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Aging Effects and Modeling Researches on 22nm FDSOI MOSFETs

Yibo Hu, Hao Ge, Zhipeng Ren, Yizhe Yin, Jing Chen
2022 Zenodo  
OUTLINE: Background Aging Effects Modeling Dynamic Voltage Stress SMI Simulator Summary
doi:10.5281/zenodo.7048176 fatcat:hgro2eowsnalpf6b5udsdcsmie

FusionMapping: Learning Depth Prediction with Monocular Images and 2D Laser Scans [article]

Peng Yin, Jianing Qian, Yibo Cao, David Held, Howie Choset
2019 arXiv   pre-print
Acquiring accurate three-dimensional depth information conventionally requires expensive multibeam LiDAR devices. Recently, researchers have developed a less expensive option by predicting depth information from two-dimensional color imagery. However, there still exists a substantial gap in accuracy between depth information estimated from two-dimensional images and real LiDAR point-cloud. In this paper, we introduce a fusion-based depth prediction method, called FusionMapping. This is the
more » ... method that fuses colored imagery and two-dimensional laser scan to estimate depth in-formation. More specifically, we propose an autoencoder-based depth prediction network and a novel point-cloud refinement network for depth estimation. We analyze the performance of our FusionMapping approach on the KITTI LiDAR odometry dataset and an indoor mobile robot system. The results show that our introduced approach estimates depth with better accuracy when compared to existing methods.
arXiv:1912.00096v1 fatcat:d5oefdevy5eupedu66sksbr4lq

Keyphrase Extraction with Span-based Feature Representations [article]

Funan Mu, Zhenting Yu, LiFeng Wang, Yequan Wang, Qingyu Yin, Yibo Sun, Liqun Liu, Teng Ma, Jing Tang, Xing Zhou
2020 arXiv   pre-print
The first sub-layer is a multi-head self-attention mechanism (Lin et al. 2017; Yin et al. 2018) , and the second sub-layer is a simple, position-wise fully connected feed-forward network.  ... 
arXiv:2002.05407v1 fatcat:oyzziprmdbcpfhdugks7uqkeou

A Personalized Compression Method for Steady-State Visual Evoked Potential EEG Signals

Sitao Zhang, Kainan Ma, Yibo Yin, Binbin Ren, Ming Liu
2022 Information  
As an informative electroencephalogram (EEG) signal, steady-state visual evoked potential (SSVEP) stands out from many paradigms for application in wireless wearable devices. However, its data are usually enormous, occupy too many bandwidth sources and require immense power when transmitted in the raw data form, so it is necessary to compress the signal. This paper proposes a personalized EEG compression and reconstruction algorithm for the SSVEP application. In the algorithm, to realize
more » ... lization, a primary artificial neural network (ANN) model is first pre-trained with the open benchmark database towards BCI application (BETA). Then, an adaptive ANN model is generated with incremental learning for each subject to compress their individual data. Additionally, a personalized, non-uniform quantization method is proposed to reduce the errors caused by compression. The recognition accuracy only decreases by 3.79% when the compression rate is 12.7 times, and is tested on BETA. The proposed algorithm can reduce signal loss by from 50.43% to 81.08% in the accuracy test compared to the case without ANN and uniform quantization.
doi:10.3390/info13040186 fatcat:qhshmwvgwvgmfl7kizz6kptaxi

Lightweight End-to-End Neural Network Model for Automatic Heart Sound Classification

Tao Li, Yibo Yin, Kainan Ma, Sitao Zhang, Ming Liu
2021 Information  
Heart sounds play an important role in the initial screening of heart diseases. However, the accurate diagnosis with heart sound signals requires doctors to have many years of clinical experience and relevant professional knowledge. In this study, we proposed an end-to-end lightweight neural network model that does not require heart sound segmentation and has very few parameters. We segmented the original heart sound signal and performed a short-time Fourier transform (STFT) to obtain the
more » ... ncy domain features. These features were sent to the improved two-dimensional convolutional neural network (CNN) model for features learning and classification. Considering the imbalance of positive and negative samples, we introduced FocalLoss as the loss function, verified our network model with multiple random verifications, and, hence, obtained a better classification result. Our main purpose is to design a lightweight network structure that is easy for hardware implementation. Compared with the results of the latest literature, our model only uses 4.29 K parameters, which is 1/10 of the size of the state-of-the-art work.
doi:10.3390/info12020054 fatcat:gxi3hzlmpzajrnflhxubzbkxs4

