Journal articles: '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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Fernández-Ahumada, Luis Manuel, Jose Ramírez-Faz, Marcos Torres-Romero, and Rafael López-Luque. "Proposal for the Design of Monitoring and Operating Irrigation Networks Based on IoT, Cloud Computing and Free Hardware Technologies." Sensors 19, no. 10 (May 20, 2019): 2318. http://dx.doi.org/10.3390/s19102318.

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In recent decades, considerable efforts have been devoted to process automation in agriculture. Regarding irrigation systems, this demand has found several difficulties, including the lack of communication networks and the large distances to electricity supply points. With the recent implementation of LPWAN wireless communication networks (SIGFOX, LoraWan, and NBIoT), and the expanding market of electronic controllers based on free software and hardware (i.e., Arduino, Raspberry, ESP, etc.) with low energy requirements, new perspectives have appeared for the automation of agricultural irrigation networks. This paper presents a low-cost solution for automatic cloud-based irrigation. In this paper, it is proposed the design of a node network based on microcontroller ESP32-Lora and Internet connection through SIGFOX network. The results obtained show the stability and robustness of the designed system.

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West, Robert, and Jure Leskovec. "Automatic Versus Human Navigation in Information Networks." Proceedings of the International AAAI Conference on Web and Social Media 6, no. 1 (August 3, 2021): 362–69. http://dx.doi.org/10.1609/icwsm.v6i1.14238.

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People regularly face tasks that can be understood as navigation in information networks, where the goal is to find a path between two given nodes. In many such situations, the navigator only gets local access to the node currently under inspection and its immediate neighbors. This lack of global information about the network notwithstanding, humans tend to be good at finding short paths, despite the fact that real-world networks are typically very large. One potential reason for this could be that humans possess vast amounts of background knowledge about the world, which they leverage to make good guesses about possible solutions. In this paper we ask the question: Are human-like high-level reasoning skills really necessary for finding short paths? To answer this question, we design a number of navigation agents without such skills, which use only simple numerical features. We evaluate the agents on the task of navigating Wikipedia, a domain for which we also possess large-scale human navigation data. We observe that the agents find shorter paths than humans on average and therefore conclude that, perhaps surprisingly, no sophisticated background knowledge or high-level reasoning is required for navigating the complex Wikipedia network.

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Yang, Weiping. "AUTOMATIC CONSTRUCTION OF HIERARCHICAL ROAD NETWORKS." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences III-2 (June 2, 2016): 37–44. http://dx.doi.org/10.5194/isprsannals-iii-2-37-2016.

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This paper describes an automated method of constructing a hierarchical road network given a single dataset, without the presence of thematic attributes. The method is based on a pattern graph which maintains nodes and paths as junctions and through-traffic roads. The hierarchy is formed incrementally in a top-down fashion for highways, ramps, and major roads directly connected to ramps; and bottom-up for the rest of major and minor roads. Through reasoning and analysis, ramps are identified as unique characteristics for recognizing and assembling high speed roads. The method makes distinctions on the types of ramps by articulating their connection patterns with highways. Major and minor roads will be identified by both quantitative and qualitative analysis of spatial properties and by discovering neighbourhood patterns revealed in the data. The result of the method would enrich data description and support comprehensive queries on sorted exit or entry points on highways and their related roads. The enrichment on road network data is important to a high successful rate of feature matching for road networks and to geospatial data integration.

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Yang, Weiping. "AUTOMATIC CONSTRUCTION OF HIERARCHICAL ROAD NETWORKS." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences III-2 (June 2, 2016): 37–44. http://dx.doi.org/10.5194/isprs-annals-iii-2-37-2016.

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This paper describes an automated method of constructing a hierarchical road network given a single dataset, without the presence of thematic attributes. The method is based on a pattern graph which maintains nodes and paths as junctions and through-traffic roads. The hierarchy is formed incrementally in a top-down fashion for highways, ramps, and major roads directly connected to ramps; and bottom-up for the rest of major and minor roads. Through reasoning and analysis, ramps are identified as unique characteristics for recognizing and assembling high speed roads. The method makes distinctions on the types of ramps by articulating their connection patterns with highways. Major and minor roads will be identified by both quantitative and qualitative analysis of spatial properties and by discovering neighbourhood patterns revealed in the data. The result of the method would enrich data description and support comprehensive queries on sorted exit or entry points on highways and their related roads. The enrichment on road network data is important to a high successful rate of feature matching for road networks and to geospatial data integration.

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Zheng, Jing, Ziren Gao, Jingsong Ma, Jie Shen, and Kang Zhang. "Deep Graph Convolutional Networks for Accurate Automatic Road Network Selection." ISPRS International Journal of Geo-Information 10, no. 11 (November 11, 2021): 768. http://dx.doi.org/10.3390/ijgi10110768.

