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Academic literature on the topic 'Network synthesi'

Author: Grafiati

Published: 11 March 2023

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Journal articles on the topic "Network synthesi"

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Hau, Than Nguyen, Hiroshi Hirai, and Nobuyuki Tsuchimura. "ON HALF-INTEGRALITY OF NETWORK SYNTHESIS PROBLEM." Journal of the Operations Research Society of Japan 57, no. 2 (2014): 63–73. http://dx.doi.org/10.15807/jorsj.57.63.

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Leoshchenko, S. D., A. O. Oliinyk, S. A. Subbotin, Ye O. Gofman, and M. B. Ilyashenko. "EVOLUTIONARY METHOD FOR SYNTHESIS SPIKING NEURAL NETWORKS USING THE NEUROPATTHERN MECHANISM." Radio Electronics, Computer Science, Control, no. 3 (October 20, 2022): 77. http://dx.doi.org/10.15588/1607-3274-2022-3-8.

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Context. The problem of synthesizing pulsed neural networks based on an evolutionary approach to the synthesis of artificial neural networks using a neuropathic mechanism for constructing diagnostic models with a high level of accuracy is considered. The object of research is the process of synthesis of pulsed neural networks using an evolutionary approach and a neuropathic mechanism. Objective of the work is to develop a method for synthesizing pulsed neural networks based on an evolutionary approach using a neuropathic mechanism to build diagnostic models with a high level of accuracy of work. Method. A method for synthesizing pulsed neural networks based on an evolutionary approach is proposed. At the beginning, a population of pulsed neural networks is generated, and a neuropathic mechanism is used for their encoding and further development, which consists in separate encoding of neurons with different activation functions that are determined beforehand. So each pattern with multiple entry points can define the relationship between a pair of points. In the future, this simplifies the evolutionary development of networks. To decipher a pulsed neural network from a pattern, the coordinates for a pair of neurons are passed to the network that creates the pattern. The network output determines the weight and delay of the connection between two neurons in a pulsed neural network. After that, you can evaluate each neuromodel after evolutionary changes and check the criteria for stopping synthesis. This method allows you to reduce the resource intensity during network synthesis by abstracting the evolutionary changes of the network pattern from itself. Results. The developed method is implemented and investigated on the example of the synthesis of a pulsed neural network for use as a model for technical diagnostics. Using the developed method to increase the accuracy of the neuromodel with a test sample by 20%, depending on the computing resources used. Conclusions. The conducted experiments confirmed the operability of the proposed mathematical software and allow us to recommend it for use in practice in the synthesis of pulsed neural networks as the basis of diagnostic models for further automation of tasks of diagnostics, forecasting, evaluation and pattern recognition using big data. Prospects for further research may lie in the use of a neuropathic mechanism for indirect encoding of pulsed neural networks, which will provide even more compact data storage and speed up the synthesis process.

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Antoniou, Josephina, Ioannis Koukoutsidis, Eva Jaho, Andreas Pitsillides, and Ioannis Stavrakakis. "Access network synthesis game in next generation networks." Computer Networks 53, no. 15 (October 2009): 2716–26. http://dx.doi.org/10.1016/j.comnet.2009.06.006.

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Góes-Neto, Aristóteles, Marcelo V. C. Diniz, Daniel S. Carvalho, Gilberto C. Bomfim, Angelo A. Duarte, Jerzy A. Brzozowski, Thierry C. Petit Lobão, Suani T. R. Pinho, Charbel N. El-Hani, and Roberto F. S. Andrade. "Comparison of complex networks and tree-based methods of phylogenetic analysis and proposal of a bootstrap method." PeerJ 6 (February 9, 2018): e4349. http://dx.doi.org/10.7717/peerj.4349.

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Complex networks have been successfully applied to the characterization and modeling of complex systems in several distinct areas of Biological Sciences. Nevertheless, their utilization in phylogenetic analysis still needs to be widely tested, using different molecular data sets and taxonomic groups, and, also, by comparing complex networks approach to current methods in phylogenetic analysis. In this work, we compare all the four main methods of phylogenetic analysis (distance, maximum parsimony, maximum likelihood, and Bayesian) with a complex networks method that has been used to provide a phylogenetic classification based on a large number of protein sequences as those related to the chitin metabolic pathway and ATP-synthase subunits. In order to perform a close comparison to these methods, we selected Basidiomycota fungi as the taxonomic group and used a high-quality, manually curated and characterized database of chitin synthase sequences. This enzymatic protein plays a key role in the synthesis of one of the exclusive features of the fungal cell wall: the presence of chitin. The communities (modules) detected by the complex network method corresponded exactly to the groups retrieved by the phylogenetic inference methods. Additionally, we propose a bootstrap method for the complex network approach. The statistical results we have obtained with this method were also close to those obtained using traditional bootstrap methods.

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Fan, Qitang, Linghao Yan, Matthias W. Tripp, Ondřej Krejčí, Stavrina Dimosthenous, Stefan R. Kachel, Mengyi Chen, et al. "Biphenylene network: A nonbenzenoid carbon allotrope." Science 372, no. 6544 (May 20, 2021): 852–56. http://dx.doi.org/10.1126/science.abg4509.

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The quest for planar sp2-hybridized carbon allotropes other than graphene, such as graphenylene and biphenylene networks, has stimulated substantial research efforts because of the materials’ predicted mechanical, electronic, and transport properties. However, their syntheses remain challenging given the lack of reliable protocols for generating nonhexagonal rings during the in-plane tiling of carbon atoms. We report the bottom-up growth of an ultraflat biphenylene network with periodically arranged four-, six-, and eight-membered rings of sp2-hybridized carbon atoms through an on-surface interpolymer dehydrofluorination (HF-zipping) reaction. The characterization of this biphenylene network by scanning probe methods reveals that it is metallic rather than a dielectric. We expect the interpolymer HF-zipping method to complement the toolbox for the synthesis of other nonbenzenoid carbon allotropes.

