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A routing algorithm based on fuzzy logics for elastic optical networks

A routing algorithm based on fuzzy logics for elastic optica...

引用

IEEE Workshop on High Performance Switching and Routing

作者： E. O. Figueirôa E. A. O. Lima C. J. A. Bastos-Filho J. C. da Silva A. V. S. Xavier D. R. B. Araújo Polytechnic School of Pernambuco University of Pernambuco Recife PE Brazil Universidade de Pernambuco Recife PE BR Department of Electronics and Systems Federal University of Pernambuco Recife PE Brazil Universidade Federal de Pernambuco Recife PE BR Department of Informatics Federal Rural University of Pernambuco Recife PE Brazil

this paper proposes a decision model based on fuzzy logics to assign the route between source-destination nodes in elastic optical networks. the route is chosen dynamically based on the lowest cost criterion, which is calculated based on a weighted summation of three metrics, called availability of contiguous slots on the route, average availability of slots on the route and physical length of the lightpath. To accomplish this task, we use the Takagi-Sugeno defuzzification process to calculate the cost of the route under analysis. By using our proposal in three different scenarios, we obtained a better network performance regarding blocking probability and an increase in the throughput when compared to other well-known routing algorithms, such as Shortest Path, Minimum Hop, Congested Aware Shortest Path and Spectrum Continuity based Shortest Path.

关键词： Measurement Routing Optical fiber networks Topology Fuzzy logic Network topology Modulation

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Ionic Polymer Gated WS2-Graphene Photodetectors

Ionic Polymer Gated WS2-Graphene Photodetectors

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IEEE conference on Nanotechnology

作者： Jake D. Mehew Selim Unal Elias Torres Alonso Gareth F. Jones Saad Fadhil Ramadhar Monica F. Craciun Saverio Russo Centre for Graphene Science University of Exeter Exeter UK

ISBN: (纸本)9781538653371;9781538653364

Layer by layer assembly of two-dimensional materials enables the tailored fabrication of atomically thin electronic devices. By using different material combinations, these van der Waals heterostructures can possess novel properties not found in the constituent materials. For example, combining graphene with semiconductor materials in heterostructure photodetectors, allows the detection of light signals down to powers in the femtowatt range. Currently, the operational speed of these detectors is limited by the presence of long-lived charge traps to a few tens of Hz. In this work, we fabricate and fully characterize atomically thin graphene-WS 2 heterostructure photodetectors, encapsulated in an ionic polymer. we demonstrate an internal gain exceeding 10 6 electrons per photon due to the high mobility graphene channel and the strong light-matter interaction of WS 2 . However, unique to our devices is the expansion of the operational bandwidth into the kHz range, whilst simultaneously observing responsivities as high as 10 6 A/W. Highly mobile ions and the nanometer Debye length of the ionic polymer are used to screen charge traps and tune the Fermi level of graphene over an unprecedented range at the graphene-WS 2 interface. this combination of both high responsivity and kHz bandwidth makes these photodetectors suitable for video-frame-rate imaging applications. Furthermore, to extend the spectral range into the near-infrared the use of alternative semiconductor materials to replace WS 2 will also be discussed.

关键词： Photodetectors Graphene Polymers Semiconductor materials Bandwidth logic gates Fabrication

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Survey Findings on Analytical Tools for Common-Mode and Dependent Outages in Power System Reliability Analysis

Survey Findings on Analytical Tools for Common-Mode and Depe...

引用

international conference on Probabilistic Methods Applied to Power Systems (PMAPS)

作者： Milorad Papic Douglas M. Logan

ISBN: (数字)9798350372786

ISBN: (纸本)9798350372793

this paper presents results from a survey conducted by the IEEE Working Group on Probability Applications for Common Mode and dependent Events (PACME WG) in electric power systems. the main goal of the survey was to assess the state of industry practice in collecting data on common mode and dependent (CMD) outage events and using such data in planning and operational studies. this paper focuses on the survey findings regarding use of the data and analytical tools incorporating CMD outages in power system reliability analysis. A key finding is that there remains a widespread perception that CMD event data and how to use them still are not well understood, and that the tools available for system reliability analysis with CMD event data are inadequate. there is an evident need to develop a new generation of probabilistic methods, tools, and business practices to replace (or complement) the existing ones.

