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European control Conference (ECC)

作者： Hakan Basargan Filip Jeniš András Mihály Péter Gáspár Department of Control for Transportation and Vehicle Systems Budapest University of Technology and Economics Stoczek u. 2 Budapest Hungary Faculty of Mechanical Engineering Institute of Machine and Industrial Designs Brno University of Technology Technicka 2 Brnos Czech Republic Systems and Control Laboratory Institute for Computer Science and Control (SZTAKI) Eötvös Loránd Research Network (ELKH) Kende u. 13-17 Budapest Hungary

The paper presents a reconfigurable fault-tolerant control strategy for a semi-active suspension using magnetorheological (MR) damper. The aim of the control reconfiguration is to handle the adverse behaviour of the MR damper due to oil leakage induced by the wear of the suspension component. The proposed method relies on the data driven model of the MR damper, using an estimation procedure to quantify the healthiness of the damper and to estimate the performance degradation due to the oil leakage. The reconfiguration control strategy is founded on the Linear Parameter Varying (LPV) framework, where a scheduling variable is defined to represent the healthiness level of the MR damper. By the scaling of the control action through the scheduling variable, the performance degradation of the MR damper can be compensated to match the behaviour of the healthy dampers. The proposed method is demonstrated through simulations, comparing the performance of the fault-tolerant LPV control to conventional semi-active control methods

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AI-aided power electronic converters automatic online real-time efficiency optimization method

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Fundamental Research 2023年第3期5卷 1111-1116页

作者： Tang, Yuanhong Cao, Di Xiao, Jian Jiang, Chenying Huang, Qi Li, Yunwei Chen, Zhe Blaabjerg, Frede Hu, Weihao Power System Wide-area Measurement and Control Sichuan Provincial Key Laboratory School of Mechanical and Electrical Engineering University of Electronic Science and Technology of China Chengdu 611731 China MORNSUN Guangzhou Science & Technology Co. Ltd. Guangzhou 510663 China School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu 611731 China Yangtze Delta Region Institute (Quzhou) University of Electronic Science and Technology of China Quzhou 324000 China Department of Chengdu State Grid Corporation of China Chengdu 610599 China Southwest University of Science and Technology Mianyang 621010 China Department of Electrical and Computer Engineering University of Alberta Edmonton AB T6G 2V4 Canada Department of Energy Technology Aalborg University Aalborg 9220 Denmark

Energy losses during the conversion and supply of electric power are considered a significant issue and cannot be estimated. Improvement in the efficiency of energy conversion systems is highly restricted because of their internal nonlinearity and complexity. Thus, inspired by the successful utilization of robotic chemists, we demonstrate a pioneering concept of artificial intelligence (AI)-aided automatic online real-time optimization of a power electronics converter using a dual active bridge (DAB) converter as an example. An optimal modulation strategy was obtained through repeated automatic exploration experiments on a practical DAB converter platform. Specifically, the DAB experimental platform operated autonomously around the clock for approximately 71 h. It performed 120,000 consecutive experiments (12,000 episodes) within a six-variable experimental space driven by a deep deterministic policy gradient (DDPG) algorithm. The proposed AI-aided automatic online real-time optimization method achieved significantly improved efficiency of power conversion and supply. Consequently, zero carbon emissions may be obtained in the future. © 2023

关键词： Artificial intelligence Automatic online real-time exploration DC-DC converter Dual active bridge converter Energy conversion system Power electronics

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Decoherence limiting the cost to simulate an anharmonic oscillator

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Physical Review A 2023年第6期108卷 062219-062219页

作者： Tzula B. Propp Sayonee Ray John B. DeBrota Tameem Albash Ivan H. Deutsch Center for Quantum Information & Control University of New Mexico Albuquerque New Mexico 87131 USA Department of Physics and Astronomy University of Waterloo Ontario N2L 3G1 Canada Department of Electrical and Computer Engineering University of New Mexico Albuquerque New Mexico 87131 USA Department of Physics and Astronomy University of New Mexico Albuquerque New Mexico 87131 USA

We study how decoherence increases the efficiency with which we can simulate the quantum dynamics of an anharmonic oscillator governed by the Kerr effect. As decoherence washes out the fine-grained sub-Planck structure associated with phase-space quantum interference in the closed quantum system, open quantum dynamics can be more efficiently simulated using a coarse-grained finite-difference numerical integration. We tie this to the way in which decoherence recovers the semiclassical truncated Wigner approximation, which strongly differs from the exact closed-system dynamics at times when quantum interference leads to cat states and more general superpositions of coherent states. The regression in quadrature measurement statistics to semiclassical dynamics becomes more pronounced as the initial amplitude of the oscillator grows, with implications for the quantum advantage that might be accessible as system size grows in noisy quantum devices. Lastly, we show that this regression does not have the form of a convex noise model, such as for a depolarizing noise channel. Instead, closed quantum system effects interact with the open-system effects, giving rise to distinct open-system behavior.