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Yang, Hae-Chan, Sang-Jun Park, Kwoan-Young Park, Jae-Hyun Sa, and Tae-Hwan Kim. "High-level Synthesis Design and Implementation of an Efficient Capsule Network Inference System in an FPGA." Journal of the Institute of Electronics and Information Engineers 58, no. 11 (November 30, 2021): 39–47. http://dx.doi.org/10.5573/ieie.2021.58.11.39.

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KURUMIDA, Junya, and Shu NAMIKI. "Demonstration of optical communication network for ultra high-definition image transmission." Synthesiology English edition 4, no. 2 (2011): 108–18. http://dx.doi.org/10.5571/syntheng.4.108.

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V.M., Sineglazov, and Chumachenko O.I. "Structural-parametric synthesis of deep learning neural networks." Artificial Intelligence 25, no. 4 (December 25, 2020): 42–51. http://dx.doi.org/10.15407/jai2020.04.042.

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The structural-parametric synthesis of neural networks of deep learning, in particular convolutional neural networks used in image processing, is considered. The classification of modern architectures of convolutional neural networks is given. It is shown that almost every convolutional neural network, depending on its topology, has unique blocks that determine its essential features (for example, Squeeze and Excitation Block, Convolutional Block of Attention Module (Channel attention module, Spatial attention module), Residual block, Inception module, ResNeXt block. It is stated the problem of structural-parametric synthesis of convolutional neural networks, for the solution of which it is proposed to use a genetic algorithm. The genetic algorithm is used to effectively overcome a large search space: on the one hand, to generate possible topologies of the convolutional neural network, namely the choice of specific blocks and their locations in the structure of the convolutional neural network, and on the other hand to solve the problem of structural-parametric synthesis of convolutional neural network of selected topology. The most significant parameters of the convolutional neural network are determined. An encoding method is proposed that allows to repre- sent each network structure in the form of a string of fixed length in binary format. After that, several standard genetic operations were identified, i.e. selection, mutation and crossover, which eliminate weak individuals of the previous generation and use them to generate competitive ones. An example of solving this problem is given, a database (ultrasound results) of patients with thyroid disease was used as a training sample.

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Gao, Wei, Linjie Zhou, and Lvfang Tao. "A Fast View Synthesis Implementation Method for Light Field Applications." ACM Transactions on Multimedia Computing, Communications, and Applications 17, no. 4 (November 30, 2021): 1–20. http://dx.doi.org/10.1145/3459098.

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View synthesis (VS) for light field images is a very time-consuming task due to the great quantity of involved pixels and intensive computations, which may prevent it from the practical three-dimensional real-time systems. In this article, we propose an acceleration approach for deep learning-based light field view synthesis, which can significantly reduce calculations by using compact-resolution (CR) representation and super-resolution (SR) techniques, as well as light-weight neural networks. The proposed architecture has three cascaded neural networks, including a CR network to generate the compact representation for original input views, a VS network to synthesize new views from down-scaled compact views, and a SR network to reconstruct high-quality views with full resolution. All these networks are jointly trained with the integrated losses of CR, VS, and SR networks. Moreover, due to the redundancy of deep neural networks, we use the efficient light-weight strategy to prune filters for simplification and inference acceleration. Experimental results demonstrate that the proposed method can greatly reduce the processing time and become much more computationally efficient with competitive image quality.

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ISHII, ISHII, Junya KURUMIDA, and Shu NAMIKI. "Towards large-capacity, energy-efficient, and sustainable communication networks." Synthesiology English edition 7, no. 1 (2014): 30–43. http://dx.doi.org/10.5571/syntheng.7.30.

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Dissertations / Theses on the topic "Network synthesi"

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Wynants, Christelle. "Network synthesis problems." Doctoral thesis, Universite Libre de Bruxelles, 1999. http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/211871.

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Hughes, Timothy Howard. "On the synthesis of passive networks without transformers." Thesis, University of Cambridge, 2014. https://www.repository.cam.ac.uk/handle/1810/265924.

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This thesis is concerned with the synthesis of passive networks, motivated by the recent invention of a new mechanical component, the inerter, which establishes a direct analogy between mechanical and electrical networks. We investigate the minimum numbers of inductors, capacitors and resistors required to synthesise a given impedance, with a particular focus on transformerless network synthesis. The conclusions of this thesis are relevant to the design of compact and cost-effective mechanical and electrical networks for a broad range of applications. In Part 1, we unify the Laplace-domain and phasor approach to the analysis of transformerless networks, using the framework of the behavioural approach. We show that the autonomous part of any driving-point trajectory of a transformerless network decays to zero as time passes. We then consider the trajectories of a transformerless network, which describe the permissible currents and voltages in the elements and at the driving-point terminals. We show that the autonomous part of any trajectory of a transformerless network is bounded into the future, but need not decay to zero. We then show that the value of the network's impedance at a particular point in the closed right half plane can be determined by finding a special type of network trajectory. In Part 2, we establish lower bounds on the numbers of inductors and capacitors required to realise a given impedance. These lower bounds are expressed in terms of the extended Cauchy index for the impedance, a property defined in that part. Explicit algebraic conditions are also stated in terms of a Sylvester and a Bezoutian matrix. The lower bounds are generalised to multi-port networks. Also, a connection is established with continued fraction expansions, with implications for network synthesis. In Part 3, we first present four procedures for the realisation of a general impedance with a transformerless network. These include two known procedures, the Bott-Duffin procedure and the Reza-Pantell-Fialkow-Gerst simplification, and two new procedures. We then show that the networks produced by the Bott-Duffin procedure, and one of our new alternatives, contain the least possible number of reactive elements (inductors and capacitors) and resistors, for the realisation of a certain type of impedance (called a biquadratic minimum function), among all series-parallel networks. Moreover, we show that these procedures produce the only series-parallel networks which contain exactly six reactive elements and two resistors and realise a biquadratic minimum function. We further show that the networks produced by the Reza-Pantell-Fialkow-Gerst simplification, and the second of our new alternatives, contain the least possible number of reactive elements and resistors for the realisation of almost all biquadratic minimum functions among the class of transformerless networks. We group the networks obtained by these two procedures into two quartets, and we show that these are the only quartets of transformerless networks which contain exactly five reactive elements and two resistors and realise all of the biquadratic minimum functions. Finally, we investigate the minimum number of reactive elements required to realise certain impedances, of greater complexity than the biquadratic minimum function, with series-parallel networks.

