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学者姓名：王普
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Abstract ：
本发明公开了一种基于自动聚类结合偏最小二乘的间歇过程质量预测方法。针对间歇过程的多阶段性特征，目前已有的阶段划分方法很少考虑质量相关变量对阶段划分结果的影响。本发明在划分阶段前利用典型相关分析(canonical correlation analysis，CCA)对间歇过程数据进行特征选择，在保证其过程变量以及质量相关变量之间相关关系最大时找到其最优的线性表示。该过程不仅可以实现数据降维，同时考虑质量相关变量对划分结果的影响。最终，在DBSCAN划分阶段内建立基于MPLS的质量预测模型。将该算法在青霉素发酵仿真实验平台进行了实验验证，实验结果证明了本方法的可行性和有效性。
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| GB/T 7714 | 王雨 , 王普 , 高学金 et al. 一种基于自动聚类结合偏最小二乘的间歇过程质量预测方法 : CN202110258605.0[P]. | 2021-03-09 . |
| MLA | 王雨 et al. "一种基于自动聚类结合偏最小二乘的间歇过程质量预测方法" : CN202110258605.0. | 2021-03-09 . |
| APA | 王雨 , 王普 , 高学金 , 高慧慧 , 韩华云 . 一种基于自动聚类结合偏最小二乘的间歇过程质量预测方法 : CN202110258605.0. | 2021-03-09 . |
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Abstract ：
We demonstrated MIR-pump NIR-probe photothermal spectroscopy with the first harmonic (PTS-1f) detection of formaldehyde, one of the most common volatile organic compounds (VOCs), in a silica hollow-core negative curvature fiber (HC-NCF). The photothermal gas sensor adopts a mid-infrared interband cascade pump laser at 3.6 mu m and a near-infrared fiber probe laser at 1.56 mu m. At the optimal modulation frequency (8 kHz) and modulation index (1.8) of the pump laser, we obtained a normalized noise equivalent absorption (NNEA) coefficient of 4 x 10(-9) cm(-1)WHz(-1/)2. The use of HC-NCF with an inner diameter of 65 mu m enables the sensitive photothermal detection even for a very low pump power of micro-watts. The background-free PTS-1f detection was observed to enhance the sensitivity by a factor of 2.4 compared to the second harmonic (2f) detection. A theoretical model was established in this work to interpret the experimental results.
Keyword ：
gas sensor gas sensor Photothermal spectroscopy Photothermal spectroscopy optical fiber sensor optical fiber sensor hollow core fiber hollow core fiber
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| GB/T 7714 | Yao, Chenyu , Gao, Shoufei , Wang, Yingying et al. MIR-Pump NIR-Probe Fiber-Optic Photothermal Spectroscopy With Background-Free First Harmonic Detection [J]. | IEEE SENSORS JOURNAL , 2020 , 20 (21) : 12709-12715 . |
| MLA | Yao, Chenyu et al. "MIR-Pump NIR-Probe Fiber-Optic Photothermal Spectroscopy With Background-Free First Harmonic Detection" . | IEEE SENSORS JOURNAL 20 . 21 (2020) : 12709-12715 . |
| APA | Yao, Chenyu , Gao, Shoufei , Wang, Yingying , Wang, Pu , Jin, Wei , Ren, Wei . MIR-Pump NIR-Probe Fiber-Optic Photothermal Spectroscopy With Background-Free First Harmonic Detection . | IEEE SENSORS JOURNAL , 2020 , 20 (21) , 12709-12715 . |
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Abstract ：
Fault monitoring can find out-of-control conditions of equipment operation in a timely manner, which is essential for eliminating faults and for stable operation of industrial systems in batch processes. Many conventional data-driven fault detection methods focus less on the non-Gaussian and Multi-stage characteristics of batch process data, which may result in degradation of monitoring performance. In this paper, a Multi-stage Fourth Order Moment Staked Autoencoder (M-FOM-SAE) is designed to solve the above problems. The proposed method firstly automatically determines the number of clusters and divides the batch process into multiple stages. After that, the FOM-SAE model is established in each sub-stage, which can not only effectively learn the nonlinear features of process data, but also extract the non-Gaussian information. The proposed strategy is applied to real-world industrial processes. Experimental results indicate that it can better capture the non-Gaussian and Multi-stage characteristics of process data, and improve the ability to monitor abnormalities.
