With the development of new media technology,vehicle matching plays a further significant role in video surveillance systems.Recent methods explored the vehicle matching based on the feature extraction.Meanwhile,simil...With the development of new media technology,vehicle matching plays a further significant role in video surveillance systems.Recent methods explored the vehicle matching based on the feature extraction.Meanwhile,similarity metric learning also has achieved enormous progress in vehicle matching.But most of these methods are less effective in some realistic scenarios where vehicles usually be captured in different times.To address this cross-domain problem,we propose a cross-domain similarity metric learning method that utilizes theGANto generate vehicle imageswith another domain and propose the two-channel Siamese network to learn a similarity metric from both domains(i.e.,Day pattern or Night pattern)for vehicle matching.To exploit properties and relationships among vehicle datasets,we first apply the domain transformer to translate the domain of vehicle images,and then utilize the two-channel Siamese network to extract features from both domains for better feature similarity learning.Experimental results illustrate that our models achieve improvements over state-of-the-arts.展开更多
窃电行为不仅会扰乱正常用电秩序,更会影响电网的供电质量和安全运行。针对窃电检测工作中所面临的用户正常用电行为与窃电行为多样化问题,该文提出一种基于多阶段递推数据分析的低压台区窃电检测方法。该方法第1阶段对嫌疑窃电台区进...窃电行为不仅会扰乱正常用电秩序,更会影响电网的供电质量和安全运行。针对窃电检测工作中所面临的用户正常用电行为与窃电行为多样化问题,该文提出一种基于多阶段递推数据分析的低压台区窃电检测方法。该方法第1阶段对嫌疑窃电台区进行判定,针对当日线损不是明显激增的情况,提出基于台区线损综合波动率、总分表电流差异率、线损和电流曲线的突变点时间重合度的三步分析法,为窃电嫌疑用户的检测提供了良好的条件;第2阶段提出基于最优特征集的时间序列相似性度量方法,基于欧氏距离度量曲线间数值特征,同时基于动态时间规整(dynamic time warping,DTW)算法度量曲线间的形态特征,实现窃电嫌疑用户的初步筛选;第3阶段提出基于核函数和惩罚参数优化的支持向量机二次深度检测模型(optimize kernel-function and penalty-parameters support vector machine,OKPSVM),其中惩罚参数采用综合改进的粒子群(improved particle swarm optimization,IPSO)算法。通过算例仿真和实际工程应用,整体优化后的支持向量机模型(IPSO-OKPSVM)能够提高深度窃电检测的精准性和适用性。展开更多
Quantitative headspace analysis of volatiles emitted by plants or any other living organisms in chemical ecology studies generates large multidimensional data that require extensive mining and refining to extract usef...Quantitative headspace analysis of volatiles emitted by plants or any other living organisms in chemical ecology studies generates large multidimensional data that require extensive mining and refining to extract useful information. More often the number of variables and the quantified volatile compounds exceed the number of observations or samples and hence many traditional statistical analysis methods become inefficient. Here, we employed machine learning algorithm, random forest (RF) in combination with distance-based procedure, similarity percentage (SIMPER) as preprocessing steps to reduce the data dimensionality in the chemical profiles of volatiles from three African nightshade plant species before subjecting the data to non-metric multidimensional scaling (NMDS). In addition, non-parametric methods namely permutational multivariate analysis of variance (PERMANOVA) and analysis of similarities (ANOSIM) were applied to test hypothesis of differences among the African nightshade species based on the volatiles profiles and ascertain the patterns revealed by NMDS plots. Our results revealed that there were significant differences among the African nightshade species when the data’s dimension was reduced using RF variable importance and SIMPER, as also supported by NMDS plots that showed S. scabrum being separated from S. villosum and S. sarrachoides based on the reduced data variables. The novelty of our work is on the merits of using data reduction techniques to successfully reveal differences in groups which could have otherwise not been the case if the analysis were performed on the entire original data matrix characterized by small samples. The R code used in the analysis has been shared herein for interested researchers to customise it for their own data of similar nature.展开更多
The aim of a recommender system is filtering the enormous quantity of information to obtain useful information based on the user’s interest. Collaborative filtering is a technique which improves the efficiency of rec...The aim of a recommender system is filtering the enormous quantity of information to obtain useful information based on the user’s interest. Collaborative filtering is a technique which improves the efficiency of recommendation systems by considering the similarity between users. The similarity is based on the given rating to data by similar users. However, user’s interest may change over time. In this paper we propose an adaptive metric which considers the time in measuring the similarity of users. The experimental results show that our approach is more accurate than the traditional collaborative filtering algorithm.展开更多
The structure of any a.s. self-similar set K(w) generated by a class of random elements {gn,wσ} taking values in the space of contractive operators is given and the approximation of K(w) by the fixed points {Pn,wσ} ...The structure of any a.s. self-similar set K(w) generated by a class of random elements {gn,wσ} taking values in the space of contractive operators is given and the approximation of K(w) by the fixed points {Pn,wσ} of {gn,ow} is obtained. It is useful to generate the fractal in computer.展开更多
We constructed a class of generalized statistically self-similar set.S and give the necessary and sufficent conditions to ensure a random recursive set being a generalized statistically self-similar set. The statist...We constructed a class of generalized statistically self-similar set.S and give the necessary and sufficent conditions to ensure a random recursive set being a generalized statistically self-similar set. The statistically self-similar sets defined by Hutchinson,Falconer,Graf are the special cases of ours.展开更多
We have studied statistically self similar measures together with statistically self similar sets in this paper.A special kind of statistically self similar measures has been constructed and a class of statisticall...We have studied statistically self similar measures together with statistically self similar sets in this paper.A special kind of statistically self similar measures has been constructed and a class of statistically self similar sets as well.展开更多
基金supported in part by the National Natural Science Foundation of China under Grant 61972205in part by the National Key R&D Program of China under Grant 2018YFB1003205.
