基于改进相容函数的超分辨率图像重建

提出了一种基于机器学习的超分辨率(SR)改进算法。首先建立一个包括低分辨率(LR)图像及其相应的高分辨率(HR)图像的训练样本集,为LR图像提供了HR的图像解释。把训练集中的每一幅图像分成若干个图像块,每一个图像块作为马尔可夫随机场(MRF)模型的结点,MRF模型参数从这些训练样本中学习得到,通过对训练样本中的LR图像块进行k-均值聚类减少计算开销,并用k-均值的聚类结果提出了一种新的相容函数形式。实验结果表明,该算法是可行的,并与同类算法相比能取得较好的结果,使得SR后的图像更平滑自然。     

Comprehensive classification of USA cannabis samples based on chemical profiles of major cannabinoids and terpenoids

Abstract

Different USA-origin cannabis samples were analyzed by GC-FID to quantify all possible cannabinoids and terpenoids prior to their clustering. Chromatographic analysis confirmed the presence of seven cannabinoids and sixteen terpenoids with variable levels. Among tested cannabinoids, Δ⁹-Tetrahydrocannabinol Δ⁹-THC and cannabinol CBN were available in excess amounts (1.2–8.0?wt%) and (0.22–1.1?wt%), respectively. Fenchol was the most abundant terpenoid with a range of (0.03–1.0?wt%). The measured chemical profile was used to cluster 23 USA states and to group plant samples using different unsupervised multivariate statistical tools. Clustering of plant samples and states was sensitive to the selected cannabinoids/terpenoids. Principal component analysis (PCA) indicated the importance of Δ⁹-THC, CBN, CBG, CBC, THCV, Δ⁸-THC, CBL, and fenchol for samples clustering. Δ⁹-THC was significant to separate California-origin samples while CBN and fenchol were dominant to separate Oregon-origin samples away from the rest of cannabis samples. A special PCA analysis was performed on cannabinoids after excluding Δ⁹-THC (due to its high variability in the same plant) and CBN (as a degradation byproduct for THC). Results indicated that CBL and Δ⁸-THC were necessary to separate Nevada and Washington samples, while, CBC was necessary to isolate Oregon and Illinois plant samples. PCA based on terpenoids content confirmed the significance of caryophyllene, guaiol, limonene, linalool, and fenchol for clustering target. Fenchol played a major role for clustering plant samples that originated from Washington and Nevada. k-means method was more flexible than PCA and generated three different classes; samples obtained from Oregon and California in comparison to the rest of other samples were obviously separated alone, which attributed to their unique chemical profile. Finally, both PCA and k-means were useful and quick guides for cannabis clustering based on their chemical profile. Thus, less effort, time, and materials will be consumed in addition to decreasing operational conditions for cannabis clustering.     

An Improved K-Means Algorithm Based on Evidence Distance

The main influencing factors of the clustering effect of the k-means algorithm are the selection of the initial clustering center and the distance measurement between the sample points. The traditional k-mean algorithm uses Euclidean distance to measure the distance between sample points, thus it suffers from low differentiation of attributes between sample points and is prone to local optimal solutions. For this feature, this paper proposes an improved k-means algorithm based on evidence distance. Firstly, the attribute values of sample points are modelled as the basic probability assignment (BPA) of sample points. Then, the traditional Euclidean distance is replaced by the evidence distance for measuring the distance between sample points, and finally k-means clustering is carried out using UCI data. Experimental comparisons are made with the traditional k-means algorithm, the k-means algorithm based on the aggregation distance parameter, and the Gaussian mixture model. The experimental results show that the improved k-means algorithm based on evidence distance proposed in this paper has a better clustering effect and the convergence of the algorithm is also better.     