Structural Health Monitoring Method of Pantograph–Catenary System Based on Strain Response Inversion

Sheng Liu, Yibo Wei, Yongxin Yin, Tangzheng Feng, Jinbao Lin
2021 Frontiers in Physics  
Pantograph-catenary system provides electric energy for the subway lines; its health status is essential to the serviceability of the vehicle. In this study, a real-time structural health monitoring method based on strain response inversion is proposed to calculate the magnitude and position of the dynamic contact force between the catenary and pantograph. The measurement principle, calibration, and installation detail of the fiber Bragg grating (FBG) sensors are also presented in this article.
more » ... Putting this monitoring system in use, an application example of a subway with a rigid overhead catenary is given to demonstrate its performance. The pantograph was monitored and analyzed, running underground at a maximum speed of 80 km/h. The results show that the strain response inversion method has high measurement accuracy, good data consistency, and flexibility on sensor installation. It can accurately calculate the magnitude and location of the contact force exerted on the pantograph.
doi:10.3389/fphy.2021.691510 fatcat:5xaygzdhcfferbzfqe6y3kvaqa

Knowledge push based on design flow and user capacity model

Hao Jiang, Pu Yin, Lin Guo, Yibo Wang, Bing Xu, Yinong Chen
2017 MATEC Web of Conferences  
The knowledge demand of the designer is determined by the knowledge structure of the knowledge user and knowledge usage scenario. Therefore, this paper proposes a knowledge push method based on design activities workflow model and user capability model. By constructing the workflow model of design activities, this paper determines the designer's knowledge demand scenario. The relationship between design knowledge and design activity is determined by calculating the similarity between the label
more » ... f the design activity and the key words of design knowledge. By constructing user capability model, we describe the designer's knowledge structure and design ability .This paper determine the designer's personalized knowledge needs according to the user capability model. Finally, this paper presents the prototype system and proves the feasibility of this method with the example of the part design. 2 Design process and user capability model MATEC Web of Conferences 139, 00012 (2017)
doi:10.1051/matecconf/201713900012 fatcat:qcvhncgudnffxg5enccf6mclmu

DNA Methylation Silences Exogenous Gene Expression in Transgenic Birch Progeny

Minghao Ma, Xiaohui Chen, Yibo Yin, Ruixin Fan, Bo Li, Yaguang Zhan, Fansuo Zeng
2020 Frontiers in Plant Science  
The genetic stability of exogenous genes in the progeny of transgenic trees is extremely important in forest breeding; however, it remains largely unclear. We selected transgenic birch (Betula platyphylla) and its hybrid F1 progeny to investigate the expression stability and silencing mechanism of exogenous genes. We found that the exogenous genes of transgenic birch could be transmitted to their offspring through sexual reproduction. The exogenous genes were segregated during genetic
more » ... on. The hybrid progeny of transgenic birch WT1×TP22 (184) and WT1×TP23 (212) showed higher Bgt expression and greater insect resistance than their parents. However, the hybrid progeny of transgenic birch TP23×TP49 (196) showed much lower Bgt expression, which was only 13.5% of the expression in its parents. To elucidate the mechanism underlying the variation in gene expression between the parents and progeny, we analyzed the methylation rates of Bgt in its promoter and coding regions. The hybrid progeny with normally expressed exogenous genes showed much lower methylation rates (0–29%) than the hybrid progeny with silenced exogenous genes (32.35–45.95%). These results suggest that transgene silencing in the progeny is mainly due to DNA methylation at cytosine residues. We further demonstrated that methylation in the promoter region, rather than in the coding region, leads to gene silencing. We also investigated the relative expression levels of three methyltransferase genes: BpCMT, BpDRM, and BpMET. The transgenic birch line 196 with a silenced Gus gene showed, respectively, 2.54, 9.92, and 4.54 times higher expression levels of BpCMT, BpDRM, and BpMET than its parents. These trends are consistent with and corroborate the high methylation levels of exogenous genes in the transgenic birch line 196. Therefore, our study suggests that DNA methylation in the promoter region leads to silencing of exogenous genes in transgenic progeny of birch.
doi:10.3389/fpls.2020.523748 pmid:33414793 pmcid:PMC7783445 fatcat:ofnoz52mpva5hkhnbpziobrwmm

