Relevant bibliographies by topics / Automatic networks

Academic literature on the topic 'Automatic networks'

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Published: 10 December 2022
Last updated: 28 January 2023

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Journal articles on the topic "Automatic networks"

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Dao-Thi, Thu-Ha, and Jean Mairesse. "Zero-Automatic Networks." Discrete Event Dynamic Systems 18, no. 4 (September 16, 2008): 499–536. http://dx.doi.org/10.1007/s10626-008-0048-1.

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Droste, Peter, Wolfgang Wiechert, and Katharina Nöh. "Semi-automatic drawing of metabolic networks." Information Visualization 11, no. 3 (August 22, 2011): 171–87. http://dx.doi.org/10.1177/1473871611413565.

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In the living cell, biochemical reactions catalyzed by enzymes are the drivers for metabolic processes like growth, energy production, and replication. Metabolic networks are the representation of these processes describing the complex interactions of biochemical compounds. The large amount of manifold data concerning metabolic networks continually arising from current research activities in biotechnology leads to the great challenge of information visualization. Visualizing information in networks first of all requires appropriate network diagrams. In the context of metabolic networks, historical conventions regarding the network layout have been established. These layouts are not realizable by prevailing algorithms for automatic graph drawing. Hence, manual graph drawing is the predominating way to set up metabolic network diagrams. This is very time-consuming without software support, especially considering large networks with more than 500 nodes. We present a semi-automatic approach to drawing networks which relies on manual editing supported by two concepts of the interactive and automatic arrangement of nodes and edges. The first concept, called the layout pattern, uses an arbitrarily shaped skeleton as a backbone for the arrangement of nodes and edges. The second concept allows us to wrap a set of repeating drawing steps onto a so-called motif stamp, which can be appended to other parts of a diagram during the drawing process. Finally, a case study demonstrates that both semi-automatic drawing techniques diminish the time to be devoted for the manual network drawing process.
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Hou, Tingting, and Zamira Madina. "Automatic Classification of Basic Nursing Teaching Resources Based on the Fusion of Multiple Neural Networks." Mathematical Problems in Engineering 2022 (February 21, 2022): 1–7. http://dx.doi.org/10.1155/2022/7176111.

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Automatic classification is one of the hot topics in the field of information retrieval and natural language processing, but it still faces many problems to be solved. The classic automated classification approach has a sluggish classification speed and poor processing accuracy for resources with a large quantity of data. Based on this, an automated classification approach based on the integration of various neural networks for fundamental nursing teaching materials was presented. The automatic classification method of teaching resources was designed by extracting the characteristics of teaching resources, establishing the model of multiple neural network integration, and designing the classification index of basic nursing teaching resources. The experimental findings suggest that this technique has higher chi-square test parameters and better outcomes for the automated classification of large instructional materials than the classic rough set automatic classification method.
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Triantafyllis, N., E. Sokos, and A. Ilias. "Automatic moment tensor determination for the Hellenic Unified Seismic Network." Bulletin of the Geological Society of Greece 47, no. 3 (December 21, 2016): 1308. http://dx.doi.org/10.12681/bgsg.10912.

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Modern seismic networks with broadband sensors and real time digital telemetry made Moment Tensor (MT) determination a routine procedure. Automatic MT’s are now provided by global networks and a few very dense regional networks, within minutes after a significant event. An automatic MT determination wasn’t possible for the broader Hellenic area since seismic station density wasn’t sufficient. The creation of the Hellenic Unified Seismic Network (HUSN) provided the opportunity to apply an automated MT procedure using the available broad band data from almost one hundred stations. Thus the ISOLA code was extended towards the automatic operation based on Linux OS shell scripts, stand alone Fortran codes and SAC2000. Software supports both manual and automatic mode; at the first case, the user manually runs the program with the desired input parameters while at the latter, the system monitors a mailbox or RSS feed and if it receives an appropriate notification triggers the MT inversion procedure based on certain conditions. As it is setup now it calculates automatically the moment tensor of earthquakes larger than 3.5M w using data from HUSN. Application of an automated MT inversion procedure for HUSN will provide important real time information for studies like ground motion evaluation, tsunami warning etc.
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Sharygin, Mikhail, Vladimir Vukolov, and Anton Petrov. "Adaptive multivariable relay protection of reconfigurable distribution networks." E3S Web of Conferences 139 (2019): 01048. http://dx.doi.org/10.1051/e3sconf/201913901048.

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Version of relay protection construction of relative selectivity is proposed, which allows increasing its technical perfection and reliability in microgrids with distributed generation sources. This is done by increasing the dimension of the measurement space along arbitrary axes, introducing new methods of recognizing emergency modes, as well as automating the selection of response parameters. Hardware implementation of protection is described. Recommendations for calculating the settings of multidimensional protection and an example of calculation are given. Protection coordination is carried out by using graphical-analytical methods in automatic mode. Automation of the calculation, elimination of the human factor will make it possible to apply the method for adaptation of protection in the conditions of dynamic change of microgrid topology.
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Barinov, Roman, Vasiliy Gai, George Kuznetsov, and Vladimir Golubenko. "Automatic Evaluation of Neural Network Training Results." Computers 12, no. 2 (January 20, 2023): 26. http://dx.doi.org/10.3390/computers12020026.

