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International Conference on Connected Vehicles and Expo (ICCVE)

作者： Zhe Wang Zhangdui Zhong Minming Ni Chih-Yung Chang State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong University Beijing China Dept. Computer Science and Information Engineering Tamkang University Taiwan

Vehicular Ad Hoc Networks (VANETs) represent promising technologies for comfort driving and entertainment applications which rely heavily on the data downloading. Due to the rapid change of network topology and intermittent connection, it is a big challenge to satisfy the download requirements from multi-vehicles at the same time. This paper proposes a Bus-Based Content Downloading (BBCD) which aims to maximize the volume of downloaded data from bus to vehicles while the download opportunity fairness of each vehicle is taken into consideration. By predicting the number of buses which the vehicle would encounter in its future path and estimating the connection duration that the vehicle stays in the coverage of bus, the proposed BBCD schedules the download service for vehicles slot by slot such that the volume of downloaded data can be guaranteed while achieving the download opportunity fairness. The effectiveness of the proposed algorithm is evaluated by extensive simulations.

关键词： Vehicles Roads Vehicular ad hoc networks Wireless communication Connected vehicles Schedules

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First results of robots crawling on a loose net in micro-gravity during a sounding rocket experiment

First results of robots crawling on a loose net in micro-gra...

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AIAA 57th International Astronautical Congress, IAC 2006

作者： Summerer, Leopold Putz, Bernhard Kopacek, Peter Kaya, Nobuyuki ESA - Advanced Concepts Team Keplerlaan 1 NL-2201AZ Noordwijk Netherlands Vienna University of Technology Institute for Handling Devices and Robotics Favoritenstr. 9-11 A-1040 Vienna Austria University of Kobe Dept. of Computer and Systems Engineering 1-1 Rokkodai Nada 657-8501 Kobe Japan

ISBN: (纸本)9781605600390

Several concepts for the construction of large antennas have been proposed and discussed. This paper presents the first results obtained during a sounding rocket experiment of the Furoshiki-type concept: a large net deployed by spacecraft forms the antenna surface, on which small robots either deploy to perform simple tasks on antenna elements or constitute individual elements of a larger retro-directive phase array antenna. The present paper discusses the trade-off of the robotic options and presents results obtained from robots on the flight experiment.

关键词： Robots

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Leveraging Artificial Intelligence for Enhanced Data Generation in Addressing Imbalance in Binary Classification System

Leveraging Artificial Intelligence for Enhanced Data Generat...

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Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI), IEEE Conference on

作者： Srijita Bandopadhyay Srimonti Dutta Imran Haider Bhavaraju Anuraag Jerry Zhu Saad Ahmed Bazaz Dept. of Electronics and Communication Engineering University of Engg. & Management Kolkata India School of Agro and Rural Technology Indian Institute of Technology Guwahati Guwahati India Dept. of Industrial and Systems Engineering Indian Institute of Technology Kharagpur Kharagpur India Dept. of Electronics and Communication Engineering Vellore Institute of Technology Vellore India Faculty of Mathematics University of Waterloo Ontario Canada Dept. of Computer Science National University of Computer & Emerging Sciences Islamabad Pakistan

This paper delves into the challenges of binary classification using imbalanced datasets, particularly when instances of interest are infrequent. It explores a comprehensive approach that integrates Synthetic Minority Over-sampling Technique (SMOTE), Generative Adversarial Networks (GANs), and Variational Autoencoders (VAEs) to enhance classification outcomes. Traditional classification models tend to favor the majority class, while the impact of imbalanced misclassification costs is often overlooked. The integration of SMOTE, GANs, and VAEs in binary classification, or SMOTE-GAN-VAE, addresses these challenges by generating synthetic instances, refining data representations, and capturing latent features. To evaluate the effectiveness of various data generation methods, a credit card fraud dataset is used. The performance metrics considered include F0.5-score, F1-score, and F2-score, which account for both precision and recall. The results indicate that SMOTE-GAN-VAE outperforms individual methods, such as SMOTE, GANs, and VAEs, demonstrating its potential to enhance data representation and classification accuracy, and outperformed the β- VAE filtered approach employed in previous literature.

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Experimental evaluation of a hybrid MPC strategy for vehicle start-up with an Automated Manual Transmission

Experimental evaluation of a hybrid MPC strategy for vehicle...

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European control Conference (ECC)

作者： Rachid Amari Paolino Tona Mazen Alamir IFP Powertrain Engineering BP3 69390 Vernaison France IFP Powertrain Engineering Vernaison France Gipsa-Lab/ Control Systems Dept. / Sysco-Group Domaine Universitaire France

In this paper, an experimental evaluation is presented for a Model Predictive control (MPC) algorithm controlling powertrains dynamics in vehicles with Automated Manual Transmissions (AMTs). This model based control enables online optimization by using sub-optimal solutions directly linked to the accelerator pedal position. Transmission stability constraints are explicitly handled as well as saturations on the control inputs. This MPC control is tested on line during vehicle start-up in a mild hybrid city car demonstrator equipped with a natural gas engine. A comparison with a PI-based control is made to show the convenience of the proposed MPC control.