Splitting Methods for Convex Clustering

Clustering is a fundamental problem in many scientific applications. Standard methods such as k-means, Gaussian mixture models, and hierarchical clustering, however, are beset by local minima, which are sometimes drastically suboptimal. Recently introduced convex relaxations of k-means and hierarchical clustering shrink cluster centroids toward one another and ensure a unique global minimizer. In this work, we present two splitting methods for solving the convex clustering problem. The first is an instance of the alternating direction method of multipliers (ADMM); the second is an instance of the alternating minimization algorithm (AMA). In contrast to previously considered algorithms, our ADMM and AMA formulations provide simple and unified frameworks for solving the convex clustering problem under the previously studied norms and open the door to potentially novel norms. We demonstrate the performance of our algorithm on both simulated and real data examples. While the differences between the two algorithms appear to be minor on the surface, complexity analysis and numerical experiments show AMA to be significantly more efficient. This article has supplementary materials available online.     

Improving the Retrieval of Arabic Web Search Results Using Enhanced k-Means Clustering Algorithm

Traditional information retrieval systems return a ranked list of results to a user’s query. This list is often long, and the user cannot explore all the results retrieved. It is also ineffective for a highly ambiguous language such as Arabic. The modern writing style of Arabic excludes the diacritical marking, without which Arabic words become ambiguous. For a search query, the user has to skim over the document to infer if the word has the same meaning they are after, which is a time-consuming task. It is hoped that clustering the retrieved documents will collate documents into clear and meaningful groups. In this paper, we use an enhanced k-means clustering algorithm, which yields a faster clustering time than the regular k-means. The algorithm uses the distance calculated from previous iterations to minimize the number of distance calculations. We propose a system to cluster Arabic search results using the enhanced k-means algorithm, labeling each cluster with the most frequent word in the cluster. This system will help Arabic web users identify each cluster’s topic and go directly to the required cluster. Experimentally, the enhanced k-means algorithm reduced the execution time by 60% for the stemmed dataset and 47% for the non-stemmed dataset when compared to the regular k-means, while slightly improving the purity.     

一种基于模糊加权的改进文本聚类方法

首先提出了一种优化初始中心点方法用以解决聚类的局部最优问题.同时通过样本的模糊加权减少边缘噪音数据对聚类效率的影响.文本聚类试验表明,该模糊文本聚类算法取得较好的聚类效果.     

基于多视图SIFT特征的三维模型检索

研究了一种基于多视图SIFT特征的三维模型检索算法。首先对三维模型进行多视图投影,得到其余方位的三维投影深度图,并在各投影深度图上提取SIFT特征。分别利用按模型特征数比例分次建立码本及建立模型库整体码本这两种方式建立了模型库的码本,继而将模型的SIFT特征聚类量化,并用一个多维向量将其表示出来。通过计算三维模型特征向...     

基于语义特征的模糊聚类算法研究

对海量数据进行聚类,从中获取有价值的隐含知识,已经成为一项迫切的需求。传统的基于词频或距离的文本聚类技术在准确度方面存在较大差距。引入文本语义信息的聚类方法,提高了聚类的准确度。实验结果表明,基于语义特征的模糊聚类算法具有较好的聚类效果。     

基于块合成的视频图像去模糊算法的改进

提出了一种对基于图像块合成的视频图像去模糊算法的改进.该算法通过建立图像模糊模型,并引入“幸运度”来代表图像的清晰程度,在当前帧的时间窗内使用基于图像块合成的方法来达到图像去模糊的目的,文中对图像块的选择用K-means聚类算法进行改进.通过对实验结果的分析,证明了改进方法的有效性.     

基于数据挖掘技术的告警恢复模型探析

随着信息通信网络设备功能和业务服务的发展,监控部门在网络维护日常工作中的作用日益突出。本文利用数据挖掘中的K-means算法对半年内的原始告警做聚类分析,论述了告警恢复所遵循的普遍规律,并分别利用离散和连续两种方法建立数学模型对最佳派单时间点的设置进行研究,为告警管理的提升以及大数据技术在电信运营商中的使用提供了思路和建议。