Search

Your search keyword '"WU Shaoqing"' showing total 39 results
Start Over Author "WU Shaoqing" Search Limiters Full Text
39 results on '"WU Shaoqing"'

1. DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

Author

DeepSeek-AI, Guo, Daya, Yang, Dejian, Zhang, Haowei, Song, Junxiao, Zhang, Ruoyu, Xu, Runxin, Zhu, Qihao, Ma, Shirong, Wang, Peiyi, Bi, Xiao, Zhang, Xiaokang, Yu, Xingkai, Wu, Yu, Wu, Z. F., Gou, Zhibin, Shao, Zhihong, Li, Zhuoshu, Gao, Ziyi, Liu, Aixin, Xue, Bing, Wang, Bingxuan, Wu, Bochao, Feng, Bei, Lu, Chengda, Zhao, Chenggang, Deng, Chengqi, Zhang, Chenyu, Ruan, Chong, Dai, Damai, Chen, Deli, Ji, Dongjie, Li, Erhang, Lin, Fangyun, Dai, Fucong, Luo, Fuli, Hao, Guangbo, Chen, Guanting, Li, Guowei, Zhang, H., Bao, Han, Xu, Hanwei, Wang, Haocheng, Ding, Honghui, Xin, Huajian, Gao, Huazuo, Qu, Hui, Li, Hui, Guo, Jianzhong, Li, Jiashi, Wang, Jiawei, Chen, Jingchang, Yuan, Jingyang, Qiu, Junjie, Li, Junlong, Cai, J. L., Ni, Jiaqi, Liang, Jian, Chen, Jin, Dong, Kai, Hu, Kai, Gao, Kaige, Guan, Kang, Huang, Kexin, Yu, Kuai, Wang, Lean, Zhang, Lecong, Zhao, Liang, Wang, Litong, Zhang, Liyue, Xu, Lei, Xia, Leyi, Zhang, Mingchuan, Zhang, Minghua, Tang, Minghui, Li, Meng, Wang, Miaojun, Li, Mingming, Tian, Ning, Huang, Panpan, Zhang, Peng, Wang, Qiancheng, Chen, Qinyu, Du, Qiushi, Ge, Ruiqi, Zhang, Ruisong, Pan, Ruizhe, Wang, Runji, Chen, R. J., Jin, R. L., Chen, Ruyi, Lu, Shanghao, Zhou, Shangyan, Chen, Shanhuang, Ye, Shengfeng, Wang, Shiyu, Yu, Shuiping, Zhou, Shunfeng, Pan, Shuting, Li, S. S., Zhou, Shuang, Wu, Shaoqing, Yun, Tao, Pei, Tian, Sun, Tianyu, Wang, T., Zeng, Wangding, Zhao, Wanjia, Liu, Wen, Liang, Wenfeng, Gao, Wenjun, Yu, Wenqin, Zhang, Wentao, Xiao, W. L., An, Wei, Liu, Xiaodong, Wang, Xiaohan, Chen, Xiaokang, Nie, Xiaotao, Cheng, Xin, Liu, Xin, Xie, Xin, Liu, Xingchao, Yang, Xinyu, Li, Xinyuan, Su, Xuecheng, Lin, Xuheng, Li, X. Q., Jin, Xiangyue, Shen, Xiaojin, Chen, Xiaosha, Sun, Xiaowen, Wang, Xiaoxiang, Song, Xinnan, Zhou, Xinyi, Wang, Xianzu, Shan, Xinxia, Li, Y. K., Wang, Y. Q., Wei, Y. X., Zhang, Yang, Xu, Yanhong, Li, Yao, Zhao, Yao, Sun, Yaofeng, Wang, Yaohui, Yu, Yi, Zhang, Yichao, Shi, Yifan, Xiong, Yiliang, He, Ying, Piao, Yishi, Wang, Yisong, Tan, Yixuan, Ma, Yiyang, Liu, Yiyuan, Guo, Yongqiang, Ou, Yuan, Wang, Yuduan, Gong, Yue, Zou, Yuheng, He, Yujia, Xiong, Yunfan, Luo, Yuxiang, You, Yuxiang, Liu, Yuxuan, Zhou, Yuyang, Zhu, Y. X., Huang, Yanping, Li, Yaohui, Zheng, Yi, Zhu, Yuchen, Ma, Yunxian, Tang, Ying, Zha, Yukun, Yan, Yuting, Ren, Z. Z., Ren, Zehui, Sha, Zhangli, Fu, Zhe, Xu, Zhean, Xie, Zhenda, Zhang, Zhengyan, Hao, Zhewen, Ma, Zhicheng, Yan, Zhigang, Wu, Zhiyu, Gu, Zihui, Zhu, Zijia, Liu, Zijun, Li, Zilin, Xie, Ziwei, Song, Ziyang, Pan, Zizheng, Huang, Zhen, Xu, Zhipeng, Zhang, Zhongyu, and Zhang, Zhen