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The selection of road networks is very important for cartographic generalization. Traditional artificial intelligence methods have improved selection efficiency but cannot fully extract the spatial features of road networks. However, current selection methods, which are based on the theory of graphs or strokes, have low automaticity and are highly subjective. Graph convolutional networks (GCNs) combine graph theory with neural networks; thus, they can not only extract spatial information but also realize automatic selection. Therefore, in this study, we adopted GCNs for automatic road network selection and transformed the process into one of node classification. In addition, to solve the problem of gradient vanishing in GCNs, we compared and analyzed the results of various GCNs (GraphSAGE and graph attention networks [GAT]) by selecting small-scale road networks under different deep architectures (JK-Nets, ResNet, and DenseNet). Our results indicate that GAT provides better selection of road networks than other models. Additionally, the three abovementioned deep architectures can effectively improve the selection effect of models; JK-Nets demonstrated more improvement with higher accuracy (88.12%) than other methods. Thus, our study shows that GCN is an appropriate tool for road network selection; its application in cartography must be further explored.

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MENEZES, TELMO, and CAMILLE ROTH. "AUTOMATIC DISCOVERY OF AGENT BASED MODELS: AN APPLICATION TO SOCIAL ANTHROPOLOGY." Advances in Complex Systems 16, no. 07 (October 2013): 1350027. http://dx.doi.org/10.1142/s0219525913500276.

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We present a methodology that applies a machine learning technique — genetic programming — to the problem of finding plausible generative models for complex networks. We specifically apply this method to the analysis of alliance networks, a type of kinship network used by social anthropologists where nodes are groups and directed edges represent a group giving a wife to another group. Network generators are represented as computer programs. Evolutionary search is used to find programs that generate networks that best approximate real networks. The quality evaluation of a model is based on a set of network metrics with anthropological meaning. We evolve generators for seventeen real alliance networks and find that our approach is capable of generating high quality results both in terms of network similarity and human readability of the programs. We present and discuss a subset of the experimental results that highlights several interesting aspects of our findings. We believe in the applicability of the methodology to complex networks in general and propose that these are the first steps towards an artificial network scientist.

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Devi, Kharibam Jilenkumari, and Khelchandra Thongam. "A Survey of Automatic Speaker Recognition System Using Artificial Neural Networks." Journal of Advanced Research in Dynamical and Control Systems 11, no. 10-SPECIAL ISSUE (October 31, 2019): 453–56. http://dx.doi.org/10.5373/jardcs/v11sp10/20192832.

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Shih, Po-Chou, Chun-Chin Hsu, and Fang-Chih Tien. "Automatic Reclaimed Wafer Classification Using Deep Learning Neural Networks." Symmetry 12, no. 5 (May 2, 2020): 705. http://dx.doi.org/10.3390/sym12050705.

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Silicon wafer is the most crucial material in the semiconductor manufacturing industry. Owing to limited resources, the reclamation of monitor and dummy wafers for reuse can dramatically lower the cost, and become a competitive edge in this industry. However, defects such as void, scratches, particles, and contamination are found on the surfaces of the reclaimed wafers. Most of the reclaimed wafers with the asymmetric distribution of the defects, known as the “good (G)” reclaimed wafers, can be re-polished if their defects are not irreversible and if their thicknesses are sufficient for re-polishing. Currently, the “no good (NG)” reclaimed wafers must be first screened by experienced human inspectors to determine their re-usability through defect mapping. This screening task is tedious, time-consuming, and unreliable. This study presents a deep-learning-based reclaimed wafers defect classification approach. Three neural networks, multilayer perceptron (MLP), convolutional neural network (CNN) and Residual Network (ResNet), are adopted and compared for classification. These networks analyze the pattern of defect mapping and determine not only the reclaimed wafers are suitable for re-polishing but also where the defect categories belong. The open source TensorFlow library was used to train the MLP, CNN, and ResNet networks using collected wafer images as input data. Based on the experimental results, we found that the system applying CNN networks with a proper design of kernels and structures gave fast and superior performance in identifying defective wafers owing to its deep learning capability, and the ResNet averagely exhibited excellent accuracy, while the large-scale MLP networks also acquired good results with proper network structures.

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Sharama, Apurva, and Dr Jaswinder Singh. "Survey of technologies of self-organizing networks (SON)." International Journal of Engineering & Technology 7, no. 4 (September 24, 2018): 2581. http://dx.doi.org/10.14419/ijet.v7i4.16831.

