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6th EAI International Conference on Intelligent Transport Systems, INTSYS 2022

作者： Kolaitis, Angelos Alexandridis, Georgios Adam, Panagiota Alexandridis, Petros Chasanis, Grigoris Breton, Fabrice Artificial Intelligence and Learning Systems Laboratory School of Electrical and Computer Engineering National Technical University of Athens Zografou157 80 Greece Extrabit L.P. 97 Nikolaou Plastira str Egaleo122 42 Greece Olympia Odos S.A. 4 Rizareiou str Chalandri152 33 Greece

ISBN: (纸本)9783031308543

Highway operators are in a constant search of techniques and methodologies that can reduce their energy footprint. In this respect, the installation of dimmable light-emitting diode lights on the open road section of highways appears to be a promising solution, due to the reduced energy consumption (compared to high pressure sodium lamps) and the ability to adjust their brightness at various levels, based on the road’s traffic load. However, setting the desired level of light intensity cannot be performed instantly, due to safety and contractual reasons that a highway operator must follow. For this reason, an adaptive and intelligent system is proposed in this work, that models traffic load and is able to predict its future trend, based on current load and light intensity measurements. In this way, the unnecessary use of the lighting equipment is avoided, as brightness is dropped to a minimum level when traffic load is predicted to be low. The proposed model is based on recurrent neural networks and more specifically on long short-term memory cells that are able to model complex dependencies in data with temporal correlations, like traffic load measurements. The overall approach is evaluated on relevant data provided by Olympia Odos S.A. that operates the Elefsina-Korinthos-Patra highway in Greece, with promising results. © 2023, ICST Institute for computer Sciences, Social Informatics and Telecommunications engineering.

关键词： Luminance

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FEMD: Feature Enhancement-aided Multimodal Feature Fusion Approach for Smart Contract Vulnerability Detection

FEMD: Feature Enhancement-aided Multimodal Feature Fusion Ap...

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International Conference on Parallel and Distributed Systems (ICPADS)

作者： Juan Li Rui Li Youshui Lu Bowen Cai Yulin Cao Hang Lin Xingxing Li Chan Li School of Computer Science Qinghai Normal University Xining China School of Computer Science and Technology Xidian University The State Key Laboratory of Tibetan Intelligent Information Processing and Application School of Computer Science Qinghai Normal University Xian China School of the Instrument Science and Technology and School of Electrical Engineering Xi’an Jiaotong University Xian China School of Science and Engineering University of Minnesota - Twin City Minneapolis USA

ISBN: (数字)9798331515966

ISBN: (纸本)9798331515973

Smart contracts, due to their immutability and transparency upon deployment, entail significant economic and systemic risks from any vulnerabilities present. Traditional vulnerability detection methods suffer from low automation and high false positive rates, while existing deep learning-based approaches inadequately extract contract features, thereby limiting detection accuracy. To address these issues, this paper proposes FEMD: a feature-enhanced aided multimodal feature fusion method for smart contract vulnerability detection. Building on the foundation of addressing the low automation of traditional detection tools, our method improves the model’s feature extraction performance and detection capabilities. Specifically, we construct a contract graph through Comprehensive Expert-Graph Fusion, combining multi-modal feature fusion using a multi-head attention mechanism with expert patterns to ensure the capture and effective preservation of all critical information during the fusion process. To delve deeper into potential information within smart contract graphs, we employ a Feature Enhancer that leverages transpose operations on feature matrices to extract complex interaction patterns across different dimensions. Our approach is validated through batches of experiments on the Ethereum open dataset focusing on reentrancy and timestamp dependency vulnerabilities, demonstrating significant improvements in detection accuracy and robustness.

关键词： Economics Automation Accuracy Limiting Smart contracts Buildings Focusing Feature extraction Robustness Data mining

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Multi-Physics Modeling and Simulation of Oil-Immersed Power Transformers Based on 3D Finite Element Analysis and Finite Volume Method

Multi-Physics Modeling and Simulation of Oil-Immersed Power ...