Subjects
Computer Science - Computation and Language, Computer Science - Artificial Intelligence, Computer Science - Machine Learning
Abstract

We introduce our first-generation reasoning models, DeepSeek-R1-Zero and DeepSeek-R1. DeepSeek-R1-Zero, a model trained via large-scale reinforcement learning (RL) without supervised fine-tuning (SFT) as a preliminary step, demonstrates remarkable reasoning capabilities. Through RL, DeepSeek-R1-Zero naturally emerges with numerous powerful and intriguing reasoning behaviors. However, it encounters challenges such as poor readability, and language mixing. To address these issues and further enhance reasoning performance, we introduce DeepSeek-R1, which incorporates multi-stage training and cold-start data before RL. DeepSeek-R1 achieves performance comparable to OpenAI-o1-1217 on reasoning tasks. To support the research community, we open-source DeepSeek-R1-Zero, DeepSeek-R1, and six dense models (1.5B, 7B, 8B, 14B, 32B, 70B) distilled from DeepSeek-R1 based on Qwen and Llama.

Published
2025

2. DeepSeek-V3 Technical Report

Author

DeepSeek-AI, Liu, Aixin, Feng, Bei, Xue, Bing, Wang, Bingxuan, Wu, Bochao, Lu, Chengda, Zhao, Chenggang, Deng, Chengqi, Zhang, Chenyu, Ruan, Chong, Dai, Damai, Guo, Daya, Yang, Dejian, Chen, Deli, Ji, Dongjie, Li, Erhang, Lin, Fangyun, Dai, Fucong, Luo, Fuli, Hao, Guangbo, Chen, Guanting, Li, Guowei, Zhang, H., Bao, Han, Xu, Hanwei, Wang, Haocheng, Zhang, Haowei, Ding, Honghui, Xin, Huajian, Gao, Huazuo, Li, Hui, Qu, Hui, Cai, J. L., Liang, Jian, Guo, Jianzhong, Ni, Jiaqi, Li, Jiashi, Wang, Jiawei, Chen, Jin, Chen, Jingchang, Yuan, Jingyang, Qiu, Junjie, Li, Junlong, Song, Junxiao, Dong, Kai, Hu, Kai, Gao, Kaige, Guan, Kang, Huang, Kexin, Yu, Kuai, Wang, Lean, Zhang, Lecong, Xu, Lei, Xia, Leyi, Zhao, Liang, Wang, Litong, Zhang, Liyue, Li, Meng, Wang, Miaojun, Zhang, Mingchuan, Zhang, Minghua, Tang, Minghui, Li, Mingming, Tian, Ning, Huang, Panpan, Wang, Peiyi, Zhang, Peng, Wang, Qiancheng, Zhu, Qihao, Chen, Qinyu, Du, Qiushi, Chen, R. J., Jin, R. L., Ge, Ruiqi, Zhang, Ruisong, Pan, Ruizhe, Wang, Runji, Xu, Runxin, Zhang, Ruoyu, Chen, Ruyi, Li, S. S., Lu, Shanghao, Zhou, Shangyan, Chen, Shanhuang, Wu, Shaoqing, Ye, Shengfeng, Ma, Shirong, Wang, Shiyu, Zhou, Shuang, Yu, Shuiping, Zhou, Shunfeng, Pan, Shuting, Wang, T., Yun, Tao, Pei, Tian, Sun, Tianyu, Xiao, W. L., Zeng, Wangding, Zhao, Wanjia, An, Wei, Liu, Wen, Liang, Wenfeng, Gao, Wenjun, Yu, Wenqin, Zhang, Wentao, Li, X