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SSRN 2024年

作者： Yang, Chao Guo, Wenxuan Shao, Yuhao Lin, Yangshu Pu, Xuesen Wang, Yifan Zheng, Chenghang Ding, Yi Fan, Haidong Zhu, Yucai Gao, Xiang State Key Lab of Clean Energy Utilization State Environmental Protection Center for Coal-Fired Air Pollution Control Zhejiang University Hangzhou310027 China State Key Lab of Clean Energy Utilization Institute of Carbon Neutrality State Environmental Protection Engineering Center for Coal-Fired Air Pollution Control Zhejiang University Hangzhou310027 China Dongfang Electric Autocontrol Engineering Co. LTD. Deyang618000 China College of Electrical Engineering Zhejiang University Hangzhou310027 China State Key Laboratory of Industrial Control Technology College of Control Science and Engineering Zhejiang University Hangzhou310027 China

Post-combustion carbon capture (PCC) with chemical absorption exhibits significant interactions with coal-fired power plants (CFPP). The mismatch in the timescales of dynamic responses between PCC and CFPP, along with their nonlinear characteristics, presents additional challenges for the design of controllers. To address these obstacles, this work presents a two-timescale nonlinear model predictive control (T-NMPC) strategy to align the operational flexibility of the CFPP-PCC coupled system. A slow timescale nonlinear model predictive controller (S-NMPC) manages the extensive set of variables within the integrated CFPP-PCC system, focusing on long-term stability. In contrast, a fast timescale nonlinear model predictive controller (F-NMPC) dynamically adjusts the CFPP power and steam pressure to meet real-time operational demands. The interaction between these two controllers, both using extraction steam as a controlled variable, increases the flexibility of the CFPP by exploiting the energy buffers in the PCC. Comprehensive simulations of the CFPP-PCC system substantiate the enhancements in automatic generation control (AGC) performance, affirming the strategy's effectiveness. © 2024, The Authors. All rights reserved.

关键词： Coal fired power plant

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Consistent and Asymptotically Statistically-Efficient Solution to Camera Motion Estimation

arXiv

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arXiv 2024年

作者： Zeng, Guangyang Zeng, Qingcheng Li, Xinghan Mu, Biqiang Chen, Jiming Shi, Ling Wu, Junfeng The School of Data Science Chinese University of Hong Kong Shenzhen Shenzhen518172 China The College of Control Science and Engineering The State Key Laboratory of Industrial Control Technology Zhejiang University Hangzhou310027 China Key Laboratory of Systems and Control Institute of Systems Science Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing100190 China The Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Hong Kong

Given 2D point correspondences between an image pair, inferring the camera motion is a fundamental issue in the computer vision community. The existing works generally set out from the epipolar constraint and estimate the essential matrix, which is not optimal in the maximum likelihood (ML) sense. In this paper, we dive into the original measurement model with respect to the rotation matrix and normalized translation vector and formulate the ML problem. We then propose a two-step algorithm to solve it: In the first step, we estimate the variance of measurement noises and devise a consistent estimator based on bias elimination;In the second step, we execute a one-step Gauss-Newton iteration on manifold to refine the consistent estimate. We prove that the proposed estimate owns the same asymptotic statistical properties as the ML estimate: The first is consistency, i.e., the estimate converges to the ground truth as the point number increases;The second is asymptotic efficiency, i.e., the mean squared error of the estimate converges to the theoretical lower bound - Cramer-Rao bound. In addition, we show that our algorithm has linear time complexity. These appealing characteristics endow our estimator with a great advantage in the case of dense point correspondences. Experiments on both synthetic data and real images demonstrate that when the point number reaches the order of hundreds, our estimator outperforms the state-of-the-art ones in terms of estimation accuracy and CPU time. Copyright © 2024, The Authors. All rights reserved.