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Due to increase in existing web availability, self organized systems are expected to give a quick, adaptable and cost-effective solution for satisfy the gap between limit and request/demand to adapt to the quick development in remote activity or wireless traffic. The main purpose of introducing SON algorithm is to simplify the network operations via automating repeated tasks in network operations. Hence, in SON empowered system operations, methods that hold the human administrator in manual system operations are substituted via programmed SON functions. Self organized systems or intelligent frameworks are required for remote portable systems to deal with broad number of clients in the meantime and to support the human administrator in manual system operations; that are substituted via automatic SON functions since the human administrators don't have to gather and examine the system information. Self organized networks are needed for the controlling the big challenge of mobile traffic growth and to reduce the cost efficiency. Efficient methods are required for automatic channel selection, power adjustment, and frequency assignment for autonomous interference coordination and coverage optimization.

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Pandey, Subash, Rabin Kumar Dhamala, Bikram Karki, Saroj Dahal, and Rama Bastola. "Automatic Image Captioning Using Neural Networks." Journal of Innovations in Engineering Education 3, no. 1 (March 31, 2020): 138–46. http://dx.doi.org/10.3126/jiee.v3i1.34335.

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Automatically generating a natural language description of an image is a major challenging task in the field of artificial intelligence. Generating description of an image bring together the fields: Natural Language Processing and Computer Vision. There are two types of approaches i.e. top-down and bottom-up. For this paper, we approached top-down that starts from the image and converts it into the word. Image is passed to Convolutional Neural Network (CNN) encoder and the output from it is fed further to Recurrent Neural Network (RNN) decoder that generates meaningful captions. We generated the image description by passing the real time images from the camera of a smartphone as well as tested with the test images from the dataset. To evaluate the model performance, we used BLEU (Bilingual Evaluation Understudy) score and match predicted words to the original caption.

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Papegnies, Etienne, Vincent Labatut, Richard Dufour, and Georges Linares. "Conversational Networks for Automatic Online Moderation." IEEE Transactions on Computational Social Systems 6, no. 1 (February 2019): 38–55. http://dx.doi.org/10.1109/tcss.2018.2887240.

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Park, Seongjin, and John Culnan. "Automatic perceptual judgment using neural networks." Journal of the Acoustical Society of America 146, no. 4 (October 2019): 2957. http://dx.doi.org/10.1121/1.5137271.

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Liu, Jun, and Kuo-Chu Chang. "Automatic Target Recognition with Bayesian Networks." IFAC Proceedings Volumes 29, no. 1 (June 1996): 7464–69. http://dx.doi.org/10.1016/s1474-6670(17)58889-8.

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Rogers, Steven K., John M. Colombi, Curtis E. Martin, James C. Gainey, Ken H. Fielding, Tom J. Burns, Dennis W. Ruck, Matthew Kabrisky, and Mark Oxley. "Neural networks for automatic target recognition." Neural Networks 8, no. 7-8 (January 1995): 1153–84. http://dx.doi.org/10.1016/0893-6080(95)00050-x.

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Avila-Campillo, I., K. Drew, J. Lin, D. J. Reiss, and R. Bonneau. "BioNetBuilder: automatic integration of biological networks." Bioinformatics 23, no. 3 (November 30, 2006): 392–93. http://dx.doi.org/10.1093/bioinformatics/btl604.

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Rodrigo, G., J. Carrera, and A. Jaramillo. "Genetdes: automatic design of transcriptional networks." Bioinformatics 23, no. 14 (May 7, 2007): 1857–58. http://dx.doi.org/10.1093/bioinformatics/btm237.

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Ferrari, S., I. Frosio, V. Piuri, and N. A. Borghese. "Automatic Multiscale Meshing Through HRBF Networks." IEEE Transactions on Instrumentation and Measurement 54, no. 4 (August 2005): 1463–70. http://dx.doi.org/10.1109/tim.2005.851471.

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Rodriguez, Marko A., Johan Bollen, and Herbert Van De Sompel. "Automatic metadata generation using associative networks." ACM Transactions on Information Systems 27, no. 2 (February 2009): 1–20. http://dx.doi.org/10.1145/1462198.1462199.

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Gruau, Frédéric. "Automatic Definition of Modular Neural Networks." Adaptive Behavior 3, no. 2 (September 1994): 151–83. http://dx.doi.org/10.1177/105971239400300202.

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Park, Young C., and Key-Sun Choi. "Automatic thesaurus construction using Bayesian networks." Information Processing & Management 32, no. 5 (September 1996): 543–53. http://dx.doi.org/10.1016/0306-4573(96)00026-x.

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Watanabe, Masataka, Kazuyuki Aihara, and Shunsuke Kondo. "Automatic learning in chaotic neural networks." Electronics and Communications in Japan (Part III: Fundamental Electronic Science) 79, no. 3 (1996): 87–93. http://dx.doi.org/10.1002/ecjc.4430790309.

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Rudnik, V. E., A. A. Suvorov, N. Yu Ruban, M. V. Andreev, and Yu D. Bay. "Operation of synthetic inertia units in electric power systems of various densities." iPolytech Journal 26, no. 3 (October 8, 2022): 465–86. http://dx.doi.org/10.21285/1814-3520-2022-3-465-486.

