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arXiv 2024年

作者： Wu, Yinyu Zhang, Xuhui Ren, Jinke Shen, Yanyan Yang, Bo Wang, Shuqiang Guan, Xinping Niyato, Dusit The Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Guangdong518055 China The University of Chinese Academy of Sciences Beijing100049 China The Shenzhen Future Network of Intelligence Institute The School of Science and Engineering The Guangdong Provincial Key Laboratory of Future Networks of Intelligence The Chinese University of Hong Kong Guangdong Shenzhen518172 China Shenzhen University of Advanced Technology Guangdong518055 China The Department of Automation The Key Laboratory of System Control and Information Processing Ministry of Education Shanghai Jiao Tong University Shanghai200240 China The College of Computing and Data Science Nanyang Technological University Singapore639798 Singapore

Intelligent reflecting surface (IRS)-assisted mobile edge computing (MEC) systems have shown notable improvements in efficiency, such as reduced latency, higher data rates, and better energy efficiency. However, the resource competition among users will lead to uneven allocation, increased latency, and lower throughput. Fortunately, the rate-splitting multiple access (RSMA) technique has emerged as a promising solution for managing interference and optimizing resource allocation in MEC systems. This paper studies an IRS-assisted MEC system with RSMA, aiming to jointly optimize the passive beamforming of the IRS, the active beamforming of the base station, the task offloading allocation, the transmit power of users, the ratios of public and private information allocation, and the decoding order of the RSMA to minimize the average delay from a novel uplink transmission perspective. Since the formulated problem is non-convex and the optimization variables are highly coupled, we propose a hierarchical deep reinforcement learning-based algorithm to optimize both continuous and discrete variables of the problem. Additionally, to better extract channel features, we design a novel network architecture within the policy and evaluation networks of the proposed algorithm, combining convolutional neural networks and densely connected convolutional network for feature extraction. Simulation results indicate that the proposed algorithm not only exhibits excellent convergence performance but also outperforms various benchmarks. Copyright © 2024, The Authors. All rights reserved.

关键词： Deep reinforcement learning

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Spatially Extended Sound Source Reconstruction Based on the Non-Synchronous Measurements of Microphone Arrays and Bayesian Compressive Sensing

SSRN

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SSRN 2023年

作者： Yu, Su Zhang, Chenyu Hu, Dingyu Yu, Liang School of Urban Rail Transportation Shanghai University of Engineering Science Shanghai201620 China College of Power and Energy EngineeringHarbin Engineering University Harbin150006 China Shanghai Engineering Research Center of Railway Noise and Vibration Control Shanghai201620 China Institute of Vibration Shock and Noi1se State Key Laboratory of Mechanical System and Vibration Shanghai Jiao Tong University Shanghai200240 China School of Civil Aviation Northwestern Polytechnical University Xi’an710072 China

The traditional near-field acoustic holography technology (NAH) needs to adapt to the Nyquist sampling theorem, which requires numerous sampling points to be arranged on the holography plane to obtain accurate sound source reconstruction results. The Non-synchronous Measurements (NSM) was proposed to enhance the accuracy of reconstruction and reduce the measurement cost. However, the investigated object of conventional NSM are only sparsely distributed sound sources, and limited attention is paid to the reconstructed accuracy of the spatially extended sound source. In this article, the traditional NSM is further investigated in the spatially extended sound source, and the sound pressure information is reconstructed by Bayesian compressive sensing algorithm (BCS). Firstly, the spatially extended sound source is scanned by moving the rectangular microphone array, and the measurement effects of the microphone array with large aperture and high density are approximately obtained by using NSM technique. Then, BCS is applied to reconstruct sound field by the NSM of sound pressure. The results of numerical simulations demonstrate that the proposed study is applicable to the spatially extended sound source. NSM has advantages in reconstruction accuracy compared with the single measurement. At the same time, the reconstructed sound pressure under NSM can replace the simultaneous measurement(SM) results of multi-array without reference microphone, which avoids the limitation of using reference microphone in increasing the number of channels and spatial layout. The effectiveness and feasibility of the investigation are further proved by the experimental results of the driven steel plate. © 2023, The Authors. All rights reserved.

