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4th IEEE International Future Energy Electronics Conference, IFEEC 2019

作者： Itoh, Jun-Ichi Mizoguchi, Kosuke Nam, Le Hoai Kusaka, Keisuke Nagaoka University of Technology Dept. of Electrical Electronics and Information Engineering Nagaoka Japan

ISBN: (纸本)9781728131535

This paper proposes a thermal design method for transmission coils with thermally conductive plastic plates considering load fluctuation of high-power wireless power transfer (WPT) systems. The high power transmission may cause melting of an insulation coating film of litz wires due to the heat generation of the windings and cores. For this reason, a cooling structure for the transmission coils is required. As the cooling structure, thermally conductive plastic plates with high thermal conductivity and resistivity are inserted between the core and the aluminum plates for an improvement of heat transfer. By representing the cooling structure as a thermal equivalent circuit, it facilitates a thermal design that takes into account load fluctuations. The thermal equivalent circuit is composed of a two-stage ladder circuit divided into the WPT system part and a cooling part. The peak temperature and the maximum allowable loss of load fluctuation are derived by simplifying the two-stage ladder circuit. In addition, the thickness of the thermally conductive plastic plates is designed by the design flowchart for the 15-kW WPT system. © 2019 IEEE.

关键词： Timing circuits

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Nonparametric development of the ranked tree structure for the accurate facial landmarking localization 1

Nonparametric development of the ranked tree structure for t...

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1st International Conference on Advances in Science, engineering and Robotics technology, ICASERT 2019

作者： Ghose, Supratip Rahman, Habibur University of Information Technology and Sciences Dept. of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9781728134451

Reliable face recognition starts with the analysis of accurate and robust facial localization component and poses an unresolved challenging computer vision problem in unconstrained settings in the backdrop of clutter, and large head poses variations. The landmark localizer needs to meet robust reasonable point coordinates in correspondence with the face alignment;despite to the extent the image in consideration is under scenarios with face variability. Correspondingly, in this paper;the task is viewed as non-parametric development of local estimator that produces learned features in the leaf node of a tree with an adopted ranking in a supervised clustering way. Then, the proposed approach helps to implement a sequence of estimators based on ensembles of regression trees. The trees use simple scale invariant, asymmetric Multi-Scale shape indexed pixel difference at each split in the internal nodes using adopted label ranking clustering methods, which make those trees able to rapid detection of the process in the region of interest (ROI). The developed system is then tested on several publicly available datasets and analyzed for the sensitivity of the experimental occlusion. The results show that the performance of our method has practical value for commercialization aspect overcoming slowness in the state-of-the-art methods and evaluates to significant accuracy compared to recent methods. © 2019 IEEE.

关键词： Face recognition

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Modeling for Malicious Traffic Detection in 6G Next Generation Networks

Modeling for Malicious Traffic Detection in 6G Next Generati...

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technology and Entrepreneurship, (ICTE-V), International Conference on

作者： Hamidreza Ghorbani M. Saeed Mohammadzadeh M. Hossein Ahmadzadegan dept. Electrical Engineering and Information Technology Azad University of Tehran Tehran Iran

ISBN: (数字)9781728193748

ISBN: (纸本)9781728193755

Based on the digitalization of the world and the increasing use of data, we can say that the next ten years will see a huge leap in the use of data in virtualization, automation, health and more. One of the infrastructures to reach the digitalization goals of the world is new communication networks with more capabilities, including high speed, low latency, more coverage and more. 5G networks and later 6G can provide us with this infrastructure. According to 6G (KPIS) targeting, IoT equipment, and distributed systems, data volumes are beyond the tolerance of cyber systems for data analysis. On the other hand, as technology evolves, its abuse will increase, and attacks on data exploitation will become more and more diverse. The main weaknesses of distributed and IoT systems, which are their computing power and power, will remain the same, and hackers will make the most of these weaknesses and will use new methods and ways to anonymize their activities. So there is a need for a system to identify and counter potential abuses of capabilities and data volume in next generation networks, especially 5G and 6G, more than ever. In this paper, we present a theory for work and activity on the creation of malicious traffic identification modeling in 5G and 6G networks. And finally we can come up with a solution to create more security in the future.

关键词： 5G mobile communication 6G mobile communication next generation networks telecommunication security telecommunication traffic

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A Comparative Study Cluster-Based Routing Protocols in VANETs for City Environment 2

A Comparative Study Cluster-Based Routing Protocols in VANET...

