A Novel Method for Prediction of Protein Domain Using Distance-Based Maximal Entropy

Detecting the boundaries of protein domains is an important and challenging task in both experimental and computational structural biology. In this paper, a promising method for detecting the domain structure of a protein from sequence information alone is presented. The method is based on analyzing multiple sequence alignments derived from a database search. Multiple measures are defined to quantify the domain information content of each position along the sequence. Then they are combined into a single predictor using support vector machine. What is more important, the domain detection is first taken as an imbal- anced data learning problem. A novel undersampling method is proposed on distance-based maximal entropy in the feature space of Support Vector Machine （SVM）. The overall precision is about 80%. Simulation results demonstrate that the method can help not only in predicting the complete 3D structure of a protein but also in the machine learning system on general im- balanced datasets.

Aggregating metasearch engine results based on maximal entropy OWA operator

The maximal entropy ordered weighted averaging （ME-OWA） operator is used to aggregate metasearch engine results, and its newly analytical solution is also applied. Within the current context of the OWA operator, the methods for aggregating metasearch engine results are divided into two kinds. One has a unique solution, and the other has multiple solutions. The proposed method not only has crisp weights, but also provides multiple aggregation results for decision makers to choose from. In order to prove the application of the ME-OWA operator method, under the context of aggregating metasearch engine results, an example is given, which shows the results obtained by the ME-OWA operator method and the minimax linear programming （ minimax-LP ） method. Comparison between these two methods are also made. The results show that the ME-OWA operator has nearly the same aggregation results as those of the minimax-LP method.

Automated Exudates Detection in Retinal Fundus Image Using Morphological Operator and Entropy Maximization Thresholding

Blindness which is considered as degrading disabling disease is the final stage that occurs when a certain threshold of visual acuity is overlapped. It happens with vision deficiencies that are pathologic states due to many ocular diseases. Among them, diabetic retinopathy is nowadays a chronic disease that attacks most of diabetic patients. Early detection through automatic screening programs reduces considerably expansion of the disease. Exudates are one of the earliest signs. This paper presents an automated method for exudates detection in digital retinal fundus image. The first step consists of image enhancement. It focuses on histogram expansion and median filter. The difference between filtered image and his inverse reduces noise and removes background while preserving features and patterns related to the exudates. The second step refers to blood vessel removal by using morphological operators. In the last step, we compute the result image with an algorithm based on Entropy Maximization Thresholding to obtain two segmented regions (optical disk and exudates) which were highlighted in the second step. Finally, according to size criteria, we eliminate the other regions obtain the regions of interest related to exudates. Evaluations were done with retinal fundus image DIARETDB1 database. DIARETDB1 gathers high-quality medical images which have been verified by experts. It consists of around 89 colour fundus images of which 84 contain at least mild non-proliferative signs of the diabetic retinopathy. This tool provides a unified framework for benchmarking the methods, but also points out clear deficiencies in the current practice in the method development. Comparing to other recent methods available in literature, we found that the proposed algorithm accomplished better result in terms of sensibility (94.27%) and specificity (97.63%).

On Utilization of K-Means for Determination of <i>q</i>-Parameter for Tsallis-Entropy-Maximized-FCM

In this paper, we consider a fuzzy c-means (FCM) clustering algorithm combined with the deterministic annealing method and the Tsallis entropy maximization. The Tsallis entropy is a q-parameter extension of the Shannon entropy. By maximizing the Tsallis entropy within the framework of FCM, membership functions similar to statistical mechanical distribution functions can be derived. One of the major considerations when using this method is how to determine appropriate q values and the highest annealing temperature, Thigh?, for a given data set. Accordingly, in this paper, a method for determining these values simultaneously without introducing any additional parameters is presented. In our approach, the membership function is approximated by a series of expansion methods and the K-means clustering algorithm is utilized as a preprocessing step to estimate a radius of each data distribution. The results of experiments indicate that the proposed method is effective and both q and Thigh can be determined automatically and algebraically from a given data set.