Keyword ：
Fault monitoring Fault monitoring Stacked Autoencoder Stacked Autoencoder Multi-stage Multi-stage non-Gaussian non-Gaussian Batch process Batch process
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| GB/T 7714 | Chen, Jin , Pu, Wang , Kai, Wang . Batch Process Monitoring Based on Multi-stage Fourth Order Moment Stacked Autoencoder [C] . 2020 : 721-728 . |
| MLA | Chen, Jin et al. "Batch Process Monitoring Based on Multi-stage Fourth Order Moment Stacked Autoencoder" . (2020) : 721-728 . |
| APA | Chen, Jin , Pu, Wang , Kai, Wang . Batch Process Monitoring Based on Multi-stage Fourth Order Moment Stacked Autoencoder . (2020) : 721-728 . |
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Abstract ：
本发明公开一种基于双核t分布随机近邻嵌入的过程监测可视化方法。包括离线建模和在线监测两个步骤。离线建模利用标准t‑SNE方法对历史正常数据降维；计算输入核矩阵到特征核矩阵的映射参数矩阵；利用PCA将特征核矩阵降至两维，然后计算平方马氏距离作为统计量并求控制限。在线监测计算采集到的数据与建模数据之间的核函数；将得到的核向量与映射参数矩阵相乘获得映射后的特征核向量；利用PCA对映射后的特征核向量降维，得到用于可视化的二维特征；绘制特征的散点图并观察是否在椭圆控制限范围内。相比于现有技术，本发明保留标准t‑SNE方法数据降维优势的同时，将其应用于工业过程故障监测可视化，降低了工业过程监测的误报和漏报率。
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| GB/T 7714 | 张海利 , 王普 , 高学金 et al. 一种基于双核t分布随机近邻嵌入的过程监测可视化方法 : CN202010550245.7[P]. | 2020-06-16 . |
| MLA | 张海利 et al. "一种基于双核t分布随机近邻嵌入的过程监测可视化方法" : CN202010550245.7. | 2020-06-16 . |
| APA | 张海利 , 王普 , 高学金 , 高慧慧 . 一种基于双核t分布随机近邻嵌入的过程监测可视化方法 : CN202010550245.7. | 2020-06-16 . |
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Abstract ：
Aiming at nonlinearity and multimodal batch trajectories in semiconductor manufacturing processes, principal component analysis and k nearest neighbor (kNN)-related methods were previously presented. However, these methods require data unfolding and are not capable of extracting crucial features, which affects the performance of fault detection. In this paper, an automated fault detection method using convolutional auto encoder (CAE) and k nearest neighbor rule is proposed. Firstly, data collected in one batch is considered as a two-dimensional gray-scale image, and is input to CAE for feature unsupervised learning, with no need of data preprocessing and data labels. Secondly, kNN rule is incorporated into CAE to construct the monitoring index and perform fault detecting. Finally, the effectiveness of the proposed method is verified with a benchmark semiconductor manufacturing process.
Keyword ：
fault detection fault detection convolutional auto encoder convolutional auto encoder k nearest neighbor k nearest neighbor semiconductor manufacturing process semiconductor manufacturing process
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| GB/T 7714 | Zhang, Haili , Wang, Pu , Gao, Xuejin et al. Automated Fault Detection Using Convolutional Auto Encoder and k Nearest Neighbor Rule for Semiconductor Manufacturing Processes [C] . 2020 : 83-87 . |
| MLA | Zhang, Haili et al. "Automated Fault Detection Using Convolutional Auto Encoder and k Nearest Neighbor Rule for Semiconductor Manufacturing Processes" . (2020) : 83-87 . |
| APA | Zhang, Haili , Wang, Pu , Gao, Xuejin , Gao, Huihui , Qi, Yongsheng . Automated Fault Detection Using Convolutional Auto Encoder and k Nearest Neighbor Rule for Semiconductor Manufacturing Processes . (2020) : 83-87 . |
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Abstract ：
Ultrasensitive mid-infrared absorption spectroscopy is demonstrated by the use of a novel silica-based hollow-core negative curvature fiber (HC-NCF). The HC-NCF used in this article consists of a single ring of six nontouching cladding capillaries around the hollow core, thus forming a unique core boundary with a negative curvature. Such a silica HC-NCF enables the broadband single-mode transmission in the mid-infrared. By using the HC-NCF as a compact gas cell, a proof-of-principle experiment is conducted to detect the N2O line at 2778.37 cm(-1) with a distributed-feedback interband cascade laser emitting at 3.6 mu m. A minimum detectable absorbance of 3 x 10(-5) is achieved for a fiber length of 120 cm, corresponding to a noise equivalent absorption (NEA) coefficient of 2.5 x 10(-7) cm(-1). Silica HC-NCFs offer a new opportunity of developing sensitive and compact gas sensors using mid-infrared absorption spectroscopy.
