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14. A safe operation system suitable for smart electric energy meters

Author

Hu, Qilong, primary, Yuan, Ruiming, additional, Wang, Xiaodong, additional, Jiang, Honglang, additional, Pang, Zhenjiang, additional, Du, Jun, additional, Duan, Xiaomeng, additional, Wang, Shuang, additional, Li, Wenwen, additional, Fan, Lin, additional, Wang, Xin, additional, and Wang, Zhiqiang, additional

Published
2022
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20. Development and preliminary experimental results of novel three-band multi-polarization airborne SAR system

Author

Kun Xing, Li Wenwen, Zhongjun Yu, Chen Qingxin, Liu Yabo, and Liu Lin

Subjects
Synthetic aperture radar, Basis (linear algebra), Computer science, business.industry, Phased array, Wireless, Ka band, Polarization (waves), business, Wireless sensor network, Time–frequency analysis, Remote sensing
Abstract

Compared with traditional single-channel synthetic aperture radar (SAR) system, the multi-band, multi-polarization SAR system can obtain more observation information to improve the ability of targets recognition, and becomes an important research direction. Therefore, we developed a new highly integrated airborne three-band multi-polarization SAR system, which works in low/medium/high (L/X/Ka band) frequency, and has the ability of full-polarity. Three-band phased array antennas are integrated a whole coplanar structure, and controlled by one task management unit. Each band of the system can work alone or together at the basis frequency and basis time, to obtain different frequency SAR images of the same area simultaneously. The results of flight experiments verify the function and performance of the system effectively. The design and application of this SAR system is significant for quantitative remote sensing observation and instructive for future radar system development.

Published
2021
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21. Comprehensive evaluation of reliability life and economy of electric energy meter considering intermittent operation mode

Author

Yuan Ruiming, Li Wenwen, Tan Zhiqiang, Zhou Lixia, and Liu Kexue

Subjects
Reliability theory, Electric energy, Operation mode, Economy, Hardware_GENERAL, Computer science, Process (computing), Mode (statistics), Metre, State (computer science), Reliability (statistics)
Abstract

In the application process of smart electric energy meter, not all components are in working state all the time. According to the working stress to predict its reliability index will lead to the conservatism of the predicted results. Therefore, it is necessary to consider the non-stress state of the intermittent mode unit, so as to make a more reasonable and accurate prediction. In this paper, a reliability prediction method considering non-stress state of components in intermittent operation mode is proposed, and a practical reliability prediction model of electric energy meter is established, which realizes the accurate reliability prediction of intelligent electric energy meter in intermittent mode. Based on the theory of reliability prediction and life cycle cost of electric energy meter, a comprehensive evaluation model of reliability and economy of smart electric energy meter is established, which provides a theoretical basis for the formulation of reliability index and related management policies of smart electric energy meter.

Published
2021
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26. Cloud Detection and Classification Algorithms for Himawari-8 Imager Measurements Based on Deep Learning.

Author

Li, Wenwen, Zhang, Feng, Lin, Han, Chen, Xiaoran, Li, Jun, and Han, Wei

Subjects
*DEEP learning, *MODIS (Spectroradiometer), *GEOSTATIONARY satellites, *CLASSIFICATION algorithms
Abstract

A deep-learning-based cloud detection and classification algorithm for advanced Himawari imager (AHI) measurements from the geostationary satellite Himawari-8 has been developed. It is found that a combination of observed radiances and simulated clear-sky radiances can substantially improve cloud phase discrimination, especially for optically thin clouds. Therefore, cloud detection, cloud phase classification, and multilayer cloud detection are obtained simultaneously from multispectral observed radiances and simulated clear-sky radiances using deep neural networks (DNNs). Two DNN models are established for all-day and daytime-only applications, respectively, using active Cloud Profiling Radar (CPR) and Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) merged cloud products from 2016 as reference labels. The independent dataset from 2017 is used to validate the DNN models. It is shown that both the DNN models outperform the official Moderate Resolution Imaging Spectroradiometer (MODIS) and AHI products in cloud detection and phase discrimination, and the enhancement is more significant over land than over water surface. For multilayer cloud detection, the probability of detecting multilayer clouds reaches ~60% for the all-day model and is increased to ~70% for the daytime model, which is substantially better than MODIS and AHI products. In practical cases, multilayer cloud detection by DNN models is more consistent with CPR/CALIOP than two official products. In addition, the DNN models have superior capability in detecting the optically thin cirrus, which is omitted by MODIS and AHI products. Specifically, the cases also demonstrate that the DNN models can provide effective mixed-phase cloud identification. This deep-learning-based algorithm has the potential for measurements from other similar instruments. [ABSTRACT FROM AUTHOR]

Published
2022
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33. Gate Stability and Robustness of In-Situ Oxide GaN Interlayer Based Vertical Trench MOSFETs (OG-FETs)

Author

Ruzzarin, Maria, primary, Borga, Matteo, additional, Zanoni, Enrico, additional, Meneghini, Matteo, additional, Meneghesso, Gaudenzio, additional, Ji, Dong, additional, Li, Wenwen, additional, Chan, Silvia H., additional, Agarwal, Anchal, additional, Gupta, Chirag, additional, Keller, Stacia, additional, Mishra, Umesh K., additional, and Chowdhury, Srabanti, additional

Published
2019
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34. The Variation in Microwave Brightness Temperature of Granite Pressed Under Weak Background Radiation.

