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Dynamic modeling of NH 3 concentration-driven SCR denitrification processes for control optimization

Process Safety and Environmental Protection 2025年 200卷

作者： Zhenlong Wu Shaoyong Sui Bingnan Li Yanhong Liu YangQuan Chen School of Electrical and Information Engineering Zhengzhou University Zhengzhou 450001 China State Key Laboratory of Intelligent Agricultural Power Equipment Zhengzhou University Luoyang 471000 China Rundian Energy Science and Technology Company Ltd. Zhengzhou Henan 450000 China Mechatronics Embedded Systems and Automation (MESA) Lab Department of Mechanical Engineering School of Engineering University of California Merced CA 95343 USA

Selective Catalytic Reduction (SCR) is widely adopted as the flue gas denitrification method in coal-fired power plants. For the denitrification process in coal-fired power plants, the amount of ammonia injected is crucial for controlling nitrogen oxide concentrations in the flue gas. This paper adjusts the denitrification process model of SCR by analyzing its internal structure characteristics. It establishes a hierarchical change calculation SCR dynamic model by improving the method for calculating changes in flue gas concentration inside the SCR reactor. Simulation results demonstrate that the improved SCR mechanism model maintains fitting accuracy within 10% across diverse actual operating data. Based on the denitrification system’s characteristics, a modified active disturbance rejection control(MADRC) is designed and applied to the actual denitrification system. The running data indicate that replacing the ammonia spray valves on both sides of the SCR with MADRC and running for some time, the positive and negative deviations of the NO concentration at the SCR outlet on both sides remained within 20 mg/m 3 , indicating that the application of MADRC in the denitrification system has significant potential.

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BiDet: An Efficient Binarized Object Detector

BiDet: An Efficient Binarized Object Detector

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Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Ziwei Wang Ziyi Wu Jiwen Lu Jie Zhou Department of Automation Tsinghua University China State Key Lab of Intelligent Technologies and Systems China Beijing National Research Center for Information Science and Technology China Tsinghua Shenzhen International Graduate School Tsinghua University China

ISBN: (数字)9781728171685

ISBN: (纸本)9781728171692

In this paper, we propose a binarized neural network learning method called BiDet for efficient object detection. Conventional network binarization methods directly quantize the weights and activations in one-stage or two-stage detectors with constrained representational capacity, so that the information redundancy in the networks causes numerous false positives and degrades the performance significantly. On the contrary, our BiDet fully utilizes the representational capacity of the binary neural networks for object detection by redundancy removal, through which the detection precision is enhanced with alleviated false positives. Specifically, we generalize the information bottleneck (IB) principle to object detection, where the amount of information in the high-level feature maps is constrained and the mutual information between the feature maps and object detection is maximized. Meanwhile, we learn sparse object priors so that the posteriors are concentrated on informative detection prediction with false positive elimination. Extensive experiments on the PASCAL VOC and COCO datasets show that our method outperforms the state-of-the-art binary neural networks by a sizable margin.

关键词： Detectors Object detection Neural networks Feature extraction Mutual information Redundancy Quantization (signal)

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Multi-MEC server multi-user resource allocation in heterogeneous network

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Journal of Physics: Conference Series 2021年第1期1792卷

作者： ZhenQin Yuanxiu Liao Dejin Shen Faculty of Computer Science and Information Engineering Guangxi Normal University Guilin Guangxi 541004 China Guangxi Key Lab of Multi-Source Information Mining and Security Guangxi Regional Multi-Source Information Integration and Intelligent Processing Collaborative Innovation Center

Aiming at the coexistence of large and small cells in heterogeneous cellular networks, the difference of computing resources between mobile equipment and base stations, and the diversity of service quality demands of users, a resource allocation algorithm based on matching game theory was proposed. We assume that some users have computing needs and can perform MEC offloading or D2D offloading. We define cost performance as the ratio of the data rate per user to its price cost, and resource allocation as the cost performance maximization problem. Considering that the formulation problem is a mixed integer nonlinear problem and it is difficult to obtain the optimal solution, we design an improved Gale-Shapley algorithm based on matching game theory to obtain the feasible solution of the problem. Simulation results show that the proposed multi-MEC server multi-user resource allocation strategy can effectively reduce user service delay and improve system performance in heterogeneous cellular networks.

