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Optimal Bilinear Equalizer for Cell-Free Massive MIMO Systems over Correlated Rician Channels

IEEE Transactions on Signal Processing 2025年

作者： Wang, Zhe Zhang, Jiayi Bjornson, Emil Niyato, Dusit Ai, Bo Beijing Jiaotong University State Key Laboratory of Advanced Rail Autonomous Operation Beijing100044 China Beijing Jiaotong University School of Electronics and Information Engineering Beijing100044 China KTH Royal Institute of Technology Department of Computer Science Stockholm114 28 Sweden Nanyang Technological University College of Computing & Data Science 639798 Singapore

In this paper, we explore the low-complexity optimal bilinear equalizer (OBE) combining scheme design for cell-free massive multiple-input multiple-output networks with spatially correlated Rician fading *** provide a spectral efficiency (SE) performance analysis framework for both the centralized and distributed processing schemes with bilinear equalizer (BE)-structure combining schemes applied. The BE-structured combining is a set of schemes that are constructed by the multiplications of channel statistics-based BE matrices and instantaneous channel estimates. Notably, we derive closed-form achievable SE expressions for centralized and distributed BE-structured combining schemes. We propose one centralized and two distributed OBE schemes: Centralized OBE (C-OBE), Distributed OBE based on Global channel statistics (DG-OBE), and Distributed OBE based on Local channel statistics (DL-OBE), which maximize their respective SE expressions. OBE matrices in these schemes are tailored based on varying levels of channel statistics. Notably, we obtain new and insightful closed-form results for the C-OBE, DG-OBE, and DL-OBE combining schemes. Numerical results demonstrate that the proposed OBE schemes can achieve excellent SE, even in scenarios with severe pilot contamination. © 2025 IEEE. All rights reserved.

关键词： Fading channels

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Image manipulation detection by multi-view multi-scale supervision

arXiv

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arXiv 2021年

作者： Chen, Xinru Dong, Chengbo Ji, Jiaqi Cao, Juan Li, Xirong MoE Key Lab of Data Engineering and Knowledge Engineering Renmin University of China AIMC Lab School of Information Renmin University of China Institute of Computing Technology Chinese Academy of Sciences State Key Laboratory of Media Convergence Production Technology and Systems

The key challenge of image manipulation detection is how to learn generalizable features that are sensitive to manipulations in novel data, whilst specific to prevent false alarms on authentic images. Current research emphasizes the sensitivity, with the specificity overlooked. In this paper we address both aspects by multi-view feature learning and multi-scale supervision. By exploiting noise distribution and boundary artifact surrounding tampered regions, the former aims to learn semantic-agnostic and thus more generalizable features. The latter allows us to learn from authentic images which are nontrivial to be taken into account by current semantic segmentation network based methods. Our thoughts are realized by a new network which we term MVSS-Net. Extensive experiments on five benchmark sets justify the viability of MVSS-Net for both pixel-level and image-level manipulation detection. Copyright © 2021, The Authors. All rights reserved.

关键词： Semantic Segmentation

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On The Decomposition of Locational Marginal Hydrogen Pricing

On The Decomposition of Locational Marginal Hydrogen Pricing

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IEEE General Meeting Power& Energy Society

作者： Qi An Gengyin Li Jianxiao Wang Yiyang Song Xuanyuan Wang Zhen Liu Guannan He State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources North China Electric Power University Beijing China National Engineering Laboratory for Big Data Analysis and Applications Peking University Beijing China State Grid Jibei Electric Power Co. Ltd. Beijing China College of Engineering Peking University Beijing China