关键词： Compressed sensing

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A moving target detection and localization strategy based on optical flow and pin-hole imaging methods using monocular vision

A moving target detection and localization strategy based on...

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2021 IEEE International Conference on Real-Time Computing and Robotics, RCAR 2021

作者： Wang, Shun Guo, Qingqiang Xu, Sheng Su, Dan School of Control Science and Engineering Shandong University Shandong Jinan250061 China Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen Institute of Advanced Technology Chinese Academy of Sciences CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institute of Advanced Technology Shenzhen518055 China Shandong Institute of Advanced Technology Chinese Academy of Sciences Shandong Jinan250102 China Orbbec Inc. Shenzhen518057 China

ISBN: (纸本)9781665436786

This paper proposes a new strategy for moving target detection and localization based on monocular vision. Firstly, to detect a moving target with large displacement and high speed accurately, two consecutive video images captured by a monocular camera are preprocessed using the enhancement and denoising methods. Then, the optical flow representing motion information is calculated iteratively by the modified Lucas-Kanade optical flow method. Secondly, a new interest region extraction method is developed to overcome the negative impacts caused by the noises in the background. Specifically, this proposed method combines a two-level image segmentation strategy from coarse to fine, including median filtering, two-direction dynamic threshold segmentation, the Otsu method, and morphological processing. Thirdly, a low computational cost target localization algorithm is proposed based on pin-hole imaging theory. Besides, it only uses two-dimensional image and camera parameters to obtain the moving target's position in the three-dimensional space. Finally, experimental results show that the proposed strategy can effectively eliminate noise interferences and realize moving target detection, extraction, and localization. © 2021 IEEE.

关键词： Image segmentation

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A Risk-Averse Just-In-Time Scheme for Learning-Based Operation of Microgrids with Coupled Electricity-Hydrogen-Ammonia under Uncertainties

arXiv

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arXiv 2024年

作者： Li, Longyan Ning, Chao Pan, Guangsheng Zhang, Leiqi Gu, Wei Zhao, Liang Du, Wenli Shahidehpour, Mohammad Department of Automation Shanghai Jiao Tong University Shanghai200240 China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China School of Electrical Engineering Southeast University Nanjing210096 China Zhejiang Key Laboratory of Distributed Generations and Microgrid Technology State Grid Zhejiang Electric Power Research Institute Hangzhou310014 China Key Laboratory of Smart Manufacturing in Energy Chemical Process Ministry of Education East China University of Science and Technology China Department of Electrical and Computer Engineering Illinois Institute of Technology ChicagoIL60616 United States

This paper proposes a Risk-Averse Just-In-Time (RAJIT) operation scheme for Ammonia-Hydrogen-based Micro-Grids (AHMGs) to boost electricity-hydrogen-ammonia coupling under uncertainties. First, an off-grid AHMG model is developed, featuring a novel multi-mode ammonia synthesis process and a hydrogen-ammonia dual gas turbine with tunable feed-in ratios. Subsequently, a state-behavior mapping strategy linking hydrogen storage levels with the operation modes of ammonia synthesis is established to prevent cost-ineffective shutdowns. The proposed model substantially improves operational flexibility but results in a challenging nonlinear fractional program. Based upon this model, a data-driven RAJIT scheme is developed for the real-time rolling optimization of AHMGs. Unlike conventional one-size-fits-all schemes using one optimization method throughout, the data-driven RAJIT intelligently switches between cost-effective deterministic optimization and risk-averse online-learning distributionally robust optimization depending on actual risk profiles, thus capitalizing on the respective strengths of these two optimization methods. To facilitate the solution of the resulting nonlinear program, we develop an equivalent-reformulation-based solution methodology by leveraging a constraint-tightening technique. Numerical simulations demonstrate that the proposed scheme guarantees safety and yields an overall cost reduction up to 14.6% compared with several state-of-the-art methods. © 2024, CC BY.

