http://chineseinput.net/에서 pinyin(병음)방식으로 중국어를 변환할 수 있습니다.
변환된 중국어를 복사하여 사용하시면 됩니다.
- 中文 을 입력하시려면 zhongwen을 입력하시고 space를누르시면됩니다.
- 北京 을 입력하시려면 beijing을 입력하시고 space를 누르시면 됩니다.
RISS 인기검색어
- 검색키워드
- 저자 : Hongxin Wu (검색결과 5 건)
- 무료
- 기관 내 무료
- 유료
-
Xiaoning Wu,Xiaoqian Xu,Jialing Zhou,Yameng Sun,Huiguo Ding,Wen Xie,Guofeng Chen,Anlin Ma,Hongxin Piao,Bingqiong Wang,Shuyan Chen,Tongtong Meng,Xiaojuan Ou,Hwai-I Yang,Jidong Jia,Yuanyuan Kong,Hong Yo 대한간학회 2023 Clinical and Molecular Hepatology(대한간학회지) Vol.29 No.3
Background/Aims: Existing hepatocellular carcinoma (HCC) prediction models are derived mainly from pretreatment or early on-treatment parameters. We reassessed the dynamic changes in the performance of 17 HCC models in patients with chronic hepatitis B (CHB) during long-term antiviral therapy (AVT). Methods: Among 987 CHB patients administered long-term entecavir therapy, 660 patients had 8 years of follow-up data. Model scores were calculated using on-treatment values at 2.5, 3, 3.5, 4, 4.5, and 5 years of AVT to predict threeyear HCC occurrence. Model performance was assessed with the area under the receiver operating curve (AUROC). The original model cutoffs to distinguish different levels of HCC risk were evaluated by the log-rank test. Results: The AUROCs of the 17 HCC models varied from 0.51 to 0.78 when using on-treatment scores from years 2.5 to 5. Models with a cirrhosis variable showed numerically higher AUROCs (pooled at 0.65–0.73 for treated, untreated, or mixed treatment models) than models without (treated or mixed models: 0.61–0.68; untreated models: 0.51–0.59). Stratification into low, intermediate, and high-risk levels using the original cutoff values could no longer reflect the true HCC incidence using scores after 3.5 years of AVT for models without cirrhosis and after 4 years of AVT for models with cirrhosis. Conclusions: The performance of existing HCC prediction models, especially models without the cirrhosis variable, decreased in CHB patients on long-term AVT. The optimization of existing models or the development of novel models for better HCC prediction during long-term AVT is warranted.
-
Tracking Control of Robotic Manipulators based on the All-Coefficient Adaptive Control Method
Yongjun Lei,Hongxin Wu 대한전기학회 2006 International Journal of Control, Automation, and Vol.4 No.2
A multi-variable Golden-Section adaptive controller is proposed for the tracking control of robotic manipulators with unknown dynamics. With a small sample time, the unknown dynamics of the robotic manipulator are denoted equivalently by a characteristic model of a 2-order multi variable time-varying difference equation. The coefficients of the characteristic model change slowly with time and some of their valuable characteristic relationships emerge. Based on the characteristic model, an adaptive algorithm with a simple form for the control of robotic manipulators is presented, which combines the multi-variable Golden-Section adaptive control law with the weighted least squares estimation method. Moreover, a compensation neural network law is incorporated into the designed controller to reduce the influence of the coefficients estimation error on the control performance. The results of the simulations indicate that the developed control scheme is effective in robotic manipulator control.
-
Lin Lin,Xiaohuan Wu,Jiajin Qi,Hongxin Ci 보안공학연구지원센터 2016 International Journal of Signal Processing, Image Vol.9 No.1
Power quality (PQ) disturbances recognition is the foundation of power quality analysis and improvement. In order to improve the classification accuracy and efficiency, a new classification approach based on modified Fourier neural networks (FNN) and Hyperbolic S-transform (HST) was designed for PQ disturbances classification. HST has better a time-frequency resolution than S-transform. The features extracted from HST results compose the input vectors of classifier. The DFP emendatory Quasi-Newton method is used to improve the learning ability of FNN and avoid local minimum problem. Three modified FNNs were used to construct a classifier with the structure of decision tree. Six types of disturbances with different noise ratio were simulated to test the classification ability of the new approach. Simulation results show that the new classifier has better classification accuracy than other classifiers based on BP neural networks and Fourier neural networks. The new approach is effective.
-
Zhang, Lichi,Wang, Qian,Gao, Yaozong,Li, Hongxin,Wu, Guorong,Shen, Dinggang Elsevier 2017 Neurocomputing Vol.229 No.-
<P><B>Abstract</B></P> <P>Automatic labeling of the hippocampus in brain MR images is highly demanded, as it has played an important role in imaging-based brain studies. However, accurate labeling of the hippocampus is still challenging, partially due to the ambiguous intensity boundary between the hippocampus and surrounding anatomies. In this paper, we propose a concatenated set of spatially-localized random forests for multi-atlas-based hippocampus labeling of adult/infant brain MR images. The contribution in our work is two-fold. <I>First</I>, each forest classifier is trained to label just a specific sub-region of the hippocampus, thus enhancing the labeling accuracy. <I>Second</I>, a novel forest selection strategy is proposed, such that each voxel in the test image can automatically select a set of optimal forests, and then dynamically fuses their respective outputs for determining the final label. <I>Furthermore</I>, we enhance the spatially-localized random forests with the aid of the auto-context strategy. In this way, our proposed learning framework can gradually refine the tentative labeling result for better performance. Experiments show that, regarding the large datasets of both adult and infant brain MR images, our method owns satisfactory scalability by segmenting the hippocampus accurately and efficiently.</P>
-
Ke Wang,Chao Sun,Povilas Dumčius,Hongxin Zhang,Hanlin Liao,Zhenlin Wu,Liangfei Tian,Wang Peng,Yongqing Fu,Jun Wei,Meng Cai,Yi Zhong,Xiaoyu Li,Xin Yang,Min Cui 한국생체재료학회 2023 생체재료학회지 Vol.27 No.00
Background Blood–brain barrier (BBB) is a crucial but dynamic structure that functions as a gatekeeper for the central nervous system (CNS). Managing sufficient substances across the BBB is a major challenge, especially in the development of therapeutics for CNS disorders. Methods To achieve an efficient, fast and safe strategy for BBB opening, an acoustofluidic transwell (AFT) was developed for reversible disruption of the BBB. The proposed AFT was consisted of a transwell insert where the BBB model was established, and a surface acoustic wave (SAW) transducer realized using open-source electronics based on printed circuit board techniques. Results In the AFT device, the SAW produced acousto-mechanical stimulations to the BBB model resulting in decreased transendothelial electrical resistance in a dose dependent manner, indicating the disruption of the BBB. Moreover, SAW stimulation enhanced transendothelial permeability to sodium fluorescein and FITC-dextran with various molecular weight in the AFT device. Further study indicated BBB opening was mainly attributed to the apparent stretching of intercellular spaces. An in vivo study using a zebrafish model demonstrated SAW exposure promoted penetration of sodium fluorescein to the CNS. Conclusions In summary, AFT effectively disrupts the BBB under the SAW stimulation, which is promising as a new drug delivery methodology for neurodegenerative diseases.
DBpia연관 검색어 추천
이 검색어로 많이 본 자료
활용도 높은 자료
