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Добірка наукової літератури з теми "ZERO INITIALIZATION"

Автор: Grafiati
Опубліковано: 11 вересня 2023

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Статті в журналах з теми "ZERO INITIALIZATION"

1

Shiratsuchi, Hiroshi, Hiromu Gotanda, Katsuhiro Inoue, and Kousuke Kumamaru. "Studies on Effects of Initialization on Structure Formationand Generalization of Structural Learning with Forgetting." Journal of Advanced Computational Intelligence and Intelligent Informatics 8, no. 6 (November 20, 2004): 621–26. http://dx.doi.org/10.20965/jaciii.2004.p0621.

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Анотація:
In this paper, our proposed initialization for multilayer neural networks (NN) applies to the structural learning with forgetting. Initialization consists of two steps: weights of hidden units are initialized so that their hyperplanes pass through the center of gravity of an input pattern set, and weights of output units are initialized to zero. Several simulations were performed to study how the initialization effects the structure formation of the NN. From the simulation result, it was confirmed that the initialization gives better network structure and higher generalization ability.
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2

Shiratsuchi, Hiroshi, Hiromu Gotanda, Katsuhiro Inoue, and Kousuke Kumamaru. "Effects of Initialization on Rule Extraction in Structural Learning." Journal of Advanced Computational Intelligence and Intelligent Informatics 12, no. 1 (January 20, 2008): 57–62. http://dx.doi.org/10.20965/jaciii.2008.p0057.

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Анотація:
This paper studies how our previously proposed initialization effects the rule extraction of neural networks by structural learning with forgetting. The proposed initialization consists of two steps: (1) initializing weights of hidden units so that their separation hyperplanes should pass through the center of an input pattern set and (2) initializing those of output units to zero. From simulation results on Boolean function discovery problems with 5 and 7 inputs, it has been confirmed that the proposed initialization yields a simpler network structure and higher rule extraction ability than the conventional initialization giving uniform random number to all the initial weights of the network.
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3

Danesh, Mohamad H. "Reducing Neural Network Parameter Initialization Into an SMT Problem (Student Abstract)." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 18 (May 18, 2021): 15775–76. http://dx.doi.org/10.1609/aaai.v35i18.17884.

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Анотація:
Training a neural network (NN) depends on multiple factors, including but not limited to the initial weights. In this paper, we focus on initializing deep NN parameters such that it performs better, comparing to random or zero initialization. We do this by reducing the process of initialization into an SMT solver. Previous works consider certain activation functions on small NNs, however the studied NN is a deep network with different activation functions. Our experiments show that the proposed approach for parameter initialization achieves better performance comparing to randomly initialized networks.
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4

Xu, Xiaozeng, and Chuanjiang He. "Implicit Active Contour Model with Local and Global Intensity Fitting Energies." Mathematical Problems in Engineering 2013 (2013): 1–13. http://dx.doi.org/10.1155/2013/367086.

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Анотація:
We propose a new active contour model which integrates a local intensity fitting (LIF) energy with an auxiliary global intensity fitting (GIF) energy. The LIF energy is responsible for attracting the contour toward object boundaries and is dominant near object boundaries, while the GIF energy incorporates global image information to improve the robustness to initialization of the contours. The proposed model not only can provide desirable segmentation results in the presence of intensity inhomogeneity but also allows for more flexible initialization of the contour compared to the RSF and LIF models, and we give a theoretical proof to compute a unique steady state regardless of the initialization; that is, the convergence of the zero-level line is irrespective of the initial function. This means that we can obtain the same zero-level line in the steady state, if we choose the initial function as a bounded function. In particular, our proposed model has the capability of detecting multiple objects or objects with interior holes or blurred edges.
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5

Ahn, Jaemyung, Jun Bang, and Sang-Il Lee. "Acceleration of Zero-Revolution Lambert’s Algorithms Using Table-Based Initialization." Journal of Guidance, Control, and Dynamics 38, no. 2 (February 2015): 335–42. http://dx.doi.org/10.2514/1.g000764.