. Q., Jin, Xiangyue, Wang, Xianzu, Bi, Xiao, Liu, Xiaodong, Wang, Xiaohan, Shen, Xiaojin, Chen, Xiaokang, Zhang, Xiaokang, Chen, Xiaosha, Nie, Xiaotao, Sun, Xiaowen, Wang, Xiaoxiang, Cheng, Xin, Liu, Xin, Xie, Xin, Liu, Xingchao, Yu, Xingkai, Song, Xinnan, Shan, Xinxia, Zhou, Xinyi, Yang, Xinyu, Li, Xinyuan, Su, Xuecheng, Lin, Xuheng, Li, Y. K., Wang, Y. Q., Wei, Y. X., Zhu, Y. X., Zhang, Yang, Xu, Yanhong, Huang, Yanping, Li, Yao, Zhao, Yao, Sun, Yaofeng, Li, Yaohui, Wang, Yaohui, Yu, Yi, Zheng, Yi, Zhang, Yichao, Shi, Yifan, Xiong, Yiliang, He, Ying, Tang, Ying, Piao, Yishi, Wang, Yisong, Tan, Yixuan, Ma, Yiyang, Liu, Yiyuan, Guo, Yongqiang, Wu, Yu, Ou, Yuan, Zhu, Yuchen, Wang, Yuduan, Gong, Yue, Zou, Yuheng, He, Yujia, Zha, Yukun, Xiong, Yunfan, Ma, Yunxian, Yan, Yuting, Luo, Yuxiang, You, Yuxiang, Liu, Yuxuan, Zhou, Yuyang, Wu, Z. F., Ren, Z. Z., Ren, Zehui, Sha, Zhangli, Fu, Zhe, Xu, Zhean, Huang, Zhen, Zhang, Zhen, Xie, Zhenda, Zhang, Zhengyan, Hao, Zhewen, Gou, Zhibin, Ma, Zhicheng, Yan, Zhigang, Shao, Zhihong, Xu, Zhipeng, Wu, Zhiyu, Zhang, Zhongyu, Li, Zhuoshu, Gu, Zihui, Zhu, Zijia, Liu, Zijun, Li, Zilin, Xie, Ziwei, Song, Ziyang, Gao, Ziyi, and Pan, Zizheng

Subjects
Computer Science - Computation and Language, Computer Science - Artificial Intelligence
Abstract

We present DeepSeek-V3, a strong Mixture-of-Experts (MoE) language model with 671B total parameters with 37B activated for each token. To achieve efficient inference and cost-effective training, DeepSeek-V3 adopts Multi-head Latent Attention (MLA) and DeepSeekMoE architectures, which were thoroughly validated in DeepSeek-V2. Furthermore, DeepSeek-V3 pioneers an auxiliary-loss-free strategy for load balancing and sets a multi-token prediction training objective for stronger performance. We pre-train DeepSeek-V3 on 14.8 trillion diverse and high-quality tokens, followed by Supervised Fine-Tuning and Reinforcement Learning stages to fully harness its capabilities. Comprehensive evaluations reveal that DeepSeek-V3 outperforms other open-source models and achieves performance comparable to leading closed-source models. Despite its excellent performance, DeepSeek-V3 requires only 2.788M H800 GPU hours for its full training. In addition, its training process is remarkably stable. Throughout the entire training process, we did not experience any irrecoverable loss spikes or perform any rollbacks. The model checkpoints are available at https://github.com/deepseek-ai/DeepSeek-V3.