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This article is dedicated to solving the problem of an insufficient degree of automation of artificial neural network training. Despite the availability of a large number of libraries for training neural networks, machine learning engineers often have to manually control the training process to detect overfitting or underfitting. This article considers the task of automatically estimating neural network training results through an analysis of learning curves. Such analysis allows one to determine one of three possible states of the training process: overfitting, underfitting, and optimal training. We propose several algorithms for extracting feature descriptions from learning curves using mathematical statistics. Further state classification is performed using classical machine learning models. The proposed automatic estimation model serves to improve the degree of automation of neural network training and interpretation of its results, while also taking a step toward constructing self-training models. In most cases when the training process of neural networks leads to overfitting, the developed model determines its onset ahead of the early stopping method by 3–5 epochs.
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Wang, Zibo, Yaofang Zhang, Zhiyao Liu, Xiaojie Wei, Yilu Chen, and Bailing Wang. "An Automatic Planning-Based Attack Path Discovery Approach from IT to OT Networks." Security and Communication Networks 2021 (October 31, 2021): 1–18. http://dx.doi.org/10.1155/2021/1444182.

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With the convergence of IT and OT networks, more opportunities can be found to destroy physical processes by cyberattacks. Discovering attack paths plays a vital role in describing possible sequences of exploitation. Automated planning that is an important branch of artificial intelligence (AI) is introduced into the attack graph modeling. However, while adopting the modeling method for large-scale IT and OT networks, it is difficult to meet urgent demands, such as scattered data management, scalability, and automation. To that end, an automatic planning-based attack path discovery approach is proposed in this paper. At first, information of the attacking knowledge and network topology is formally represented in a standardized planning domain definition language (PDDL), integrated into a graph data model. Subsequently, device reachability graph partitioning algorithm is introduced to obtain subgraphs that are small enough and of limited size, which facilitates the discovery of attack paths through the AI planner as soon as possible. In order to further cope with scalability problems, a multithreading manner is used to execute the attack path enumeration for each subgraph. Finally, an automatic workflow with the assistance of a graph database is provided for constructing the PDDL problem file for each subgraph and traversal query in an interactive way. A case study is presented to demonstrate effectiveness of attack path discovery and efficiency with the increase in number of devices.
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Mossin, Eduardo André, Dennis Brandão, Guilherme Serpa Sestito, and Renato Veiga Torres. "Automatic Diagnosis for Profibus Networks." Journal of Control, Automation and Electrical Systems 27, no. 6 (July 25, 2016): 658–69. http://dx.doi.org/10.1007/s40313-016-0261-3.

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Gurskiy, A. A., A. E. Goncharenko, and S. M. Dubna. "AUTOMATIC SYNTHESIS OF PETRI NETS AT TUNING UP OF THE COORDINATING AUTOMATIC CONTROL SYSTEMS." ELECTRICAL AND COMPUTER SYSTEMS 33, no. 108 (November 30, 2020): 34–44. http://dx.doi.org/10.15276/eltecs.32.108.2020.4.

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The process of automated tuning for the coordinating automatic control system is considered in this paper. This process of tuning for the coordinating control system is linked to the automatic synthesis of Petri nets based on functioning of the artificial neural network. Thereby, we can automate the process of tuning and synthesis of system models and also solve the urgent task linked to the minimization of tuning time for the multilevel control systems. The purposes of the scientific work are time reduction of the tuning and automatization of the tuning for the multilevel coordinating systems of the automatic control. In order to achieve this purpose in the MATLAB \ Simulink software environment it is necessary to devel- op the system for automated tuning of the regulators of various levels for the coordinating automatic control system. The application of artificial neural network with automatic synthesis of Petri nets allows to introduce intelligent technology in the automated tuning system. In this work we have presented the corresponding block diagrams of considered automated tuning system and the principles of its functioning. The certain principle of the formation of Petri nets is proposed. These Petri nets represent the algorithms of tuning in the systems for analysis the corresponding processes. The formation of the composition in the scheme from Petri net during the functioning of the artificial neural network is presented in the paper. The results of experiment are presented in the final part of this work. This time characteristics of the pro- cess of setting up for the coordinating automatic control system of foodstuffs cooling in tunnel chamber. The experiments were conducted in the Matlab 2012a environment. Based on the results of the experiment we have depicted the process of synthesis of the Petri net representing the system tuning algorithm. The performed experiments have showed the principal suitability of the automated search system for the settings of the regulators of various levels of the coordinating control system. The technique of automatic synthesis of Petri nets based on the functioning of artificial neural networks has obtained the further devel- opment while performing the approved task in the scientific paper.
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Wang, Shuai, and Chunwu Liu. "Automatic Modulation Classification with Neural Networks via Knowledge Distillation." Electronics 11, no. 19 (September 22, 2022): 3018. http://dx.doi.org/10.3390/electronics11193018.