关键词： Maximum likelihood estimation

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Potential Transmission Choice for Internet of Things(IoT):Wireless and Batteryless Communications and Open Problems

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China Communications 2021年第2期18卷 241-249页

作者： Zhan Xu Guanjie Hu Minzheng Jia Lan Dong School of Information and Communication Engineering and with Key Laboratory of Modern Measurement&Control Technology Ministry of EducationBeijing Information Science&Technology UniversityChina Beijing Key Lab of Transportation Data Analysis and Mining School of Computer and Information TechnologyBeijing Jiaotong UniversityBeijingChina Department of Information Engineering Beijing Polytechnic CollegeBeijingChina

The 5th generation mobile communications aims at connecting everything and future Internet of Things(IoT)will get everything smartly *** realize it,there exist many *** key challenge is the battery problem for small devices,such as sensors or *** backscatter,also referred to as or battery-free backscatter,is a new potential technology to address this *** early and typical type of batteryless backscatter is ambient ***,batteryless backscatter utilizes environmental wireless signals to enable battery-free devices to communicate with each *** devices first harvest energy from ambient wireless signals and then backscatter these signals so as to transmit their own *** paper reviews the current studies about batteryless backscatter,including various backscatter schemes and theoretical works,and then introduces open problems for future research.

关键词： batteryless backscatter battery-free channel state information(CSI) channel estimation multiple antennas signal detection symbiotic communication

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Efficient View Path Planning for Autonomous Implicit Reconstruction

Efficient View Path Planning for Autonomous Implicit Reconst...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Jing Zeng Yanxu Li Yunlong Ran Shuo Li Fei Gao Lincheng Li Shibo He Jiming Chen Qi Ye Zhejiang University Hangzhou China Fuxi AI Lab NetEase Hangzhou China State Key Laboratory of Industrial Control Technology and also the Key Laboratory of Collaborative Sensing and Autonomous Unmanned Systems of Zhejiang Province College of Control Science and Engineering Zhejiang University China

Implicit neural representations have shown promising potential for 3D scene reconstruction. Recent work applies it to autonomous 3D reconstruction by learning information gain for view path planning. Effective as it is, the computation of the information gain is expensive, and compared with that using volumetric representations, collision checking using the implicit representation for a 3D point is much slower. In the paper, we propose to 1) leverage a neural network as an implicit function approximator for the information gain field and 2) combine the implicit fine-grained representation with coarse volumetric representations to improve efficiency. Further with the improved efficiency, we propose a novel informative path planning based on a graph-based planner. Our method demonstrates significant improvements in the reconstruction quality and planning efficiency compared with autonomous reconstructions with implicit and explicit representations. We deploy the method on a real UAV and the results show that our method can plan informative views and reconstruct a scene with high quality.

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An Efficient Smoke Detection Approach Based on Dual-Channel Neural Network

An Efficient Smoke Detection Approach Based on Dual-Channel ...

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2022 International Symposium on Intelligent Signal Processing and Communication Systems, ISPACS 2022

作者： Zhou, Chengxu Wang, Dongxia Cai, Haoran School of Electronics and Information Engineering Liaoning University of Technology China Dalian University of Technology Key Lab. of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education Jinzhou Dalian China School of Electronics and Information Engineering Tianjin University of Technology and Education Tianjin China School of Electronics and Information Engineering Liaoning University of Technology Jinzhou China

ISBN: (纸本)9798350332421

Smoke is the key feature of fire detection in its early age. Thus, an efficient smoke detection approach (i. e. an accurate and rapid method) is essential important to prevent fires. However, it is difficult to obtain an efficient method due to non-obvious details and monotonous color of smoke images. Moreover, traditional methods of smoke detection based on CNN contains lots of parameters and operations, which severely influents the computing efficiency in practical applications. Thus, we propose an efficient dual-channel neural network (EDCNN) on the basis of the state-of-The-Art DCNN. Concretely, we use the linear inverted bottleneck (LIB) to replace the traditional convolution layers on DCNN to build a light weight deep neural network. The introduction of the LIB block can efficiently trade off between accuracy and latency. Moreover, ReLU6 is used as the activation function, because it is more suitable for low-precision hardware devices. We then use some experimental results to demonstrate the effectiveness of EDCNN compared with the competitors for smoke detection in terms of model and computational complexity. © 2022 IEEE.

关键词： Smoke

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Risk Identification Method for Smuggled Frozen Meat in Urban Public Safety Awareness and Early Warning

Risk Identification Method for Smuggled Frozen Meat in Urban...