Published
2024

3. DeepSeek-V2: A Strong, Economical, and Efficient Mixture-of-Experts Language Model

Author

DeepSeek-AI, Liu, Aixin, Feng, Bei, Wang, Bin, Wang, Bingxuan, Liu, Bo, Zhao, Chenggang, Dengr, Chengqi, Ruan, Chong, Dai, Damai, Guo, Daya, Yang, Dejian, Chen, Deli, Ji, Dongjie, Li, Erhang, Lin, Fangyun, Luo, Fuli, Hao, Guangbo, Chen, Guanting, Li, Guowei, Zhang, H., Xu, Hanwei, Yang, Hao, Zhang, Haowei, Ding, Honghui, Xin, Huajian, Gao, Huazuo, Li, Hui, Qu, Hui, Cai, J. L., Liang, Jian, Guo, Jianzhong, Ni, Jiaqi, Li, Jiashi, Chen, Jin, Yuan, Jingyang, Qiu, Junjie, Song, Junxiao, Dong, Kai, Gao, Kaige, Guan, Kang, Wang, Lean, Zhang, Lecong, Xu, Lei, Xia, Leyi, Zhao, Liang, Zhang, Liyue, Li, Meng, Wang, Miaojun, Zhang, Mingchuan, Zhang, Minghua, Tang, Minghui, Li, Mingming, Tian, Ning, Huang, Panpan, Wang, Peiyi, Zhang, Peng, Zhu, Qihao, Chen, Qinyu, Du, Qiushi, Chen, R. J., Jin, R. L., Ge, Ruiqi, Pan, Ruizhe, Xu, Runxin, Chen, Ruyi, Li, S. S., Lu, Shanghao, Zhou, Shangyan, Chen, Shanhuang, Wu, Shaoqing, Ye, Shengfeng, Ma, Shirong, Wang, Shiyu, Zhou, Shuang, Yu, Shuiping, Zhou, Shunfeng, Zheng, Size, Wang, T., Pei, Tian, Yuan, Tian, Sun, Tianyu, Xiao, W. L., Zeng, Wangding, An, Wei, Liu, Wen, Liang, Wenfeng, Gao, Wenjun, Zhang, Wentao, Li, X. Q., Jin, Xiangyue, Wang, Xianzu, Bi, Xiao, Liu, Xiaodong, Wang, Xiaohan, Shen, Xiaojin, Chen, Xiaokang, Chen, Xiaosha, Nie, Xiaotao, Sun, Xiaowen, Wang, Xiaoxiang, Liu, Xin, Xie, Xin, Yu, Xingkai, Song, Xinnan, Zhou, Xinyi, Yang, Xinyu, Lu, Xuan, Su, Xuecheng, Wu, Y., Li, Y. K., Wei, Y. X., Zhu, Y. X., Xu, Yanhong, Huang, Yanping, Li, Yao, Zhao, Yao, Sun, Yaofeng, Li, Yaohui, Wang, Yaohui, Zheng, Yi, Zhang, Yichao, Xiong, Yiliang, Zhao, Yilong, He, Ying, Tang, Ying, Piao, Yishi, Dong, Yixin, Tan, Yixuan, Liu, Yiyuan, Wang, Yongji, Guo, Yongqiang, Zhu, Yuchen, Wang, Yuduan, Zou, Yuheng, Zha, Yukun, Ma, Yunxian, Yan, Yuting, You, Yuxiang, Liu, Yuxuan, Ren, Z. Z., Ren, Zehui, Sha, Zhangli, Fu, Zhe, Huang, Zhen, Zhang, Zhen, Xie, Zhenda, Hao, Zhewen, Shao, Zhihong, Wen, Zhiniu, Xu, Zhipeng, Zhang, Zhongyu, Li, Zhuoshu, Wang, Zihan, Gu, Zihui, Li, Zilin, and Xie, Ziwei

Subjects
Computer Science - Computation and Language, Computer Science - Artificial Intelligence
Abstract

