894 resultados para Computer-Aided Engineering (CAD, CAE) and design
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This chapter presents some of the issues with holonic manufacturing systems. It starts by presenting the current manufacturing scenario and trends and then provides some background information on the holonic concept and its application to manufacturing. The current limitations and future trends of manufacturing suggest more autonomous and distributed organisations for manufacturing systems; holonic manufacturing systems are proposed as a way to achieve such autonomy and decentralisation. After a brief literature survey a specific research work is presented to handle scheduling in holonic manufacturing systems. This work is based on task and resource holons that cooperate with each other based on a variant of the contract net protocol that allow the propagation of constraints between operations in the execution plan. The chapter ends by presenting some challenges and future opportunities of research.
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SUMMARY The aim of this study was to evaluate the influence of surface roughness on surface hardness (Vickers; VHN), elastic modulus (EM), and flexural strength (FLS) of two computer-aided design/computer-aided manufacturing (CAD/CAM) ceramic materials. One hundred sixty-two samples of VITABLOCS Mark II (VMII) and 162 samples of IPS Empress CAD (IPS) were ground according to six standardized protocols producing decreasing surface roughnesses (n=27/group): grinding with 1) silicon carbide (SiC) paper #80, 2) SiC paper #120, 3) SiC paper #220, 4) SiC paper #320, 5) SiC paper #500, and 6) SiC paper #1000. Surface roughness (Ra/Rz) was measured with a surface roughness meter, VHN and EM with a hardness indentation device, and FLS with a three-point bending test. To test for a correlation between surface roughness (Ra/Rz) and VHN, EM, or FLS, Spearman rank correlation coefficients were calculated. The decrease in surface roughness led to an increase in VHN from (VMII/IPS; medians) 263.7/256.5 VHN to 646.8/601.5 VHN, an increase in EM from 45.4/41.0 GPa to 66.8/58.4 GPa, and an increase in FLS from 49.5/44.3 MPa to 73.0/97.2 MPa. For both ceramic materials, Spearman rank correlation coefficients showed a strong negative correlation between surface roughness (Ra/Rz) and VHN or EM and a moderate negative correlation between Ra/Rz and FLS. In conclusion, a decrease in surface roughness generally improved the mechanical properties of the CAD/CAM ceramic materials tested. However, FLS was less influenced by surface roughness than expected.
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Manipulator systems are rather complex and highly nonlinear which makes difficult their analysis and control. Classic system theory is veil known, however it is inadequate in the presence of strong nonlinear dynamics. Nonlinear controllers produce good results [1] and work has been done e. g. relating the manipulator nonlinear dynamics with frequency response [2–5]. Nevertheless, given the complexity of the problem, systematic methods which permit to draw conclusions about stability, imperfect modelling effects, compensation requirements, etc. are still lacking. In section 2 we start by analysing the variation of the poles and zeros of the descriptive transfer functions of a robot manipulator in order to motivate the development of more robust (and computationally efficient) control algorithms. Based on this analysis a new multirate controller which is an improvement of the well known “computed torque controller” [6] is announced in section 3. Some research in this area was done by Neuman [7,8] showing tbat better robustness is possible if the basic controller structure is modified. The present study stems from those ideas, and attempts to give a systematic treatment, which results in easy to use standard engineering tools. Finally, in section 4 conclusions are presented.
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This paper describes a formal component language, used to support automated component-based program development. The components, referred to as templates, are machine processable, meaning that appropriate tool support, such as retrieval support, can be developed. The templates are highly adaptable, meaning that they can be applied to a wide range of problems. Some of the main features of the language are described, including: higher-order parameters; state variable declarations; specification statements and conditionals; applicability conditions and theories; meta-level place holders; and abstract data structures.