Electrolytic hydrogen is going to be dominant in hydrogen production industry, which calls for a novel hydrogen pricing mechanism suitable for high proportion of electricity-hydrogen interaction. Compared with traditional hydrogen pricing methods such as cost plus and pay-as-bids, marginal-pricing (MP) mechanism could characterize the correlation between hydrogen price (locational marginal hydrogen price, LMHP) and electricity price (locational marginal price, LMP). However, the decomposition of LMHP has not been investigated, which is crucial for the operation of a future hydrogen market. Due to physical characteristics of hydrogen system, the decomposition of LMHP is quite different from that of LMP. In view of the above knowledge gap, we put forward the concept and decomposition method of LMHP for the first time in this paper. Based on the cross-period dynamic characteristics of hydrogen system, the difference between the composition of LMHP and LMP is explored. The hydrogen market clearing model is proposed and the derivation method of composition of LMHP is proved. On top of the conventional three LMP components, we theoretically derive four components of LMHP: energy, loss, congestion, and time-dependent components. Case studies based on Belgium 20-node system demonstrate the effectiveness of the proposed concept and decomposition method of LMHP.

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An improved LED junction temperature test method based on FCM 5

An improved LED junction temperature test method based on FC...

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2022 5th International Conference on Power Electronics and Control engineering, ICPECE 2022

作者： Zhang, Jie Zhang, Junchao Xu, Bingshe Fu, Qiang Wang, Chen Zhao, Haodi College of Electrical and Power Engineering Taiyuan University of Technology Shanxi Taiyuan030024 China Key Laboratory of Interface Science and Engineering of New Materials. Ministry of Education Taiyuan University of Technology Shanxi Taiyuan030024 China Shanxi Electric Drive and Internet of Things Engineering Research Center Shanxi Taiyuan030024 China Intelligent City Lighting Data Sharing and Pub. Serv. Platform Eng. Res. Ctr. of 1331 Proj. in Shanxi Prov. Based on Big Data Shanxi Taiyuan030024 China Shanxi Intelligent Digital Lighting Union Laboratory Shanxi Taiyuan030024 China Shanxi Intelligent Digital Cultural Tourism Industry Research Institute Shanxi Taiyuan030024 China

High junction temperature (Tj) of LED will affect the performance and life of LED, so this paper proposes a high-power LED junction temperature test method. Mainly aiming at the problem that the accuracy of the forward current method (FCM) will be affected by the calibration procedure, equipment aging, and technical differences when batch testing LED Tj, this paper uses the relationship between relative current and Tj to calibrate the procedure based on this method, to limit the negative affection of sample heterogeneity. Ten LED samples were tested in the experiment, and the Tj errors under two test methods were obtained. The results showed that the improved method was more accurate than the traditional method. Then, the Tj error of the improved method was compared with that of the forward voltage method, and the error of the new method was smaller. © Published under licence by IOP Publishing Ltd.

关键词： Light emitting diodes

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Image Manipulation Detection by Multi-View Multi-Scale Supervision

Image Manipulation Detection by Multi-View Multi-Scale Super...

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International Conference on Computer Vision (ICCV)

作者： Xinru Chen Chengbo Dong Jiaqi Ji Juan Cao Xirong Li MoE Key Lab of Data Engineering and Knowledge Engineering Renmin University of China AIMC Lab School of Information Renmin University of China Institute of Computing Technology Chinese Academy of Sciences State Key Laboratory of Media Convergence Production Technology and Systems

ISBN: (纸本)9781665428132

关键词： Representation learning Image segmentation Computer vision Sensitivity Costs Image edge detection Semantics

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Technical Function-effect Based Patent Multi-to-One Negation Game Model 25

Technical Function-effect Based Patent Multi-to-One Negation...