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2022 IEEE Industry Applications Society Annual Meeting, IAS 2022

作者： Ilka, Reza He, JiangBiao Yin, Weijun Contreras, Jose E. Cavazos, Carlos G. University of Kentucky Electrical and Computer Engineering LexingtonKY United States Ge Research Dielectrics and Insulation Group NiskayunaNY United States Prolec Ge Applied Research Center Insulating Materials Laboratory Nuevo Leon Apodaca Mexico

ISBN: (数字)9781665478151

ISBN: (纸本)9781665478151

Power transformers are the essential components in almost every electric power network. Uninterrupted operation of power transformers plays a critical role in guaranteeing the reliability and safety of the power grid. In this paper, aiming at predicting the reliability of large power transformers, multi-physics modeling and simulations are carried out based on three-dimensional (3D) finite element analysis (FEA) and finite volume method (FVM). Specifically, FEA electromagnetic modeling and simulation is performed in Ansys Maxwell to extract the transformer winding losses. Afterwards, thermal model is established in Ansys Fluent to obtain the temperature distribution, and more importantly to identify the transformer winding hot-spot temperature (HST). Accordingly, aging acceleration factor is determined by the winding HST. A sensitivity analysis is also conducted to determine the effects of oil properties on the temperature distribution and HST. © 2022 IEEE.

关键词： Finite element method

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Scalable single-mode lasing from open-Dirac cavities

Scalable single-mode lasing from open-Dirac cavities

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Laser Science, LS 2022

作者： Contractor, R. Noh, W. Redjem, W. Qarony, W. Martin, E. Dhuey, Scott Schwartzberg, Adam Kante, B. Department of Electrical Engineering and Computer Sciences University of California Berkeley California 94720 USA Materials Sciences Division Lawrence Berkeley National Laboratory 1 Cyclotron Road Berkeley California 94720 USA Molecular Foundry Lawrence Berkeley National Laboratory Berkeley California 94720 USA

We experimentally demonstrate Berkeley surface-emitting lasers (BerkSELs) from open-Dirac cavities. BerkSELs exploit symmetry-dependent scaling of losses in cavities with a linear dispersion and remain single-mode eve...

ISBN: (纸本)9781557528209

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Evolutionary Competitive Multiobjective Multitasking: One-Pass Optimization of Heterogeneous Pareto Solutions

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IEEE Transactions on Evolutionary Computation 2024年

作者： Li, Yanchi Wu, Xinyi Gong, Wenyin Xu, Meng Wang, Yubo Gu, Qiong China University of Geosciences School of Computer Wuhan430074 China Nanyang Technological University College of Computing and Data Science 639798 Singapore Hubei University of Arts and Science School of Computer Engineering Xiangyang441053 China Hubei University of Arts and Science Hubei Key Laboratory of Power System Design and Test for Electrical Vehicle Xiangyang441053 China

Competitive multiobjective multitask optimization (CMO-MTO) problems involve multiple tasks with comparable objectives but heterogeneous decision variables. The final Pareto front in CMO-MTO consists of multiple subsets corresponding to different tasks. Since the Pareto front subset of one task may be dominated by that of another, competition arises among tasks. Additionally, there may be exploitable similarities among tasks that evolutionary multitasking methods can leverage. For a comprehensive study of CMO-MTO, we construct 12 benchmark CMO-MTO problems with varied competitive relationships and inter-task similarities. To effectively solve CMO-MTO problems, we propose a reference vector contribution-based multitask evolutionary algorithm (RVC-MTEA). RVC-MTEA facilitates both global and local knowledge transfer based on vector contributions and integrates global archives to gather non-dominated solutions across multiple competitive tasks. Comparative results with four popular single-task and six state-of-the-art multitask evolutionary algorithms demonstrate the efficacy of RVC-MTEA. Finally, we apply RVC-MTEA to several real-world applications, showcasing the potential of CMO-MTO in practical decision-making scenarios. © 1997-2012 IEEE.