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Khor, Cheng Seong. "Optimization of water network synthesis." Thesis, Imperial College London, 2012. http://hdl.handle.net/10044/1/39370.

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Water is a key component in most industries. It has become a crucial resource today particularly in the process and allied industries due to increasingly higher demand for water use, scarcities in water resources, and ever more stringent regulations on wastewater discharges. Hence, this thesis addresses water network synthesis with the goal of developing a systematic approach for optimizing water recovery through regeneration-reuse and regeneration-recycle schemes. A water network super structure is first develop ed that consists of three elements similar to a pooling problem formulation: sources for reuse/recycle, regenerators for contaminants removal, and sinks for acceptance of water for reuse/recycle. The superstructure encompasses membrane separation-based technologies for water regeneration, particularly ultrafiltration and reverse osmosis, which are gaining widespread industrial applications. For the membrane regenerators, we formulate simplified linear models that admit a more general concentration expression as functions of both the liquid phase recovery factors and contaminant removal ratios. The overall superstructure leads to a mixed-integer nonlinear programming (MINLP) optimization model formulation, with continuous variables on water flowrates and contaminant concentrations while binary 0?1 variables are used for selection of piping interconnections. The resultant model is nonconvex particularly in bilinear terms due to contaminant mixing in the regenerators. Realizing the important influence of the physical parameters of a membrane regenerator, the network design is refined by proposing the use of a more detailed nonlinear preliminary design model of this regenerator type that also accounts for various cost elements of the associated equipment components. The more detailed model is applied to a single-stage reverse osmosis network that is incorporated within an overall water network MINLP. To address uncertainty in the formulation, this work develops a recourse-based two-stage stochastic programming framework by using multiple discrete scenarios to approximate the underlying probability distribution of the uncertain parameters. The model is extended with risk management considerations by using the conditional value-at-risk (CVaR) metric. However, a large number of scenarios are often required to capture the uncertainty meaningfully, causing the model to suffer from the curse of dimensionality. Hence, a stepwise solution strategy is propose d to reduce the computational load. This framework is appl ied to reformulate the original deterministic water network synthesis model as a multiscenario stochastic MINLP consisting of a first -stage network design and a second-stage operation as recourse. The thesis handles these challenging nonconvex formulations, which can result in multiple local optimal solutions, by employing global optimization techniques to ensure reliable solutions. To enhance convergence, a solution strategy is presented that incorporates additional constraints into the model in the form of logic-based linear inequalities by exploiting the physics of the underpinning problem. These logical constraints enforce certain design and structural specifications that consequently reduce the solution time. The proposed modeling and solution strategy is implemented on industrial-size case studies of the water systems in an actual operating petroleum refinery in Malaysia and obtained promising results by employing a state-of-the-art general purpose global solver GAMS/BARON. For the stochastic model formulation, computational comparisons are also conducted with the performance of a recently available global solver, GloMIQO. Finally, the main contributions of this thesis are consolidated and perspectives for future work are offered.

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Hagvall, Hörnstedt Julia. "Synthesis of Thoracic Computer Tomography Images using Generative Adversarial Networks." Thesis, Linköpings universitet, Avdelningen för medicinsk teknik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-158280.

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The use of machine learning algorithms to enhance and facilitate medical diagnosis and analysis is a promising and an important area, which could improve the workload of clinicians’ substantially. In order for machine learning algorithms to learn a certain task, large amount of data needs to be available. Data sets for medical image analysis are rarely public due to restrictions concerning the sharing of patient data. The production of synthetic images could act as an anonymization tool to enable the distribution of medical images and facilitate the training of machine learning algorithms, which could be used in practice. This thesis investigates the use of Generative Adversarial Networks (GAN) for synthesis of new thoracic computer tomography (CT) images, with no connection to real patients. It also examines the usefulness of the images by comparing the quantitative performance of a segmentation network trained with the synthetic images with the quantitative performance of the same segmentation network trained with real thoracic CT images. The synthetic thoracic CT images were generated using CycleGAN for image-to-image translation between label map ground truth images and thoracic CT images. The synthetic images were evaluated using different set-ups of synthetic and real images for training the segmentation network. All set-ups were evaluated according to sensitivity, accuracy, Dice and F2-score and compared to the same parameters evaluated from a segmentation network trained with 344 real images. The thesis shows that it was possible to generate synthetic thoracic CT images using GAN. However, it was not possible to achieve an equal quantitative performance of a segmentation network trained with synthetic data compared to a segmentation network trained with the same amount of real images in the scope of this thesis. It was possible to achieve equal quantitative performance of a segmentation network, as a segmentation network trained on real images, by training it with a combination of real and synthetic images, where a majority of the images were synthetic images and a minority were real images. By using a combination of 59 real images and 590 synthetic images, equal performance as a segmentation network trained with 344 real images was achieved regarding sensitivity, Dice and F2-score. Equal quantitative performance of a segmentation network could thus be achieved by using fewer real images together with an abundance of synthetic images, created at close to no cost, indicating a usefulness of synthetically generated images.

