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| 1 | 组织支持感对研发人员创新行为的影响机制研究显示文摘研发人员的创造力是企业技术进步的重要源泉。基于组织支持感理论,从认知和情绪视角,将创造力效能感和积极情绪引入到组织支持感与研发人员创新行为的关系分析框架中,构建组织支持感→创造力效能感/积极情绪→研发人员创新行为理论模型,采用自填问卷法调查248名研发人员,在检验同源方差和问卷信效度的基础上,通过结构方程建模方法检验理论模型及相应的研究假设。研究结果表明,①在中国情境下,组织支持感对研发人员创新行为有显著正向影响,组织支持感及其各维度对研发人员创新行为有预测力,其中主管支持的预测力最强;②创造力效能感对研发人员创新行为有显著正向影响,并在组织支持感与研发人员创新行为间起部分中介作用;③积极情绪对研发人员创新行为有显著正向影响,在组织支持感与研发人员创新行为间起部分中介作用;④将创造力效能感和积极情绪同时并入中介模型后,两者的中介作用完全解释了组织支持感对研发人员创新行为的影响。 | 顾远东 周文莉 彭纪生 | 2014 | 管理科学2014，27，1： | 99 |
| 2 | 应用支持向量机评价土壤环境质量显示文摘基于野外采样和室内分析相结合的方法,采用电感耦合等离子体质谱法(ICP-MS)对羊草沟煤矿研究区表层土壤样品中的Cd、Cr、Zn、Pb和Cu含量进行测定,应用非线性支持向量机模型中的分类支持向量机,选用sigmoid核函数,利用MATLAB编写程序,进行土壤环境质量评价,并利用模糊综合评判法对评价结果进行验证.在此基础上,运用对应分析方法对样品和变量进行了关联分析,进一步了解重金属污染特征.评价结果表明,研究区土壤环境质量多为Ⅰ类,与模糊综合评判法的相同率达到91.67%,将支持向量机用于土壤环境质量评价是可行的.相比于传统的评价方法,支持向量机采用结构风险最小化原则,将复杂的非线性问题转化为线性问题,成功的解决了多分类、高维运算等问题. | 姜雪 卢文喜 杨青春 赵海卿 | 2014 | 中国环境科学2014，34，5： | 25 |
| 3 | Efects of a bioartificial liver support system on a cetaminophen-induced acute liver failure canines显示文摘Seeinvitedcommentaryonpage286Subjectheadingsliversupportsystem;acuteliverfailure;canines;porcinehepatocytes;bioartificiallive... | XUE Yi Long 1, ZHAO Shi Feng 2, ZHANG Zuo Yun 1, WANG Yue Feng 1, LI Xin Jian 1, HUANG Xiao Qiang 3, LUO Yun 1, HUANG Ying Cai 4 and LIU Cheng Gui 1 | 1999 | World Journal of Gastroenterology1999，5，4： | 19 |
| 4 | Machine learning methods for rockburst prediction-state-of-the-art review显示文摘One of the most serious mining disasters in underground mines is rockburst phenomena.They can lead to injuries and even fatalities as well as damage to underground openings and mining equipment.This has forced many researchers to investigate alternative methods to predict the potential for rockburst occurrence.However,due to the highly complex relation between geological,mechanical and geometric parameters of the mining environment,the traditional mechanics-based prediction methods do not always yield precise results.With the emergence of machine learning methods,a breakthrough in the prediction of rockburst occurrence has become possible in recent years.This paper presents a state-ofthe-art review of various applications of machine learning methods for the prediction of rockburst potential.First,existing rockburst prediction methods are introduced,and the limitations of such methods are highlighted.A brief overview of typical machine learning methods and their main features as predictive tools is then presented.The current applications of machine learning models in rockburst prediction are surveyed,with related mechanisms,technical details and performance analysis. | Yuanyuan Pu Derek B.Apel Victor Liu Hani Mitri | 2019 | International Journal of Mining Science and Technology2019，29，4： | 19 |
| 5 | Chinese expert consensus statement on the diagnosis and treatment of fulminant myocarditis显示文摘Fulminant myocarditis is primarily caused by infection with any number of a variety of viruses. It arises quickly, progresses rapidly, and may lead to severe heart failure or circulatory failure presenting as rapid-onset hypotension and cardiogenic shock,with mortality rates as high as 50%–70%. Most importantly, there are no treatment options, guidelines or an expert consensus statement. Here, we provide the first expert consensus, the Chinese Society of Cardiology Expert Consensus Statement on the Diagnosis and Treatment of Fulminant Myocarditis, based on data from our recent clinical trial(NCT03268642). In this statement, we describe the clinical features and diagnostic criteria of fulminant myocarditis, and importantly, for the first time,we describe a new treatment regimen termed life support-based comprehensive treatment regimen. The core content of this treatment regimen includes(i) mechanical life support(applications of mechanical respirators and