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Photon subtraction-based continuous-variable measurement-device-independent quantum key distribution with discrete modulation over a fiber-to-water channel

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Communications in Theoretical Physics 2022年第3期74卷 36-43页

作者： Chao Yu Yin Li Jianzhi Ding Yun Mao Ying Guo School of Computer Science and Engineering Central South UniversityChangsha 410083China School of Automation Central South UniversityChangsha 410075China School of Computer Science Beijing University of Posts and TelecommunicationsBeijing 100876China

We propose a discrete-modulated continuous-variable measurement-device-independent quantum key distribution protocol over a fiber-to-water *** from optical fibers,the underwater channel has more severe optical attenuation because of optical absorption and scattering,which reduces the maximum communication *** enhance the performance of the protocol,the photon subtraction operation is implemented at the modulator *** carry out a performance simulation in two different kinds of seawater channel,and the result shows that the scheme with photon subtraction has a longer secure communication distance under certain conditions.

关键词： measurement-device-independent discrete modulation fiber-to-water channel continuous variable quantum key distribution

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On the Enhancement of Energy Efficiency of Wireless Rechargeable Sensor Networks through Mobile Charger Scheduling 2

On the Enhancement of Energy Efficiency of Wireless Recharge...

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2nd International Conference on Distributed Computing and Optimization Techniques, ICDCOT 2024

作者： Kamaruzzaman, Md. Chandra, Abhijit Azharuddin, Md. The Neotia University Dept.of Robotics and Automation Kolkata India Jadavpur University Dept. of Instrumentation and Electronics Engineering Kolkata India Aliah University Dept. of Computer Science and Engineering Kolkata India

ISBN: (纸本)9798350382952

In recent years, wireless rechargeable sensor networks (WRSNs) have become more popular because of its diverse array of applications. However, the limited energy resources of individual sensors pose a significant challenge in maintaining network operations over an extended period. To address this challenge, this research paper presents an energy-efficient scheduling scheme based on distance based scheduling for mobile charging vehicle (MCV) in WRSNs. The proposed scheme capitalizes on the MCV, allowing them to dynamically adjust their charging schedules to cater to the energy demands of sensor nodes (SNs). By efficiently allocating charging tasks to MCV, the scheme aims to balance the energy distribution and mitigate the energy imbalance across the network. By conducting comprehensive simulations with various scenarios, the proposed scheme has been compared with some existing schemes in relation to charging path length, total energy received and charging utility. The simulation results manifest significant advantages of the proposed scheme. © 2024 IEEE.

关键词： Energy efficiency

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Generation of Indian Sign Language Letters, Numbers, and Words

Generation of Indian Sign Language Letters, Numbers, and Wor...

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2024 International Conference on Intelligent Algorithms for Computational Intelligence Systems, IACIS 2024

作者： Yadav, Ajeet Kumar Kumar, Nishant Rathna, G.N. Indian Institute of Science Electrical Communication Engineering Banglore Bengaluru India Indian Institute of Science Computer Science and Automation Banglore Bengaluru India Indian Institute of Science Electrical Engineering Banglore Bengaluru India

ISBN: (纸本)9798350360660

Sign language, which contains hand movements, facial expressions and bodily gestures, is a significant medium for communicating with hard-of-hearing people. A well-trained sign language community communicates easily, but those who don't know sign language face significant challenges. Recognition and generation are basic communication methods between hearing and hard-of-hearing individuals. Despite progress in recognition, sign language generation still needs to be explored. The Progressive Growing of Generative Adversarial Network (ProGAN) excels at producing high-quality images, while the Self-Attention Generative Adversarial Network (SAGAN) generates feature-rich images at medium resolutions. Balancing resolution and detail is crucial for sign language image generation. We are developing a Generative Adversarial Network (GAN) variant that combines both models to generate feature-rich, high-resolution, and class-conditional sign language images. Our modified Attention-based model generates high-quality images of Indian Sign Language letters, numbers, and words, outperforming the traditional ProGAN in Inception Score (IS) and Frechet Inception Distance (FID), with improvements of 3.2 and 30.12, respectively. Additionally, we are publishing a large dataset incorporating high-quality images of Indian Sign Language alphabets, numbers, and 129 words. © 2024 IEEE.