关键词： Ammonia

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Expression of Concern for: Fog-Cloud-IoT Enabled WSN Architecture

Expression of Concern for: Fog-Cloud-IoT Enabled WSN Archite...

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Intelligent engineering and Management (ICIEM), International Conference on

作者： P Anitha Christy Angelin. P.V Samuel Devakumar. Liwa H. Al-Farhani Gandam Vijay Kumar Shaurya Deep Shoaib Mohammad PSNA College of Engineering and Technology Dindigul Tamilnadu India Karunya Institute of Technology and Sciences Coimbatore Tamilnadu India System Analysis Control and Information Processing Academy of Engineering RUDN University Moscow Russia Computer Science and Engineering Career Point University Rajasthan India IIMT College of Management Greater Noida India Department of Law IMS Unison University Dehradun Uttarakhand India

ISBN: (数字)9781665467568

ISBN: (纸本)9781665467575

Remote Because of the exciting innovation it introduces, the WSN likely to be a subject of research in recent time. Because of the impromptu distant connections, adaptability, and simplicity of execution, this has all the makings of becoming the most maintainable innovation for ecological detecting, whether it's about limited or huge scope watching. In any case, the main drawbacks stem from the limited capacity, processing, and accessibility of organization centers for information. Virtual machines resources have been adopted to overcome such constraints, devices to expanded capacity, computation, and easy-to-understand availability. This was a natural progression from traditional WSN designs among pattern in recent theories evolved along advent of Internet-of-Things advancements. Against the growing popularity of WSN associated cloud checking frameworks, still the issues are detected because of the distributed comp ting's disadvantages, like dormancy with the capacity charges. In this paper, the advancements achieved to a fog-cloud-IoT associated with WSN architecture of putting a bit of computing at the network part are elaborated, a process, which follows the smart Fog associated with Cloud-computing concept of dissecting also following up on IoT data. A similar investigation was carried out to illustrate the improvements made possible by processing at the organization's edge.

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S4TP: Social-Suitable and Safety-Sensitive Trajectory Planning for Autonomous Vehicles

arXiv

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arXiv 2024年

作者： Wang, Xiao Tang, Ke Dai, Xingyuan Xu, Jintao Du, Quancheng Ai, Rui Wang, Yuxiao Gu, Weihao Engineering Research Center of Autonomous Unmanned System Technology Ministry of Education Anhui University Hefei230031 China Haomo Technology Co. Ltd. Beijing100192 China The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China Qingdao Academy of Intelligent Industries Qingdao266114 China The School of Computer and Communication Engineering University of Science and Technology Beijing Beijing100083 China The School of Artificial Intelligence University of Chinese Academy of Sciences Beijing100049 China

In public roads, autonomous vehicles (AVs) face the challenge of frequent interactions with human-driven vehicles (HDVs), which render uncertain driving behavior due to varying social characteristics among humans. To effectively assess the risks prevailing in the vicinity of AVs in social interactive traffic scenarios and achieve safe autonomous driving, this article proposes a social-suitable and safety-sensitive trajectory planning (S4TP) framework. Specifically, S4TP integrates the Social-Aware Trajectory Prediction (SATP) and Social-Aware Driving Risk Field (SADRF) modules. SATP utilizes Transformers to effectively encode the driving scene and incorporates an AV’s planned trajectory during the prediction decoding process. SADRF assesses the expected surrounding risk degrees during AVs-HDVs interactions, each with different social characteristics, visualized as two-dimensional heat maps centered on the AV. SADRF models the driving intentions of the surrounding HDVs and predicts trajectories based on the representation of vehicular interactions. S4TP employs an optimization-based approach for motion planning, utilizing the predicted HDVs’ trajectories as input. With the integration of SADRF, S4TP executes real-time online optimization of the planned trajectory of AV within low-risk regions, thus improving the safety and the interpretability of the planned trajectory. We have conducted comprehensive tests of the proposed method using the SMARTS simulator. Experimental results in complex social scenarios, such as unprotected left-turn intersections, merging, cruising, and overtaking, validate the superiority of our proposed S4TP in terms of safety and rationality. S4TP achieves a pass rate of 100% across all scenarios, surpassing the current state-of-the-art methods Fanta of 98.25% and Predictive-Decision of 94.75%. Copyright © 2024, The Authors. All rights reserved.