关键词： Surveys Industries Probabilistic logic Power system reliability Planning Reliability Business

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LSTM Based Bearing Fault Diagnosis of Electrical Machines using Motor Current Signal

LSTM Based Bearing Fault Diagnosis of Electrical Machines us...

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international conference on Machine Learning and Applications (ICMLA)

作者： Russell Sabir Daniele Rosato Sven Hartmann Clemens Guehmann SEG Automotive Germany GmbH Stuttgart Germany Chair of Electronic Measurement and Diagnostic Technology & Technische Universität Berlin Berlin Germany Technische Universität Berlin Berlin Germany

Rolling element bearings are one of the most critical components of rotating machinery, with bearing faults amounting up to 50% of the faults in electrical machines. therefore, the bearing fault diagnosis has attracted attention of many researchers. Typically, the bearing fault diagnosis is performed using vibration signals from the machine. In addition, by using deep learning algorithms on the vibration signals, the fault detection accuracy close to 100% can be achieved. However, measurement of vibration signals requires an additional sensor, which is not present in majority of the machines. Nevertheless, with an alternative approach, the stator current can be used for diagnosis. therefore, the paper emphasizes on the diagnosis of bearing faults using the stator current. the diagnosis requires signal processing for the fault signature extraction that is buried underneath the noise in the current signal. the paper uses the Paderborn University damaged bearing dataset, which contains stator current data from healthy, real damaged inner raceway and real damaged outer raceway bearings with different fault severity. For fault signature extraction from the current signals, the redundant frequencies in the signals are filtered, then from the filtered signals eight features are extracted from the time and time-frequency domain by using the wavelet packet decomposition (WPD). then, using these features and the well known deep learning algorithm Long Short-Term Memory (LSTM), bearing fault classification is made. the deep learning LSTM algorithm is mostly used in speech recognition due to its time coherence, but in this paper, the ability of LSTM is also demonstrated with the fault classification accuracy of 96%, which outperforms most of the present algorithms that perform bearing fault diagnosis using stator current. the method developed is independent of the speed and the loading conditions.

关键词： Fault diagnosis Feature extraction Machine learning Stators Vibrations logic gates Signal processing algorithms

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New High Efficiency Models for Generation of Synthetic Time Series of Wind Speed and Direction

New High Efficiency Models for Generation of Synthetic Time ...

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international conference on Probabilistic Methods Applied to Power Systems (PMAPS)

作者： R. Langella A. Testa University of Campania "Luigi Vanvitelli" Aversa Italy

ISBN: (数字)9798350372786

ISBN: (纸本)9798350372793

New High efficiency models (H) for generation of synthetic time series of correlated wind speed (WS) and wind direction (WD) are proposed. they strongly reduce the dimension of a complete Markov Chain model (CM) preserving the number of classes used to discretize WS and WD without ignoring any possible single transition among classes; the states of the CM matrix columns are compacted into a reduced number of macro-states of the H matrix columns, each characterised by a transition probability obtained summing the transition probabilities of the corresponding compacted CM states. the computational burden and accuracy of the proposed models are compared with those of the CM and of the two simplified models proposed by the authors in previous papers, through numerical experiments based on field data.

关键词： Dimensionality reduction Accuracy Computational modeling Wind speed Time series analysis Probabilistic logic Data models

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Impact of Non-Rectangular Cross-Section on Electrical Performances of GAA FETs

Impact of Non-Rectangular Cross-Section on Electrical Perfor...