关键词： Cavity quantum electrodynamics Open quantum systems & decoherence Computational complexity Phase space dynamics Semiclassical methods

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Design and implementation virtual environment network base on DMVPN

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AIP Conference Proceedings 2023年第1期2591卷

作者： Ahmed Abbas Fadel Safa Ihsan Matloob Mohamed Ibrahim Shujaa 1Department of Computer Engineering Techniques AL-Mustafa University College Iraq 2Department of Computer Engineering Techniques Middle Technical University (MTU) Iraq

Due to network advancements and network user growth, network security challenges and approaches have grown in the recent years. The current trend of network security focuses on those networks which are operating over the internet (web-based). Since unpredictable users can be presented over the internet networks from various places, malicious activities are highly expected. Several technologies were produced for ensuring the required security and privacy enforcement. Virtual private network is proposed for ensuring the required security over particular connections over wide networks. In this work proposed use the "show dmvpn" to make sure the connectivity of dmvpn works fine (10.20.20.2, 10.30.30.2 is referred as NBMA (non-broadcast multiple access network) these Internet Protocol (ip's) assigned in spokes routers in their physical interfaces and connecting directly in ISP routers and routed with default route.

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Adaptation Via Proxy: Building Instance-Aware Proxy for Unsupervised Domain Adaptive 3d Object Detection

SSRN

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SSRN 2023年

作者： Li, Ziyu Yao, Yuncong Quan, Zhibin Qi, Lei Feng, Zhenhua Yang, Wankou School of Automation Southeast University Nanjing210096 China Key Lab of Measurement and Control of Complex Systems of Engineering Ministry of Education Southeast University Nanjing210096 China School of Computer Science and Engineering Southeast University Nanjing210096 China Department of Computer Science University of Surrey GuildfordGU2 7XH United Kingdom

LiDAR-based 3D detectors perform well on particular autonomous driving benchmarks, but may poorly generalize to other domains. Existing 3D domain adaptive detection methods usually require annotation-related statistics or continuous refinement of pseudo-labels. The former is not always feasible for practical applications, while the latter lacks sufficient accurate supervision. In this work, we propose a novel unsupervised domain adaptive framework, namely Adaptation Via Proxy (AVP), that explicitly leverages cross-domain relationships to generate adequate high-quality samples without any target statistics, thus mitigating domain shifts for existing LiDAR-based 3D detectors. Specifically, we first train the detector on the source domain with curriculum example mining to enhance its generalization capability. Then, we fine-tune the pre-trained detector on the instance-aware proxy that is a data collection with stronger supervision. The instance-aware proxy utilizes the profitable instances from the source domain to provide more robust supervision for optimizing the detector to overcome domain gaps. Note that AVP is architecture-agnostic thus it can be easily injected with any LiDAR-based 3D detectors. Extensive experiments on Waymo, nuScenes, KITTI and Lyft demonstrate the superiority of the proposed method over the state-of-the-art approaches for different adaptation scenarios. © 2023, The Authors. All rights reserved.

关键词： Object detection

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Network Traffic Co-Movement Assessment via Oriented Basis Signal Processing and Ensemble Decision Trees

Network Traffic Co-Movement Assessment via Oriented Basis Si...

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International Conference on Intelligent Transportation

作者： Mohammad Reza Mohebbi Mohammad Javad Hassan Zada Majid Rostami-Shahrbabaki Mario Döller Iuliia Yamnenko Josef Ressel Center Vision2Move project University of Applied Sciences Kufstein Tirol Austria Department of Computer Science University of Passau Germany Chair of Traffic Engineering and Control Technical University of Munich Germany Department of Electronic Devices and Systems National Technical University of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute” Kyiv Ukraine

ISBN: (数字)9798331505929

ISBN: (纸本)9798331505936

Accurate short-term network-wide traffic prediction is essential to guarantee high service quality in urban traffic control systems. Nevertheless, traffic state time series represent network-scale spatiotemporal co-movement patterns and location-specific features. Therefore, hybrid statistical machine learning (ml) algorithms could be utilized to accommodate the aforementioned characteristics. In this paper, a hybrid random forest (rf) and extreme gradient boosting tree (XGBoost) model is introduced for network-wide traffic prediction. Moreover, a multi-noise oriented basis wavelet transform (OBT) filter is employed to pre-process the original time series, and improve the predictive accuracy. The RF model captures the traffic co-movements and the XGBoost extracts the local information. Comparative analysis of the hybrid algorithm and deep learning-based benchmarks indicate better performance of the proposed methodology in hourly traffic state prediction of 30 loop detectors, located in the paris city center. Hence, the hybrid decision-tree-based mechanism is a useful framework for real-time network traffic forecasting offering fewer trainable (hyper-)parameters, and thus, lower computational cost.