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6

Yang, Fuxin, Chuanlei Zheng, Hui Li, Liang Li, Jie Zhang, and Lin Zhao. "Continuity Enhancement Method for Real-Time PPP Based on Zero-Baseline Constraint of Multi-Receiver." Remote Sensing 13, no. 4 (February 8, 2021): 605. http://dx.doi.org/10.3390/rs13040605.

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Анотація:
Continuity is one of the metrics that characterize the required navigation performance of global navigation satellite system (GNSS)-based applications. Data outage due to receiver failure is one of the reasons for continuity loss. Although a multi-receiver configuration can maintain position solutions in case a receiver has data outage, the initialization of the receiver will also cause continuous high-precision positioning performance loss. To maintain continuous high-precision positioning performance of real-time precise point positioning (RT-PPP), we proposed a continuity enhancement method for RT-PPP based on zero-baseline constraint of multi-receiver. On the one hand, the mean time to repair (MTTR) of the multi-receiver configuration is improved to maintain continuous position solutions. On the other hand, the zero-baseline constraint of multi-receiver including between-satellite single-differenced (BSSD) ambiguities, zenith troposphere wet delay (ZWD), and their suitable stochastic models are constructed to achieve instantaneous initialization of back-up receiver. Through static and kinematic experiments based on real data, the effectiveness and robustness of proposed method are evaluated comprehensively. The experiment results show that the relationship including BSSD ambiguities and ZWD between receivers can be determined reliably based on zero-baseline constraint, and the instantaneous initialization can be achieved without high-precision positioning continuity loss in the multi-receiver RT-PPP processing.
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7

Zhang, Shuiping, Shun Li, Tianrui Luan, Yixing Chen, and Suirong Li. "A Zero-injection Initialization Numerical Method for Ill-conditioned Power Flow." IOP Conference Series: Earth and Environmental Science 1050, no. 1 (July 1, 2022): 012009. http://dx.doi.org/10.1088/1755-1315/1050/1/012009.

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Анотація:
Abstract Regarding the initial value sensitivity issues commonly found in Newton-like power flow calculation algorithms, a zero-injection initialization numerical method for ill-conditioned power flow is proposed in this paper, which improves the convergence of power flow calculation in ill-conditioned power systems. Based on the initial power flow provided by this method, the unbalanced active power of each bus (the slack bus excluded) relates to the net injection of active power of that bus and the unbalanced reactive power of each PQ bus relates to the net injection of reactive power of that bus, thus avoiding significant power unbalance. The method put forward herein can provide a reasonable initial value for the power flow calculation so that difficulty in power flow convergence caused by the unreasonable initial power flow is effectively avoided. The Newton’s method and the optimal multiplier method are implemented respectively on a small-scale ill-conditioned test system to verify the feasibility of the proposed zero-injection initialization numerical method.
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8

Chen, Chi-Chung, and Yi-Ting Liu. "Enhanced Ant Colony Optimization with Dynamic Mutation and Ad Hoc Initialization for Improving the Design of TSK-Type Fuzzy System." Computational Intelligence and Neuroscience 2018 (2018): 1–15. http://dx.doi.org/10.1155/2018/9485478.

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Анотація:
This paper proposes an enhanced ant colony optimization with dynamic mutation and ad hoc initialization, ACODM-I, for improving the accuracy of Takagi-Sugeno-Kang- (TSK-) type fuzzy systems design. Instead of the generic initialization usually used in most population-based algorithms, ACODM-I proposes an ad hoc application-specific initialization for generating the initial ant solutions to improve the accuracy of fuzzy system design. The generated initial ant solutions are iteratively improved by a new approach incorporating the dynamic mutation into the existing continuous ACO (ACOR). The introduced dynamic mutation balances the exploration ability and convergence rate by providing more diverse search directions in the early stage of optimization process. Application examples of two zero-order TSK-type fuzzy systems for dynamic plant tracking control and one first-order TSK-type fuzzy system for the prediction of the chaotic time series have been simulated to validate the proposed algorithm. Performance comparisons with ACOR and different advanced algorithms or neural-fuzzy models verify the superiority of the proposed algorithm. The effects on the design accuracy and convergence rate yielded by the proposed initialization and introduced dynamic mutation have also been discussed and verified in the simulations.
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9

Eng, Wei Yong, Yang Lang Chang, Tien Sze Lim, and Voon Chet Koo. "A Dense Optical Flow Field Estimation with Variational Refinement." Journal of Engineering Technology and Applied Physics 1, no. 2 (December 17, 2019): 10–13. http://dx.doi.org/10.33093/jetap.2019.1.2.3.