关键词： Autonomous vehicles

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Application of the High-Q Bouncing Ball Model Based on Fabry-Pérot Cavity for Alcohol Gas Concentration Detection

SSRN

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SSRN 2023年

作者： Sang, Luxiao Wang, Teng Zhao, Biao Ding, Jinyu Jian, Aoqun Sang, Shengbo Shanxi Key Laboratory of Micro Nano Sensors & Artificial Intelligence Perception College of Electronic Information and Optical Engineering Taiyuan University of Technology Taiyuan030024 China Key Lab of Advanced Transducers and Intelligent Control System Ministry of Education Taiyuan University of Technology Taiyuan030024 China Shanxi Research Institute of 6D Artificial Intelligence Biomedical Science Taiyuan030031 China

This paper presents a method to enhance the Q factor of Fabry-Pérot cavities using a vertical dielectric interface (DI) made of silicon and air, which generates a bouncing ball model. The method is applied to detect alcohol gas concentrations with high resolution. Specifically, we introduce a 450 µm-thick silicon wafer into the Fabry-Pérot (FP) cavity, where it is positioned at an angle of inclination. This addition considerably augments the light-matter interaction, effectively confining the light within the cavity. The Q factor of the FP cavity with DI is always higher than that of the open FP cavity in the inclination angle range of 0 to 1°. Moreover, the effective mode area of the FP cavity with DI is smaller. Furthermore, the FP cavity with DI reduces the effective mode area and diminishes the lateral walk-off loss experienced in the open FP cavity. Finally, we experimentally verified the effectiveness of this method by detecting alcohol gas concentrations ranging from 28.5 ppm to 142.5 ppm and achieving a Q factor improvement from 5.9×104 to 1.2×105. The system sensitivity was calculated to be 0.5294 pm/ppm, and the resolution obtained was 28.5 ppm. © 2023, The Authors. All rights reserved.

关键词： Silicon wafers

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Influence of Electrical Tree Aging on the Dielectric and Trap Properties of XLPE Insulation 4

Influence of Electrical Tree Aging on the Dielectric and Tra...

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4th IEEE China International Electrical and Energy Conference, CIEEC 2021

作者： Xie, Kun Ren, Chengyan Lin, Tiandi Zhang, Cheng Zhang, Wei Hu, Libin Chen, Jie Hubei University of Technology Hubei Key Lab. for High-Efficiency Utiliz. of Solar Ener. and Oper. Control of Energy Storage System Wuhan China Chinese Academy of Sciences Beijing International St Cooperation Base for Plasma Science and Energy Conversion Institute of Electrical Engineering Beijing China State Grid Jiangsu Electric Power Co. Ltd. Research Institute Nanjing China

ISBN: (纸本)9781728171494

The dielectric and charge properties of XLPE insulation with different service periods are important to estimate the aging state of high voltage cables. The accelerated aging experiment was carried out to obtain the aged XLPE samples with the needle-plate electrodes using power frequency AC source. The surface composition and dielectric parameters of XLPE samples were measured and analyzed with the Fourier Transform Infrared Spectroscopy (FTIR) and impedance analyzer. The surface potential and trap distribution of XLPE samples were measured by the isothermal surface potential decay (ISPD) method. The tested objects include the new XLPE sample, the samples with different electrical tree lengths, and the one with electrical tree breakdown. The results show that the aging of the electrical tree changes the dielectric parameters and trap distribution of XLPE samples. The dielectric constant increases with the length of electrical tree. The deeper and shallower traps appear simultaneously in the surface layer of XLPE insulation after electrical tree aging, and the trap density increases with the length of electrical tree. The trap energy level is the lowest and the trap density is the largest in the sample with electrical tree breakdown. The FTIR result shows that the absorption peaks of XLPE backbone structure decline obviously after electrical tree aging. The fracture of molecular chain and formation of low density area in XLPE sample are the main reasons for the change of dielectric and trap after electrical tree aging. © 2021 IEEE.

