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43rd Chinese Control Conference, CCC 2024

作者： Zhi, Jiawen Chen, Zhenping Fan, Songli Zhu, Tong Suzhou University of Science Technology School of Electronic and Information Engineering Suzhou China Jiangsu Industrial Intelligent and Low-carbon Technology Engineering Center Suzhou China Suzhou Key Laboratory of Intelligent Low-carbon Technology Application Suzhou China

ISBN: (纸本)9789887581581

Considering the directional characteristics of communication topology due to heterogeneity in distance, power, and so on, a fully distributed consensus-based low-carbon economic dispatch method with directed topology is investigated in this paper. First, a low-carbon economic dispatching model is established with the consideration of dual goals of maximizing economic benefits and reducing carbon emissions, which employs quadratic functions to calculate operation and maintenance costs of subsystems along with carbon trading costs and introduces the Lagrange multiplier method to solve the optimization problem. Second, a fully distributed dispatching algorithm is proposed for large-scale and complex power networks. Our proposed algorithm treats incremental costs as consensus variables based on consensus theory, which realizes distributed dispatching through iterative computation. Third, the algorithm's applicability under random directed communication topology is thoroughly investigated. With the introduction of a surplus variable, the effectiveness and convergence of the algorithm have been confirmed through the analysis of communication topology and adjacency weights. Finally, some numerical experiments under the IEEE-14 power system are carried out to show the effectiveness of our proposed method. © 2024 Technical Committee on Control Theory, Chinese Association of Automation.

关键词： Low emission

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HVAC Optimal Control Based on the Sensitivity Analysis:An Improved SA Combination Method Based on a Neural Network

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Computer Modeling in Engineering & Sciences 2023年第9期136卷 2741-2758页

作者： Lifan Zhao Zetian Huang Qiming Fu Nengwei Fang Bin Xing Jianping Chen School of Electronic and Information Engineering Suzhou University of Science and TechnologySuzhou215009China Jiangsu Key Laboratory of Intelligent Building Energy Efficiency Suzhou University of Science and TechnologySuzhou215009China Suzhou Key Laboratory of Mobile Network Technology and Application Suzhou University of Science and TechnologySuzhou215009China Chongqing Industrial Big Data Innovation Center Co. Ltd.Chongqing400707China

Aiming at optimizing the energy consumption of HVAC,an energy conservation optimization method was proposed for HVAC systems based on the sensitivity analysis(SA),named the sensitivity analysis combination method(SAC).Based on the SA,neural network and the related settings about energy conservation of HVAC systems,such as cooling water temperature,chilled water temperature and supply air temperature,were ***,based on the data of the existing HVAC system,various optimal control methods ofHVAC systems were tested and evaluated by a simulated HVAC system in *** results show that the proposed SA combination method can reduce significant computational load while maintaining an equivalent energy performance compared with traditional optimal control methods.

关键词： Energy conservation sensitivity analysis HVAC system neural network

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HPHR-SAR-Net: Hyperpixel High-Resolution SAR Imaging Network Based on Nonlocal Total Variation

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IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2023年 16卷 8595-8608页

作者： Zhou, Guoru Xu, Zhongqiu Fan, Yizhe Zhang, Zhe Qiu, Xiaolan Zhang, Bingchen Fu, Kun Wu, Yirong Chinese Academy of Sciences Key Laboratory of Technology in Geo-spatial Information Processing and Application System Beijing100045 China University of Chinese Academy of Sciences Aerospace Information Research Institute Chinese Academy of Sciences School of Electronic Electrical and Communication Engineering Beijing100045 China Suzhou Aerospace Information Research Institute Suzhou Key Laboratory of Microwave Imaging Processing and Application Technology Suzhou215009 China

High resolution is a key trend in the development of synthetic aperture radar (SAR), which enables the capture of fine details and accurate representation of backscattering properties. However, traditional high-resolution SAR imaging algorithms face several challenges. First, these algorithms tend to focus on local information, neglecting nonlocal information between different pixel patches. Second, speckle is more pronounced and difficult to filter out in high-resolution SAR images. Third, the process of high-resolution SAR imaging generally involves high time and computational complexity, making real-time imaging difficult to achieve. To address these issues, we propose a hyperpixel high-resolution SAR imaging network (HPHR-SAR-Net) for rapid despeckling in high-resolution modes. Based on the concept of hyperpixel techniques, we initially combine nonconvex and nonlocal total variation as compound regularization. This approach more effectively despeckles and manages the relationship between pixels while reducing bias effects caused by convex constraints. Subsequently, we solve the compound regularization model using the alternating direction method of multipliers (ADMM) algorithm and unfold it into a deep unfolding network (DUN). The network's parameters are adaptively learned in a data-driven manner, and the learned network significantly increases imaging speed. Additionally, the proposed HPHR-SAR-Net is compatible with high-resolution imaging modes, such as spotlight, staring spotlight, and sliding spotlight. In this article, we demonstrate the superiority of HPHR-SAR-Net through experiments in both simulated and real SAR scenarios. The results indicate that HPHR-SAR-Net can rapidly perform high-resolution SAR imaging from raw echo data, producing accurate imaging results. © 2008-2012 IEEE.

