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学者姓名：顾锞
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Abstract ：
This article irons out the issue of recursive state estimation for mobile robot localization under a multiple description coding scheme. For the sake of optimizing the utilization of channel resources, a novel two-description coding scheme is first introduced to facilitate measurements transmission by encoding the data into two equally important descriptions. The raw data is then reconstructed according to the number of the descriptions received by the decoders. Meanwhile, two random variables with Bernoulli distribution are used to display the occurrences of the packet dropouts in both parallel independent channels from the encoders to the decoders. The primary objective of this article is to develop a desired estimator tailored to the mobile robot localization problem in the presence of the data encoding-decoding mechanism, where the upper bound on the estimation error covariance is first guaranteed by virtue of mathematical induction and then is minimized by designing the estimator gain appropriately. Furthermore, the estimation performance is analyzed through the implementation of a sufficient condition. Finally, experimental examples are employed to verify the applicability of the proposed encoding-decoding-based recursive state estimation scheme for mobile robot localization.
Keyword ：
Mobile robots Mobile robots Quantization (signal) Quantization (signal) multiple description coding scheme multiple description coding scheme Encoding Encoding Upper bound Upper bound Accuracy Accuracy Mobile robot localization (MRL) Mobile robot localization (MRL) Mechatronics Mechatronics Decoding Decoding Indexes Indexes State estimation State estimation packet dropout packet dropout recursive state estimation recursive state estimation Kinematics Kinematics
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| GB/T 7714 | Huang, Cong , Zhu, Li , Ding, Weiping et al. EncodingDecoding-Based Recursive State Estimation for Mobile Robot Localization: A Multiple Description Case [J]. | IEEE-ASME TRANSACTIONS ON MECHATRONICS , 2025 . |
| MLA | Huang, Cong et al. "EncodingDecoding-Based Recursive State Estimation for Mobile Robot Localization: A Multiple Description Case" . | IEEE-ASME TRANSACTIONS ON MECHATRONICS (2025) . |
| APA | Huang, Cong , Zhu, Li , Ding, Weiping , Gu, Ke , Mei, Peng , Yang, Shichun . EncodingDecoding-Based Recursive State Estimation for Mobile Robot Localization: A Multiple Description Case . | IEEE-ASME TRANSACTIONS ON MECHATRONICS , 2025 . |
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Abstract ：
一种VOCs燃烧残留量精确检测方法属于智能环保领域。本发明步骤：基于计算流体力学，针对双股蒸汽助燃型火炬进行仿真建模，构建放空火炬系统最终生成混合气体成分的仿真数据集，基于烟气分析仪测量放空火炬系统混合气体成分构建测量数据集；针对因仪器检测过程耗时导致测量数据集存在时间滞后的问题，采用延迟消除方法修正数据集中的VOCs燃烧残留量的时间戳，实现VOCs燃烧残留量的预测；基于构建数据集使用RBF网络建立放空火炬VOCs燃烧残留量预测模型；针对RBF网络的设计，设计基于密度的Canopy‑K均值算法初始化网络的结构和参数，提高网络性能；采用微调和基于梯度的算法调整RBF网络参数，提高网络的逼近能力。
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| GB/T 7714 | 郭楠 , 乔俊飞 , 顾锞 et al. 一种VOCs燃烧残留量精确检测方法 : CN202310714365.X[P]. | 2023-06-15 . |
| MLA | 郭楠 et al. "一种VOCs燃烧残留量精确检测方法" : CN202310714365.X. | 2023-06-15 . |
| APA | 郭楠 , 乔俊飞 , 顾锞 , 李鹏宇 , 武利 , 贾丽杰 et al. 一种VOCs燃烧残留量精确检测方法 : CN202310714365.X. | 2023-06-15 . |
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本发明公开了一种面向污染监测的多通路深度神经网络高效训练方法，其中多通路深度神经网络高效训练方法先通过分步筛选集成所有单通道神经网络及其融合部分的最优参数，再对融合后的多通路深度神经网络进行微调得到污染监测模型的最优参数，将污染物图像样本输入网络进行训练，能有效提高污染监测模型精度。本发明针对不同子网络及其组合进行网络训练，集成了所有子网络及其融合部分的最优参数，解决了随机初始化参数容易使网络陷入局部最小值的问题，从而提高了神经网络模型监测精度；提高污染监测模型的监测精度。
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| GB/T 7714 | 顾锞 , 谢双憶 , 刘静 . 一种面向污染监测的多通路深度神经网络高效训练方法 : CN202310106807.2[P]. | 2023-02-13 . |
| MLA | 顾锞 et al. "一种面向污染监测的多通路深度神经网络高效训练方法" : CN202310106807.2. | 2023-02-13 . |
| APA | 顾锞 , 谢双憶 , 刘静 . 一种面向污染监测的多通路深度神经网络高效训练方法 : CN202310106807.2. | 2023-02-13 . |
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Abstract ：