关键词： Fourier transform infrared spectroscopy

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Unraveling climate change-induced compound low-solar-low-wind extremes in China

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National science Review 2025年第1期 99-112页

作者： Licheng Wang Yawen Liu Lei Zhao Xi Lu Liangdian Huang Yana Jin Steven J.Davis Amir Aghakouchak Xin Huang Tong Zhu Yue Qin College of Environmental Sciences and Engineering Peking University Institute of Carbon Neutrality Peking University School of Atmospheric Sciences Nanjing University Department of Civil and Environmental Engineering University of Illinois at Urbana-Champaign National Center for Supercomputing Applications University of Illinois at Urbana-Champaign School of Environment State Key Joint Laboratory of Environment Simulation and Pollution Control Tsinghua University Department of Earth System Science University of California Department of Civil and Environmental Engineering University of California

China's pursuit of carbon neutrality targets hinges on a profound shift towards low-carbon energy, primarily reliant on intermittent and variable, yet crucial, solar and wind power sources. In particular,low-solar-low-wind(LSLW) compound extremes present a critical yet largely ignored threat to the reliability of renewable electricity generation. While existing studies have largely evaluated the impacts of average climate-induced changes in renewable energy resources, comprehensive analyses of the compound extremes and, particularly, the underpinning dynamic mechanisms remain scarce. Here we show the dynamic evolution of compound LSLW extremes and their underlying mechanisms across China via coupling multi-model simulations with diagnostic analysis. Our results unveil a strong topographic dependence in the frequency of compound LSLW extremes, with a national average frequency of 16.4(10th–90th percentile interval ranges from 5.3 to 32.6) days/yr, when renewable energy resources in eastern China are particularly compromised(～80% lower than that under an average climate). We reveal a striking increase in the frequency of LSLW extremes, ranging from 12.4% under SSP126 to 60.2% under SSP370,primarily driven by both renewable energy resource declines and increasingly heavily-tailed distributions,resulting from weakened meridional temperature(pressure) gradient, increased frequency of extremely dense cloud cover and additional distinctive influence of increased aerosols under SSP370. Our study underscores the urgency of preparing for significantly heightened occurrences of LSLW events in a warmer future, emphasizing that such climate-induced compound LSLW extreme changes are not simply by chance,but rather projectable, thereby underscoring the need for proactive adaptation strategies. Such insights are crucial for countries navigating a similar transition towards renewable energy.

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Predictive Vector Quantized Variational AutoEncoder for Spectral Envelope Quantization

Predictive Vector Quantized Variational AutoEncoder for Spec...

引用

International Conference on Electronics, Information and Communications (ICEIC)

作者： Tanasan Srikotr Kazunori Mano Division of Functional Control System Graduate School of Engineering and Science Shibaura Institute of Technology Japan

ISBN: (数字)9781728162898

ISBN: (纸本)9781728162904

The Predictive Vector Quantized Variational AutoEncoder is proposed to improve the reconstruction error of the conventional VQ-VAE. The proposed model can predict the current data from the previous data. The performance of the quantized spectral envelope parameters of the high-quality 48 kHz WORLD vocoder is evaluated. The results indicate that the Predictive Vector Quantized Variational AutoEncoder has a lower distortion with four target bitrates in term of log-spectral distortion, compared with the conventional VQ-VAE.

关键词： audio coding distortion spectral analysis vector quantisation vocoders Vocoders Vector quantization Audio coding Spectrum Analysis distortion reconstruction error abnormal shapes envelopes Predictive Current data

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