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Lee, Robert. "ON THE APPLICATION OF LOCALITY TO NETWORK INTRUSION DETECTION: WORKING-SET ANALYSIS OF REAL AND SYNTHETIC NETWORK SERVER TRAFFIC." Doctoral diss., Orlando, Fla. : University of Central Florida, 2009. http://purl.fcla.edu/fcla/etd/CFE0002718.

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Zhang, Ying. "Passive network synthesis for vibration suppression." Thesis, University of Bristol, 2017. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.730880.

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Jiang, Z. "Passive electrical and mechanical network synthesis." Thesis, University of Cambridge, 2010. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.605602.

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This dissertation is concerned with low-complexity mechanical and electrical network synthesis. This dissertation first formalises the concept of regular positive real function and develops a series of lemmas characterising the basic properties of regularity. This concept will be shown to be useful in the classification of low-complexity two-terminal networks. We classify the positive-real biquadratic functions which can be realised by five-element networks. It will be shown that a biquadratic can be realised by a series-parallel network with two reactive elements if and only if it is regular. Moreover, there are two such networks quartets which can realise all regular biquadratics. It will also be shown that the only five-element networks which can realise non-regular biquadratics can be arranged into three network quartets. We then investigate the series-parallel six-element networks with three reactive elements. We will describe a classification procedure to find an efficient subset of such networks which may realise any non-regular biquadratic that can be synthesised by this class of networks. Four network quartets will be identified which serve this purpose. We will then derive the non-regular biquadratics which can be realised by each quartet. We will show that the set of non-regular realisable biquadratics are identical for three of the quartets. The series-parallel six-element networks with four reactive elements will then be investigated. We describe a classification procedure to find an efficient subset of such networks which may realise any non-regular biquadratic that can be synthesised by this class of networks. Five network quartets will be identified which serve this purpose. We will then derive the non-regular biquadratics which can be realised by each quartet.

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Chen, Z. "Passive network synthesis of restricted complexity." Thesis, University of Cambridge, 2007. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.597545.

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This dissertation is concerned with passive network synthesis in a mechanical context and applications to vehicle suspensions. This dissertation first presents a modified test for positive-realness of real-rational functions which appears only subtly different from a known condition. The test allows existing results to be derived more simply and allows more general results to be established. We then consider a realisation problem of restricted complexity where the number of dampers and inerters is restricted to one in each case, while allowing an arbitrary number of springs and no transformers (levers). The solution uses element extraction of the damper and inerter followed by the derivation of a necessary and sufficient condition for the one-element-kind (transformerless) realisation of an associated three-port network. This involves the derivation of a necessary and sufficient condition for a third-order non-negative definite matrix to be reducible to a paramount matrix using a diagonal transformation. It is shown that the relevant class of mechanical admittances can be parametrised in terms of five circuit arrangements each containing four springs. We investigate and compare the performances of the five circuit arrangements proposed when applied to suspension systems. One of the five circuits has appeared in the literature and therefore serves as the benchmark. One or more circuit arrangements appear to outperform the benchmark in terms of each individual performance measure among the three of interest and a multi-objective performance measure incorporating two of the three individual performance measures. Finally, we consider the minimum reactance synthesis of a class of biquadratic functions by reactance extraction. We show that at most four dampers are needed to synthesise the remaining resistive 3-port network when explicit conditions are met. The results are an advancement on an equivalent problem studied in the electrical network case.

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Chen, Chia-Hsin Ph D. Massachusetts Institute of Technology. "On-Chip Network exploration and synthesis." Thesis, Massachusetts Institute of Technology, 2012. http://hdl.handle.net/1721.1/70792.

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Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2012.
Cataloged from PDF version of thesis.
Includes bibliographical references (p. 57-60).
As CMOS technology improves, the trend of processor designs has gone towards multi-core architectures. Networks-on-Chips (NoCs) have become popular on-chip interconnect fabrics that connect the ever-increasing cores because of their ability to provide high-bandwidth. However, as the number of cores keeps increasing, the endto- end packet latency and the total network power begin to pose tight constraints on NoC designs. In this thesis, we studied architecture proposals designed to tackle this latency and power budget issue. We also studied the impact of applying advanced circuit techniques to these architecture proposals and how to implement these techniques while realizing a NoC design. The thesis begins with an evaluation of physical express topologies and the virtual express topologies that enable the bypassing of intermediate router pipelines. The bypassing of pipeline stages help reduce both end-to-end latency and power consumption since fewer resources are used. We observed that both topologies have similar low-traffic-load latencies and that virtual express topologies result in higher throughput and are more robust across traffic patterns. Physical express topologies, however, deliver a better throughput/watt and can leverage the low-swing link circuits to lower the latency and increase the throughput. Next, then we identified that crossbars, in addition to links, can obtain benefit from the low-swing circuit techniques. We thus developed a layout generation tool for low-swing crossbars and links due to the inability of the existing tools for physical designs to generate these low-swing circuits automatically. The generated crossbars and links using our tool showed 50% energy saving compared to the full-swing synthesized counterpart. We also demonstrated a case study with a router synthesized with the generated crossbar and links.
by Chia-Hsin Chen.
S.M.

