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作者： Yang, Weidong Shen, Erbo Wang, Xuyu Mao, Shiwen Gong, Yuehong Hu, Pengming College of Information Science and Engineering Henan University of Technology Zhengzhou450001 China Henan Key Laboratory of Grain Photoelectric Detection and Control Zhengzhou450001 China College of International Education Kaifeng University Henan Kaifeng475004 China Knight Foundation School of Computing & Information Sciences Florida International University MiamiFL33199 United States Department of Electrical and Computer Engineering Auburn University AuburnAL36849-5201 United States

In this paper, we propose a contact-free wheat moisture monitoring system, termed Wi-Wheat+, to address the several limitations of the existing grain moisture detection technologies, such as time-consuming process, expensive equipment, low accuracy, and difficulty in real-time monitoring. The proposed system is based on Commodity WiFi and is easy to deploy. Leveraging WiFi CSI data, this paper proposes a feature extraction method based on multi-scale and multi-channel entropy. The feasibility and stability of the system are validated through experiments in both Line-Of-Sight (LOS) and Non-Line-Of-Sight (NLOS) scenarios, where ten types of wheat moisture content are tested using multi-class Support Vector Machine (SVM). Compared with the Wi-Wheat system proposed in our prior work, Wi-Wheat+ has higher efficiency, requiring only a simple training process, and can sense more wheat moisture content levels. © 2022 Chongqing University of Posts and Telecommunications

关键词： Channel state information (CSI) Multi-class support vector machine (SVM) Multi-scale entropy Radio frequency (RF) sensing WiFi

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Reinforcement Learning-Based Sensitive Semantic Location Privacy Protection for VANETs

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China Communications 2021年第6期18卷 244-260页

作者： Minghui Min Weihang Wang Liang Xiao Yilin Xiao Zhu Han Department of Information and Communication Engineering Xiamen UniversityXiamen 361005China School of Information and Control Engineering ChinaUniversity of Mining and TechnologyXuzhou 221116China The Affiliated Hospital of China University of Mining and Technology Xuzhou 221116China Department of Electrical and Computer Engineering University of HoustonHouston TX 77004USA Beijing Key Laboratory of Mobile Computing and Pervasive Device No.6 Kexueyuan South RoadBeijing 100190China

Location-based services(LBS)in vehicular ad hoc networks(VANETs)must protect users’privacy and address the threat of the exposure of sensitive locations during LBS *** release not only their geographical but also semantic information of the visited places(e.g.,hospital).This sensitive information enables the inference attacker to exploit the users’preferences and life *** this paper we propose a reinforcement learning(RL)based sensitive semantic location privacy protection *** scheme uses the idea of differential privacy to randomize the released vehicle locations and adaptively selects the perturbation policy based on the sensitivity of the semantic location and the attack *** scheme enables a vehicle to optimize the perturbation policy in terms of the privacy and the quality of service(QoS)loss without being aware of the current inference attack model in a dynamic privacy protection *** solve the location protection problem with high-dimensional and continuous-valued perturbation policy variables,a deep deterministic policy gradientbased semantic location perturbation scheme(DSLP)is *** actor part is used to generate continuous privacy budget and perturbation angle,and the critic part is used to estimate the performance of the *** demonstrate the DSLP-based scheme outperforms the benchmark schemes,which increases the privacy,reduces the QoS loss,and increases the utility of the vehicle.

关键词： semantic location sensitivity locationbased services VANET differential privacy reinforcement learning

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Computational Discovery of Microstructured Composites with Optimal Stiffness-Toughness Trade-Offs

arXiv

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arXiv 2023年

作者： Li, Beichen Deng, Bolei Shou, Wan Oh, Tae-Hyun Hu, Yuanming Luo, Yiyue Shi, Liang Matusik, Wojciech Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology CambridgeMA02139 United States Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology CambridgeMA02139 United States Department of Mechanical Engineering Massachusetts Institute of Technology CambridgeMA02139 United States Department of Mechanical Engineering University of Arkansas FayettevilleAR72701 United States Department of Electrical Engineering Graduate School of AI POSTECH Gyeongsangbuk-do Pohang-si37673 Korea Republic of

The conflict between stiffness and toughness is a fundamental problem in engineering materials design. However, the systematic discovery of microstructured composites with optimal stiffness-toughness trade-offs has never been demonstrated, hindered by the discrepancies between simulation and reality and the lack of data-efficient exploration of the entire Pareto front. We introduce a generalizable pipeline that integrates physical experiments, numerical simulations, and artificial neural networks to address both challenges. Without any prescribed expert knowledge of material design, our approach implements a nested-loop proposal-validation workflow to bridge the simulation-to-reality gap and discover microstructured composites that are stiff and tough with high sample efficiency. Further analysis of Pareto-optimal designs allows us to automatically identify existing toughness enhancement mechanisms, which were previously discovered through trial-and-error or biomimicry. On a broader scale, our method provides a blueprint for computational design in various research areas beyond solid mechanics, such as polymer chemistry, fluid dynamics, meteorology, and robotics. Copyright © 2023, The Authors. All rights reserved.