Keyword ：
hollow core fiber hollow core fiber mid-infrared absorption spectroscopy mid-infrared absorption spectroscopy Gas sensor Gas sensor microstructured optical fiber microstructured optical fiber optical fiber sensor optical fiber sensor
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| GB/T 7714 | Yao, Chenyu , Gao, Shoufei , Wang, Yingying et al. Silica Hollow-Core Negative Curvature Fibers Enable Ultrasensitive Mid-Infrared Absorption Spectroscopy [J]. | JOURNAL OF LIGHTWAVE TECHNOLOGY , 2020 , 38 (7) : 2067-2072 . |
| MLA | Yao, Chenyu et al. "Silica Hollow-Core Negative Curvature Fibers Enable Ultrasensitive Mid-Infrared Absorption Spectroscopy" . | JOURNAL OF LIGHTWAVE TECHNOLOGY 38 . 7 (2020) : 2067-2072 . |
| APA | Yao, Chenyu , Gao, Shoufei , Wang, Yingying , Wang, Pu , Jin, Wei , Ren, Wei . Silica Hollow-Core Negative Curvature Fibers Enable Ultrasensitive Mid-Infrared Absorption Spectroscopy . | JOURNAL OF LIGHTWAVE TECHNOLOGY , 2020 , 38 (7) , 2067-2072 . |
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Abstract ：
A visual-inertial odometer is used to fuse the image information obtained by a vision sensor with the data measured by an inertial sensor and recover the motion track online in a global frame. However, in an indoor environment, geometric transformation, sparse features, illumination changes, blurring, and noise will occur, which will either cause a reduction in or failure of the positioning accuracy. To solve this problem, a map matching algorithm based on an indoor plane structure map is proposed to improve the positioning accuracy of the system; this algorithm was implemented using a conditional random field model. The output of the attitude information from the visual-inertial odometer was used as the input of the conditional random field model. The feature function between the attitude information and the expected value was established, and the maximum probabilistic value of the attitude was estimated. Finally, the closed-loop feedback correction of the visual-inertial system was carried out with the probabilistic attitude value. A number of experiments were designed to verify the feasibility and reliability of the positioning method proposed in this paper.
Keyword ：
map matching map matching visual-inertial odometer visual-inertial odometer indoor positioning system indoor positioning system conditional random field conditional random field
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| GB/T 7714 | Meng, Juan , Ren, Mingrong , Wang, Pu et al. Improving Positioning Accuracy via Map Matching Algorithm for Visual-Inertial Odometer [J]. | SENSORS , 2020 , 20 (2) . |
| MLA | Meng, Juan et al. "Improving Positioning Accuracy via Map Matching Algorithm for Visual-Inertial Odometer" . | SENSORS 20 . 2 (2020) . |
| APA | Meng, Juan , Ren, Mingrong , Wang, Pu , Zhang, Jitong , Mou, Yuman . Improving Positioning Accuracy via Map Matching Algorithm for Visual-Inertial Odometer . | SENSORS , 2020 , 20 (2) . |
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Abstract ：
High-precision indoor localization plays a vital role in various places. In recent years, visual inertial odometry (VIO) system has achieved outstanding progress in the field of indoor localization. However, it is easily affected by poor lighting and featureless environments. For this problem, we propose an indoor localization algorithm based on VIO system and three-dimensional (3D) map matching. The 3D map matching is to add height matching on the basis of previous two-dimensional (2D) matching so that the algorithm has more universal applicability. Firstly, the conditional random field model is established. Secondly, an indoor three-dimensional digital map is used as a priori information. Thirdly, the pose and position information output by the VIO system are used as the observation information of the conditional random field (CRF). Finally, the optimal states sequence is obtained and employed as the feedback information to correct the trajectory of VIO system. Experimental results show that our algorithm can effectively improve the positioning accuracy of VIO system in the indoor area of poor lighting and featureless.
