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摘要 ：
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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摘要 ：
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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摘要 ：
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.
关键词 ：
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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摘要 ：
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 . |
| 导入链接 | NoteExpress RIS BibTex |
摘要 ：
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 . |
| 导入链接 | NoteExpress RIS BibTex |
摘要 ：
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.
关键词 ：
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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摘要 ：
This article devises a photograph-based monitoring model to estimate the real-time PM2.5 concentrations, overcoming currently popular electrochemical sensor-based PM2.5 monitoring methods' shortcomings such as low-density spatial distribution and time delay. Combining the proposed monitoring model, the photographs taken by various camera devices (e.g., surveillance camera, automobile data recorder, and mobile phone) can widely monitor PM2.5 concentration in megacities. This is beneficial to offering helpful decision-making information for atmospheric forecast and control, thus reducing the epidemic of COVID-19. To specify, the proposed model fuses Information Abundance measurement and Wide and Deep learning, dubbed as IAWD, for PM2.5 monitoring. First, our model extracts two categories of features in a newly proposed DS transform space to measure the information abundance (IA) of a given photograph since the growth of PM2.5 concentration decreases its IA. Second, to simultaneously possess the advantages of memorization and generalization, a new wide and deep neural network is devised to learn a nonlinear mapping between the above-mentioned extracted features and the groundtruth PM2.5 concentration. Experiments on two recently established datasets totally including more than 100 000 photographs demonstrate the effectiveness of our extracted features and the superiority of our proposed IAWD model as compared to state-of-the-art relevant computing techniques.
关键词 ：
Atmospheric measurements Atmospheric measurements Transforms Transforms information abundance (IA) information abundance (IA) Temperature measurement Temperature measurement Atmospheric modeling Atmospheric modeling Monitoring Monitoring DS transform space DS transform space photograph-based PM2.5 monitoring photograph-based PM2.5 monitoring COVID-19 COVID-19 wide and deep learning wide and deep learning Feature extraction Feature extraction
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| GB/T 7714 | Gu, Ke , Liu, Hongyan , Xia, Zhifang et al. PM2.5 Monitoring: Use Information Abundance Measurement and Wide and Deep Learning [J]. | IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS , 2021 , 32 (10) : 4278-4290 . |
| MLA | Gu, Ke et al. "PM2.5 Monitoring: Use Information Abundance Measurement and Wide and Deep Learning" . | IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 32 . 10 (2021) : 4278-4290 . |
| APA | Gu, Ke , Liu, Hongyan , Xia, Zhifang , Qiao, Junfei , Lin, Weisi , Thalmann, Daniel . PM2.5 Monitoring: Use Information Abundance Measurement and Wide and Deep Learning . | IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS , 2021 , 32 (10) , 4278-4290 . |
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摘要 ：
In submarine and underwater detection tasks, conventional optical imaging and analysis methods are not universally applicable due to the limited penetration depth of visible light. Instead, sonar imaging has become a preferred alternative. However, the capture and transmission conditions in complicated and dynamic underwater environments inevitably lead to visual quality degradation of sonar images, which might also impede further recognition, analysis and understanding. To measure this quality decrease and provide a solid quality indicator for sonar image enhancement, we propose a task- and perception-oriented sonar image quality assessment (TPSIQA) method, in which a semi-reference (SR) approach is applied to adapt to the limited bandwidth of underwater communication channels. In particular, we exploit reduced visual features that are critical for both human perception of and object recognition in sonar images. The final quality indicator is obtained through ensemble learning, which aggregates an optimal subset of multiple base learners to achieve both high accuracy and a high generalization ability. In this way, we are able to develop a compact but generalized quality metric using a small database of sonar images. Experimental results demonstrate competitive performance, high efficiency, and strong robustness of our method compared to the latest available image quality metrics.
关键词 ：
Feature extraction Feature extraction image quality asse-ssment (IQA) image quality asse-ssment (IQA) Sonar detection Sonar detection Image quality Image quality Sonar image Sonar image semi-reference semi-reference Sonar measurements Sonar measurements task-aware quality assessment task-aware quality assessment Task analysis Task analysis
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| GB/T 7714 | Chen, Weiling , Gu, Ke , Zhao, Tiesong et al. Semi-Reference Sonar Image Quality Assessment Based on Task and Visual Perception [J]. | IEEE TRANSACTIONS ON MULTIMEDIA , 2021 , 23 : 1008-1020 . |
| MLA | Chen, Weiling et al. "Semi-Reference Sonar Image Quality Assessment Based on Task and Visual Perception" . | IEEE TRANSACTIONS ON MULTIMEDIA 23 (2021) : 1008-1020 . |
| APA | Chen, Weiling , Gu, Ke , Zhao, Tiesong , Jiang, Gangyi , Le Callet, Patrick . Semi-Reference Sonar Image Quality Assessment Based on Task and Visual Perception . | IEEE TRANSACTIONS ON MULTIMEDIA , 2021 , 23 , 1008-1020 . |
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摘要 ：
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.
关键词 ：
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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摘要 ：
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.
关键词 ：
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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