Screen content, which is often computer-generated, has many characteristics distinctly different from conventional camera-captured natural scene content. Such characteristic differences impose major challenges to the corresponding content quality assessment, which plays a critical role to ensure and improve the final user-perceived quality of experience (QoE) in various screen content communication and networking systems. Quality assessment of such screen content has attracted much attention recently, primarily because the screen content grows explosively due to the prevalence of cloud and remote computing applications in recent years, and due to the fact that conventional quality assessment methods can not handle such content effectively. As the most technology-oriented part of QoE modeling, image/video content/media quality assessment has drawn wide attention from researchers, and a large amount of work has been carried out to tackle the problem of screen content quality assessment. This article is intended to provide a systematic and timely review on this emerging research field, including (1) background of natural scene vs. screen content quality assessment; (2) characteristics of natural scene vs. screen content; (3) overview of screen content quality assessment methodologies and measures; (4) relevant benchmarks and comprehensive evaluation of the state-of-the-art; (5) discussions on generalizations from screen content quality assessment to QoE assessment, and other techniques beyond QoE assessment; and (6) unresolved challenges and promising future research directions. Throughout this article, we focus on the differences and similarities between screen content and conventional natural scene content. We expect that this review article shall provide readers with an overview of the background, history, recent progress, and future of the emerging screen content quality assessment research.
Keyword ：
quality of experience quality of experience Screen content Screen content natural scene natural scene quality assessment quality assessment
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| GB/T 7714 | Min, Xiongkuo , Gu, Ke , Zhai, Guangtao et al. Screen Content Quality Assessment: Overview, Benchmark, and Beyond [J]. | ACM COMPUTING SURVEYS , 2022 , 54 (9) . |
| MLA | Min, Xiongkuo et al. "Screen Content Quality Assessment: Overview, Benchmark, and Beyond" . | ACM COMPUTING SURVEYS 54 . 9 (2022) . |
| APA | Min, Xiongkuo , Gu, Ke , Zhai, Guangtao , Yang, Xiaokang , Zhang, Wenjun , Le Callet, Patrick et al. Screen Content Quality Assessment: Overview, Benchmark, and Beyond . | ACM COMPUTING SURVEYS , 2022 , 54 (9) . |
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一种放空火炬燃烧状态定量预测及最佳助燃蒸汽量寻优方法，属于智能环保技术领域。本发明所述方法包括以下步骤：基于计算流体力学，针对双股蒸汽助燃型火炬进行仿真建模，获得废气成分流速、助燃蒸汽量和燃尽率数据；根据仿真数据使用LSTM网络建立放空火炬燃烧状态预测模型；采用NSGA‑Ⅲ算法，对放空火炬所需最佳助燃蒸汽量进行寻优；将优化算法改进为动态优化算法。本发明通过在软件中仿真实际工况获得大量可靠数据解决了实际放空火炬数据稀缺问题，通过神经网络建模解决了仿真模型计算过慢、难以实际应用的问题，通过优化算法解决能耗与燃烧状态之间的耦合关系，能够快速准确地定量判断放空火炬燃烧状态和所需助燃蒸汽量。
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| GB/T 7714 | 乔俊飞 , 彭益新 , 郭楠 et al. 一种放空火炬燃烧状态定量预测及最佳助燃蒸汽量寻优方法 : CN202211129496.3[P]. | 2022-09-15 . |
| MLA | 乔俊飞 et al. "一种放空火炬燃烧状态定量预测及最佳助燃蒸汽量寻优方法" : CN202211129496.3. | 2022-09-15 . |
| APA | 乔俊飞 , 彭益新 , 郭楠 , 顾锞 . 一种放空火炬燃烧状态定量预测及最佳助燃蒸汽量寻优方法 : CN202211129496.3. | 2022-09-15 . |
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本发明公开了一种基于回声状态网络的放空火炬燃烧状态精确控制方法，该方法基于回声状态网络的模型预测控制技术、设定值跟踪控制技术，利用Fluent软件对放空火炬的湍动燃烧过程进行模拟，计算其燃尽率和破坏去除率来精确判断燃烧状态，然后根据公式计算出精确的助燃蒸汽流量，从而对助燃蒸汽流量进行精确调控以实现高效燃烧。本发明通过建立放空火炬机理模型，筛选出高质量的数据建立回声状态网络模型，并预测最佳助燃蒸汽流量，随后设计回声状态网络辨识器和预测控制器，对助燃蒸汽流量进行设定值在线跟踪控制。基于设定值跟踪控制研究，可及时地校正控制过程中出现的各种复杂情况，在火炬高效燃烧和节约资源方面都提升了很多。
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| GB/T 7714 | 乔俊飞 , 刘佳晖 , 郭楠 et al. 一种基于回声状态网络的放空火炬燃烧状态精确控制方法 : CN202211119832.6[P]. | 2022-09-15 . |
| MLA | 乔俊飞 et al. "一种基于回声状态网络的放空火炬燃烧状态精确控制方法" : CN202211119832.6. | 2022-09-15 . |
| APA | 乔俊飞 , 刘佳晖 , 郭楠 , 顾锞 . 一种基于回声状态网络的放空火炬燃烧状态精确控制方法 : CN202211119832.6. | 2022-09-15 . |