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Анотація:
Optical flow has long been a focus of research study in computer vision community. Researchers have established extensive work to solve the optical flow estimation. Among the published works, a notable work using variational energy minimization has been a baseline of optical flow estimation for a long time. Variational optical flow optimization solves an approximate global minimum in a well-defined nonlinear Markov Energy formulation. It works by first linearizing the energy model and uses a numerical method specifically successive over-relaxation (SOR) method to solve the resulting linear model. An initialization scheme is required for optical flow field in this iterative optimization method. In the original work, a zero initialization is proposed and it works well on the various environments with photometric and geometric distortion. In this work, we have experimented with different flow field initialization scheme under various environment setting. We found out that variational refinement with a good initial flow estimate using state-of-art optical flow algorithms can further improve its accuracy performance.
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10

Yong Eng, Wei, Yang Lang Chang, Tien Sze Lim, and Voon Chet Koo. "A Dense Optical Flow Field Estimation with Variational Refinement." Journal of Engineering Technology and Applied Physics 1, no. 2 (December 17, 2019): 10–13. http://dx.doi.org/10.33093/jetap.2019.1.2.30.

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Анотація:
Optical flow has long been a focus of research study in computer vision community. Researchers have established extensive work to solve the optical flow estimation. Among the published works, a notable work using variational energy minimization has beena baseline of optical flow estimation for a long time. Variational optical flow optimization solves an approximate global minimum in a well-defined non-linear Markov Energy formulation. It works by first linearizing the energy model and uses a numerical method specifically successive over-relaxation (SOR) method to solve the resulting linear model. An initialization scheme is required for optical flow field in this iterative optimization method. In the original work, a zero initialization is proposed and it works well on the various environments with photometric and geometric distortion. In this work, we have experimented with different flow field initialization scheme under various environment setting. We found out that variational refinement with a good initial flow estimate using state-of-art optical flow algorithms can further improve its accuracy performance.
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Дисертації з теми "ZERO INITIALIZATION"

1

Hakala, Tim. "Settling-Time Improvements in Positioning Machines Subject to Nonlinear Friction Using Adaptive Impulse Control." BYU ScholarsArchive, 2006. https://scholarsarchive.byu.edu/etd/1061.

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Анотація:
A new method of adaptive impulse control is developed to precisely and quickly control the position of machine components subject to friction. Friction dominates the forces affecting fine positioning dynamics. Friction can depend on payload, velocity, step size, path, initial position, temperature, and other variables. Control problems such as steady-state error and limit cycles often arise when applying conventional control techniques to the position control problem. Studies in the last few decades have shown that impulsive control can produce repeatable displacements as small as ten nanometers without limit cycles or steady-state error in machines subject to dry sliding friction. These displacements are achieved through the application of short duration, high intensity pulses. The relationship between pulse duration and displacement is seldom a simple function. The most dependable practical methods for control are self-tuning; they learn from online experience by adapting an internal control parameter until precise position control is achieved. To date, the best known adaptive pulse control methods adapt a single control parameter. While effective, the single parameter methods suffer from sub-optimal settling times and poor parameter convergence. To improve performance while maintaining the capacity for ultimate precision, a new control method referred to as Adaptive Impulse Control (AIC) has been developed. To better fit the nonlinear relationship between pulses and displacements, AIC adaptively tunes a set of parameters. Each parameter affects a different range of displacements. Online updates depend on the residual control error following each pulse, an estimate of pulse sensitivity, and a learning gain. After an update is calculated, it is distributed among the parameters that were used to calculate the most recent pulse. As the stored relationship converges to the actual relationship of the machine, pulses become more accurate and fewer pulses are needed to reach each desired destination. When fewer pulses are needed, settling time improves and efficiency increases. AIC is experimentally compared to conventional PID control and other adaptive pulse control methods on a rotary system with a position measurement resolution of 16000 encoder counts per revolution of the load wheel. The friction in the test system is nonlinear and irregular with a position dependent break-away torque that varies by a factor of more than 1.8 to 1. AIC is shown to improve settling times by as much as a factor of two when compared to other adaptive pulse control methods while maintaining precise control tolerances.
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2