Temporal Convolutional Network Connected with an Anti-Arrhythmia Hidden Semi-Markov Model for Heart Sound Segmentation

Yibo Yin, Kainan Ma, Ming Liu
2020 Applied Sciences  
Heart sound segmentation (HSS) is a critical step in heart sound processing, where it improves the interpretability of heart sound disease classification algorithms. In this study, we aimed to develop a real-time algorithm for HSS by combining the temporal convolutional network (TCN) and the hidden semi-Markov model (HSMM), and improve the performance of HSMM for heart sounds with arrhythmias. We experimented with TCN and determined the best parameters based on spectral features, envelopes, and
more » ... one-dimensional CNN. However, the TCN results could contradict the natural fixed order of S1-systolic-S2-diastolic of heart sound, and thereby the Viterbi algorithm based on HSMM was connected to correct the order errors. On this basis, we improved the performance of the Viterbi algorithm when detecting heart sounds with cardiac arrhythmias by changing the distribution and weights of the state duration probabilities. The public PhysioNet Computing in Cardiology Challenge 2016 data set was employed to evaluate the performance of the proposed algorithm. The proposed algorithm achieved an F1 score of 97.02%, and this result was comparable with the current state-of-the-art segmentation algorithms. In addition, the proposed enhanced Viterbi algorithm for HSMM corrected 30 out of 30 arrhythmia errors after checking one by one in the dataset.
doi:10.3390/app10207049 fatcat:yd4kdxxjenhmho6pu3nwb4by74

FLAX: A Flexible Architecture for Large Scale Cloud Fabric

Yiyang Shao, Yihang Luo, Xiaohe Hu, Yibo Xue, Yang Xiang, Kevin Yin
2015 2015 IEEE International Conference on Smart City/SocialCom/SustainCom (SmartCity)  
The scalability and geographical location agility of data centers have become two key concerns for those critical cloud applications. However, it is still infeasible to build nonblocking data centers which are scalable, agile and cost-effective, given that current network devices are either closed high-end or performance limited, and the dedicated fiber is expensive and hard to expand. This paper proposes FLAX, a flexible architecture consolidating intra-and inter-cloud networks for large scale
more » ... fabric. By leveraging on Software-Defined Networking techniques, FLAX can provide non-blocking application networks and scale out to millions of 10 gigabit ethernet ports across geographically-separated and arbitrarily-connected cloud data centers. Under the global view of network controllers, uniformed design of switches in different hierarchies and involving Wide Area Networks make it possible to fully use all network elements, and hence driving down the cost of network infrastructure. We present the architecture design and future work in this paper, and also a prototype deployed in one of the largest third-party data centers in eastern China.
doi:10.1109/smartcity.2015.226 dblp:conf/smartcity/ShaoLHXXY15 fatcat:mogl7fcpvfeh5oqqtdgu2tvyr4

Challenges and Perspectives in the Study of Self-Incompatibility in Orchids

Xiaojing Zhang, Yin Jia, Yang Liu, Duanfen Chen, Yibo Luo, Shance Niu
2021 International Journal of Molecular Sciences  
Self-incompatibility affects not only the formation of seeds, but also the evolution of species diversity. A robust understanding of the molecular mechanisms of self-incompatibility is essential for breeding efforts, as well as conservation biology research. In recent years, phenotypic and multiple omics studies have revealed that self-incompatibility in Orchidaceae is mainly concentrated in the subfamily Epidendroideae, and the self-incompatibility phenotypes are diverse, even in the same
more » ... , and hormones (auxin and ethylene), and new male and female determinants might be involved in SI response. This work provides a good foundation for future studies of the evolution and molecular mechanisms of self-incompatibility. We review recent research progress on self-incompatibility in orchids at the morphological, physiological, and molecular levels, provide a general overview of self-incompatibility in orchids, and propose future research directions.
doi:10.3390/ijms222312901 pmid:34884706 pmcid:PMC8657995 fatcat:7fpdcn23snf4jiztk3ygdhlxy4

Question Generation from SQL Queries Improves Neural Semantic Parsing [article]

