Showing papers in "Journal of Computer Applications in Technology in 2010"
TL;DR: A computer vision imaging system for the capture of 3D breast surface motion for the DIET system, including the image acquisition system, camera calibration, and 3D surface and motion reconstruction is presented.
Abstract: Breast cancer is one of the most prevalent forms of cancer in the world today. The search for effective treatment and screening methods is a highly active area of research. The Digital Image-based Elasto-Tomography (DIET) project is a new breast cancer screening system under development, where surface motion from the mechanically actuated breast is measured in 3D, and used as input to an inverse problem solving for breast elasticity. Cancerous lesions appear as high contrast features, being an order of magnitude stiffer than healthy tissue. The 3D motion capture is measured by an array of digital cameras using computer vision techniques. This paper presents a computer vision imaging system for the capture of 3D breast surface motion for the DIET system, including the image acquisition system, camera calibration, and 3D surface and motion reconstruction. Results are presented for experiments performed with silicone gel phantoms, with conditions designed to replicate the clinical procedure. Full 3D surface motion is successfully captured using an array of five cameras. Some successful results from the DIET inverse problem are also presented to demonstrate the viability of the system in practice.
22 citations
TL;DR: It will be shown that by using a head and shoulders avatar that is both photo-realistic and with appealing personality, that the experience of a speech interface is vastly enhanced and the convergence of many of them in the current Massey smart-office is investigated.
Abstract: This paper explores the problems of speech recognition in a (sometimes) noisy environment. An adaptive acoustic beamformer is proposed based on the Griffiths-Jim method and a 'hot-spot' where speech can be received within a geometric-defined boundary and rejected outside of it will be shown to give a certain amount of noise immunity and improve the signal-to-noise ratio for the second stage, which is the speech recognition engine. The recognition engine used has a limited vocabulary which gives rise to an excellent hit-rate and less training than unlimited vocabulary. The technology here has improved vastly within the last decade and it will be shown that by using a head and shoulders avatar that is both photo-realistic and with appealing personality, the experience of a speech interface is vastly enhanced. The paper will explore these technologies and investigate the convergence of many of them in the current Massey smart-office.
21 citations
TL;DR: The purpose of the study is to develop a flexible, lightweight and compact control valve which can be safely attached to the human body and find that the valve can control a larger flow rate relative to its weight and size.
Abstract: Owing to the recent ageing and decreasing birth rate, providing nursing care to the elderly has become very crucial. As a result, it is necessary to develop wearable systems to aid in nursing care. To realise such a system, we require not only wearable soft actuators, but also a compact and flexible control valve. The purpose of our study is to develop a flexible, lightweight and compact control valve which can be safely attached to the human body. In this study, we proposed and tested a new type of control valve that consists of a vibration motor and a check valve. By giving continuous vibration, the valve can be kept open. As a result, we find that the valve can control a larger flow rate relative to its weight and size. We also apply the control device to a wearable actuator in the form of a McKibben artificial muscle.
21 citations
TL;DR: Four types of DSMs and their applications in engineering are reviewed; several algorithms (i.e. partitioning, tearing, banding, clustering) are introduced in brief; after that, the applications of DSM in concurrent engineering, virtual enterprise and other fields are introduced.
Abstract: Firms rely on new product development to succeed in competitive global markets. Competition forces these firms to launch more innovation products in shorter periods of time. However, owing to complexity of product development and innovation, it is difficult to model product development process with traditional modelling tools, such as directed graph, Petri-Nets and so on. Design Structure Matrix (DSM) has attracted extensive attention among scholars due to its visual and compact matrix expression format. This paper first reviews four types of DSMs and their applications in engineering; and then, several algorithms (i.e. partitioning, tearing, banding, clustering) are introduced in brief; after that, the applications of DSM in concurrent engineering, virtual enterprise and other fields are introduced. In addition, hybrid model of four DSM types as well as numerical DSM (NDSM) and its applications are discussed. Finally, the limitations and expansions of DSM are made as the promising area for further research.
