求线性方程组极小l_1范数解的一种算法

陈中文研究中关于求线性方程组极小l1范数解问题有着较为广泛的应用.本文研究了该问题的最优性条件,给出最优解的充分必要条件.进一步研究了该问题最优解的一种表现形式,提出一个单纯形方法的算法,该算法解决了退化问题,且收敛速度较快,同时给出确定初始基的方法.

求解极小l_1模问题的修正Bland规则

改进了Bland规则 ,给出了一个解极小l1模问题的有效算法 .

关于双向加细方程的L^1-解的一点注记

研究了双向加细方程f(x)=sum(c(n,1)f(αx-β_n))from n=0 to N+sum(c(n,-1)f(-αx-β_n))from n=0 to N的L1-解,其中α∈R且α>1,β0<…<βN∈R.利用傅里叶方法和迭代函数系将证明双向加细方程的所有L1-解所做成的解空间至少是1维的,并且给出了双向加细方程非平凡L1-解存在的充分条件与必要条件,同时给出非平凡L1-解不存在的条件,所得结果容易验证.

基于l_1范数稀疏解的水下双层圆柱壳振动声辐射预报影响因素研究

基于模态叠加法理论,采用l1范数稀疏解方法,实现了水下双层圆柱壳由内壳有限测点振速值重构得到内、外壳振速空间分布,进而基于边界元理论对结构水下辐射声场进行预报。通过数值计算,分析了模态数目、测点数目和模态振型误差等因素对振动声辐射预报结果的影响,为指导速度场重构时模态数目、测点数目的选取提供了一定的理论依据;结果表明基于l1范数稀疏解声学预报方法对模态振型误差有一定的鲁棒性。最后开展了水下典型双层圆柱壳结构振动声辐射预报的试验研究,可为工程领域结构的声振预报提供一定的指导思路。

L1 norm optimal solution match processing in the wavelet domain

Greater attention has been paid to vintage-merge processing of seismic data and extracting more valuable information by the geophysicist. A match filter is used within many important areas such as splicing seismic data, matching seismic data with different ages and sources, 4-D seismic monitoring, and so on. The traditional match filtering method is subject to many restrictions and is usually difficult to overcome the impact of noise. Based on the traditional match filter, we propose the wavelet domain L1 norm optimal matching filter. In this paper, two different types of seismic data are decomposed to the wavelet domain, different detailed effective information is extracted for Ll-norm optimal matching, and ideal results are achieved. Based on the model test, we find that the L1 norm optimal matching filter attenuates the noise and the waveform, amplitude, and phase coherence of result signals are better than the conventional method. The field data test shows that, with our method, the seismic events in the filter results have better continuity which achieves the high precision seismic match requirements.

一致连续的多维倒向随机微分方程的L^1解

建立了一致连续的多维倒向随机微分方程(BSDE)L1解的一个新的存在唯一性结果,其中生成元g关于y满足Osgood条件,关于z是α-Hlder(0<α<1)连续的,并且g的第i个分量仅仅依赖于矩阵z的第i行.

Seizure detection using earth movers' distance and SVM in intracranial EEG

Seizure detection is extremely essential for long-term monitoring of epileptic patients. This paper investigates the detection of epileptic seizures in multi-channel long-term intracranial electroencephalogram （iEEG）. The algorithm conducts wavelet decomposition of iEEGs with five scales, and transforms the sum of the three frequency bands into histogram for computing the distance. The proposed method combines a novel feature called EMD-L1, which is an efficient algorithm of earth movers＇ distance （EMD）, with support vector machine （SVM） for binary classification between seizures and non-sei- zures. The EMD-LI used in this method is characterized by low time complexity and high processing speed by exploiting the L~ metric structure. The smoothing and collar technique are applied on the raw outputs of SVM classifier to obtain more ac- curate results. Several evaluation criteria are recommended to compare our algorithm with other conventional methods using the same dataset from the Freiburg EEG database. Experiment results show that the proposed method achieves a high sensi- tivity, specificity and low false detection rate, which are 95.73 %, 98.45 % and 0.33/h, respectively. This algorithm is char- acterized by its robustness and high accuracy with the possibility of performing real-time analysis of EEG data, and may serve as a seizure detection tool for monitoring long-term EEG.

小波变换的稀疏最优化信号趋势项提取方法

针对非线性信号的趋势项,提出一种基于小波变换的稀疏最优化方法(WT-SO)。该方法通过设置两个边界约束条件,求取l1范数稀疏最优解重构信号趋势项。仿真信号与实测信号处理结果表明:该方法依据信号自身的特性来定义趋势项,不需要对信号作任何假设,比传统的提取趋势项的方法具有更高的精度和可靠性,且在各种噪声环境下均具有良好的鲁棒性。

Bis(acetylacetonato)-oxidovanadium(Ⅳ) inhibits isoproterenol-induced lipolysis in insulin-resistant 3T3-L1 adipocytes by reducing phosphorylation of perilipin and hormone-sensitive lipase

Insulin resistance is characterized as one of crucial pathological changes in type 2 diabetes mellitus（T2DM）, and dyslipidaemia is frequently detected in T2DM. A variety of vanadium compounds have been studied as drug candidates for diabetes based on their insulin-like action. However, few studies focus on their antilipolytic effect. In the present study, we established an insulin-resistant model in 3T3-L1 adipocytes to mimic pathological conditions of T2DM according to a well-established method by the treatment of high concentrations of glucose and insulin, which was validated by oil red O staining and the decreased levels of phosphorylated Akt, AS160 and GSK3 after insulin treatment. The results demonstrated that bis（acetylacetonato）-oxidovanadium（Ⅳ）（VO（acac）_2） could inhibit isoproterenol-stimulated lipolysis through the reduction of the phosphorylated HSL and perilipin levels in both insulin-sensitive and insulin-resistant 3T3-L1 adipocytes. Moreover, although the levels of phosphorylated Akt induced by VO（acac）_2 were decreased, the rates of lipolytic inhibition were not significantly altered compared with those under insulin-sensitive condition, indicating that the anti-lipolytic effect of VO（acac）_2 might also function in an Akt-independent way in insulin-resistant adipocytes. Our work here help elucidate the anti-diabetic effects of vanadium compounds. It may not only shed light on the utility of vanadium-based compounds as potential anti-diabetic drugs but also serve as a useful screening model for new anti-diabetic drugs.