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This study is aimed at improving the efficiency of photovoltaic plants operated in the electric networks of various densities by adjusting the synthetic inertia algorithm and automatic frequency control circuits. To this end, the automatic control system of a photovoltaic plant was investigated using hybrid modelling methods in an all-mode online simulation complex of electric power systems. It was shown that the stability of photovoltaic power plants could be improved through the use of synthetic inertia. According to the conducted research, effective operation of this algorithm can be ensured by a correct determination of the bandwidth of automatic frequency control. Operation of this automatic frequency control circuit can lead to the oscillations of various frequencies during the installation of photovoltaic power plants in low-current electrical networks (electrical networks with the short circuit coefficient of less than 10 a.u.) and, subsequently, negatively affect the operability of the synthetic inertia algorithm. In addition, in high-current networks with an increased bandwidth of the automatic frequency control unit, the value of the network frequency reduction decreases (optimal bandwidth of 50 Hz). Conversely, in low-current networks, the automatic frequency control unit, under an increase in the bandwidth, decreases the response rate of the synthetic inertia algorithm, which leads to an increase in the frequency reduction value (optimal bandwidth of 0.3 Hz). Thus, the conducted investigations showed that the automatic frequency control circuit in the control system of a photovoltaic power plant can be used to alter the operation of the synthetic inertia algorithm. However, the nature of this effect depends on the electrical network density and can be both positive and negative. The effect observed in the tested power system was confirmed for a real-dimension power system.

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Parol, Miroslaw, Jacek Wasilewski, Tomasz Wojtowicz, Bartlomiej Arendarski, and Przemyslaw Komarnicki. "Reliability Analysis of MV Electric Distribution Networks Including Distributed Generation and ICT Infrastructure." Energies 15, no. 14 (July 21, 2022): 5311. http://dx.doi.org/10.3390/en15145311.

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In recent years, the increased distributed generation (DG) capacity in electric distribution systems has been observed. Therefore, it is necessary to research existing structures of distribution networks as well as to develop new (future) system structures. There are many works on the reliability of distribution systems with installed DG sources. This paper deals with a reliability analysis for both present and future medium voltage (MV) electric distribution system structures. The impact of DG technology used and energy source location on the power supply reliability has been analyzed. The reliability models of electrical power devices, conventional and renewable energy sources as well as information and communications technology (ICT) components have been proposed. Main contribution of this paper are the results of performed calculations, which have been analyzed for specific system structures (two typical present network structures and two future network structures), using detailed information on DG types, their locations and power capacities, as well as distribution system automation applied (automatic stand-by switching on—ASS and automatic power restoration—APR). The reliability of the smart grid consisting of the distribution network and the coupled communications network was simulated and assessed. The observations and conclusions based on calculation results have been made. More detailed modeling and consideration of system automation of distribution grids with DG units coupled with the communication systems allows the design and application of more reliable MV network structures.

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Tao, Tao, Jiada Li, Kunlun Xin, Peng Liu, and Xiaolan Xiong. "Division method for water distribution networks in hilly areas." Water Supply 16, no. 3 (December 23, 2015): 727–36. http://dx.doi.org/10.2166/ws.2015.182.

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Water distribution systems in hilly areas are always divided into several zones due to the undulating terrain. The present approach of dividing water distribution systems lacks an assessment index and is characterized by a low degree of automation. With the building of a mathematical model, this paper introduces two indicators – pressure limitation and pressure variation – to enable the automatic division of the water supply pipe network. It prioritizes economic index as the objective function in the evaluation of the division of water distribution systems in hilly areas, and then selects the optimal division scheme by generic algorithm in a large number of candidates. The SY terrain in YW City China is used for verification. Compared to traditional water supply partition methods, this procedure is easier to operate time-savingly by staff and is more automatic.

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Mapayi, Temitope, Pius A. Owolawi, and Adedayo O. Adio. "Automated Detection and Tortuosity Characterization of Retinal Vascular Networks." Journal of Biomimetics, Biomaterials and Biomedical Engineering 50 (April 2021): 89–102. http://dx.doi.org/10.4028/www.scientific.net/jbbbe.50.89.

