Deformable Object Modelling Through Cellular Neural Network

This paper presents a new methodology for thedeformable object modelling by drawing an analogybetween cellular neural network (CNN) and elasticdeformation. The potential energy stored in an elasticbody as a result of a deformation caused by an externalforce is propagated among mass points by the non-linearCNN activity. An improved autonomous CNN model isdeveloped for propagating the energy generated by theexternal force on the object surface in the naturalmanner of heat conduction. A heat flux based method ispresented to derive the internal forces from the potentialenergy distribution established by the CNN. Theproposed methodology models non-linear materials withnon-linear CNN rather than geometric non-linearity inthe most existing deformation methods. It can not onlydeal with large-range deformations due to the localconnectivity of cells and the CNN dynamics, but it canalso accommodate both isotropic and anisotropicmaterials by simply modifying conductivity constants.Examples are presented tThis paper presents a new methodology for the deformable object modelling by drawing an analogy between cellular neural network (CNN) and elastic deformation. The potential energy stored in an elastic body as a result of a deformation caused by an external force is propagated among mass points by the non-linear CNN activity. An improved autonomous CNN model is developed for propagating the energy generated by the external force on the object surface in the natural manner of heat conduction. A heat flux based method is presented to derive the internal forces from the potential energy distribution established by the CNN. The proposed methodology models non-linear materials with non-linear CNN rather than geometric non-linearity in the most existing deformation methods. It can not only deal with large-range deformations due to the local connectivity of cells and the CNN dynamics, but it can also accommodate both isotropic and anisotropic materials by simply modifying conductivity constants. Examples are presented to demonstrate the efficacy of the proposed methodology

Efficient techniques for soft tissue modeling and simulation

Performing realistic deformation simulations in real time is a challenging problem in computer graphics. Among numerous proposed methods including Finite Element Modeling and ChainMail, we have implemented a mass spring system because of its acceptable accuracy and speed. Mass spring systems have, however, some drawbacks such as, the determination of simulation coefficients with their iterative nature. Given the correct parameters, mass spring systems can accurately simulate tissue deformations but choosing parameters that capture nonlinear deformation behavior is extremely difficult. Since most of the applications require a large number of elements i. e. points and springs in the modeling process it is extremely difficult to reach realtime performance with an iterative method. We have developed a new parameter identification method based on neural networks. The structure of the mass spring system is modified and neural networks are integrated into this structure. The input space consists of changes in spring lengths and velocities while a "teacher" signal is chosen as the total spring force, which is expressed in terms of positional changes and applied external forces. Neural networks are trained to learn nonlinear tissue characteristics represented by spring stiffness and damping in the mass spring algorithm. The learning algorithm is further enhanced by an adaptive learning rate, developed particularly for mass spring systems. In order to avoid the iterative approach in deformation simulations we have developed a new deformation algorithm. This algorithm defines the relationships between points and springs and specifies a set of rules on spring movements and deformations. These rules result in a deformation surface, which is called the search space. The deformation algorithm then finds the deformed points and springs in the search space with the help of the defined rules. The algorithm also sets rules on each element i. e. triangle or tetrahedron so that they do not pass through each other. The new algorithm is considerably faster than the original mass spring systems algorithm and provides an opportunity for various deformation applications. We have used mass spring systems and the developed method in the simulation of craniofacial surgery. For this purpose, a patient-specific head model was generated from MRI medical data by applying medical image processing tools such as, filtering, the segmentation and polygonal representation of such model is obtained using a surface generation algorithm. Prism volume elements are generated between the skin and bone surfaces so that different tissue layers are included to the head model. Both methods produce plausible results verified by surgeons

Reusable modelling and simulation of flexible manufacturing for next generation semiconductor manufacturing facilities

Automated material handling systems (AMHS) in 300 mm semiconductor manufacturing facilities may need to evolve faster than expected considering the high performance demands on these facilities. Reusable simulation models are needed to cope with the demands of this dynamic environment and to deliver answers to the industry much faster. One vision for intrabay AMHS is to link a small group of intrabay AMHS systems, within a full manufacturing facility, together using what is called a Merge/Diverge link. This promises better operational performance of the AMHS when compared to operating two dedicated AMHS systems, one for interbay transport and the other for intrabay handling. A generic tool for modelling and simulation of an intrabay AMHS (GTIA-M&S) is built, which utilises a library of different blocks representing the different components of any intrabay material handling system. GTIA-M&S provides a means for rapid building and analysis of an intrabay AMHS under different operating conditions. The ease of use of the tool means that inexpert users have the ability to generate good models. Models developed by the tool can be executed with the merge/diverge capability enabled or disabled to provide comparable solutions to production demands and to compare these two different configurations of intrabay AMHS using a single simulation model. Finally, results from simulation experiments on a model developed using the tool were very informative in that they include useful decision making data, which can now be used to further enhance and update the design and operational characteristics of the intrabay AMHS

Conical spring and localised region methodologies for modelling of soft tissue deformation.

