Quantitative Investigation of Tomographic Effects in Abnormal Regions of Complex Structures

The detection of abnormal regions in complex structures is one of the most challenging targets for underground space engineering.Natural or artificial geologic variations reduce the effectiveness of conventional exploration methods.With the emergence of real-time monitoring,seismic wave velocity tomography allows the detection and imaging of abnormal regions to be accurate,intuitive,and quantitative.Since tomographic results are affected by multiple factors in practical small-scale applications,it is necessary to quantitatively investigate those influences.We adopted an improved three-dimensional(3D)tomography method combining passive acoustic emission acquisition and active ultrasonic measurements.By varying individual parameters(i.e.,prior model,sensor configuration,ray coverage,event distributions,and event location errors),37 comparative tests were conducted.The quantitative impact of different factors was obtained.Synthetic experiments showed that the method could effectively adapt to complex structures.The optimal input parameters based on quantization results can significantly improve the detection reliability in abnormal regions.

Exploring the Abnormal Brain Regions and Abnormal Functional Connections in SZ by Multiple Hypothesis Testing Technique

Schizophrenia(SZ)is one of the most common mental diseases.Its main characteristics are abnormal social behavior and inability to correctly understand real things.In recent years,the magnetic resonance imaging(MRI)technique has been popularly utilized to study SZ.However,it is still a great challenge to reveal the essential information contained in the MRI data.In this paper,we proposed a biomarker selection approach based on the multiple hypothesis testing techniques to explore the difference between SZ and healthy controls by using both functional and structural MRI data,in which biomarkers represent both abnormal brain functional connectivity and abnormal brain regions.By implementing the biomarker selection approach,six abnormal brain regions and twenty-three abnormal functional connectivity in the brains of SZ are explored.It is discovered that compared with healthy controls,the significantly reduced gray matter volumes are mainly distributed in the limbic lobe and the basal ganglia,and the significantly increased gray matter volumes are distributed in the frontal gyrus.Meanwhile,it is revealed that the significantly strengthened connections are those between the middle frontal gyrus and the superior occipital gyrus,the superior occipital gyrus and the middle occipital gyrus as well as the middle occipital gyrus and the fusiform gyrus,and the rest connections are significantly weakened.

基于多声源波速结构成像的岩体异常区域超前辨识方法

异常区域超前辨识对于预防地下岩土工程灾害具有重要作用。为了满足地下工程高精度探测的需求,本文提出一种层析成像方法以辨识复杂岩体结构中的异常区域,结合了走时层析、阻尼最小二乘和高斯滤波等技术。该方法克服了空洞区域辨识中速度差限制,减轻了迭代中孤立速度突变所带来的影响。我们开展了数值和室内实验量化评估最短路径法(Shortest-Path Method,SPM)、动态最短路径法(Dynamic Shortest-Path Method,DSPM)和快速扫描法(Fast Sweeping Method,FSM)等正演模拟的识别精度和计算效率。结果表明,在数值和室内实验中DSPM和FSM均能清晰地辨识出异常区域。陕西震奥矿山现场应用结果证明了该方法可利用矿山开采中爆破、微震等多类声源对矿山内部未知结构进行波速场成像。本研究不仅实现了走时层析成像方法在异常区域识别中的应用,而且为地下岩土工程中潜在风险源的探测提供了新的思路。

Role of intraoperative transesophageal echocardiography in coronary artery bypass grafting

Transesophapeal echocardiography （TEE） can be used as a diagnostic tool during cardiac surgery to direct the surgical procedure and diagnose unanticipated problems. TEE has also been one of the most important means of monitoring myocardial ischemia dur- ing coronary artery bypas grafting procedures. The cardiac anesthesiologist can apply intraoperative TEE in evaluating coronary artery anatomy and aorta atherosclerosis, assessing diastolic left ventricular function and preload,measuring intracardiac pressure and cardiac output,detecting ischaemic mitral regurgitation,intracardiac air and pericardial effusion.

Road traffic anomaly monitoring and warning based on DeepWalk algorithm

In the complex urban road traffic network,a sudden accident leads to rapid congestion in the nearby traffic region,which even makes the local traffic network capacity quickly reduce.Therefore,an efficient monitoring system for abnormal conditions of the urban road network plays a crucial role in the tolerance of the urban road network.The traditional traffic monitoring system not only costs a lot in construction and maintenance,but also may not cover the road network comprehensively,which could not meet the basic needs of traffic management.Only a more comprehensive and intelligent monitoring method is able to identify traffic anomalies more effectively and quickly,so that it can provide more effective support for traffic management decisions.The extensive use of positioning equipment made us able to obtain accurate trajectory data.This paper presents a traffic anomaly monitoring and prediction method based on vehicle trajectory data.This model uses deep learning to detect abnormal trajectory on the traffic road network.The method effectively analyses the abnormal source and potential anomaly to judge the abnormal region,which provides an important reference for the traffic department to take effective traffic control measures.Finally,the paper uses Internet vehicle trajectory data from Chengdu(China)to test and obtains an accurate result.