关键词： Neural networks

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Structural and mechanical properties of monolayer amorphous carbon and boron nitride

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Physical Review B 2024年第17期109卷 174106-174106页

作者： Xi Zhang Yu-Tian Zhang Yun-Peng Wang Shiyu Li Shixuan Du Yu-Yang Zhang Sokrates T. Pantelides University of Chinese Academy of Sciences and Institute of Physics Chinese Academy of Sciences Beijing 100049 China CAS Key Laboratory of Theoretical Physics Institute of Theoretical Physics Chinese Academy of Sciences Beijing 100190 China Hunan Key Laboratory for Super Microstructure and Ultrafast Process School of Physics and Electronics Central South University Changsha 410083 China Songshan Lake Materials Laboratory Dongguan Guangdong 523808 China Department of Physics and Astronomy and Department of Electrical and Computer Engineering Vanderbilt University Nashville Tennessee 37235 USA

Amorphous materials exhibit various characteristics that are not featured by crystals and can sometimes be tuned by their degree of disorder (DOD). Here, we report results on the mechanical properties of monolayer amorphous carbon (MAC) and monolayer amorphous boron nitride (maBN) with different DOD. The pertinent structures are obtained by kinetic Monte Carlo (kMC) simulations using machine-learning potentials with density-functional theory-level accuracy. An intuitive order parameter, namely, the areal fraction Fx occupied by crystallites within the continuous random network, is proposed to describe the DOD. We find that Fx captures the essence of the DOD: Samples with the same Fx but different sizes and arrangements of crystallites, obtained using two distinct kMC procedures, have virtually identical radial distribution functions as well as bond-length and bond-angle distributions. Furthermore, by simulating the fracture process with molecular dynamics, we found that the mechanical responses of MAC and maBN before fracture are mainly determined by Fx and are insensitive to the sizes and specific arrangements and to some extent the numbers and area distributions of the crystallites. The behavior of cracks in the two materials is analyzed and found to mainly propagate in meandering paths in the continuous random network region and to be influenced by crystallites in distinct ways that toughen the material. The present results reveal the relation between structure and mechanical properties in amorphous monolayers and may provide a universal toughening strategy for two-dimensional materials.

关键词： Cracking Defects Fracture Material failure Mechanical deformation Microstructure Plasticity Stress 2-dimensional systems Amorphous materials Boron nitride Graphene Artificial neural networks Density functional calculations First-principles calculations High-throughput calculations Machine learning Materials modeling Molecular dynamics Monte Carlo methods

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CNN to Mitigate Atmospheric Turbulence Effect on Shack-Hartmann Wavefront Sensing: A Case Study on The Magdalena Ridge Observatory Interferometer

CNN to Mitigate Atmospheric Turbulence Effect on Shack-Hartm...

引用

IS and T International Symposium on Electronic Imaging: Machine Learning for Scientific Imaging, MLSI 2022

作者： Norouzi, Siavash Luis, James J.D. Ramyaa, Ramyaa Young, John S. Seneta, Eugene B. Hosseini, Morteza Darvish Morshedi Ligon, E. Robert Department of Computer Science New Mexico Institute of Mining and Technology SocorroNM United States Magdalena Ridge Observatory New Mexico Institute of Mining and Technology SocorroNM United States Cavendish Laboratory University of Cambridge Cambridge United Kingdom Department of Electrical and Computer Engineering Binghamton University BinghamtonNY United States CHARA Array Mount WilsonCA United States

The Magdalena Ridge Observatory Interferometer (MROI) utilizes Shack-Hartmann Wavefront Sensing (SH-WFS) for the back-end stability of its beam relay systems in a unique design. The SH-WFS, however, is sensitive to atmospheric turbulence scintillation which can drastically affect its precision in calculating the position of the beam profile it sees. A large number of images are needed to counteract the turbulence effect. Here we use deep learning as an alternative to long averaging cycles. A CNN was trained to map from a number of initial images of a series of star frames to the average image of the entire series at different positions of the beam profile. Under typical seeing conditions expected at MROI, the results showed that the network can map 10 input frames to the average of 100 within the permissible error margin of 0.1 pixels and furnish proper generalization to beam position movements not seen during training. The network can also perform better than the averaging technique when both techniques operate on small numbers of input frames such as 10 or 20. © 2022, Society for Imaging Science and Technology

关键词： Interferometers

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A Low-Code Development Platform for Industrialized Insect-based Bioconversion Systems

A Low-Code Development Platform for Industrialized Insect-ba...