关键词： Radar imaging

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A Novel Gradient Descent Least-Squares (GDLSs) Algorithm for Efficient Gridless Line Spectrum Estimation With applications in Tomographic SAR Imaging

A Novel Gradient Descent Least-Squares (GDLSs) Algorithm for...

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作者： Shi, Ruizhe Zhang, Zhe Qiu, Xiaolan Ding, Chibiao University of Chinese Academy of Sciences School of Electronic Electrical and Communication Engineering Beijing100190 China Suzhou Aerospace Information Research Institute Suzhou Key Laboratory of Intelligent Aerospace Big Data Application Technology Suzhou Jiangsu215123 China CAS Key Laboratory of Technology in Geo-Spatial Information Processing and Application System Beijing100190 China

This article presents a novel efficient method for gridless line spectrum estimation problems with single snapshot and sparse signals, namely the gradient descent least-squares (GDLS) method. Conventional single-snapshot (a.k.a. single-measure vector or SMV) line spectrum estimation methods either rely on smoothing techniques that sacrifice the range and/or azimuth resolution or adopt the sparsity constraint and utilize the compressed sensing (CS) method by defining prior grids and resulting in the off-grid problem. Recently emerged atomic norm minimization (ANM) methods achieved gridless SMV line spectrum estimation, but its computational complexity is extremely high;thus, it is practically infeasible in real applications with large problem scales. Our proposed GDLS method reformulates the line spectrum estimations problem into a least-squares (LSs) estimation problem and solves the corresponding objective function via a gradient descent algorithm in an iterative fashion with efficiency. The convergence guarantee, computational complexity, and performance analysis for evenly distributed antenna array cases are discussed in this article. Numerical simulations show that the proposed GDLS algorithm outperforms the state-of-the-art methods, for example, CS and ANM, in terms of estimation performances. It can completely avoid the off-grid problem, and its computational complexity is significantly lower than ANM. Our method has been tested in tomographic SAR (TomoSAR) imaging applications via simulated and real experimental data. Results show the great potential of the proposed method in terms of better cloud point performance and eliminating the gridding effect. © 1980-2012 IEEE.

关键词： Spectrum analysis

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Semantics-Based Analysis for Multi-source Remote Sensing Image Retrieval 8th

Semantics-Based Analysis for Multi-source Remote Sensing Ima...

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8th China High Resolution Earth Observation Conference, CHREOC 2022

作者： Su, Xiaolu Zhu, Lilu Wang, Yang Hu, Yanfeng Suzhou Aerospace Information Research Institute Suzhou China Aerospace Information Research Institute Chinese Academy of Sciences Beijing China Suzhou Key Laboratory of Aerospace Big Data Intelligent Application Technology Suzhou China

ISBN: (纸本)9789811982019

With the continuous increasing diversity and volume of the remote sensing data, the urgent need of efficient approaches for multi-source remote sensing data retrieval has attracted a lot of attention in the remote sensing field. However, there are limitations to existing remote sensing image retrieval approaches, such as high use threshold caused by excessive reliance on users’ domain knowledge, and imperfect multi-dimensional retrieval ability caused by insufficient data feature utilization. To address these problems, we propose an approach of semantics-based analysis for multi-source remote sensing image retrieval (SBARSIR), to make multi-source remote sensing data more accessible. In more detail, a convolutional neural network (CNN) is exploited for semantic feature extraction, and the global and partial classification results are integrated to improve the accuracy on remote sensing images with differences resolutions. Then, we cross-encode the visual semantic, temporal, and spatial features, and establish the mapping between features and indexing structures to provide retrieval capabilities. Extensive experiments have been carried out on several typical public remote sensing data sets to validate the effectiveness of the SBARSIR proposed. It turns out that the SBARSIR proposed outperforms the main stream approaches under the examined conditions. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Semantics

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Research on Optimized Deployment Methods for Indoor UWB Base Stations Based on Three-Dimensional spatial Analysis 2

Research on Optimized Deployment Methods for Indoor UWB Base...