18 citations
TL;DR: This work shows how to solve the problem in SOD-M, a model-driven approach for the development of Service-Oriented Web applications, based on the use of weaving models as annotation models that can be easily generalised to other domains and contexts.
Abstract: Model-Driven Web Engineering is a new approach for Web Information Systems development whose basic assumption is the consideration of models as first-class entities. Basically, each step of the process consists of a model transformation that generates one or more target models from one or more source models. However, the special nature of the behavioural models implied at the early stages of a Model-Driven Web Engineering process complicates the specification of a model transformation that works for any input model. In such situations, it is not feasible to automate the whole development process since some design decisions have to be considered before executing each transformation. This work shows how we solve this problem in SOD-M, a model-driven approach for the development of Service-Oriented Web applications. The technique proposed is based on the use of weaving models as annotation models and it can be easily generalised to other domains and contexts.
17 citations
TL;DR: A coaxial powder feeding system is developed to meet the requirement of three-dimensional laser cladding and the geometric and mechanical properties of metal parts of layers fabricated by LRS are explored.
Abstract: Laser remanufacturing has been used as an approach to refurbish or to improve the surface quality of high-priced parts. However, most of the existing systems lack measuring and modelling functions, which results in the uncertainty of the quality of end products. This paper presents a three-dimensional Laser Remanufacturing System (LRS) based on the integration of reverse engineering and laser cladding. A coaxial powder feeding system is developed to meet the requirement of three-dimensional laser cladding. Meanwhile, the geometric and mechanical properties of metal parts of layers fabricated by LRS are explored. In addition, the principal, advantages and applications of the LRS system are described.
17 citations
TL;DR: The purpose of the paper is to bridge pervasive computing with agents through context-awareness and apply it in cooperative design.
Abstract: This paper presents a context-aware multi-agent cooperative design system framework. It analyses the related work of multi-agent cooperative design system and context-awareness computing first. Then, it presents the architecture of a multi-agent cooperative design system and the structure of a design agent in cooperative environment. Subsequently, it introduces the XML-based context modelling, capturing and processing in the environment. Finally, the design process is illustrated by a gear reducer design instance. The purpose of the paper is to bridge pervasive computing with agents through context-awareness and apply it in cooperative design.
13 citations
TL;DR: To enhance skin color separation, this paper presents a honeycomb model to recognize the real human skin and the skin color items and it is observed that the proposed model can effectively improve the segmentation results.
Abstract: Skin colour is an important feature for face detection and recognition in colour images. To obtain the possible face regions in colour images, skin colour models are always constructed by statistical analysis. Owing to low accuracy of the static models, researchers have discussed several dynamic models to correct input images. Unfortunately, it is possible that some objects whose colour is the same as the definition exist, and the previous methods cannot separate real skin item from skin colour background. Thus, this paper presents a honeycomb model to recognise the real human skin colour. The performance of the new skin colour detector technique has been tested under complex lighting source and background environments. It is observed that the proposed model can effectively improve the segmentation results. Especially, the honeycomb model is capable of separating the human face which connected with other face or skin colour background.
13 citations
TL;DR: It is found that the proposed Genetic-RBFNN (GRBFNN) method not only makes the original neural network smaller in terms of computation and realization but also improves diagnosis speed and accuracy.
Abstract: This paper presents development of an automatic fault diagnosis system in the nuclear power plants to minimise the possible nuclear disasters caused by inaccurate diagnoses done by operators. Combined binary and decimal coding methods are employed in this work based on Radial Basis Function Neural Network (RBFNN) structure. This underlying RBFNN structure is further trained through genetic optimisation algorithm based on known frequent failure conditions from a nuclear power plant's condensation and feed-water system. It is found that the proposed Genetic-RBFNN (GRBFNN) method not only makes the original neural network smaller in terms of computation and realisation but also improves the diagnosis speed and accuracy.
11 citations
TL;DR: Aiming at simplifying and real-time rendering of large-scale terrain, the whole terrain is first divided into terrain blocks, and for each block, feature points are picked out to generate a Triangulated Irregular Network (TIN) model.