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An Adaptive Model Update Object Tracking Algorithm based on DenseNet Features

An Adaptive Model Update Object Tracking Algorithm based on ...

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International Conference on Networking and Network Applications (NaNA)

作者： Sugang Ma Lei Zhang Lei Pu Xiaobao Yang Zhiqiang Hou Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Xi'an University of Posts and Telecommunications Xi'an shaanxi China School of Computer Science and Technology Xi'an University of Posts and Telecommunications Xi'an shaanxi China School of Information and Navigation Air Force Engineering University Xi'an shaanxi China

ISBN: (数字)9781728189543

ISBN: (纸本)9781728189550

In order to further improve the ability to deal with complex scenes, a visual tracking algorithm based on DenseNet features and model adaptive updating is proposed. Aiming to improve the feature representation ability of correlation filtering algorithm, a more discriminative DenseNet model is used as feature extractor. Meanwhile, in order to improve the long-term tracking performance, an adaptive model update strategy is proposed. The average peak correlation energy (APCE) based on response map is used to judge and evaluate the tracking result and the model is updated only when the confidence is high. The experimental results show that the proposed algorithm achieves higher success rate and accuracy compared to the other five related algorithms, and improves the robustness of object tracking in complex scenes such as occlusion and deformation.

关键词： Adaptation models Visualization Correlation Target tracking Feature extraction Object tracking Strain

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Advances in information processing and biological imaging using flat optics

Nature Reviews Electrical Engineering

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Nature Reviews Electrical Engineering 2024年 391-411页

作者： Xinwei Wang Huijie Hao Jian Liu Jiubin Tan Haoyu Li Xumin Ding Xiaoyuan He Peng Xie Guangwei Hu Hao Wang Patrice Genevet Yu Luo Advanced Microscopy and Instrumentation Research Center School of Instrumentation Science and Engineering Harbin Institute of Technology Harbin China Key Lab of Ultra-Precision Intelligent Instrumentation Harbin Institute of Technology Ministry of Industry and Information Technology Harbin China Center of Ultra-Precision Optoelectronic Instrument Engineering Harbin Institute of Technology Harbin China School of Electrical and Electronic Engineering Nanyang Technological University Singapore Singapore Engineering Product Development Singapore University of Technology and Design Singapore Singapore Physics Department Colorado School of Mines Golden CO USA National Key Laboratory of Microwave Photonics Nanjing University of Aeronautics and Astronautics Nanjing China

Flat optical components (metasurfaces) made from artificial electromagnetic materials (metamaterials) have opened new possibilities for the manipulation of electromagnetic waves within compact multifunctional devices. This field encompasses the development of individual optical elements and their integration into systems for use in real-world applications, especially in optical information processing, light detection and ranging (LiDAR), augmented or virtual reality, and biological imaging. This comprehensive Review highlights advances in the use of flat optics in analog computational information processing and imaging applications and emphasizes their fundamental role in transfer function engineering. The natural synergy between flat optics and computational technologies is described in relation to advanced imaging and microscopy solutions that have the potential for simultaneous information acquisition and processing. An outlook on future developments, including critical insights for both newcomers and experts in this field, is also provided.

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Anchor-based spatio-temporal attention 3D convolutional networks for dynamic 3D point cloud sequences

arXiv

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arXiv 2020年

作者： Wang, Guangming Chen, Muyao Liu, Hanwen Yang, Yehui Liu, Zhe Wang, Hesheng The Department of Automation Institute of Medical Robotics Key Laboratory of System Control and Information Processing Ministry of Education Key Laboratory of Marine Intelligent Equipment System of Ministry of Education Shanghai Jiao Tong University Shanghai200240 China Beijing Advanced Innovation Center for Intelligent Robots and Systems Beijing Institute of Technology China The Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai200240 China The Department of Computer Science and Technology University of Cambridge