circulatory support systems,including intraaortic balloon pump and extracorporeal membrane oxygenation),(ii) immunological modulation by using sufficient doses of glucocorticoid, immunoglobulin and(iii) antiviral reagents using neuraminidase inhibitor. The proper application of this treatment regimen may and has helped to save the lives of many patients with fulminant myocarditis. | Daowen Wang Sheng Li Jiangang Jiang Jiangtao Yan Chunxia Zhao Yan Wang Yexin Ma Hesong Zeng Xiaomei Guo Hong Wang Jiarong Tang Houjuan Zuo Li Lin Guanglin Cui Section of Precision Medicine Group of Chinese Society of Cardiology,Editorial Board of Chinese Journal of Cardiology &Working Group of Adult Fulminant Myocarditis | 2019 | Science China(Life Sciences)2019，62，2： | 18 |
| 6 | Extracorporeal membrane oxygenation for pediatric respiratory failure: History, development and current status显示文摘Extracorporeal membrane oxygenation(ECMO) is currently used to support patients of all ages with acute severe respiratory failure non-responsive to conventional treatments, and although initial use was almost exclusively in neonates, use for this age group is decreasing while use in older children remains stable(300-500 cases annually) and support for adults is increasing. Recent advances in technology include: refinement of double lumen veno-venous(VV) cannulas to support a large range of patient size, pumps with lower prime volumes, more efficient oxygenators, changes in circuit configuration to decrease turbulent flow and hemolysis. Veno-arterial(VA) mode of support remains the predominant type used; however, VV support has lower risk of central nervous injury and mortality. Key to successful survival is implementation of ECMO before irreversible organ injury develops, unless support with ECMO is used as a bridge to transplant. Among pediatric patients treated with ECMO mortality varies by pulmonary diagnosis, underlying condition, other nonpulmonary organ dysfunction as well as patient age, but has remained relatively unchanged overall(43%)over the past several decades. Additional risk factors associated with death include prolonged use of mechanical ventilation(> 2 wk) prior to ECMO, use of VA ECMO, older patient age, prolonged ECMO support as well as complications during ECMO. Medical evidence regarding daily patient management specifically related to ECMO is scant, it usually mirrors care recommended for similar patients treated without ECMO. Linkage of the Extracorporeal Life Support Organization dataset with other databases and collaborative research networks will be required to address this knowledge deficit as most centers treat only a few pediatric respiratory failure patients each year. | Anna Maslach-Hubbard Susan L Bratton | 2013 | World Journal of Critical Care Medicine2013，2，4： | 17 |
| 7 | Effect of extracorporeal bioartificial liver support system on fulminant hepatic failure rabbits显示文摘AIM To evaluate the possibility of usingcultured human hepatocytes as a bridge betweenbioartificial liver and liver transplantation.METHODS In this experiment,the efficacy ofextracorporeal bioartificial liver support system(EBLSS)consisting of spheriodal human livercells and cultured hepatocytes supernatant wasassessed in vivo using galactosamine inducedrabbit model of fulminant hepatic failure.RESULTS There was no difference of survivalbetween the two groups of rabbits,but in thesupported rabbits serum alanineaminotransferase,total bilirubin and creatininewere significantly lower and hepatocyte necrosiswas markedly milder than those in controlanimals.In addition,a good viability of humanliver cells was noted after the experiment.CONCLUSION EBLSS plays a biologic role inmaintaining and compensating the function ofthe liver. | Wang YJ Li MD Wang YM Chen GZ Lu GD Tan ZX | 2000 | World Journal of Gastroenterology2000，6，2： | 17 |