VEDANSHU. "MODELLING TECHNIQUES FOR ELECTRICITY LOAD FORECASTING." Thesis, 2019. http://dspace.dtu.ac.in:8080/jspui/handle/repository/17239.

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Анотація:
SinglelayerFeedforwardNeuralNetwork(FNN)isusedmanyatime asalastlayerinmodelssuchasseq2seqorasimpleRNNnetwork. The importance of such layer is to transform the output to our required dimensions. When it comes to weights and biases initialization, there is no such specific technique that could speed up the learning process. We could depend on deep network initialization techniques such as Xavier or He initialization. But such initialization fails to show much improvement in learning speed or accuracy. Zero Initialization (ZI) for weights of a single layer network is proposed here. We first test this technique with on a simple RNN network and compare the results against Xavier, He and Identity initialization. As a final test we implement it on a seq2seq network. It was found that ZI considerably reduces the number of epochs used and improve the accuracy. Multi-objective swarm intelligence is also utilized for weights and biases initialization for quicker learning. The developed model has been applied for short-term load forecastingusingtheloaddataofAustralianEnergyMarket. Themodel is able to forecast the day ahead price accurately with error of 0.94%.
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Частини книг з теми "ZERO INITIALIZATION"

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Janovic, Jan. "Fabric Initialization and Management." In Cisco ACI: Zero to Hero, 61–145. Berkeley, CA: Apress, 2022. http://dx.doi.org/10.1007/978-1-4842-8838-2_3.

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2

Krishnamurti, T. N., H. S. Bedi, and V. M. Hardiker. "Initialization Procedures." In An Introduction to Global Spectral Modeling. Oxford University Press, 1998. http://dx.doi.org/10.1093/oso/9780195094732.003.0011.

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In this chapter we describe two of the most commonly used initialization procedures. These are the dynamic normal mode initialization and the physical initialization methods. Historically, initialization for primitive equation models started from a hierarchy of static initialization methods. These include balancing the mass and the wind fields using a linear or nonlinear balance equation (Charney 1955; Phillips 1960), variational techniques for such adjustments satisfying the constraints of the model equations (Sasaki 1958), and dynamic initialization involving forward and backward integration of the model over a number of cycles to suppress high frequency gravity oscillations before the start of the integration (Miyakoda and Moyer 1968; Nitta and Hovermale 1969; Temperton 1976). A description of these classical methods can be found in textbooks such as Haltiner and Williams (1980). Basically, these methods invoke a balanced relationship between the mass and motion fields. However, it was soon realized that significant departures from the balance laws do occur over the tropics and the upperlevel jet stream region. It was also noted that such departures can be functions of the heat sources and sinks and dynamic instabilities of the atmosphere. The procedure called nonlinear normal mode initialization with physics overcomes some of these difficulties. Physical initialization is a powerful method that permits the incorporation of realistic rainfall distribution in the model’s initial state. This is an elegant and successful initialization procedure based on selective damping of the normal modes of the atmosphere, where the high-frequency gravity modes are suppressed while the slow-moving Rossby modes are left untouched. Williamson (1976) used the normal modes of a shallow water model for initialization by setting the initial amplitudes of the high frequency gravity modes equal to zero. Machenhauer (1977) and Baer (1977) developed the procedure for nonlinear normal mode initialization (NMI), which takes into account the nonlinearities in the model equations. Kitade (1983) incorporated the effect of physical processes in this initialization procedure. We describe here the normal mode initialization procedure. Essentially following Kasahara and Puri (1981), we first derive the equations for vertical and horizontal modes of the linearized form of the model equations.
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3

Labiod, Salim, Hamid Boubertakh, and Thierry Marie Guerra. "Indirect Adaptive Fuzzy Control for a Class of Uncertain Nonlinear Systems with Unknown Control Direction." In Contemporary Theory and Pragmatic Approaches in Fuzzy Computing Utilization, 139–54. IGI Global, 2013. http://dx.doi.org/10.4018/978-1-4666-1870-1.ch010.