关键词： Vehicles Engines Torque Mechanical power transmission Gears control systems Mathematical model

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USDOT Tier-1 University Transportation Center for Advancing Cybersecurity Research and Education

USDOT Tier-1 University Transportation Center for Advancing ...

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Mobility, Operations, Services and Technologies (MOST), IEEE International Conference on

作者： Yunpeng Jack Zhang Kailai Wang Lingguang Song Amaury Lendasse Houbing Herbert Song Zhu Han Rakesh Verma Arlei Silva Carlos Rubio-Medrano Zhixia Richard Li Guohui Zhang Dept. of Information Science Technology University of Houston Houston TX USA Dept. of Construction Management University of Houston Houston TX USA Dept. of Information Systems University of Maryland Baltimore County Baltimore MD USA Dept. of Electrical & Computer Engineering University of Houston Houston TX USA Dept. of Computer Science University of Houston Houston TX USA Dept. of Computer Science Rice University Houston TX USA Dept. of Computer Science Texas A&M University-Corpus Christi Corpus Christi TX USA Dept. of Civil and Architectural Engineering and Construction Management University of Cincinnati Cincinnati OH USA Dept. Civil and Environmental Engineering University of Hawai’i at Mānoa Honolulu HI USA

The Transportation Cybersecurity Center for Advanced Research and Education (CYBER-CARE) is a US Department of Transportation (USDOT) Tier-1 University Transportation Center (UTC) funded in 2023. CYBER-CARE primarily focuses on the USDOT statutory research priority area of “Reducing Transportation Cybersecurity Risks.” CYBER-CARE aims to establish a fundamental knowledge basis and explore advanced theory to mitigate the impacts of large-scale cyberattacks on transportation infrastructure and connected and automated vehicle (CAV) systems. The research projects at CYBER-CARE will develop conceptual frameworks, construct comprehensive datasets, explore novel analytical approaches, support the implementation of public policies and infrastructure investments, and build a high-quality industry workforce through education. All CYBER-CARE research projects can be organized into four thrusts: CAV cybersecurity, transportation data security, advanced traffic management system (ATMS) cybersecurity, and next-generation transportation cybersecurity systems. In addition, CYBER-CARE will accelerate industry collaborations, foster new technologies, and provide professionals with the skills and opportunities needed to become successful leaders in their fields. Notably, as CYBER-CARE will prioritize engagement with underrepresented minorities, these communities stand to benefit from professional development training in transportation cybersecurity.

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Extended Adaptive Data-Rate (X-ADR) Technique for Optimal Resource Allocation in Smart City Applications

Extended Adaptive Data-Rate (X-ADR) Technique for Optimal Re...

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IEEE International Symposium on World of Wireless Mobile and Multimedia Networks (WoWMoM)

作者： Nikumani Choudhury Manik Gupta Moustafa M. Nasralla Satoshi Fujita Dept. of Computer Science and Information Systems Birla Institute of Technology & Science Pilani Hyderabad India Dept. of Communications & Networks Engineering Prince Sultan University Riyadh Saudi Arabia Graduate School of Advanced Science and Engineering Hiroshima University Japan

With the rapid growth of urbanization, the need for sustainable, energy-efficient, and smart solutions for home, industry, governance, traffic, and in general, the need for improved quality of life and health has risen. As an enabling technology, the Internet of Things (IoT) must facilitate several advanced applications with varied QoS requirements for smart cities. In this regard, Long Range (LoRa) technologies can be an ideal communication protocol for resource-constrained IoT devices. The LoRa Wide Area Network (LoRaWAN) defines physical layer options and the medium access control (MAC) sub-layer protocols for facilitating low-power, low-rate communications among battery-operated wireless IoT devices. With the enormous number of IoT devices and growing QoS requirements, it is imperative to optimally allocate resources (bandwidth, spreading factor, and transmit power) to these constrained devices so that network lifetime can be improved. The Adaptive Data-Rate (ADR) technique is used by the Network Servers (NS) to adapt the transmission parameters of the devices optimally. This is based on several received parameters from the end devices as well as the network settings. In this work, we extend the ADR technique to the LoRa gateways to consider the class of LoRa devices and the frequency of transmissions further to extend the energy efficiency and lifespan of IoT devices. Additionally, there is a considerable delay in adapting to optimal settings for the end-devices resulting in excess power dissipation. Further, a novel mechanism is proposed to address the congestion issue in the network using the Spreading Factor parameter (SF).

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Efficient public key cryptosystem resilient to key leakage chosen ciphertext attacks

Efficient public key cryptosystem resilient to key leakage c...