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Deep learning is used for automatic modulation recognition in neural networks, and because of the need for high classification accuracy, deeper and deeper networks are used. However, these are computationally very expensive for neural network training and inference, so its utility in the case of a mobile with memory limitations or weak computational power is questionable. As a result, a trade-off between network depth and network classification accuracy must be considered. To address this issue, we used a knowledge distillation method in this study to improve the classification accuracy of a small network model. First, we trained Inception–Resnet as a teacher network, which has a size of 311.77 MB and a final peak classification accuracy of 93.09%. We used the method to train convolutional neural network 3 (CNN3) and increase its peak classification accuracy from 79.81 to 89.36%, with a network size of 0.37 MB. It was also used similarly to train mini Inception–Resnet and increase its peak accuracy from 84.18 to 93.59%, with a network size of 39.69 MB. When we compared all classification accuracy peaks, we discover that knowledge distillation improved small networks and that the student network had the potential to outperform the teacher network. Using knowledge distillation, a small network model can achieve the classification accuracy of a large network model. In practice, choosing the appropriate student network based on the constraints of the usage conditions while using knowledge distillation (KD) would be a way to meet practical needs.
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Dissertations / Theses on the topic "Automatic networks"

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Grau, Leguia Marc. "Automatic reconstruction of complex dynamical networks." Doctoral thesis, Universitat Pompeu Fabra, 2019. http://hdl.handle.net/10803/666631.

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Un problema principal de la ciència de xarxes és com reconstruir (inferir) la topologia d’una xarxa real a partir de senyals mesurades de les seves unitats internes. Entendre la arquitectura d’una xarxa complexa és clau, no només per comprendre el seu funcionament, sinó també per predir i controlar el seu comportament. Els mètodes actualment disponibles es centren principalment en la detecció d’enllaços de xarxes no direccio- nals i sovint requereixen suposicions fortes sobre el sistema. Tanmateix, molts d’aquests mètodes no es poden aplicar a xarxes amb connexions direccionals. Per abordar aquest problema, en aquesta tesis ens centrarem en la inferència de xarxes direccionals. Concretament, desenvolupem un mètode de reconstrucció de xarxes basat en models que combina estadístiques de correlacions de derivades amb recuit simulat. A més, desenvolupem un mètode de reconstrucció basat en dades cimentat en una mesura d’interpedendència no lineal. Aquest mètode permet inferir la topologia de xarxes direccionals d’oscil.ladors caòtics de Lorenz per un subordre de la força d’acoblament i la densitat de la xarxa. Finalment, apliquem el mètode basat en dades a gravacions electroencefalogràfiques d’un pacient amb epilèpsia. Les xarxes cerebrals funcionals obtingu- des a partir d’aquest mètode són coherents amb la informació mèdica disponible.
Un problema principal de la ciencia de redes es cómo reconstruir (inferir) la topología de una red real usando la señales medidas de sus unidades internas. Entender la arquitectura de redes complejas es clave, no solo para entender su funcionamiento pero también para predecir y controlar su comportamiento. Los métodos existentes se focalizan en la detección de redes no direccionales y normalmente requieren fuertes suposicio- nes sobre el sistema. Sin embargo, muchos de estos métodos no pueden ser aplicados en redes con conexiones direccionales. Para abordar este problema, en esta tesis estudiamos la reconstrucción de redes direccio- nales. En concreto, desarrollamos un método de reconstrucción basado en modelos que combina estadísticas de correlaciones de derivadas con recocido simulado. Además, desarrollamos un método basado en datos cimentado en una medida d’interdependencia no lineal. Este método permite inferir la topología de redes direccionales de osciladores caóticos de Lorenz para un subrango de la fuerza de acoplamiento y densidad de la red. Finalmente, aplicamos el método basado en datos a grabaciones electroencefalográficas de un paciente con epilepsia. Las redes cerebra- les funcionales obtenidas usando este método son consistentes con la información médica disponible.
A foremost problem in network science is how to reconstruct (infer) the topology of a real network from signals measured from its internal units. Grasping the architecture of complex networks is key, not only to understand their functioning, but also to predict and control their behaviour. Currently available methods largely focus on the detection of links of undirected networks and often require strong assumptions about the system. However, many of these methods cannot be applied to networks with directional connections. To address this problem, in this doctoral work we focus at the inference of directed networks. Specifically, we develop a model-based network reconstruction method that combines statistics of derivative-variable correlations with simulated annealing. We furthermore develop a data-driven reconstruction method based on a nonlinear interdependence measure. This method allows one to infer the topology of directed networks of chaotic Lorenz oscillators for a subrange of the coupling strength and link density. Finally, we apply the data-driven method to multichannel electroencephalographic recordings from an epilepsy patient. The functional brain networks obtained from this approach are consistent with the available medical information.
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Glöckner, Reinhard Jörg. "Power transfer optimised automatic matching networks." Thesis, Northumbria University, 2009. http://nrl.northumbria.ac.uk/1622/.