With the rapid development of measurwement technology, LiDAR and depth cameras are widely used in the perception of the 3D environment. Recent learning based methods for robot perception most focus on the image or video, but deep learning methods for dynamic 3D point cloud sequences are underexplored. Therefore, developing efficient and accurate perception method compatible with these advanced instruments is pivotal to autonomous driving and service robots. An Anchor-based Spatio-Temporal Attention 3D Convolution operation (ASTA3DConv) is proposed in this paper to process dynamic 3D point cloud sequences. The proposed convolution operation builds a regular receptive field around each point by setting several virtual anchors around each point. The features of neighborhood points are firstly aggregated to each anchor based on the spatio-temporal attention mechanism. Then, anchor-based 3D convolution is adopted to aggregate these anchors' features to the core points. The proposed method makes better use of the structured information within the local region and learns spatio-temporal embedding features from dynamic 3D point cloud sequences. Anchor-based Spatio-Temporal Attention 3D Convolutional Neural Networks (ASTA3DCNNs) are built for classification and segmentation tasks based on the proposed ASTA3DConv and evaluated on action recognition and semantic segmentation tasks. The experiments and ablation studies on MSRAction3D and Synthia datasets demonstrate the superior performance and effectiveness of our method for dynamic 3D point cloud sequences. Our method achieves the state-of-the-art performance among the methods with dynamic 3D point cloud sequences as input on MSRAction3D and Synthia datasets. Copyright © 2020, The Authors. All rights reserved.

关键词： Embeddings

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BoostTree and BoostForest for Ensemble Learning

arXiv

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arXiv 2020年

作者： Zhao, Changming Wu, Dongrui Huang, Jian Yuan, Ye Zhang, Hai-Tao Peng, Ruimin Shi, Zhenhua Key Laboratory of the Ministry of Education for Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China Shenzhen Huazhong University of Science and Technology Research Institute Shenzhen China The Autonomous Intelligence Unmanned Systems Engineering Research Center of Ministry of Education of China The State Key Lab of Digital Manufacturing Equipment and Technology Wuhan China

Bootstrap aggregating (Bagging) and boosting are two popular ensemble learning approaches, which combine multiple base learners to generate a composite model for more accurate and more reliable performance. They have been widely used in biology, engineering, healthcare, etc. This paper proposes BoostForest, which is an ensemble learning approach using BoostTree as base learners and can be used for both classification and regression. BoostTree constructs a tree model by gradient boosting. It increases the randomness (diversity) by drawing the cut-points randomly at node splitting. BoostForest further increases the randomness by bootstrapping the training data in constructing different BoostTrees. BoostForest generally outperformed four classical ensemble learning approaches (Random Forest, Extra-Trees, XGBoost and LightGBM) on 35 classification and regression datasets. Remarkably, BoostForest tunes its parameters by simply sampling them randomly from a parameter pool, which can be easily specified, and its ensemble learning framework can also be used to combine many other base learners. Copyright © 2020, The Authors. All rights reserved.

关键词： Classification (of information)

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An overview on visualization of ontology alignment and ontology entity 5th

An overview on visualization of ontology alignment and ontol...

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5th Euro-China Conference on intelligent Data Analysis and Applications, ECC 2018

作者： Chen, Jie Xue, Xingsi Huang, Lili Ren, Aihong College of Information Science and Engineering Fujian University of Technology FuzhouFujian350118 China Intelligent Information Processing Research Center Fujian University of Technology FuzhouFujian350118 China Fujian Provincial Key Laboratory of Big Data Mining and Applications FuzhouFujian350118 China Fujian Key Lab for Automotive Electronics and Electric Drive Fujian University of Technology FuzhouFujian350118 China College of Humanities and Law Fujian Agriculture and Forestry University FuzhouFujian350002 China School of Mathematics and Information Science Baoji University of Arts and Sciences BaojiShaanxi721013 China

ISBN: (纸本)9783030037659

Visualization of ontology alignment and ontology entity is becoming a method to help Solve the problem of semantic heterogeneity. This paper first introduces the basic concepts of ontology and ontology matching, and then illustrates the importance of ontology visualization technique. The existing partial visualization tools are discussed respectively in two aspects of visualization of ontology alignment and visualization entity on ontology. Finally, four researching directions on ontology visualization technique are pointed out for the future work. © Springer Nature Switzerland AG 2019.

