Academic literature on the topic 'Network Monitoring Objects'
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Journal articles on the topic "Network Monitoring Objects"
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Полковникова, Н. А., Е. В. Тузинкевич, and А. Н. Попов. "Application of convolutional neural networks for monitoring of marine objects." MORSKIE INTELLEKTUAL`NYE TEHNOLOGII), no. 4(50) (December 17, 2020): 53–61. http://dx.doi.org/10.37220/mit.2020.50.4.097.
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В статье рассмотрены технологии компьютерного зрения на основе глубоких свёрточных нейронных сетей. Применение нейронных сетей особенно эффективно для решения трудно формализуемых задач. Разработана архитектура свёрточной нейронной сети применительно к задаче распознавания и классификации морских объектов на изображениях. В ходе исследования выполнен ретроспективный анализ технологий компьютерного зрения и выявлен ряд проблем, связанных с применением нейронных сетей: «исчезающий» градиент, переобучение и вычислительная сложность. При разработке архитектуры нейросети предложено использовать функцию активации RELU, обучение некоторых случайно выбранных нейронов и нормализацию с целью упрощения архитектуры нейросети. Сравнение используемых в нейросети функций активации ReLU, LeakyReLU, Exponential ReLU и SOFTMAX выполнено в среде Matlab R2020a. На основе свёрточной нейронной сети разработана программа на языке программирования Visual C# в среде MS Visual Studio для распознавания морских объектов. Программапредназначена для автоматизированной идентификации морских объектов, производит детектирование (нахождение объектов на изображении) и распознавание объектов с высокой вероятностью обнаружения. The article considers computer vision technologies based on deep convolutional neural networks. Application of neural networks is particularly effective for solving difficult formalized problems. As a result convolutional neural network architecture to the problem of recognition and classification of marine objects on images is implemented. In the research process a retrospective analysis of computer vision technologies was performed and a number of problems associated with the use of neural networks were identified: vanishing gradient, overfitting and computational complexity. To solve these problems in neural network architecture development, it was proposed to use RELU activation function, training some randomly selected neurons and normalization for simplification of neural network architecture. Comparison of ReLU, LeakyReLU, Exponential ReLU, and SOFTMAX activation functions used in the neural network implemented in Matlab R2020a.The computer program based on convolutional neural network for marine objects recognition implemented in Visual C# programming language in MS Visual Studio integrated development environment. The program is designed for automated identification of marine objects, produces detection (i.e., presence of objects on image), and objects recognition with high probability of detection.
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Zhang, Fan, Shui Yuan Cheng, Ping Zhong, Rui Wu, Ming Juan Ma, and Wei Wei Gong. "Study of China’s Highway Environmental Monitoring Network Planning." Applied Mechanics and Materials 675-677 (October 2014): 318–24. http://dx.doi.org/10.4028/www.scientific.net/amm.675-677.318.
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Environmental monitoring of national highway is an important content of China’s environmental monitoring network in the transport field, a part of national environmental monitoring system, and also a significant basis for environmental protection supervision and management of the transport industry. Using the methods of spatial analysis, grid analysis, and optimization through ArcGIS, monitoring objects of Chinese national highway environmental monitoring network had been screened out. Among which, the environmental quality monitoring objects are composed of the national highway sensitive sections nearby or passing through 277 natural reserves, 32 national scenic areas, 7 of the world’s natural and cultural heritage sites, 8 important wetlands, 398 important reservoirs, and 193 state-level sensitive aquatic germ plasm resources conservation areas; and the pollutant emission monitoring objects include 316 long and extra-long tunnels, 539 service areas and 601 toll stations with large traffic volume. Environmental quality monitoring objects and pollutant emission monitoring objects form China’s National Highway Environmental Monitoring Network, which has filled the blank of highway environmental monitoring in the national environmental monitoring network. It will play a positive role in promoting the green development of China's transport industry.
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Bao, Ke, and Yourong Ding. "Multiobjects Association and Abnormal Behavior Detection for Massive Data Analysis in Multisensor Monitoring Network." Mathematical Problems in Engineering 2020 (November 3, 2020): 1–9. http://dx.doi.org/10.1155/2020/8858416.
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With the rapid increase in the number of large-scale distributed cameras and the rapid increase in the monitoring range of the camera network, how to accurately recognize and analyze abnormal behavior is still a challenging problem. In addition, the appearance of moving objects between different cameras without overlapping fields of view undergoes significant changes, making it difficult to obtain accurate association Therefore, multiobjects association and abnormal behavior detection for massive data analysis in multisensor monitoring network are proposed in this paper, which firstly uses belief propagation to associate multiple objects, extracts the object’s behavior trajectory characteristics, and then builds a long short-term memory classification network to realize automatic classification of abnormal behaviors. Multiobject association fully considers the timing correlation and object detection probability, as well as the statistical dependence of the measurement on the association matrix. The experimental results show that our proposed method can achieve a high classification accuracy and sensitivity, which meets the requirements of automatic classification of abnormal behavior in complex monitoring network. This further shows that this research has practical application value.
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Loktev, Daniil, and Olga Lokteva. "Image processing of transport objects using neural networks." E3S Web of Conferences 164 (2020): 03036. http://dx.doi.org/10.1051/e3sconf/202016403036.
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The paper is devoted to the development of an automated system model for monitoring and control of transport objects, based on the processing of images obtained using photo or video detectors, which can be installed on a fixed base near the transport highway for monitoring traffic flows and individual vehicles, and on rolling stock for monitoring transport infrastructure facilities. Image processing occurs by determining the function of blurring the image of an object, algorithms for extracting an image of an object using cascading classifiers and characteristic points, depending on the behavior of the object itself, as well as using a convolutional neural network. Machine learning of the convolutional neural network occurs when using the back propagation method of error. A neural network allows detecting objects of certain classes in the image, determining the parameters of their state and behavior. The proposed model with a movable hardware, which is responsible for obtaining the primary image, was tested on a section of the railway track to identify deviations of the state of the superstructure from the content standards, and a system with stationary photodetectors was tested to determine the parameters of moving vehicles. The obtained results of processing experimental data allowed drawing qualitative conclusions about the possibility of using the proposed algorithms and schemes for monitoring and control of various transport objects.
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Rodimtsev, Sergey, Alexander Psaryov, and Andrey Chuykin. "Monitoring of moving objects in the absence of a GSM signal." MATEC Web of Conferences 341 (2021): 00029. http://dx.doi.org/10.1051/matecconf/202134100029.