| 8 | Liver transplantation in China: problems and their solutions显示文摘BACKGROUND: The past decade has witnessed the rapid development of liver transplantation in China. The 1-year survival of liver transplant patients comes to 80% in many leading medical centers and the number of liver transplanta- tion is increasing. However, liver transplantation in China is facing several challenges including recipient with hepato- cellular carcinoma (HCC), recurrence of HCC and hepati- tis B, long-term postoperative care, the bridge to liver transplantation, and shortage of liver donor. This review was to understand the status of and problems in liver trans- plantation in China. DATA RESOURCES: An English-language literature search using MEDLINE (1990-2003) on liver transplantation and other related reports and review articles in Chinese from major transplant centers in China. RESULTS: HCC is one of the main indications for liver transplantation in China but different centers adopted dif- ferent criteria for selection of patients. Hepatitis B virus re- infection is a vital problem after liver transplantation in HBV-related patients. More and more attention was fo- cused on long-term postoperative care and donor shortage. Artificial liver support system has been applied in patients waiting for a graft in many centers. CONCLUSIONS: HCC remains to be one of the main indi- cations for liver transplantation in China; combined hepati- tis B immune globulin and lamivudine is considered effec- tive to prevent hepatitis B virus reinfection. Apart from long-term postoperative care for the improvement of the survival rate, early steroid withdrawal is feasible in liver transplantation. Living donor liver transplantation, split liv- er transplantation, and marginal donor transplantation can deal with donor shortage to some extent. Artificial liver as- sist system serves as a bridge to liver transplantation. | Jian Wu and Shu-Sen Zheng Hangzhou, China Department of Hepatobiliary Surgery, First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310003 , Chi- na | 2004 | Hepatobiliary & Pancreatic Diseases International2004，3，2： | 15 |
| 9 | 基于PCA-LSSVM的机车走行部滚动轴承故障诊断研究显示文摘机车走行部的故障诊断是保障机车安全运行的重要手段之一。鉴于目前机车走行部滚动轴承故障诊断中存在的特征提取困难、故障诊断不准确等问题,提出一种基于最小二乘支持向量机(LSSVM)的故障诊断方法。通过主成分分析方法(PCA)对机车走行部滚动轴承的故障特征进行特征提取,将提取后的特征向量输入到故障诊断模型中,结合最小二乘支持向量机原理实现机车走行部滚动轴承的故障诊断。仿真结果表明,相比其他方法而言分类准确率达到了100%,模型构建时间为3.642 s,满足机车走行部滚动轴承的诊断要求。将最小二乘支持向量机引入并应用到机车走行部故障诊断领域中,为机车走行部故障诊断系统的研究与开发提供新思路和新方法。 | 钟小凤 贺德强 苗剑 | 2014 | 广西大学学报(自然科学版)2014，39，2： | 15 |
| 10 | A reliability assessment method based on support vector machines for CNC equipment显示文摘With the applications of high technology,a catastrophic failure of CNC equipment rarely occurs at normal operation conditions.So it is difficult for traditional reliability assessment methods based on time-to-failure distributions to deduce the reliability level.This paper presents a novel reliability assessment methodology to estimate the reliability level of equipment with machining performance degradation data when only a few samples are available.The least squares support vector machines(LS-SVM) are introduced to analyze the performance degradation process on the equipment.A two-stage parameter optimization and searching method is proposed to improve the LS-SVM regression performance and a reliability assessment model based on the LS-SVM is built.A machining performance degradation experiment has been carried out on an OTM650 machine tool to validate the effectiveness of the proposed reliability assessment methodology. | WU Jun DENG Chao SHAO XinYu XIE S Q | 2009 | Science China(Technological Sciences)2009，52，7： | 14 |