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Matching networks are widely used to enhance active power transfer when radio frequency generators drive complex loads. The tuning of the network for varying loads typically involves searching for optimum matching conditions. However, improving the matching condition of the network does not necessarily indicate an increase in active power transfer. As an example, a 71 network with three adjustable elements may achieve comparable matches for a variety of elements' settings, each matching triple exhibiting a different transferred active power. Furthermore, the influence of the transmission lines used to connect the matching network to its source and load is rarely taken into account. The purpose of the work is to optimise the power gain of a narrowband matching sys-tem in the frequency range of 1.8 — 30 MHz. The system consists of a source, a match-ing network, a load and two interconnecting lines whose characteristic impedance is complex. The optimisation process involves optimum choice of the transmission lines' lengths and development of a matching strategy. Its objective is to ensure automatic and continuous adjustment of the matching network for optimum ac¬tive power transfer to its load while matching the network's input impedance to a resistive source. The network topologies employed are limited to the most common 71 and T networks consisting of two variable capacitors and one central inductor. Losses are assumed to be mainly caused by the inductor. An appropriate simple and synthetic model of the losses is proposed which is suitable for active power transfer optimisation. The model is validated against losses of inductors derived by different works. After choosing a proper network parametrisation and exact inclusion of the losses during network design, the losses of a network terminated by a resistance and de¬signed to match (exactly) a source resistance at its input are derived. Then its power gain is optimised by a proper choice of the network's parameter and the impact of changing the purely resistive termination to impedances exhibiting capacitive or in¬ductive imaginary parts is considered. An explicit solution is calculated for networks with a constant Q factor central inductor, its differences from the approximate solu¬tion (network elements designed as if the network would be lossless) are considered. Example diagrams are given illustrating those differences and power gain contour Smith charts are drawn for typical ranges of the L, iv, and T networks' elements. Combining the results of the different approaches yields an optimum matching strat¬egy. The losses of transmission lines connecting source and network of load and network are determined, where the lines' complex characteristic impedance is taken into account. Those losses are included in a power gain optimisation of the complete matching system. Finally, an experimental setup is designed under which the matching strategy of the network is tested and validated.
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Li, Ping. "Neural networks for automatic arc welding." Thesis, University of Liverpool, 1995. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.284264.

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Rivera, Corban G. "Automatic Reconstruction of the Building Blocks of Molecular Interaction Networks." Diss., Virginia Tech, 2008. http://hdl.handle.net/10919/28752.

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High-throughput whole-genome biological assays are highly intricate and difficult to interpret. The molecular interaction networks generated from evaluation of those experiments suggest that cellular functions are carried out by modules of interacting molecules. Reverse-engineering the modular structure of cellular interaction networks has the promise of significantly easing their analysis. We hypothesize that:
  1. cellular wiring diagrams can be decomposed into overlapping modules, where each module is a set of coherently-interacting molecules and
  2. a cell responds to a stress or a stimulus by appropriately modulating the activities of a subset of these modules.
Motivated by these hypotheses, we develop models and algorithms that can reverse-engineer molecular modules from large-scale functional genomic data. We address two major problems:
  1. Given a wiring diagram and genome-wide gene expression data measured after the application of a stress or in a disease state, compute the active network of molecular interactions perturbed by the stress or the disease.
  2. Given the active networks for multiple stresses, stimuli, or diseases, compute a set of network legos, which are molecular modules with the property that each active network can be expressed as an appropriate combination of a subset of modules.
To address the first problem, we propose an approach that computes the most-perturbed subgraph of a curated pathway of molecular interactions in a disease state. Our method is based on a novel score for pathway perturbation that incorporates both differential gene expression and the interaction structure of the pathway. We apply our method to a compendium of cancer types. We show that the significance of the most perturbed sub-pathway is frequently larger than that of the entire pathway. We identify an association that suggests that IL-2 infusion may have a similar therapeutic effect in bladder cancer as it does in melanoma. We propose two models to address the second problem. First, we formulate a Boolean model for constructing network legos from a set of active networks. We reduce the problem of computing network legos to that of constructing closed biclusters in a binary matrix. Applying this method to a compendium of 13 stresses on human cells, we automatically detect that about four to six hours after treatment with chemicals cause endoplasmic reticulum stress, fibroblasts shut down the cell cycle far more aggressively than fibroblasts or HeLa cells do in response to other treatments. Our second model represents each active network as an additive combination of network legos. We formulate the problem as one of computing network legos that can be used to recover active networks in an optimal manner. We use existing methods for non-negative matrix approximation to solve this problem. We apply our method to a human cancer dataset including 190 samples from 18 cancers. We identify a network lego that associates integrins and matrix metalloproteinases in ovarian adenoma and other cancers and a network lego including the retinoblastoma pathway associated with multiple leukemias.
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Prager, Richard William. "Parallel processing networks for automatic speech recognition." Thesis, University of Cambridge, 1987. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.238443.