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25th IEEE International Conference on Computer Supported Cooperative Work in Design, CSCWD 2022

作者： Du, Lu Liu, Weidong Xiao, Kaiyang Gao, Shixuan Han, Yong College of Computer Science Inner Mongolia University Hohhot China Institute of Scientific and Technical Information of China Beijing China Natl. and Loc. Jt. Eng. Res. Center of Intelligent Information Processing Technology for Mongolian Hohhot China Inner Mongolia Key Laboratory of Social Computing and Data Processing Hohhot China Inner Mongolia Engineering Laboratory for Big Data Analysis Technology Hohhot China Inner Mongolia Intellectual Property Service Center Hohhot China

ISBN: (纸本)9781665405270

As the number of patent applications increases yearly, the negation relation between patents has become intertwined, which makes it difficult for constructing negation relation in patent examination manually. Therefore, how to automatically construct negation relation between patents has become the hot research. Constructing negation relation is the process of comparing technical features between patents, which depends on the technical features in the patent claims and the technical function-effect. Although there are many researches on patent one-to-one negation, few researches on patent multi-to-one negation. The research on negation relation of multi-to-one patents, has the following challenges: (1) the definition of patent features is ambiguous in comparison of negation relation;(2) the role of patent function-effect is often ignored in judgement of negation relation. To solve the above issues, we propose a technical function-effect based patent multi-to-one negation game model. In the model, we decompose patent technical features into technical utility and technical function-effect. Patent technical utility is mainly realized by using the valence theory in linguistics. After obtaining technical utility, we choose to have the same technical effect, express the technical features of different technical functions, and obtain the patent technical function-effect. Finally, we combine the technical utility and function-effect to construct a technical utility/function-effect game tree to judge the patent negation relation. According to the two-tailed test in the A/B-test, our model outperforms the baseline model in the evaluation measurement. This model can subsequently be used to calculate the portfolio value and the strategic value of the patents. © 2022 IEEE.

关键词： Patents and inventions

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A Revocable Fast and Lightweight Parallel Encryption Scheme for IIoT

引用

IEEE Internet of Things Journal 2025年

作者： Lu, Junze Hu, Chunqiang Chen, Jiajun Xia, Hui Chongqing University Key Laboratory of Dependable Service Computing in Cyber Physical Society Ministry of Education China Chongqing University School of Big Data and Software Engineering Chongqing400030 China Ocean University of China College of Computer Science and Technology China

As the Industrial Internet of Things (IIoT) expands, the number of stakeholders increases. Many entities require significant amounts of data from industrial devices. These data streams improve information sharing and optimize the industrial chain. However, IIoT’s enormous data volumes pose challenges to traditional encryption methods, which are unable to meet efficiency and energy consumption requirements. Thus, a new, efficient encryption algorithm is essential for managing data flows among IIoT subscribers. In this paper, we propose a fast, low-energy, high-security encryption scheme to manage multi-entity data streams. First, we introduce a novel encryption scheme based on the Subset Sum Problem (SSP), which improves energy efficiency and speed. Second, to meet subscription requirements, we employ attribute-based encryption (ABE) for key forwarding. Finally, we handle subscription revocations with device key updates. The device owner utilizes their secret value to generate an identity proof with a key update request. © 2014 IEEE.

关键词： Information dissemination

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Narrow-Band Interference Suppression Method for SAR Based on CEEMD

Narrow-Band Interference Suppression Method for SAR Based on...

引用

2021 CIE International Conference on Radar, Radar 2021

作者： Zhang, Hengrui Lv, Zongsen Min, Lin Li, Ning College of Computer and Information Engineering Henan University Kaifeng China Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng China Network Information Center Office Henan University Kaifeng China

ISBN: (纸本)9781665498142

Narrow-band interference (NBI) is a common form of interference in SAR systems, severely reducing the quality of SAR images. The NBI suppression method based on empirical mode decomposition (EMD) can adaptively split the raw signal into multiple independent intrinsic modal functions (IMFs), and reconstruct the useful signal after removing the IMFs of the NBI. However, the mode mixing problem it brings will cause part of the useful signal to be removed simultaneously when removing the NBI. A novel NBI suppression method based on complementary ensemble EMD (CEEMD) is proposed in this paper to solve this problem. First, the moment kurtosis coefficient is used to detect the presence of NBI in the raw echo, and CEEMD is used to decompose the NBI-contained raw echo into a series of IMFs. Then, the second-order difference of permutation entropy (PE) of all IMFs is calculated to obtain the global threshold for distinguishing IMFs with NBI and useful signal. Finally, the target IMF is used to reconstruct useful signals to remove the NBI. Experimental results based on real C-band spaceborne SAR data with simulated NBI verify the performance of the proposed method. © 2021 IEEE.