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Burniston, J. D. "A neural network/rule-based architecture for continuous function approximation." Thesis, University of Nottingham, 1994. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.387198.

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Books on the topic "Network synthesi"

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Wynants, Christelle. Network Synthesis Problems. Boston, MA: Springer US, 2001. http://dx.doi.org/10.1007/978-1-4757-3349-5.

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Wynants, Christelle. Network synthesis problems. Dordrecht: Kluwer Academic Publishers, 2001.

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Wynants, Christelle. Network Synthesis Problems. Boston, MA: Springer US, 2001.

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Wynants, Christelle. Network synthesis problems. Dordrecht: Kluwer Academic Publishers, 2001.

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Weber, Wilfried, and Martin Fussenegger, eds. Synthetic Gene Networks. Totowa, NJ: Humana Press, 2012. http://dx.doi.org/10.1007/978-1-61779-412-4.

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T, Stokke B., Elgsaeter A, and Polymer Networks Group International Conference., eds. Synthetic versus biological networks. Chichester, England: Wiley, 1999.

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Mueller, F. U. Optimisation strategies for heat exchanger network synthesis. Manchester: UMIST, 1994.

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Aleksandrovich, Rozenberg Boris, and Sigalov Grigori M, eds. Heterophase network polymers: Synthesis, characterization, and properties. London: Taylor & Francis, 2002.

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S, Palli Shyammohan, ed. Circuits and networks: Analysis and synthesis. Boston, [Mass.]: McGraw-Hill Higher Education, 2008.

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Marcoulaki, E. C. Chemical reactor network synthesis using stochastic optimization methods. Manchester: UMIST, 1994.

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Book chapters on the topic "Network synthesi"

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Keskin, Ali Ümit. "Network Synthesis." In Electrical Circuits in Biomedical Engineering, 535–646. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-55101-2_8.

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Vilgis, T. A. "Novel Network Structures: Fractal-Rigid-Flexible Networks." In Synthesis, Characterization, and Theory of Polymeric Networks and Gels, 13–30. Boston, MA: Springer US, 1992. http://dx.doi.org/10.1007/978-1-4615-3016-9_2.

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Wynants, Christelle. "Network Synthesis Problem." In Combinatorial Optimization, 11–29. Boston, MA: Springer US, 2001. http://dx.doi.org/10.1007/978-1-4757-3349-5_2.

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Winter, Pawel. "Topological network synthesis." In Lecture Notes in Mathematics, 282–303. Berlin, Heidelberg: Springer Berlin Heidelberg, 1989. http://dx.doi.org/10.1007/bfb0083472.

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Hagn, Korbinian, and Oliver Grau. "Optimized Data Synthesis for DNN Training and Validation by Sensor Artifact Simulation." In Deep Neural Networks and Data for Automated Driving, 127–47. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-01233-4_4.

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AbstractSynthetic, i.e., computer-generated imagery (CGI) data is a key component for training and validating deep-learning-based perceptive functions due to its ability to simulate rare cases, avoidance of privacy issues, and generation of pixel-accurate ground truth data. Today, physical-based rendering (PBR) engines simulate already a wealth of realistic optical effects but are mainly focused on the human perception system. Whereas the perceptive functions require realistic images modeled with sensor artifacts as close as possible toward the sensor, the training data has been recorded. This chapter proposes a way to improve the data synthesis process by application of realistic sensor artifacts. To do this, one has to overcome the domain distance between real-world imagery and the synthetic imagery. Therefore, we propose a measure which captures the generalization distance of two distinct datasets which have been trained on the same model. With this measure the data synthesis pipeline can be improved to produce realistic sensor-simulated images which are closer to the real-world domain. The proposed measure is based on the Wasserstein distance (earth mover’s distance, EMD) over the performance metric mean intersection-over-union (mIoU) on a per-image basis, comparing synthetic and real datasets using deep neural networks (DNNs) for semantic segmentation. This measure is subsequently used to match the characteristic of a real-world camera for the image synthesis pipeline which considers realistic sensor noise and lens artifacts. Comparing the measure with the well-established Fréchet inception distance (FID) on real and artificial datasets demonstrates the ability to interpret the generalization distance which is inherent asymmetric and more informative than just a simple distance measure. Furthermore, we use the metric as an optimization criterion to adapt a synthetic dataset to a real dataset, decreasing the EMD distance between a synthetic and the Cityscapes dataset from 32.67 to 27.48 and increasing the mIoU of our test algorithm () from 40.36 to $$47.63\%$$ 47.63 % .

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Jiang, Bin. "A Complex-Network Perspective on Alexander’s Wholeness." In Spatial Synthesis, 339–54. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-52734-1_20.

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Granat, Janusz, and Andrzej P. Wierzbicki. "Multiple Criteria Ranking in Future Network Management." In Knowledge Synthesis, 15–26. Tokyo: Springer Japan, 2016. http://dx.doi.org/10.1007/978-4-431-55218-5_2.

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Paulsen, Brandon, and Chao Wang. "Example Guided Synthesis of Linear Approximations for Neural Network Verification." In Computer Aided Verification, 149–70. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-13185-1_8.