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Many modern transport processes require constant on-line monitoring and management. However, the main difficulty lies in the lack of a stable GSM connection. One of the possible solutions to this issue is the use of MESH networks. These represent the possibility of wireless communication between multiple entities, which are nodes on a distributed network. The article presents the results of the development of original local radio modules and a base station to ensure uninterrupted transmission of digital data from the monitoring object, in conditions of insufficient GSM coverage. The complex provides the following operating modes: simple interaction via a GSM channel, data transmission via a self-organizing network, as well as sensory interaction between objects. In the absence of GSM communication, the repeater provides on-line data transmission from the monitoring object, at a distance of up to 860 m, at a distance from the base station - at least 500 m. The use of the new complex makes it possible to promptly respond to changes in operating factors and prevent emergency situations during the operation of vehicles.
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Novokreschenova, Regina, and Olga Nikolaeva. "RELEVANCE OF ENVIRONMENTAL MONITORING OF MUNICIPAL WATER OBJECTS." Interexpo GEO-Siberia 4, no. 2 (2019): 112–17. http://dx.doi.org/10.33764/2618-981x-2019-4-2-112-117.
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The article substantiates the relevance of detailing the existing network of state monitoring of water objects by forming an additional observation network for water objects being in municipal ownership. A brief description of the existing observation network of surface water monitoring carried out by Roshydromet is given. The main documents regulating the order of surface water monitoring are listed. The insufficient coverage of water bodies in municipal ownership by the observation program was noted. The layout of surface water monitoring stations in the Novosibirsk Region, confirming this fact, is given. The main types of water bodies that are the municipal ownership are listed. The list of the water objects that are the municipal property of Novosibirsk is resulted. The fact of active economic and recreational use of these objects that contradicts the municipal legislation and safety requirements is noted. A list of the possible use of these facilities for the benefit of the municipality is stated. Conclusions about the need for the formulation of water bodies in municipal ownership, cadastral registration for their effecient use in the economy are drawn. The main types of work planned to solve this problem are listed.
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Nikoletseas, Sotiris, and Paul Spirakis. "Efficient sensor network design for continuous monitoring of moving objects." Theoretical Computer Science 402, no. 1 (July 2008): 56–66. http://dx.doi.org/10.1016/j.tcs.2008.03.005.
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Jasim Saud, Laith, and Zainab Kudair Abass. "A Comparison between Multi-Layer Perceptron and Radial Basis Function Networks in Detecting Humans Based on Object Shape." Ibn AL- Haitham Journal For Pure and Applied Science 31, no. 2 (September 12, 2018): 210. http://dx.doi.org/10.30526/31.2.1950.
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Human detection represents a main problem of interest when using video based monitoring. In this paper, artificial neural networks, namely multilayer perceptron (MLP) and radial basis function (RBF) are used to detect humans among different objects in a sequence of frames (images) using classification approach. The classification used is based on the shape of the object instead of depending on the contents of the frame. Initially, background subtraction is depended to extract objects of interest from the frame, then statistical and geometric information are obtained from vertical and horizontal projections of the objects that are detected to stand for the shape of the object. Next to this step, two types of neural networks are used to classify the extracted objects. Tests have been performed on a sequence of frames, and the simulation results by MATLAB showed that the RBF neural network gave a better performance compared with the MLP neural network where the RBF model gave a mean squared error (MSE) equals to 2.36811e-18 against MSE equals to 2.6937e-11 achieved by the MLP model. The more important thing observed is that the RBF approach required less time to classify the detected object as human compared to the MLP, where the RBF took approximately 86.2% lesser time to give the decision.
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Jagdale, Balaso, and Jagdish Bakal. "Privacy Aware Monitoring of Mobile Users in Sensor Networks Environment." International Journal of Interactive Mobile Technologies (iJIM) 13, no. 02 (February 22, 2019): 128. http://dx.doi.org/10.3991/ijim.v13i02.10023.
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<p><strong>Abstract—</strong>Due to complex monitoring systems in various business domains, secrecy and privacy has become critical issue for movable resources including human being. In a mobile object monitoring systems, balancing computing resources and quality of privacy is required. Objects can be mobile devices or users or any other moving entity. This work is presented in wireless sensor network environment. Earlier work does not consider presence of mobile objects in 3D space. We can observe that objects also carry Z axis in city area where high rise buildings are present. Earlier monitoring applications are mainly designed in two dimensional space to protect privacy. Our novelty is to suggest and design mechanism that reflect Z position (height) of mobile objects for protecting privacy. While calculating cloaking area and counting of objects, height is also considered as third dimension. This results in better location privacy as compared to the privacy delivered by the scheme that considers two dimensional space. We have presented performance, communications cost and privacy strength of modified 3D quality algorithm and 3D resource algorithm. Moreover, we present novel containment resolution algorithm that handles duplicate counting due to 3D presence of wireless system and mobile devices.</p>
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Ouldzira, Hicham, Ahmed Mouhsen, Hajar Lagraini, Mostafa Chhiba, Abdelmoumen Tabyaoui, and Said Amrane. "Remote monitoring of an object using a wireless sensor network based on NODEMCU ESP8266." Indonesian Journal of Electrical Engineering and Computer Science 16, no. 3 (December 1, 2019): 1154. http://dx.doi.org/10.11591/ijeecs.v16.i3.pp1154-1162.
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<p>In recent years, wireless sensors networks (WSNs) have been imposed as an effective means of interconnection with simultaneous communication and information processing. They allow operating with sensors at low cost and low power consumption in various application areas such as ecosystem monitoring, detection and monitoring of objects and smart cities, etc.This paper describes the development of a system to detect the presence of an object and monitor it. This prototype is based on four NodeMcu modules (a static access point that provides the WIFI network, a server, a client and a mobile access point attached to the remote surveillance object) programmed under Arduino IDE and communicating between them via the HTTP protocol. The remote monitoring of the object for a linear disposition of the nodes used is based on the existence of the mobile access point in the HTTP client field.</p>
Dissertations / Theses on the topic "Network Monitoring Objects"
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Arvedal, David. "Analyzing network monitoring systems and objects for a telecommunications company." Thesis, Mälardalens högskola, Akademin för innovation, design och teknik, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:mdh:diva-35610.