Daya Guo, Yibo Sun, Duyu Tang, Nan Duan, Jian Yin, Hong Chi, James Cao, Peng Chen, Ming Zhou
2018 arXiv   pre-print
We study how to learn a semantic parser of state-of-the-art accuracy with less supervised training data. We conduct our study on WikiSQL, the largest hand-annotated semantic parsing dataset to date. First, we demonstrate that question generation is an effective method that empowers us to learn a state-of-the-art neural network based semantic parser with thirty percent of the supervised training data. Second, we show that applying question generation to the full supervised training data further
more » ... mproves the state-of-the-art model. In addition, we observe that there is a logarithmic relationship between the accuracy of a semantic parser and the amount of training data.
arXiv:1808.06304v2 fatcat:4jgswmw2pvfqbakowhxrdkedgq

Blood vessel assessment using computed tomography : Effects of ephedrine on uterine artery

Yibo Yin, Can Liu, Guangjian Gao, Jingjing Li, Xuechen Long, Peijin Zhang, Wenjun Guo
2022 Frontiers in Pharmacology  
Ephedrine increased blood pressure due to the contractile properties of resistance vessels. Excessive contraction of the uterine arteries might cause fetal distress. This study was to determine the diameter of the uterine artery of female New Zealand rabbits after the administration of different doses of ephedrine using CT.Methods: Thirty-two rabbits were randomly divided into a control group (Group C), low dosage group (Group L), medium dosage group (Group M) and high dosage group (Group H).
more » ... rmal saline and doses corresponding to the human dose of 7.5, 15 and 30 mg of ephedrine were injected respectively. The marginal ear and uterine artery diameters were measured 5, 10, 15, 30, and 45 min after injection using CT, and the hemodynamic changes were recorded.Results: The increase in mean arterial pressure in group M (p = 0.009), and H (p = 0.013) was higher than that in group C. Compared with group C, substantial contraction of the marginal ear artery was observed at the three doses of ephedrine. There were no differences in the uterine artery diameter among groups L, M and C, However, in Group H, a significant contraction of the uterine artery compared with the other groups (p < 0.001) was observed.Discussion: CT can be used to evaluate the effects of drugs on organs and blood vessels. Ephedrine can not only constrict the peripheral blood vessels but also do not affect the uterine artery at a dose of 15 mg or less. However, the dose should not exceed 30 mg, which may cause severe uterine artery depression.
doi:10.3389/fphar.2022.890246 pmid:36081950 pmcid:PMC9448417 fatcat:l3p4tnkcsngqdgwr3ipcllcpe4

Comparison of postoperative complications between different operation methods for esophageal cancer

Qingqing Ding, Wenyin Zhou, Yibo Xue, Xiao Han, Dandan Yin, Lei Xue, Jinhua Luo
2019 Thoracic Cancer  
We explored the selection of surgical method and differences in postoperative complications in patients with esophageal cancer (EC).
doi:10.1111/1759-7714.13092 pmid:31245903 pmcid:PMC6669799 fatcat:5zimurtanvc2nbmm7tvoimpjey

A Low-Cost Improved Method of Raw Bit Error Rate Estimation for NAND Flash Memory of High Storage Density

Kainan Ma, Ming Liu, Tao Li, Yibo Yin, Hongda Chen
2020 Electronics  
Cells wear fast in NAND flash memory of high storage density (HSD), so it is very necessary to have a long-term frequent in-time monitoring on its raw bit error rate (RBER) changes through a fast RBER estimation method. As the flash of HSD already has relatively lower reading speed, the method should not further degrade its read performance. This paper proposes an improved estimation method utilizing known data comparison, includes interleaving to balance the uneven error distribution in the
more » ... sh of HSD, a fast RBER estimation module to make the estimated RBER highly linearly correlated with the actual RBER, and enhancement strategies to accelerate the decoding convergence of low-density parity-check (LDPC) codes and thereby make up the rate penalty caused by the known data. Experimental results show that when RBER is close to the upper bound of LDPC code, the reading efficiency can be increased by 35.8% compared to the case of no rate penalty. The proposed method only occupies 0.039mm2 at 40nm process condition. Hence, the fast, read-performance-improving, and low-cost method is of great application potential on RBER monitoring in the flash of HSD.
doi:10.3390/electronics9111900 fatcat:tn4h44x62rfqvbvp3aijx66cii
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