基于新交叉熵的灰关联双向投影多属性决策方法

文章针对属性权重完全未知的犹豫模糊多属性决策问题,提出一种基于新交叉熵的灰关联双向投影多属性决策方法。首先,结合基本不等式概念提出犹豫模糊数中新的交叉熵公式,并证明其满足熵的基本性质。其次,以新的交叉熵公式为距离公式,应用离差最大化方法求得属性权重。再次,将灰关联双向投影法拓展到一般犹豫模糊环境下,提出了基于新交叉熵的灰关联双向投影多属性决策方法。最后,通过一个算例验证了所提方法的可行性和有效性。该方法为解决犹豫模糊信息中属性权重完全未知的决策问题提供了新的方法和思路,且具有较高的区分度和灵敏度,提高了决策可信度。

伪对立学习与差分进化的改进鲸鱼优化算法及图像分割应用

针对多阈值图像分割计算代价高、分割精度差的不足,提出伪对立学习与差分进化的改进鲸鱼优化最大熵多阈值图像分割算法。为了提升传统鲸鱼算法的寻优精度和收敛速率,引入伪对立学习和混沌Tent映射进行种群初始化,提升种群多样性和初始解质量,扩大精英个体对种群进化的引领作用;引入差分进化增强种群全局搜索能力,避免迭代后期陷入局部最优,进而实现改进算法OLDWOA。以最大熵函数评估适应度,利用OLDWOA对图像分割多阈值组合寻优,确定最优阈值。利用经典图像做图像分割实验,在计算效率、峰值信噪比、结构相似度和特征相似度指标上对比,证实该方法分割精度和分割效率优于同类算法。

Complexity analysis and dynamic characteristics of EEG using MODWT based entropies for identification of seizure onset

In this paper,complexity analysis and dynamic characteristics of electroencephalogram(EEG) signal based on maximal overlap discrete wavelet transform(MODWT) has been exploited for the identification of seizure onset.Since wavelet-based studies were well suited for classification of normal and epileptic seizure EEG,we have applied MODWT which is an improved version of discrete wavelet transform(DWT).The selection of optimal wavelet sub-band and features plays a crucial role to understand the brain dynamics in epileptic patients.Therefore,we have investigated MODWT using four different wavelets,namely Haar,Coif4,Dmey,and Sym4 sub-bands until seven levels.Further,we have explored the potentials of six entropies,namely sigmoid,Shannon,wavelet,Renyi,Tsallis,and Steins unbiased risk estimator(SURE) entropies in each sub-band.The sigmoid entropy extracted from Haar wavelet in sub-band D4 showed the highest accuracy of 98.44% using support vector machine classifier for the EEG collected from Ramaiah Medical College and Hospitals(RMCH).Further,the highest accuracy of 100% and 94.51% was achieved for the University of Bonn(UBonn) and CHB-MIT databases respectively.The findings of the study showed that Haar and Dmey wavelets were found to be computationally economical and expensive respectively.Besides,in terms of dynamic characteristics,MODWT results revealed that the highest energy present in sub-bands D2,D3,and D4 and entropies in those respective sub-bands outperformed other entropies in terms of classification results for RMCH database.Similarly,using all the entropies,sub-bands D5 and D6 outperformed other sub-bands for UBonn and CHB-MIT databases respectively.In conclusion,the comparison results of MODWT outperformed DWT.

基于社区与结构熵的异质网络影响力最大化

影响力最大化的目的是在网络中发现能够触发最大数量的剩余节点参与到信息传播过程的一小群节点.目前异质信息网络中影响力最大化的研究通常从网络中抽取同质子图、或基于节点局部结构的元路径进行节点影响力的评估,没有考虑节点的全局特征和网络中高影响力节点间的集群现象给种子集合最终扩散范围造成的影响损失.文中提出了一种基于社区与结构熵的异质信息网络影响力最大化算法,该算法能够有效地从局部和全局两个方面度量节点的影响.首先,通过构建元结构保留节点在网络中的局部结构信息和异质信息度量节点的局部影响;其次,利用节点所属社区在整个网络中的权重占比对节点的全局影响进行度量;最后,综合求出节点的最终影响并选出种子集合.在真实数据集上进行的大量实验结果表明所提算法有较好的有效性和效率.