Considerable research efforts have been dedicated to the development of virtual reality simulators that facilitate medical students in learning anatomy and surgery in the virtual environment and to allow surgeons to rehearse the surgical procedures. The level of realism depends upon the simulation accuracy and the computational efficiency of underlying deformable models. Ideally, the deformable models should be able to simulate accurately mechanical behaviours of soft tissues with real-time visual and force feedback. Modelling soft tissue deformation is not an easy task. Due to the complexity of soft tissue properties, many methods have been proposed to model soft tissue properties. One of the most well-known methods is the Finite Element Method (FEM). In this method, the soft tissue is represented by multiple elements that are derived based on complex mathematical formulations. It has been proven that the method is able to simulate soft tissue properties accurately, but it requires high computational cost to produce real-time interaction. In this regard, the Mass Spring Method (MSM) has been proposed as an alternative. The traditional MSM model simulates soft tissue deformation by discretising the soft tissue into several mass points that are connected to each other by linear springs. The major advantage of MSM is it has an excellent computational performance. However, the MSM application is limited to linear deformation, which does not represent the actual behaviour of the soft tissue deformation. In this thesis, an improved MSM model has been proposed to simulate the complex behaviour of soft tissue deformations. The improved MSM model is called conical spring model which considers the general behaviour of soft tissue deformation that is a combination of linear and nonlinear responses. Piecewise approach is used to discretise each response individually, and the conical spring methodology is used to model the deformation behaviours during all the responses. The piecewise approach gives precision in modelling while the conical spring methodology that was founded on stiffness variation, has improved the accuracy of the simulation due to its ability to model any type of linear and nonlinear responses. Moreover, the generated conical spring model is based on the force propagation approach. The computational performance of the model relies on the number of nodes involved in the propagation of the force. Inherently, computational time can be improved by considering the nodes only in a deformation area, and ignoring the other nodes. Soft tissue deformation commonly occurs only within a local region. As the effect of the deformation outside the local region is very little, it can be ignored in real practice. In this thesis, methods to define the local region were proposed. The methods are based on the linear elastic theory. As reported in Chapter 4 of this thesis, the localised region was generated based on displacement value induced when the simulation model was subjected to an external load. The Boussinesq method, which is widely used in the soil mechanics, was used to estimate the induced displacement value. However, the Boussinesq method is limited to the isotropic material. Therefore, as described in Chapter 5, the study has extended the isotropic localised region to anisotropic localised region by introducing an anisotropic factor which was derived based on cross-anisotropic properties. By using the anisotropic factor, the anisotropic localised region is determined from the corresponding isotropic case. Alternatively, in Chapter 6, we have presented a localised region that was generated based on stress value induced during a loading process. It is shown for point load type of contact, in comparison to ABAQUS analysis, stress based localisation has a better accuracy than the displacement based localisation. However, the stress value that is also determined using the Boussinesq method, has no relation to the material properties. Hence, a combination of the Hertzian and the Boussinesq method was used to generate localised regions with respect to the material properties and loading conditions. In the final chapter, contributions of the study were discussed, and some of the future works to expand the research were listed out

Efficient techniques for soft tissue modeling and simulation

Performing realistic deformation simulations in real time is a challenging problem in computer graphics. Among numerous proposed methods including Finite Element Modeling and ChainMail, we have implemented a mass spring system because of its acceptable accuracy and speed. Mass spring systems have, however, some drawbacks such as, the determination of simulation coefficients with their iterative nature. Given the correct parameters, mass spring systems can accurately simulate tissue deformations but choosing parameters that capture nonlinear deformation behavior is extremely difficult. Since most of the applications require a large number of elements i. e. points and springs in the modeling process it is extremely difficult to reach realtime performance with an iterative method. We have developed a new parameter identification method based on neural networks. The structure of the mass spring system is modified and neural networks are integrated into this structure. The input space consists of changes in spring lengths and velocities while a "teacher" signal is chosen as the total spring force, which is expressed in terms of positional changes and applied external forces. Neural networks are trained to learn nonlinear tissue characteristics represented by spring stiffness and damping in the mass spring algorithm. The learning algorithm is further enhanced by an adaptive learning rate, developed particularly for mass spring systems. In order to avoid the iterative approach in deformation simulations we have developed a new deformation algorithm. This algorithm defines the relationships between points and springs and specifies a set of rules on spring movements and deformations. These rules result in a deformation surface, which is called the search space. The deformation algorithm then finds the deformed points and springs in the search space with the help of the defined rules. The algorithm also sets rules on each element i. e. triangle or tetrahedron so that they do not pass through each other. The new algorithm is considerably faster than the original mass spring systems algorithm and provides an opportunity for various deformation applications. We have used mass spring systems and the developed method in the simulation of craniofacial surgery. For this purpose, a patient-specific head model was generated from MRI medical data by applying medical image processing tools such as, filtering, the segmentation and polygonal representation of such model is obtained using a surface generation algorithm. Prism volume elements are generated between the skin and bone surfaces so that different tissue layers are included to the head model. Both methods produce plausible results verified by surgeons.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Equality Index And Learning In Recurrent Fuzzy Neural Networks