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Lee, George J. (George Janbing) 1979. "Automatic service selection in dynamic wireless networks." Thesis, Massachusetts Institute of Technology, 2003. http://hdl.handle.net/1721.1/87434.

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Crestel, Léopold. "Neural networks for automatic musical projective orchestration." Electronic Thesis or Diss., Sorbonne université, 2018. http://www.theses.fr/2018SORUS625.

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L’orchestration est l’art de composer un discours musical en combinant les timbres instrumentaux. La complexité de la discipline a longtemps été un frein à l’élaboration d’une théorie de l’orchestration. Ainsi, contrairement à l’harmonie ou au contrepoint qui s’appuient sur de solides constructions théoriques, l’orchestration reste de nos jours encore essentiellement enseignée à travers l’observation d’exemples canoniques. Notre objectif est de développer un système d’orchestration automatique de pièce pour piano en nous appuyant sur des méthodes d’apprentissage statistique. Nous nous focalisons sur le répertoire classique, cette technique d’écriture étant courante pour des compositeurs tels que Mozart ou Beethoven qui réalisaient d’abord une ébauche pianistique de leurs pièces orchestrales. En observant une large base de donnée de pièces pour orchestre et leurs réductions pour piano, nous évaluons l'aptitude des réseaux de neurones à apprendre les mécanismes complexes qui régissent l’orchestration. La vaste capacité d’apprentissage des architectures profondes semble adaptée à la difficulté du problème. Cependant, dans un contexte orchestrale, les représentations musicales symboliques traditionnelles donnent lieu à des vecteurs parcimonieux dans des espaces de grande dimension. Nous essayons donc de contourner ces difficultés en utilisant des méthodes auto-régressives et des fonctions d’erreur adaptées. Finalement, nous essayons de développer un système capable d'orchestrer en temps réel l'improvisation d'un pianiste
Orchestration is the art of composing a musical discourse over a combinatorial set of instrumental possibilities. For centuries, musical orchestration has only been addressed in an empirical way, as a scientific theory of orchestration appears elusive. In this work, we attempt to build the first system for automatic projective orchestration, and to rely on machine learning. Hence, we start by formalizing this novel task. We focus our effort on projecting a piano piece onto a full symphonic orchestra, in the style of notable classic composers such as Mozart or Beethoven. Hence, the first objective is to design a system of live orchestration, which takes as input the sequence of chords played by a pianist and generate in real-time its orchestration. Afterwards, we relax the real-time constraints in order to use slower but more powerful models and to generate scores in a non-causal way, which is closer to the writing process of a human composer. By observing a large dataset of orchestral music written by composers and their reduction for piano, we hope to be able to capture through statistical learning methods the mechanisms involved in the orchestration of a piano piece. Deep neural networks seem to be a promising lead for their ability to model complex behaviour from a large dataset and in an unsupervised way. More specifically, in the challenging context of symbolic music which is characterized by a high-dimensional target space and few examples, we investigate autoregressive models. At the price of a slower generation process, auto-regressive models allow to account for more complex dependencies between the different elements of the score, which we believe to be of the foremost importance in the case of orchestration
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Newman, Rhys A. "Automatic learning in computer vision." Thesis, University of Oxford, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.390526.

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Winsnes, Casper. "Automatic Subcellular Protein Localization Using Deep Neural Networks." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-189991.

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Protein localization is an important part in understanding the functionality of a protein. The current method of localizing proteins is to manually annotate microscopy images. This thesis investigates the feasibility of using deep artificial neural networks to automatically classify subcellular protein locations based on immunoflourescent images. We investigate the applicability in both single-label and multi-label classification, as well as cross cell line classification. We show that deep single-label neural networks can be used for protein localization with up to 73% accuracy. We also show the potential of deep multi-label neural networks for protein localization and cross cell line classification but conclude that more research is needed before we can say for certain that the method is applicable.
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Howell, Andrew Jonathan. "Automatic face recognition using radial basis function networks." Thesis, University of Sussex, 1997. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.241635.

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Books on the topic "Automatic networks"

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Bowen, Jacqueline D. Automatic fingerprint pattern classification using neutral networks. London: Home Office, Science and Technology Group, 1992.

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Rost, Burkhard. Automatic sensor networks of verifying disarmament of aircraft. Bochum: UVB Universitätsverlag Dr. N. Brockmeyer, 1991.

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Can, Vuran Mehmet, ed. Wireless sensor networks. Chichester, West Sussex, U.K: Wiley, 2010.

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Azzouz, Elsayed Elsayed. Automatic modulation recognition of communication signals. Boston: Kluwer Academic Publishers, 1996.