Keyword ：
map matching map matching visual inertial odometry visual inertial odometry conditional random field conditional random field three-dimensional three-dimensional indoor localization indoor localization
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| GB/T 7714 | Zhang, Jitong , Ren, Mingrong , Wang, Pu et al. Indoor Localization Based on VIO System and Three-Dimensional Map Matching [J]. | SENSORS , 2020 , 20 (10) . |
| MLA | Zhang, Jitong et al. "Indoor Localization Based on VIO System and Three-Dimensional Map Matching" . | SENSORS 20 . 10 (2020) . |
| APA | Zhang, Jitong , Ren, Mingrong , Wang, Pu , Meng, Juan , Mu, Yuman . Indoor Localization Based on VIO System and Three-Dimensional Map Matching . | SENSORS , 2020 , 20 (10) . |
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Abstract ：
序批式反应器(SBR)的处理过程的数据具有非高斯分布和高度非线性的特点,传统特征提取方法在进行特征提取时仅仅考虑信息最大化而忽略数据的簇结构信息导致数据特征提取的不完整.由于多向核熵成分分析是一种新的监测方法,在监测过程中的应用表明能够克服传统监测方法的缺陷,减少误报警率.因此本文结合多向核熵成分分析的的优势,提出多向核熵独立成分分析方法用于SBR过程监测及故障诊断.首先,将三维SBR过程数据利用一种新的数据展开技术变为二维数据;其次,利用核熵成分分析将展开后的二维数据映射到高维空间用独立成分分析进行独立成分提取;最后提出一种基于多向核熵独立成分分析的故障诊断方法进行故障诊断.将该方法和传统方法应用于80升的SBR处理过程的监测结果表明,本文提出的方法优于传统的多向独立成分分析方法.
Keyword ：
多向核熵独立成分 多向核熵独立成分 序批式反应器 序批式反应器 故障诊断 故障诊断 故障检测 故障检测
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| GB/T 7714 | 常鹏 , 乔俊飞 , 王普 et al. 非线性和非高斯性共存的序批次反应处理过程故障诊断 [J]. | 控制理论与应用 , 2019 , 36 (5) : 728-736 . |
| MLA | 常鹏 et al. "非线性和非高斯性共存的序批次反应处理过程故障诊断" . | 控制理论与应用 36 . 5 (2019) : 728-736 . |
| APA | 常鹏 , 乔俊飞 , 王普 , 高学金 . 非线性和非高斯性共存的序批次反应处理过程故障诊断 . | 控制理论与应用 , 2019 , 36 (5) , 728-736 . |
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Abstract ：
The common energy efficiency model of chiller in subway station has nonlinear interference, which leads to low prediction accuracy and reliability. To solve the above problems, this paper proposes a model of chiller efficiency prediction based on error correction. This model focuses on the error, trains the historical data of prediction error by SVR, and then corrects the prediction result of traditional model to get the final prediction value of energy efficiency. Through the verification of operation data from air conditioning units on a Beijing subway platform , the results show that this model is superior to the traditional model , the average relative error of predictive value decreased from 4.04*10(-2) to 6.69*10(-)(11).
Keyword ：
error correction error correction thermodynamics thermodynamics support vector machine support vector machine chiller chiller
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| GB/T 7714 | Wang Pu , Zeng Wenhao , Gao Xuejin . Research on The Model of Chiller Efficiency Prediction Based on Error Correction [C] . 2019 : 1660-1665 . |
| MLA | Wang Pu et al. "Research on The Model of Chiller Efficiency Prediction Based on Error Correction" . (2019) : 1660-1665 . |
| APA | Wang Pu , Zeng Wenhao , Gao Xuejin . Research on The Model of Chiller Efficiency Prediction Based on Error Correction . (2019) : 1660-1665 . |
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