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Cryptographers' Track at the RSA Conference 2013, CT-RSA 2013

作者： Liu, Shengli Weng, Jian Zhao, Yunlei Dept. of Computer Science and Engineering Shanghai Jiao Tong University Shanghai 200240 China Department of Computer Science Emergency Technology Research Center of Risk Evaluation and Prewarning on Public Network Security Jinan University China Software School Fudan University Shanghai 201203 China State Key Laboratory of Information Security Institute of Information Engineering Chinese Academy of Sciences Beijing 100093 China Shanghai Key Laboratory of Scalable Computing and Systems Shanghai China

ISBN: (纸本)9783642360947

Leakage-resilient public key encryption (PKE) schemes are designed to resist "memory attacks", i.e., the adversary recovers the cryptographic key in the memory adaptively, but subject to constraint that the total amount of leaked information about the key is bounded by some parameter λ. Among all the IND-CCA2 leakage-resilient PKE proposals, the leakage-resilient version of the Cramer-Shoup cryptosystem (CS-PKE), referred to as the KL-CS-PKE scheme proposed by Naor and Segev in Crypto09, is the most practical one. But, the key leakage parameter λ and plaintext length m of KL-CS-PKE are subject to λ + m ≤ log q - ω(log κ), where κ is security parameter and q is the prime order of the group on which the scheme is based. Such a dependence between λ and m is undesirable. For example, when λ (resp., m) approaches to log q, m (resp., λ) approaches to 0. In this paper, we designed a new variant of CS-PKE that is resilient to key leakage chosen ciphertext attacks. Our proposal is λ ≤ log q - ω(log κ) leakage-resilient, and the leakage parameter λ is independent of the plaintext space that has the constant size q (exactly the same as that in CS-PKE). The performance of our proposal is almost as efficient as the original CS-PKE. As far as we know, this is the first leakage-resilient CS-type cryptosystem whose plaintext length is independent of the key leakage parameter, and is also the most efficient IND-CCA2 PKE scheme resilient to up to log q - ω(log κ) leakage. © 2013 Springer-Verlag.

关键词： Public key cryptography

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Mobile Robot Manipulator Localization in Indoor Environments for Object Picking Tasks

Mobile Robot Manipulator Localization in Indoor Environments...

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International Conference on control and Robots (ICCR)

作者： Yunfan Xie Goragod Pongthanisorn Shin-ichiro Kaneko Genci Capi Dept. of Mechanical Eng. Graduate School of Science and Engineering Hosei University Tokyo Japan Department of Electrical and Control Systems Engineering National Institute of Technology Toyama College Toyama Japan

ISBN: (数字)9798331518158

ISBN: (纸本)9798331518165

In dynamic and unpredictable work environments such as manufacturing, logistics, and automated warehouses, achieving high-precision self-localization estimation for efficient object picking are critical challenges for mobile robots. Ensuring accuracy and adaptability is essential for their successful operation. This paper presents a new method that integrates RGB cameras with Contrastive Learning (CL) for self-localization estimation and advanced object picking for mobile manipulators. The effectiveness of the proposed method for location estimation in object picking is verified in indoor environments.

关键词： Adaptation models Accuracy Robot vision systems Estimation Contrastive learning Predictive models Manipulators Robustness Indoor environment Mobile robots

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Enhancing Robot Self-Localization Accuracy and Robustness: A Variational Autoencoder-Based Approach

Enhancing Robot Self-Localization Accuracy and Robustness: A...

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International Conference on control and Robots (ICCR)

作者： Asuma Abe Goragod Pongthanisorn Shin-ichiro Kaneko Genci Capi Dept. of Mechanical Eng. Graduate School of Science and Engineering Hosei University Tokyo Japan Department of Electrical and Control Systems Engineering National Institute of Technology Toyama College Toyama Japan

Self-localization is a crucial task for robots, demanding high accuracy. In this work, we propose a new robot localization method based on the Variational Autoencoder (VAE). In our method, the robot utilizes the captured image to generate robot localization in indoor environments. The utilization of VAE makes the system adaptive to varying environmental conditions. Our findings demonstrate that utilizing both the robot's coordinates and images as training data significantly enhances the accuracy of robot self-localization estimation and improves the robustness of the system due to sensor data noise.

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Feedback Optimizing MPC for Load Frequency control and Economic Dispatch

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IFAC-PapersOnLine 2023年第2期56卷 10935-10940页

作者： Amba Asuk Paul Trodden National Grid ESO Faraday House Warwick CV34 6DA UK Dept. of Automatic Control & Systems Engineering University of Sheffield S1 3JD UK

With increasing renewable generation, demand response, and deregulation, power networks are becoming more uncertain, time-varying, and strongly coupled. As a result, the conventional approach of performing separate economic dispatch (ED) and load-frequency control (LFC) operations may no longer guarantee smooth and cost-efficient regulation of frequency across interconnected power networks. To address this, we present a tracking model predictive control (MPC) algorithm which simultaneously achieves economic dispatch and secondary frequency control in a multi-area power network. A unique feature of the proposed algorithm is that it exploits the implicit feedback in MPC to regulate the interconnected power system towards steady-state equilibria that solve a multi-area economic dispatch problem, without explicitly computing the latter as a reference to be followed or estimating the unknown disturbances. This feedback-based optimization approach endows the algorithm with inherent robustness to uncertainty (such as unknown step changes in the demand). Simulation results for a two-area power network show improved steady-state economic performance compared to standard MPC-based frequency control schemes, and better dynamic performance compared to other feedback-based optimization schemes.

关键词： control and optimization of power systems Renewable energy sources Smart grids control of energy storage systems Power system planning and operation

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