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2nd International Conference on Geographic information and Remote Sensing technology, GIRST 2023

作者： Zou, Dexu Zhao, Xiangwei Zhang, Lixing Ma, Haoquan Kong, Xianghui Key Laboratory of Geomatics and Digital Technology of Shandong Province Shandong University of Science and Technology Qingdao China Qingdao Key Laboratory of Beidou Navigation and Intelligent Spatial Information Technology Application Shandong University of Science and Technology Qingdao China

ISBN: (纸本)9781510668478

Ultra-wideband (UWB) positioning technology is prone to be obscured by various objects in indoor complex environments and generates non-line-of-sight errors (NLOS). In view of the current situation that there are few studies on how to deploy UWB in indoor complex environments to ensure positioning accuracy while minimizing the use of UWB base stations and saving costs, this study generates positioning ranges based on AGV tracking routes;divides positioning ranges based on UWB positioning ranges to generate basic UWB deployment points;analyzes blind areas of UWB signals through three-dimensional spatial analysis technology;encrypts and deploys UWB base stations for blind areas;and analyzes the rationality of dense deployment points through three-dimensional spatial analysis technology. Finally, through simulation experiment, a scientific and reasonable base station deployment process is provided for UWB deployment in indoor complex environment. © 2023 SPIE.

关键词： Base stations

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STUDY ON THE THREE DIMENSION IMAGING METHODS OF FULLY-POLARISED ARRAY INSAR

STUDY ON THE THREE DIMENSION IMAGING METHODS OF FULLY-POLARI...

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IET International Radar Conference 2023, IRC 2023

作者： Song, Shujie Qiu, Xiaolan Shangguan, Songtao Key Laboratory of Technology in Geo-spatial Information Processing and Application System Chinese Academy of Sciences Beijing China Suzhou Key Laboratory of Microwave Imaging Processing and Application Technology Suzhou China Suzhou Aerospace Information Research Institute Suzhou China School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Beijing China Aerospace Information Research Institute Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781839539954

Fully-polarised (FP) array interferometric Synthetic Aperture Radar (FP-Array-InSAR) is an important technology in three-dimensional (3D) reconstruction and image interpretation of various scattering mechanisms (SMs) by exploiting the structural and polarisation properties of the targets. There has been a solid foundation for multi-baseline PolInSAR, but lack of systematic comparison and analysis for FP-Array-InSAR. Relying on the UAV-borne FP-Array-InSAR system developed by our research team, this paper applies two 3D imaging methods, one based on polarisation decomposition and the other based on multibaseline (MB) polarimetric coherence optimization, to four-channel FP-array-InSAR datasets from suzhou, China. Finally, this paper compares and analyses the experimental results of these two methods, and then gives their applicable scenarios. Such comparative experiments are positive for the rational selection and application of different methods in FP-array-InSAR. © The Institution of Engineering & technology 2023.

关键词： Radar imaging

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GRIDLESS DOA ESTIMATION FOR AUTOMOTIVE RADARS WITH VARIOUS ARRAY GEOMETRIES: THE NON-VANDERMONDE ATOMIC SOFT THRESHOLDING APPROACH

GRIDLESS DOA ESTIMATION FOR AUTOMOTIVE RADARS WITH VARIOUS A...

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IET International Radar Conference 2023, IRC 2023

作者： Gao, Silin Wang, Muhan Zhang, Zhe Zhang, Bingchen Wu, Yirong Aerospace Information Research Institute Chinese Academy of Sciences Beijing100094 China Key Laboratory of Technology in Geo-spatial Information Processing and Application System Chinese Academy of Sciences Beijing100190 China School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Beijing100049 China Key Laboratory of Intelligent Aerospace Big Data Application Technology Suzhou215123 China Suzhou Aerospace Information Research Institute Suzhou215123 China

ISBN: (纸本)9781839539954

This paper is centered on gridless direction of arrival (DoA) estimation for single-snapshot data collected by non-uniform linear arrays (NLAs) in the context of automotive applications. While recent single-snapshot DoA estimation algorithms rooted in grid-based compressed sensing (CS) offer super-resolution capabilities, they are notably sensitive to discrepancies between the assumed sparsity basis and the actual basis. Furthermore, the existing atomic soft thresholding (AST) algorithm, a promising gridless sparse recovery technique within the Toeplitz model, is constrained to uniform linear arrays (ULAs) exhibiting Vandermonde structure in their array manifolds. However, in automotive scenarios, it becomes imperative to efficiently apply gridless DoA estimation to NLAs featuring non-Vandermonde array manifolds. In this paper, we introduce the non-Vandermonde atomic soft thresholding (NVAST) algorithm and employ it for DoA estimation in NLAs. The novel algorithm establishes a link between Vandermonde and non-Vandermonde atoms by decomposing non-Vandermonde vectors and addressing them using semi-definite programming (SDP). Simulation and measurement experiments conducted with automotive radars underscore the remarkable performance of our proposed method. © The Institution of Engineering & technology 2023.