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2023 International Conference on Intelligent Communication and Networking, ICN 2023

作者： Li, Xueru Zhang, Yan Liu, Lingjie Tian, Ye Zhang, Yue Harbin Engineering University College of Computer Science and Technology Harbin China China Industrial Control Systems Cyber Emergency Response Team Beijing China Environment and Food and Drug Crime Investigation Detachment of Hangzhou Public Security Bureau Hangzhou China Xidian University Hangzhou Institute of Technology Hangzhou China Key Laboratory of Peace-Building Big Data of Zhejiang Province Zhejiang China

ISBN: (纸本)9798350357677

With the continuous growth in demand for meat and frozen products in the cold chain market, illegally smuggled frozen goods have quickly entered the market due to their low prices, leading to a series of food safety issues. This article proposes a method for identifying the risks of smuggled frozen meat for urban public safety awareness using the YOLOv7 model. The YOLOv7 model is used to detect data captured at various traffic checkpoints, obtaining spatiotemporal information of frozen goods vehicles. This information is then combined with map data to generate vehicle trajectory information. The trajectory similarity between the frozen vehicle trajectories and the specialized database of frozen transportation routes is analyzed using the EDwP distance algorithm. The EDwP algorithm employs a threshold-free approach, dynamically interpolating using the concept of projection, and automatically adapting to non-uniform sampling rates to address the issue of inconsistent trajectory data sampling rates. The experimental results of the YOLOv7-EDwP algorithm were verified by verifying the alerts generated in the recent period through on-site verification by a third-party inspection agency, with a recognition accuracy of 90.77%. This holds significant significance in the detection and warning of smuggled frozen meat. © 2023 IEEE.

关键词： Transportation routes

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Self‑Generated Buried Submicrocavities for High‑Performance Near‑Infrared Perovskite Light‑Emitting Diode

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Nano-Micro Letters 2023年第8期15卷 355-367页

作者： Jiong Li Chenghao Duan Qianpeng Zhang Chang Chen Qiaoyun Wen Minchao Qin Christopher C.S.Chan Shibing Zou Jianwu Wei Zuo Xiao Chuantian Zuo Xinhui Lu Kam Sing Wong Zhiyong Fan keyou Yan School of Environment and Energy State Key Lab of Luminescent Materials and DevicesGuangdong Provincial Key Laboratory of Solid Wastes Pollution Control and RecyclingSouth China University of TechnologyGuangzhou 510000People’s Republic of China Department of Electronic and Computer Engineering The Hong Kong University of Science and TechnologyClear Water BayKowloonHong KongPeople’s Republic of China Department of Physics The Chinese University of Hong KongShatinHong KongPeople’s Republic of China Department of Physics The Hong Kong University of Science and TechnologyClear Water BayKowloon 999077Hong KongPeople’s Republic of China School of Chemistry and Chemical Engineering Guangxi UniversityNanning 530004People’s Republic of China Center for Excellence in Nanoscience(CAS) Key Laboratory of Nanosystem and Hierarchical Fabrication(CAS)National Center for Nanoscience and TechnologyBeijing 100190People’s Republic of China

ABSTRACT Embedding submicrocavities is an effective approach to improve the light out-coupling efficiency(LOCE)for planar perovskite light-emitting diodes(PeLEDs).In this work,we employ phenethylammonium iodide(PEAI)to trigger the Ostwald ripening for the downward recrystallization of perovskite,resulting in spontaneous formation of buried submicrocavities as light output *** simulation suggests the buried submicrocavities can improve the LOCE from 26.8 to 36.2%for near-infrared ***,PeLED yields peak external quantum efficiency(EQE)increasing from 17.3%at current density of 114 mA cm^(−2)to 25.5%at current density of 109 mA cm^(−2)and a radiance increasing from 109 to 487 W sr^(−1)m^(−2)with low *** turn-on voltage decreased from 1.25 to 1.15 V at 0.1 W sr^(−1)m^(−2).Besides,downward recrystallization process slightly reduces the trap density from 8.90×10^(15)to 7.27×10^(15)cm^(−3).This work provides a self-assembly method to integrate buried output coupler for boosting the performance of PeLEDs.

关键词： Perovskite light-emitting diodes Downward recrystallization Buried submicrocavities Light out-coupling efficiency Radiative recombination

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SliceProp: A Slice-Wise Bidirectional Propagation Model for Interactive 3D Medical Image Segmentation 1

SliceProp: A Slice-Wise Bidirectional Propagation Model for ...