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IEEE International Symposium on Industrial Electronics (ISIE)

作者： Cheng Pang Jinxian Liang Guangtao Yu Jibin Zhang Hiroaki Nishi Kevin I-Kai Wang Zeyuan Li Wenbin Dai Valeriy Vyatkin INSPRO Science Ltd. Shenzhen China Shenzhen Cynovan Technologies Co. Ltd. Shenzhen China National Key Laboratory of Agricultural Microbiology Huazhong Agricultural University Wuhan China Graduate School of Science and Technology Keio University Tokyo Japan Department of Electrical Computer and Software Engineering The University of Auckland Auckland New Zealand Googol Technology Co. Ltd. Shenzhen China Department of Automation Shanghai Jiao Tong University Shanghai China Department of Electrical Engineering and Automation Aalto University Espoo Finland Department of Computer Science Electrical and Space Engineering Luleå Tekniska Universitet Luleå Sweden

ISBN: (数字)9798350394085

ISBN: (纸本)9798350394092

Industrialized insect-based bioconversion is a technologically promising and economical viable approach for solving global crisis of food security and waste management. The successful implementation of such a multi-disciplinary system requires comprehensive expertise and skills from biotechnology, automation, information, and circular economy domains. This is rather difficult for existing practitioners. To simplify their jobs, by leveraging recent advances in industrial informatics, this work proposed and developed a low-code development platform for engineering the automation logic of industrial insect rearing and processing. For feasibility demonstration, a pilot facility has been designed, deployed, and benchmarked in this work. When compared with the conventional pro-code practice, the proposed low-code approach can significantly reduce development costs, shorten delivery times, and improve communication efficiency.

关键词： Waste management Industrial electronics Biotechnology Automation Costs Insects Food security

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Intelligent Fracture Diagnostic Procedure Using Smart Microchip Proppants Data

Intelligent Fracture Diagnostic Procedure Using Smart Microc...

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2021 Abu Dhabi International Petroleum Exhibition and Conference, ADIP 2021

作者： Van Pham, Vuong Dahaghi, Amirmasoud Kalantari Negahban, Shahin Fincham, William Babakhani, Aydin Chemical and Petroleum Engineering University of Kansas Lawrence KS United States National Energy Technology Laboratory US Department of Energy MorgantownWV United States Samueli Electrical and Computer Engineering Department Los AngelesCA United States

ISBN: (纸本)9781613998342

Unconventional oil and gas reservoir development requires an understanding of the geometry and complexity of hydraulic fractures. The current categories of fracture diagnostic approaches include methods for near-wellbore (production and temperature logs, tracers, borehole imaging) and far-field techniques (micro-seismic fracture mapping). These techniques provide an indirect and/or interpreted fracture geometry. Therefore, none of these methods consistently provides a fully detailed and accurate description of the character of created hydraulic fractures. This study proposes a novel approach that uses direct data from the injected fine size and battery-less Smart MicroChip Proppants (SMPs) to map the fracture geometry. This novel approach enables direct, fast, and smart of the received high-resolution geo-sensor data from the SMPs collected in high pressure and high-temperature environment and maps the fracture network using the proposed Intelligent and Integrated Fracture Diagnostic Platform (IFDP), which is a closed-loop architecture and is based on multi-dimensional projection, unsupervised clustering, and surface reconstruction. Affine transformation and a shallow ANN are integrated to control the stochasticity of clustering. IFDP proves its efficacy in fracture diagnostics for 3 in-house design synthetic fracture networks, with 100% consistency, rated "fairly satisfied" to "highly satisfied" in prediction capability, and between 85-100% in execution robustness. The integration of the couple affine transformation-ANN increases the performance of unsupervised clustering in IFDP. © Copyright 2021, Society of Petroleum Engineers

关键词： Fracture

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Unpaired Quad-Path Cycle Consistent Adversarial Networks for Single Image Defogging

SSRN

引用

SSRN 2023年

作者： Liu, Wei Chen, Cheng Jiang, Rui Lu, Tao Xiong, Zixiang School of Computer Science and Engineering Wuhan Institute of Technology Wuhan430205 China Hubei Key Laboratory of Intelligent Robot Wuhan Institute of Technology Wuhan430205 China Department of Electrical and Computer Engineering Texas A&M University College StationTX77843 United States