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The goal with this thesis work has been to identify what a telecommunications company should monitor and to find a network monitoring system that can monitor these identified objects on two different platforms: Windows and Linux. The network monitoring system has been implemented in a telecommunications company’s environment and this thesis presents how the system monitors their environment. The subject for this thesis work is within network monitoring. The problem formulation has been answered by conducting a literature study and by testing network monitoring systems’ features in a lab environment. The sources used in the literature study consists of scientific articles and other articles found on the web. The lab environment consisted of virtual machines that runs Linux or Windows as an operating system. The purpose of the work was to enlighten Cellip in what objects they should monitor and to help the company to monitor them by implementing a network monitoring system. Cellip is a telecommunications company that provides IP-telephony services through Session Initiation Protocol. The limits of this thesis work are based on what their environment supports in terms of monitoring. Cellip’s environment consists of Linux and Windows servers, Cisco switches and firewalls, and Sonus Session Border Controllers. In summary, the result of this thesis gives the reader information about what a telecommunications company with a similar environment to Cellip should monitor, what three systems that can monitor these objects, which of the three systems that has most automatized features and finally how the chosen system Datadog monitors and presents the objects. Some of the objects that are important to monitor is: memory, disk storage, latency, packet loss. In conclusion, this thesis presents a monitoring baseline for telecommunication companies with a similar environment to Cellip.
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Boschin, Erica. "Dynamics of cognitive control and flexibility in the anterior cingulate and prefrontal cortices." Thesis, University of Oxford, 2013. http://ora.ox.ac.uk/objects/uuid:31cdb9f1-8107-4431-bc7a-2e0dbb9885a1.
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The body of work hereby presented aims at better defining the specific mechanisms underlying cognitive control and flexibility, and to investigate the neural substrates that might support these dynamics. More specifically, the anterior cingulate (ACC), dorsolateral prefrontal (dlPFC) and frontopolar (FPC) cortices have been proposed to play a fundamental role in monitoring and detecting the presence of environmental contingencies that require the recruitment of cognitive control (such as competition between responses in the presence of conflicting information), implementing cognitive control, and supporting higher-order cognitive processing, respectively. This thesis investigates the effects of damage to these regions, and of interference with their activity, on these processes. It also argues for the importance of dissociating possible separate cognitive control components that might differently contribute to behavioural adjustments (such as caution and attention/task-relevant processing), and provides one of the first attempts to quantify them within the parameters of a mathematical model of choice response-time, the Linear Ballistic Accumulator (LBA). The results confirm the crucial role of the dlPFC in modulating behavioural adjustments, as both damage and interference with this region’s activity significantly affect measures of conflict-induced behavioural adaptation. It is hypothesized that dlPFC might drive behavioural adjustments by encoding recent conflict history and/or supporting the automatization of a newly advantageous behavioural strategy during the early stages after a change in conflict levels. When a task does not involve competition between a habit and instructed behaviour, lesions or interference with ACC’s activity do not appear to affect behaviour in a manner that is consistent with the classic conflict-monitoring framework. It is suggested that its role might be better described as a more general monitoring and confirmatory mechanism that evaluates both actual and potential outcomes of an action, in order to proactively guide adjustments away from contextually disadvantageous responses. Finally, lesions to the FPC do not affect abstract-rule integration, but do impair the early stages of acquisition of a new abstract rule, when a previously rewarded rule stops being rewarded, and specifically when acquisition is dependent on self-initiated exploration. This suggests a role for FPC in the evaluation of multiple concurrent options in order to aid the development of new behavioural strategies.
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Dvorský, Petr. "Monitoring stokové sítě ve městě Brně." Master's thesis, Vysoké učení technické v Brně. Fakulta stavební, 2019. http://www.nusl.cz/ntk/nusl-392025.
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This thesis deals with the monitoring of the sewerage system in the city of Brno. In the research part, there is made an overview of the most used measuring equipment for the monitoring of the sewerage system – level meters, flowmeters, automatic samplers. The practical part is devoted to the monitoring of the sewerage system in Brno for partial evaluation of the benefits of the project „Reconstruction and completion of sewerage in Brno“, realized in 2012 – 2014. Measuring campaign has taken place from June to October 2017. In the course of the work, the instalation of measuring equipment and the collection of measured data was done. After finishing measuring campaign, the data were evaluated from different aspects – the number of overflows, the quantity of overflowing water, the dilution ratio and the quantity of discharged pollution. Then, the hydraulic and construction technical assement of the pipelines of sewer network and overflow chambers and assements of the quantity of the rainwater were made. The results of the evaluation were compared with the results of the previous monitoring campaingns and with the data obtained before the project has been realized. Based on this, the benefit of the project was determined.
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Hassan, Basma Mostafa. "Monitoring the Internet of Things (IoT) Networks." Thesis, Montpellier, 2019. http://www.theses.fr/2019MONTS100.
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Les réseaux « Internet des Objets » se composent de plusieurs millions d’objets qui possèdent une adresse IP et qui peuvent connecter sur Internet. En général, ces objets sont supposé d’être autonomes et peuvent résoudre des tâches; mesurer, traiter et fournir des informations pour les systèmes connectés et pour les utilisateurs. Aussi, ces réseaux sont vulnérables (c.