We present DeepSeek-V2, a strong Mixture-of-Experts (MoE) language model characterized by economical training and efficient inference. It comprises 236B total parameters, of which 21B are activated for each token, and supports a context length of 128K tokens. DeepSeek-V2 adopts innovative architectures including Multi-head Latent Attention (MLA) and DeepSeekMoE. MLA guarantees efficient inference through significantly compressing the Key-Value (KV) cache into a latent vector, while DeepSeekMoE enables training strong models at an economical cost through sparse computation. Compared with DeepSeek 67B, DeepSeek-V2 achieves significantly stronger performance, and meanwhile saves 42.5% of training costs, reduces the KV cache by 93.3%, and boosts the maximum generation throughput to 5.76 times. We pretrain DeepSeek-V2 on a high-quality and multi-source corpus consisting of 8.1T tokens, and further perform Supervised Fine-Tuning (SFT) and Reinforcement Learning (RL) to fully unlock its potential. Evaluation results show that, even with only 21B activated parameters, DeepSeek-V2 and its chat versions still achieve top-tier performance among open-source models.

Published
2024

14. DNA methylation markers in the diagnosis and prognosis of common leukemias

Author

Jiang, Hua, Ou, Zhiying, He, Yingyi, Yu, Meixing, Wu, Shaoqing, Li, Gen, Zhu, Jie, Zhang, Ru, Wang, Jiayi, Zheng, Lianghong, Zhang, Xiaohong, Hao, Wenge, He, Liya, Gu, Xiaoqiong, Quan, Qingli, Zhang, Edward, Luo, Huiyan, Wei, Wei, Li, Zhihuan, Zang, Guangxi, Zhang, Charlotte, Poon, Tina, Zhang, Daniel, Ziyar, Ian, Zhang, Run-ze, Li, Oulan, Cheng, Linhai, Shimizu, Taylor, Cui, Xinping, Zhu, Jian-kang, Sun, Xin, and Zhang, Kang

Published
2020
Full Text
View/download PDF

20. Time-Varying Statistics Identification of Nonstationary Random Fluctuating Pressure via Orthogonal Polynomial Representation and Karhunen–Loève Expansion.

Author

Wu, Shaoqing, Zheng, Yi, and Geng, Yincen

Subjects
*ORTHOGONAL polynomials, *STATISTICS, *COMPUTER simulation
Abstract

A novel time domain method is proposed to estimate the time-varying statistics of nonstationary random pressure load from structural strain response samples. Orthogonal polynomials and Karhunen–Loève expansion are adopted to represent the spatial distribution of the stochastic pressure load and the random structural strain data, respectively, which transform the random pressure load identification problem to a problem of estimating a few deterministic time-varying coefficients. Numerical simulation on a plate structure subjected to nonstationary random fluctuating load is conducted to validate the proposed method, and the influence of the load's correlation length on the identification accuracy is also discussed. To show the application perspective of the proposed method, the nonstationary gust-induced random pressure load on a wing structure is reconstructed from structural strain data. Some practical aspects, such as the number of response samples, the level of noise in the strain response, the order of orthogonal polynomial used, and the regularization method used, on the performance of the load identification method are also discussed. [ABSTRACT FROM AUTHOR]

Published
2023
Full Text
View/download PDF

21. Ring Finger Protein 213 in Moyamoya Disease With Pulmonary Arterial Hypertension: A Mini-Review

Author

Luo, Yuting, Cao, Zhixin, Wu, Shaoqing, and Sun, Xunsha

Subjects
Neurology, Neurology (clinical)
Abstract

Moyamoya disease (MMD), most often diagnosed in children and adolescents, is a chronic cerebrovascular disease characterized by progressive stenosis at the terminal portion of the internal carotid artery and an abnormal vascular network at the base of the brain. Recently, many investigators show a great interest in MMD with pulmonary arterial hypertension (PAH). Ring finger protein 213 (RNF213) is a major susceptibility gene for MMD and also has strong correlations with PAH. Therefore, this review encapsulates current cases of MMD with PAH and discusses MMD with PAH in the aspects of epidemiology, pathology, possible pathogenesis, clinical manifestations, diagnosis, and treatment.