基于IAHP-EWM-LDM的L3级自动驾驶接管过程安全性评价方法

在L3级自动驾驶阶段,驾驶人需要在系统发出接管请求时,及时响应并接管车辆。因此为了准确评估L3级自动驾驶车辆接管过程的安全性,构建了自动驾驶接管过程的安全性评价指标体系。本文采用4×2×2的接管场景因子设计了驾驶模拟试验,利用驾驶模拟器采集各类驾驶数据;基于变异系数法和Spearman相关性判别法从风险感知、避险操纵和接管绩效3个方面分析得到了13个安全性评价指标;使用能够表征专家经验的改进层次分析法求取指标的主观权重,使用能够反映数据特征的熵权法求取指标的客观权重;为综合2种方法的优点,利用级差最大化法获得了融合主、客观权重的综合权重,并通过计算得出风险感知、避险操纵、接管绩效的综合权重分别为0.259、0.475、0.271,以此结果来构建接管过程的安全性评价指标体系。本文运用该体系对驾驶模拟试验所获得的655个接管过程进行了综合评价,根据评价结果将其划分为A、B、C这3类接管过程。对比3类接管过程在风险感知、避险操纵、接管绩效3个方面的得分发现,A类接管过程在3个方面均表现较好,C类接管过程在避险操纵和接管绩效2个方面表现较差,B类指标的表现介于A类和C类之间,不同类别的接管过程在各个指标上均具有较好的区分度。本文构建的评价指标体系有效结合了专家经验和指标特征,能够为更加全面、合理和科学地评价自动驾驶接管过程中的安全性提供理论支持。

Stronger Version Sensitivity, Almost Finite to One Extension and Maximal Pattern Entropy

In this paper,we introduce thickr-sensitivity,multi-r-sensitivity and block thick r-sensitivity for r≥2.We first give a characterization of a minimal system which is block thickly r-sensitive.Then we obtain a sufficient condition of a minimal system which is thickly r-sensitive.The maximal pattern entropy of a multi-r-sensitive topological dynamical system is also discussed.

Maximal Abelian Subalgebras of the Hyperfinite Factor,Entropy and Ergodic Theory

Given a free ergodic action of a discrete abelian group G on a measure space (X, 7), the crossed product LX (X, 7)p G contains two distinguished maximal abelian subalgebras. We discuss what kind of information about the action can be extracted from the positions of these two subalgebras inside the crossed product algebra.

Measurable n-sensitivity and maximal pattern entropy

We introduce the notion of measurable n-sensitivity for measure preserving systems,and study the relation between measurable n-sensitivity and the maximal pattern entropy.We prove that,if(X,ℬ,μ,T)is ergodic,then(X,ℬ,μ,T)is measurable n-sensitive but not measurable(n+1)-sensitive if and only if h_(μ)^(*)(T)=log n,where h_(μ)^(*)(T)is the maximal pattern entropy of T.

效用最大化、logit变换和城市地理学的数量分析模型

城市地理学的主要数学模型 ,包括城市化进程的logistic模型 ,城市位序 -规模分布的幂指数模型以及城市人口密度衰减的负指数模型 ,都可以从两个简单的科学假设出发推导出来。文章证明 ,上述假设在理论上可以归结为效用最大化原理 ,其本质与信息熵最大化有着深刻的内在关系。城市地理学的主要模型作为标度定律都可以借助logit变换进行简化处理 ,从而为理论模型的实际应用以及城市演化动力学的模拟实验分析打开了方便之门。

基于互信息最大化的特征选择算法及应用

该文以互信息最大化原则为指导,经过推导和分析后提出了一种基于信息论模型的新的特征选择算法,称之为基于互信息最大化的特征选择算法(MaxMI)。基本思想就是特征选择后,应当尽可能多地保留关于类别的信息。该算法与传统的信息增益、互信息和交叉熵在表达形式上具有一定的相似性,但是并不完全相同。从实验上验证了基于互信息最大化的特征选择算法优于其它三种算法。