-à-d. : les éléments peuvent être mobiles et la topologie du réseau peut changer dynamiquement), les changements peuvent influencer le (bon) fonctionnement du réseau. De plus, ils peuvent être alimentés par des batteries de durée de vie limitée, ce que nécessite la réduction de leur consommation.Ce travail de thèse aborde un sujet important dans le domaine de l’Internet des Objets, qui consiste à savoir comment assurer la robustesse et le fonctionnement tolérant aux pannes du réseau pour répondre aux exigences des missions critiques. Avec le large déploiement des services IdO, ce problème est deventé ou de détection de pannes et de sécurité industriel où l’état des objets communicants doit être constamment vérifié pour le rétablissement rapide en cas de problème particulièrement crucial pour les applications telles que le monitorage intelligent de sames de communication inattendus. On cherche alors de minimiser le coût du monitorage et l’utilisation de l’énergie, et aussi les charges additionnelles sur les réseaux.Nous avons proposé un algorithme qui vise à réaliser un placement distribué des moniteurs avec une complexité minimale pour le calcul. L’algorithme proposé fonctionne avec RPL. L’objectif principal est d’augmenter la robustesse dans les réseaux IdO ciblant les applications critiques en temps réel via le monitorage des liaisons dans les DODAGs construits par RPL. Dans notre première contribution, le problème est modélisé comme un problème de couverture minimale des sommets (VCP) sur le DODAG. Nous avons développé un algorithme à temps polynomial qui transforme le DODAG en une décomposition arborescente (Nice-Tree Decomposition) avec une largeur arborescente (treewidth) d’unité. Cette stratégie profite de la spécificité des DODAG et a abouti à une réduction significative de la complexité de la résolution du VCP sur les DODAG. Elle peut être résolue en temps polynomial.La deuxième proposition est un modèle approché pour l’optimisation de l’ordonnancement du rôle de monitorage des nœuds dans les réseaux IdO, afin de maximiser la durée de vie des dispositifs embarqués à ressources limitées, tout en minimisant le coût global du monitorage de réseau. Le monitorage de réseau est très coûteux, en particulier pour les réseaux à ressources limitées tels que l’IdO. Par conséquent, le monitorage doit être économe en énergie et avec des frais généraux minimaux sur la performance normale du réseau. Notre travail correspondant contient une proposition d’un modèle mathématique en trois phases pour assurer l’exigence d’une couverture des moniteurs tout en minimisant la consommation d’énergie de monitorage et les frais de communication.Notre modèle proposé décompose le problème abordé en trois problèmes d’optimisation bien connus, il s’agit du problème de couverture de sommets, problème d’affectation généralisé multi-objectives et problème de voyageur de commerce.Dans cette troisième partie, une approche exacte est proposée pour résoudre le problème décrit dans (Contribution 2). Comme nous avons vu, la décomposition en trois phases ne donne pas la solution exacte. Nous avons donc proposé une formulation exacte du problème qui consiste en un problème de l'affectation minimum des tâches de surveillance avec un fonctionnement de surveillance cyclique. Pour cela, nous avons formulé un programme en nombres entiers binaires. L'ordonnancement optimal garantit la couverture du graphe pour la surveillance avec une consommation d'énergie minimaleBy connecting billions of things to the Internet, IoT created a plethora of applications that touch every aspect of human life. Time-sensitive, mission-critical services, require robust connectivity and strict reliability constraints. On the other hand, the IoT relies mainly on Low-power Lossy Networks, which are unreliable by nature due to their limited resources, hard duty cycles, dynamic topologies, and uncertain radio connectivity. Faults in LLNs are common rather than rare events, therefore, maintaining continuous availability of devices and reliability of communication, are critical factors to guarantee a constant, reliable flow of application data.After a comprehensive literature review, and up to our knowledge, it is clear that there is a call for a new approach to monitoring the unreliable nodes and links in an optimized, energy-efficient, proactive manner, and complete interoperability with IoT protocols. To target this research gap, our contributions address the correct assignment (placement) of the monitoring nodes. This problem is known as the minimum assignment problem, which is NP-hard. We target scalable monitoring by mapping the assignment problem into the well-studied MVC problem, also NP-hard. We proposed an algorithm to convert the DODAG into a nice-tree decomposition with its parameter (treewidth) restricted to the value one. As a result of these propositions, the monitor placement becomes only Fixed-Parameter Tractable, and can also be polynomial-time solvable.To prolong network longevity, the monitoring role should be distributed and balanced between the entire set of nodes. To that end, assuming periodical functioning, we propose in a second contribution to schedule between several subsets of nodes; each is covering the entire network. A three-phase centralized computation of the scheduling was proposed. The proposition decomposes the monitoring problem and maps it into three well-known sub-problems, for which approximation algorithms already exist in the literature. Thus, the computational complexity can be reduced.However, the one major limitation of the proposed three-phase decomposition is that it is not an exact solution. We provide the exact solution to the minimum monitor assignment problem with a duty-cycled monitoring approach, by formulating a Binary Integer Program (BIP). Experimentation is designed using network instances of different topologies and sizes. Results demonstrate the effectiveness of the proposed model in realizing full monitoring coverage with minimum energy consumption and communication overhead while balancing the monitoring role between nodes.The final contribution targeted the dynamic distributed monitoring placement and scheduling. The dynamic feature of the model ensures real-time adaptation of the monitoring schedule to the frequent instabilities of networks, and the distributed feature aims at reducing the communication overhead
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Scalamandrè, Davide. "Sistema di visione per le gestione automatica dei posti in un parcheggio." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2018.
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Questo elaborato si propone di risolvere il problema della ricerca di un parcheggio. Sono tante le grandi città che oggi investono in tecnologia, nel tentativo di fornire servizi intelligenti ai propri cittadini. Questa pratica si è notevolmente sviluppata negli ultimi anni, tanto che è stato coniato il termine Smart City. Spesso, in alcune aree urbane ed in determinati orari, il cittadino per trovare un parcheggio libero impiega molto tempo ed è costretto ad effettuare una ricerca nelle zone limitrofe, sperando che questa porti ad un esito positivo, nel più breve tempo possibile. Ciò non accadrebbe se ci fosse un servizio capace di informare l’utente sull’ubicazione del parcheggio libero più vicino rispetto alla propria posizione. La tecnologia che manca è un sistema di visione robusto per il monitoraggio degli spazi di parcheggio. Si è proceduto, quindi, con una ricerca su come il problema sia stato già affrontato in letteratura. Da tale ricerca è emerso che le attuali tecnologie consentono solo di configurare il sistema e le telecamere in modo da analizzare determinate aree delle immagini prodotte. Ciò consentirebbe soltanto la realizzazione di questo servizio per pochi parcheggi, e non si applicherebbe ad un numero più elevato quale quello relativo alle aree parcheggio di un’intera città. Tali considerazioni ci hanno portato ad individuare una tecnologia capace di localizzare un parcheggio, estrapolando le informazioni da una base di conoscenza. Tutto ciò è realizzabile tramite l’Object Detection. Uno dei sistemi di Object Detection più all’avanguardia è Yolo, un sistema real-time che si occupa del rilevamento di oggetti. Questo sistema utilizza una Convolutional Neural Network per effettuare la previsione di una Bounding Box, che delimita l’oggetto all’interno dell’immagine ed effettua la classificazione dell’oggetto presente all’interno di tale Box. Per poter addestrare questo tipo di sistema è stato utilizzato un Dataset presente in letteratura, PKLot.
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Sadler, Jeffrey Michael. "Hydrologic Data Sharing Using Open Source Software and Low-Cost Electronics." BYU ScholarsArchive, 2015. https://scholarsarchive.byu.edu/etd/4425.