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Automated retinal vascular network detection and analysis using digital retinal images continue to play a major role in the field of biomedicine for the diagnosis and management of various forms of human ailments like hypertension, diabetic retinopathy, retinopathy of prematurity, glaucoma and cardiovascular diseases. Although several literature have implemented different automatic approaches of detecting blood vessels in the retinal and also determining their tortuous states, the results obtained show that there are needs for further investigation on more efficient ways to detect and characterize the blood vessel network tortuosity states. This paper implements the use of an adaptive thresholding method based on local spatial relational variance (LSRV) for the detection of the retinal vascular networks. The suitability of a multi-layer perceptron artificial neural network (MLP-ANN) technique for the tortuosity characterization of retinal blood vascular networks is also presented in this paper. Some vessel geometric features of detected vessels are fed into ANN classifier for the automatic classification of the retinal vascular networks as being tortuous vessels or normal vessels. Experimental studies conducted on DRIVE and STARE databases show that the vascular network detection results obtained from the method implemented in this paper detects large and thin vascular networks in the retina. In comparison to preious methods in the literature, the proposed method for vascular network segmentation achieved better performance than several methods in the literature with a mean accuracy value of 95.04% and mean sensitivity value of 75.16% on DRIVE and mean accuracy value of 94.02% and average sensitivity value of 76.55% on STARE with computational processing time of 4.5 seconds and 9.4 seconds on DRIVE and STARE respectively. The MLP-ANN method proposed for the vascular network tortuosity characterization achieves promising accuracy rates of 77.5%, 80%, 83.33%, 85%, 86.67% and 100% for varying training sample sizes.

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Zang, Ke, Wenqi Wu, and Wei Luo. "Deep Sparse Learning for Automatic Modulation Classification Using Recurrent Neural Networks." Sensors 21, no. 19 (September 25, 2021): 6410. http://dx.doi.org/10.3390/s21196410.

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Deep learning models, especially recurrent neural networks (RNNs), have been successfully applied to automatic modulation classification (AMC) problems recently. However, deep neural networks are usually overparameterized, i.e., most of the connections between neurons are redundant. The large model size hinders the deployment of deep neural networks in applications such as Internet-of-Things (IoT) networks. Therefore, reducing parameters without compromising the network performance via sparse learning is often desirable since it can alleviates the computational and storage burdens of deep learning models. In this paper, we propose a sparse learning algorithm that can directly train a sparsely connected neural network based on the statistics of weight magnitude and gradient momentum. We first used the MNIST and CIFAR10 datasets to demonstrate the effectiveness of this method. Subsequently, we applied it to RNNs with different pruning strategies on recurrent and non-recurrent connections for AMC problems. Experimental results demonstrated that the proposed method can effectively reduce the parameters of the neural networks while maintaining model performance. Moreover, we show that appropriate sparsity can further improve network generalization ability.

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Vervečka, Martynas. "SENSOR NETWORK DATA FUSION METHODS." Mokslas - Lietuvos ateitis 2, no. 1 (February 28, 2010): 50–53. http://dx.doi.org/10.3846/mla.2010.011.

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Sensor network data fusion is widely used in warfare, in areas such as automatic target recognition, battlefield surveillance, automatic vehicle control, multiple target surveillance, etc. Non-military use example are: medical equipment status monitoring, intelligent home. The paper describes sensor networks topologies, sensor network advantages against the isolated sensors, most common network topologies, their advantages and disadvantages.

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Al Smadi, Takialddin. "Automatic detection technique for voice quality inter-disciplinary methodologies." Journal of advanced Sciences and Engineering Technologies 1, no. 1 (April 29, 2018): 1–6. http://dx.doi.org/10.32441/jaset.v1i1.54.

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This paper mainly studies process of dynamic routing in a multi-level perspective the mobile radio network based on the new generation of radio enhance the mobility a new and higher quality of service is required for different types of traffics,The Routing protocol in data networks will understand a formal set of rules and agreements on sharing network information between routers to determine the route of data transmission that satisfies a given quality of service requirements and provides a balanced load across the mobile radio network as a whole. including routing issues, devoted to the work of scientists, For modern computer networks of large dimension is typical multilevel routing in which in some way divided into a subnet routing domains, with at the most efficient protocol subnets group IGP, EGP group and protocols between networks. It is proposed to use a well-known proactive routing protocol OLSR multipoint handoff service packages as part of a hybrid protocol (HWMP). The description, the algorithm and the features of the implementation of the proactive protocol (OLSR). © 2018 JASET, International Scholars and Researchers Association

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Zhou, Guang-Dong, Mei-Xi Xie, Ting-Hua Yi, and Hong-Nan Li. "Optimal wireless sensor network configuration for structural monitoring using automatic-learning firefly algorithm." Advances in Structural Engineering 22, no. 4 (October 4, 2018): 907–18. http://dx.doi.org/10.1177/1369433218797074.