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AbstractLinear approximations of nonlinear functions have a wide range of applications such as rigorous global optimization and, recently, verification problems involving neural networks. In the latter case, a linear approximation must be hand-crafted for the neural network’s activation functions. This hand-crafting is tedious, potentially error-prone, and requires an expert to prove the soundness of the linear approximation. Such a limitation is at odds with the rapidly advancing deep learning field – current verification tools either lack the necessary linear approximation, or perform poorly on neural networks with state-of-the-art activation functions. In this work, we consider the problem of automatically synthesizing sound linear approximations for a given neural network activation function. Our approach is example-guided: we develop a procedure to generate examples, and then we leverage machine learning techniques to learn a (static) function that outputs linear approximations. However, since the machine learning techniques we employ do not come with formal guarantees, the resulting synthesized function may produce linear approximations with violations. To remedy this, we bound the maximum violation using rigorous global optimization techniques, and then adjust the synthesized linear approximation accordingly to ensure soundness. We evaluate our approach on several neural network verification tasks. Our evaluation shows that the automatically synthesized linear approximations greatly improve the accuracy (i.e., in terms of the number of verification problems solved) compared to hand-crafted linear approximations in state-of-the-art neural network verification tools. An artifact with our code and experimental scripts is available at: https://zenodo.org/record/6525186#.Yp51L9LMIzM."Image missing""Image missing"

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Yang, Haoran, and Yongling Li. "Complex Network Theory on High-Speed Transportation Systems." In Spatial Synthesis, 147–62. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-52734-1_11.

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Yang, Zhengfeng, Yidan Zhang, Wang Lin, Xia Zeng, Xiaochao Tang, Zhenbing Zeng, and Zhiming Liu. "An Iterative Scheme of Safe Reinforcement Learning for Nonlinear Systems via Barrier Certificate Generation." In Computer Aided Verification, 467–90. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-81685-8_22.

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AbstractIn this paper, we propose a safe reinforcement learning approach to synthesize deep neural network (DNN) controllers for nonlinear systems subject to safety constraints. The proposed approach employs an iterative scheme where a learner and a verifier interact to synthesize safe DNN controllers. The learner trains a DNN controller via deep reinforcement learning, and the verifier certifies the learned controller through computing a maximal safe initial region and its corresponding barrier certificate, based on polynomial abstraction and bilinear matrix inequalities solving. Compared with the existing verification-in-the-loop synthesis methods, our iterative framework is a sequential synthesis scheme of controllers and barrier certificates, which can learn safe controllers with adaptive barrier certificates rather than user-defined ones. We implement the tool SRLBC and evaluate its performance over a set of benchmark examples. The experimental results demonstrate that our approach efficiently synthesizes safe DNN controllers even for a nonlinear system with dimension up to 12.

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Conference papers on the topic "Network synthesi"

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Zhu, Mingrui, Nannan Wang, Xinbo Gao, Jie Li, and Zhifeng Li. "Face Photo-Sketch Synthesis via Knowledge Transfer." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. California: International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/147.

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Despite deep neural networks have demonstrated strong power in face photo-sketch synthesis task, their performance, however, are still limited by the lack of training data (photo-sketch pairs). Knowledge Transfer (KT), which aims at training a smaller and fast student network with the information learned from a larger and accurate teacher network, has attracted much attention recently due to its superior performance in the acceleration and compression of deep neural networks. This work has brought us great inspiration that we can train a relatively small student network on very few training data by transferring knowledge from a larger teacher model trained on enough training data for other tasks. Therefore, we propose a novel knowledge transfer framework to synthesize face photos from face sketches or synthesize face sketches from face photos. Particularly, we utilize two teacher networks trained on large amount of data in related task to learn the knowledge of face photos and face sketches separately and transfer them to two student networks simultaneously. In addition, the two student networks, one for photo ? sketch task and the other for sketch ? photo task, can transfer their knowledge mutually. With the proposed method, we can train our model which has superior performance using a small set of photo-sketch pairs. We validate the effectiveness of our method across several datasets. Quantitative and qualitative evaluations illustrate that our model outperforms other state-of-the-art methods in generating face sketches (or photos) with high visual quality and recognition ability.

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Zhu, Kefeng, Peilin Tong, Hongwei Kan, and Rengang Li. "You Get What You Sow: High Fidelity Image Synthesis with a Single Pretrained Network." In Thirtieth International Joint Conference on Artificial Intelligence {IJCAI-21}. California: International Joint Conferences on Artificial Intelligence Organization, 2021. http://dx.doi.org/10.24963/ijcai.2021/479.

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State-of-the-art image synthesis methods are mostly based on generative adversarial networks and require large dataset and extensive training. Although the model-inversion-oriented branch of methods eliminate the training requirement, the quality of the resulting image tends to be limited due to the lack of sufficient natural and class-specific information. In this paper, we introduce a novel strategy for high fidelity image synthesis with a single pretrained classification network. The strategy includes a class-conditional natural regularization design and a corresponding metadata collecting procedure for different scenarios. We show that our method can synthesize high quality natural images that closely follow the features of one or more given seed images. Moreover, our method achieves surprisingly decent results in the task of sketch-based image synthesis without training. Finally, our method further improves the performance in terms of accuracy and efficiency in the data-free knowledge distillation task.

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Benmaghnia, Hanane, Matthieu Martel, and Yassamine Seladji. "Fixed-Point Code Synthesis for Neural Networks." In 6th International Conference on Artificial Intelligence, Soft Computing and Applications (AISCA 2022). Academy and Industry Research Collaboration Center (AIRCC), 2022. http://dx.doi.org/10.5121/csit.2022.120202.

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Over the last few years, neural networks have started penetrating safety critical systems to take decisions in robots, rockets, autonomous driving car, etc. A problem is that these critical systems often have limited computing resources. Often, they use the fixed-point arithmetic for its many advantages (rapidity, compatibility with small memory devices.) In this article, a new technique is introduced to tune the formats (precision) of already trained neural networks using fixed-point arithmetic, which can be implemented using integer operations only. The new optimized neural network computes the output with fixed-point numbers without modifying the accuracy up to a threshold fixed by the user. A fixed-point code is synthesized for the new optimized neural network ensuring the respect of the threshold for any input vector belonging the range [xmin, xmax] determined during the analysis. From a technical point of view, we do a preliminary analysis of our floating neural network to determine the worst cases, then we generate a system of linear constraints among integer variables that we can solve by linear programming. The solution of this system is the new fixed-point format of each neuron. The experimental results obtained show the efficiency of our method which can ensure that the new fixed-point neural network has the same behavior as the initial floating-point neural network.