Abstract: Aiming at simplifying and real-time rendering of large-scale terrain, the whole terrain is first divided into terrain blocks, and for each block, feature points are picked out to generate a Triangulated Irregular Network (TIN) model. Then, the hierarchical multi-resolution model of the terrain is created in a bottom-to-up mode. Cracks between terrain blocks can be easily remedied by boundary sharing. At run-time, whether a terrain block is to be rendered or not depends both on its isotropic error metric and the result of view frustum culling. Experimental results show that the proposed algorithm performs efficiently.
11 citations
TL;DR: A methodology is proposed for using roadmaps and conceptual frameworks within the context of integrated knowledge networks for improving efficient innovation.
Abstract: Knowledge management and innovation management are logically linked. However, the alignment of their respective deployment mechanisms is still not obvious. An analysis of the Innovation and Knowledge Life Cycles shows that the Knowledge Life Cycle can be deployed (partially) at each step of the Innovation Life Cycle. This implies that different, specific knowledge management tools could be used to increase innovation. Two knowledge management tools are considered in this paper: roadmaps and conceptual frameworks. A methodology is proposed for using roadmaps and conceptual frameworks within the context of integrated knowledge networks for improving efficient innovation. These two approaches aim to ease the knowledge structuring and identification in order to facilitate innovation. Two knowledge management examples in the financial services highlight how these tools contribute to the increased efficiency of the innovation process, leading to a more mature innovation deployment.
TL;DR: Based on generic data structure of product family (GBOM), association rules analysis is introduced to construct the classification mechanism between customer requirements and product architecture to furthest satisfy customer needs.
Abstract: Customer requirements analysis is the key step for product variety design of mass customisation. Quality Function Deployment (QFD) is a widely used management technique for comprehending the 'Voice of the Customer' (VOC), however, QFD in excess depends on human subjective judgement during extracting customer requirements and determination of the importance weights of customer requirements. And also, QFD process and related problems are complicated. In this paper, based on generic data structure of product family (GBOM), association rules analysis is introduced to construct the classification mechanism between customer requirements and product architecture. The new method can map customer requirements to the items of product family architecture respectively, accomplish the mapping process from customer domain to physical domain directly, decrease mutual process between customer and designer, improve the product design quality, and thus furthest satisfy customer needs. Finally, an example of customer requirements mapping of the elevator cabin is used to illustrate the proposed method.
TL;DR: Methods of linking design and manufacturing domains using disparate technologies, which include knowledge management supporting for product lifecycle management systems, Enterprise Resource Planning (ERP) systems, aggregate process planning systems, workflow management and data exchange formats are introduced.
Abstract: The manufacturing industry faces many challenges such as reducing time-to-market and cutting costs. In order to meet these increasing demands, effective methods are need to support the early product development stages by bridging the gap of communicating early design ideas and the evaluation of manufacturing performance. This paper introduces methods of linking design and manufacturing domains using disparate technologies. The combined technologies include knowledge management supporting for product lifecycle management systems, Enterprise Resource Planning (ERP) systems, aggregate process planning systems, workflow management and data exchange formats. A case study has been used to demonstrate the use of these technologies, illustrated by adding manufacturing knowledge to generate alternative early process plan which are in turn used by an ERP system to obtain and optimise a rough-cut capacity plan.
TL;DR: This paper tries to implement log-polar mapping techniques on Xilinx FPGA (field programmable gate array) board for unwarping the omnidirectional images into panoramic images to present a wide angle of view by preserving fine output image quality in a higher data compression manner.
Abstract: Log-polar or spatially variant image representation is an important component of active vision system in tracking process for many robotic applications due to its data compression ability, faster sampling rates and hence, direct to faster image-processing speed in machine vision system. In this paper, we try to implement log-polar mapping techniques on Xilinx FPGA (Field Programmable Gate Array) board for unwarping the omnidirectional images into panoramic images to present a wide angle of view by preserving fine output image quality in a higher data compression manner. Simulations are also run on MATLAB to find out the optimum mapping criterion. Some significant advantages of this new approach are: lighter processing system, lesser space utilisation, cost saving, faster processing speed and faster reset time (boot time) compared to a laptop computer that uses MATLAB for doing the unwarping process.