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Wireless sensor networks are becoming attractive data communication patterns in structural health monitoring systems. Designing and applying effective wireless sensor network–based structural health monitoring systems for large-scale civil infrastructure require a great number of wireless sensors and the optimal wireless sensor networks configuration becomes critical for such spatially separated large structures. In this article, optimal wireless sensor network configuration for structural health monitoring is treated as a discrete optimization problem, where parameter identification and network performance are simultaneously addressed. To solve this rather complicated optimization problem, a novel swarm intelligence algorithm called the automatic-learning firefly algorithm is proposed by integrating the original firefly algorithm with the Lévy flight and the automatic-learning mechanism. In the proposed algorithm, the Lévy flight is adopted to maximize the searching capability in unknown solution space and avoid premature convergence and the automatic-learning mechanism is designed to drive fireflies to move toward better locations at high speed. Numerical experiments are performed on a long-span bridge to demonstrate the effectiveness of the proposed automatic-learning firefly algorithm. Results indicate that automatic-learning firefly algorithm can find satisfactory wireless sensor network configurations, which facilitate easy discrimination of identified mode vectors and long wireless sensor network lifetime, and the innovations in automatic-learning firefly algorithm make it superior to the simple discrete firefly algorithm as to solution quality and convergence speed.

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Golikov, I. O., A. V. Vinogradov, V. E. Bolshev, A. V. Vinogradova, M. Jasinski, and R. R. Gibadullin. "Structure of many-level adaptive automatic voltage regulation system." E3S Web of Conferences 178 (2020): 01068. http://dx.doi.org/10.1051/e3sconf/202017801068.

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This article describes the features of voltage regulation in electrical networks of 35, 110, 220 kV. The structural diagram of the 35/10/0.4 kV network is presented. The paper also describes the adaptive automatic voltage regulation system which allows regulating the voltage taking into account the actual voltage values at the consumers’ inputs. The structural diagram of the adaptive automatic voltage regulation system in the 0.4 kV electrical network using a boost transformer as an additional means of voltage regulation is given. The system is based on voltage sensors installed in different parts of an eletcrical network sending information on voltage values to to the processing unit which generates a signal for voltage regulating supplied to the executive device and the working body whuch, in turn, change the on-load tap-changer position of a transformer. The paper justifies the need for the enhancement of the adaptive automatic voltage regulation system for different voltage classes wich allows controlling a voltage change at different power supply system levels and regulating voltage level in accordance with this change. For this problem the multi-level adaptive automatic voltage regulation systemis proposed. The system allows regulating the voltage not only in the 0.4 kV network but also in networks of higher voltage classes. The proposed system can be integrated into the structure of intelligent electrical networks.

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Smith, Steven T., Edward K. Kao, Erika D. Mackin, Danelle C. Shah, Olga Simek, and Donald B. Rubin. "Automatic detection of influential actors in disinformation networks." Proceedings of the National Academy of Sciences 118, no. 4 (January 7, 2021): e2011216118. http://dx.doi.org/10.1073/pnas.2011216118.

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The weaponization of digital communications and social media to conduct disinformation campaigns at immense scale, speed, and reach presents new challenges to identify and counter hostile influence operations (IOs). This paper presents an end-to-end framework to automate detection of disinformation narratives, networks, and influential actors. The framework integrates natural language processing, machine learning, graph analytics, and a network causal inference approach to quantify the impact of individual actors in spreading IO narratives. We demonstrate its capability on real-world hostile IO campaigns with Twitter datasets collected during the 2017 French presidential elections and known IO accounts disclosed by Twitter over a broad range of IO campaigns (May 2007 to February 2020), over 50,000 accounts, 17 countries, and different account types including both trolls and bots. Our system detects IO accounts with 96% precision, 79% recall, and 96% area-under-the precision-recall (P-R) curve; maps out salient network communities; and discovers high-impact accounts that escape the lens of traditional impact statistics based on activity counts and network centrality. Results are corroborated with independent sources of known IO accounts from US Congressional reports, investigative journalism, and IO datasets provided by Twitter.

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Gafurov, Artur M., and Oleg P. Yermolayev. "Automatic Gully Detection: Neural Networks and Computer Vision." Remote Sensing 12, no. 11 (May 28, 2020): 1743. http://dx.doi.org/10.3390/rs12111743.

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Transition from manual (visual) interpretation to fully automated gully detection is an important task for quantitative assessment of modern gully erosion, especially when it comes to large mapping areas. Existing approaches to semi-automated gully detection are based on either object-oriented selection based on multispectral images or gully selection based on a probabilistic model obtained using digital elevation models (DEMs). These approaches cannot be used for the assessment of gully erosion on the territory of the European part of Russia most affected by gully erosion due to the lack of national large-scale DEM and limited resolution of open source multispectral satellite images. An approach based on the use of convolutional neural networks for automated gully detection on the RGB-synthesis of ultra-high resolution satellite images publicly available for the test region of the east of the Russian Plain with intensive basin erosion has been proposed and developed. The Keras library and U-Net architecture of convolutional neural networks were used for training. Preliminary results of application of the trained gully erosion convolutional neural network (GECNN) allow asserting that the algorithm performs well in detecting active gullies, well differentiates gullies from other linear forms of slope erosion — rills and balkas, but so far has errors in detecting complex gully systems. Also, GECNN does not identify a gully in 10% of cases and in another 10% of cases it identifies not a gully. To solve these problems, it is necessary to additionally train the neural network on the enlarged training data set.