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This paper aims to crystallize recent research performed at the University of Worcester to investigate the feasibility of using the commercial game engine ‘Unreal Tournament 2004’ (UT2004) to produce ‘Educational Immersive Environments’ (EIEs) suitable for education and training. Our research has been supported by the UK Higher Education Academy. We discuss both practical and theoretical aspects of EIEs. The practical aspects include the production of EIEs to support high school physics education, the education of architects, and the learning of literacy by primary school children. This research is based on the development of our novel instructional medium, ‘UnrealPowerPoint’. Our fundamental guiding principles are that, first, pedagogy must inform technology, and second, that both teachers and pupils should be empowered to produce educational materials. Our work is informed by current educational theories such as constructivism, experiential learning and socio-cultural approaches as well as elements of instructional design and game principles.
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The paper discusses facilities of computer systems for editing scientific and technical texts, which partially automate functions of human editor and thus help the writer to improve text quality. Two experimental systems LINAR and CONUT developed in 90s to control the quality of Russian scientific and technical texts are briefly described; and general principles for designing more powerful editing systems are pointed out. Features of an editing system being now under development are outlined, primarily the underlying linguistic knowledge base and procedures controlling the text.
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In this paper, a computer-aided diagnostic (CAD) system for the classification of hepatic lesions from computed tomography (CT) images is presented. Regions of interest (ROIs) taken from nonenhanced CT images of normal liver, hepatic cysts, hemangiomas, and hepatocellular carcinomas have been used as input to the system. The proposed system consists of two modules: the feature extraction and the classification modules. The feature extraction module calculates the average gray level and 48 texture characteristics, which are derived from the spatial gray-level co-occurrence matrices, obtained from the ROIs. The classifier module consists of three sequentially placed feed-forward neural networks (NNs). The first NN classifies into normal or pathological liver regions. The pathological liver regions are characterized by the second NN as cyst or "other disease." The third NN classifies "other disease" into hemangioma or hepatocellular carcinoma. Three feature selection techniques have been applied to each individual NN: the sequential forward selection, the sequential floating forward selection, and a genetic algorithm for feature selection. The comparative study of the above dimensionality reduction methods shows that genetic algorithms result in lower dimension feature vectors and improved classification performance.
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OBJECTIVES Optical scanners combined with computer-aided design and computer-aided manufacturing (CAD/CAM) technology provide high accuracy in the fabrication of titanium (TIT) and zirconium dioxide (ZrO) bars. The aim of this study was to compare the precision of fit of CAD/CAM TIT bars produced with a photogrammetric and a laser scanner. METHODS Twenty rigid CAD/CAM bars were fabricated on one single edentulous master cast with 6 implants in the positions of the second premolars, canines and central incisors. A photogrammetric scanner (P) provided digitized data for TIT-P (n=5) while a laser scanner (L) was used for TIT-L (n=5). The control groups consisted of soldered gold bars (gold, n=5) and ZrO-P with similar bar design. Median vertical distance between implant and bar platforms from non-tightened implants (one-screw test) was calculated from mesial, buccal and distal scanning electron microscope measurements. RESULTS Vertical microgaps were not significantly different between TIT-P (median 16μm; 95% CI 10-27μm) and TIT-L (25μm; 13-32μm). Gold (49μm; 12-69μm) had higher values than TIT-P (p=0.001) and TIT-L (p=0.008), while ZrO-P (35μm; 17-55μm) exhibited higher values than TIT-P (p=0.023). Misfit values increased in all groups from implant position 23 (3 units) to 15 (10 units), while in gold and TIT-P values decreased from implant 11 toward the most distal implant 15. SIGNIFICANCE CAD/CAM titanium bars showed high precision of fit using photogrammetric and laser scanners. In comparison, the misfit of ZrO bars (CAM/CAM, photogrammetric scanner) and soldered gold bars was statistically higher but values were clinically acceptable.