关键词： Competition

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A cross Transformer for image denoising

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Information Fusion 2024年 102卷

作者： Tian, Chunwei Zheng, Menghua Zuo, Wangmeng Zhang, Shichao Zhang, Yanning Lin, Chia-Wen School of Software Northwestern Polytechnical University Xi'anShaanxi710129 China National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology Xi'anShaanxi710129 China School of Computer Science and Technology Harbin Institute of Technology Heilongjiang Harbin150001 China Peng Cheng Laboratory Guangdong Shenzhen518055 China School of Computer Science and Engineering Central South University Hunan Changsha410083 China School of Computer Science Northwestern Polytechnical University Shaanxi Xi'an710129 China Department of Electrical Engineering and the Institute of Communications Engineering National Tsing Hua University Hsinchu30013 Taiwan

Deep convolutional neural networks (CNNs) depend on feedforward and feedback ways to obtain good performance in image denoising. However, how to obtain effective structural information via CNNs to efficiently represent given noisy images is key for complex scenes. In this paper, we propose a cross Transformer denoising CNN (CTNet) with a serial block (SB), a parallel block (PB), and a residual block (RB) to obtain clean images for complex scenes. A SB uses an enhanced residual architecture to deeply search structural information for image denoising. To avoid loss of key information, PB uses three heterogeneous networks to implement multiple interactions of multi-level features to broadly search for extra information for improving the adaptability of an obtained denoiser for complex scenes. Also, to improve denoising performance, Transformer mechanisms are embedded into the SB and PB to extract complementary salient features for effectively removing noise in terms of pixel relations. Finally, a RB is applied to acquire clean images. Experiments illustrate that our CTNet is superior to some popular denoising methods in terms of real and synthetic image denoising. It is suitable to mobile digital devices, i.e., phones. Codes can be obtained at https://***/hellloxiaotian/CTNet. © 2023 Elsevier B.V.

关键词： Image denoising

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3D ferroelectric phase field simulations of polycrystalline multiphase hafnia and zirconia based ultra-thin films

arXiv

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arXiv 2024年

作者： Kumar, Prabhat Hoffmann, Michael Nonaka, Andy Salahuddin, Sayeef Yao, Zhi Applied Mathematics and Computational Research Division Lawrence Berkeley National Laboratory CA94720 United States Department of Electrical Engineering and Computer Sciences University of California BerkeleyCA94720 United States

HfO2- and ZrO2-based ferroelectric thin films have emerged as promising candidates for the gate oxides of next generation electronic devices. Recent work has experimentally demonstrated that a tetragonal/orthorhombic (t/o-) phase mixture with partially in-plane polarization can lead to negative capacitance (NC) stabilization. However, there is a discrepancy between experiments and the theoretical understanding of domain formation and domain wall motion in these multi-phase, polycrystalline materials. Furthermore, the effect of anisotropic domain wall coupling on NC has not been studied so far. Here we apply 3D phase field simulations of HfO2- and ZrO2-based mixed-phase ultra-thin films on silicon to understand the necessary and beneficial conditions for NC stabilization. We find that smaller ferroelectric grains and a larger angle of the polar axis with respect to the out-of-plane direction enhances the NC effect. Furthermore, we show that theoretically predicted negative domain wall coupling even along only one axis prevents NC stabilization. Therefore, we conclude that topological domain walls play a critical role in experimentally observed NC phenomena in HfO2- and ZrO2-based ferroelectrics. © 2024, CC BY.

关键词： Stabilization

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Digital Tavis-Cummings Simulation on Superconducting Quantum Hardware with Error Mitigation

Digital Tavis-Cummings Simulation on Superconducting Quantum...

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Optica Quantum 2.0 Conference and Exhibition, Quantum 2.0

作者： Marinelli, Brian Rubin, Alex H. Norman, Victoria A. Rizvi, Zainab Naik, Ravi Santiago, David I. Spitzer, Christopher Kreikebaum, John Mark Krstic-Marinkovic, Marina Siddiqi, Irfan Radulaski, Marina Quantum Nanoelectronics Laboratory Department of Physics University of California at Berkeley BerkeleyCA94720 United States Computational Research Division Lawrence Berkeley National Laboratory BerkeleyCA94720 United States Department of Electrical and Computer Engineering University of California DavisCA95616 United States Department of Physics University of California DavisCA95616 United States Department of Computer Science University of California DavisCA95616 United States Materials Science Division Lawrence Berkeley National Laboratory BerkeleyCA94720 United States Institute for Theoretical Physics ETH Zurich Zurich8093 Switzerland