关键词： Wavelet transforms Radio frequency Accuracy Machine learning algorithms Urban areas Signal processing algorithms Detectors Prediction algorithms Decision trees Random forests

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Joint Uplink and Downlink Rate Splitting for Fog Computing-Enabled Internet of Medical Things

arXiv

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arXiv 2024年

作者： Zhou, Jiasi Chen, Yan Zhou, Cong Sun, Yanjing Tellambura, Chintha School of Medical Information and Engineering Xuzhou Medical University Xuzhou221004 China Faculty of Information Technology and Electrical Engineering University of Oulu Oulu Finland School of Electronic and Information Engineering Harbin Institute of Technology Harbin150001 China School of Information and Control Engineering China University of Mining and Technology Xuzhou221116 China Department of Electrical and Computer Engineering University of Alberta EdmontonABT6G 2R3 Canada

The Internet of Medical Things (IoMT) facilitates in-home electronic healthcare, transforming traditional hospitalbased medical examination approaches. This paper proposes a novel transmit scheme for fog computing-enabled IoMT that leverages uplink and downlink rate splitting (RS). Fog computing allows offloading partial computation tasks to the edge server and processing the remainder of the tasks locally. The uplink RS and downlink RS utilize their flexible interference management capabilities to suppress offloading and feedback delay. Our overarching goal is to minimize the total time cost for task offloading, data processing, and result feedback. The resulting problem requires the joint design of task offloading, computing resource allocation, uplink beamforming, downlink beamforming, and common rate allocation. To solve the formulated nonconvex problem, we introduce several auxiliary variables and then construct accurate surrogates to smooth the achievable rate. Moreover, we derive the optimal computation resource allocation per user with closed-form expressions. On this basis, we recast the computing resource allocation and energy consumption at the base station to a convex constraint set. We finally develop an alternating optimization algorithm to update the auxiliary variable and inherent variable alternately. Simulation results show that our transmit scheme and algorithm exhibit considerable performance enhancements over several benchmarks. © 2024, CC BY.

关键词： Fog computing

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Effectiveness of Image-Based Classifiers for On-Site Identification of Lichens in Reserved Forest

SSRN

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SSRN 2024年

作者： Sankar, Karthikumar Rajaram, Shyam Kumar Karuppiah, Ponmurugan Periyasami, Govindasami Karthikeyan, Perumal Selvi, Chengathir M. Selvan, Basil Tamil E. Department of Computer Science and Engineering Mepco Schlenk Engineering College Tamil Nadu Sivakasi626005 India Department of Biotechnology Kamaraj College of Engineering and Technology S.P.G.C.Nagar K.Vellakulam Tamil Nadu Virudhunagar626001 India Department of Botany and Microbiology College of Science King Saud University P.O. Box 2455 Riyadh11451 Saudi Arabia 2Department of Chemistry College of Science King Saud University P.O. Box 2455 Riyadh11451 Saudi Arabia Department of Chemistry and Biochemistry Ohio State University 170A CBEC 151 Woodruff Avenue ColumbusOH43210 United States Department of Computer Science and Engineering Kamaraj College of Engineering and Technology S.P.G.C.Nagar K.Vellakulam Tamil Nadu Virudhunagar626001 India

The collection and identification of lichen samples within the reserved forest pose significant challenges, leading researchers to seek alternative methodologies. In this context, an in-depth study was conducted using two approaches to evaluate the efficacy of several network models for the identification of lichens in digital images. In the initial method, a total of 119 lichen images, comprising 15 images from experimental work and additional images from open sources, were employed for the traditional classification approach utilizing MATLAB. In which, 90 different features were extracted from various color models such as RGB, YCbCr, HSV, CMYK, LAB, YIQ and subjected for classification in ANN model, which resulted 76.6% accuracy for the class of lichen family. In another approach, each species of lichen images was augmented using generative augmentation tool and subjected for multiclass deep learning network models, precisely transfer learning model. In total, 10410 images comprising 5 thallus type, 8 order and 77 distinct lichen species were used to train three different network models (VGG16, VGG19, ***50). It was found that ***50 model exhibited highest sensitivity (>0.99) and precision. The class lichen thallus type achieved a maximum accuracy of 86.6%, while the order exhibited 80%. Overall, the lichen species under the order of Laccanorals with fruticose thallus type showed highest classification accuracy in all the three models attempted in this study. The study concluded that utilizing transfer learning with a multilayer perceptron model proved to be more effective and accurate for the lichen dataset, especially when dealing with greater complexity in picture information. The obtained results were comparable to those of other recent deep learning approaches. © 2024, The Authors. All rights reserved.

