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Start Over Author "SONG, Hao" Publication Type Academic Journals
2,750 results on '"SONG, Hao"'

13. Research on decision-making of wartime ammunition replenishment mode based on AHP-Ideal point method

Author

FENG Qianli, SHI Xianming, WU Zaogui, LYU Shuai, SONG Hao

Subjects
ahp, ideal point method, wartime, ammunition supply, decision research, Military Science
Abstract

This article adopts a combination of Analytic Hierarchy Process (AHP) and Ideal Point Method (IPM) to construct a decision-making model for ammunition supply methods, which includes six influencing factors: operational process time dimension, battlefield advance distance, task importance, task urgency, possibility of resource allocation around and ammunition demand. Firstly, we select four typical ammunition supply methods: fixed point supply, mobile supply, adjustment supply, and accompanying supply, and determine the decision-making attributes of the ideal situation. Then, we calculate the weights of each influencing factor through expert evaluation and scoring. Finally, based on the numerical calculation of each influencing factor given by various experts on the battlefield and the closeness of each ideal wartime ammunition supply method, the one with the highest closeness is the optimal wartime ammunition supply method. Through case analysis, the effectiveness of this method is verified, which has guiding significance for the decision-making of ammunition supply methods in actual wartime, and provides methods and ideas for in-depth research on ammunition supply decision-making.

Published
2024
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14. Prediction Method of the Low-Frequency Multistatic Scattering Sound Field for Underwater Spherical Targets Based on Limited Data

Author

CHEN Changxiong, PENG Zilong, SONG Hao, XUE Yaqiang, ZHOU Fulin

Subjects
acoustic scattering, multistatic scattering sound field, spherical target, sound field prediction, bistatic configuration, Engineering (General). Civil engineering (General), TA1-2040, Chemical engineering, TP155-156, Naval architecture. Shipbuilding. Marine engineering, VM1-989
Abstract

A prediction method based on the information data of finite scattering acoustic pressure is proposed for the prediction of underwater spherical target multistatic acoustic scattering sound field, which represents the scattering sound field as the product of the acoustic scattering transfer function and the source density function, obtains the acoustic scattering transfer function by numerical integration with the model surface grid information as input, and acquires the source density function by combining acoustic reciprocity, least squares, and matrix transformation with small amount of known scattered sound pressure data as input. The multistatic scattering sound field prediction method is validated by using the Rayleigh normal mode solution and the finite element method (FEM) for double-layered spherical shells and spherical shell with the.pngfener. The target strength experiment is conducted for the spherical shell with the.pngfener, and the experiment results are compared with the finite element calculations. Several test data and FEM calculation results data are used as input to predict the multistatic scattering sound field of the spherical shell with.pngfener and compared with the FEM results, respectively. It is found that the simulation and test results are in good agreement, which verifies the feasibility of the prediction method.

Published
2024
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21. Addendum: Cellular differentiation into hyphae and spores in halophilic archaea

Author

Tang, Shu-Kun, Zhi, Xiao-Yang, Zhang, Yao, Makarova, Kira S., Liu, Bing-Bing, Zheng, Guo-Song, Zhang, Zhen-Peng, Zheng, Hua-Jun, Wolf, Yuri I., Zhao, Yu-Rong, Jiang, Song-Hao, Chen, Xi-Ming, Li, En-Yuan, Zhang, Tao, Chen, Pei-Ru, Feng, Yu-Zhou, Xiang, Ming-Xian, Lin, Zhi-Qian, Shi, Jia-Hui, Chang, Cheng, Zhang, Xue, Li, Rui, Lou, Kai, Wang, Yun, Chang, Lei, Yin, Min, Yang, Ling-Ling, Gao, Hui-Ying, Zhang, Zhong-Kai, Tao, Tian-Shen, Guan, Tong-Wei, He, Fu-Chu, Lu, Yin-Hua, Cui, Heng-Lin, Koonin, Eugene V., Zhao, Guo-Ping, and Xu, Ping