TL;DR: An integrated system employing both a novel PDMS device and a visual feedback from the device is reported, which has the potential to significantly facilitate the study on the relationship between muscle arms and force patterns of C. elegans in motion, and thus gives a better understanding of muscle arms development and modelling.
Abstract: Caenorhabditis elegans is a worm that could be mutated to have different muscle arms, which may generate distinct force patterns when the worm moves. In this paper, an integrated system employing both a novel PDMS device and a visual feedback from the device is reported. The silicone elastomer-based PDMS device consists of arrays of pillars, which form open channels for the worm to move in and bend the pillars in contact. Enabled by a single vision sensor (CCD/CMOS) camera, the computer vision system is able to transform the forces generated by C. elegans, through detecting the deflection of the pillars with sub-pixel accuracy. The experimental results demonstrate that the current vision-based force sensing system is capable of performing robust force measurements at a full 30 Hz with a 1.52 μN resolution. The framework has the potential to significantly facilitate the study on the relationship between muscle arms and force patterns of C. elegans in motion, and thus gives a better understanding of muscle arms development and modelling.
TL;DR: A color camera based technique which could be used to discriminate Bidens from wheat to be used in operations such as autonomous weed destruction and it is shown that the color based segmentation followed by shape based validation algorithm gives rise to high detection rates with lower false detections.
Abstract: Bidens pilosa L. (commonly known as cobbler's peg) is an annual broad leaf weed widely distributed in tropical and subtropical regions of the world and is reported to be a weed of 31 crops, including wheat. Automatic detection of Bidens in wheat farms is a non-trivial problem due to their similarity in colour and presence of occlusions. This paper proposes a methodology which could be used to discriminate Bidens from wheat to be used in operations such as autonomous weed destruction. A spectrometer is used to analyse the optical properties of Bidens and wheat leaves while achieving high classification results. However, due to the practical constraints of using spectrometers, a colour camera-based technique is proposed. It is shown that the colour-based segmentation followed by shape-based validation algorithm gives rise to high detection rates with lower false detections. We have experimentally evaluated the algorithm with Bidens detection rate of 80% and a false alarm rate of 10%.
TL;DR: An approach to integrate SaaS services is proposed using the integration architecture discussed, and several critical problems in the presentation, function and data layers are solved so developers can easily combine the SAAS offerings to meet new business scenarios.
Abstract: With the development of internet and web2.0 technology, Software as a Service (SaaS) has been accepted as the main way of software service delivery. However, the SaaS applications are isolated and cannot fulfill the rapidly change of business requirements. In this paper, an approach to integrate SaaS services is proposed using the integration architecture discussed, and several critical problems in the presentation, function and data layers are solved. So developers can easily combine the SaaS offerings to meet new business scenarios. We implemented the supporting platform and put it into real practice in a large-scale car manufacturing factory in China.
TL;DR: The proposed DSS was successful in decreasing inventories holding costs significantly by modifying the unreasonable purchase applications while maintaining the predefined target service level.
Abstract: This paper presents a hybrid Decision Support System (DSS) for slow moving spare parts joint replenishment in a nuclear power plant. In this study, we integrate the fuzzy and grey theory-based spare parts criticality class evaluation model to confirm the target service level, and the web-based joint replenishment DSS to obtain reasonable purchase parameters that can be helpful for reducing total inventory holding costs. The proposed DSS was successful in decreasing inventories holding costs significantly by modifying the unreasonable purchase applications while maintaining the predefined target service level.
TL;DR: A system dynamics model of new product diffusion with negative appraise is established based on innovation diffusion theory, and thenegative appraise effect is analysed on macro-level aspect.