Published
2022
Full Text
View/download PDF

29. Learning attribute network algorithm based on high-order similarity.

Author

WU Shaoqing, DONG Yihong, WANG Xiong, CAO Yan, and XIN Yu

Abstract

Due to the lack of deep-level information mining and utilization in the existing network representation learning methods, the potential pattern structure similarity was proposed by further exploring the potential information in the network. The similarity score between network structures was defined to measure the similarity between various structures so that nodes could cross irrelevant vertices to obtain high-order similarities on the global structure. In order to achieve the best effect, deep learning was used to fuse multiple information sources to participate in training together to make up for the deficiency of random walks. In the experiment, Lap, DeepWalk, TADW, SDNE and CANE were selected as comparison methods, and three real-world networks were used as data sets to verify the validity of the model, and experiments of node classification and link reconstruction are carried out. In the node classification, the average performance is improved by 1.7 percentage points for different datasets and training proportions. In the link reconstruction experiment, only half the dimension is needed to achieve better performance. Finally, the performance improvement of the model under different network depths was discussed. By increasing the depth of the model, the average performance of node classification increased by 1.1 percentage points. [ABSTRACT FROM AUTHOR]

Published
2020
Full Text
View/download PDF

32. Frequency-dependent random fatigue of panel-type structures made of ceramic matrix composites

Author

Zhou, Yadong, Hang, Xiaochen, Wu, Shaoqing, Fei, Qingguo, Trišović, Nataša, Zhou, Yadong, Hang, Xiaochen, Wu, Shaoqing, Fei, Qingguo, and Trišović, Nataša

Abstract

The panel-type structures used in aerospace engineering can be subjected to severe high-frequency acoustic loadings in service. This paper evaluates the frequency-dependent random fatigue of panel-type structures made of ceramic matrix composites (CMCs) under acoustic loadings. Firstly, the high-frequency random responses from the broadband random excitation will result in more stress cycles in a definite period of time. The probability density distributions of stress amplitudes will be different in different frequency band-widths, though the peak stress estimations are identical. Secondly, the fatigue properties of CMCs can be highly frequency-dependent. The fatigue evaluation method for the random vibration case is adopted to evaluate the fatigue damage of a representative stiffened panel structure. The frequency effect through S-N curves on random fatigue damage is numerically verified. Finally, a parameter is demonstrated to characterize the mean vibration frequency of a random process, and hence this parameter can further be considered as a reasonable loading frequency in the fatigue tests of CMCs to obtain more reliable S-N curves. Therefore, the influence of vibration frequency can be incorporated in the random fatigue model from the two perspectives.

Published
2017

33. Modal Strain Based Method for Dynamic Design of Plate-Like Structures

Author

Zhou, Yadong, Wu, Shaoqing, Trišović, Nataša, Fei, Qingguo, Tan, Zhiyong, Zhou, Yadong, Wu, Shaoqing, Trišović, Nataša, Fei, Qingguo, and Tan, Zhiyong

Abstract

Design optimization of dynamic properties, for example, modal frequencies, can be of much importance when structures are exposed to the shock and/or vibration environments. A modal strain based method is proposed for fast design of natural frequencies of plate-like structures. The basic theory of modal strains of thin plates is reviewed. The capability of determining the highly sensitive elements by means of modal strain analysis is theoretically demonstrated. Finite element models were constructed in numerical simulations. Firstly, the application of the proposed method is conducted on a central-massed flat plate which was topologically optimized by the Reference. The results of modal strain analysis at the first mode have good agreement with the results from the topology optimization. Furthermore, some features of the strain mode shapes (SMSs) of the flat plate are investigated. Finally, the SMSs are applied to the optimization of a stiffened plate. Attention is focused on the distributions of the SMSs of the stiffeners, which also shows good agreement with the results from the topology optimization in the previous study. Several higher orders of SMSs are extracted, which can visualize the most sensitive elements to the corresponding modal frequency. In summary, both the theory and simulations validate the correctness and convenience of applying SMSs to dynamic design of plate-like structures.