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Hasoon, Safwan, and Fatima Younis. "Constructing Expert System to Automatic Translation for Software development." Al-Kitab Journal for Pure Sciences 2, no. 2 (December 30, 2018): 231–47. http://dx.doi.org/10.32441/kjps.02.02.p16.

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the development in computer fields, especially in the software engineering, emerged the need to construct intelligence tool for automatic translation from design phase to coding phase, for producing the source code from the algorithm model represented in pseudo code, and execute it depending on the constructing expert system which reduces the cost, time and errors that may occur during the translation process, which has been built the knowledge base, inference engine, and the user interface. The knowledge bases consist of the facts and the rules for the automatic transition. The results are compared with a set of neural networks, which are Back propagation neural network, Cascade-Forward network, and Radial Basis Function network. The results showed the superiority of the expert system in automatic transition process speed, as well as easy to add, delete or modify process for rules or data of the pseudo code compared with previously mentioned neural networks.

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Hanumantha Rao, T. V. K., Saurabh Mishra, and Sudhir Kumar Singh. "Automatic Electrocardiographic Analysis Using Artificial Neural Network Models." Advanced Materials Research 403-408 (November 2011): 3587–93. http://dx.doi.org/10.4028/www.scientific.net/amr.403-408.3587.

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In this paper, the artificial neural network method was used for Electrocardiogram (ECG) pattern analysis. The analysis of the ECG can benefit from the wide availability of computing technology as far as features and performances as well. This paper presents some results achieved by carrying out the classification tasks by integrating the most common features of ECG analysis. Four types of ECG patterns were chosen from the MIT-BIH database to be recognized, including normal sinus rhythm, long term atrial fibrillation, sudden cardiac death and congestive heart failure. The R-R interval features were performed as the characteristic representation of the original ECG signals to be fed into the neural network models. Two types of artificial neural network models, SOM (Self- Organizing maps) and RBF (Radial Basis Function) networks were separately trained and tested for ECG pattern recognition and experimental results of the different models have been compared. The trade-off between the time consuming training of artificial neural networks and their performance is also explored. The Radial Basis Function network exhibited the best performance and reached an overall accuracy of 93% and the Kohonen Self- Organizing map network reached an overall accuracy of 87.5%.

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LEE, HANG-MAO, KARL-JOSEF DIETZ, and RALF HOFESTÄDT. "COMPUTATIONAL CONSTRUCTION OF SPECIALIZED BIOLOGICAL NETWORKS." Journal of Bioinformatics and Computational Biology 11, no. 01 (February 2013): 1340003. http://dx.doi.org/10.1142/s0219720013400039.

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Today we have access to more than 1500 molecular database systems inside the internet. Based on these databases and information systems, computer scientists developed and implemented different methods for the automatic integration and prediction of biological networks. The idea is to use such methods for the automatic prediction and expansion of rudimentary molecular knowledge. However, the inherent data deficiency problem concerning the properties of specialized network hampers the database- and text-mining-based network construction. This paper presents the concept concerning the computational network expansion, namely for the specific biological network–thiol-disulfide redox regulatory network. Besides, a network-contexted document retrieval system (ncDocReSy) is also introduced to assist the network reduction by providing indirectly relevant literature for user's manual curation. NcDocReSy combines literature search with biological network and ranks the retrieved literature according to the network topology. NcDocReSy is implemented as a Cytoscape plugin.

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Astashev, Mikhail G., Artem S. Vanin, Vladimir M. Korolev, Dmitriy I. Panfilov, Pavel A. Rashitov, and Vladimir N. Tulskii. "Assessment of the Technical and Economic Effect from Using Automatic Voltage Control Devices on 10/0.4 kV Transformers in Power Distribution Networks." Vestnik MEI, no. 5 (2021): 27–36. http://dx.doi.org/10.24160/1993-6982-2021-5-27-36.