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1st IEEE International Conference on Medical Artificial Intelligence, MedAI 2023

作者： Xu, Xin Lu, Wenjing Lei, Jiahao Qiu, Peng Shen, Hong-Bin Yang, Yang Shanghai Jiao Tong University Key Lab. of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Department of Computer Science and Engineering Shanghai200240 China Shanghai Ninth People's Hospital Shanghai Jiao Tong University School of Medicine Department of Vascular Surgery China Shanghai Jiao Tong University Institute of Image Processing and Pattern Recognition Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China

ISBN: (纸本)9798350358780

Interactive medical image segmentation methods have become increasingly popular in recent years. These methods combine manual labeling and automatic segmentation, reducing the workload of annotation while maintaining high accuracy. However, most current interactive segmentation frameworks are limited to 2D image data, and are not suitable for 3D image data due to the large size and high complexity of 3D data, as well as the challenges posed by information asymmetry and sparse annotation. In this paper, we propose SliceProp, an interactive segmentation framework that implements slice-wise label Bidirectional Propagation (LBP) for 3D medical image segmentation. SliceProp extends the interactive 2D image segmentation algorithm to 3D image segmentation, and can handle 3D data with large size and high complexity. Moreover, equipped with a Backtracking Feedback Check (BFC) module, SliceProp effectively addresses the issues of information asymmetry and spatial sparse annotation in 3D medical image segmentation. Additionally, we adopt an uncertainty-based criterion to pri-oritize the slices to be refined interactively, which enhances the efficiency of the interaction process by enabling the model to focus on the regions with the most unreliable predictions. SliceProp is evaluated on two datasets and achieves promising results compared to state-of-the-art methods. © 2023 IEEE.

关键词： Medical imaging

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Expression recognition algorithm based on local and global feature reconstruction residuals 7

Expression recognition algorithm based on local and global f...

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7th International Conference on Information Science and control engineering, ICISCE 2020

作者： Li, Yaqian Lei, Ya Wu, Chao Li, Haibin Yanshan University Key Laboratory of Industrial Computer Control Engineering of Hebei Province Qinhuangdao China

ISBN: (纸本)9781728164069

In this paper, an algorithm is proposed to improve expression recognition by combining reconstructed residuals of local and global information. Firstly, positional relation is introduced into local binary features encoding, and a non-negative matrix decomposition method with sparse correlation constraints is employed to perform reducing of dimension. Then, the features are encoded by bag-of-words model to form local features. Secondly, a discrete wavelet transform is used to emphasis on expression information and extract expression-enhanced global features. Thirdly, collaboration Represents is used to calculate local partial reconstruction residuals and is combined with global reconstruction residuals to construct the final residual to be classified at the decision level. Experiments on Jaffe, TEFID and CK+ data sets prove the effectiveness of the proposed algorithm. © 2020 IEEE.

关键词： Discrete wavelet transforms

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MCMFuse:a Multi-level Co-selection Model in the Frequency Domain to Enhance EEG Signal Analysis for Emotion Recognition

MCMFuse:a Multi-level Co-selection Model in the Frequency Do...

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Youth Academic Annual Conference of Chinese Association of Automation (YAC)

作者： Yuxin Yang Youhan Li Tianyu Zeng Tianqi Fan Hongyu Zhao Sen Qiu School of Future Technology School of Artificial Intelligence Dalian University of Technology Dalian China School of Computer Science and Technology Dalian University of Technology Dalian China The Key Laboratory of Intelligent Control Optimization for Industrial Equipment of Ministry of Education and the School of Control Science and Engineering Dalian University of Technology Dalian China

ISBN: (数字)9798350379228

ISBN: (纸本)9798350390780

Electroencephalogram signals, as physiological signals unaffected by subjective consciousness and physiological dysfunctions, can objectively reflect a patient's physiological data and serve as a reliable basis for emotion recognition. Prior research has highlighted the temporal and spatial features of EEG signals. However, extracting frequency-domain features is difficult due to EEG's non-stationary. This study introduces an inception structure to reuse more EEG features. Then, we carry out an enhanced co-selection in the frequency domain by combining the shallow features autonomously selected by the inception structure with the deep features extracted from the temporal and spatial domains. Ultimately, deep features and co-selected features are fused. Our model MCMFuse was tested on the DEAP dataset with cross-validation, showing promising results in emotion recognition.

关键词： Emotion recognition Frequency-domain analysis Neural networks Feature extraction Brain modeling Information retrieval Electroencephalography

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