引用

IEEE India conference (INDICON)

作者： Tripty Kumari Jawar Singh Pramod Kumar Tiwari Indian Institute of Technology Patna India

ISBN: (纸本)9781665441766

We investigate the short-channel immunity of various cross-section gate-all-around (GAA) field-effect transistors (FETs): Square (Sq), Rectangular (Re), Trapezoidal (Tz), Circular (Cr), Elliptical (Ep), and triangular with rounded corners (Tr) in terms of above-threshold inversion electron charge density, drain induced barrier lowering (DIBL), subthreshold swing (SS) degradation, gate capacitance (C gg ), threshold voltage roll-off, and ON-by-OFF ratio. these cross-sections are obtained by varying one or the other key parameters viz. Fin top width (W top ), Fin height (H Fin ), inclination angle ($\theta$), and radius of curvature (R). the impact on electrical performances of these parameters as dictated by the processing technology has been analyzed. It is observed that at channel length, $L_{G}=20nm$ the trapezoidal GAA (TzGAA) FET offers 30% reduction in DIBL, 6.4% less degradation in SS, 57% reduction in threshold voltage roll-off, and 22% higher gate capacitance compared to the rectangular counterpart if their bottom widths are equal. Despite ReGAA FET has slightly higher C gg compared to EpGAA, the later shows enhanced immunity to short-channel effects. the electron density of TzGAA FET has been observed to decrease with an increase in $\theta,\ H_{Fin},\ W_{top}$, and R. Besides, we calculated the analoyRF parameter dependence on $\theta$ of TzGAA FET. We believe our findings could represent guidelines for the design of high-performance GAA FETs.

关键词： Degradation Electron traps Field effect transistors Gallium arsenide logic gates Capacitance Controllability

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LoRaDrone: Enabling Low-Power LoRa Data Transmission via a Mobile Approach

LoRaDrone: Enabling Low-Power LoRa Data Transmission via a M...

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international conference on Mobile Ad-hoc and Sensor Networks, MSN

作者： Ciyuan Chen Junzhou Luo Zhuqing Xu Runqun Xiong Zhimeng Yin Jingkai Lin Dian Shen School of Computer Science and Engineering Southeast University Department of Computer Science City University of Hong Kong

Low-Power Wide Area Networks (LPWANs) are widely used to connect large-scale Internet of things (IoT) applications. Long Range (LoRa) is a promising LPWAN technology sensitive to energy consumption, since LoRa nodes are generally battery-powered, and the battery life will influence the lifetime of the LoRa network. In practice, the battery life of LoRa nodes is short in many scenarios, due to the long transmission distance form the gateway leading to high energy consumption. Existing techniques for energy-efficient data transmission mainly focus on static gateways, and will consume huge energy of remote nodes. In this paper, we propose to integrate LoRa with mobility to minimize the energy consumption of nodes by effectively shortening the transmission distances, and design the first mobile LoRa data transmission system called LoRaDrone by leveraging the unmanned aerial vehicle (UAV) gateway flying close to nodes. Specifically, we present a low-power communication mechanism and a dynamic channel allocation policy to minimize the energy consumed in sensing and communicating with the UAV gateway, while considering the distinctive LoRa parallel reception and complex transmission collisions. then, an optimal speed scheduling strategy is designed to ensure the reliability of data transmission, and minimize the energy consumption of the UAV. Evaluations on various scales verify the effectiveness of LoRaDrone under different nodes' distributions and UAV paths. Compared with the baselines, the energy consumption of nodes using LoRaDrone is at most reduced by $\mathbf{70.37}\times$ at 5000 nodes.

关键词： Energy consumption logic gates Autonomous aerial vehicles Reliability engineering Sensors Batteries Data communication

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Adaptive Group-based Signal Control Using Reinforcement Learning with Eligibility Traces

Adaptive Group-based Signal Control Using Reinforcement Lear...

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international IEEE conference on Intelligent Transportation Systems

作者： Junchen Jin Xiaoliang Ma Div. of Transp. Planning Econ. & Eng. KTH R. Inst. of Technol. Stockholm Sweden

ISBN: (纸本)9781467365970

Group-based signal controllers are widely deployed on urban networks in the European countries. However, group-based signal controls are usually implemented with rather simple timing logics, e.g. vehicle actuated timing. In addition, group-based signal control systems with pre-defined signal parameter settings show relatively poor performances in a dynamically changed traffic environment. this study, therefore, presents an adaptive group-based signal control system capable of changing control strategies with respect to non-stationary traffic demands. In this study, signal groups are formulated as individual agents. the signal group agent learns from traffic environments and makes intelligent timing decisions according to the perceived system states. Reinforcement learning with multiple-step backups is applied as the learning algorithm. Agents on-line update their knowledge based on a sequence of states during the learning process rather than purely on the basis of single previous state. the proposed signal control system is integrated into a software-in-the-loop simulation (SILS) framework for evaluation purpose. In the testbed experiments, the proposed adaptive group-based control system is compared to a benchmark signal control system, the well-established group-based fixed-time control system. the simulation results demonstrate that learning-based and adaptive group-based signal control system owns its advantage in dealing with dynamic traffic environments in terms of improving traffic mobility efficiency.