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Dib, Mohammed. Automatic Speech Recognition of Arabic Phonemes with Neural Networks. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-319-97710-2.

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Azzouz, Elsayed Elsayed. Automatic Modulation Recognition of Communication Signals. Boston, MA: Springer US, 1996.

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Kocheisen, Michael. Neutral networks in photofinishing. Konstanz: Hartung-Gorre, 1997.

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Zheng-Hua, Tan, and Lindberg Børge, eds. Automatic speech recognition on mobile devices and over communication networks. London: Springer, 2008.

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Tan, Zheng-Hua, and Børge Lindberg. Automatic Speech Recognition on Mobile Devices and over Communication Networks. London: Springer London, 2008. http://dx.doi.org/10.1007/978-1-84800-143-5.

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Sophie, Tarbouriech, Abdallah C. T, and Chiasson John Nelson, eds. Advances in communication control networks. Berlin: Springer, 2005.

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Book chapters on the topic "Automatic networks"

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Kłosowski, Piotr, and Adam Dustor. "Automatic Speech Segmentation for Automatic Speech Translation." In Computer Networks, 466–75. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-38865-1_47.

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Yu, Dong, and Li Deng. "Deep Neural Networks." In Automatic Speech Recognition, 57–77. London: Springer London, 2014. http://dx.doi.org/10.1007/978-1-4471-5779-3_4.

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Gotzhein, Reinhard. "Automatic Topology Detection." In Computer Communications and Networks, 79–98. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-33319-5_4.

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Yu, Dong, and Li Deng. "Adaptation of Deep Neural Networks." In Automatic Speech Recognition, 193–215. London: Springer London, 2014. http://dx.doi.org/10.1007/978-1-4471-5779-3_11.

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Yu, Dong, and Li Deng. "Recurrent Neural Networks and Related Models." In Automatic Speech Recognition, 237–66. London: Springer London, 2014. http://dx.doi.org/10.1007/978-1-4471-5779-3_13.

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Czyszczoń, Adam, and Aleksander Zgrzywa. "Automatic Customer Segmentation for Social CRM Systems." In Computer Networks, 552–61. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-38865-1_55.

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Yu, Dong, and Li Deng. "Feature Representation Learning in Deep Neural Networks." In Automatic Speech Recognition, 157–75. London: Springer London, 2014. http://dx.doi.org/10.1007/978-1-4471-5779-3_9.

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Czyszczoń, Adam, and Aleksander Zgrzywa. "Automatic RESTful Web Service Identification and Information Extraction." In Computer Networks, 318–27. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-07941-7_32.

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Wehenkel, Louis A. "Artificial Neural Networks." In Automatic Learning Techniques in Power Systems, 71–98. Boston, MA: Springer US, 1998. http://dx.doi.org/10.1007/978-1-4615-5451-6_4.

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Yu, Dong, and Li Deng. "Representation Sharing and Transfer in Deep Neural Networks." In Automatic Speech Recognition, 219–35. London: Springer London, 2014. http://dx.doi.org/10.1007/978-1-4471-5779-3_12.

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Conference papers on the topic "Automatic networks"

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Dao-Thi, Thu-Ha, and Jean Mairesse. "Zero-automatic networks." In the 1st international conference. New York, New York, USA: ACM Press, 2006. http://dx.doi.org/10.1145/1190095.1190098.

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Singh, Chandan Kumar, Anima Majumder, Swagat Kumar, and Laxmidhar Behera. "Deep Network based Automatic Annotation for Warehouse Automation." In 2018 International Joint Conference on Neural Networks (IJCNN). IEEE, 2018. http://dx.doi.org/10.1109/ijcnn.2018.8489424.

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Cornelissen, Willy, and Maurício Loureiro. "Automatic onset detection using convolutional neural networks." In Simpósio Brasileiro de Computação Musical. Sociedade Brasileira de Computação - SBC, 2019. http://dx.doi.org/10.5753/sbcm.2019.10446.

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A very significant task for music research is to estimate instants when meaningful events begin (onset) and when they end (offset). Onset detection is widely applied in many fields: electrocardiograms, seismographic data, stock market results and many Music Information Research(MIR) tasks, such as Automatic Music Transcription, Rhythm Detection, Speech Recognition, etc. Automatic Onset Detection(AOD) received, recently, a huge contribution coming from Artificial Intelligence (AI) methods, mainly Machine Learning and Deep Learning. In this work, the use of Convolutional Neural Networks (CNN) is explored by adapting its original architecture in order to apply the approach to automatic onset detection on audio musical signals. We used a CNN network for onset detection on a very general dataset, well acknowledged by the MIR community, and examined the accuracy of the method by comparison to ground truth data published by the dataset. The results are promising and outperform another methods of musical onset detection.
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Liu, Huajie, and Jian (John) Dong. "Artificial Neural Networks for Automatic Dispatching Rule Selection." In ASME 1995 15th International Computers in Engineering Conference and the ASME 1995 9th Annual Engineering Database Symposium collocated with the ASME 1995 Design Engineering Technical Conferences. American Society of Mechanical Engineers, 1995. http://dx.doi.org/10.1115/cie1995-0832.