| 11 | Experimental study of bioartificial liver with cultured human liver cells显示文摘METHODSTheliversupportexperimentofEBLSSconsistingofaggregatesculturedhumanlivercels,holowfiberbioreactor,andcirculationunit... | WANG Ying Jie, LI Meng Dong, WANG Yu Ming, NIE Qing He and CHEN Guo Zheng | 1999 | World Journal of Gastroenterology1999，5，2： | 14 |
| 12 | Real-time transient stability assessment in power system based on improved SVM显示文摘Due to the strict requirements of extremely high accuracy and fast computational speed, real-time transient stability assessment(TSA) has always been a tough problem in power system analysis.Fortunately, the development of artificial intelligence and big data technologies provide the new prospective methods to this issue, and there have been some successful trials on using intelligent method, such as support vector machine(SVM) method.However, the traditional SVM method cannot avoid false classification, and the interpretability of the results needs to be strengthened and clear.This paper proposes a new strategy to solve the shortcomings of traditional SVM,which can improve the interpretability of results, and avoid the problem of false alarms and missed alarms.In this strategy, two improved SVMs, which are called aggressive support vector machine(ASVM) and conservative support vector machine(CSVM), are proposed to improve the accuracy of the classification.And two improved SVMs can ensure the stability or instability of the power system in most cases.For the small amount of cases with undetermined stability, a new concept of grey region(GR) is built to measure the uncertainty of the results, and GR can assessment the instable probability of the power system.Cases studies on IEEE 39-bus system and realistic provincial power grid illustrate the effectiveness and practicability of the proposed strategy. | Wei HU Zongxiang LU Shuang WU Weiling ZHANG Yu DONG Rui YU Baisi LIU | 2019 | Journal of Modern Power Systems and Clean Energy2019，7，1： | 14 |
| 13 | 基于改进层次聚类和SVM的图像型火焰识别显示文摘为了提高大空间建筑内实时监控的火灾检出率,提出基于改进分层聚类和支持向量机(SVM)的火灾识别算法。首先建立火焰颜色模型,用像素运动累积法获取疑似目标,借助改进层次聚类法对其进行合并,形成少量疑似区域。然后提取疑似区域相邻帧间相关性、面积变化率、质心偏移距离、红绿分量比、平均亮度这五个特征量。最后将特征输入到SVM进行二分类,判断是否有火。实验结果表明该算法提高了聚类算法在实际应用中的效率,克服了已有火灾识别算法过分依赖阈值的局限性,适用于室内大空间基于视频监控的火灾探测。 | 贾阳 王慧琴 胡燕 党勃 | 2014 | 计算机工程与应用2014，50，5： | 13 |
| 14 | Predicting the onset temperature(Tg) of GexSe1-x glass transition: a feature selection based two-stage support vector regression method显示文摘Despite the usage of both experimental and topological methods, realizing a rapid and accurate measurement of the onset temperature(Tg ) of GexSe1-xglass transition remains an open challenge. In this paper, a predictive model for the Tg in GexSe1-xglass system is presented by a machine learning method named feature selection based two-stage support vector regression(FSTS-SVR). Firstly, Pearson correlation coefficient(PCC) is used to select features highly correlated with Tg from the candidate features of GexSe1-x glass system. Secondly, in order to simulate the two-stage characteristic of Tg which is caused by structural variation with a turning point at x = 0.33 via the structural analysis, SVR is utilized to build predictive models for two stages separately and then the two achieved models are synthesized using a minimum error based model for Tg prediction. Compared with the topological and other methods based on SVR, the FSTS-SVR gives the highest predictive accuracy with the root mean square error(RMSE) and mean absolute percentage error(MAPE) of 10.64 K and 2.38%, respectively. This method is also expected to be more efficient for the prediction of Tg of other glass systems with the multi-stage characteristic. | Yue Liu Junming Wu Guang Yang Tianlu Zhao Siqi Shi | 2019 | Science Bulletin2019，64，16： | 12 |
| 15 | 基于边缘检测和颜色纹理直方图的车牌定位方法显示文摘针对已有车牌定位算法在分辨率高、背景复杂图像上存在准确率下降的问题,提出了一种基于边缘检测和颜色纹理直方图的车牌定位算法。该定位算法分为两个阶段:首先利用结合了Canny和Sobel算法的改进边缘检测算法提取图像的垂直边缘,并结合滤波、投影等方法进行车牌粗定位;然后提取候选区域的颜色纹理直方图,与训练好的分类器进行匹配,实现车牌的精确定位。实验表明,该方法对于背景复杂、光照不均等情况均有良好的鲁棒性,在白天和晚上都能取得较好的定位效果。 | 郭延祥 陈耀武 | 2014 | 计算机科学与探索2014，8，6： | 12 |