We explore digital quantum simulation of the dynamics of N emitters coupled to an optical cavity (Tavis-Cummings model) on superconducting quantum hardware and successfully mitigate errors using randomized compiling a... 详细信息

ISBN: (纸本)9781957171272

关键词： Cavity quantum electrodynamics Crystalline materials Defect center materials Photonic devices Quantum technology Two level systems

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Bayesian Optimization of Double-SAC Robotic Arm Path Planning Using Recent Prioritized Experience Replay

Bayesian Optimization of Double-SAC Robotic Arm Path Plannin...

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WRC Symposium on Advanced Robotics and Automation (WRC SARA)

作者： Shuai Yuan Ke Wen Feng Zhang Jianqiang Xiong School of Computer Science and Engineering Shenyang Jianzhu University Liaoning Province Big Data Management and Analysis Laboratory of Urban Construction Shenyang Branch of National Special Computer Engineering Technology Research Center Shengyang China School of Electrical and Control Engineering Shenyang Jianzhu University Shengyang China

As for the path planning of robotic arms, Deep Reinforcement Learning (DRL) algorithms have recently been developed. However, the high-dimensional action space and state space will result in reward sparsity and low training efficiency of robotic arm path planning. We propose a Bayesian optimization of Double-SAC algorithm using recent priority experience replay to solve these issues. We first propose the sampling method of recent prioritized experience replay (RPER), which combines recent sampling and prioritized experience replay (PER). The interval of experience pool is divided according to the recent sampling method, and sampling is performed according to the sample priority of the interval. Then we adopt a Bayesian optimization method that reduces the variance of the random strategy and improve the quality of the sampled actions. The larger variance is more beneficial for exploring the environment. Smaller variance is advantageous for exploiting good experiences to obtain higher returns. Our approach finds a good balance between exploration and exploitation. We train two Soft Actor Critic (SAC) agents to obtain two independent probability distributions, and a Bayesian optimizer is adopted to combine the two distributions to design a hybrid strategy. The hybrid strategy can output better actions according to the uncertainty estimation of the distribution, and further enhance training efficiency and stability. Finally, simulation tests are performed on the ROS-Gazebo platform, and the simulation results illustrate that the algorithm proposed in this paper converges faster and has a shorter training time than the original algorithm in terms of path planning performance.

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Joint Transceiver Optimization for MmWave/THz MU-MIMO ISAC Systems

arXiv

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arXiv 2024年

作者： Wang, Peilan Fang, Jun Zeng, Xianlong Chen, Zhi Li, Hongbin National Key Laboratory of Wireless Communications University of Electronic Science and Technology of China Chengdu611731 China The Department of Electrical and Computer Engineering Stevens Institute of Technology HobokenNJ07030 United States

In this paper, we consider the problem of joint transceiver design for millimeter wave (mmWave)/Terahertz (THz) multi-user MIMO integrated sensing and communication (ISAC) systems. Such a problem is formulated into a nonconvex optimization problem, with the objective of maximizing a weighted sum of communication users’ rates and the passive radar’s signal-to-clutter-and-noise-ratio (SCNR). By exploring a low-dimensional subspace property of the optimal precoder, a low-complexity block-coordinate-descent (BCD)-based algorithm is proposed. Our analysis reveals that the hybrid analog/digital beamforming structure can attain the same performance as that of a fully digital precoder, provided that the number of radio frequency (RF) chains is no less than the number of resolvable signal paths. Also, through expressing the precoder as a sum of a communication-precoder and a sensing-precoder, we develop an analytical solution to the joint transceiver design problem by generalizing the idea of block-diagonalization (BD) to the ISAC system. Simulation results show that with a proper tradeoff parameter, the proposed methods can achieve a decent compromise between communication and sensing, where the performance of each communication/sensing task experiences only a mild performance loss as compared with the performance attained by optimizing exclusively for a single task. Copyright © 2024, The Authors. All rights reserved.

关键词： Millimeter waves

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