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Abstract To schedule a job shop, the first task is to select an appropriate scheduling algorithm or rules. Because of the complexity of scheduling problems, no general algorithmic approach sufficient for solving all scheduling problems has been developed yet. Most scheduling systems offer multiple alternative algorithms to various circumstances, and experienced human schedulers are needed to select the appropriate dispatching rules in these systems. To automate scheduling process, this paper discusses the use of neural networks for scheduling decision making. For a given workshop, various situations are simulated to identify the best dispatching rule. The inputs and outputs of the simulation model are used to train an artificial neural network. The trained neural network then will be used to automatically selecting an appropriate dispatching rule for a given situation. Research results have shown great potential in using a neural network to replace human schedulers in selecting an appropriate approach for real time scheduling. This research is a part of an ongoing project of developing a real-time planning and scheduling system.
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Matic, Ivana D., Antonio Pedro Oliveira, and Amilcar Cardoso. "Automatic melody generation using Neural Networks and Cellular Automata." In 2012 11th Symposium on Neural Network Applications in Electrical Engineering (NEUREL 2012). IEEE, 2012. http://dx.doi.org/10.1109/neurel.2012.6419972.

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Rizzi, A., F. M. F. Mascioli, and G. Martinelli. "Automatic training of ANFIS networks." In Proceedings of 8th International Fuzzy Systems Conference. IEEE, 1999. http://dx.doi.org/10.1109/fuzzy.1999.790153.

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Sharma, S., D. Staessens, D. Colle, M. Pickavet, and P. Demeester. "Automatic bootstrapping of OpenFlow networks." In 2013 IEEE 19th International Workshop on Local & Metropolitan Area Networks (LANMAN). IEEE, 2013. http://dx.doi.org/10.1109/lanman.2013.6528283.

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Geyer, Fabien, and Georg Carle. "Towards automatic performance optimization of networks using machine learning." In 2016 17th International Telecommunications Network Strategy and Planning Symposium (Networks). IEEE, 2016. http://dx.doi.org/10.1109/netwks.2016.7751147.

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Li, C. James, and T. Y. Huang. "Automatic Structure and Parameter Training Methods for Modeling of Mechanical System by Recurrent Neural Networks." In ASME 1997 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 1997. http://dx.doi.org/10.1115/imece1997-0402.

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Abstract Automatic nonlinear-system identification is very useful for various disciplines including, e.g., automatic control, mechanical diagnostics, and financial market prediction. This paper describes a fully automatic structural and weight learning method for recurrent neural networks (RNN). The basic idea is training with residuals, i.e., a single hidden neuron RNN is trained to track the residuals of an existing network before it is augmented to the existing network to form a larger and better network. The network continues to grow until either a desired level of accuracy or a preset maximal number of neurons is reached. The method requires neither guess of initial weight values nor the number of neurons in the hidden layer from users. This new structural and weight learning algorithm is used to find RNN models for a two-degree-of-freedom planar robot, a Van der Pol oscillator and a Mackey-Glass equation using their simulated responses to excitations. In addition, a RNN model is obtained for a real robot using its input and output measurements. The algorithm is effective in all four cases and RNN models were shown to be superior to linear models and hybrid models wherever the comparison was made.
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Pudovkin, Mikhail I., Ake Steen, N. V. Nikolaev, O. I. Kornilov, Urban Brandstrom, Bjorn Gustavsson, and Peter Rydesater. "Automatic recognition of auroral forms." In Ninth Workshop on Virtual Intelligence/Dynamic Neural Networks: Neural Networks Fuzzy Systems, Evolutionary Systems and Virtual Re, edited by Thomas Lindblad, Mary Lou Padgett, and Jason M. Kinser. SPIE, 1999. http://dx.doi.org/10.1117/12.343030.

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Reports on the topic "Automatic networks"

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Hillard, D., M. Ostendorf, A. Stolcke, Y. Liu, and E. Shriberg. Improving Automatic Sentence Boundary Detection with Confusion Networks. Fort Belvoir, VA: Defense Technical Information Center, January 2004. http://dx.doi.org/10.21236/ada460954.

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Lenz, Lutz. Automatic Tuning of Integrated Filters Using Neural Networks. Portland State University Library, January 2000. http://dx.doi.org/10.15760/etd.6488.

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Shah, S., and M. Yip. Extreme Networks' Ethernet Automatic Protection Switching (EAPS) Version 1. RFC Editor, October 2003. http://dx.doi.org/10.17487/rfc3619.

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Greamo, Chris. Automatic Identification & Mitigation of Unauthorized Information Leaking from Tactical Mobile Networks. Fort Belvoir, VA: Defense Technical Information Center, October 2012. http://dx.doi.org/10.21236/ada586817.