关键词： adaptive control control engineering computing digital simulation learning (artificial intelligence) road traffic control traffic engineering computing Nordic countries SILS framework adaptive group-based signal control system agent learning group-based fixed-time control system reinforcement learning algorithm signal parameter settings software-in-the-loop simulation timing logic traffic mobility efficiency urban networks Adaptive systems Control systems Delays Green products Learning (artificial intelligence) Vehicles

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Reliability Evaluation of Smart Distribution Networks Considering AC and DC Microgrids

Reliability Evaluation of Smart Distribution Networks Consid...

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international conference on Probabilistic Methods Applied to Power Systems (PMAPS)

作者： Elson Silva Anselmo Rodrigues José Filho Castro Maria da Guia Silva PPGEE UFMA São Luís Brazil DEE UFPE Recife Brazil

ISBN: (数字)9798350372786

ISBN: (纸本)9798350372793

the main objective of this paper is to propose a probabilistic method to evaluate reliability indices in radial power distribution networks considering AC and DC microgrids subject to technical constraints associated with voltage limits and angular frequency. Violations of these constraints are mapped to partial and total load loss events to evaluate reliability indices. these reliability indices were evaluated using the Sequential Monte Carlo Simulation. the tests results showed that the application of DC microgrids caused reduction in SAIFI and SAIDI indices. Furthermore, it can be observed that the relaxation of network constraints causes an underestimation of reliability indices in both AC and DC microgrids.

关键词： Photovoltaic systems Monte Carlo methods Loading Microgrids Distribution networks Voltage Probabilistic logic

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RF Transmitter Fingerprinting Exploiting Spatio-Temporal Properties in Raw Signal Data

RF Transmitter Fingerprinting Exploiting Spatio-Temporal Pro...

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international conference on Machine Learning and Applications (ICMLA)

作者： Debashri Roy Tathagata Mukherjee Mainak Chatterjee Eduardo Pasiliao Computer Science University of Central Florida Orlando FL Computer Science University of Alabama Huntsville AL Munitions Directorate Air Force Research Laboratory Eglin AFB FL

the recent advances of wireless technologies in RF environments coupled with large scale usage of such technologies has warranted more autonomous deployments of wireless systems. Machine learning techniques, that include recurrent structures, have shown promise in creating such autonomous deployments using the idea of Radio Frequency Machine Learning (RFML). In large scale autonomous deployments of wireless communication networks, the signals received from one component play a crucial role in the decision making process of other components. In order to efficiently implement such systems each component of the network should be uniquely identifiable. In this paper we propose a transmitter fingerprinting technique for radio device identification using recurrent structures, by exploiting the temporal property of the received radio signal. We design and implement three recurrent neural networks (RNNs) using different types of cell models: (i) long short term memory (LSTM); (ii) gated recurrent unit (GRU) and (iii) convolutional long short term memory (ConvLSTM), for this task. We program 8 universal software radio peripheral (USRP) software defined radios (SDRs) as transmitters and collect over-the-air raw in-phase (I) and quadrature (Q) (I/Q) time series data from them using a DVB-T RTL-SDR receiver, in a laboratory setting. We exploit both the temporal variations as well as the inherent spatial dependencies in the collected I/Q time series data, to learn unique feature representations and use these as "fingerprints'" for identifying the transmitters. Experimental results reveal that the RNNs with LSTM, GRU, and ConvLSTM cells are able to correctly distinguish between the 8 transmitters with 92%, 95.3%, 97.2% accuracy respectively.

关键词： Radio transmitters logic gates Recurrent neural networks Computer architecture Microprocessors Radio frequency Task analysis

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