-85-850, and draws comparisons with the carbonation of a calcined limestone. In-situ XRD has verified the inertness of mayenite, which neither interacts with the active CaO nor does it significantly alter the CaO carbonation–calcination equilibrium. An overlapping grain model was developed to predict the rate and extent of carbonation of HA-85-850 and limestone. In the model, the initial microstructure of the sorbent was defined by a discretised grain size distribution, assuming spherical grains. The initial input to the model – the size distribution of grains – was a fitted parameter, which was in good agreement with measurements made with mercury porosimetry and by the analysis of SEM images of sectioned particles. It was found that the randomly overlapping spherical grain assumption offered great simplicity to the model, despite its approximation to the actual porous structure within a particle. The model was able to predict the performance of the materials well and, particularly, was able to account for changes in rate and extent of reaction as the structure evolved after various numbers of cycles of calcination and carbonation.

The retrieval of structured design rationale for the re-use of design knowledge with an integrated representation

Design knowledge can be acquired from various sources and generally requires an integrated representation for its effective and efficient re-use. Though knowledge about products and processes can illustrate the solutions created (know-what) and the courses of actions (know-how) involved in their creation, the reasoning process (know-why) underlying the solutions and actions is still needed for an integrated representation of design knowledge. Design rationale is an effective way of capturing that missing part, since it records the issues addressed, the options considered, and the arguments used when specific design solutions are created and evaluated. Apart from the need for an integrated representation, effective retrieval methods are also of great importance for the re-use of design knowledge, as the knowledge involved in designing complex products can be huge. Developing methods for the retrieval of design rationale is very useful as part of the effective management of design knowledge, for the following reasons. Firstly, design engineers tend to want to consider issues and solutions before looking at solid models or process specifications in detail. Secondly, design rationale is mainly described using text, which often embodies much relevant design knowledge. Last but not least, design rationale is generally captured by identifying elements and their dependencies, i.e. in a structured way which opens the opportunity for going beyond simple keyword-based searching. In this paper, the management of design rationale for the re-use of design knowledge is presented. The retrieval of design rationale records in particular is discussed in detail. As evidenced in the development and evaluation, the methods proposed are useful for the re-use of design knowledge and can be generalised to be used for the retrieval of other kinds of structured design knowledge. © 2012 Elsevier Ltd. All rights reserved.

The retrieval of structured design knowledge

Design rationale is an effective way of capturing knowledge, since it records the issues addressed, the options considered, and the arguments used when specific decisions are made during the design process. Design rationale is generally captured by identifying elements and their dependencies, i.e. in a structured way. Current retrieval methods focus mainly on either the classification of rationale or on keyword-based searches of records. Keyword-based retrieval is reasonably effective as the information in design rationale records is mainly described using text. However, most of the current keyword-based retrieval methods discard the implicit structures of these records, resulting either in poor precision of retrieval or in isolated pieces of information that are difficult to understand. This ongoing research aims to go beyond keyword-based retrieval by developing methods and tools to facilitate the provision of useful design knowledge in new design projects. Our first step is to understand the structured information derived from the relationship between lumps of text held in different nodes in the design rationale captured via a software tool currently used in industry, and study how this information can be utilised to improve retrieval performance. Specifically, methods for utilising various structured information are developed and implemented on a prototype keyword-based retrieval system developed in our earlier work. The implementation and evaluation of these methods shows that the structured information can be utilised in a number of ways, such as filtering the results and providing more complete information. This allows the retrieval system to present results that are easy to understand, and which closely match designers' queries. Like design rationale, other methods for representing design knowledge also in essence involve structured information and thus the methods proposed can be generalised to be adapted and applied for the retrieval of other kinds of design knowledge. Copyright © 2002-2012 The Design Society. All rights reserved.

Visual retrieval of concrete crack properties for automated post-earthquake structural safety evaluation

The safety of post-earthquake structures is evaluated manually through inspecting the visible damage inflicted on structural elements. This process is time-consuming and costly. In order to automate this type of assessment, several crack detection methods have been created. However, they focus on locating crack points. The next step, retrieving useful properties (e.g. crack width, length, and orientation) from the crack points, has not yet been adequately investigated. This paper presents a novel method of retrieving crack properties. In the method, crack points are first located through state-of-the-art crack detection techniques. Then, the skeleton configurations of the points are identified using image thinning. The configurations are integrated into the distance field of crack points calculated through a distance transform. This way, crack width, length, and orientation can be automatically retrieved. The method was implemented using Microsoft Visual Studio and its effectiveness was tested on real crack images collected from Haiti.

Construction site image retrieval based on material cluster recognition

The capability to automatically identify shapes, objects and materials from the image content through direct and indirect methodologies has enabled the development of several civil engineering related applications that assist in the design, construction and maintenance of construction projects. This capability is a product of the technological breakthroughs in the area of image processing that has allowed for the development of a large number of digital imaging applications in all industries. In this paper, an automated and content based construction site image retrieval method is presented. This method is based on image retrieval techniques, and specifically those related with material and object identification and matches known material samples with material clusters within the image content. The results demonstrate the suitability of this method for construction site image retrieval purposes and reveal the capability of existing image processing technologies to accurately identify a wealth of materials from construction site images.

Identification of Materials from Construction Site Images Using Content Based Image Retrieval Techniques

The capability to automatically identify shapes, objects and materials from the image content through direct and indirect methodologies has enabled the development of several civil engineering related applications that assist in the design, construction and maintenance of construction projects. Examples include surface cracks detection, assessment of fire-damaged mortar, fatigue evaluation of asphalt mixes, aggregate shape measurements, velocimentry, vehicles detection, pore size distribution in geotextiles, damage detection and others. This capability is a product of the technological breakthroughs in the area of Image and Video Processing that has allowed for the development of a large number of digital imaging applications in all industries ranging from the well established medical diagnostic tools (magnetic resonance imaging, spectroscopy and nuclear medical imaging) to image searching mechanisms (image matching, content based image retrieval). Content based image retrieval techniques can also assist in the automated recognition of materials in construction site images and thus enable the development of reliable methods for image classification and retrieval. The amount of original imaging information produced yearly in the construction industry during the last decade has experienced a tremendous growth. Digital cameras and image databases are gradually replacing traditional photography while owners demand complete site photograph logs and engineers store thousands of images for each project to use in a number of construction management tasks. However, construction companies tend to store images without following any standardized indexing protocols, thus making the manual searching and retrieval a tedious and time-consuming effort. Alternatively, material and object identification techniques can be used for the development of automated, content based, construction site image retrieval methodology. These methods can utilize automatic material or object based indexing to remove the user from the time-consuming and tedious manual classification process. In this paper, a novel material identification methodology is presented. This method utilizes content based image retrieval concepts to match known material samples with material clusters within the image content. The results demonstrate the suitability of this methodology for construction site image retrieval purposes and reveal the capability of existing image processing technologies to accurately identify a wealth of materials from construction site images.