关键词： Generative adversarial networks

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ControlSynth Neural ODEs: Modeling Dynamical Systems with Guaranteed Convergence 38

ControlSynth Neural ODEs: Modeling Dynamical Systems with Gu...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Mei, Wenjie Zheng, Dongzhe Li, Shihua The School of Automation and the Key Laboratory of MCCSE of the Ministry of Education Southeast University Nanjing China The Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai China

Neural ODEs (NODEs) are continuous-time neural networks (NNs) that can process data without the limitation of time intervals. They have advantages in learning and understanding the evolution of complex real dynamics. Many previous works have focused on NODEs in concise forms, while numerous physical systems taking straightforward forms, in fact, belong to their more complex quasi-classes, thus appealing to a class of general NODEs with high scalability and flexibility to model those systems. This, however, may result in intricate nonlinear properties. In this paper, we introduce ControlSynth Neural ODEs (CSODEs). We show that despite their highly nonlinear nature, convergence can be guaranteed via tractable linear inequalities. In the composition of CSODEs, we introduce an extra control term for learning the potential simultaneous capture of dynamics at different scales, which could be particularly useful for partial differential equation-formulated systems. Finally, we compare several representative NNs with CSODEs on important physical dynamics under the inductive biases of CSODEs, and illustrate that CSODEs have better learning and predictive abilities in these settings. © 2024 Neural information processing systems foundation. All rights reserved.

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Scalable Reliable Control for Cyber- Physical Microgrids Using Chordal Graph 13

Scalable Reliable Control for Cyber- Physical Microgrids Usi...

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13th IEEE Data Driven Control and Learning Systems Conference, DDCLS 2024

作者： Zhao, Yunlong Song, Yang Wang, Aimin Chen, Yonghui Mehran, Kamyar School of Mechatronic Engineering and Automation Shanghai University Shanghai201900 China School of Electronic Engineering and Computer Science Queen Mary University LondonE1 4NS United Kingdom

ISBN: (纸本)9798350361674

We consider a microgrid system with sparse structure, where the security is susceptible to the impact of potential faults. To enhance the reliability and scalability of the microgrid system, this paper investigates the structured controller design problem for the microgrid system under fault conditions using chordal decomposition. By establishing a microgrid fault model with a sparse structure, the controller design problem for large-scale microgrid systems is decomposed into a set of smaller-scale problems using chordal decomposition, and then solved clique-by-clique in a sequential approach. The results demonstrate that this approach reduces the sensitivity of the microgrid system to faults and improves solution efficiency and scalability. © 2024 IEEE.

关键词： Microgrids

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Automatic generation of repair suggestions for overall I&C architecture represented with an ontology 28

Automatic generation of repair suggestions for overall I&C a...

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28th IEEE International Conference on Emerging Technologies and Factory automation, ETFA 2023

作者： Ovsiannikova, Polina Pakonen, Antti Vyatkin, Valeriy VTT Technical Research Centre of Finland Ltd. Espoo Finland Aalto University Department of Electrical Engineering and Automation Espoo Finland Luleiå Tekniska Universitet Department of Computer Science Computer and Space Engineering Sweden

ISBN: (纸本)9798350339918

We present an approach for suggesting possible fixes to an overall I&C nuclear architecture during its design phase. Despite the I&C architecture, in our case, being represented with an ontology, we do not aim to change the properties of an ontology per se. Instead, we focus on the subset of ABox triples that do not contain terminological elements in either subject, predicate, or object parts. Such a subset we call the design artifacts. When the ontology is filled with the design artifacts, the analyst runs the check of non-functional requirements using SPARQL queries. The requirements associated with the queries that returned results do not hold. The goal of the current work is to provide support for the next stage when the analyst has to change the design artifacts so that the queries no longer return results. Our method is based on representing the results of queries as graphs, intersecting them, and finding the minimal changes that prevent the results from being mapped on their (or other) queries. © 2023 IEEE.

关键词： Ontology

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AggFollower: Aggressiveness Informed Car-Following Modeling

IEEE Transactions on Intelligent Vehicles

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IEEE Transactions on Intelligent Vehicles 2024年 1-15页

作者： Chen, Xianda Yuan, Xinyi Zhu, Meixin Zheng, Xinhu Shen, Shaojie Wang, Xuesong Wang, Yinhai Wang, Fei-Yue Guangzhou China Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Hong Kong SAR China School of Transportation Engineering Tongji University Shanghai China Department of Civil and Environmental Engineering University of Washington Seattle WA USA State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China

Car-following is the most common driving scenario where a following vehicle follows a lead vehicle in the same lane. One crucial factor of car-following behavior is driving style which affects speed and gap selection, acceleration pattern, and fuel consumption. However, existing car-following research used limited categories of driving style through pre-defined patterns and failed to encode driving style into data-driven car-following models. To address these limitations, we propose the Aggressiveness Informed Car-Following (AICF) modeling approach, which embeds driving style as a dynamic input feature in data-driven car-following models. In detail, We design driving aggressiveness tokens using four physical quantities (jerk, acceleration, relative speed, and relative spacing) to capture the heterogeneity of driving aggressiveness. These tokens were then embedded into a physics-informed Long Short-Term Memory (LSTM) based car-following model for trajectory prediction. To evaluate the effectiveness of our approach, we conducted extensive experiments based on 12,540 car-following events extracted from the HighD dataset and 24,093 events from the Lyft dataset. Compared to models devoid of considerations for driving aggressiveness levels, AICF exhibits superior efficacy in mitigating the Mean Square Error (MSE) of spacing and collision rate. To the best of our knowledge, this is the first work to directly incorporate real-time driving aggressiveness tokens as input features into data-driven car-following models, enabling a more comprehensive understanding of aggressiveness in car-following behavior. IEEE

关键词： Long short-term memory

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Fault Diagnosis of Electric Drive Systems Based on Dynamic Independent Component Analysis and A Hidden Markov Model 8

Fault Diagnosis of Electric Drive Systems Based on Dynamic I...