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2nd International Seminar on Research of information technology and Intelligent Systems, ISRITI 2019

作者： Youneng, Kim Sulistyo, Selo Wayan Mustika, I. Universitas Gadjah Mada Dept. Electrical Engineering and Information Technology Yogyakarta Indonesia

ISBN: (纸本)9781728145204

Vehicular Ad-hoc Networks (VANETs) is consid-ered as an extension of Mobile Ad-hoc Networks (MANETs) which the vehicles have high mobility on the street and in-Termittent connectivity. The vehicles move on the street and communicate with each other to share the information such as traffic information, road condition, warning message at the corner, and browsing the internet at the car's backseat, etc. Because of the quick speed of vehicles and intermittent connectivity that making routing protocol plays a significant role in VANETs to deliver the packets among the nodes. There have bio-inspired routing protocols and traditional routing protocols, as well as mobility models, are proposed to obtain an optimal solution for this challenge. In this paper, we illustrate the essential information about the cluster-based technique in VANETs and do a comparative study on cluster-based routing protocols between ACO and AODV, and its mobility for the city environment. © 2019 IEEE.

关键词： Vehicular ad hoc networks

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Towards complete tree crown delineation by instance segmentation with Mask R–CNN and DETR using UAV-based multispectral imagery and lidar data

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ISPRS Open Journal of Photogrammetry and Remote Sensing 2023年 8卷

作者： Dersch, S. Schöttl, A. Krzystek, P. Heurich, M. Dept. of Geoinformatics Munich University of Applied Sciences Munich 80333 Germany Faculty of Environment and Natural Resources University of Freiburg Germany Bavarian Forest National Park Dept. of Visitor Management and National Park Monitoring Grafenau 94481 Germany Institute of Forestry and Wildlife Management Inland Norway University of Applied Science Koppang NO-2480 Norway Dept. of Electrical Engineering and Information Technology Munich University of Applied Sciences Munich 80335 Germany Institute for Applications of Machine Learning and Intelligent Systems Munich University of Applied Sciences Munich 80335 Germany

Precise single tree delineation allows for a more reliable determination of essential parameters such as tree species, height and vitality. Methods of instance segmentation are powerful neural networks for detecting and segmenting single objects and have the potential to push the accuracy of tree segmentation methods to a new level. In this study, two instance segmentation methods, Mask R–CNN and DETR, were applied to precisely delineate single tree crowns using multispectral images and images generated from UAV lidar data. The study area was in Bavaria, 35 km north of Munich (Germany), comprising a mixed forest stand of around 7 ha characterised mainly by Norway spruce (Picea abies) and large groups of European beeches (Fagus sylvatica) with 181–236 trees per ha. The data set, consisting of multispectral images and lidar data, was acquired using a Micasense RedEdge-MX dual camera system and a Riegl miniVUX-1UAV lidar scanner, both mounted on a hexacopter (DJI Matrice 600 Pro). At an altitude of approximately 85 m, two flight missions were conducted at an airspeed of 5 m/s, leading to a ground resolution of 5 cm and a lidar point density of 560 points/m². In total, 1408 trees were marked by visual interpretation of the remote sensing data for training and validating the classifiers. Additionally, 125 trees were surveyed by tacheometric means used to test the optimized neural networks. The evaluations showed that segmentation using only multispectral imagery performed slightly better than with images generated from lidar data. In terms of F1 score, Mask R–CNN with color infrared (CIR) images achieved 92% in coniferous, 85% in deciduous and 83% in mixed stands. Compared to the images generated by lidar data, these scores are the same for coniferous and slightly worse for deciduous and mixed plots, by 4% and 2%, respectively. DETR with CIR images achieved 90% in coniferous, 81% in deciduous and 84% in mixed stands. These scores were 2%, 1%, and 2% worse, res

关键词： Instance segmentation Lidar Multispectral imagery Single tree delineation

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Passivity-Based Analysis and Nudging Design for Dynamic Social Model with Bounded Rationality

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IFAC-PapersOnLine 2020年第5期53卷 338-343页

作者： Shunya Yamashita Takeshi Hatanaka Yasuaki Wasa Naoki Hayashi Kenji Hirata Kenko Uchida Dept. of Systems and Control Engineering Tokyo Institute of Technology Tokyo Japan Dept. of Electrical Engineering and Bioscience Waseda University Tokyo Japan Division of Electrical Electronic and Infocommunications Engineering Osaka University Osaka Japan Dept. of Electric and Electronic Engineering University of Toyama Toyama Japan

In this paper, we address a design problem of nudging mechanism for dynamical decision makers with bounded rationality. We first present a mathematical model for dynamic decision makers with psychological biases. We then analyze the dynamics based on so-called δ -passivity, and reveal that the bias violates passivity of decision makers. We moreover propose a nudging mechanism based on the passivity paradigm to achieve desired social behavior. The presented mechanism is finally demonstrated through numerical simulation.

关键词： population dynamics bounded rationality nudge passivity

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The Research on Target Detection of MIMO Radar Based on Fornasini-Marchesini II Model 5

The Research on Target Detection of MIMO Radar Based on Forn...