关键词： Fungi

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Review of Approaches to the Use of UAVs, Remote Sensing and GIS in Humanitarian Demining: Ukrainian Case

SSRN

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SSRN 2024年

作者： Hutsul, Taras Khobzei, Mykola Tkach, Vladyslav Krulikovskyi, Oleh Moisiuk, Oleksandr Ivashko, Victor Samila, Andrii Department of Geomatics Land and Agromanagement Yuriy Fedkovych Chernivtsi National University Kotsyubynsky 2 Chernivtsi58002 Ukraine Department of Radio Engineering and Information Security Yuriy Fedkovych Chernivtsi National University Kotsyubynsky 2 Chernivtsi58002 Ukraine Department of Computer Sciences Yuriy Fedkovych Chernivtsi National University Kotsyubynsky 2 Chernivtsi58002 Ukraine Integrated Center for Research Development and Innovation in Advanced Materials Nanotechnologies and Distributed Systems for Fabrication and Control Stefan cel Mare University of Suceava Street University 13 Suceava720229 Romania Faculty of Electrical Engineering and Computer Science Stefan cel Mare University of Suceava Street University 13 Suceava720229 Romania

The history of the use of mines dates back almost two centuries. The geography of their use and the associated social harm have made them, without exaggeration, a global problem. At the same time, searches were underway for safe methods of their neutralization using various technical means. In so doing, until now, none of the existing methods provides a 100% guarantee of cleaning the territory, which determines the purpose of finding innovative methods and the possibility of combining them with existing ones. Unmanned aerial vehicles (UAVs) are becoming a real modern breakthrough in the field of intellectual achievements. Obtaining optimal results when solving a wide range of different problems, together with the development of composite materials, software, and the latest navigation equipment, make the tasks assigned to them and the expected results more and more difficult. UAVs allow people not to be in life-threatening conditions, to conduct activities beyond their physiological and psychophysiological abilities. The combination of the ability to collect spatial data during flight in various ranges of remote sensing with the possibility of carrying variants of useful equipment opens up prospects for their use in the field of demining territories. Supplementing UAV technologies with modern information systems for processing and analysis of information (expert systems, machine learning, computational intelligence, distributed artificial intelligence, neural networks, etc.), including spatial (GIS), opens up great prospects in the field of humanitarian demining of territories. © 2024, The Authors. All rights reserved.

关键词： Geographic information systems

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Performance Creativity Enhancement Method Based on Emotional Analysis and Concentration

Journal of Network Intelligence

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Journal of Network Intelligence 2023年第3期8卷 1019-1048页

作者： Liu, Chang Zhang, Longfei Huang, Tianyu Wu, Yufeng Zhang, Fuquan Lin, Qi Department of Computer Science and Technology Beijing Institute of Technology 100081 China Beijing Key Laboratory of Digital Performance and Simulation Technology 100081 China College of Computer and Control Engineering Minjiang University Fuzhou350108 China Digital Media Art Key Laboratory of Sichuan Province Sichuan Conservatory of Music Chengdu610021 China Informatics King’s College London LondonWC2R 2LS United Kingdom

Intelligent performance creativity is a new research direction of the intersection of technology and art. At present, the cutting-edge technologies such as computer simulation, emotional computing and machine learning have been applied to the evaluation and enhancement of intelligent performance creativity. The application of these technologies has greatly increased the diversity of performance forms and the complexity of performance content. For the traditional stage performance, due to the lack of timely feedback from the audience, it is difficult for the director to enhance the creativity effec-tively. In order to solve this problem, this paper proposes concentration level enhancement method (CLEM). The CLEM extracts and analyzes the physiological and emotional characteristics of the audience by collecting the multimodal physiological signal data of the audience. Through the analysis of the features, the concentration information of the audience is obtained and the concentration level (CL) is defined. Based on the physiological and emotional induction mechanism, a performance creativity enhancement strategy is proposed, which enhances the performance creativity in two aspects: " Visual-based" and " Content-based". Through the concentration level enhancement method, compare the audience’s performance attention changes before and after the performance creativity enhancement, select the effective strategies to enhance the performance creativity, and provide the director with quantifiable performance creativity enhancement feedback results. The experimental results show that the CLEM proposed in this paper can effectively induce significant changes in the audience’s emotion, and the selected creative enhancement strategy can significantly improve the audience’s concentration and achieve the purpose of enhancing the creativity of the performance. © 2023, Journal of Network Intelligence.

关键词： Physiology

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