文摘With the development of new media technology,vehicle matching plays a further significant role in video surveillance systems.Recent methods explored the vehicle matching based on the feature extraction.Meanwhile,similarity metric learning also has achieved enormous progress in vehicle matching.But most of these methods are less effective in some realistic scenarios where vehicles usually be captured in different times.To address this cross-domain problem,we propose a cross-domain similarity metric learning method that utilizes theGANto generate vehicle imageswith another domain and propose the two-channel Siamese network to learn a similarity metric from both domains(i.e.,Day pattern or Night pattern)for vehicle matching.To exploit properties and relationships among vehicle datasets,we first apply the domain transformer to translate the domain of vehicle images,and then utilize the two-channel Siamese network to extract features from both domains for better feature similarity learning.Experimental results illustrate that our models achieve improvements over state-of-the-arts.
文摘窃电行为不仅会扰乱正常用电秩序,更会影响电网的供电质量和安全运行。针对窃电检测工作中所面临的用户正常用电行为与窃电行为多样化问题,该文提出一种基于多阶段递推数据分析的低压台区窃电检测方法。该方法第1阶段对嫌疑窃电台区进行判定,针对当日线损不是明显激增的情况,提出基于台区线损综合波动率、总分表电流差异率、线损和电流曲线的突变点时间重合度的三步分析法,为窃电嫌疑用户的检测提供了良好的条件;第2阶段提出基于最优特征集的时间序列相似性度量方法,基于欧氏距离度量曲线间数值特征,同时基于动态时间规整(dynamic time warping,DTW)算法度量曲线间的形态特征,实现窃电嫌疑用户的初步筛选;第3阶段提出基于核函数和惩罚参数优化的支持向量机二次深度检测模型(optimize kernel-function and penalty-parameters support vector machine,OKPSVM),其中惩罚参数采用综合改进的粒子群(improved particle swarm optimization,IPSO)算法。通过算例仿真和实际工程应用,整体优化后的支持向量机模型(IPSO-OKPSVM)能够提高深度窃电检测的精准性和适用性。
文摘Quantitative headspace analysis of volatiles emitted by plants or any other living organisms in chemical ecology studies generates large multidimensional data that require extensive mining and refining to extract useful information. More often the number of variables and the quantified volatile compounds exceed the number of observations or samples and hence many traditional statistical analysis methods become inefficient. Here, we employed machine learning algorithm, random forest (RF) in combination with distance-based procedure, similarity percentage (SIMPER) as preprocessing steps to reduce the data dimensionality in the chemical profiles of volatiles from three African nightshade plant species before subjecting the data to non-metric multidimensional scaling (NMDS). In addition, non-parametric methods namely permutational multivariate analysis of variance (PERMANOVA) and analysis of similarities (ANOSIM) were applied to test hypothesis of differences among the African nightshade species based on the volatiles profiles and ascertain the patterns revealed by NMDS plots. Our results revealed that there were significant differences among the African nightshade species when the data’s dimension was reduced using RF variable importance and SIMPER, as also supported by NMDS plots that showed S. scabrum being separated from S. villosum and S. sarrachoides based on the reduced data variables. The novelty of our work is on the merits of using data reduction techniques to successfully reveal differences in groups which could have otherwise not been the case if the analysis were performed on the entire original data matrix characterized by small samples. The R code used in the analysis has been shared herein for interested researchers to customise it for their own data of similar nature.
文摘The aim of a recommender system is filtering the enormous quantity of information to obtain useful information based on the user’s interest. Collaborative filtering is a technique which improves the efficiency of recommendation systems by considering the similarity between users. The similarity is based on the given rating to data by similar users. However, user’s interest may change over time. In this paper we propose an adaptive metric which considers the time in measuring the similarity of users. The experimental results show that our approach is more accurate than the traditional collaborative filtering algorithm.
基金Supported by NNSF of China and the Foundation of Wuhan University
文摘The structure of any a.s. self-similar set K(w) generated by a class of random elements {gn,wσ} taking values in the space of contractive operators is given and the approximation of K(w) by the fixed points {Pn,wσ} of {gn,ow} is obtained. It is useful to generate the fractal in computer.
基金the National Natural Science Foundation of China
文摘We constructed a class of generalized statistically self-similar set.S and give the necessary and sufficent conditions to ensure a random recursive set being a generalized statistically self-similar set. The statistically self-similar sets defined by Hutchinson,Falconer,Graf are the special cases of ours.
文摘We have studied statistically self similar measures together with statistically self similar sets in this paper.A special kind of statistically self similar measures has been constructed and a class of statistically self similar sets as well.