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Stevenson, G. Analysis of Pre-Trained Deep Neural Networks for Large-Vocabulary Automatic Speech Recognition. Office of Scientific and Technical Information (OSTI), July 2016. http://dx.doi.org/10.2172/1289367.

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Croft, Elizabeth, and Barbara Spencer. Fees and Surcharging in automatic teller machine networks: Non-bank ATM providers versus large banks. Cambridge, MA: National Bureau of Economic Research, August 2003. http://dx.doi.org/10.3386/w9883.

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Arhin, Stephen, Babin Manandhar, Hamdiat Baba Adam, and Adam Gatiba. Predicting Bus Travel Times in Washington, DC Using Artificial Neural Networks (ANNs). Mineta Transportation Institute, April 2021. http://dx.doi.org/10.31979/mti.2021.1943.

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Washington, DC is ranked second among cities in terms of highest public transit commuters in the United States, with approximately 9% of the working population using the Washington Metropolitan Area Transit Authority (WMATA) Metrobuses to commute. Deducing accurate travel times of these metrobuses is an important task for transit authorities to provide reliable service to its patrons. This study, using Artificial Neural Networks (ANN), developed prediction models for transit buses to assist decision-makers to improve service quality and patronage. For this study, we used six months of Automatic Vehicle Location (AVL) and Automatic Passenger Counting (APC) data for six Washington Metropolitan Area Transit Authority (WMATA) bus routes operating in Washington, DC. We developed regression models and Artificial Neural Network (ANN) models for predicting travel times of buses for different peak periods (AM, Mid-Day and PM). Our analysis included variables such as number of served bus stops, length of route between bus stops, average number of passengers in the bus, average dwell time of buses, and number of intersections between bus stops. We obtained ANN models for travel times by using approximation technique incorporating two separate algorithms: Quasi-Newton and Levenberg-Marquardt. The training strategy for neural network models involved feed forward and errorback processes that minimized the generated errors. We also evaluated the models with a Comparison of the Normalized Squared Errors (NSE). From the results, we observed that the travel times of buses and the dwell times at bus stops generally increased over time of the day. We gathered travel time equations for buses for the AM, Mid-Day and PM Peaks. The lowest NSE for the AM, Mid-Day and PM Peak periods corresponded to training processes using Quasi-Newton algorithm, which had 3, 2 and 5 perceptron layers, respectively. These prediction models could be adapted by transit agencies to provide the patrons with accurate travel time information at bus stops or online.
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Dannemann Dugick, Fransiska, Sarah Albert, Gil Averbuch, and Stephen Arrowsmith. Utilizing the Dynamic Networks Data Processing and Analysis Experiment (DNE18) to Establish Methodologies for the Comparison of Automatic Infrasonic Signal Detectors. Office of Scientific and Technical Information (OSTI), November 2021. http://dx.doi.org/10.2172/1832306.

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Yaroshchuk, Svitlana O., Nonna N. Shapovalova, Andrii M. Striuk, Olena H. Rybalchenko, Iryna O. Dotsenko, and Svitlana V. Bilashenko. Credit scoring model for microfinance organizations. [б. в.], February 2020. http://dx.doi.org/10.31812/123456789/3683.

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The purpose of the work is the development and application of models for scoring assessment of microfinance institution borrowers. This model allows to increase the efficiency of work in the field of credit. The object of research is lending. The subject of the study is a direct scoring model for improving the quality of lending using machine learning methods. The objective of the study: to determine the criteria for choosing a solvent borrower, to develop a model for an early assessment, to create software based on neural networks to determine the probability of a loan default risk. Used research methods such as analysis of the literature on banking scoring; artificial intelligence methods for scoring; modeling of scoring estimation algorithm using neural networks, empirical method for determining the optimal parameters of the training model; method of object-oriented design and programming. The result of the work is a neural network scoring model with high accuracy of calculations, an implemented system of automatic customer lending.
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Beiker, Sven. Unsettled Issues in Remote Operation for On-road Driving Automation. SAE International, December 2021. http://dx.doi.org/10.4271/epr2021028.

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On-road vehicles equipped with driving automation features—where a human might not be needed for operation on-board—are entering the mainstream public space. However, questions like “How safe is safe enough?” and “What to do if the system fails?” persist. This is where remote operation comes in, which is an additional layer to the automated driving system where a human remotely assists the so-called “driverless” vehicle in certain situations. Such remote-operation solutions introduce additional challenges and potential risks as the entire vehicle-network-human now needs to work together safely, effectively, and practically. Unsettled Issues in Remote Operation for On-road Driving Automation highlights technical questions (e.g., network latency, bandwidth, cyber security) and human aspects (e.g., workload, attentiveness, situational awareness) of remote operation and introduces evolving solutions. The report also discusses standards development and regulations—both of which are needed to provide frameworks for the deployment of driving automation with remote operation.
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