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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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Optical Memory Elements Based on Hetero-nipi Superlattice Asymmetric Cavity Spatial Light Modulators

Optical Memory Elements Based on Hetero-nipi Superlattice As...

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Spatial Light Modulators and Applications, SLM 1993

作者： Hibbs-Brenner, M.K. Mukherjee, S.D. Lehman, J. Ruden, P.P. Liu, J.J. Sawchuk, A.A. Hsu, W.-F. Honeywell Systems and Research Center 10701 Lyndale Avenue South BloomingtonMN55420 United States Dept. of Electrical Engineering and Computer Science University of Minnesota MinneapolisMN55455 United States Signal and Image Processing Institute University of Southern California Los AngelesCA90089-2564 United States

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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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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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Attribute Weighting: How and When Does It Work for Bayesian Network Classification

Attribute Weighting: How and When Does It Work for Bayesian ...

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International Joint Conference on Neural Networks

作者： Jia Wu Zhihua Cai Shirui Pan Xingquan Zhu Chengqi Zhang Centre for Quantum Computation & Intelligent Systems University of Technology Sydney Australia Department of Computer Science China University of Geosciences Wuhan China Dept. of Computer and Electrical Engineering & Computer Science Florida Atlantic University USA

A Bayesian Network (BN) is a graphical model which can be used to represent conditional dependency between random variables, such as diseases and symptoms. A Bayesian Network Classifier (BNC) uses BN to characterize the relation-ships between attributes and the class labels, where a simplified approach is to employ a conditional independence assumption between attributes and the corresponding class labels, i.e., the Naive Bayes (NB) classification model. One major approach to mitigate NB's primary weakness (the conditional independence assumption) is the attribute weighting, and this type of approach has been proved to be effective for NB with simple structure. However, for weighted BNCs involving complex structures, in which attribute weighting is embedded into the model, there is no existing study on whether the weighting will work for complex BNCs and how effective it will impact on the learning of a given task. In this paper, we first survey several complex structure models for BNCs, and then carry out experimental studies to investigate the effectiveness of the attribute weighting strategies for complex BNCs, with a focus on Hidden Naive Bayes (HNB) and Averaged One-Dependence Estimation (AODE). Our studies use classification accuracy (ACC), area under the ROC curve ranking (AUC), and conditional log likelihood (CLL), as the performance metrics. Experiments and comparisons on 36 benchmark data sets demonstrate that attribute weighting technologies just slightly outperforms unweighted complex BNCs with respect to the ACC and AUC, but significant improvement can be observed using CLL.

关键词： Niobium Bayes methods Training Mutual information Estimation Educational institutions Annealing

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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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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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Nonlinear dynamic modelling of flexible beam structures using neural networks

Nonlinear dynamic modelling of flexible beam structures usin...

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International Conference on Mechatronics (ICM)

作者： S.Z.M. Hashim M.O. Tokhi I.Z.M. Darus Department of Automatic Control and Systems Engineering University of Sheffield Sheffield UK The University of Sheffield Sheffield Sheffield GB Dept. of Autom. Control & Syst. Eng. Univ. of Sheffield UK

This paper investigates the utilisation of back propagation neural networks (NNs) for modelling flexible beam structures in fixed-free mode; a simple representation of an aircraft wing or robot arm. A comparative performance of the NN model and conventional recursive least square scheme, in characterising the system is carried out in the time and frequency domains. Simulated results demonstrate that using NN approach the system is modelled well than with the conventional linear modelling approach. The developed neuro-modelling approach would further be utilized in the design and implementation of suitable controllers, for vibration suppression in such system.

关键词： Neural networks System identification Robots Least squares methods control systems Predictive models Error correction Testing systems engineering and theory Frequency domain analysis

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Data Selection Using Genetic Algorithm to Improve Transfer Learning Efficiency in Brain-Machine-Interface systems

Data Selection Using Genetic Algorithm to Improve Transfer L...

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International Conference on control and Robots (ICCR)

作者： Shuichi Suguro 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

Data selection has a great effect on the performance of trained Convolutional Neural Networks (CNNs) for Brain Machine Interface systems. In this paper, we combine transfer learning and Genetic Algorithms to optimize the training data of CNNs. We implement transfer learning between different subjects. The data selected by GA to improve the performance of the CNN after transfer learning can be used to compare the similarity of the brain activity between subjects. The results show that brain data selected by GA improves the CNN recognition accuracy and reduces the training time. In addition, the performance of trained CNN using transfer learning on different days shows that although the performance deteriorates still it is acceptable.

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