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The article addresses the problem of ensuring permissible voltage levels in distribution electrical networks of various types: distribution networks of large cities, regional distribution electrical networks, and distribution electrical networks containing renewable energy sources. The most typical factors causing the voltage to go beyond the permissible limits specified by the relevant regulatory documents are pointed out. The negative factors conducive to the voltage at the consumer end deviating from the permissible limits, including a long length of network lines, high network load, low controllability of the network, load schedule nonuniformity, and poor observability of the network, are analyzed. The existing principles of voltage control in electrical distribution networks, namely, automatic and seasonal regulation, are studied. A distribution electrical network test model representing a real network fragment is developed. The model operation modes have been verified based on the data of measurements carried out in the original distribution electrical network. The voltage distributions in a medium voltage network during its operation under the conditions of the highest and lowest loads are demonstrated. It is shown, on the test model example, how the network voltage can be controlled by automatically regulating the voltage at the power supply center and selecting a fixed position of the NLTC at 10/0.4 kV transformer substations. It is shown that the use of power transformer OLTCs does not ensure sufficient means for adequately controlling the voltage in networks containing long power lines and featuring highly nonuniform seasonal and daily load schedules. The technical efficiency and economic feasibility of using automatic voltage regulation devices on 10/0.4 kV transformers for local voltage control are analyzed. The economic efficiency of applying automatic voltage regulation devices at 6--10/0.4 kV substations was evaluated in comparison with other means for improving the power distribution network voltage quality by upgrading the 10 kV feeder lines or installing a voltage booster at the inlet to the problematic 10 kV network section. The application field of automatic voltage regulators in the form of semiconductor devices for regulating the transformer output voltage at distribution transformer substations is shown.

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Xue, Yu, Pengcheng Jiang, Ferrante Neri, and Jiayu Liang. "A Multi-Objective Evolutionary Approach Based on Graph-in-Graph for Neural Architecture Search of Convolutional Neural Networks." International Journal of Neural Systems 31, no. 09 (July 24, 2021): 2150035. http://dx.doi.org/10.1142/s0129065721500350.

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With the development of deep learning, the design of an appropriate network structure becomes fundamental. In recent years, the successful practice of Neural Architecture Search (NAS) has indicated that an automated design of the network structure can efficiently replace the design performed by human experts. Most NAS algorithms make the assumption that the overall structure of the network is linear and focus solely on accuracy to assess the performance of candidate networks. This paper introduces a novel NAS algorithm based on a multi-objective modeling of the network design problem to design accurate Convolutional Neural Networks (CNNs) with a small structure. The proposed algorithm makes use of a graph-based representation of the solutions which enables a high flexibility in the automatic design. Furthermore, the proposed algorithm includes novel ad-hoc crossover and mutation operators. We also propose a mechanism to accelerate the evaluation of the candidate solutions. Experimental results demonstrate that the proposed NAS approach can design accurate neural networks with limited size.

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Aribowo, Widi. "Slime Mould Algorithm Training Neural Network in Automatic Voltage Regulator." Trends in Sciences 19, no. 3 (January 20, 2022): 2145. http://dx.doi.org/10.48048/tis.2022.2145.

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The research is proposed a new method of artificial intelligence (AI) to control automatic voltage regulators. A neural network has improved using a metaheuristic method, namely the slime mould algorithm (SMA). SMA has an algorithm based on the mode of slime mold in nature. SMA has characteristics that use adaptive weights to simulate the process to generate feedback from the movement of bio-oscillator-based slime molds in foraging, exploring, and exploiting areas. The performance of the proposed method is focused on speed and rotor angle. To know the competence and potency of the proposed method, a comparison with feed-forward backpropagation neural networks (FFBNN), cascade-forward backpropagation neural networks (CFBNN), Elman-recurrent neural networks (E-RNN), focused time delay neural network (FTDNN), and Distributed Time Delay Neural Network (DTDNN) method are applied. It can be concluded that the proposed method has the best ability. The Proposed method has ability to reduce the overshoot speed with an average value of 0.78 % and the overshoot rotor angle with an average value of 2.134 %.

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Rivera, Diego, Fernando Monje, Victor Villagrá, Mario Vega-Barbas, Xavier Larriva-Novo, and Julio Berrocal. "Automatic Translation and Enforcement of Cybersecurity Policies Using A High-Level Definition Language." Entropy 21, no. 12 (November 30, 2019): 1180. http://dx.doi.org/10.3390/e21121180.

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The increasing number of cyber-attacks, their potential destructive capabilities, and the enormous threat they pose for organizations, require the constant design and development of new, faster, and easier to use systems to address them. The automation of security enforcement systems is one of the most important techniques for enabling a fast response to security challenges, but the complexity of security management might hinder the successful achievement of the desired security. Our proposal integrates the automatic enforcement of security rules based on intrusion detection systems with the definition of a high-level user-centered language for the definition of policies. We have designed a translation process from this language to specific network-wise and device-aware rules that can be installed and enforced. The deployment of these rules is determined by an automatic risk assessment process ruled by the detection system monitoring the network. This way, both the automation and easiness of use goals can be achieved using an integrated system. The solution was tested and validated in two different virtualized networks.

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El-Kasasy, Mohamed Sherif, K. Soliman, and Rasha Mahmoud. "Automatic Speaker Identification Using Neural Networks.(Dept.E)." MEJ. Mansoura Engineering Journal 26, no. 2 (January 30, 2021): 12–21. http://dx.doi.org/10.21608/bfemu.2021.144790.

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Journal articles on the topic 'Automatic networks'

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