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While it is generally accepted that environmental data are critical to understanding environmental phenomena, there are yet improvements to be made in their consistent collection, curation, and sharing. This thesis describes two research efforts to improve two different aspects of hydrologic data collection and management. First described is a recipe for the design, development, and deployment of a low-cost environmental data logging and transmission system for environmental sensors and its connection to an open source data-sharing network. The hardware is built using several low-cost, open-source, mass-produced components. The system automatically ingests data into HydroServer, a standards-based server in the open source Hydrologic Information System (HIS) created by the Consortium of Universities for the Advancement of Hydrologic Sciences Inc (CUAHSI). A recipe for building the system is provided along with several test deployment results. Second, a connection between HydroServer and HydroShare is described. While the CUAHSI HIS system is intended to empower the hydrologic sciences community with better data storage and distribution, it lacks support for the kind of “Web 2.0” collaboration and social-networking capabilities that are increasing scientific discovery in other fields. The design, development, and testing of a software system that integrates CUAHSI HIS with the HydroShare social hydrology architecture is presented. The resulting system supports efficient archive, discovery, and retrieval of data, extensive creator and science metadata, assignment of a persistent digital identifier such as a Digital Object Identifier (DOI), scientific discussion and collaboration around the data and other basic social-networking features. In this system, HydroShare provides functionality for social interaction and collaboration while the existing HIS provides the distributed data management and web services framework. The system is expected to enable scientists, for the first time, to access and share both national- and research lab-scale hydrologic time series in a standards-based web services architecture combined with a social network developed specifically for the hydrologic sciences.These two research projects address and provide a solution for significant challenges in the automatic collection, curation, and feature-rich sharing of hydrologic data.
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Leone, Rémy. "Passerelle intelligente pour réseaux de capteurs sans fil contraints." Thesis, Paris, ENST, 2016. http://www.theses.fr/2016ENST0038/document.
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Les réseaux de capteurs sans fil (aussi appelés LLNs en anglais) sont des réseaux contraints composés de nœuds ayant de faibles ressources (mémoire, CPU, batterie). Ils sont de nature très hétérogène et utilisés dans des contextes variés comme la domotique ou les villes intelligentes. Pour se connecter nativement à l’Internet, un LLN utilise une passerelle, qui a une vue précise du trafic transitant entre Internet et le LLN du fait de sa position. Le but de cette thèse est d’exposer comment des fonctionnalités peuvent être ajoutées à une passerelle d’un LLN dans le but d’optimiser l’utilisation des ressources limitées des nœuds contraints et d’améliorer la connaissance de leur état de fonctionnement. La première contribution est un estimateur non intrusif utilisant le trafic passant par la passerelle pour inférer l’utilisation de la radio des nœuds contraints. La seconde contribution adapte la durée de vie d’informations mises en cache (afin d’utiliser les ressources en cache au lieu de solliciter le réseau) en fonction du compromis entre le coût et l’efficacité. Enfin, la troisième contribution est Makesense, un framework permettant de documenter, d’exécuter et d’analyser une expérience pour réseaux de capteurs sans fil de façon reproductible à partir d’une description uniqueLow-Power and Lossy Network (LLN)s are constrained networks composed by nodes with little resources (memory, CPU, battery). Those networks are typically used to provide real-time measurement of their environment in various contexts such as home automation or smart cities. LLNs connect to other networks by using a gateway that can host various enhancing features due to its key location between constrained and unconstrained devices. This thesis shows three contributions aiming to improve the reliability and performance of a LLN by using its gateway. The first contribution introduce a non-intrusive estimator of a node radio usage by observing its network traffic passing through the gateway. The second contribution offers to determine the validity time of an information within a cache placed at the gateway to reduce the load on LLNs nodes by doing a trade-off between energy cost and efficiency. Finally, we present Makesense, an open source framework for reproducible experiments that can document, execute and analyze a complete LLN experiment on simulation or real nodes from a unique description
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Benoît, Lionel. "Positionnement GPS précis et en temps-réel dans le contexte de réseaux de capteurs sans fil type Geocube : application à des objets géophysiques de taille kilométrique." Thesis, Paris, Ecole normale supérieure, 2014. http://www.theses.fr/2014ENSU0014/document.
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Les réseaux de capteurs permettent une surveillance multi-paramètres de zones d'étendue limitée grâce à la coopération d'un ensemble de récepteurs déployés in-situ qui gèrent l'acquisition, le traitement et le transfert de données. Afin de coupler le concept de réseaux de capteurs et un positionnement précis des récepteurs à l'aide de puces GPS mono-fréquence, l'Institut National de l'Information Géographique et Forestière (IGN) a mis au point le Geocube.La première partie de ce travail a été consacrée au développement d'une stratégie d'acquisition, de transfert et de traitement des données GPS des Geocubes pour permettre un positionnement précis et en temps-réel des récepteurs au sein du réseau. Un traitement utilisant les observations de phase GPS ainsi qu'un filtrage de Kalman a été adopté. Cependant, les séries temporelles de positions brutes sont entachées de l'effet des multitrajets. Diverses méthodes d'atténuation de ce phénomène sont alors proposées. Au final, une précision infra-centimétrique à millimétrique est atteinte.La seconde partie de cette thèse a été consacrée à l'application de réseaux de Geocubes pour l'étude d'objets géophysiques. Deux sites d'étude ont été sélectionnés: le glissement de terrain de Super-Sauze dans la vallée de l'Ubaye et le glacier d'Argentière dans le massif du Mont-Blanc. La dynamique des objets d'intérêt peut alors être étudiée à une échelle infra-journalière grâce à la précision et à la grande résolution temporelle du positionnement des Geocubes. De plus, la densité des réseaux de mesure et leur facilité d'installation permet d'instrumenter la grande majorité des points où un besoin de surveillance est identifiéWireless Sensor Networks (WSN) allow a multi-parameters monitoring of small extend areas thanks to cooperative data acquisition, transfer and processing. In order to combine WSN with a precise positioning of the receivers within the network using single frequency GPS modules, the Geocube has been developed by the French National Institute of Geographic and Forest Information (IGN-France). The first part of this work focused on GPS data management and processing to allow the relative positioning of the Geocubes within a local network. To this end, a processing method customized for Geocube data and WSN environment was developed. It is based on the use of GPS carrier phase double differences and a Kalman filtering. Due to the basic GPS antenna used into the Geocube to minimize its price and its size, multipath affect position time series. Various strategies are proposed for multipath mitigation, and finally a sub-centimeter to millimeter level accuracy is reached for relative positioning depending on measurement conditions.The second part of this work was devoted to the use of Geocube networks for geophysical structures monitoring. Two test sites were selected: the Super-Sauze landslide (Ubaye valley, Alpes de Haute-Provence, France) and the Argentière glacier (Mont-Blanc massif, Haute-Savoie, France). The dynamics of the studied areas was investigated at a sub-daily time scale thanks to the high accuracy and the high time resolution of positioning time series derived from Geocubes. In addition, positioning data were acquired quite everywhere a deformation measurement was needed thanks to the low-cost of Geocubes and their easy set up
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Moussallik, Laila. "Towards Condition-Based Maintenance of Catenary wires using computer vision : Deep Learning applications on eMaintenance & Industrial AI for railway industry." Thesis, Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-83123.