关键词： Automotive radar

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AN AUTOFOCUS NETWORK FOR MULTI-CHANNEL PHASE ERRORS WITH application TO TOMOSAR IMAGING

AN AUTOFOCUS NETWORK FOR MULTI-CHANNEL PHASE ERRORS WITH APP...

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IET International Radar Conference 2023, IRC 2023

作者： Wang, Muhan Gao, Silin Zhang, Zhe Qiu, Xiaolan Key Laboratory of Technology in Geo-spatial Information Processing and Application System Chinese Academy of Sciences Beijing100190 China Key Laboratory of Intelligent Aerospace Big Data Application Technology Suzhou215123 China Suzhou Aerospace Information Research Institute Suzhou215123 China Aerospace Information Research Institute Chinese Academy of Sciences Beijing100094 China School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Beijing100049 China

ISBN: (纸本)9781839539954

Synthetic aperture radar (SAR) tomography (TomoSAR) has garnered significant attention due to its capability for three-dimensional reconstruction. Compressed sensing (CS) methods are widely employed to address the TomoSAR inversion challenge. Nevertheless, practical applications reveal phase errors among different channels, resulting in defocusing and blurring when relying solely on CS for 3D reconstruction. Current state-of-the-art autofocus techniques suffer from prohibitive computational complexity, limiting their applicability to large-scale 3D imaging. In pursuit of efficient TomoSAR 3-D autofocusing, we proposed ASAMP-Net, an innovative deep unfolding network. Operating within a two-step framework, each layer comprises two stages: phase error estimation and iterative scattering coefficient reconstruction using the sparse adaptive matching pursuit (SAMP) algorithm. Additionally, phase error estimation is obtained through mathematical derivation, while challenges associated with fixed sparsity and limited efficiency in conventional methods are mitigated through deep learning techniques. Simulation experiments and real data validation affirm the effectiveness and superiority of the proposed method. © The Institution of Engineering & technology 2023.

关键词： Radar imaging

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Semiautomatic 3D modeling and entity separation method for lower extremity bones 3

Semiautomatic 3D modeling and entity separation method for l...

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3rd International Symposium on Computer applications and information Systems, ISCAIS 2024

作者： Zhao, Xiangwei Zhang, Lixing Cui, Weiding Zou, Dexu Zhong, Yani Bai, Congshuo Key Laboratory of Geomatics and Digital Technology of Shandong Province Shandong University of Science and Technology Qingdao China First Affiliated Hospital Nanjing Medical University Nanjing China Qingdao Key Laboratory of Beidou Navigation and Intelligent Spatial Information Technology Application Shandong University of Science and Technology Qingdao China

ISBN: (纸本)9781510681316

Three-dimensional (3D) reconstruction has been widely used in bone correction. However, some key problems need to be urgently solved in the current 3D reconstruction algorithms for lower extremity bones based on CT DICOM images. A method of semi-automatic 3D reconstruction and entity separation for lower extremity bones is proposed. Firstly, an improved aT-Mask (adaptiveThreshold-Mask) algorithm is researched to remove the patient couch. Then, the S-PP (Sample-Point Picking) bone-separation algorithm is proposed to well extract and separate the lower extremity bone entities. To automatically batch-repair holes on the reconstructed 3D models, the M-IM (Model-Image Matching) hole repair algorithm is implemented based on the spectral characteristics of the lower extremity bone in the DICOM images. Finally, the surface-rendering algorithm is applied to reconstruct the 3D model of the lower extremity bone. One hundred sets of DICOM images are utilized to experiment, and the fine 3D models for femur, fibula and tibia are well reconstructed and separated for each case. The 3D reconstruction of lower extremity bones can be easily established, and the entities can be separated efficiently based on the method proposed in this study. The efficiency and accuracy of deformity recognition and correction can be significantly improved by using the reconstructed 3D models. © 2024 SPIE.

关键词： Image reconstruction

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