| 16 | Neurologic complications and neurodevelopmental outcome with extracorporeal life support显示文摘Extracorporeal life support is used to support patients of all ages with refractory cardiac and/or respiratory failure. Extracorporeal membrane oxygenation(ECMO)has been used to rescue patients whose predicted mortality would have otherwise been high. It is associated with acute central nervous system(CNS) complications and with long- term neurologic morbidity. Many patients treated with ECMO have acute neurologic complications, including seizures, hemorrhage, infarction, and brain death. Various pre-ECMO and ECMO factors have been found to be associated with neurologic injury, including acidosis, renal failure, cardiopulmonary resuscitation, and modality of ECMO used. The risk of neurologic complication appears to vary by age of the patient, with neonates appearing to have the highest risk of acute central nervous system complications. Acute CNS injuries are associated with increased risk of death in a patient who has received ECMO support. ECMO is increasingly used during cardiopulmonary resuscitation when return of spontaneous circulation is not achieved rapidly and outcomes may be good in select populations. Economic analyses have shown that neonatal and adult respiratory ECMO are cost effective. There have been several intriguing reports of active physical rehabilitation of patients duringECMO support that is well tolerated and may improve recovery. Although there is evidence that some patients supported with ECMO appear to have very good outcomes, there is limited understanding of the longterm impact of ECMO on quality of life and long-term cognitive and physical functioning for many groups, especially the cardiac and pediatric populations. This deserves further study. | Amit Mehta Laura M Ibsen | 2013 | World Journal of Critical Care Medicine2013，2，4： | 12 |
| 17 | Fault diagnosis of wind turbine bearing based on stochastic subspace identification and multi-kernel support vector machine显示文摘In order to accurately identify a bearing fault on a wind turbine, a novel fault diagnosis method based on stochastic subspace identification(SSI) and multi-kernel support vector machine(MSVM) is proposed. Firstly, the collected vibration signal of the wind turbine bearing is processed by the SSI method to extract fault feature vectors. Then, the MSVM is constructed based on Gauss kernel support vector machine(SVM) and polynomial kernel SVM. Finally, fault feature vectors which indicate the condition of the wind turbine bearing are inputted to the MSVM for fault pattern recognition. The results indicate that the SSI-MSVM method is effective in fault diagnosis for a wind turbine bearing and can successfully identify fault types of bearing and achieve higher diagnostic accuracy than that of K-means clustering, fuzzy means clustering and traditional SVM. | Hongshan ZHAO Yufeng GAO Huihai LIU Lang LI | 2019 | Journal of Modern Power Systems and Clean Energy2019，7，2： | 12 |
| 18 | How do machine learning techniques help in increasing accuracy of landslide susceptibility maps?显示文摘Landslides are abundant in mountainous regions.They are responsible for substantial damages and losses in those areas.The A1 Highway,which is an important road in Algeria,was sometimes constructed in mountainous and/or semi-mountainous areas.Previous studies of landslide susceptibility mapping conducted near this road using statistical and expert methods have yielded ordinary results.In this research,we are interested in how do machine learning techniques help in increasing accuracy of landslide susceptibility maps in the vicinity of the A1 Highway corridor.To do this,an important section at Ain