关键词： complementary ensemble empirical mode decomposition (CEEMD) empirical mode decomposition (EMD) Narrow-band interference (NBI) permutation entropy (PE) synthetic aperture radar (SAR)

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LSTM and Statistical Learning for Dynamic Inferential Modeling with Applications to a 660MW Boiler

引用

IFAC-PapersOnLine 2022年第7期55卷 600-605页

作者： Jicheng Li Peng Tan S. Joe Qin School of Data Science City University of Hong Kong Kowloon Hong Kong State Key Laboratory of Coal Combustion School of Energy and Power Engineering Huazhong University of Science and Technology Wuhan China School of Data Science and Hong Kong Institute for Data Science City University of Hong Kong Kowloon Hong Kong

Statistical learning methods have been widely studied and practiced in the past for inferential modeling. In recent years, deep learning methods have been implemented for inferential sensor modeling. As a popular deep learning model, the long short-term memory (LSTM) network is capable of handling data nonlinearity and dynamics and is therefore applied for dynamic inferential modeling. In this paper, we analyze and compare LSTM with other statistical learning methods for the dynamic NOx emission prediction of a 660 MW industrial boiler. Support vector regression (SVR), partial least squares (PLS), and Least absolute shrinkage and selection operator (Lasso) with embedded dynamics are compared with LSTM for dynamic inferential modeling. The experimental results indicate that SVR, PLS, and Lasso outperform LSTM. By disabling the LSTM gates to realize a simple memory structure, the LSTM performance is signifcantly improved. The main goal of the paper is to demonstrate that a deep neural network that is effective in other domains requires close scrutiny and detailed study to show its superiority in process applications.

关键词： Dynamic inferential modeling LSTM Statistical learning Dynamic modeling

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Synthetic Instance Segmentation from Semantic Image Segmentation Masks

arXiv

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arXiv 2023年

作者： Shen, Yuchen Zhang, Dong Zhang, Zhao Fu, Liyong Ye, Qiaolin College of Information Science and Technology & Artificial Intelligence Nanjing Forestry University Jiangsu Nanjing210037 China Department of Computer Science and Engineering Hong Kong University of Science and Technology Hong Kong Key Laboratory of Knowledge Engineering with Big Data Hefei University of Technology Hefei230009 China College of Information Science and Technology Nanjing Forestry University Jiangsu Nanjing210037 China Institute of Forest Resource Information Techniques Chinese Academy of Forestry Beijing100091 China

In recent years, instance segmentation has garnered significant attention across various applications. However, training a fully-supervised instance segmentation model requires costly both instance-level and pixel-level annotations. In contrast, weakly-supervised instance segmentation methods, such as those using image-level class labels or point labels, often struggle to satisfy the accuracy and recall requirements of practical scenarios. In this paper, we propose a novel paradigm called Synthetic Instance Segmentation (SISeg). SISeg achieves instance segmentation results by leveraging image masks generated by existing semantic segmentation models, and it is highly efficient as we do not require additional training for semantic segmentation or the use of instance-level image annotations. In other words, the proposed model does not need extra manpower or higher computational expenses. Specifically, we first obtain a semantic segmentation mask of the input image via an existent semantic segmentation model. Then, we calculate a displacement field vector for each pixel based on the segmentation mask, which can indicate representations belonging to the same class but different instances, i.e., obtaining the instance-level object information. Finally, the instance segmentation results are refined by a learnable category-agnostic object boundary branch. Extensive experimental results on two challenging datasets highlight the effectiveness of SISeg in achieving competitive results when compared to state-of-the-art methods, especially fully-supervised methods. The code will be released at: SISeg Copyright © 2023, The Authors. All rights reserved.

关键词： Semantic Segmentation

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