A novel learning algorithm for recurrent neurofuzzy networks is introduced in this paper. The core of the learning algorithm uses equality index as the performance measure to be optimized. Equality index is especially important because its properties reflect the fuzzy set-based structure of the neural network and nature of learning. Equality indexes are strongly tied with the properties of the fuzzy set theory and logic-based techniques. The neural network recurrent topology is built with fuzzy neuron units and performs neural processing consistent with fuzzy system methodology. Therefore neural processing and learning are fully embodied within fuzzy set theory. The performance recurrent neurofuzzy network is verified via examples of nonlinear systems modeling. Computational experiments show that the recurrent fuzzy neural models developed are simpler and that learning is faster than both, static neural and neural fuzzy networks and alternative recurrent fuzzy neural networks.1155160Ku, C.C., Lee, K.L., Diagonal recurrent neural networks for dynamic systems control (1995) IEEE Trans. on Neural Networks, 6, pp. 144-156. , JanuaryWilliams, R.J., Zipser, D., A learning algorithm for continually running fully recurrent neural networks (1989) Neural Computation, (1), pp. 270-280Lee, C.H., Teng, C.C., Identification and control of dynamic systems using recurrent fuzzy neural networks (2000) IEEE Trans. on Fuzzy Systems, 8 (4), pp. 3493-366. , AugustBuckley, A., Hayashi, Y., Fuzzy neural networks: A survey (1994) Fuzzy Sets and Systems, 66, pp. 41-49Lin, C.T., Lee, C.S., (1996) Neural Fuzzy Systems: A Neuro-Fuzzy Synergism to Intelligent Systems, , Prentice Hall, Upper Saddle River, N.JNürnberger, A., Radetzky, A., Kruse, R., Using recurrent neuro-fuzzy techniques for the identification and simulation of dynamic systems (2001) Neurocomputing, 36, pp. 123-147Nürnberger, A., A hierarchical recurrent neuro-fuzzy system (2001) Proc. of Joint 9th IFSA World Congress and 20th NAFIPS International Conference, pp. 1407-1412. , IEEEPedrycz, W., Neurocomputations in relational systems (1991) IEEE Trans. Pattern Analysis and Machine Intelligence, 13 (3), pp. 289-297. , MarchPedrycz, W., Gomide, F., (1998) An Introduction to Fuzzy Sets: Analysis and Design, , MIT Press, Cambridge, MACaminhas, W., Tavares, H., Gomide, F., Pedrycz, W., Fuzzy set based neural networks: Structure, learning and application (1999) Journal of Advanced Computational Intelligence, 3 (3), pp. 151-157Ballini, R., Scares, S., Gomide, F., A recurrent neurofuzzy network structure and learning procedure (2001) Proc. of the 10th IEEE International Conference on Fuzzy Systems - FUZZ-IEEE'2001, 3Pedrycz, W., Rocha, A., Fuzzy-set based models of neuron and knowledge-based networks (1998) IEEE Trans. on Fuzzy Systems, 4 (1), pp. 254-266Nozaki, K., Ishibuchi, H., Tanaka, H., Trainable fuzzy classification systems based on fuzzy if-then rules (1994) Proc. of the 3th IEEE International Conference on Fuzzy Systems, pp. 408-502Oliveira, M., Figueiredo, M., Gomide, F., A neurofuzzy approach for autonomous control (1994) Proc. of the 3th IEEE International Conference on Fuzzy Systems, Neural Nets and Soft Computing, pp. 597-598Rumelhart, D., Hinten, G.E., Williams, R.J., Learning representations by back-propagating errors (1986) Nature (London), 323, pp. 533-536Barto, A.G., Jordan, M.I., Gradient following without back-propagation in layered networks (1987) Proc. of the IEEE 1st International Conference on Neural Networks, 2, pp. 629-636. , San DiegoNarendra, K.S., Parthasarathy, K., Identification and control of dynamical systems using neural networks (1990) IEEE Trans. on Neural Networks, 1 (1)Wang, L.X., (1994) Adaptive Fuzzy Systems and Control, , Prentice-Hal