Bouziane(NE,Algeria) is chosen as a case study to evaluate the landslide susceptibility using three different machine learning methods,namely,random forest(RF),support vector machine(SVM),and boosted regression tree(BRT).First,an inventory map and nine input factors were prepared for landslide susceptibility mapping(LSM) analyses.The three models were constructed to find the most susceptible areas to this phenomenon.The results were assessed by calculating the receiver operating characteristic(ROC) curve,the standard error(Std.error),and the confidence interval(CI) at 95%.The RF model reached the highest predictive accuracy(AUC=97.2%) comparatively to the other models.The outcomes of this research proved that the obtained machine learning models had the ability to predict future landslide locations in this important road section.In addition,their application gives an improvement of the accuracy of LSMs near the road corridor.The machine learning models may become an important prediction tool that will identify landslide alleviation actions. | Yacine Achour Hamid Reza Pourghasemi | 2020 | Geoscience Frontiers2020，11，3： | 11 |
| 19 | Combination of Model-based Observer and Support Vector Machines for Fault Detection of Wind Turbines显示文摘Support vector machines and a Kalman-like observer are used for fault detection and isolation in a variable speed horizontalaxis wind turbine composed of three blades and a full converter. The support vector approach is data-based and is therefore robust to process knowledge. It is based on structural risk minimization which enhances generalization even with small training data set and it allows for process nonlinearity by using flexible kernels. In this work, a radial basis function is used as the kernel. Different parts of the process are investigated including actuators and sensors faults. With duplicated sensors, sensor faults in blade pitch positions,generator and rotor speeds can be detected. Faults of type stuck measurements can be detected in 2 sampling periods. The detection time of offset/scaled measurements depends on the severity of the fault and on the process dynamics when the fault occurs. The converter torque actuator fault can be detected within 2 sampling periods. Faults in the actuators of the pitch systems represents a higher difficulty for fault detection which is due to the fact that such faults only affect the transitory state(which is very fast) but not the final stationary state. Therefore, two methods are considered and compared for fault detection and isolation of this fault: support vector machines and a Kalman-like observer. Advantages and disadvantages of each method are discussed. On one hand, support vector machines training of transitory states would require a big amount of data in different situations, but the fault detection and isolation results are robust to variations in the input/operating point. On the other hand, the observer is model-based, and therefore does not require training, and it allows identification of the fault level, which is interesting for fault reconfiguration. But the observability of the system is ensured under specific conditions, related to the dynamics of the inputs and outputs. The whole fault detection and isolation scheme is evaluated using a wind turbine benchmark with a real sequence of wind speed. | Nassim Laouti Sami Othman Mazen Alamir Nida Sheibat-Othman | 2014 | International Journal of Automation and computing2014，11，3： | 11 |
| 20 | 基于ACO-LSSVM的网络流量预测显示文摘为了提高了网络流量的预测精度,提出一种蚁群算法(ACO)优化最小二乘支持向量机(LSSVM)参数的网络流量预测算法(ACO-LSSVM)。将LSSVM算法参数作为蚂蚁的位置向量,采用动态随机抽取的方法来确定目标个体引导蚁群进行全局搜索,并在最优蚂蚁邻域内进行小步长局部搜索,找到算法的最优参数,建立了基于ACO-LSSVM的网络流量预测模型。仿真结果表明,相对其他网络流量预测算法,ACO-LSSVM算法提高了网络流量预测精度,更能准确地描述网络流量变化规律。 | 田海梅 黄楠 | 2014 | 计算机工程与应用2014，50，1： | 11 |
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