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Abstract ：
Tone mapping operators (TMOs) are developed to convert a high dynamic range (HDR) image into a low dynamic range (LDR) one for display with the goal of preserving as much visual information as possible. However, image quality degradation is inevitable due to the dynamic range compression during the tone-mapping process. This accordingly raises an urgent demand for effective quality evaluation methods to select a high-quality tone-mapped image (TMI) from a set of candidates generated by distinct TMOs or the same TMO with different parameter settings. A key element to the success of TMI quality evaluation is to extract effective features that are highly consistent with human perception. Towards this end, this paper proposes a novel blind TMI quality metric by exploiting both local degradation characteristics and global statistical properties for feature extraction. Several image attributes including texture, structure, colorfulness and naturalness are considered either locally or globally. The extracted local and global features are aggregated into an overall quality via regression. Experimental results on two benchmark databases demonstrate the superiority of the proposed metric over both the state-of-The-Art blind quality models designed for synthetically distorted images (SDIs) and the blind quality models specifically developed for TMIs. © 1999-2012 IEEE.
Keyword ：
Image quality Image quality Quality control Quality control Benchmarking Benchmarking Textures Textures Mapping Mapping
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| GB/T 7714 | Wang, Xuejin , Jiang, Qiuping , Shao, Feng et al. Exploiting Local Degradation Characteristics and Global Statistical Properties for Blind Quality Assessment of Tone-Mapped HDR Images [J]. | IEEE Transactions on Multimedia , 2021 , 23 : 692-705 . |
| MLA | Wang, Xuejin et al. "Exploiting Local Degradation Characteristics and Global Statistical Properties for Blind Quality Assessment of Tone-Mapped HDR Images" . | IEEE Transactions on Multimedia 23 (2021) : 692-705 . |
| APA | Wang, Xuejin , Jiang, Qiuping , Shao, Feng , Gu, Ke , Zhai, Guangtao , Yang, Xiaokang . Exploiting Local Degradation Characteristics and Global Statistical Properties for Blind Quality Assessment of Tone-Mapped HDR Images . | IEEE Transactions on Multimedia , 2021 , 23 , 692-705 . |
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Recent years have witnessed numerous successful applications of incorporating attention module into feed-forward convolutional neural networks. Along this line of research, we design a novel lightweight general-purpose attention module by simultaneously taking channel attention and spatial attention into consideration. Specifically, inspired by the characteristics of channel attention and spatial attention, a nonlinear hybrid method is proposed to combine such two types of attention feature maps, which is highly beneficial to better network fine-tuning. Further, the parameters of each attention branch can be adjustable for the purpose of making the attention module more flexible and adaptable. From another point of view, we found that the currently popular SE, and CBAM modules are actually two particular cases of our proposed attention module. We also explore the latest attention module ADCM. To validate the module, we conduct experiments on CIFAR10, CIFAR100, Fashion MINIST datasets. Results show that, after integrating with our attention module, existing networks tend to be more efficient in training process and have better performance as compared with state-of-the-art competitors. Also, it is worthy to stress the following two points: (1) our attention module can be used in existing state-of-the-art deep architectures and get better performance at a small computational cost; (2) the module can be added to existing deep architectures in a simple way through stacking the integration of networks block and our module.