基于最大熵原理的小波去噪方法

小波变换去噪中最关键的问题是如何确定小波系数的阈值 ,使其能够将与噪声和信号相对应的小波系数合理地区分开来。根据概率论的基本原理可以推断 ,随机序列的细节小波变换系数符合正态分布。基于此结论 ,可以利用最大熵原理确定一个阈值 ,使得绝对值小于此阈值的小波系数组成的序列符合正态分布。该阈值在统计意义下能够最佳地区分信号与噪声的小波系数。采用光谱数据的仿真分析以及与其他方法的对比实验证明 ,这种最大熵小波去噪方法不仅在提高信噪比方面显示出了其优势 ,而且去噪效果不易受信噪比变化的影响。

视觉显著目标的自适应分割

基于视觉注意模型和最大熵分割算法,提出了一种自适应显著目标分割方法来分离目标和复杂背景,以便快速准确地从场景图像中检测出显著目标。首先,通过颜色、强度、方向和局部能量4个特征通道获取图像的显著图;通过引入局部能量通道来更好地描述了显著目标的轮廓。然后,根据显著图中像素灰度的强弱构建不同的目标检测蒙板,将每个蒙板作用于原图像作为预分割的结果,再计算每个预分割图像的熵。最后,利用最大熵准则估计图像目标熵,根据预分割图像的熵和目标熵判断选取最优显著目标分割图像。实验结果表明:本文算法检测的显著目标更为完整,分割性能F-measure达到0.56,查全率和查准率分别为0.69和0.41,相对于传统方法更为有效准确,实现了在复杂背景下对显著目标的有效准确检测。

基于最大熵原理与最优化方法的隧道衬砌结构可靠度分析

针对隧道衬砌结构参数的随机性、分布的多样性与极限状态功能函数高度非线性的特征,运用最大熵原理对隧道衬砌结构参数进行估计,得到符合实际的参数估计值;然后,根据隧道衬砌结构稳定的极限状态方程,从结构可靠度指标的几何涵义出发,建立其可靠度指标计算的优化数学模型,并运用Microsoft Excel工作表中的规划求解功能得到其可靠度指标。结合工程实际,给出具体算例,且指出提高隧道衬砌结构可靠度的途径。研究结果表明:该方法用于计算工程结构可靠度指标无需将状态函数线性化,不受基本变量维数限制,收敛速度快,计算效率高,且与蒙特卡罗100万次直接抽样法计算结果相比,具有很高的精度,能广泛适用于隧道及地下工程领域的可靠度计算和分析。

基于熵权-离差聚类法的城市公共安全舆情评估

为了提高我国平安城市和安全社区建设水平,以天涯社区论坛为例,评估我国城市公共安全舆情。基于安全社区指标体系,采用火车头采集器对北京、天津、上海、重庆和广州5个城市2010年1月—2012年5月的公共安全舆情数据进行挖掘,采用熵权法和离差最大化法确定安全舆情指标的权重值,建立城市公共安全舆情评价的多属性决策模型。利用ward系统聚类法对城市公共安全舆情进行分类评价。计算结果表明,城市公共安全舆情指数的排序依次为上海、重庆、北京、广州、天津。这5个城市的公共安全舆情指数存在明显差异。

极大熵Relief特征加权

Relief特征加权的最新研究进展表明其可近似地表述为一个间距最大化优化问题.尽管该类算法广为应用,但仍然存在一些缺陷.为了提高Relief特征加权的适应性和鲁棒性,融合间距最大化和极大熵理论,并由此探讨了新的鲁棒的具有更好适应性的Relief特征加新方法.首先,构造了一个结合极大熵原理的间距最大化目标函数.对于该目标函数,运用优化理论得到一些重要的理论结果.在此基础上,对于两类数据、多类数据和在线数据,提出了一组鲁棒的Relief特征加权算法.利用UCI基准数据集和基因数据集进行了实验验证,结果表明提出的新Relief特征加权算法对噪音和例外点显示出了更好的适应性和鲁棒性.

交通分布与熵

介绍在气象等学科中成功应用的方法论———最大信息熵原理， 并扩充为一般熵原理； 通过对交通分布与熵原理的关系的分析研究， 揭示交通特性的内在涵义， 以熵原理作为判断交通特性分布的准则。