Adversarial learning-based image defogging methods have been extensively studied in computer vision due to their remarkable performance. However, most existing methods have limited defogging capabilities for real cases because they are trained on the paired clear and synthesized foggy images of the same scenes. In addition, they have limitations in preserving vivid color and rich textual details in defogging. To address these issues, we develop a novel generative adversarial network, called quad-path cycle consistent adversarial network (QPC-Net), for single image defogging. QPC-Net consists of a Fog2Fogfree block and a Fogfree2Fog block. In each block, there are three learning-based modules, namely, fog removal, color-texture recovery, and fog synthetic, which sequentially compose dual-path that constrain each other to generate high quality images. Specifically, the color-texture recovery model is designed to exploit the self-similarity of texture and structure information by learning the holistic channel-spatial feature correlations between the foggy image with its several derived images. Moreover, in the fog synthetic module, we utilize the atmospheric scattering model to guide it to improve the generative quality by focusing on an atmospheric light optimization with a novel sky segmentation network. Extensive experiments on both synthetic and real-world datasets show that QPC-Net outperforms state-of-the-art defogging methods in terms of quantitative accuracy and subjective visual quality. © 2023, The Authors. All rights reserved.

关键词： Fog

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Exploring Fairness for FAS-assisted Communication Systems: from NOMA to OMA

arXiv

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arXiv 2024年

作者： Yao, Junteng Zhou, Liaoshi Wu, Tuo Jin, Ming Pan, Cunhua Elkashlan, Maged Wong, Kai-Kit The Faculty of Electrical Engineering and Computer Science Ningbo University Ningbo315211 China The School of Electronic Engineering and Computer Science Queen Mary University of London LondonE1 4NS United Kingdom The National Mobile Communications Research Laboratory Southeast University Nanjing210096 China The Department of Electronic and Electrical Engineering University College London LondonWC1E 6BT United Kingdom The Yonsei Frontier Laboratory The School of Integrated Technology Yonsei University Seoul03722 Korea Republic of

This paper addresses the fairness issue within fluid antenna system (FAS)-assisted non-orthogonal multiple access (NOMA) and orthogonal multiple access (OMA) systems, where a single fixed-antenna base station (BS) transmits superposition-coded signals to two users, each with a single fluid antenna. We define fairness through the minimization of the maximum outage probability for the two users, under total resource constraints for both FAS-assisted NOMA and OMA systems. Specifically, in the FAS-assisted NOMA systems, we study both a special case and the general case, deriving a closed-form solution for the former and applying a bisection search method to find the optimal solution for the latter. Moreover, for the general case, we derive a locally optimal closed-form solution to achieve fairness. In the FAS-assisted OMA systems, to deal with the non-convex optimization problem with coupling of the variables in the objective function, we employ an approximation strategy to facilitate a successive convex approximation (SCA)-based algorithm, achieving locally optimal solutions for both cases. Empirical analysis validates that our proposed solutions outperform conventional NOMA and OMA benchmarks in terms of fairness. © 2024, CC BY.

关键词： Approximation algorithms

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Co-Fix3D: Enhancing 3D Object Detection With Collaborative Refinement

arXiv

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arXiv 2024年

作者： Li, Wenxuan Zou, Qin Chen, Chi Du, Bo Chen, Long Zhou, Jian Yu, Hongkai The School of Computer Science Wuhan University Wuhan430072 China The State Key Laboratory of Surveying Mapping and Remote Sensing Information Engineering Wuhan University Wuhan430079 China The Institute of Automation Chinese Academy of Sciences Beijing130028 China The Department of Electrical and Computer Engineering Cleveland State University ClevelandOH44115 United States

3D object detection in driving scenarios faces the challenge of complex road environments, which can lead to the loss or incompleteness of key features, thereby affecting perception performance. To address this issue, we propose an advanced detection framework called Co-Fix3D. Co-Fix3D integrates Local and Global Enhancement (LGE) modules to refine Bird’s Eye View (BEV) features. The LGE module uses Discrete Wavelet Transform (DWT) for pixel-level local optimization and incorporates an attention mechanism for global optimization. To handle varying detection difficulties, we adopt multi-head LGE modules, enabling each module to focus on targets with different levels of detection complexity, thus further enhancing overall perception capability. Experimental results show that on the nuScenes dataset’s LiDAR benchmark, Co-Fix3D achieves 69.4% mAP and 73.5% NDS, while on the multimodal benchmark, it achieves 72.3% mAP and 74.7% NDS. The source code is publicly available at https://***/rubbish001/Co-Fix3d. © 2024, CC BY.

关键词： Discrete wavelet transforms

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