Keyword ：
Hybrid attention mechanism Hybrid attention mechanism Convolutional neural networks Convolutional neural networks Feature map combination Feature map combination General module General module
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| GB/T 7714 | Guo Nan , Gu Ke , Qiao Junfei et al. Improved deep CNNs based on Nonlinear Hybrid Attention Module for image classification. [J]. | Neural networks : the official journal of the International Neural Network Society , 2021 , 140 : 158-166 . |
| MLA | Guo Nan et al. "Improved deep CNNs based on Nonlinear Hybrid Attention Module for image classification." . | Neural networks : the official journal of the International Neural Network Society 140 (2021) : 158-166 . |
| APA | Guo Nan , Gu Ke , Qiao Junfei , Bi Jing . Improved deep CNNs based on Nonlinear Hybrid Attention Module for image classification. . | Neural networks : the official journal of the International Neural Network Society , 2021 , 140 , 158-166 . |
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Abstract ：
In each petrochemical plant around the world, the flare stack as a requisite facility produces a large amount of soot due to the incomplete combustion of flare gas, and this strongly endangers air quality and human health. Despite severe damages, the abovementioned abnormal conditions rarely occur, and, thus, only few-shot samples are available. To address such difficulty, in this article, we design an image-based flare soot density recognition network (FSDR-Net) via a new ensemble meta-learning technology. More particularly, we first train a deep convolutional neural network (CNN) by applying the model-agnostic meta-learning algorithm on a variety of learning tasks that are relevant to the flare soot recognition so as to obtain the general-purpose optimized initial parameters (GOIP). Second, for the new task of recognizing the flare soot density via only few-shot instances, a new ensemble is developed to selectively aggregate several predictions that are generated based on a wide range of learning rates and a small number of gradient steps. Results of experiments conducted on the density recognition of flare soot corroborate the superiority of our proposed FSDR-Net as compared with the popular and state-of-the-art deep CNNs. © 2005-2012 IEEE.
Keyword ：
Air quality Air quality Dust Dust Convolutional neural networks Convolutional neural networks Deep neural networks Deep neural networks Soot Soot Learning algorithms Learning algorithms Optical character recognition Optical character recognition Petrochemical plants Petrochemical plants
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| GB/T 7714 | Gu, Ke , Zhang, Yonghui , Qiao, Junfei . Ensemble Meta-Learning for Few-Shot Soot Density Recognition [J]. | IEEE Transactions on Industrial Informatics , 2021 , 17 (3) : 2261-2270 . |
| MLA | Gu, Ke et al. "Ensemble Meta-Learning for Few-Shot Soot Density Recognition" . | IEEE Transactions on Industrial Informatics 17 . 3 (2021) : 2261-2270 . |
| APA | Gu, Ke , Zhang, Yonghui , Qiao, Junfei . Ensemble Meta-Learning for Few-Shot Soot Density Recognition . | IEEE Transactions on Industrial Informatics , 2021 , 17 (3) , 2261-2270 . |
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Abstract ：
In this article, we propose an efficient joint image quality assessment and enhancement algorithm for the 3-D-synthesized images using a global predictor, namely, kernel ridge regression (KRR). Recently, a few prediction-based image quality assessment (IQA) algorithms have been proposed for 3-D-synthesized images. These algorithms use efficient prediction algorithms and try to predict all the regions efficiently, except the boundaries of the regions with geometric distortions. Unfortunately, these algorithms only count the number of pixels along the boundaries of the regions with geometric distortions and subsequently, calculate the quality scores. With this view, we propose a new algorithm for 3-D-synthesized images based upon the global KRR-based predictor, which estimates the complete distortion surface with geometric distortions. Further, it uses the distortion surface to estimate the perceptual quality of the 3-D-synthesized images. Also, the joint quality assessment and enhancement algorithms for 3-D-synthesized images are missing in literature. With this view, we propose to estimate the distortion map of the geometric distortions via the same predictor used in quality estimation and it subsequently enhances the perceptual quality of the 3-D-synthesized images. The performance of the proposed quality assessment algorithm is better than the existing IQA algorithms. Also, the proposed quality enhancement algorithm is promising, significantly enhancing the perceptual quality of 3-D-synthesized images. © 1982-2012 IEEE.
Keyword ：
Forecasting Forecasting Image quality Image quality Image enhancement Image enhancement Regression analysis Regression analysis Geometry Geometry Quality control Quality control
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| GB/T 7714 | Jakhetiya, Vinit , Gu, Ke , Jaiswal, Sunil P. et al. Kernel-Ridge Regression-Based Quality Measure and Enhancement of Three-Dimensional-Synthesized Images [J]. | IEEE Transactions on Industrial Electronics , 2021 , 68 (1) : 423-433 . |
| MLA | Jakhetiya, Vinit et al. "Kernel-Ridge Regression-Based Quality Measure and Enhancement of Three-Dimensional-Synthesized Images" . | IEEE Transactions on Industrial Electronics 68 . 1 (2021) : 423-433 . |
| APA | Jakhetiya, Vinit , Gu, Ke , Jaiswal, Sunil P. , Singhal, Trisha , Xia, Zhifang . Kernel-Ridge Regression-Based Quality Measure and Enhancement of Three-Dimensional-Synthesized Images . | IEEE Transactions on Industrial Electronics , 2021 , 68 (1) , 423-433 . |
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