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Анотація:
In this paper, the authors propose two indirect adaptive fuzzy control schemes for a class of uncertain continuous-time single-input single-output (SISO) nonlinear dynamic systems with known and unknown control direction. Within these schemes, fuzzy systems are used to approximate unknown nonlinear functions and the Nussbaum gain technique is used to deal with the unknown control direction. This paper first presents a singularity-free indirect adaptive control algorithm for nonlinear systems with known control direction, and then this control algorithm is generalized for the case of unknown control direction. The proposed adaptive controllers are free from singularity, allow initialization to zero of all adjustable parameters of the used fuzzy systems, and guarantee asymptotic convergence of the tracking error to zero. Simulations performed on a nonlinear system are given to show the feasibility of the proposed adaptive control schemes.
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4

Singh, Uday Pratap, Sanjeev Jain, Akhilesh Tiwari, and Rajeev Kumar Singh. "Nature-Inspired-Based Adaptive Neural Network Approximation for Uncertain System." In Handbook of Research on Emergent Applications of Optimization Algorithms, 439–61. IGI Global, 2018. http://dx.doi.org/10.4018/978-1-5225-2990-3.ch019.

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Анотація:
In this chapter, we focus and studied on some important nature inspired optimization methods like Particle Swarm Optimization (PSO), Firefly Algorithm (FA), Cuckoo Search (CS), Bat Algorithm (BA) and Flower Pollination Algorithm (FPA) are used for assign initial weights of Back-Propagation Neural Network (BPN). Success of neural networks are sturdily depends on different parameters and initialization weight is one, these nature inspired methods are used for optimization of mean square error (MSE) and mean absolute percentage error (MAPE) are used as test functions. The proposed method is based updating population, moving positions and obtain best solution space. The combination of nature inspired method and neural network were developed with the scope of creating an improved balance between premature convergence and stagnation. The performance of the proposed method is tested on two nonlinear systems. Results of FANN, CSNN, BANN and FPANN envisage that the proposed method exhibit high level of identification accuracy and its tracking error is closed in the neighbourhood of zero.
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Тези доповідей конференцій з теми "ZERO INITIALIZATION"

1

Masood, Sarfaraz, and Pravin Chandra. "Training neural network with zero weight initialization." In the CUBE International Information Technology Conference. New York, New York, USA: ACM Press, 2012. http://dx.doi.org/10.1145/2381716.2381761.

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2

Adams, Jay L., and Tom T. Hartley. "Finite-Time Controllability of Fractional-Order Systems." In ASME 2007 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2007. http://dx.doi.org/10.1115/detc2007-35746.

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In this paper the conditions that lead to a system output remaining at zero with zero input are considered. It is shown that the initialization of fractional-order integrators plays a key role in determining whether the integrator output will remain at a zero with zero input. Three examples are given that demonstrate the importance of initialization for integrators of order less than unity, inclusive. Two examples give a concrete illustration of the role that initialization plays in keeping the output of a fractional-order integrator at zero once it has been driven to zero. The implications of these results are considered, with special consideration given to the formulation of the fractional-order optimal control problem.
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3

Xia, Lanfang, and Peng Hu. "Data Mining for Spatial Relations Based on Zero Initialization." In 2008 International Conference on Advanced Language Processing and Web Information Technology. IEEE, 2008. http://dx.doi.org/10.1109/alpit.2008.24.

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4

Lorenzo, Carl F., and Tom T. Hartley. "Time-Varying Initialization and Laplace Transform of the Caputo Derivative: With Order Between Zero and One." In ASME 2011 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2011. http://dx.doi.org/10.1115/detc2011-47396.