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5th IEEE International Conference on Computer and Communications, ICCC 2019

作者： Cao, Zhongyong Hua, Cheng Xudongzi, Wu Chang, Liu Wuhan University of Science and Technology Dept. of Information Science and Engineering Wuhan China

ISBN: (纸本)9781728147437

This paper introduces the Fornasini-Marchesini second model (FM-II) realization problem for the target detection of multi-input and multi-output (MIMO) radar. A new operation approach to the realization of MIMO radar based on Fornasini-Marchesini second model will be presented. The constructive procedure that can generate a Fornasini-Marchesini state-space model realization for a given n-D system with lower order than the existing procedure. By utilizing the new method to reduce the dimension of the realization matrix of the multidimensional system which improve the radar's ability to detect dynamic targets. And a non-trival example will be given to show not only the details but also the effectiveness of the proposed approach. © 2019 IEEE.

关键词： Tracking radar

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Graph neural network based service function chaining for automatic network control

arXiv

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arXiv 2020年

作者： Heo, Dong Nyeong Lange, Stanislav Kim, Hee-Gon Choi, Heeyoul Dept. of Information and Communication Engineering Handong Global University Pohang Korea Republic of Dept. of Information Security and Communication Technology Norwegian University of Science and Technology Trondheim Norway Dept. of Computer Science and Engineering Pohang University of Science and Technology Pohang Korea Republic of

Software-defined networking (SDN) and the network function virtualization (NFV) led to great developments in software based control technology by decreasing expenditures. Service function chaining (SFC) is an important technology to find efficient paths in network servers to process all of the requested virtualized network functions (VNF). However, SFC is challenging since it has to maintain high Quality of Service (QoS) even for complicated situations. Although some works have been conducted for such tasks with high-level intelligent models like deep neural networks (DNNs), those approaches are not efficient in utilizing the topology information of networks and cannot be applied to networks with dynamically changing topology since their models assume that the topology is fixed. In this paper, we propose a new neural network architecture for SFC, which is based on graph neural network (GNN) considering the graph-structured properties of network topology. The proposed SFC model consists of an encoder and a decoder, where the encoder finds the representation of the network topology, and then the decoder estimates probabilities of neighborhood nodes and their probabilities to process a VNF. In the experiments, our proposed architecture outperformed previous performances of DNN based baseline model. Moreover, the GNN based model can be applied to a new network topology without re-designing and re-training. Copyright © 2020, The Authors. All rights reserved.

关键词： Deep neural networks

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Tinyradarnn: Combining spatial and temporal convolutional neural networks for embedded gesture recognition with short range radars

arXiv

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arXiv 2020年

作者： Scherer, Moritz Magno, Michele Erb, Jonas Mayer, Philipp Eggimann, Manuel Benini, Luca The Dept. of Information Technology and Electrical Engineering ETH Zürich Switzerland The Dept. of Project-based Learning ETH Zürich Switzerland The Dept. of Electrical Electronic and Information Engineering University of Bologna Italy

This work proposes a low-power high-accuracy embedded hand-gesture recognition algorithm targeting battery-operated wearable devices using low power short-range RADAR sensors. A 2D Convolutional Neural Network (CNN) using range frequency Doppler features is combined with a Temporal Convolutional Neural Network (TCN) for time sequence prediction. The final algorithm has a model size of only 46 thousand parameters, yielding a memory footprint of only 92 KB. Two datasets containing 11 challenging hand gestures performed by 26 different people have been recorded containing a total of 20'210 gesture instances. On the 11 hand gesture dataset, accuracies of 86.6% (26 users) and 92.4% (single user) have been achieved, which are comparable to the state-of-the-art, which achieves 87% (10 users) and 94% (single user), while using a TCN-based network that is 7500× smaller than the state-of-the-art. Furthermore, the gesture recognition classifier has been implemented on a Parallel Ultra-Low Power Processor, demonstrating that real-time prediction is feasible with only 21 mW of power consumption for the full TCN sequence prediction network, while a system-level power consumption of less than 120 mW is achieved. We provide open-source access to example code and all data collected and used in this work on ***. © 2020, CC BY-SA.

关键词： Internet of things

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Live Demo: E2P–Events to Polarization Reconstruction from PDAVIS Events

Live Demo: E2P–Events to Polarization Reconstruction from P...

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IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

作者： Tobi Delbruck Zuowen Wang Haiyang Mei Germain Haessig Damien Joubert Justin Haque Yingkai Chen Moritz B. Milde Viktor Gruev Sensors Group Institute of Neuroinformatics Univ. of Zurich and ETH Zurich Switzerland AIT Austrian Institute of Technology Center for Vision Automation & Control High-Performance Vision Systems Vienna Austria Intl. Centre for Neuromorphic Systems The MARCS Institute Western Sydney University Sydney Australia Dept. of Electrical and Computer Engineering University of Illinois at Urbana-Champaign Urbana IL USA

This demonstration shows live operation of of PDAVIS polarization event camera reconstruction by the E2P DNN reported in the main CVPR conference paper Deep Polarization Reconstruction with PDAVIS Events (paper 9149 [...

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