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B. Cohen, obert. "Manufacturers, AI Models and Machine Learning, Value Chains, and 5th Generation Wireless Networks." In 2nd International Conference on Soft Computing, Artificial Intelligence and Machine Learning (SAIM 2021). AIRCC Publishing Corporation, 2021. http://dx.doi.org/10.5121/csit.2021.111003.

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When AI models and machine learning are fully interconnected in factories with cabling-free 5G wireless networks, firms become “fully digital”. This analysis argues that it is not the initial efficiencies gained by optimizing a plant’s operations but rather a firm’s ability to build a collection of knowledge about each step of its operations, what we call “knowledge synthesis”. This is information about how each product is produced, how the process to produce it is managed and optimized, and the software and systems required. This knowledge is important because it permits firms to exploit network effects based upon connecting plants together or sharing expertise with partners. This greatly expands the potential for economic benefits from the use of AI and 5G. This review explores cases from firms with smart factories that have adopted AI and 5G communications including Moderna, Sanofi, Mercedes, Ford, and VW. It examines how these firms have benefitted from the move to smart factories with 5G communications networks. It also explores how firms have improved their value chains by building smart factories that connect nearly all manufacturing processes to machine learning and AI models that analyze machine and process data rapidly. Next, they take advantage of network effects – due to “knowledge synthesis” that permits early smart factories with 5G networks --to derive even larger benefits inside their production operations and in their supply chains. In both phases, the adoption of 5th Generation wireless in plants ramps up firms’ abilities to interconnect their digital systems. Once the interconnected systems exist, firms exploit network effects to create “knowledge synthesis” or knowledge platforms to consolidate insights gained from optimizing many machines and processes. Using “knowledge synthesis”, firms can also transfer knowledge from one group of equipment to another that is not optimized even when the equipment is in different facilities. This makes firms far more flexible, interoperable, and scalable.

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Ickin, Selim, Konstantinos Vandikas, Farnaz Moradi, Jalil Taghia, and Wenfeng Hu. "Ensemble-based Synthetic Data Synthesis for Federated QoE Modeling." In 2020 6th IEEE International Conference on Network Softwarization (NetSoft). IEEE, 2020. http://dx.doi.org/10.1109/netsoft48620.2020.9165379.

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Vukelic, Djordje, and Verka Jovanović. "Applying GIS in GeoModeling of Social Networks." In Synthesis 2015. Belgrade, Serbia: Singidunum University, 2015. http://dx.doi.org/10.15308/synthesis-2015-620-623.

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Babichev, Andrew, and Vladimir Alexandrovich Frolov. "Structure Preserving Exemplar-Based 3D Texture Synthesis." In 31th International Conference on Computer Graphics and Vision. Keldysh Institute of Applied Mathematics, 2021. http://dx.doi.org/10.20948/graphicon-2021-3027-433-442.

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In this paper we propose exemplar-based 3D texture synthesis method which unlike existing neural network approaches preserve structural elements in texture. The proposed approach does this by accounting additional image properties which stand for the preservation of the structure with the help of a specially constructed error function used for training neural networks. Thanks to the proposed solution we can apply 2D texture to any 3D model (even without texture coordinates) by synthesizing high quality 3D texture and using local or world space position of surface instead 2D texture coordinates (fig. 1). Our solution is based on introducing 3 different error components in to the process of neural network fitting which helps to preserve desired properties of generated texture. The first component is for structuredness of the generated texture and the sample, the second component increases the diversity of the generated textures and the third one prevents abrupt transitions between individual pixels.

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Ibadah, Nisrine, Khalid Minaoui, Mohammed Rziza, and Mohammed Oumsis. "Experimental Synthesis of Routing Protocols and Synthetic Mobility Modeling for MANET." In 6th International Conference on Sensor Networks. SCITEPRESS - Science and Technology Publications, 2017. http://dx.doi.org/10.5220/0006203601680173.

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Melo, Renato Silva, and André Luís Vignatti. "Preprocessing Rules for Target Set Selection in Complex Networks." In Brazilian Workshop on Social Network Analysis and Mining. Sociedade Brasileira de Computação, 2020. http://dx.doi.org/10.5753/brasnam.2020.11167.

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In the Target Set Selection (TSS) problem, we want to find the minimum set of individuals in a network to spread information across the entire network. This problem is NP-hard, so find good strategies to deal with it, even for a particular case, is something of interest. We introduce preprocessing rules that allow reducing the size of the input without losing the optimality of the solution when the input graph is a complex network. Such type of network has a set of topological properties that commonly occurs in graphs that model real systems. We present computational experiments with real-world complex networks and synthetic power law graphs. Our strategies do particularly well on graphs with power law degree distribution, such as several real-world complex networks. Such rules provide a notable reduction in the size of the problem and, consequently, gains in scalability.

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Lacombe, Théo, Yuichi Ike, Mathieu Carrière, Frédéric Chazal, Marc Glisse, and Yuhei Umeda. "Topological Uncertainty: Monitoring Trained Neural Networks through Persistence of Activation Graphs." In Thirtieth International Joint Conference on Artificial Intelligence {IJCAI-21}. California: International Joint Conferences on Artificial Intelligence Organization, 2021. http://dx.doi.org/10.24963/ijcai.2021/367.