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Анотація:
This paper derives the time-varying initialization function for the Caputo derivative with order between zero and one. The derivative is redefined to include this initialization function. Then, the Laplace transform for the redefined Caputo derivative is determined which corrects (supplants) that given for the derivative in the literature and properly accounts for time-varying initialization effects.
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5

Raca, Dejan, Michael C. Harke, and Robert D. Lorenz. "Robust Magnet Polarity Estimation for Initialization of PM Synchronous Machines with Near Zero Saliency." In Conference Record of the 2006 IEEE Industry Applications Conference Forty-First IAS Annual Meeting. IEEE, 2006. http://dx.doi.org/10.1109/ias.2006.256563.

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6

Cao, Chengyu, and Naira Hovakimyan. "Effect of Non-Zero Initialization Error on the Performance Bounds in L1 Adaptive Control Architecture." In AIAA Guidance, Navigation and Control Conference and Exhibit. Reston, Virigina: American Institute of Aeronautics and Astronautics, 2007. http://dx.doi.org/10.2514/6.2007-6645.

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7

Jiang, Long, and Shikui Chen. "Parametric Structural Shape and Topology Optimization With a Variational Distance-Regularized Level Set Method." In ASME 2016 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2016. http://dx.doi.org/10.1115/detc2016-59350.

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Анотація:
In conventional level set methods, the slope of the level set function needs to be well controlled to maintain the numerical stability during the topology optimization process. One common solution is to regularize the level set function to be a signed distance function, which is usually achieved by periodically implementing the so called re-initialization scheme to force the level set function to gain the desired signed distance property. However, the re-initialization scheme will bring some unwanted drawbacks to the optimization process, such as zero level set drifting, time consuming etc. In addition, re-initialization is usually implemented outside the optimization loop, which will cause convergence issues. In this paper, a distance regularization functional is introduced to the structural topology optimization objective functional to ensure the signed distance property of the level set function near the structure boundaries. This functional can also keep the level set function to be constant-value at positions far away from the structural boundaries. The radial basis function (RBF) based parameterization technique together with the mathematical programming are utilized to improve the potential capability of handling multiple constraints for the topology optimization. The combination of these two techniques makes the level set based topology optimization be capable of handling complicated multi-constrained problems with higher numerical efficiency, leaving no compromise to multiple drawbacks. To demonstrate the validity of the proposed scheme, benchmark examples on minimum compliance structural optimization are employed. This type of problem is computed by the conventional level set method with the introduced distance regularization functional, the RBF based parametric level set and at last, the distance regularized RBF based parametric level set separately to demonstrate their differences.
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8

Schmidt, Robert, Matthew Begneaud, and Joshua Vaughan. "Tracking of a Target Payload via a Combination of Input Shaping, Zero Phase Error Tracking Control, and Fuzzy Logic." In ASME 2016 Dynamic Systems and Control Conference. American Society of Mechanical Engineers, 2016. http://dx.doi.org/10.1115/dscc2016-9890.

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Анотація:
During crane operation, the task of retrieval and deployment of payloads can be partitioned into two components: the initial move towards the target or deployment location and the retrieval or deployment of the payload. If the payload is not stationary, as is the case in the retrieval of a sea-going vessel, a third component, tracking, must be included. The target payload in this research is an Autonomous Surface Vehicle (ASV) primarily used for surveying. This paper studies the transition between the initial move towards the payload and the initialization of tracking. Input Shaping is used to limit residual vibration caused by the initial move to the ASV. A set of Fuzzy Logic membership functions are then used to transition from the initial move to the tracking portion of the retrieval process. These membership functions map position and velocity error to a gain that is applied to the tracking controller. As the gain increases, the contribution of the tracking controller input is increased. Zero Phase Error Tracking Control is utilized for accurate tracking of the target payload. Through a combination of these control methods, the tracking accuracy is improved.
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Veenu, M. P. S. Bhatia, and P. Chandra. "Impact of Gaussian learning rate on training of sigmoidal FFANN using zero and random weight initializations." In Fifth International Conference on Advances in Recent Technologies in Communication and Computing (ARTCom 2013). Institution of Engineering and Technology, 2013. http://dx.doi.org/10.1049/cp.2013.2239.

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