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Railways are a main element of a sustainable transport policy in several countries as they are considered a safe, efficient and green mode of transportation. Owing to these advantages, there is a cumulative request for the railway industry to increase the performance, the capacity and the availability in addition to safely transport goods and people at higher speeds. To meet the demand, large adjustment of the infrastructure and improvement of maintenance process are required. Inspection activities are essential in establishing the required maintenance, and it is periodically required to reduce unexpected failures and to prevent dangerous consequences. Maintenance of railway catenary systems is a critical task for warranting the safety of electrical railway operation.Usually, the catenary inspection is performed manually by trained personnel. However, as in all human-based inspections characterized by slowness and lack of objectivity, might have a number of crucial disadvantages and potentially lead to dangerous consequences. With the rapid progress of artificial intelligence, it is appropriate for computer vision detection approaches to replace the traditional manual methods during inspections. In this thesis, a strategy for monitoring the health of catenary wires is developed, which include the various steps needed to detect anomalies in this component. Moreover, a solution for detecting different types of wires in the railway catenary system was implemented, in which a deep learning framework is developed by combining the Convolutional Neural Network (CNN) and the Region Proposal Network (RPN).
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Bou, Tayeh Gaby. "Towards smart firefighting using the internet of things and machine learning." Thesis, Bourgogne Franche-Comté, 2020. http://www.theses.fr/2020UBFCD015.
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L’objectif de cette de thèse est d’étudier à la fois des solutions matérielles et logicielles pour améliorer les conditions de travail des sapeurs-pompiers. Il s’agit de développer un système intelligent basé sur l’internet des objets pour surveiller l'état de santé des pompiers et aider à les localiser lors des interventions. Dans la première partie de la thèse, nous avons étudié et proposé plusieurs approches permettant de réduire la consommation d’énergie du système afin de maximiser sa durée de vie. La première approche présente un modèle de prédiction basé sur la corrélation temporelle entre les mesures collectées par le même capteur. Il permet de réduire la quantité de données collectées et transmises au centre de contrôle. Ce modèle est exécuté à la fois par le capteur et le centre et qui s'auto-adapte en fonction de l’écart constaté entre les mesures réelles collectées et les mesures prédites. Une deuxième version de cette approche a été étudiée pour prendre en considération la perte de message et la synchronisation entre le capteur et le centre de contrôle. D’un autre côté et pour réduire davantage la consommation d’énergie, nous avons couplé l’approche de prédiction avec un algorithme de collecte de données adaptatif permettant de réduire l’activité du capteur et le taux d’échantillonnage. Toutes ces approches ont été testées via des simulations et de l’implémentation réelle. Les résultats obtenus montrent l’efficacité de ces approches en termes de réduction de la consommation d’énergie tout en gardant l’intégrité de données. La deuxième partie de cette thèse est dédiée au traitement des données issues des interventions des sapeurs-pompiers. Nous avons étudié plusieurs méthodes de clustérisation permettant un prétraitement de données avant l’extraction des connaissances. D’un autre côté, nous avons appliqué des méthodes d'apprentissage profond sur un grand ensemble de données concernant 200.000 interventions qui ont eu lieu pendant une période de 6 ans dans le département du Doubs, en France. Le but de cette partie était de prédire le nombre d’interventions futures en fonction de variables explicatives externes, pour aider les pompiers à bien gérer leurs ressourcesIn this thesis, we present a multilevel scheme consisting of both hardware and software solutions to improve the daily operational life of firefighters. As a core part of this scheme, we design and develop a smart system of wearable IoT devices used for state assessment and localization of firefighters during interventions. To ensure a maximum lifetime for this system, we propose multiple data-driven energy management techniques for resource constraint IoT devices. The first one is an algorithm that reduces the amount of data transmitted between the sensor and the destination (Sink). This latter exploits the temporal correlation of collected sensor measurements to build a simple yet robust model that can forecast future observations. Then, we coupled this approach with a mechanism that can identify lost packets, force synchronization, and reconstruct missing data. Furthermore, knowing that the sensing activity does also require a significant amount of energy, we extended the previous algorithm and added an additional adaptive sampling layer. Finally, we also proposed a decentralized data reduction approach for cluster-based sensor networks. All the previous algorithms have been tested and validated in terms of energy efficiency using custom-built simulators and through implementation on real sensor devices. The results were promising as we were able to demonstrate that our proposals can significantly improve the lifetime of the network. The last part of this thesis focusses on building data-centric decision-making tools to improve the efficiency of interventions. Since sensor data clustering is an important pre-processing phase and a stepstone towards knowledge extraction, we review recent clustering techniques for massive data management in IoT and compared them using real data for a gas leak detection sensor network. Furthermore, with our hands on a large dataset containing information on 200,000 interventions that happened during a period of 6 years in the region of Doubs, France. We study the possibility of using Machine Learning to predict the number of future interventions and help firefighters better manage their mobile resources according to the frequency of events
Books on the topic "Network Monitoring Objects"
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Co-Operative and Energy Efficient Body Area and Wireless Sensor Networks for Healthcare Applications. Elsevier Science & Technology Books, 2014.
Find full textBook chapters on the topic "Network Monitoring Objects"
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Rasti, Pejman, Tõnis Uiboupin, Sergio Escalera, and Gholamreza Anbarjafari. "Convolutional Neural Network Super Resolution for Face Recognition in Surveillance Monitoring." In Articulated Motion and Deformable Objects, 175–84. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-41778-3_18.
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Pensa, Antonio F., and R. Sridharan. "Monitoring Objects in Space with the U.S. Space Surveillance Network." In Mission Design & Implementation of Satellite Constellations, 305–15. Dordrecht: Springer Netherlands, 1998. http://dx.doi.org/10.1007/978-94-011-5088-0_27.
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Chemerys, Oleksandr, Oleksandr Bushma, Oksana Lytvyn, Alexei Belotserkovsky, and Pavel Lukashevich. "Network of Autonomous Units for the Complex Technological Objects Reliable Monitoring." In Studies in Computational Intelligence, 261–74. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-74556-1_16.