Published
2016

34. Vibro-acoustic coupled analysis excited by correlated turbulent boundary layer

Author

Li, Yanbin, Fei, Qingguo, Wu, Shaoqing, Zhang, Peng, Jiang, Dong, Li, Yanbin, Fei, Qingguo, Wu, Shaoqing, Zhang, Peng, and Jiang, Dong

Abstract

Vibro-acoustic coupling is one of the most concerned problems in the design stage of aircraft and aerospace vehicle. An algorithm that integrates the finite element method (FEM), the boundary element method (BEM) and an acquisition method of excitation correlation is proposed to conduct the vibro-acoustic coupled analysis under correlated excitations in time and spatial domains. The Corcos/Smol’yakov-Tkachenko (ST) power spectral density models are adopted for the correlated excitation with a divisional method, and the vibro-acoustic coupled analysis of complex structures can be greatly simplified. First, a simply supported panel under correlated TBL is investigated to validate the proposed algorithm. Then, the proposed algorithm is applied to a stiffened panel to carry out the vibro-acoustic coupled analysis under three types of excitation: (a) perfectly correlated TBL, (b) partially correlated TBL based on the Corcos or ST model, and (c) uncorrelated TBL. Parameters which may affect the vibro-acoustic coupled analysis, such as the coupled effect, the correlation of excitation and the speed and thickness of the turbulence, are also discussed. Results show that the proposed method is suitable for the vibro-acoustic coupled analysis of complex systems under correlated random excitations. The vibro-acoustic coupling effect will result in a decrease in both the natural frequency and structural response under perfectly correlated excitation, and an increase in the structural response for partial correlated and uncorrelated excitations. However, the coupling effect has little influence on the acoustical response. The structural and acoustic responses due to partially correlated excitation are larger than that due to perfectly correlated excitation. Moreover, the structural and acoustic responses increase with the increment in turbulence speed and thickness, and the difference among the responses under the three different types of excitations rapidly increase with the inc

Published
2016

37. Comparative study of model updating methods using frequency response function data

Author

Jiang, Dong, Zhang, Peng, Fei, Qingguo, Wu, Shaoqing, Jiang, Dong, Zhang, Peng, Fei, Qingguo, and Wu, Shaoqing

Abstract

Constructing accurate finite element models for engineering structures plays a key role in structural dynamic design and analysis. Finite element model updating using frequency response function data arises great attention. In this paper, a comparison of two model updating approaches by using frequency response function data is investigated. The first method is based on sensitivity analysis, which has been regarded as one of the most successful approaches in model updating. The second one is based on the representation of modeling errors as linear combinations of the individual element matrices, which can be used for both error locating and model updating. The basic formulations of these two methods are introduced and the possible solution strategies are discussed. Numerical simulations are conducted to compare the two model updating methods employing the GARTEUR Truss, two aspects effect on the updating solution including magnitude of initial modeling errors and the completeness of measured coordinates are studied. At last, an experimental cantilever beam is updated by adopting the sensitivity method with tested frequency response function, it is shown that the sensitivity method is effective even when the test data are extremely incomplete.

Published
2014

38. A two-step method for determination of mode order in structural damage identification

Author

Fei, Qingguo, Jiang, Dong, Han, Xiaolin, Wu, Shaoqing, Fei, Qingguo, Jiang, Dong, Han, Xiaolin, and Wu, Shaoqing

Abstract

Varieties of structural damage identification methods have been proposed in the past years. Most of the methods adopt modal information including modal frequencies and mode shapes. These methods provide excellent references for researchers and engineers in this field. However, there is still an unsolved critical problem regarding the number of modes that should be used in the identification. To circumvent this problem, a two-step method for determination of mode order is proposed. Modal effective mass analysis is used to estimate the range of mode orders in the first step. Stabilization diagram analysis is then employed in the second step to decide the reasonable mode order. In the numerical simulation, the proposed method is used to determine the mode order in the damage localization of a truss structure. Results demonstrate that all of the damaged elements are correctly identified when adopting reasonable mode order determined by the proposed method.

Published
2013

Catalog

Books, media, physical & digital resources
See catalog results