Author

Gao, Xiang, Liu, Shanjun, Wu, Lixin, Mao, Wenfei, Li, Wenwen, and Huang, Jianwei

Subjects
BACKGROUND radiation, GRANITE, BRIGHTNESS temperature, MICROWAVES, MICROWAVE radiometers, COMPASSION, QUARTZ, CYCLIC loads
Abstract

Generally, ground stress accumulates in the process of earthquake (EQ) preparation. The change trend of microwave brightness temperature ($T_{B}$) of rock with stress change is an important factor for the understanding of the microwave anomalies associated with EQs. However, it is not yet clear whether the downtrend of rocks’ $T_{B}$ is associated with increased stress. To confirm this, in this article, the instantaneous field of view of the microwave radiometer was identified first. Then, the microwave observation experiments were conducted on granite samples at 6.6 GHz under cyclic loading and outdoor conditions with weak background radiation. It was found that besides uptrend and fluctuation, the downtrend of granite samples’ $T_{B}$ also correlates with stress, with an occurrence probability of 47.62% and a maximum rate of −0.038 K/MPa. The variation trends of $T_{B}$ with stress are not uniform across different areas of the same sample. To reveal the cause of this phenomenon, the permittivity of single-crystal quartz, one of the main mineral compositions of granite, was measured when it was under compassion loading at the direction perpendicular or parallel to the optical axis. For quartz, the real part of the permittivity rises (falls) when the two directions are perpendicular (parallel), causing the $T_{B}$ to fall (rise). The optic axes of minerals are randomly distributed in granite samples, which make the variation in the permittivity of minerals also random, thereby resulting in the nonuniformity of stress-induced $T_{B}$ variation in granite samples. Finally, the implications of these results were discussed. [ABSTRACT FROM AUTHOR]

Published
2021
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43. The construction of knowledge innovation model for strategic alliance of industrial technology innovation

Author

Wang Yumei, Li Wenwen, and Zhou Guangju

Subjects
ComputingMilieux_GENERAL, Knowledge management, Industrial technology, Knowledge innovation, business.industry, Process (engineering), Knowledge economy, Innovation management, ComputingMilieux_LEGALASPECTSOFCOMPUTING, business, Strategic alliance, System dynamics
Abstract

This paper analyzed the influencing factors of industrial technology innovation alliances knowledge innovation from the perspective of system dynamics, and established the causal relationship of the factors in the process of alliances knowledge innovation. Based on those, this paper establishes the model of alliances knowledge innovation motivation. This will reveal the alliances knowledge innovation motivation and provide theoretical basis for improving alliances knowledge innovation capacity and efficiency.

Published
2015
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44. A Learning Automata-Based Multiobjective Hyper-Heuristic.

Author

Li, Wenwen, Ozcan, Ender, and John, Robert

Subjects
MACHINE learning, METAHEURISTIC algorithms, MATHEMATICAL optimization, HEURISTIC algorithms, HEURISTIC
Abstract

Metaheuristics, being tailored to each particular domain by experts, have been successfully applied to many computationally hard optimization problems. However, once implemented, their application to a new problem domain or a slight change in the problem description would often require additional expert intervention. There is a growing number of studies on reusable cross-domain search methodologies, such as selection hyper-heuristics, which are applicable to problem instances from various domains, requiring minimal expert intervention or even none. This paper introduces a new learning automata-based selection hyper-heuristic controlling a set of multiobjective metaheuristics. The approach operates above three well-known multiobjective evolutionary algorithms and mixes them, exploiting the strengths of each algorithm. The performance and behavior of two variants of the proposed selection hyper-heuristic, each utilizing a different initialization scheme are investigated across a range of unconstrained multiobjective mathematical benchmark functions from two different sets and the real-world problem of vehicle crashworthiness. The empirical results illustrate the effectiveness of our approach for cross-domain search, regardless of the initialization scheme, on those problems when compared to each individual multiobjective algorithm. Moreover, both variants perform significantly better than some previously proposed selection hyper-heuristics for multiobjective optimization, thus significantly enhancing the opportunities for improved multiobjective optimization. [ABSTRACT FROM AUTHOR]

Published
2019
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50. Research of PCA and KPCA in the characteristics simplicity of the gene data

Author

Li Wenwen, Zhang Yu, Liu Fu, Shang Weiwei, and Xing Xiaoxue

Subjects
Clustering high-dimensional data, Computer science, business.industry, Dimensionality reduction, media_common.quotation_subject, Experimental data, Pattern recognition, computer.software_genre, Kernel principal component analysis, Support vector machine, ComputingMethodologies_PATTERNRECOGNITION, Principal component analysis, Range (statistics), Artificial intelligence, Data mining, Simplicity, business, computer, media_common
Abstract

Although SVM method is suitable for high dimensional data analysis, gene expression data are often thousands of dimensions, the time cost of this algorithm is quite high. The method of dimension reduction based on principal component analysis (PCA) and kernel principal component analysis (KPCA) can not only shorten the algorithm's running time, but also integrate the useful characteristics information. This paper compares the classification accuracy of the search range with different parameters between PCA-SVM and KPCA-SVM when the cumulative contribution rate reaches 100%, 95% and 90% by the experimental data of the Singh-2002. The experimental results show that the change of classification accuracy based on PCA-SVM is none of the business of the change of the cumulative contribution rate, but change of classification accuracy based on KPCA-SVM decreases or remains unchanged with the loss of the cumulative contribution.

Published
2013
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