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In this work, we take advantage of association rule mining to support two types of medical systems: the Content-based Image Retrieval (CBIR) systems and the Computer-Aided Diagnosis (CAD) systems. For content-based retrieval, association rules are employed to reduce the dimensionality of the feature vectors that represent the images and to improve the precision of the similarity queries. We refer to the association rule-based method to improve CBIR systems proposed here as Feature selection through Association Rules (FAR). To improve CAD systems, we propose the Image Diagnosis Enhancement through Association rules (IDEA) method. Association rules are employed to suggest a second opinion to the radiologist or a preliminary diagnosis of a new image. A second opinion automatically obtained can either accelerate the process of diagnosing or to strengthen a hypothesis, increasing the probability of a prescribed treatment be successful. Two new algorithms are proposed to support the IDEA method: to pre-process low-level features and to propose a preliminary diagnosis based on association rules. We performed several experiments to validate the proposed methods. The results indicate that association rules can be successfully applied to improve CBIR and CAD systems, empowering the arsenal of techniques to support medical image analysis in medical systems. (C) 2009 Elsevier B.V. All rights reserved.
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This paper describes a case of a rehabilitation involving Computer Aided Design/Computer Aided Manufacturing (CAD-CAM) system in implant supported and dental supported prostheses using zirconia as framework. The CAD-CAM technology has developed considerably over last few years, becoming a reality in dental practice. Among the widely used systems are the systems based on zirconia which demonstrate important physical and mechanical properties of high strength, adequate fracture toughness, biocompatibility and esthetics, and are indicated for unitary prosthetic restorations and posterior and anterior framework. All the modeling was performed by using CAD-CAM system and prostheses were cemented using resin cement best suited for each situation. The rehabilitation of the maxillary arch using zirconia framework demonstrated satisfactory esthetic and functional results after a 12-month control and revealed no biological and technical complications. This article shows the important of use technology CAD/CAM in the manufacture of dental prosthesis and implant-supported.
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Quantitative characterisation of carotid atherosclerosis and classification into symptomatic or asymptomatic is crucial in planning optimal treatment of atheromatous plaque. The computer-aided diagnosis (CAD) system described in this paper can analyse ultrasound (US) images of carotid artery and classify them into symptomatic or asymptomatic based on their echogenicity characteristics. The CAD system consists of three modules: a) the feature extraction module, where first-order statistical (FOS) features and Laws' texture energy can be estimated, b) the dimensionality reduction module, where the number of features can be reduced using analysis of variance (ANOVA), and c) the classifier module consisting of a neural network (NN) trained by a novel hybrid method based on genetic algorithms (GAs) along with the back propagation algorithm. The hybrid method is able to select the most robust features, to adjust automatically the NN architecture and to optimise the classification performance. The performance is measured by the accuracy, sensitivity, specificity and the area under the receiver-operating characteristic (ROC) curve. The CAD design and development is based on images from 54 symptomatic and 54 asymptomatic plaques. This study demonstrates the ability of a CAD system based on US image analysis and a hybrid trained NN to identify atheromatous plaques at high risk of stroke.
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Swallowable capsule endoscopy is used for non-invasive diagnosis of some gastrointestinal (GI) organs. However, control over the position of the capsule is a major unresolved issue. This study presents a design for steering the capsule based on magnetic levitation. The levitation is stabilized with the aid of a computer-aided feedback control system and diamagnetism. Peristaltic and gravitational forces to be overcome were calculated. A levitation setup was built to analyze the feasibility of using Hall Effect sensors to locate the in- vivo capsule. CAD software Maxwell 3D (Ansoft, Pittsburgh, PA) was used to determine the dimensions of the resistive electromagnets required for levitation and the feasibility of building them was examined. Comparison based on design complexity was made between positioning the patient supinely and upright.
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Liver steatosis is a common disease usually associated with social and genetic factors. Early detection and quantification is important since it can evolve to cirrhosis. In this paper, a new computer-aided diagnosis (CAD) system for steatosis classification, in a local and global basis, is presented. Bayes factor is computed from objective ultrasound textural features extracted from the liver parenchyma. The goal is to develop a CAD screening tool, to help in the steatosis detection. Results showed an accuracy of 93.33%, with a sensitivity of 94.59% and specificity of 92.11%, using the Bayes classifier. The proposed CAD system is a suitable graphical display for steatosis classification.