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Although neural networks are capable of reaching astonishing performance on a wide variety of contexts, properly training networks on complicated tasks requires expertise and can be expensive from a computational perspective. In industrial applications, data coming from an open-world setting might widely differ from the benchmark datasets on which a network was trained. Being able to monitor the presence of such variations without retraining the network is of crucial importance. In this paper, we develop a method to monitor trained neural networks based on the topological properties of their activation graphs. To each new observation, we assign a Topological Uncertainty, a score that aims to assess the reliability of the predictions by investigating the whole network instead of its final layer only as typically done by practitioners. Our approach entirely works at a post-training level and does not require any assumption on the network architecture, optimization scheme, nor the use of data augmentation or auxiliary datasets; and can be faithfully applied on a large range of network architectures and data types. We showcase experimentally the potential of Topological Uncertainty in the context of trained network selection, Out-Of-Distribution detection, and shift-detection, both on synthetic and real datasets of images and graphs.

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Reports on the topic "Network synthesi"

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Le, Hoang. Novel View Synthesis - A Neural Network Approach. Portland State University Library, January 2000. http://dx.doi.org/10.15760/etd.7409.

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Hilton, Paul K. Expeditionary Maneuver: A Synthesis of Network Centric Concepts. Fort Belvoir, VA: Defense Technical Information Center, May 2003. http://dx.doi.org/10.21236/ada420212.

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Shanmugan, K. S., Victor S. Frost, G. J. Minden, and E. Komp. A Packet Communication Network Synthesis and Analysis System. Fort Belvoir, VA: Defense Technical Information Center, August 1986. http://dx.doi.org/10.21236/ada174316.

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Singh, Tarunraj. Synthesis of Road Networks by Data Conflation. Fort Belvoir, VA: Defense Technical Information Center, April 2014. http://dx.doi.org/10.21236/ada603970.

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Andersson, Göran. Thematic synthesis “Energy Networks” of the NRP “Energy”. Swiss National Science Foundation (SNSF), December 2019. http://dx.doi.org/10.46446/publication_nrp70_nrp71.2019.2.en.

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Switzerland’s energy grids are reliable and stable – but they are facing new challenges. These include the fact that the new renewable energy sources, namely photovoltaic systems and wind farms, only produce electricity on an irregular basis. Greater flexibility is therefore required in the energy grid: with new storage solutions for electricity and heat on the supply side and automated load management on the demand side. The potential synergies between the various energy sources must also be exploited.

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Lundquist, J., B. Kosovic, and R. Belles. Synthetic Event Reconstruction Experiments for Defining Sensor Network Characteristics. Office of Scientific and Technical Information (OSTI), December 2005. http://dx.doi.org/10.2172/894010.

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Orkwis, Paul D., and Terry Daviaux. Advanced Neural Network Modeling of Synthetic Jet Flow Fields. Fort Belvoir, VA: Defense Technical Information Center, March 2006. http://dx.doi.org/10.21236/ada473581.

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Fujimoto, Richard M. Distributed Simulation of Synthetic Environments and Wireless Networks. Fort Belvoir, VA: Defense Technical Information Center, September 1999. http://dx.doi.org/10.21236/ada369488.

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Ray, Chris, Robert Wilkerson, Rodney Siegel, Mandy Holmgren, and Sylvia Haultain. Landbird population trends in parks of the Sierra Nevada Network: 2011–2019 synthesis. National Park Service, June 2022. http://dx.doi.org/10.36967/nrr-2293643.

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Delmer, Deborah, Nicholas Carpita, and Abraham Marcus. Induced Plant Cell Wall Modifications: Use of Plant Cells with Altered Walls to Study Wall Structure, Growth and Potential for Genetic Modification. United States Department of Agriculture, May 1995. http://dx.doi.org/10.32747/1995.7613021.bard.

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Our previous work indicated that suspension-cultured plant cells show remarkable flexibility in altering cell wall structure in response either to growth on saline medium or in the presence of the cellulose synthesis inhibitor 2,-6-dichlorobenzonitrile (DCB). We have continued to analyze the structure of these modified cell walls to understand how the changes modify wall strength, porosity, and ability to expand. The major load-bearing network in the walls of DCB-adapted dicot cells that lack a substantial cellulose-xyloglucan network is comprised of Ca2+-bridged pectates; these cells also have an unusual and abundant soluble pectic fraction. By contrast, DCB-adapted barley, a graminaceous monocot achieves extra wall strength by enhanced cross-linking of its non-cellulosic polysaccharide network via phenolic residues. Our results have also shed new light on normal wall stucture: 1) the cellulose-xyloglucan network may be independent of other wall networks in dicot primary walls and accounts for about 70% of the total wall strength; 2) the pectic network in dicot walls is the primary determinant of wall porosity; 3) both wall strength and porosity in graminaceous monocot primary walls is greatly influenced by the degree of phenolic cross-linking between non-cellulosic polysaccharides; and 4) the fact that the monocot cells do not secrete excess glucuronoarabinoxylan and mixed-linked glucan in response to growth on DCB, suggests that these two non-cellulosic polymers do not normally interact with cellulose in a manner similar to xyloglucan. We also attempted to understand the factors which limit cell expansion during growth of cells in saline medium. Analyses of hydrolytic enzyme activities suggest that xyloglucan metabolism is not repressed during growth on NaCl. Unlike non-adapted cells, salt-adapted cells were found to lack pectin methyl esterase, but it is not clear how this difference could relate to alterations in wall expansibility. Salt-adaped cell walls contain reduced hyp and secrete two unique PRPP-related proteins suggesting that high NaCl inhibits the cross-linking of these proteins into the walls, a finding that might relate to their altered expansibility.

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