Abstract: The enterprises often neglect the consumers' negative appraise, and as a result it leads to shrinkage in market and operating landslide In our former research, the multi-agent model has been established to study the negative appraise on new product diffusion and some meaningful results have been obtained But it is still unclear on the macro-level factors' relationships In this paper, a system dynamics model of new product diffusion with negative appraise is established based on innovation diffusion theory, and the negative appraise effect is analysed on macro-level aspect Through simulation experiments, the new product diffusion process is affected obviously by the negative appraise The diffusion process is sensitive to the probability of negative appraise and produce life cycle By comparing with multi-agent simulation, the results of macro-level simulation are the same as the multi-agent ones, and the two simulation ways can testify the effectiveness by each other
TL;DR: This paper first extracts the low-level features by characterising the point spatial density distributions, and train one feed-forward neural network to learn these features by examples, and compares this classifier to the k nearest neighbours classifier for 3D shapes.
Abstract: The task of 3D shape classification is to assign a set of unordered shapes into pre-tagged classes with class labels. In this paper, we present a 3D shape classifier approach based on supervision of the learning of point spatial distributions. We first extract the low-level features by characterising the point spatial density distributions, and train one feed-forward neural network to learn these features by examples. The Konstanz shape database was chosen as the test database to evaluate the accuracy rate of classification. We also compared this classifier to the k nearest neighbours classifier for 3D shapes.
TL;DR: This paper presents a method for semi-automatically building tailored application ontologies from a set of data acquisition forms intended to facilitate the integration of very heterogeneous data generation processes and their linkage to well-known external resources.
Abstract: This paper presents a method for semi-automatically building tailored application ontologies from a set of data acquisition forms. Such ontologies are intended to facilitate the integration of very heterogeneous data generation processes and their linkage to well-known external resources. The resulting tool is being applied to the medical domain, where a wide variety of knowledge and linguistic resources are available. The proposed method consists of first inferring the implicit structure of the forms and then semantically annotating all their textual elements. Finally, by applying a set of patterns over the form inferred structure, the tool generates the ontology axioms that describe it. Our initial results demonstrate that the approach can perform effectively.
TL;DR: The proposed method consists of three stages, a region segmentation technique is used to find reasonably good regions in image pairs, and an adjacency relationship based region matching technique is proposed to find corresponding regions between image pairs.
Abstract: In this paper, a method for image registration using 2D regions as correspondence features is proposed. The proposed method consists of three stages. First, a region segmentation technique is used to find reasonably good regions in image pairs. Second, an adjacency relationship based region matching technique is proposed to find corresponding regions between image pairs. Lastly, a method for finding the affine transformation between regions based on minimum enclosing ellipses is presented. Extensive experiments are performed to demonstrate the efficacy of the proposed method in registering real image pairs subject to large perspective distortions.
TL;DR: This paper presents simple methods of projecting a space curve onto a surface and derives the differential equations of the projection curve on both parametrically and implicitly defined surface.
Abstract: This paper presents simple methods of projecting a space curve onto a surface. Here, the parallel projection and the central projection are particularly considered. We derive the differential equations of the projection curve on both parametrically and implicitly defined surface. The projection curve is obtained by numerically solving the initial-value problem for a system of first-order Ordinary Differential Equations (ODEs) in the parametric domain associated with the surface representation for parametric case or in 3D space for implicit case. Some examples are also given to demonstrate that the presented methods are effective and potentially useful in computer-aided design.
TL;DR: The perfect sharing contract may achieve greater effective coordination than non-linear transfer payment contract, along with the strengthening of the innovation basis and the extent to which partners absorb and transform technological innovation knowledge, and the improvement of intellectual property protection environment and the degree of Intellectual property protection.
Abstract: Information goods supply chain partnership is the contract relationship of co-opetition (Brandenburger and Nalebuf, 1996) innovation in nature. The main findings are as following: in the threat of strategic substituting from industrial competitor of information goods, the government subsidy policy for information goods supply chain makes up the lack of incentives to original innovation due to innovation externalities, improves information goods supply chain partners' incentives to cooperative innovation, reduces industrial competitor's incentives to imitative innovation, and makes supply chain system profit of information goods and social welfare improvement in the incentive policy of government subsidies. The perfect sharing contract may achieve greater effective coordination than non-linear transfer payment contract, along with the strengthening of the innovation basis and the extent to which partners absorb and transform technological innovation knowledge, and the improvement of intellectual property protection environment and the degree of intellectual property protection.
