An Experimental Digital Library Platform - A Demonstrator Prototype for the DigLib Project at SICS

Within the framework of the Digital Library project at SICS, this thesis describes the implementation of a demonstrator prototype of a digital library (DigLib); an experimental platform integrating several functions in one common interface. It includes descriptions of the structure and formats of the digital library collection, the tailoring of the search engine Dienst, the construction of a keyword extraction tool, and the design and development of the interface. The platform was realised through sicsDAIS, an agent interaction and presentation system, and is to be used for testing and evaluating various tools for information seeking. The platform supports various user interaction strategies by providing: search in bibliographic records (Dienst); an index of keywords (the Keyword Extraction Function (KEF)); and browsing through the hierarchical structure of the collection. KEF was developed for this thesis work, and extracts and presents keywords from Swedish documents. Although based on a comparatively simple algorithm, KEF contributes by supplying a long-felt want in the area of Information Retrieval. Evaluations of the tasks and the interface still remain to be done, but the digital library is very much up and running. By implementing the platform through sicsDAIS, DigLib can deploy additional tools and search engines without interfering with already running modules. If wanted, agents providing other services than SICS can supply, can be plugged in

The development of a knowledge-based system for the preliminary investigation of contaminated land.

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Manufacturing code generation for rotational parts in a feature based product modelling environment

An important element for the integration of CAD/CAM is the representation and handling of data used during the design and manufacturing activities. The use of features and product modelling techniques bring a better handling of this data and provide CAD/CAM with an excellent platform for integration. The thesis explores the use of a predefined set of features in a product modelling environment for the design and machining of rotational components. Theword features in this research implies a set of functional, geometrical and technological information with a unique form. Those features are pre-defined and comprise of a limited number of elements which carry the information related to design and manufacturing activities. The thesis is divided into three main parts. The first part contains a review of topics related to the research e. g. group technology, component features, CAD/CAM and also contains a literature survey of related research works. In the second part the "features" are defined and presented. Also the product modelling environment is explained and the basic rule based procedures which are used to automatize the operation planning activities are presented. In the last part a description of the case-studies used for automatic NC code generation is presented followed by a discussion of the results. Lastly, the conclusions are drawn and ideas for further work presented

Automatic Identification of Algae using Low-cost Multispectral Fluorescence Digital Microscopy, Hierarchical Classification & Deep Learning

Harmful algae blooms (HABs) can produce lethal toxins and are a rising global concern. In response to this threat, many organizations are monitoring algae populations to determine if a water body might be contaminated. However, identifying algae types in a water sample requires a human expert, a taxonomist, to manually identify organisms using an optical microscope. This is a tedious, time-consuming process that is prone to human error and bias. Since many facilities lack on-site taxonomists, they must ship their water samples off site, further adding to the analysis time. Given the urgency of this problem, this thesis hypothesizes that multispectral fluorescence microscopy with a deep learning hierarchical classification structure is the optimal method to automatically identify algae in water on-site. To test this hypothesis, a low-cost system was designed and built which was able generate one brightfield image and four fluorescence images. Each of the four fluorescence images was designed to target a different pigment in algae, resulting in a unique autofluorescence spectral fingerprint for different phyla groups. To complement this hardware system, a software framework was designed and developed. This framework used the prior taxonomic structure of algae to create a hierarchical classification structure. This hierarchical classifier divided the classification task into three steps which were phylum, genus, and species level classification. Deep learning models were used at each branch of this hierarchical classifier allowing the optimal set of features to be implicitly learned from the input data. In order to test the efficacy of the proposed hardware system and corresponding software framework, a dataset of nine algae from 4 different phyla groups was created. A number of preprocessing steps were required to prepare the data for analysis. These steps were flat field correction, thresholding and cropping. With this multispectral imaging data, a number of spatial and spectral features were extracted for use in the feature-extraction-based models. This dataset was used to determine the relative performance of 12 different model architectures, and the proposed multispectral hierarchical deep learning approach achieved the top classification accuracy of 97% to the species level. Further inspection revealed that a traditional feature extraction method was able to achieve 95% to the phyla level when only using the multispectral fluorescence data. These observations strongly support that: (1) the proposed low-cost multispectral fluorescence imaging system, and (2) the proposed hierarchical structure based on the taxonomy prior, in combination with (3) deep learning methods for feature learning, is an effective method to automatically classify algae

Collaborative semantic editing of linked data lexica.