Published
2024
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24. Combining Google traffic map with deep learning model to predict street-level traffic-related air pollutants in a complex urban environment

Author

Peng Wei, Song Hao, Yuan Shi, Abhishek Anand, Ya Wang, Mengyuan Chu, and Zhi Ning

Subjects
Machine learning, Deep learning, Mobile measurement, Street-level pollution, Air quality sensor, Crowd-sourced, Environmental sciences, GE1-350
Abstract

Background: Traffic-related air pollution (TRAP) is a major contributor to urban pollution and varies sharply at the street level, posing a challenge for air quality modeling. Traditional land use regression models combined with data from fixed monitoring stations may be unable to predict and characterize fine-scale TRAP, especially in complex urban environments influenced by various features. This study aims to estimate fine-scale (50 m) concentrations of nitrogen oxides (NO and NO₂) in Hong Kong using a deep learning (DL) structured model. Methods: We collected data from mobile air quality sensors on buses and crowd-sourced Google real-time traffic status as a proxy for real-time traffic emissions. Our DL model was compared with existing machine learning models to assess performance improvements. Using an interpretable machine learning method, we hierarchically evaluated the global, local, and interaction effects for different features. Results: Our DL model outperformed existing machine learning models, achieving R2 values of 0.72 for NO and 0.69 for NO₂. The incorporation of traffic status as a key predictor improved model performance by 9% to 17%. The interpretable machine learning method revealed the importance of traffic-related features and their pairwise interactions. Conclusion: The results indicate that traffic-related features significantly contribute to TRAP and provide insights and guidance for urban planning. By incorporating crowd-sourced Google traffic information, we assessed traffic abatement scenarios that could inform targeted strategies for improving urban air quality.

Published
2024
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31. Multi-dimensional data transmission using inverse-designed silicon photonics and microcombs.

Author

Yang, Ki Youl, Shirpurkar, Chinmay, White, Alexander D, Zang, Jizhao, Chang, Lin, Ashtiani, Farshid, Guidry, Melissa A, Lukin, Daniil M, Pericherla, Srinivas V, Yang, Joshua, Kwon, Hyounghan, Lu, Jesse, Ahn, Geun Ho, Van Gasse, Kasper, Jin, Yan, Yu, Su-Peng, Briles, Travis C, Stone, Jordan R, Carlson, David R, Song, Hao, Zou, Kaiheng, Zhou, Huibin, Pang, Kai, Hao, Han, Trask, Lawrence, Li, Mingxiao, Netherton, Andy, Rechtman, Lior, Stone, Jeffery S, Skarda, Jinhee L, Su, Logan, Vercruysse, Dries, MacLean, Jean-Philippe W, Aghaeimeibodi, Shahriar, Li, Ming-Jun, Miller, David AB, Marom, Dan M, Willner, Alan E, Bowers, John E, Papp, Scott B, Delfyett, Peter J, Aflatouni, Firooz, and Vučković, Jelena

Abstract

The use of optical interconnects has burgeoned as a promising technology that can address the limits of data transfer for future high-performance silicon chips. Recent pushes to enhance optical communication have focused on developing wavelength-division multiplexing technology, and new dimensions of data transfer will be paramount to fulfill the ever-growing need for speed. Here we demonstrate an integrated multi-dimensional communication scheme that combines wavelength- and mode- multiplexing on a silicon photonic circuit. Using foundry-compatible photonic inverse design and spectrally flattened microcombs, we demonstrate a 1.12-Tb/s natively error-free data transmission throughout a silicon nanophotonic waveguide. Furthermore, we implement inverse-designed surface-normal couplers to enable multimode optical transmission between separate silicon chips throughout a multimode-matched fibre. All the inverse-designed devices comply with the process design rules for standard silicon photonic foundries. Our approach is inherently scalable to a multiplicative enhancement over the state of the art silicon photonic transmitters.

Published
2022

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