TL;DR: A mathematical model of hydrodynamic torque converter applied to construction optimisation using a genetic algorithm as an optimisation method and a modelling error of steady-state characteristic was considered.
Abstract: This paper describes a mathematical model of hydrodynamic torque converter applied to construction optimisation using a genetic algorithm as an optimisation method. The model estimation was performed for a pre-selected hydrodynamic torque converter. A modelling error of steady-state characteristic was considered as the quality criterion. During estimation calculations, the Monte Carlo method and a genetic algorithm were applied as optimisation methods. Verification of the applied mathematical model of hydrodynamic torque converter revealed a modelling error of about 5.8%.
TL;DR: The goal of the paper is not only to reduce energy consumption, but also to improve the system utility in the grid environment, ensuring the battery lifetime and the deadlines of the grid applications.
Abstract: Energy efficiency for high-performance computing and communication systems has recently become an important concern, but most current grid environments do not implement energy-aware resource management. This paper proposes an energy-aware grid resource scheduling scheme. Energy-aware grid resource scheduling optimisation is formulated as utility optimisation. The goal of the paper is not only to reduce energy consumption, but also to improve the system utility in the grid environment, ensuring the battery lifetime and the deadlines of the grid applications. To reduce the computational complexity, we decompose the energy-aware grid resource scheduling optimisation problem into two sub-problems; the interaction between the two sub-problems is controlled through the use of the pricing variable. The paper proposes an energy-aware grid resource scheduling optimisation algorithm. The performance evaluation of the algorithm is conducted by comparing with other related algorithms.
TL;DR: The concept of link power as well as modifications of the rules used to determine the FA terms level depending on the concept of links will be presented, and the results showed that the precession and recall were improved.
Abstract: Studying co-word relations between field association (FA) terms and candidate terms can provide valuable information which helps the machine to take the correct decision and append the candidate terms in their suitable place inside FA terms dictionary. In this paper, a pure automatic tool to build a dynamic FA terms dictionary is described. The concept of link power as well as modifications of the rules used to determine the FA terms level depending on the concept of links will be presented. The results showed that the precession and recall were improved. In our simulation, the average improvement was about 16%. Also the complete dynamic system was built and tested.
TL;DR: A new approach for texture classification, which adopts ten words describing textures in natural language, is proposed, which shows that this approach of texture classification for natural texture is feasible.
Abstract: In the field of Content-Based Image Retrieval (CBIR), the semantic understanding of textures has long been a difficult problem, especially the texture classification. This paper proposes a new approach for texture classification, which adopts ten words describing textures in natural language. Texture features of an image are extracted by Discrete Wavelet Transform (DWT), and then classified through both Back Propagating Neural Network (BPNN) and Support Vector Machine (SVM) classifiers. Experimental results show that this approach of texture classification for natural texture is feasible.
TL;DR: A method to analyse clearance distribution between rotors with theoretical, modified design and machined profiles was developed and a localisation algorithm was developed to evaluate contact point on rotors.
Abstract: Twin screw compressors have been widely used in industry owing to their advantages. Owing to machining imperfections, it is necessary to apply clearance between main and gate rotors, which will still provide minimum clearance with lowest leakage. The aim of this study is to develop a method to analyse clearance distribution between rotors with theoretical, modified design and machined profiles. In order to evaluate contact point on rotors, a localisation algorithm was developed. The clearances were then calculated along sealing line that was originally derived from theoretical profile. The validity of the method was verified by computer simulation and experiments.
TL;DR: A direct and accurate approach for local resampling in vector fields is developed, and the approach is used to synthesise textures on 2D manifold surfaces directly from a texture exemplar.
Abstract: We develop a direct and accurate approach for local resampling in vector fields, and then use the approach to synthesise textures on 2D manifold surfaces directly from a texture exemplar. Regular-grid patches produced by the local resampling are used as building blocks for texture synthesis. Then, texture optimisation and patch-based sampling are generalised to synthesise texture directly in vector fields. Users can control the vector field on the mesh to generate textures with local variations including the orientation and scale. Many experimental results are presented to demonstrate the ease of use and reliable results provided by our system.