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Ohsawa, Yutaka, and Htoo Htoo. "Versatile Safe-Region Generation Method for Continuous Monitoring of Moving Objects in the Road Network Distance." In Database Systems for Advanced Applications, 377–92. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-32055-7_32.
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Qin, Lu, Jeffrey Xu Yu, Bolin Ding, and Yoshiharu Ishikawa. "Monitoring Aggregate k-NN Objects in Road Networks." In Lecture Notes in Computer Science, 168–86. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-69497-7_13.
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Stratman, Robert H. "Integrated State and Alarm Monitoring Across Heterogeneous Networks Using OSI Standards and Object-Oriented Techniques." In Network Management and Control, 39–51. Boston, MA: Springer US, 1994. http://dx.doi.org/10.1007/978-1-4899-1298-5_6.
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Gura, Dmitry A., Irina S. Gribkova, Nafset I. Khusht, and Saida K. Pshidatok. "Knowledge Base as a Part of Intelligent System for Security Monitoring of Infrastructure Objects." In Lecture Notes in Networks and Systems, 46–52. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-80485-5_7.
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Stramana, Franco, Juan Pablo D’amato, Leonardo Dominguez, Aldo Rubiales, and Alejandro Perez. "Object Extraction and Encoding for Video Monitoring Through Low-Bandwidth Networks." In Communications in Computer and Information Science, 431–41. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-61834-6_37.
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Vinogradenko, Aleksey, Pavel Budko, and Vladimir Fedorenko. "Adaptive System Monitoring of the Technical Condition Technological Objects Based on Wireless Sensor Networks." In Convergent Cognitive Information Technologies, 200–210. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-37436-5_18.
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Attique, Muhammad, Hyung-Ju Cho, and Tae-Sun Chung. "CORE: Continuous Monitoring of Reverse k Nearest Neighbors on Moving Objects in Road Networks." In Computer and Information Science 2015, 109–24. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-23467-0_8.
Full textConference papers on the topic "Network Monitoring Objects"
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Boldyrikhin, Nickolay V., Olga A. Safaryan, Pavel V. Razumov, Vitaliy M. Porksheyan, Ivan A. Smirnov, Denis A. Korochentsev, Larissa V. Cherckesova, and Artem M. Romanov. "Controlling the Resources of the Intrusion Detection System at Network Objects Monitoring." In 2020 3rd International Conference on Computer Applications & Information Security (ICCAIS). IEEE, 2020. http://dx.doi.org/10.1109/iccais48893.2020.9096741.
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Su, Hongguo, Mingyuan Zhang, Shengyuan Li, and Xuefeng Zhao. "Dangerous Scenes Recognition During Hoisting Based on Faster Region-Based Convolutional Neural Network." In ASME 2018 Conference on Smart Materials, Adaptive Structures and Intelligent Systems. American Society of Mechanical Engineers, 2018. http://dx.doi.org/10.1115/smasis2018-8226.
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In the last couple of years, advancements in the deep learning, especially in convolutional neural networks, proved to be a boon for the image classification and recognition tasks. One of the important practical applications of object detection and image classification can be for security enhancement. If dangerous objects or scenes can be identified automatically, then a lot of accidents can be prevented. For this purpose, in this paper we made use of state-of-the-art implementation of Faster Region-based Convolutional Neural Network (Faster R-CNN) based on the monitoring video of hoisting sites to train a model to detect the dangerous object and the worker. By extracting the locations of them, object-human interactions during hoisting, mainly for changes in their spatial location relationship, can be understood whereby estimating whether the scene is safe or dangerous. Experimental results showed that the pre-trained model achieved good performance with a high mean average precision of 97.66% on object detection and the proposed method fulfilled the goal of dangerous scenes recognition perfectly.
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Chen, Jiehui, and Mitsuji Matsumoto. "EUCOW: Energy-efficient boundary monitoring for unsmoothed continuous objects in wireless sensor network." In 2009 IEEE 6th International Conference on Mobile Adhoc and Sensor Systems (MASS). IEEE, 2009. http://dx.doi.org/10.1109/mobhoc.2009.5337035.
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Fuadi, Dendi Hazik, Dessy Novita, and Mohammad Taufik. "Socially Assistive Robot Interaction by Objects Detection and Face Recognition on Convolutional Neural Network for Parental Monitoring." In 2021 International Conference on Artificial Intelligence and Mechatronics Systems (AIMS). IEEE, 2021. http://dx.doi.org/10.1109/aims52415.2021.9466091.
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Ninov, Plamen, and Tzviatka Karagiozova. "MONITORING AND INVESTIGATION OF INTERMITTENT RIVERS IN BULGARIA." In XXVII Conference of the Danubian Countries on Hydrological Forecasting and Hydrological Bases of Water Management. Nika-Tsentr, 2020. http://dx.doi.org/10.15407/uhmi.conference.01.01.
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River flows could be defined according to their surface hydrologic stream duration as either perennial or temporary. Normally perennial streams flow throughout the year, while temporary streams lack surface flow for some periods of the year. Temporary streams are classified as intermittent or ephemeral. Intermittent streams flow in some periods as result of snowmelt and eventually elevated groundwater tables during the periods of increased precipitations. Intermittent streams are poorly represented in existing river monitoring programs in Bulgaria and seldom are objects of regular monitoring. Only in several gauging stations exist hydrological time series. Furthermore, intermittent and ephemeral streams are not adequately protected by current legislation and management strategies in Bulgaria and generally are neglected. The authors discuss the climatic, hydrological and soil conditions in different part of the country as the major factors determining their origin and distribution. Covering the whole territory of Bulgaria the authors identify four main types of intermittent streams as: 1) intermittent flows as result of Mediterranean climatic impact located in the southern part of the country; 2) sinking intermittent flows as result of specific geological and soil characteristics, 3) intermittent flows in large karst and loess areas and finally 4) the sinking flows in alluvium depositions mainly along the large mainstreams. Nevertheless, the limited number of gauging stations built up at these rivers some hydrological information is collected and statistical results are presented as duration curves of temporal rivers, hydrographs with seasonal characteristics etc. Intermittent streams have a hydrologic flow regime with very specific characteristics that place them as interact between land and water. Unfortunately, in Bulgaria there are poorly mapped, recognized, and protected but they have a critical influence on the ecological health of networks. There exists a strong need for new approaches to scientifically study, the structure and function of temporal streams. The construction of monitoring network for the regular registration of their hydrological regime is surely the first required step for their future detailed ingestions, use and protection.