关键词： Ontology

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HLO: Half-kernel laplacian operator for surface smoothing

arXiv

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arXiv 2019年

作者： Pan, Wei Lu, Xuequan Gong, Yuanhao Tang, Wenming Liu, Jun He, Ying Qiu, Guoping College of Information Engineering Shenzhen University Guangdong Key Laboratory of Intelligent Information Processing School of Information Technology Deakin University School of Computer Science and Engineering Nanyang Technological University

This paper presents a simple yet effective method for feature-preserving surface smoothing. Through analyzing the differential property of surfaces, we show that the conventional discrete Laplacian operator with uniform weights is not applicable to feature points at which the surface is non-differentiable and the second order derivatives do not exist. To overcome this difficulty, we propose a Half-kernel Laplacian Operator (HLO) as an alternative to the conventional Laplacian. Given a vertex v, HLO first finds all pairs of its neighboring vertices and divides each pair into two subsets (called half windows);then computes the uniform Laplacians of all such subsets and subsequently projects the computed Laplacians to the full-window uniform Laplacian to alleviate flipping and degeneration. The half window with least regularization energy is then chosen for v. We develop an iterative approach to apply HLO for surface denoising. Our method is conceptually simple and easy to use because it has a single parameter, i.e., the number of iterations for updating vertices. We show that our method can preserve features better than the popular uniform Laplacian-based denoising and it significantly alleviates the shrinkage artifact. Extensive experimental results demonstrate that HLO is better than or comparable to state-of-the-art techniques both qualitatively and quantitatively and that it is particularly good at handling meshes with high noise. We will make our source code publicly available. Copyright © 2019, The Authors. All rights reserved.

关键词： Laplace transforms

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Reverberant speech recognition with actual cochlear implants: Verifying a pulsatile vocoder simulation method 23

Reverberant speech recognition with actual cochlear implants...

引用

23rd International Congress on Acoustics: Integrating 4th EAA Euroregio, ICA 2019

作者： Kong, Fanhui Wang, Xianren Teng, Xiangbin Zheng, Nengheng Yu, Guangzheng Meng, Qinglin Guangdong Key Laboratory of Intelligent Information Processing College of Information Engineering Shenzhen University Shenzhen China Department of Otorhinolaryngology First Affiliated Hospital Sun Yat-Sen University Institute of Otorhinolaryngology Sun Yat-Sen University Guangzhou China Department of Neuroscience Max-Planck-Institute for Empirical Aesthetics Frankfurt Germany Acoustics Lab of School of Physics and Optoelectronics State Key Laboratory of Subtropical Building Science South China University of Technology Guangzhou China

ISBN: (纸本)9783939296157

Classic channel vocoders have been widely used for simulating cochlear implants (CIs). However, some important features of state-of-the-art CI strategies were usually ignored, such as pulsatile current, broad current spread, and low quantization resolution of intensity. To solve this problem, a novel vocoder was proposed, which is combined from Gaussian-shaped acoustic pulses each corresponding to an electric pulse and can directly transformed CI-electrodograms to sounds. Compared with the classic channel vocoders on a reverberant speech recognition task, this new vocoder was considerably more sensitive to the reverberant condition (published in APSIPA-2018). Moreover, its performance is comparable to actual CI users in previous CI-reverberation studies. Here, the same paradigm as in the APSIPA-2018 paper was tested on actual CI users. We found that under reverberant conditions the CI users got comparable scores to the simulated performance from the new vocoder simulation in the NH cohort, while the classic vocoder predicted significantly much higher performance This study demonstrated that the classic vocoders overestimated the task performance and further lent great support to the usage of the novel vocoder for the simulation of reverberant speech recognition in adult post-lingually deafened CI users. © 2019 Proceedings of the International Congress on Acoustics. All rights reserved.

关键词： Cochlear implant Reverberation Vocoder

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