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8th IEEE International Conference on Advanced Robotics and Mechatronics, ICARM 2023

作者： Wang, Jingkun Wang, Hongmei Cheng, Chao Chen, Hongtian School of Computer Science and Engineering Changchun University of Technology Changchun China Shanghai Jiao Tong University Department of Automation Shanghai China

ISBN: (纸本)9798350300178

Due to the rapid development of high-speed trains, the fault diagnosis of electrical drive systems is a hot issue. A fault diagnosis method for electrical drive systems based on dynamic inner independent component analysis and the hidden Markov model is proposed. First, the offline data of the electric drive systems under different operating states are processed by using the dynamic inner independent component analysis algorithm. The dynamic features in the offline data are extracted by establishing equations that maximize the non-Gaussianity of the predicted latent variables. Second, these dynamic features are used to train a Hidden Markov Model to represent various states of the electric drive systems. Finally, the online data are injected into the Hidden Markov Model for fault diagnosis, and the experimental results on the electric drive systems simulation platform show the effectiveness of the method. © 2023 IEEE.

关键词： Failure analysis

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Visible and Infrared Image Fusion for Object Detection: A Survey

Visible and Infrared Image Fusion for Object Detection: A Su...

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International Conference on Image, Vision and Intelligent Systems, ICIVIS 2023

作者： Sun, Yuxuan Meng, Yuanqin Wang, Qingbo Tang, Minghua Shen, Tao Wang, Qingwang School of Information Engineering and Automation Yunnan Key Laboratory of Computer Technologies Application Kunming University of Science and Technology Kunming China Naval Research Academy Beijing China

ISBN: (纸本)9789819708543

Thanks to the advancements of Deep Learning (DL) Algorithms. DL-based object detection models has witnessed remarkable success in the past few years. By leveraging deep convolutional neural networks (CNNs) and other deep learning models, rich feature representations can be effectively extracted from visible (RGB) images for object detection. However, for challenging scenarios such as low-light conditions and haze, the performance of object detection using visible images is often not satisfactory. On the other hand, thermal camera, which is unaffected by lighting conditions, can penetrate through low-light and hazy environments to capture object images. However, infrared images lack edge and texture information of objects. Recognizing the complementary nature of visible and infrared images, researchers have explored the fusion of visible and infrared images for object detection, yielding promising research outcomes. This paper provides an analysis of the current research status of visible and infrared image fusion for object detection. Firstly, the fusion models are categorized into three kinds: pixel-level fusion, feature-level fusion, and decision-level fusion. Several models within each category are discussed. Furthermore, this paper summarizes five datasets that can be utilized for training RGB-Infrared object detection models and compares the experimental results of selected models on the KAIST dataset. Lastly, the paper concludes with a summary of existing challenges in the field and offers some reflections on future directions. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Object detection

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A Digital Decoupling Control Scheme for Synchronous-Frame Current Regulators in High-Speed Motor Drives 26

A Digital Decoupling Control Scheme for Synchronous-Frame Cu...

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26th International Conference on Electrical Machines and Systems, ICEMS 2023

作者： Mon-Nzongo, Daniel L. Lin, Shengyu Ipoum-Ngome, Paul G. Lai, Chunyan Concordia University Department of Electrical and Computer Engineering Canada Fuzhou University College of Electrical Engineering and Automation Fuzhou China Research and Development Department of Peal Electric China

ISBN: (纸本)9798350317589

This paper introduces a digital current regulator with a dynamic decoupling scheme for three-phase AC motor drives that operate under low sampling to fundamental frequency ratios for high-speed motor drives. Cross-coupling terms introduced by the motor and inverter models in synchronous-frame are the main cause of poor dynamic performances. These disturbances cannot be mitigated due to the bandwidth frequency limitation under high-speed operations. This paper introduces a decoupling matrix emulated in the digital domain aiming to dynamically cancel these effects. In addition, two compensation methods are proposed to reduce the discretization due to low sampling ratio. The proposed approach allows the dynamic cancellation of the plant poles that change their location depending on the synchronous speed. The time delay effect of the plant is compensated using the phase-lead compensation method. Simulation results discussed in this paper demonstrated good decoupling performances with improved dynamic responses. © 2023 IEEE.

关键词： Chemical reactions

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