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Wu, Hang, Wei Wang, Dengji Zhou, Shixi Ma, and Huisheng Zhang. "Distributed Training for Data Driven Models in Power Machinery Online Monitoring." In ASME 2019 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2019. http://dx.doi.org/10.1115/imece2019-11282.
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Abstract Prognostics and Health Management (PHM) Systems have been widely applied in online monitoring of modern mechanical devices, and models in PHM systems are of vital importance. Traditional mechanism models are able to simulate parameters and evaluate the overall machine state accurately, but their defect is the high requirements in understanding the objects. Therefore, data driven models could be a suitable substitute. Data driven models take the advantage of machine learning technique and are able to be established only on the basis of past data. Models applied in this paper are based on Artificial Neural Network (ANN), but the complexity of network structures will occupy huge amount of computing resources and time in training. For online monitoring, spending too much time on training will postpone real time prediction and decrease the reaction speed of PHM systems in abnormal conditions. In this paper distributed training based on computer cluster is proposed to decrease training time. By dispatching computing task into several workers, the severs in the cluster will only undertake a small fraction of the total computing load and therefore accelerate the overall training process. On the other hand, there are some risks of losing prediction accuracy and stability because of the nonlinear gradient deviation in data parallelism. Aggregation period (AP) is an important factor in balancing the requirements of both ends. This paper analyzes the influence of AP on training speed, accuracy and stability, then proposes a novel distributed training algorism according to the regulations achieved. Then a distributed training and online monitoring process for a typical two-shaft gas turbine is taken as an example in the result and discussion section. It turns out that the experimental results fit the theoretical regulations well, and the revised distributed learning algorism is able to meet all the requirements of training speed, prediction accuracy and stability.
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Pham, Kinh D., Kai Looijenga, Gene Wallis, and Thomas Heilig. "Track-to-Earth Potentials and Stray Current Monitoring on Portland TriMet MAX Light Rail System." In IEEE/ASME/ASCE 2008 Joint Rail Conference. ASMEDC, 2008. http://dx.doi.org/10.1115/jrc2008-63067.
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The track is insulated in a DC railway system. Even when the track is new, the insulation between track and earth is never ideal. The insulating capacity of the new track diminishes in the course of time due to ageing and dirt. The insulation can also be affected due to improper track construction. It is, therefore, necessary to monitor stray current activity to detect early insulation deficiencies to prevent any possible damage due to stray current corrosion of metal objects such as underground utility pipelines or building reinforcements. One approach to monitoring stray current activities is through the measurement of track-to-earth potentials. Dynamic stray current effects can normally be identified by fluctuations in the track-to-earth potentials with time. This paper describes the Siemens SITRAS SMS stray current monitoring system used on the Portland IMAX Light Rail extension. The SITRAS SMS system consists of a central evaluation PC located at TriMet Systems Engineering Office with track-to-earth voltage sensors installed at each of the six traction power substations on the IMAX line rail extension. The sensors and the PC are connected to each other through an ETHERNET network. The measured data are stored and archived in three different stages: instantaneous, short-term, and long term average values. This paper will present and analyze the data colleted on the IMAX line rail extension. Basic stray current modeling and analysis will also be discussed.
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Kaminskis, Janis, Lubova Sulakova, Kalvis Salmins, Janis Kaulins, and Lauris Goldbergs. "SLR and GNSS Test Field for Global Geodetic Network Assessment in Riga." In 11th International Conference “Environmental Engineering”. VGTU Technika, 2020. http://dx.doi.org/10.3846/enviro.2020.718.
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The basic aim is to contribute to the world geodetic space in line with today’s scientific achievements. Riga geodynamic site is a thankful place for this, as it has long-term SLR observations and the longest GNSS records in Latvia. The goal is establishment of regional long-term geodetic monitoring station at LU Institute of Astronomy, Riga, Kandavas street 2, by joining at least two space geodetic technologies – the already installed laser-telescope LS-105 and GNSS − collocated, but not sufficiently linked. The capability of geodetic GNSS observations would uniquely complement Riga GNSS station and allow to determine more accurate coordinates of the LS-105 laser telescope and the long-term changes needed to accurately measure the positions of Earth satellites and other similar space objects. GNSS Observation Station will contribute to the development of positioning and position long-term change to accuracy of less than 1mm, one of the current global goals of GGOS. We plan to solve the problem with the exact position of the telescope LS-105 it will contribute to the development of scientific research and applied potential of the LU Satellite Laser Ranging station. From the national point of view geodetic station serves as an important point for Latvian National Geodetic Network, long term large infrastructure planning, engineering communications, cartography, etc. From a global perspective the station will be one of very few such stations in the region and the only one in the Baltics capable of valuable contribution to ITRF network. Preparatory work for the study has started by selecting and consolidating geodetic points for further measurements.
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HAN, QIANG, SHENGCHUN WANG, YUE FANG, and PENG DAI. "Real-time Object Detection Based on R-FCN Network Under Structured Scene of High-speed Railway." In Structural Health Monitoring 2019. Lancaster, PA: DEStech Publications, Inc., 2019. http://dx.doi.org/10.12783/shm2019/32441.
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Bizyukin, M., and G. V. Abrahamyan. "Technology for developing a prototype of an information system for monitoring remote sensing data for the arctic region." In III Международная научно-практическая конференция. Нижневартовский государственный университет, 2021. http://dx.doi.org/10.36906/ap-2020/25.
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The work is aimed at developing a model of an information system for the analysis and monitoring of remote sensing data by the example of processing hyper- and multispectral satellite images, which are widely used to analyze the state of static and dynamic objects in the Arctic region of the Russian Federation. For automatic analysis and decryption of Arctic data in the development of the model, methods of high-performance computing, radiometric calibration, filtering and clustering of images, as well as intelligent data processing methods using deep learning convolutional neural networks were used. Object-oriented design and united modeling language notation were used to develop the model. A data-level model, a conceptual model of the structure of system modules, including a resource storage center, a resource and results management center, and a presentation-level interface have been developed. To develop a diagram of the use cases of the information system, the structure of actors, use cases and their interrelations were identified. The logical model of the information system was created based on a class diagram consisting of the Resource and Results Manager Center, Intellectual Information System, Functional Neural Modules packages. The practical significance of the study is due to the fact that the results obtained will allow the development of a prototype of an information system that can be used for effective monitoring of “useful data" of the Arctic region of the Russian Federation, as well as to automate the processes of analysis, updating, storage and processing of data from objects in various areas of the Arctic infrastructure.