The creation of language resources is a time-consuming process requiring the efforts of many people. The use of resources collaboratively created by non-linguists can potentially ameliorate this situation. However, such resources often contain more errors compared to resources created by experts. For the particular case of lexica, we analyse the case of Wiktionary, a resource created along wiki principles and argue that through the use of a principled lexicon model, namely lemon, the resulting data could be better understandable to machines. We then present a platform called lemon source that supports the creation of linked lexical data along the lemon model. This tool builds on the concept of a semantic wiki to enable collaborative editing of the resources by many users concurrently. In this paper, we describe the model, the tool and present an evaluation of its usability based on a small group of users

Methodologies for CIM systems integration in small batch manufacturing

This thesis is concerned with identifying the problems and constraints faced by small batch manufacturing companies during the implementation of Computer Integrated Manufacturing (CIM). The main aim of this work is to recommend generic solutions to these problems with particular regard to those constraints arising because of the need for ClM systems integration involving both new and existing systems and procedures. The work has involved the application of modern computer technologies, including suitable hardware and software tools, in an industrial environment. Since the research has been undertaken with particular emphasis on the industrial implementor's viewpoint, it is supported by the results of a two phased implementation of computer based control systems within the machine shop of a manufacturing company. This involved the specific implementation of a Distributed Numerical Control system on a single machine in a group technology cell of machines followed by the evolution of this system into Cell and Machine Management Systems to provide a comprehensive decision support and information distribution facility for the foremen and uperators within the cell. The work also required the integration of these systems with existing Factory level manufacturing control and CADCAM functions. Alternative approaches have been investigated which may have been applicable under differing conditions and the implications that this specific work has for CIM systems integration in small batch manufacturing companies evaluated with regard not only to the users within an industrial company but also the systems suppliers external to the company. The work has resulted in certain generic contributions to knowledge by complementing ClM systems integration research with regard to problems encountered; cost implications; the use of appropriate methodologies including the role of emerging international standard methods, tools and technologies and also the importance of 'human integration' when implementing CIM systems in a real industrial situation

Advancement in robot programming with specific reference to graphical methods

This research study is concerned with the derivation of advanced robot programming methods. The methods include the use of proprietary simulation modelling and design software tools for the off-line programming of industrial robots. The study has involved the generation of integration software to facilitate the co-operative operation of these software tools. The three major researcli'themes7of "ease of usage", calibration and the integration of product design data have been followed to advance robot programming. The "ease of usage" is concerned with enhancements in the man-machine interface for robo t simulation systems in terms of computer assisted solid modelling and computer assisted task generation. Robot simulation models represent an idealised situation, and any off-line robot programs generated from'them may contain'discrepancies which could seriously effect thq programs' performance; Calibration techniques have therefore been investigated as 'a method of overcoming discrepancies between the simulation model and the real world. At the present time, most computer aided design systems operate as isolated islands of computer technology, whereas their product databases should be used to support decision making processes and ultimately facilitate the generation of machine programs. Thus the integration of product design data has been studied as an important step towards truly computer integrated manufacturing. The functionality of the three areas of study have been generalised and form the basis for recommended enhancements to future robot programming systems

A pattern language for evolution reuse in component-based software architectures

Context: Modern software systems are prone to a continuous evolution under frequently varying requirements and changes in operational environments. Architecture-Centric Software Evolution (ACSE) enables changes in a system’s structure and behaviour while maintaining a global view of the software to address evolution-centric trade-offs. Lehman’s law of continuing change demands for long-living and continuously evolving architectures to prolong the productive life and economic value of software. Also some industrial research shows that evolution reuse can save approximately 40% effort of change implementation in ACSE process. However, a systematic review of existing research suggests a lack of solution(s) to support a continuous integration of reuse knowledge in ACSE process to promote evolution-off-the-shelf in software architectures. Objectives: We aim to unify the concepts of software repository mining and software evolution to discover evolution-reuse knowledge that can be shared and reused to guide ACSE. Method: We exploit repository mining techniques (also architecture change mining) that investigates architecture change logs to discover change operationalisation and patterns. We apply software evolution concepts (also architecture change execution) to support pattern-driven reuse in ACSE. Architecture change patterns support composition and application of a pattern language that exploits patterns and their relations to express evolution-reuse knowledge. Pattern language composition is enabled with a continuous discovery of patterns from architecture change logs and formalising relations among discovered patterns. Pattern language application is supported with an incremental selection and application of patterns to achieve reuse in ACSE. The novelty of the research lies with a framework PatEvol that supports a round-trip approach for a continuous acquisition (mining) and application (execution) of reuse knowledge to enable ACSE. Prototype support enables customisation and (semi-) automation for the evolution process. Results: We evaluated the results based on the ISO/IEC 9126 - 1 quality model and a case study based validation of the architecture change mining and change execution processes. We observe consistency and reusability of change support with pattern-driven architecture evolution. Change patterns support efficiency for architecture evolution process but lack a fine-granular change implementation. A critical challenge lies with the selection of appropriate patterns to form a pattern language during evolution. Conclusions: The pattern language itself continuously evolves with an incremental discovery of new patterns from change logs over time. A systematic identification and resolution of change anti-patterns define the scope for future research